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Abstract Background NUAK family kinase 2 (NUAK2) has been identified as an important mediator for tumor progression in multiple malignancies. Nevertheless, its role in lung adenocarcinoma (LUAD) remains unclear. Methods Bioinformatic analysis was performed to assess the expression and prognosis of NUAK2 in patients with LUAD. The NUAK2 expression was measured in multiple LUAD cell lines, and the loss-of-function experiment was conducted. Cell proliferation ability was assessed using CCK-8 and colony formation assays. Spheroid formation, alkaline phosphatase (AP) staining, tube formation and SA-β-gal staining assays were performed to examine stemness, angiogenesis and senescence. Lipid peroxidase was assessed by TBARS production and lipid ROS. Western blot was used to detect critical proteins. In addition, A549 cells were treated with ferroptosis inhibitor ferrostatin-1 (Fer-1) for a rescue assay. Finally, A549 cells were subcutaneously injected into the right flank of mice to establish LUAD-bearing mouse model, and the tumor weight and size were detected. Results NUAK2 was upregulated in patients with LUAD and LUAD cell lines. NUAK2 depletion inhibited cell viability, colonies, tumor spheres and decreased Oct4 and Nanog expression, confirming NUAK2 depletion inhibited proliferation and stemness of A549 cells. Meanwhile, NUAK2 depletion blocked angiogenesis via reducing formed tubes and VEGFR1/2 expression, and promoted senescence of A549 cells by elevating SA-β-gal-positive cells and p16, p21 and p53 expression. Moreover, NUAK2 depletion elevated lipid ROS, TBARS production and Fe2+ level, demonstrating that NUAK2 depletion could trigger ferroptosis in A549 cells. Furthermore, the rescue experiments revealed that the impacts of NUAK2 depletion on malignant behaviors in A549 cells were partly weakened by additional Fer-1 treatment. Finally, in vivo experiments demonstrated that NUAK2 knockdown greatly inhibited tumor growth in LUAD-bearing mice. Conclusion In summary, NUAK2 depletion impeded oncogenic phenotypes of A549 cells partly via triggering ferroptosis, suggesting NUAK2 as a novel target for treating LUAD.

In this paper, the microstructure and mechanical properties of SLM-GH3230 superalloy treated by different post treatments (single hot isostatic pressing and heat treatments after hot isostatic pressing) were studied. The micro-morphologies show that the grains along building direction after HIP + HT are mainly columnar grains, which is consistent to that after single HIP. The granular precipitates distribute intergranularly and intragranularly after HIP, but gradually re-dissolve into matrix with the increase of the temperature of the post HT processes. XRD and EDS analyses results indicate that the matrix mainly consists of austenitic γ phase and the granular precipitates are mainly M6C carbides enriched in W element. The average grain size decreases after HT compared to that after HIP, and the crystallographic orientation of grains tends to be more uniform. The Vickers hardness decreases after HIP and HIP + HT processes within a certain degree. According to tensile test results, the sample shows a better combination of strength and plastic performance under HIP+1250 °C/30 min processing, which can also be verified by the characteristic of the fracture morphology with more tearing dimples.

Significant coupling exists between DC and AC lines erected on the same tower or sharing transmission corridors. Electromagnetic and electrostatic coupling between DC and AC lines are investigated based on ± 500 kV MMC-HVDC and 500 kV double-circuit AC lines on the same tower. The interactive coupling characteristics and their impacts on protective relaying are analyzed during normal operation and after DC or AC faults. Voltage coupling ratio and current coupling ratio are defined. The 50 Hz components in MMC-HVDC line may be higher than ripple criterion under the coupling of AC line and can be even more significant when a fault occurs on adjacent AC line. The DC reactor with larger inductance can reduce its impact on MMC-HVDC system. Zero-sequence overcurrent relaying and zero-sequence directional relaying for AC lines may mal-operate when a fault occurs on adjacent MMC-HVDC line. And there is also an interruption risk for AC systems. Improvement schemes are proposed to avoid the mal-operation of protective relaying. Simulation case studies verify the correctness of coupling characteristic analysis and the effectiveness of proposed improvement scheme.

Application of deep learning (DL) and machine learning (ML) is rapidly increasing in the medical field. DL is gaining significance for medical image analysis, particularly, in oral and maxillofacial surgeries. Owing to the ability to accurately identify and categorize both diseased and normal soft- and hard-tissue structures, DL has high application potential in the diagnosis and treatment of tumors and in orthognathic surgeries. Moreover, DL and ML can be used to develop prediction models that can aid surgeons to assess prognosis by analyzing the patient's medical history, imaging data, and surgical records, develop more effective treatment strategies, select appropriate surgical modalities, and evaluate the risk of postoperative complications. Such prediction models can play a crucial role in the selection of treatment strategies for oral and maxillofacial surgeries. Their practical application can improve the utilization of medical staff, increase the treatment accuracy and efficiency, reduce surgical risks, and provide an enhanced treatment experience to patients. However, DL and ML face limitations, such as data drift, unstable model results, and vulnerable social trust. With the advancement of social concepts and technologies, the use of these models in oral and maxillofacial surgery is anticipated to become more comprehensive and extensive.

IntroductionAssessing the influence of respiratory assistive devices on the diaphragm mobility is essential for advancing patient care and improving treatment outcomes. Existing respiratory assistive robots have not yet effectively assessed their impact on diaphragm mobility. In this study, we introduce for the first time a non-invasive, real-time clinically feasible ultrasound method to evaluate the impact of soft wearable robots on diaphragm displacement.MethodsWe measured and compared diaphragm displacement and lung volume in eight participants during both spontaneous and robotic-assisted respiration. Building on these measurements, we proposed a human-robot coupled two-compartment respiratory mechanics model that elucidates the underlying mechanism by which our extracorporeal wearable robots augments respiration. Specifically, the soft robot applies external compression to the abdominal wall muscles, inducing their inward movement, which consequently pushes the diaphragm upward and enhances respiratory function. Finally, we investigated the level and shape of various robotic assistive forces on diaphragm motion.ResultsThis robotic intervention leads to a significant increase in average diaphragm displacement by 1.95 times and in lung volume by 2.14 times compared to spontaneous respiration. Furthermore, the accuracy of the proposed respiratory mechanics model is confirmed by the experimental results, with less than 7% error in measurements of both diaphragm displacement and lung volume. Finally, the magnitude of robotic assistive forces positively correlates with diaphragm movement, while the shape of the forces shows no significant relationship with diaphragm activity.ConclusionOur experimental findings validate the effective assistance mechanism of the proposed robot, which enhances diaphragm mobility and assists in ventilation through extracorporeal robotic intervention. This robotic system can assist with ventilation while increasing diaphragm mobility, potentially resolving the issue of diaphragm atrophy. Additionally, this work paves the way for improved robotic designs and personalized assistance, tailored to the dynamics of the diaphragm in respiratory rehabilitation.

As the utilization of renewable energy sources continues to expand, energy storage systems assume a crucial role in enabling the effective integration and utilization of renewable energy. This underscores their fundamental significance in mitigating the inherent intermittency and variability associated with renewable energy sources. This study focuses on the involvement of photovoltaic (PV) plants in medium and long-term transactions. It also explores the participation of battery energy storage system (BESS) in electricity trading and frequency regulation ancillary services. The objective is to establish a strategic research model for maximizing the benefits of PV plant and the BESS in the energy arbitrage and frequency regulation markets. Sensitivity analysis was conducted to assess the impact of variations in both the rated power and maximum continuous energy storage duration of the BESS. Base on the NSGA-II algorithm and TOPSIS algorithm, an optimization model for energy storage capacity configuration is developed. The optimal capacity configuration and maximum continuous energy storage duration are determined through computational analysis, yielding values of 30.8 MW and 4.521 h, respectively. At this configuration, the daily average revenue is 2.362 × 105 yuan, the initial investment cost is 1.45 × 109 yuan, and the payback period is 4.562 years.

Abstract The significance means of the seismic reinforcement effect of a pile-anchor system for slope reinforcement has been widely recognized. However, cases of deformation failure and instability sliding of the pile-anchor system itself and the reinforced slope under seismic action continue to be recorded. Therefore, it is crucial to evaluate the seismic performance of slopes reinforced by a pile-anchor system to prevent the system’s failure. Current evaluation models of a slope reinforced by a pile-anchor system mainly focus on slope stability; however, the safety of the pile-anchor system itself is not sufficiently considered in these models. Consequently, in this study, we propose a multi-objective optimization evaluation (MOE) model for evaluating the seismic performance of slopes reinforced by a pile-anchor system that considers slope stability, safety of the pile-anchor system, and dynamic response of the slope. This model considers slope displacement, acceleration amplification factor of a slope, pile displacement, and anchor displacement as negative indexes, and anti-slide pile bending moment, shear force, and anchor axial force as intermediate indexes. The comprehensive weight of relevant indexes is obtained by combining subjective and objective weights, and the seismic reinforcement effect of the pile-anchor system is evaluated subsequently. In conclusion, the MOE model proposed in this study provides a novel solution for the optimization evaluation of a slope reinforced by a pile-anchor system in forthcoming projects.

Background: Anesthesiology plays a crucial role in perioperative care, critical care, and pain management, impacting patient experiences and clinical outcomes. However, our understanding of the anesthesiology research landscape is limited. Accordingly, we initiated a data-driven analysis through topic modeling to uncover research trends, enabling informed decision-making and fostering progress within the field. Methods: The easyPubMed R package was used to collect 32,300 PubMed abstracts spanning from 2000 to 2022. These abstracts were authored by 737 Anesthesiology Principal Investigators (PIs) who were recipients of National Institute of Health (NIH) funding from 2010 to 2022. Abstracts were preprocessed, vectorized, and analyzed with the state-of-the-art BERTopic algorithm to identify pillar topics and trending subtopics within anesthesiology research. Temporal trends were assessed using the Mann-Kendall test. Results: The publishing journals with most abstracts in this dataset were Anesthesia & Analgesia 1133, Anesthesiology 992, and Pain 671. Eight pillar topics were identified and categorized as basic or clinical sciences based on a hierarchical clustering analysis. Amongst the pillar topics, “Cells & Proteomics” had both the highest annual and total number of abstracts. Interestingly, there was an overall upward trend for all topics spanning the years 2000–2022. However, when focusing on the period from 2015 to 2022, topics “Cells & Proteomics” and “Pulmonology” exhibit a downward trajectory. Additionally, various subtopics were identified, with notable increasing trends in “Aneurysms”, “Covid 19 Pandemic”, and “Artificial intelligence & Machine Learning”. Conclusion: Our work offers a comprehensive analysis of the anesthesiology research landscape by providing insights into pillar topics, and trending subtopics. These findings contribute to a better understanding of anesthesiology research and can guide future directions.

Mitochondrial respiratory chain complex I is an important component of the oxidative respiratory chain, with the mitochondrially encoded NADH:ubiquinone oxidoreductase core subunit 1 (MT-ND1) being one of the core subunits. MT-ND1 plays a role in the assembly of complex I and its enzymatic function. MT-ND1 gene mutation affects pathophysiological processes, such as interfering with the early assembly of complex I, affecting the ubiquinone binding domain and proton channel of complex I, and affecting oxidative phosphorylation, thus leading to the occurrence of diseases. The relationship between MT-ND1 gene mutation and disease has been has received increasing research attention. Therefore, this article reviews the impact of MT-ND1 mutations on disease progression, focusing on the impact of such mutations on diseases and their possible mechanisms, as well as the application of targeting MT-ND1 gene mutations in disease diagnosis and treatment. We aim to provide a new perspective leading to a more comprehensive understanding of the relationship between MT-ND1 gene mutations and diseases.

Ice cream has the characteristic of thermodynamic instability. During processing, storage, transportation, and sales, temperature fluctuations cause recrystallization of ice crystals and then increase the average size of ice crystals, which leads to rough texture and poor taste of ice cream. Therefore, inhibiting the recrystallization of ice crystals in ice cream is the key to ensure the quality of ice cream. This paper reviews the formation mechanism and research progress of supercooling, nucleation, growth and recrystallization of ice crystals during freezing. The inhibitory effects of emulsifiers, stabilizers, sweeteners, and proteins in ice cream raw materials on ice crystal recrystallization are introduced in detail. Meanwhile, it also list the contents of freezing technologies such as ultrasonic assisted freezing, magnetic field assisted freezing, high-pressure assisted freezing, and electric field assisted freezing to inhibit ice cream recrystallization. The development direction of that is prospected, which provides a theoretical reference for adapting to the trend of online sales, developing new ice cream products and solving the problem of its crystal recrystallization.

Background: Cannabinoid compounds have been approved as prescription drugs for treating various human ailments. However, the production using both microbial and plant-based sources is time-consuming and expensive because their yield is extremely low. Tetraketide synthase (TKS), the key enzyme in the biosynthesis of cannabinoid compounds, produces only 4% of the intermediate compound olivetolic acid. However, it may be possible to rearrange the carbon metabolic flux of TKS using genetic methods to increase the overall yields of cannabinoid compounds. In this context, protein engineering is an economically beneficial and viable solution to improve the catalytic activity of TKS. However, the ability to produce enzyme variants significantly exceeds the capacity to screen and identify high producers, creating a bottleneck in the enzyme engineering process. Results: This study constructed an Escherichia coli-based biosensor workflow for detecting the byproduct pentyl diacetic acid lactone (PDAL). Rational design was used to generate E. coli strains with mutant regulatory protein AraC and an altered effector PDAL to control the transcription of gfp and kanamycin. The developed biosensor could detect PDAL at the concentrations of the operational range from microbial cell culture and cell-free catalytic system. Conclusions: The E. coli-based biosensor developed in this study efficiently detected PDAL with high throughput and low cost.How to cite: Gao M, Zhang Y, Xue L, et al. Screening microbially produced pentyl diacetic acid lactone using an Escherichia coli biosensor workflow. Electron J Biotechnol 2023;66. https://doi.org/10.1016/j.ejbt.2023.09.002.

Abstract Background The c-Jun N-terminal kinase (JNK) pathway is an evolutionarily conserved regulator of cell death, which is essential for coordinating tissue homeostasis. In this study, we have characterized the Drosophila Ste20-like kinase Slik as a novel modulator of JNK pathway-mediated apoptotic cell death. Results First, ectopic JNK signaling-triggered cell death is enhanced by slik depletion whereas suppressed by Slik overexpression. Second, loss of slik activates JNK signaling, which results in enhanced apoptosis and impaired tissue homeostasis. In addition, genetic epistasis analysis suggests that Slik acts upstream of or in parallel to Hep to regulate JNK-mediated apoptotic cell death. Moreover, Slik is necessary and sufficient for preventing physiologic JNK signaling-mediated cell death in development. Furthermore, introduction of STK10, the human ortholog of Slik, into Drosophila restores slik depletion-induced cell death and compromised tissue homeostasis. Lastly, knockdown of STK10 in human cancer cells also leads to JNK activation, which is cancelled by expression of Slik. Conclusions This study has uncovered an evolutionarily conserved role of Slik/STK10 in blocking JNK signaling, which is required for cell death inhibition and tissue homeostasis maintenance in development.

A fundamental question in the oxygen reduction reaction (ORR) is how to rationally control the electrocatalytic selectivity for opening a four-electron reaction pathway. However, it still lacks direct experimental evidence to understand the reaction mechanism. This work unravels that Ag nanoparticles and carbonizing halloysite nanotubes (CHNTs) can trigger the construction of oxygen defects in the MnO2, which contribute to the generation of active sites. The Ag/MnO2-CHNTs delivers a superior activity toward ORR with high onset potential, half-wave potential, diffusion-limited current density, long-term durability and methanol tolerance. More importantly, combined with density functional theory calculations, triggering manganese dioxide defects upon the introduction of Ag nanoparticles and CHNTs can alter the electrocatalytic pathway from a two-electron to a direct four-electron direction for ORR, which is the nature of enhanced ORR activity. Based on the analysis of the results, this finding points out a very effective approach for exploring catalysts with the improved performance and durability for ORR reaction.

Climbing robots, with their expansive workspace and flexible deployment modes, have the potential to revolutionize the manufacturing processes of large and complex components. Given that the surfaces to be machined typically exhibit variable curvature, good surface adaptability, load capacity, and motion accuracy are essential prerequisites for climbing robots in manufacturing tasks. This paper addresses the manufacturing requirements of climbing robots by proposing an asymmetric independently steerable wheel (AISW) for climbing robots, along with the motion control method. Firstly, for the adaptability issue of the locomotion mechanism on curved surfaces under heavy load, an asymmetric independently steerable wheel motion module is proposed, which improves the steering difficulty of the traditional independently steerable wheel (ISW) based on the principle of steering assisted by wheels. Secondly, a kinematic model of the AISW chassis is established and, on this basis, a trajectory tracking method based on feedforward and proportional–integral feedback is proposed. Comparative experimental results on large, curved surface components show that the asymmetric independently steerable wheel has lower steering resistance and higher motion accuracy, significantly enhancing the reachability of climbing robots and facilitating their application in the manufacturing of large and complex components.

Probiotics provide health benefits and are used as feed supplements as an alternative prophylactic strategy to antibiotics. However, the effects of Bacillus-based probiotics containing more than two strains when supplemented to pigs are rarely elucidated. SOLVENS (SLV) is a triple-strain Bacillus-based probiotic. In this study, we investigate the effects of SLV on performance, immunity, intestinal morphology, and microbial community in piglets. A total of 480 weaned pigs [initial body weight (BW) of 8.13 ± 0.08 kg and 28 days of age] were assigned to three treatments in a randomized complete block design: P0: basal diet (CON); P200: CON + 200 mg SLV per kg feed (6.5 × 108 CFU/kg feed); and P400: CON + 400 mg SLV per kg feed (1.3 × 109 CFU/kg feed). Each treatment had 20 replicated pens with eight pigs (four male/four female) per pen. During the 31 d feeding period (Phase 1 = wean to d 14, Phase 2 = d 15 to 31 after weaning), all pigs were housed in a temperature-controlled nursery room (23 to 25 °C). Feed and water were available ad libitum. The results showed that the pigs in the P400 group increased (p < 0.05) average daily gain (ADG) in phase 2 and tended (p = 0.10) to increase ADG overall. The pigs in the P200 and P400 groups tended (p = 0.10) to show improved feed conversion ratios overall in comparison with control pigs. The pigs in the P200 and P400 groups increased (p < 0.05) serum immunoglobulin A, immunoglobulin G, and haptoglobin on d 14, and serum C-reactive protein on d 31. The pigs in the P200 group showed an increased (p < 0.01) villus height at the jejunum, decreased (p < 0.05) crypt depth at the ileum compared with other treatments, and tended (p = 0.09) to have an increased villus–crypt ratio at the jejunum compared with control pigs. The pigs in the P200 and P400 groups showed increased (p < 0.05) goblet cells in the small intestine. Moreover, the pigs in the P400 group showed down-regulated (p < 0.05) interleukin-4 and tumor necrosis factor-α gene expressions, whereas the pigs in the P400 group showed up-regulated occludin gene expression in the ileum. These findings suggest that SLV alleviates immunological reactions, improves intestinal microbiota balance, and reduces weaning stress in piglets. Therefore, SOLVENS has the potential to improve health and performance for piglets.

IntroductionThe coronavirus disease 2019 (COVID-19) pandemic has affected billions of people worldwide, and the lessons learned need to be concluded to get better prepared for the next pandemic. Early identification of high-risk patients is important for appropriate treatment and distribution of medical resources. A generalizable and easy-to-use COVID-19 severity stratification model is vital and may provide references for clinicians.MethodsThree COVID-19 cohorts (one discovery cohort and two validation cohorts) were included. Longitudinal peripheral blood mononuclear cells were collected from the discovery cohort (n = 39, mild = 15, critical = 24). The immune characteristics of COVID-19 and critical COVID-19 were analyzed by comparison with those of healthy volunteers (n = 16) and patients with mild COVID-19 using mass cytometry by time of flight (CyTOF). Subsequently, machine learning models were developed based on immune signatures and the most valuable laboratory parameters that performed well in distinguishing mild from critical cases. Finally, single-cell RNA sequencing data from a published study (n = 43) and electronic health records from a prospective cohort study (n = 840) were used to verify the role of crucial clinical laboratory and immune signature parameters in the stratification of COVID-19 severity.ResultsPatients with COVID-19 were determined with disturbed glucose and tryptophan metabolism in two major innate immune clusters. Critical patients were further characterized by significant depletion of classical dendritic cells (cDCs), regulatory T cells (Tregs), and CD4+ central memory T cells (Tcm), along with increased systemic interleukin-6 (IL-6), interleukin-12 (IL-12), and lactate dehydrogenase (LDH). The machine learning models based on the level of cDCs and LDH showed great potential for predicting critical cases. The model performances in severity stratification were validated in two cohorts (AUC = 0.77 and 0.88, respectively) infected with different strains in different periods. The reference limits of cDCs and LDH as biomarkers for predicting critical COVID-19 were 1.2% and 270.5 U/L, respectively.ConclusionOverall, we developed and validated a generalizable and easy-to-use COVID-19 severity stratification model using machine learning algorithms. The level of cDCs and LDH will assist clinicians in making quick decisions during future pandemics.

Biogenic volatile organic compounds (VOCs) play key roles in coral reef ecosystems, where, together with dimethylated sulfur compounds, they are indicators of ecosystem health and are used as defense strategies and infochemicals. Assessment and prediction of the exchange rates of VOCs between the oceans and atmosphere, with implications for atmospheric reactivity and climate, are hampered by poor knowledge of the regulating processes and their temporal variability, including diel cycles. Here, we measured the variation over 36h of the concentrations of DMSPCs (dimethylsulfoniopropionate (DMSP)-related compounds, namely DMSP, dimethylsulfoxide, acrylate, dimethylsulfide, and methanethiol as dimethyl disulfide) and VOCs (COS, CS2, isoprene, the iodomethanes CH3I and CH2ClI, and the bromomethanes CHBr3 and CH2Br2), in surface waters inside the shallow, northern coral-reef lagoon of Mo’orea (French Polynesia) and 4 km offshore, in the tropical open ocean. Comparisons with concurrent measurements of sea surface temperature, solar radiation, biogeochemical variables (nutrients, organic matter), and the abundances and taxonomic affiliations of microbial plankton were conducted with the aim to explain interconnections between DMSPCs, VOCs, and their environment across diel cycles. In open ocean waters, deeper surface mixing and low nutrient levels resulted in low phytoplankton biomass and bacterial activity. Consequently, the diel patterns of VOCs were more dependent on photochemical reactions, with daytime increases for several compounds including dissolved dimethylsulfoxide, COS, CS2, CH3I, and CH2ClI. A eukaryotic phytoplankton assemblage dominated by dinoflagellates and haptophytes provided higher cell-associated DMSP concentrations, yet the occurrence of DMSP degradation products (dimethylsulfide, dimethyl disulfide) was limited by photochemical loss. Conversely, in the shallow back reef lagoon the proximity of seafloor sediments, corals and abundant seaweeds resulted in higher nutrient levels, more freshly-produced organic matter, higher bacterial activity, and larger algal populations of Mamiellales, diatoms and Cryptomonadales. Consequently, DMSP and dimethylsulfoxide concentrations were lower but those of most VOCs were higher. A combination of photobiological and photochemical processes yielded sunny-daytime increases and nighttime decreases of dimethylsulfide, dimethyl disulfide, COS, isoprene, iodomethanes and bromomethanes. Our results illustrate the important role of solar radiation in DMSPC and VOC cycling, and are relevant for the design of sampling strategies that seek representative and comparable measurements of these compounds.

Stock price prediction is crucial in stock market research, yet existing models often overlook interdependencies among stocks in the same industry, treating them as independent entities. Recognizing and accounting for these interdependencies is essential for precise predictions. Propensity score matching (PSM), a statistical method for balancing individuals between groups and improving causal inferences, has not been extensively applied in stock interdependence investigations. Our study addresses this gap by introducing PSM to examine interdependence among pharmaceutical industry stocks for stock price prediction. Additionally, our research integrates Improved particle swarm optimization (IPSO) with long short-term memory (LSTM) networks to enhance parameter selection, improving overall predictive accuracy. The dataset includes price data for all pharmaceutical industry stocks in 2022, categorized into chemical pharmaceuticals, biopharmaceuticals, and traditional Chinese medicine. Using Stata, we identify significantly correlated stocks within each sub-industry through average treatment effect on the treated (ATT) values. Incorporating PSM, we match five target stocks per sub-industry with all stocks in their respective categories, merging target stock data with weighted data from non-target stocks for validation in the IPSO-LSTM model. Our findings demonstrate that including non-target stock data from the same sub-industry through PSM significantly improves predictive accuracy, highlighting its positive impact on stock price prediction. This study pioneers PSM’s use in studying stock interdependence, conducts an in-depth exploration of effects within the pharmaceutical industry, and applies the IPSO optimization algorithm to enhance LSTM network performance, providing a fresh perspective on stock price prediction research.

Glucocorticoids are primary stress hormones that exert neuronal effects via both genomic and non-genomic signaling pathways. However, their rapid non-genomic effects and underlying mechanisms on neural activities remain elusive. In the present study, we investigated the rapid non-genomic effect of glucocorticoids on Kv2.2 channels in cultured HEK293 cells and acute brain slices including cortical pyramidal neurons and calyx-type synapses in the brain stem. We found that cortisol, the endogenous glucocorticoids, rapidly increased Kv2.2 currents by increasing the single-channel open probability in Kv2.2-expressing HEK293 cells through activation of the membrane-associated glucocorticoid receptor. Bovine serum albumin-conjugated dexamethasone, a membrane-impermeable agonist of the glucocorticoid receptor, could mimic the effect of cortisol on Kv2.2 channels. The cortisol-increased Kv2.2 currents were induced by activation of the extracellular signal-regulated protein kinase (ERK) 1/2 kinase, which could be inhibited by U0126, an antagonist of the ERK signaling pathway. In layer 2 cortical pyramidal neurons and the calyx of Held synapses, cortisol suppressed the action potential firing frequency during depolarization and reduced the successful rate upon high-frequency stimulation by activating Kv2.2 channels. We further examined the postsynaptic responses and found that cortisol did not affect the mEPSC and evoked EPSC, but increased the activity-dependent synaptic depression induced by a high-frequency stimulus train. In conclusion, glucocorticoids can rapidly activate Kv2.2 channels through membrane-associated glucocorticoid receptors via the ERK1/2 signaling pathway, suppress presynaptic action potential firing, and inhibit synaptic transmission and plasticity. This may be a universal mechanism of the glucocorticoid-induced non-genomic effects in the central nervous system.

Abstract Preterm birth (PTB) is the leading cause of infant mortality globally. Research has focused on developing predictive models for PTB without prioritizing cost-effective interventions. Physical activity and sleep present unique opportunities for interventions in low- and middle-income populations (LMICs). However, objective measurement of physical activity and sleep remains challenging and self-reported metrics suffer from low-resolution and accuracy. In this study, we use physical activity data collected using a wearable device comprising over 181,944 h of data across N = 1083 patients. Using a new state-of-the art deep learning time-series classification architecture, we develop a ‘clock’ of healthy dynamics during pregnancy by using gestational age (GA) as a surrogate for progression of pregnancy. We also develop novel interpretability algorithms that integrate unsupervised clustering, model error analysis, feature attribution, and automated actigraphy analysis, allowing for model interpretation with respect to sleep, activity, and clinical variables. Our model performs significantly better than 7 other machine learning and AI methods for modeling the progression of pregnancy. We found that deviations from a normal ‘clock’ of physical activity and sleep changes during pregnancy are strongly associated with pregnancy outcomes. When our model underestimates GA, there are 0.52 fewer preterm births than expected (P = 1.01e − 67, permutation test) and when our model overestimates GA, there are 1.44 times (P = 2.82e − 39, permutation test) more preterm births than expected. Model error is negatively correlated with interdaily stability (P = 0.043, Spearman’s), indicating that our model assigns a more advanced GA when an individual’s daily rhythms are less precise. Supporting this, our model attributes higher importance to sleep periods in predicting higher-than-actual GA, relative to lower-than-actual GA (P = 1.01e − 21, Mann-Whitney U). Combining prediction and interpretability allows us to signal when activity behaviors alter the likelihood of preterm birth and advocates for the development of clinical decision support through passive monitoring and exercise habit and sleep recommendations, which can be easily implemented in LMICs.

Abstract Assay for Transposase Accessible Chromatin by sequencing (ATAC-seq) accurately depicts the chromatin regulatory state and altered mechanisms guiding gene expression in disease. However, bulk sequencing entangles information from different cell types and obscures cellular heterogeneity. To address this, we developed Cellformer, a deep learning method that deconvolutes bulk ATAC-seq into cell type-specific expression across the whole genome. Cellformer enables cost-effective cell type-specific open chromatin profiling in large cohorts. Applied to 191 bulk samples from 3 brain regions, Cellformer identifies cell type-specific gene regulatory mechanisms involved in resilience to Alzheimer’s disease, an uncommon group of cognitively healthy individuals that harbor a high pathological load of Alzheimer’s disease. Cell type-resolved chromatin profiling unveils cell type-specific pathways and nominates potential epigenetic mediators underlying resilience that may illuminate therapeutic opportunities to limit the cognitive impact of the disease. Cellformer is freely available to facilitate future investigations using high-throughput bulk ATAC-seq data.

The precise wake model is crucial for accurately estimating wind farm loads and power, playing a key role in wake control within wind farms. This study proposes a segmented dual-Gaussian wake model, which is built upon existing dual-Gaussian wake models but places greater emphasis on the influence of initial wake generation and evolution processes on the wind speed profile in the near-wake region. The enhanced model optimizes the wake speed profile in the near-wake region and improves the accuracy of wake diffusion throughout the entire flow field. Furthermore, the optimized dual-Gaussian wake model is utilized to estimate the power output and blade root vibration loads in offshore wind farms. Through comparative analysis of high-fidelity simulation results and actual measurement data, the accuracy of the optimized dual-Gaussian wake model is validated. This approach offers high computational efficiency and provides valuable insights for load fluctuations and power estimation, thereby advancing the development of wake control strategies rapidly.

Abstract Water-induced strength deterioration of rock mass is a crucial factor for rock slope instability. To better show the degradation process of rock slope water–rock interaction, we used bentonite as a water-sensitive regulator to build a new rock-like material that matches the features of water-induced strength degradation based on the cement-gypsum bonded materials. Twenty-five schemes of the material mixture proportion were designed using the orthogonal design method considering four factors with five variable levels, and a variety of experiments were conducted to obtain physico-mechanical parameters. In addition, one group of rock-like material proportion was selected and applied to the large-scale physical model test. The experiment results reveal that: (1) The failure mode of this rock-like material is highly similar to that of natural rock masses, and the physico-mechanical parameters vary over a wide range; (2) The bentonite content has a significant influence on the density, elastic modulus, and tensile strength of rock-like materials; (3) It is feasible to obtain the regression equation based on the linear regression analysis to determine the proportion of rock-like material; (4) Through application, the new rock-like material can effectively simulate or reveal the startup mechanism and instability characteristics of rock slopes under water-induced degradation. These studies can serve as a guide for the fabrication of rock-like material in the other model tests.

Abstract This study aims to give a new perspective to the biomarkers in the lung adenocarcinoma (LUAD) brain metastasis, pathways involved and potential therapeutics. We performed a comprehensive single-cell level transcriptomic analysis on one LUAD patient with circulating tumor cells (CTCs), primary tumor tissue and metastatic tumor tissue using scRNA-seq approach to identify metastasis related biomarkers. Further scRNA-seq were performed on 7 patients to validate the cancer metastatic hallmark. with single cells collected from either metastatic or primary LUAD tissues. Pathological and functional studies were also performed to evidence the critical role of RAC1 in the LUAD metastasis. Hallmark gene was verified based on immunohistochemistry staining, cytological experiment, survival information from The Cancer Genome Atlas (TCGA), and staining results from Human Protein Atlas (HPA) databases. PCA analysis revealed that CTCs were in the intermediate place between the metastatic group and primary group. In the unsupervised clustering analysis CTCs were closer to one of the metastatic tumor cells, implying heterogeneity of the metastatic tumor and origin of the CTCs were from metastatic site. Transitional phase related gene analysis identified RAC1 was enriched in metastatic tumor tissue (MTT) preferred gene set functioning as regulated cell death and apoptosis as well as promoted macromolecule organization. Compared with normal tissue, expression levels of RAC1 increased significantly in LUAD tissue based on HPA database. High expression of RAC1 predicts worse prognosis and higher-risk. EMT analysis identified the propensity of mesenchymal state in primary cells while epithelial signals were higher in the metastatic site. Functional clustering and pathway analyses suggested genes in RAC1 highly expressed cells played critical roles in adhesion, ECM and VEGF signaling pathways. Inhibition of RAC1 attenuates the proliferation, invasiveness and migration ability of lung cancer cells. Besides, through MRI T2WI results, we proved that RAC1 can promote brain metastasis in the RAC1-overexpressed H1975 cell burden nude mouse model. RAC1 and its mechanisms might promote drug design against LUAD brain metastasis.

For patients exhibiting a suboptimal response to the first chimeric antigen receptor (CAR) T-cell therapy (CART1) or relapse after remission, secondary CAR T-cell therapy (CART2) for the same target may be an option. We retrospectively analyzed patients with acute B-cell lymphoblastic leukemia (B-ALL) receiving CD19 CART1 at our center (n = 84) to report the clinical outcomes of CART2 and to identify the factors that may influence the outcomes. Twenty-six patients received CART2 for suboptimal response or relapse post-CART1. The incidence of cytokine release syndrome (CRS) after CART2 was 65.4% (17/26), with 11 cases classified as grade 1 (42.3%), four cases as grade 2 (15.4%), and two cases as grade 3 (7.7%). Neurotoxicity was observed in one patient (3.8%) after CART2 infusion. Fourteen patients (53.8%) achieved complete remission (CR) after CART2. CART2 exhibited an inferior response rate (CART2: 53.8%, 14/26; CART1: 81.0%, 64/79; P = 0.006) and a lower incidence of severe CRS (CART2: 7.7%, 2/26; CART1: 30.4%, 24/79; P = 0.020) compared with CART1, with a median progression-free survival (PFS) and a median overall survival (OS) of 6.2 months and 11.2 months, respectively. In particular, patients who progressed after consolidative allogeneic hematopoietic stem cell transplantation (allo-HSCT) following CART1 and then received CART2 demonstrated promising outcomes with a response rate of 80.0% (8/10), a median PFS of 7.9 months, and a median OS of 25.1 months. After adjusting for the confounding factors, the response rate (85.7%, 6/7) of CART2 administered to this cohort was better than those who did not bridge to allo-HSCT receiving CART2 (28.6%, 2/7) or non-CART2 treatments (13.3%, 2/15). The median OS after CART2, which was not reached, was significantly better than the median OS after CART2 (3.9 months, P = 0.014) and non-CART2 treatments (6.0 months, P = 0.012) administered in patients who did not undergo consolidative allo-HSCT post-CART1. Our results indicated that, although less effective than CART1, a subset of patients can still benefit from CART2 with mild adverse effects. For patients who relapsed after consolidative allo-HSCT post-CART1, treatment with CART2 is a viable option.

Rock bridges control the stability of rock slopes with intermittent joints. To better understand the evolution of water-induced damage in rock bridges, we developed new, water-sensitive similar materials and investigated their physical, mechanical, and hydraulic properties. The reasonable proportions of the material components required to simulate a rock bridge were systematically determined based on deformation failure modes, shear creep characteristics, deterioration degree, and the brittleness characteristics after water-rock interaction. Sensitivity analysis and contribution rates were used to characterize the effects of different influencing factors on the mechanical properties of similar materials. We found that one similar material specimen, SM-24, exhibited the analogous characteristics of natural rock bridge in shear creep behavior, crack propagation, and ruptured surfaces. Such similar material for modeling rock bridges has the following mechanical characteristics: reproducing the volume-expansion point, showing high deterioration degree and specific brittle failure characteristics after water-rock interaction, and reproducing the identifiable precursor of accelerated creep and stepped fracture morphology subjected to shear creep loading. The bone-binder ratio dominated mechanical properties of similar materials, while physical and hydraulic properties depended mainly on the bentonite content. These findings provide guidelines for reliably producing similar materials that meet the requirements of different geomechanical model tests in solving geotechnical engineering problems considering the water-rock interactions.

Abstract Incorporating positive temperature coefficient (PTC) ceramic particles into polymers provides a prospective alternative solution for suppressing severe electric field distortions caused by negative temperature coefficient (NTC) of electrical resistivity within polymer insulation in high voltage direct current cable. The effect of the Curie temperature of PTC particles on the inhibition of the NTC effect of cross‐linked polyethylene (XLPE) is investigated in this study. Positive temperature coefficient particles with varied Curie temperatures are surface‐modified and melt blending with XLPE. The modified particles are dispersed on averagely in the matrix. The electrical resistivity, space charge behaviour, and direct current (DC) electrical breakdown strength of the samples are investigated at different temperatures, exploring the effect of the Curie temperature of PTC particles on suppressing the NTC effect of XLPE. It is demonstrated that the reduction in Curie temperature can further suppress the NTC effect of XLPE, enhance the DC strength at elevated temperatures, and inhibit the internal space charge accumulation. The reduction in the Curie temperatures means that the initiation of the PTC effect advances, exhibiting a higher potential barrier and inhibiting the NTC effect more effectively. This work may give a reference for improving the temperature stability of DC properties for XLPE cable insulation.

Steel plate shear walls (SPSWs) are widely used in earthquake-prone areas, and they usually undergo multiple earthquakes throughout their service lives. The performances of SPSWs under a single shot of an earthquake have been widely studied, although the secondary seismic performance of earthquake-damaged structures remains unclear. Damage to an SPSW structure during an earthquake is difficult to evaluate. In this study, the degradation of the seismic performance of SPSWs during earthquakes was investigated. A test specimen was subjected to a two-stage quasistatic load. The plastic development and failure modes of SPSW specimens were investigated. The degradation of bearing capacity, stiffness, and energy dissipation performance was analyzed. On the basis of the experimental investigation, finite-element models introducing the ductile damage criterion and the cyclic constitutive model of steel were established. The degradation of the seismic performance of SPSWs under secondary earthquakes was studied. The results indicate that the stiffness of the SPSWs degrades more significantly than the bearing capacity. The larger the height-to-thickness ratio of the embedded plate, the more obvious the decrease in the bearing capacity, and the smaller the decrease in stiffness. With the increase in the maximum lateral displacement reached by the structure in an earthquake, and as the pinch phenomenon of the hysteresis curve of the SPSW becomes more serious, the energy dissipation capacity shows a uniform downward trend.

The differences in geomechanical properties and the uncertainty in the spatial distribution of Bimrock pose significant challenges to the construction and disaster prediction of geotechnical engineering. To clarify the geomechanical characteristics of Bimrock, this paper summarizes the basic concepts and classification methods of Bimrock at home and abroad. It discusses the methods and characteristics of determining the geometric features of Bimrock blocks and explores the influencing factors and laws of failure modes and strength under different stress states of Bimrock. The study finds that the failure mode of Bimrock is mainly influenced by factors such as block proportion, degree of welding between blocks and matrix, strength ratio between blocks and matrix, and geometric properties of blocks. Among these factors, block proportion is the most significant, and the degree of welding is a controlling factor. However, due to the complexity of Bimrock structures, there is a lack of applicable methods and mechanical models for the evaluation of geomechanical characteristics of Bimrock in engineering practice. This article also explores the influence and research methods of the geological characteristics of Bimrock in slope and tunnel engineering and, finally, provides prospects for the future research trends relating to Bimrock.

Abstract It is a critical quality assurance activity to effectively detect the root cause of faults causing the software crashes (i.e. crashing faults). Previous studies extracted features to characterise crash instances and built models to identify whether the residences of crashing faults locate inside the stack traces. These models all belong to supervised learning methods which require labelled crash data to be involved. In this study, the introduction of an unsupervised model, called Transfer Spectral Clustering (TSC), for the task of crashing fault residence identification under the unlabelled data scenario is proposed. Unlike traditional unsupervised methods which are applied to individual project data, TSC transfers the knowledge of auxiliary unlabelled data from the source project to assist the clustering task on the unlabelled data from the target project. TSC is an unsupervised transfer learning method, and simultaneously considers the data manifold information of the individual project and feature manifold information across projects to facilitate the clustering effect. Extensive experiments are conducted on a benchmark dataset containing seven software projects. Five indicators were chosen for performance evaluation. The results show that TSC achieves better performance than four clustering based unsupervised methods, and competitive performance compared with eight supervised cross‐project methods.

Vegetation slope protection has become more popular in recent years as an environmentally friendly support approach for slope protection projects. Thus, it's critical to study how to properly plant vegetation to maximize its slope protection effect. As a result, this work uses arbor as the research object and 3D printing technology to construct a model of the arbor root. The slope protection effect of arbor was studied by using a self-designed landslide model test system and conducting systematic physical model tests with three different root arrangement modes and three different root spacings. The results reveal that: ①With tree roots supporting the slope, the peak value of anti-sliding force increases, the time to reach the peak value of anti-sliding force is substantially delayed, and anti-sliding force attenuation is reduced. ②Anti-arch arrangement>positive arch arrangement>linear arrangement>no root support in terms of anti-sliding force improvement. S=1.5D (root coverage range D=7 cm) has a similar improved impact to S= 2.0D, while S=2.5D has the highest peak value of anti-sliding force. ③In terms of the root arrangement mode, the anti-arch arrangement has the most obvious improvement effect on the slope displacement, while the positive arch arrangement and linear arrangement are close in terms of the improvement effect on the displacement. In terms of the root spacing, S=2.5D has the most obvious improvement effect on the slope displacement, while S=1.5D and S=2.0D are close in terms of the improvement effect on the slope. ④Under the linear arrangement, the slope's sliding range expands, whereas the positive arch and anti-arch arrangements effectively limit slope deformation. Root spacing has no obvious effect on the slope deformation field.In conclusion, managing the arrangement mode of trees and root spacing in the vegetation protection project can increase the slope protection effect.

In order to increase the economic benefits of offshore wind farms, reducing the wake effect on the power and fatigue load of wind farms is one of the fundamental ways. In this study, the optimal method of combing yaw offset and pitch control (CYOPC) is put forward for wind farm power and fatigue loads below rated wind speed. To quantify potential power increase and fatigue loads decrease, three turbines in China’s Binhai offshore wind farm are investigated using FAST.Farm. The simulation studies are carried out in region 2 of turbine operation and compared with under yaw control only. Results indicate that the CYOPC method has the potential to increase power and reduce fatigue load.

Ciphertext-policy attribute-based encryption(CP-ABE) can be used to realize secure data sharing in cloud computing environments.However,user attribute change(attribute revocation and addition) in CP-ABE is a tricky problem.Generally,attribute change is realized via the proxy server’s secondary encryption of ciphertext and key update.However,when enforcing an attribute change,all ciphertexts related to this attribute should be updated.This paper proposes a user attribute change approach based on lazy-mode ciphertext-update.It analyzes the user’s access ability(before attribute revocation or after attribute addition) to the ciphertexts involved in attribute change and determines if these ciphertexts need to be updated,minimizing the scope of the ciphertexts that need to be updated and reducing the number of updates.This approach improves its efficiency by avoiding unnece-ssary ciphertext updates and shortening the ciphertext while preserving the original security features of the CP-ABE.Finally,a small-size test is conducted to verify the correctness of the proposed approach.

Abstract Background Cell invasion is a crucial step of tumor metastasis, finding new regulators of which offers potential drug targets for cancer therapy. Aberrant GLYAT expression is associated with human cancers, yet its role in cancer remains unknown. This study aims to understand the function and mechanism of Drosophila GLYAT in cell invasion. Results We found that dGLYAT regulates Gadd45-mediated JNK pathway activation and cell invasion. Firstly, loss of dGLYAT suppressed scrib depletion- or Egr overexpression-induced JNK pathway activation and invasive cell migration. Secondary, mRNA-seq analysis identified Gadd45 as a potential transcriptional target of dGLYAT, as depletion of dGLYAT decreased Gadd45 mRNA level. Finally, Gadd45 knockdown suppressed scrib depletion-induced JNK pathway activation and cell invasion. Conclusions These evidences reveal the role of dGLYAT and Gadd45 in JNK-dependent cell invasion, and provide insight for the roles of their human homologs in cancers.

Abstract Background Papiliotrema flavescens is a rare environmental yeast, which has been isolated from air, trees, kernels of wheat and corn, fermenting soya sauce, and cerebrospinal fluid of patient with AIDS. Additionally, it is also reported to cause subcutaneous infection in a dog. In this case, we describe primary lung adenocarcinoma coexisting with Papiliotrema flavescens infection in a female patient diagnosed by next-generation sequencing (NGS) technique, which is the first such reported case. Case presentation The patient was a 52-year-old female with recurrent cough for 3 months. Chest CT examination revealed a ground glass nodule of 17 * 23 * 18 mm in the right upper lung, and 3 new pulmonary nodules appeared around it 2 months later. The patient underwent right upper lobe lobectomy and pathology confirmed that the primary 2-cm-lesion in the right upper lobe was invasive lung adenocarcinoma, and two of the three surrounding lung nodules were pathologically suggestive of pulmonary fungal infection (not known in specific fungal types). Hence, the patient received empirical anti-fungal treatments with fluconazole 400 mg/day for a week and follow-up CT scanning showed no tumor progression and no relapse of fungal infection. The specific pathogen was eventually identified as Papiliotrema flavescens by the next-generation sequencing of pathogen. Discussion and conclusion We first reported that lung cancer coexisting with Papiliotrema flavescens infection in a female patient. The diagnosis of lung cancer with typical CT imaging features is relatively simple, while the diagnosis of lung cancer coexisting with rare fungal infection is challenging. NGS technique is an effective supplementary technique for clinical diagnosis of bacterial or fungal infectious diseases, enabling precise clinical decision-making and appropriate treatment. In this case, the lung cancer may result in a degree of immune suppression, at least locally, resulting in the formation of pulmonary fungal nodular lesions around the tumor.

Testicular germ cell tumors (TGCTs) commonly occur in males between the ages of 15 and 34, accounting for 98% of testicular malignancies. Long non-coding RNAs (LncRNAs) have been reported to play important roles in TGCT proliferation, invasion, and functioned as prognostic biomarkers. Testis-specific transcript, Y-linked 14 (TTTY14), a long non-coding RNA localized on Chr Y q11.222, has been found to be a potential prognostic biomarker for laryngeal squamous cell carcinoma, gastric cancer, and osteosarcoma. The biological role of TTTY14 in TGCT is not well understood. In this study, we aim to clarify the biological role of TTTY14 in TGCT, as well as its role in TGCT survival prognosis and immunotherapy efficacy prediction through the deep mining of public data combined with the verification of cell biological experiments. We found that high TTTY14 expression was a poor survival prognostic factor in TGCT patients and the expression of TTTY14 might be regulated by copy number variation and DNA methylation. TTTY14 knockdown significantly inhibited the proliferation of TGCT in vitro. TTTY14 expression was positively correlated with immune cell dysfunction, and significantly negatively correlated with B cells, CD8+ T cells, and macrophages, suggesting that TTTY14 may also affect the drug sensitivity by regulating the tumor immune microenvironment. In conclusion, we revealed that lncRNA TTTY14 was a novel oncogene and a biomarker in TGCT. TTTY14 may influence the drugs sensitivity through regulating the tumor immune microenvironment.

Hepatocellular carcinoma (HCC) accounts for approximately 90% of all primary liver cancers and is one of the main malignant tumor types globally. It is essential to develop rapid, ultrasensitive, and accurate strategies for the diagnosis and surveillance of HCC. In recent years, aptasensors have attracted particular attention owing to their high sensitivity, excellent selectivity, and low production costs. Optical analysis, as a potential analytical tool, offers the advantages of a wide range of targets, rapid response, and simple instrumentation. In this review, recent progress in several types of optical aptasensors for biomarkers in early diagnosis and prognosis monitoring of HCC is summarized. Furthermore, we evaluate the strengths and limitations of these sensors and discuss the challenges and future perspectives for their use in HCC diagnosis and surveillance.

Abstract Android apps have played important roles in daily life and work. To meet the new requirements from users, the apps encounter frequent updates, which involves a large quantity of code commits. Previous studies proposed to apply Just‐in‐Time (JIT) defect prediction for apps to timely identify whether the new code commits can introduce defects into apps, aiming to assure their quality. In general, high‐quality features are benefits for improving the classification performance. In addition, the number of defective commit instances is much fewer than that of clean ones, that is the defect data is class imbalanced. In this study, a novel compositional model, called KPIDL, is proposed to conduct the JIT defect prediction task for Android apps. More specifically, KPIDL first exploits a feature learning technique to preprocess original data for obtaining better feature representation, and then introduces a state‐of‐the‐art cost‐sensitive cross‐entropy loss function into the deep neural network to alleviate the class imbalance issue by considering the prior probability of the two types of classes. The experiments were conducted on a benchmark defect data consisting of 15 Android apps. The experimental results show that the proposed KPIDL model performs significantly better than 25 comparative methods in terms of two effort‐aware performance indicators in most cases.

IntroductionWe firstly investigate based on 2022 European Leukemia Net (ELN) risk stratification, whether standard-dose cytarabine based multiagent sequential chemotherapy (SDMSC) is more beneficial than high-dose cytarabine (HDAC) monotherapy in consolidation for the survival of adult acute myeloid leukemia (AML) patients.MethodsOne hundred and eighty-three AML patients with complete remission (CR) were evaluated.Results and discussionThe 3-year relapse rate was 33.4% in the HDAC group and 50.5% in the SDMSC group (p=0.066). The 3-year overall survival (OS) and event-free survival (EFS) rates in the HDAC group (69.2%, 60.7%) were significantly higher than that in the SDMSC group (50.8%, 42.1%) (p=0.025, 0.019). For patients in the intermediate risk group, the 3-year OS and EFS rates in the HDAC group (72.5%, 56.7%) were higher than that in the SDMSC group (49.1%, 38.0%) (p=0.028, 0.093). This study indicates that for young adult AML patients, HDAC consolidation achieves a higher long-term survival than SDMSC, especially for patients in the intermediate-risk group according to the 2022 ELN risk stratification.

Abstract Metastasis is the most important reason for the poor prognosis of gastric cancer (GC) patients, and the mechanism urgently needs to be clarified. Here, we explored a prognostic model for the estimation of tumor-associated mortality in GC patients and revealed the RNA-binding protein RBMS1 as a candidate promoter gene for GC metastasis by analyzing GOBO and Oncomine high-throughput sequencing datasets for 408 GC patients. Additionally, RBMS1 was observed with overexpression in 85 GC patient clinical specimens by IHC staining and further be verified its role in GC metastasis via inducing EMT process both in in vitro and in vivo experiments. Moreover, we identified that IL-6 was predicted to be one of the most significant upstream cytokines in the RBMS1 overexpression gene set based on the Ingenuity Pathway Analysis (IPA) algorithm. Most importantly, we also revealed that RBMS1 could promote migration and invasion through IL6 transactivation and JAK2/STAT3 downstream signaling pathway activation by influencing histone modification in the promoter regions after binding with the transcription factor MYC in the HGC-27 and SGC-7901 GC cell lines. Hence, we shed light on the potential molecular mechanisms of RBMS1 in the promotion of GC metastasis, which suggests that RBMS1 may be a potential therapeutic target for GC patients.

Abstract Acoustic emission (AE) characterization is an effective technique to indirectly capture the failure process of quasi brittle rock. In previous studies, both experiments and numerical simulations were adopted to investigate the AE characteristics of rocks. However, as the most popular numerical model, the moment tensor model (MTM) cannot be constrained by the experimental result because there is a gap between MTM and experiments in principle, signal processing and energy analysis. In this paper, we developed a particle-velocity-based model (PVBM) that enabled direct monitoring and analysis of the particle velocity in the numerical model and had good robustness. The PVBM imitated the actual experiment and could fill in gaps between the experiment and MTM. AE experiments of marine shale under uniaxial compression were carried out, and the results were simulated by MTM. In general, the variation trend of the experimental result could be presented by MTM. Nevertheless, the magnitudes of AE parameters by MTM presented notable differences of more than several orders of magnitude compared with those by the experiment. We sequentially used PVBM as a proxy to analyse these discrepancies and systematically evaluate the AE characterization of rocks from the experiment to numerical simulation, considering the influence of wave reflection, energy geometrical diffusion, viscous attenuation, particle size and progressive deterioration of rock material. The combination of MTM and PVBM could reasonably and accurately acquire AE characteristics of the actual AE experiment of rocks by making full use of their respective advantages.

The prediction of stock prices holds significant implications for researchers and investors evaluating stock value and risk. In recent years, researchers have increasingly replaced traditional machine learning methods with deep learning approaches in this domain. However, the application of deep learning in forecasting stock prices is confronted with the challenge of overfitting. To address the issue of overfitting and enhance predictive accuracy, this study proposes a stock prediction model based on a gated recurrent unit (GRU) with reconstructed datasets. This model integrates data from other stocks within the same industry, thereby enriching the extracted features and mitigating the risk of overfitting. Additionally, an auxiliary module is employed to augment the volume of data through dataset reconstruction, thereby enhancing the model’s training comprehensiveness and generalization capabilities. Experimental results demonstrate a substantial improvement in prediction accuracy across various industries.

Psychosocial and stress-related factors (PSFs), defined as internal or external stimuli that induce biological changes, are potentially modifiable factors and accessible targets for interventions that are associated with adverse pregnancy outcomes (APOs). Although individual APOs have been shown to be connected to PSFs, they are biologically interconnected, relatively infrequent, and therefore challenging to model. In this context, multi-task machine learning (MML) is an ideal tool for exploring the interconnectedness of APOs on the one hand and building on joint combinatorial outcomes to increase predictive power on the other hand. Additionally, by integrating single cell immunological profiling of underlying biological processes, the effects of stress-based therapeutics may be measurable, facilitating the development of precision medicine approaches.ObjectivesThe primary objectives were to jointly model multiple APOs and their connection to stress early in pregnancy, and to explore the underlying biology to guide development of accessible and measurable interventions.Materials and MethodsIn a prospective cohort study, PSFs were assessed during the first trimester with an extensive self-filled questionnaire for 200 women. We used MML to simultaneously model, and predict APOs (severe preeclampsia, superimposed preeclampsia, gestational diabetes and early gestational age) as well as several risk factors (BMI, diabetes, hypertension) for these patients based on PSFs. Strongly interrelated stressors were categorized to identify potential therapeutic targets. Furthermore, for a subset of 14 women, we modeled the connection of PSFs to the maternal immune system to APOs by building corresponding ML models based on an extensive single cell immune dataset generated by mass cytometry time of flight (CyTOF).ResultsJointly modeling APOs in a MML setting significantly increased modeling capabilities and yielded a highly predictive integrated model of APOs underscoring their interconnectedness. Most APOs were associated with mental health, life stress, and perceived health risks. Biologically, stressors were associated with specific immune characteristics revolving around CD4/CD8 T cells. Immune characteristics predicted based on stress were in turn found to be associated with APOs.ConclusionsElucidating connections among stress, multiple APOs simultaneously, and immune characteristics has the potential to facilitate the implementation of ML-based, individualized, integrative models of pregnancy in clinical decision making. The modifiable nature of stressors may enable the development of accessible interventions, with success tracked through immune characteristics.

Shallow-water coral reefs hold large quantities of acrylate and its precursor dimethylsulfoniopropionate (DMSP), but production and removal processes for these compounds are poorly characterized. Here we determined the concentrations and cycling of acrylate and DMSP in a transect from a coral reef ecosystem to the open ocean, 2 km beyond the reef in Mo’orea, French Polynesia, during April 2018. Concentrations of dissolved acrylate and DMSP were low throughout the reef-ocean transect, ranging from 0.8–3.9 nM and 0.2–3.0 nM, respectively, with no difference observed between the coral reef and open ocean when comparing mean concentrations (± std dev) of dissolved acrylate (1.7 ± 0.7 vs 2.3 ± 0.8 nM) or DMSP (0.9 ± 0.7 vs 1.3 ± 0.6 nM). In the coral reef, dissolved acrylate was rapidly taken up by the heterotrophic community with a fast turnover time averaging ~ 6 h, six times faster than in the open ocean, and nearly as fast as the average turnover time of dissolved DMSP (~ 3 h). A clear diel trend was observed for the heterotrophic consumption of dissolved acrylate and DMSP in the coral reef, with higher uptake rate constants during daylight hours, synchronized with the larger daytime release of acrylate and DMSP from the coral compared to the nighttime release of these compounds. We also measured photochemical production rates of acrylate in Mo’orean waters, but rates were one to two orders of magnitude slower compared to its rates of biological consumption. Coral and macroalgae were the main sources of dissolved acrylate and DMSP to the reef ecosystem. Our results indicate there is rapid turnover of acrylate and DMSP in the coral reef with a tight coupling between production and removal pathways that maintain dissolved concentrations of these two compounds at very low levels. These algal and coral-derived substrates serve as important chemical links between the coral and heterotrophic communities, two fundamental components in the ecological network in coral reefs.

Volatile organic compounds (VOCs) are constituents of marine ecosystems including coral reefs, where they are sources of atmospheric reactivity, indicators of ecosystem state, components of defense strategies, and infochemicals. Most VOCs result from sunlight-related processes; however, their light-driven dynamics are still poorly understood. We studied the spatial variability of a suite of VOCs, including dimethylsulfide (DMS), and the other dimethylsulfoniopropionate-derived compounds (DMSPCs), namely, DMSP, acrylate, and dimethylsulfoxide (DMSO), in waters around colonies of two scleractinian corals (Acropora pulchra and Pocillopora sp.) and the brown seaweed Turbinaria ornata in Mo’orean reefs, French Polynesia. Concentration gradients indicated that the corals were sources of DMSPCs, but less or null sources of VOCs other than DMS, while the seaweed was a source of DMSPCs, carbonyl sulfide (COS), and poly-halomethanes. A focused study was conducted around an A. pulchra colony where VOC and DMSPC concentrations and free-living microorganism abundances were monitored every 6 h over 30 h. DMSPC concentrations near the polyps paralleled sunlight intensity, with large diurnal increases and nocturnal decrease. rDNA metabarcoding and metagenomics allowed the determination of microbial diversity and the relative abundance of target functional genes. Seawater near coral polyps was enriched in DMS as the only VOC, plus DMSP, acrylate, and DMSO, with a large increase during the day, coinciding with high abundances of symbiodiniacean sequences. Only 10 cm below, near the coral skeleton colonized by a turf alga, DMSPC concentrations were much lower and the microbial community was significantly different. Two meters down current from the coral, DMSPCs decreased further and the microbial community was more similar to that near the polyps than that near the turf alga. Several DMSP cycling genes were enriched in near-polyp with respect to down-current waters, namely, the eukaryotic DMS production and DMS oxidation encoding genes, attributed to the coral and the algal symbiont, and the prokaryotic DMS production gene dddD, harbored by coral-associated Gammaproteobacteria. Our results suggest that solar radiation-induced oxidative stress caused the release of DMSPCs by the coral holobiont, either directly or through symbiont expulsion. Strong chemical and biological gradients occurred in the water between the coral branches, which we attribute to layered hydrodynamics.

Additive Manufacturing (AM) has quickly emerged as a promising technology to manufacture Shape Memory Alloy (SMA)-based components of complex geometries. 3D-printed High Temperature SMAs (HT-SMAs) components can enable applications that demand operating temperatures well beyond conventional NiTi SMAs. Here, a detailed printability assessment of NiTiHf HT-SMAs fabricated using laser powder bed fusion (LPBF) AM is conducted for the first time. Specifically, the regions associated with lack of fusion, keyholing, and balling regimes are quantitatively classified through an efficient printability assessment framework. Nearly porosity-free specimens are achieved from the predicted good printable region. The effects of key processing parameters (laser power, scanning speed, and hatch spacing) on the microstructure variation and phase transformation characteristics are studied systematically. A positive correlation is observed between the transformation temperatures and volumetric energy density (EV). Beyond a critical EV of 100 J/mm3, the transformation temperatures become insensitive to further increases in EV. At identical or similar energy density levels, the individual change of laser power, scanning speed, or hatch spacing alters the transformation behavior to a certain extent. It is hypothesized that to a large extent, this behavior is due to the effects that processing conditions have on the differential evaporation of Ni from the melt pool. A model connecting thermal histories, scanning strategy and chemistry changes is found to be consistent with this hypothesis. It is shown that the transformation temperatures in AM NiTiHf HT-SMAs can be varied over a range of more than 160 °C by controlling Ni evaporation through altering processing parameters.

Abstract IL6 is one of the most elevated cytokines during chimeric antigen receptor (CAR) T cell cytokine release syndrome (CRS), and IL6R blockade by Tocilizumab has successfully relieved the most life-threatening aspects of CRS in patients. In addition, latest studies demonstrated the essential role of IL1 in driving CART induced neurotoxicity in mouse models. Here we present a clinical investigation (ChiCTR2000032124; ChiCTR2000031868) of anti-CD19 and anti-BCMA CART (41BBζ) secreting an anti-IL6 scFv and IL1 receptor antagonist (IL1RA) in treating patients with hematologic malignancy. Our results revealed that IL6 and IL1B were maintained at low levels without significant elevation during CRS, rendering Tocilizumab dispensable. Moreover, treated patients did not show neurotoxicity during CRS and exhibited mild to moderate CRS. Notably, we observed high rate of complete response (CR) and significant CART expansion during treatment. In sum, we conclude that CART-secreting anti-IL6 scFv and IL1RA could self-neutralize IL6 storm and maintain low levels of IL1B during CART therapy to minimize IL6- and IL1-associated cytokine toxicity and neurotoxicity without impairing therapeutic efficacy.

Abstract Cancer is one of the most fatal diseases that threaten human health, whereas more than 90% mortality of cancer patients is caused by tumor metastasis, rather than the growth of primary tumors. Thus, how to effectively control or even reverse the migration of tumor cells is of great significance for cancer therapy. CtBP, a transcriptional cofactor displaying high expression in a variety of human cancers, has become one of the main targets for cancer prediction, diagnosis, and treatment. The roles of CtBP in promoting tumorigenesis have been well studied in vitro, mostly based on gain-of-function, while its physiological functions in tumor invasion and the underlying mechanism remain largely elusive. Snail (Sna) is a well-known transcription factor involved in epithelial-to-mesenchymal transition (EMT) and tumor invasion, yet the mechanism that regulates Sna activity has not been fully understood. Using Drosophila as a model organism, we found that depletion of CtBP or snail (sna) suppressed RasV12/lgl -/- -triggered tumor growth and invasion, and disrupted cell polarity-induced invasive cell migration. In addition, loss of CtBP inhibits RasV12/Sna-induced tumor invasion and Sna-mediated invasive cell migration. Furthermore, both CtBP and Sna are physiologically required for developmental cell migration during thorax closure. Finally, Sna activates the JNK signaling and promotes JNK-dependent cell invasion. Given that CtBP physically interacts with Sna, our data suggest that CtBP and Sna may form a transcriptional complex that regulates JNK-dependent tumor invasion and cell migration in vivo.

Abstract Background Radiation-induced dermatitis is a serious side effect of radiotherapy, and very few effective treatments are currently available for this condition. We previously demonstrated that apoptosis is an important feature of radiation-induced dermatitis and adipose-derived stem cells (ADSCs) are one of the most promising types of stem cells that have a protective effect on acute radiation-induced dermatitis. Cathepsin F (CTSF) is a recently discovered protein that plays an important role in apoptosis. In this study, we investigated whether ADSCs affect chronic radiation-induced dermatitis, and the underlying mechanisms involved. Methods ADSCs were isolated from male Sprague-Dawley (SD) rats and characterized. For in vivo studies, rats were randomly divided into control and ADSC-treated groups, and cultured ADSCs were transplanted into radiation-induced dermatitis model rats. The effects of ADSC transplantation were determined by skin damage scoring, histopathological analysis, electron microscopy, immunohistochemical staining, and western blotting analysis of apoptosis-related proteins. To evaluate the effects of ADSCs in vitro, radiation-induced apoptotic cells were treated with ADSC culture supernatant, and apoptosis-related protein expression was investigated by TUNEL staining, flow cytometry, and western blotting. Results In the in vivo studies, skin damage, inflammation, fibrosis, and apoptosis were reduced and hair follicle and sebaceous gland regeneration were enhanced in the ADSC group compared with the control group. Further, CTSF and downstream pro-apoptotic proteins (Bid, BAX, and caspase 9) were downregulated, while anti-apoptotic proteins (Bcl-2 and Bcl-XL) were upregulated. In vitro, ADSCs markedly attenuated radiation-induced apoptosis, downregulated CTSF and downstream pro-apoptotic proteins, and upregulated anti-apoptotic proteins. Conclusion ADSCs protect against radiation-induced dermatitis by exerting an anti-apoptotic effect through inhibition of CTSF expression. ADSCs may be a good therapeutic candidate to prevent the development of radiation-induced dermatitis.

AbstractLandslides have posed a huge threat to the ecological environment and human society all over the world. As the most conventional reinforcement method, anti-slide piles are widely used in the reinforcement of slopes. Currently, more and more attention has been paid to the low-cost and high-efficiency optimal design of anti-slide piles. However, limitations in the method of the optimization design for slopes reinforced with piles still exist. In this paper, a new multi-objective comprehensive optimization method was proposed for the optimization of the slope reinforced with anti-slide piles. The factor of safety, internal force, and deflection of piles were selected as the optimization indexes, and the optimization index weight was determined by integrating the subjective and objective weights. The influence of pile locations, pile lengths, and pile spacings on the reinforcement effect of a homogeneous slope was analyzed via the numerical simulation. Through the simulation case analysis, the proposed model had achieved good effects on the optimization design of anti-slide piles, which could effectively reduce the engineering costs. The optimization results showed that the best reinforcement effect for the homogeneous slope could be obtained when the anti-slide piles with the critical pile length and small pile spacing were located in the middle of the slope. This provides a new solution for the optimization design of other types of complex slopes and has broad application prospects.