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Load prediction tasks aim to predict the dynamic trend of future load based on historical performance sequences, which are crucial for cloud platforms to make timely and reasonable task scheduling. However, existing prediction models are limited while capturing complicated temporal patterns from the load sequences. Besides, the frequently adopted global weighting strategy (e.g., the self-attention mechanism) in temporal modeling schemes has quadratic computational complexity, hindering the immediate response of cloud servers in complex real-time scenarios. To address the above limitations, we propose a Wavelet decomposition-enhanced External Transformer (WETformer) to provide accurate yet efficient load prediction for cloud servers. Specifically, we first incorporate discrete wavelet transform to progressively extract long-term trends, highlighting the intrinsic attributes of temporal sequences. Then, we propose a lightweight multi-head External Attention (EA) mechanism to simultaneously consider the inter-element relationships within load sequences and the correlations across different sequences. Such an external component has linear computational complexity, mitigating the encoding redundancy prevalent and enhancing prediction efficiency. Extensive experiments conducted on Alibaba Cloud's cluster tracking dataset demonstrate that WETformer achieves superior prediction accuracy and the shortest inference time compared to several state-of-the-art baseline methods. [ABSTRACT FROM AUTHOR]

It is a time-consuming and costly process to develop affordable and high-performance organic photovoltaic materials. Computational methods are essential for accelerating the material discovery process by predicting power conversion efficiencies (PCE). In this study, we propose a deep learning-based framework (DeepAcceptor) to design and discover highly efficient small molecule acceptor materials. Specifically, an experimental dataset is constructed by collecting acceptor data from publications. Then, a deep learning-based model is customized to predict PCEs by applying graph representation learning to Bidirectional Encoder Representations from Transformers (BERT), with the atom, bond, and connection information in acceptor molecular structures as the input (abcBERT). The computational dataset derived from density functional theory (DFT) calculations and the experimental dataset from literature are used to pre-train and fine-tune the model, respectively. The abcBERT model outperforms other state-of-the-art models for the PCE prediction with MAE = 1.78 and R2 = 0.67 on the test set. A molecular generation and screening process is built to find new high-performance acceptors for PM6. Three discovered candidates are further validated by experiment, and the best PCE reaches 14.61%. The released user-friendly interface of DeepAcceptor greatly boosts the accessibility and efficiency of designing and discovering high-performance acceptors. Altogether, the DeepAcceptor framework with abcBERT is promising to predict the PCE and accelerate the discovery of high-performance acceptor materials. [ABSTRACT FROM AUTHOR]

This study aims to understand the connectivity characteristics of construction land during the spatial evolution of historic urban areas. The complex network analysis method is employed to examine the evolution of the spatial structure of construction land in the four periods of the Changsha historic urban area. The results indicate that the spatial network morphology of construction land follows a repeated development pattern resembling a cluster–mother–child relationship. Additionally, the vulnerability in the evolution of land use spatial relationships is very low, while the overall stability of the land use network structure decreases. The spatial equilibrium of land use shows a downwards trend, the accessibility of land where existing cultural relics and historic sites are located is generally moderate to low, and some plots require improvement. In the evolution of connectivity in the construction land of historic urban areas, the main development challenges are the uneven growth caused by imbalanced land value influenced by capital and the need to balance the preservation of land use patterns with improved accessibility to cultural relics and historic sites. Therefore, it is highly important to dynamically detect and optimize the network structure of construction land to protect and develop historic urban areas. [ABSTRACT FROM AUTHOR]

In the present study, the novel environmental-friendly adsorbent (PAA-g-CMCDAS) obtained from carboxymethyl chitosan (CMC), dialdehyde starch (DAS) and polyacrylic acid (PAA) is used for removing silver ion. The adsorbent characterized by XRD, SEM, TGA, BET and FTIR, was evaluated for removal of silver ion from aqueous solution. The adsorption process of silver ions conformed to Langmuir isotherm and the second-order kinetic mode. The maximum adsorption capacity for silver ions was 404.77 mg·g−1, and the maximum removal efficiency was 95.05%. Also, the reusability and selectivity of PAA-g-CMCDAS was investigated, and the adsorption amount was still 388.77 mg·g−1 and the removal efficiency was up to 91.29% after five times recycle; the removal efficiency of silver ions in mixed metal ions solution was 24.24% with the total removal efficiency of mixed metal ions 58%. Therefore, PAA-g-CMCDAS was an excellent silver ion adsorbent with good reusability. [ABSTRACT FROM AUTHOR]

With the development of convolutional neural networks (CNNs), salient object detection methods have made great progress in performance. Most methods are designed with complex structures to aggregate the multi-level feature maps, to reach the goal of filtering noise and obtaining rich information. However, there is no differentiation when dealing with the multi-level features, and only a uniform treatment is used in general. Based on the above considerations, in this paper, we propose a multi-branch feature fusion and refinement network (MFFRNet), which is a framework for treating low-level features and high-level features differently, and effectively fuses the information of multi-level features to make the results more accurate. We propose a detail optimization module (DOM) designed for the rich detail information in low-level features and a pyramid feature extraction module (PFEM) designed for the rich semantic information in high-level features, as well as a feature optimization module (FOM) for refining the fused feature of multiple levels. Extensive experiments are conducted on six benchmark datasets, and the results show that our approach outperforms the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

PM2.5, as one of the most harmful pollutant in the atmospheric environment and population health, has received much attention. We monitored PM2.5 levels at five sampling sites in the Lanzhou City and collected PM2.5 particles from two representative sites for cytotoxicity experiment. The cytotoxicity of PM2.5 samples on A549 cells and migration ability of the cells were respectively detected by Cell Counting kit-8 (CCK-8) assay and scratch assay. We detected the levels of cellular inflammatory factors and oxidative damage-related biochemical indexes. RT-qPCR was used to detect the mRNA levels of NF-κB and epithelial-mesenchymal transition (EMT)-related genes. We found that the Lanlian Hotel station had the highest PM2.5 annual average concentration. The annual average concentration change curve of PM2.5 showed a roughly "U"-shaped distribution during the whole sampling period. The cytotoxicity experiment showed the viability of A549 cells decreased and the scratch healing rate increased in the 200 and 400 μg/mL PM2.5-treated groups. We also found 400 μg/mL PM2.5 induced changes in the mRNA levels of NF-κB and EMT-related genes, the mRNA levels of IKK-α, NIK, and NF-κB in the 400 μg/mL PM2.5 group were higher than those in the control group. The mRNA levels of E-cadherin decreased and α-SMA increased in the 400 μg/mL PM2.5 groups, and the mRNA levels of Fibronectin increased in the 400 μg/mL PM2.5 groups. Moreover, we found hydroxyl radical scavenging ability and T-AOC levels were lower, and LPO levels were higher in the 200 and 400 μg/mL PM2.5 groups, and the SOD activity of cells in the 400 µg/mL PM2.5 group decreased. And compared with the control group, the levels of TNF-α were higher in the 200 and 400 μg/mL PM2.5 groups and the levels of IL-1 were higher in the 400 μg/mL PM2.5 group. The results indicated that the cytotoxicity of atmospheric PM2.5 was related to oxidative damage, inflammatory response, NF-κB activity and EMT. [ABSTRACT FROM AUTHOR]

Aiming at the received signal strength index (RSSI) in wireless positioning system, an improved weighted KNN fingerprint positioning algorithm is proposed in this paper. The algorithm pre-processes fingerprint data in offline stage that including eliminating outliers and Kalman filtering first, in order to improve the accuracy of data acquisition. Secondly, the fingerprint data is partitioned by using RSSI to attenuate obstacles such as walls. Then, points with significant RSSI differences in each region are selected as regional feature points, and the distance between RSSI of test points and feature points in each region is calculated respectively to determine the region in which the test points are located. Geometric method is used to analyse and define the correlation degree, and KNN is re-weighted to achieve accurate positioning in the region. Finally, experiments were carried out in the indoor environment to complete the establishment of the fingerprint database. Compared with the existing NN, KNN and WKNN, the experimental analysis results show that the accumulated error and average error are better than the traditional algorithm with the increase of measurement points, which has reference value for the complex environment positioning technology. [ABSTRACT FROM AUTHOR]

Vanadium and its derivatives are used in various industries, including steel, metallurgy, pharmaceuticals, and aerospace engineering. Although China has massive reserves of stone coal resources, these resources have low grades. Therefore, the effective extraction and recovery of metallic vanadium from stone coal is an important way to realize the efficient resource utilization of stone coal vanadium ore. Herein, Bacillus mucilaginosus was selected as the leaching strain. The vanadium leaching rate reached 35.5% after 20 d of bioleaching under optimal operating conditions. The cumulative vanadium leaching rate in the contact group reached 35.5%, which was higher than that in the noncontact group (9.3%). The metabolites of B. mucilaginosus, such as oxalic, tartaric, citric, and malic acids, dominated in bioleaching, accounting for 73.8% of the vanadium leaching rate. Interestingly, during leaching, the presence of stone coal stimulated the expression of carbonic anhydrase in bacterial cells, and enzyme activity increased by 1.335–1.905 U. Enzyme activity positively promoted the production of metabolite organic acids, and total organic acid content increased by 39.31 mg·L−1, resulting in a reduction of 2.51 in the pH of the leaching system with stone coal. This effect favored the leaching of vanadium from stone coal. Atomic force microscopy illustrated that bacterial leaching exacerbated corrosion on the surface of stone coal beyond 10 nm. Our study provides a clear and promising strategy for exploring the bioleaching mechanism from the perspective of microbial enzyme activity and metabolites. [ABSTRACT FROM AUTHOR]

Background: The P-wave terminal force in lead V1 (PTFV1) is a marker of cardiomyopathy and risk of atrial fibrillation (AF). Low-voltage area (LVA) in the left atrium (LA), which indicates underlying atrial fibrosis, could predict AF recurrence. This study aimed to investigate the correlation between PTFV1 and LVA in older patients with paroxysmal AF. Methods: From May 1, 2020, to October 31, 2021, a total of 162 patients aged 65–80 years with paroxysmal AF who underwent index ablation procedures were enrolled. PTFV1 was measured in sinus rhythm (SR) using 12-lead electrocardiograms prior to the ablation. Abnormal PTFV1 was defined as a ≥ 4 mVms depression. Additional LVA ablation beyond circumferential pulmonary vein isolation (CPVI) was performed if LVAs were found. Results: Among the 162 patients, 88 had a normal PTFV1 and 74 had an abnormal PTFV1 prior to ablation. There was a significant difference in LVA in patients with and without an abnormal PTFV1 (LVA, 11.0 vs. 5.1 cm2, P < 0.001; LVA burden, 8.9% vs. 4.5%, P < 0.001). PTFV1 and PTAV1 were highest in the upper tertile with extensive LVAs (P < 0.001). Multivariate analysis revealed that abnormal PTFV1 was an independent predictor of LVAs (β = 4.961; 95% CI, 2.135–7.788; P < 0.001). After a median follow-up of 23 months, the AF-free survival rate was similar between the normal PTFV1 group and the abnormal PTFV1 group (13/88 vs. 12/74, hazard ratio [HR], 0.933 [95% CI, 0.425–2.047]; P = 0.861). Conclusions: Abnormal PTFV1 at baseline was independently associated with the extent of LVA in older patients with paroxysmal AF. [ABSTRACT FROM AUTHOR]

In this paper, strong consistency of tail value-at-risk (TVaR) estimator under widely orthant dependent (WOD) samples is established, and a numerical simulation is performed to verify the validity of the theoretical results. To reveal the essence of the result, theoretical discussion on complete and complete moment convergence corresponding to the Baum–Katz law, as well as the Marcinkiewicz–Zygmund type strong law of large numbers (MZSLLN) for maximal weighted sums and maximal product sums of widely orthant dependent (WOD) random variables are investigated. The results obtained in the context extend the corresponding ones for independent and some dependent random variables. [ABSTRACT FROM AUTHOR]

Endoplasmic reticulum (ER) stress and oxidative stress (OS) are often related states in pathological conditions including Parkinson's disease (PD). This study investigates the role of anti-oxidant protein paraoxonase 2 (PON2) in ER stress and OS in PD, along with its regulatory molecule. PD was induced in C57BL/6 mice using 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine hydrochloride (MPTP) treatment and in SH-SY5Y cells using 1-methyl-4-phenylpyridinium. PON2 was found to be poorly expressed in the substantia nigra pars compacta (SNc) of PD mice, and its overexpression improved motor coordination of mice. Through the evaluation of tyrosine hydroxylase, dopamine transporter, reactive oxygen species (ROS), and C/EBP homologous protein (CHOP) levels and neuronal loss in mice, as well as the examination of CHOP, glucose-regulated protein 94 (GRP94), GRP78, caspase-12, sarco/endoplasmic reticulum calcium ATPase 2, malondialdehyde, and superoxide dismutase levels in SH-SY5Y cells, we observed that PON2 overexpression mitigated ER stress, OS, and neuronal apoptosis both in vivo and in vitro. Forkhead box A1 (FOXA1) was identified as a transcription factor binding to the PON2 promoter to activate its transcription. Upregulation of FOXA1 similarly protected against neuronal loss by alleviating ER stress and OS, while the protective roles were abrogated by additional PON2 silencing. In conclusion, this study demonstrates that FOXA1-mediated transcription of PON2 alleviates ER stress and OS, ultimately reducing neuronal apoptosis in PD. [ABSTRACT FROM AUTHOR]

Light-driven rotary molecular motors are among the most promising classes of responsive molecular machines and take advantage of their intrinsic chirality which governs unidirectional rotation. As a consequence of their dynamic function, they receive considerable interest in the areas of supramolecular chemistry, asymmetric catalysis and responsive materials. Among the emerging classes of responsive photochromic molecules, multistate first-generation molecular motors driven by benign visible light remain unexplored, which limits the exploitation of the full potential of these mechanical light-powered systems. Herein, we describe a series of all-visible-light-driven first-generation molecular motors based on the salicylidene Schiff base functionality. Remarkable redshifts up to 100 nm in absorption are achieved compared to conventional first-generation motor structures. Taking advantage of all-visible-light-driven multistate motor scaffolds, adaptive behaviour is found as well, and potential application in multistate photoluminescence is demonstrated. These functional visible-light-responsive motors will likely stimulate the design and synthesis of more sophisticated nanomachinery with a myriad of future applications in powering dynamic systems. First-generation light-driven rotary molecular motors received considerable interest in various areas of chemistry, but those driven by benign visible light remain less explored. Here the authors describe all-visible-light-driven first-generation molecular motors which show an adaptive behaviour and explore application in multistate photoluminescence. [ABSTRACT FROM AUTHOR]

Lead-barium silicate system products, represented by barium copper silicate and lead-barium glass, epitomize a unique artistic craftsmanship of ancient China. Specifically, barium copper silicate products were the only known artificially synthesized blue-purple inorganic pigments in ancient China. Painted pottery decorated with barium copper silicate pigment, predominantly found in elite tombs after 2nd century B.C., is exceedingly rare and holds immense archaeological significance. This case employed multi micro-scale analytical techniques, such as ultra-depth-of-field microscopy, Raman spectroscopy, µ X-ray Diffraction, and electron probe microanalysis, for comprehensive microscopic examination of a rare Chinese Purple (CP, BaCuSi2O6) pigments on painted pottery. The analytical results confirm that the purple pigment is BaCuSi2O6, accompanied by cinnabar for the red and bone white for the white sections, suggesting the probable use of organic residues as an adhesive. Additionally, the study reveals microstructural characteristics in CP faience and CP pigments, notably the encapsulation of CP particles by quartz particles. Therefore, it can be hypothesized that the process of manufacturing purple pigment was achieved by grinding faience into fine particles and then using an organic-based adhesive to adhere these particles to the pottery. This implies that the evolutions in CP products circa the 2nd century B.C. were driven not by technological advances from lead-barium glass, but rather by shifts in application methods, reflecting an adaptation process among different cultures. This study provides new perspectives and significant insights for comprehending the interplay between barium copper silicate and lead-barium glass, reconstructing ancient pottery painting techniques, and exploring the era's technological and cultural interactions. [ABSTRACT FROM AUTHOR]

Ancient calligraphy manuscripts often suffered from degradation due to human factors and natural weathering. Image restoration techniques restore the degraded contents in calligraphy character images to help text-related research and have great significance for cultural heritage protection. Traditional manual calligraphy restoration relies heavily on professional skills and identifying experts for this process is time-consuming. Automatic digital methods do not usually require experts, which have an unsatisfactory performance for severely degraded calligraphy works restoration. In this paper, we first propose a crowdsourcing-based method to identify experts by a novel character similarity inference (CSI) algorithm to restore severely degraded calligraphy characters. Then, we develop LanT, a system to implement crowdsourcing tasks of restoration. We performed an evaluation on restoring a famous Chinese calligraphy work. The experimental results show that CSI can reliably identify experts. Additionally, we evaluate the usability of LanT and the generalization of CSI for Latin calligraphy character restoration. [ABSTRACT FROM AUTHOR]

The intracellular bacterial pathogen Legionella pneumophila modulates host cell functions by secreting multiple effectors with diverse biochemical activities. In particular, effectors of the SidE family interfere with host protein ubiquitination in a process that involves production of phosphoribosyl ubiquitin (PR-Ub). Here, we show that effector LnaB converts PR-Ub into ADP-ribosylated ubiquitin, which is further processed to ADP-ribose and functional ubiquitin by the (ADP-ribosyl)hydrolase MavL, thus maintaining ubiquitin homeostasis in infected cells. Upon being activated by actin, LnaB also undergoes self-AMPylation on tyrosine residues. The activity of LnaB requires a motif consisting of Ser, His and Glu (SHxxxE) present in a large family of toxins from diverse bacterial pathogens. Thus, our study sheds light on the mechanisms by which a pathogen maintains ubiquitin homeostasis and identifies a family of enzymes capable of protein AMPylation. The bacterial pathogen Legionella pneumophila secretes effectors that affect protein ubiquitination within host cells, involving production of phosphoribosyl ubiquitin (PR-Ub). Here, the authors identify additional effectors that convert PR-Ub back into functional ubiquitin, thus maintaining ubiquitin homeostasis in infected cells. [ABSTRACT FROM AUTHOR]

Challenges in direct catalytic oxidation of biomass-derived aldehyde and alcohol into acid with high activity and selectivity hinder the widespread biomass application. Herein, we demonstrate that a Pd/Ni(OH)2 catalyst with abundant Ni2+-O-Pd interfaces allows electrooxidation of 5-hydroxymethylfurfural to 2, 5-furandicarboxylic acid with a selectivity near 100 % and 2, 5-furandicarboxylic acid yield of 97.3% at 0.6 volts (versus a reversible hydrogen electrode) in 1 M KOH electrolyte under ambient conditions. The rate-determining step of the intermediate oxidation of 5-hydroxymethyl-2-furancarboxylic acid is promoted by the increased OH species and low C–H activation energy barrier at Ni2+-O-Pd interfaces. Further, the Ni2+-O-Pd interfaces prevent the agglomeration of Pd nanoparticles during the reaction, greatly improving the stability of the catalyst. In this work, Pd/Ni(OH)2 catalyst can achieve 100% 5-hydroxymethylfurfural conversion and >90% 2, 5-furandicarboxylic acid selectivity in a flow-cell and work stably over 200 h under a fixed cell voltage of 0.85 V. Catalytic oxidation of biomass-derived aldehydes and alcohols into acids is challenging due to low activity and selectivity. Here the authors report a Pd/Ni(OH)2 electrocatalyst with abundant Ni2+-O-Pd interfaces that enables efficient electrooxidation of 5-hydroxymethylfurfural to 2,5-furandicarboxylic acid with high selectivity and product yield. [ABSTRACT FROM AUTHOR]

Background: Short-term personalized vestibular rehabilitation (ST-PVR) can establish stable vestibular compensation. However, there is a lack of a clear definition for clinical indicators that can dynamically reflect the progress of vestibular rehabilitation (VR). Objective: To explore the clinical indicators suitable for evaluating the effectiveness of ST-PVR in treating benign recurrent vertigo (BRV). Methods: In total, 50 patients diagnosed with BRV were enrolled. All patients received the ST-PVR treatment program. At 2 and 4 weeks after rehabilitation, subjective scales, including the visual analogue scale (VAS), dizziness handicap inventory scale (DHI), activities‐specific balance confidence scale (ABC) and generalized anxiety disorder (GAD-7) were assessed. Objective vestibular function tests were performed. VR grading was determined. Results: At 2 weeks after rehabilitation, significant enhancements were observed in VAS, DHI, ABC, GAD-7, UW, vHIT results, and VR grading scores (p < 0.05). The sensory organization test (SOT) results demonstrated statistically significant improvements at 2 weeks and 4 weeks after rehabilitation (p < 0.05). Conclusion and significance: Both subjective scales and partial examination results in objective assessment can serve as indicators to dynamically monitor the compensatory process of vestibular function in patients with BRV. The VR efficacy grading score, which incorporates the above indicators, allows for quantification of the changes that occur during the vestibular rehabilitation process. [ABSTRACT FROM AUTHOR]

Ratiometric fluorescence and colorimetric strategies for detecting activity of butyrylcholinesterase (BChE) in human serum were developed by using g-C3N4 nanosheets, silver ion (Ag+) and o-phenylenediamine (OPD) as chromogenic agents. The oxidation-reduction reaction of OPD and Ag+ generates 2,3-diaminophenazine (oxOPD). Under exciation at 370 nm, g-C3N4 nanosheets and oxOPD emit fluorescence at 440 nm (F440) and 560 nm (F560), respectively. Additionally, oxOPD exhibits quenching ability towards g-C3N4 nanosheets via photoinduced electron transfer (PET) process. Thiocholine (TCh), as a product of BChE-catalyzed hydrolysis reaction of butylthiocholine iodide (BTCh), can coordinate with Ag+ intensively, and consequently diminish the amount of free Ag+ in the testing system. Less amount of free Ag+ leads to less production of oxOPD, resulting in less fluorescence quenching towards g-C3N4 nanosheets as well as less fluorescence emission of oxOPD. Therefore, by using g-C3N4 nanosheets and oxOPD as fluorescence indicators, the intensity ratio of their fluorescence (F440/F560) was calculated and employed to evaluate the activity of BChE. Similarly, the color variation of oxOPD indicated by the absorbance at 420 nm (ΔA420) was monitored for the same purpose. These strategies were validated to be sensitive and selective for detecting BChE activity in human serum, with limits of detection (LODs) of 0.1 U L−1 for ratiometric fluorescence mode and 0.7 U L−1 for colorimetric mode. [ABSTRACT FROM AUTHOR]

Frequency estimation of sinusoid is an important research topic in the field of statistical signal processing. Efficient frequency estimation is of great significance for applications with high real-time requirements. In this paper, two efficient frequency estimators of complex sinusoid by using autocorrelation and discrete time Fourier transform (DTFT) are presented in additive white noise background. Firstly, autocorrelation algorithm is adopted to obtain the coarse frequency estimate. Then, three DTFT samples with arbitrary intervals located near the coarse estimate are used to get the fine frequency estimate. In order to evaluate the calculation complexity and estimation accuracy, the proposed estimators are compared with the competing algorithms. The computational complexity of the presented estimators is much lower than that of the competitive estimators. Simulation results show that the mean square errors of the presented estimators are close to the Cramer–Rao lower bound when the signal-to-noise ratio varies in a large range. [ABSTRACT FROM AUTHOR]

C9orf72 genetic mutation is the most common genetic cause of ALS/FTD accompanied by abnormal protein insufficiency. Induced pluripotent stem cell (iPSC)-derived two-dimensional (2D) and three-dimensional (3D) cultures are providing new approaches. Therefore, this study established neuronal cell types and generated spinal cord organoids (SCOs) derived from C9orf72 knockdown human iPSCs to model ALS disease and screen the unrevealed phenotype. Wild-type (WT) iPSC lines from three healthy donor fibroblasts were established, and pluripotency and differentiation ability were identified by RT-PCR, immunofluorescence and flow cytometry. After infection by the lentivirus with C9orf72-targeting shRNA, stable C9-knockdown iPSC colonies were selected and differentiated into astrocytes, motor neurons and SCOs. Finally, we analyzed the extracted RNA-seq data of human C9 mutant/knockout iPSC-derived motor neurons and astrocytes from the GEO database and the inflammatory regulation-related genes in function and pathways. The expression of inflammatory factors was measured by qRT-PCR. The results showed that both WT-iPSCs and edited C9-iPSCs maintained a similar ability to differentiate into the three germ layers, astrocytes and motor neurons, forming SCOs in a 3D culture system. The constructed C9-SCOs have features of spinal cord development and multiple neuronal cell types, including sensory neurons, motor neurons, and other neurons. Based on the bioinformatics analysis, proinflammatory factors were confirmed to be upregulated in C9-iPSC-derived 2D cells and 3D cultured SCOs. The above differentiated models exhibited low C9orf72 expression and the pathological characteristics of ALS, especially neuroinflammation. [ABSTRACT FROM AUTHOR]

In order to study the dynamic characteristics and destabilisation damage mode of the accumulation body slope under the action of ground vibration, and to provide theoretical guidance and technical support for the optimization design of reinforcement of similar slopes, a large shaking table model (scale 1:16) test with a specific accumulation body slope was conducted as a prototype. The dynamic response and deformation characteristics of the model slopes were observed by inputting sine, Wolong, and EI Centro waves to the bottom of the model slopes, respectively. The test results showed that in the vertical direction and on the slope surface, the acceleration amplification factor (AAF) increased with the increasing of altitude, and presented a nonlinear change. In the horizontal direction, the AAF increased with distance from the slope surface. There was also a slope surface amplification effect with the AAF reaching its maximum at the slope shoulder. Similar laws were obtained after numerical simulation of the prototype slope by FLAC3D software. Different types of seismic waves exhibited different effects on the AAF. Sine waves showed the largest effect, followed by Wolong waves, and EI centro waves exhibited the smallest effect. The AAF of the modelled slope was different for different input wave frequencies. As the input frequency of Sine waves increased, the AAF increased first and then decreased. This change coincided with the AAF reaching its maximum value at 25–30 Hz. The AAF of the modelled slope varied when the input wave amplitude values were different. When the AAF reached its maximum value, the input amplitude was 0.4 g. By analysing the slope failure progression, it showed that the slope of an accumulation body began with local sliding at the front edge, followed by internal sliding of it, and the overall sliding. [ABSTRACT FROM AUTHOR]

The spatiotemporal regulation of inflammasome activation remains unclear. To examine the mechanism underlying the assembly and regulation of the inflammasome response, here we perform an immunoprecipitation-mass spectrometry analysis of apoptosis-associated speck-like protein containing a CARD (ASC) and identify NCF4/1/2 as ASC-binding proteins. Reduced NCF4 expression is associated with colorectal cancer development and decreased five-year survival rate in patients with colorectal cancer. NCF4 cooperates with NCF1 and NCF2 to promote NLRP3 and AIM2 inflammasome activation. Mechanistically, NCF4 phosphorylation and puncta distribution switches from the NADPH complex to the perinuclear region, mediating ASC oligomerization, speck formation and inflammasome activation. NCF4 functions as a sensor of ROS levels, to establish a balance between ROS production and inflammasome activation. NCF4 deficiency causes severe colorectal cancer in mice, increases transit-amplifying and precancerous cells, reduces the frequency and activation of CD8+ T and NK cells, and impairs the inflammasome-IL-18-IFN-γ axis during the early phase of colorectal tumorigenesis. Our study implicates NCF4 in determining the spatial positioning of inflammasome assembly and contributing to inflammasome-mediated anti-tumor responses. The NADPH oxidase 2 (NOX2) complex is a main reactive oxygen species (ROS) source during inflammation. Here the authors report that NCF4, a subunit of the NOX2 complex, acts a sensor of ROS levels and regulates NLRP3 and AIM2 inflammasome activation, associated with attenuated colorectal cancer progression. [ABSTRACT FROM AUTHOR]

There are hundreds of genes typically overexpressed in breast cancer cells and it's often assumed that their overexpression contributes to cancer progression. However, the precise proportion of these overexpressed genes contributing to tumorigenicity remains unclear. To address this gap, we undertook a comprehensive screening of a diverse set of seventy-two genes overexpressed in breast cancer. This systematic screening evaluated their potential for inducing malignant transformation and, concurrently, assessed their impact on breast cancer cell proliferation and viability. Select genes including ALDH3B1, CEACAM5, IL8, PYGO2, and WWTR1, exhibited pronounced activity in promoting tumor formation and establishing gene dependencies critical for tumorigenicity. Subsequent investigations revealed that CEACAM5 overexpression triggered the activation of signaling pathways involving β-catenin, Cdk4, and mTOR. Additionally, it conferred a growth advantage independent of exogenous insulin in defined medium and facilitated spheroid expansion by inducing multiple layers of epithelial cells while preserving a hollow lumen. Furthermore, the silencing of CEACAM5 expression synergized with tamoxifen-induced growth inhibition in breast cancer cells. These findings underscore the potential of screening overexpressed genes for both oncogenic drivers and tumor dependencies to expand the repertoire of therapeutic targets for breast cancer treatment. [ABSTRACT FROM AUTHOR]

Human multidrug resistance protein 5 (hMRP5) effluxes anticancer and antivirus drugs, driving multidrug resistance. To uncover the mechanism of hMRP5, we determine six distinct cryo-EM structures, revealing an autoinhibitory N-terminal peptide that must dissociate to permit subsequent substrate recruitment. Guided by these molecular insights, we design an inhibitory peptide that could block substrate entry into the transport pathway. We also identify a regulatory motif, comprising a positively charged cluster and hydrophobic patches, within the first nucleotide-binding domain that modulates hMRP5 localization by engaging with membranes. By integrating our structural, biochemical, computational, and cell biological findings, we propose a model for hMRP5 conformational cycling and localization. Overall, this work provides mechanistic understanding of hMRP5 function, while informing future selective hMRP5 inhibitor development. More broadly, this study advances our understanding of the structural dynamics and inhibition of ABC transporters. Human multidrug resistance protein 5 (hMRP5) effluxes anticancer and antivirus drugs, driving multidrug resistance. Here, the authors present cryo-EM structures of hMRP5 in different states, showing that hMRP5 can be autoinhibited by a short peptide from its N-terminal tail, which prevents the entry of substrates into hMRP5's transport pathway. [ABSTRACT FROM AUTHOR]

Summer precipitation over the Upper Yellow River Basin (UYRB), China plays a significant role in the water supply in the upstream region of Yellow River. In this study, the main modes and variability of summer precipitation over the UYRB were diagnosed. Summer precipitation over the UYRB showed an overall increase during 1980–2020, with increasing rate weakening from southwest to northeast. The empirical orthogonal analysis revealed that the first main mode (EOF1) of UYRB precipitation was spatially uniform, while EOF2 had a "northeast-southwest" dipolar structure, both of which showed significant interannual variations. In addition, the potential causes of the dipolar mode in EOF2 were investigated. The results show that the weakened East Asian subtropical jet, South Asian high and West Pacific Subtropical High (WPSH) are favorable for the decreased/increased precipitation over the western/eastern UYRB regions, while the strengthened Ural blocking, East Asian subtropical jet and the southwestward extended WPSH are favorable for the opposite distribution of precipitation during negative EOF2 years. [ABSTRACT FROM AUTHOR]

The onset and progression of periodontitis involves complicated interactions between the dysbiotic oral microbiota and disrupted host immune-inflammatory response, which can be mirrored by the changes in salivary metabolites profile. This pilot study sought to examine the saliva microbiome and metabolome in the Chinese population by the combined approach of 16s rRNA sequencing and high-throughput targeted metabolomics to discover potential cues for host-microbe metabolic interactions. Unstimulated whole saliva samples were collected from eighteen Stage III and IV periodontitis patients and thirteen healthy subjects. Full-mouth periodontal parameters were recorded. The taxonomic composition of microbiota was obtained by 16s rRNA sequencing, and the metabolites were identified and measured by ultra-high performance liquid chromatography and mass spectrometry-based metabolomic analysis. The oral microbiota composition displayed marked changes where the abundance of 93 microbial taxa differed significantly between the periodontitis and healthy group. Targeted metabolomics identified 103 differential metabolites between the patients and healthy individuals. Functional enrichment analysis demonstrated the upregulation of protein digestion and absorption, histidine metabolism, and nicotinate and nicotinamide metabolism pathways in the dysbiotic microbiota, while the ferroptosis, tryptophan metabolism, glutathione metabolism, and carbon metabolism pathways were upregulated in the patients. Correlation analysis confirmed positive relationships between the clinical parameters, pathogen abundances, and disease-related metabolite levels. The integral analysis of the saliva microbiome and metabolome yielded an accurate presentation of the dysbiotic oral microbiome and functional alterations in host-microbe metabolism. The microbial and metabolic profiling of the saliva could be a potential tool in the diagnosis, prognosis evaluation, and pathogenesis study of periodontitis. [ABSTRACT FROM AUTHOR]

As one of the major greenhouse gases, CO2 is significantly influencing global climate change. Efficient utilization of CO2 is considered an important approach to address the current environmental and energy challenges. In particular, using CO2 as a raw material for the production of hydrocarbons is an attractive strategy for reducing carbon emissions while ensuring a stable energy supply. In this study, fluorine (F) doped ZnLa layered double hydroxide (F-ZL-LDH) and F-doped ZnCe layered double hydroxide (F-ZC-LDH) were prepared using a hydrothermal synthesis method. The materials' structural composition and morphology were analyzed using X-ray diffractometry (XRD), X-ray photoelectron spectroscopy (XPS), scanning electron microscopy (SEM), and transmission electron microscopy (TEM). The research results demonstrate that both catalysts exhibit abundant active sites, high selectivity, and excellent electrocatalytic performance for CO2RR to generate CO, with Faradaic efficiencies reaching 89.29% and 89.87% for F-ZL-LDH and F-ZC-LDH, respectively. The doping of fluorine results in the presence of numerous defects and pore structures on the surfaces of F-ZC-LDH and F-ZL-LDH, increasing the surface active area and enhancing charge transfer rates. Meanwhile, F doping facilitates CO2 adsorption and mass transfer on the electrode surface. Moreover, F doping in the catalysts also restricts the adsorption and desorption of *H during the competing hydrogen evolution reaction (HER) process. The effective management and rapid conversion of CO2 to various organic compounds and chemical fuels to facilitate carbon cycle and reduce atmospheric CO2 concentration have become a hot topic in current technological advances worldwide. Among the various electrocatalytic CO2 reduction reaction (CO2RR) products, carbon monoxide (CO) is the product with the highest kinetic accessibility. In this paper, F-ZL-LDH and F-ZC-LDH composite materials were prepared using a hydrothermal synthesis method. Based on preliminary studies, a molar ratio of M2+:M3+ of 3:1 was determined to provide better catalytic performance,and ammonium fluoride content of 30% of the cation molar ratio of M2+ + M3+. The introduction of fluorine (F) improves the catalytic performance of both materials, with a greater enhancement observed for ZL-LDH. At − 1.3V vs. RHE potential, The research results demonstrate that both catalysts exhibit abundant active sites, high selectivity, and excellent electrocatalytic performance for CO2RR to generate CO, with Faradaic efficiencies reaching 89.29% and 89.87% for F-ZL-LDH and F-ZC-LDH, respectively. the fluorination treatment of ZC-LDH and ZL-LDH catalysts significantly enhances their electrocatalytic activity for the CO2 reduction reaction, particularly for the production of CO. These findings contribute to the development of efficient catalysts for CO2 electrochemical reduction and hold promise for advancing the field of CO2 utilization and renewable energy conversion. [ABSTRACT FROM AUTHOR]

In this work, we study the problem of partitioning a set of graphs into different groups such that the graphs in the same group are similar while the graphs in different groups are dissimilar. This problem was rarely studied previously, although there has been a lot of work on node clustering and graph classification. The problem is challenging because it is difficult to measure the similarity or distance between graphs. One feasible approach is using graph kernels to compute a similarity matrix for the graphs and then performing spectral clustering, but the effectiveness of existing graph kernels in measuring the similarity between graphs is very limited. To solve the problem, we propose a novel method called Deep Graph-Level Clustering (DGLC). DGLC utilizes a graph isomorphism network to learn graph-level representations by maximizing the mutual information between the representations of entire graphs and sub-structures, under the regularization of a clustering module that ensures discriminative representations via pseudo-labels. DGLC achieves graph-level representation learning and graph-level clustering in an end-to-end manner. The experimental results on six benchmark datasets of graphs show that our DGLC has state-of-the-art performance in comparison to many baselines. [ABSTRACT FROM AUTHOR]

In order to understand the delamination growth under mode II loading from the perspective of composite damage mechanisms, this study conducted in-depth research on the damage evolution of laminated plates using acoustic emission (AE) technology and signal analysis methods. Delamination mainly includes two stages: initiation and propagation, in which the initiation is the more important stage in the process of material delamination. Three damage modes of laminated materials during loading were identified by combining peak frequency statistics of AE signals with continuous wavelet transform (CWT). The initial time and stage of delamination can be determined by two methods. (1) The evolution process of damage mechanisms can be characterized by the fast fourier transform (FFT) of AE signals in several time periods. (2) The cumulative energy of three damage modes separated by variational mode decomposition (VMD) can be used to characterize the damage evolution process. The initial time determined by integrating several methods is much earlier than the occurrence time of the interlaminar fracture toughness value defined by ASTM standard. The strain energy release rate (SERR) determined at the initial time of delamination is taken as a design reference, which will be of guiding significance for ensuring the safety of laminates. [ABSTRACT FROM AUTHOR]

Kidney ultrasound (US) images are primarily employed for diagnosing different renal diseases. Among them, one is renal localization and detection, which can be carried out by segmenting the kidney US images. However, kidney segmentation from US images is challenging due to low contrast, speckle noise, fluid, variations in kidney shape, and modality artifacts. Moreover, well-annotated US datasets for renal segmentation and detection are scarce. This study aims to build a novel, well-annotated dataset containing 44,880 US images. In addition, we propose a novel training scheme that utilizes the encoder and decoder parts of a state-of-the-art segmentation algorithm. In the pre-processing step, pixel intensity normalization improves contrast and facilitates model convergence. The modified encoder–decoder architecture improves pyramid-shaped hole pooling, cascaded multiple-hole convolutions, and batch normalization. The pre-processing step gradually reconstructs spatial information, including the capture of complete object boundaries, and the post-processing module with a concave curvature reduces the false positive rate of the results. We present benchmark findings to validate the quality of the proposed training scheme and dataset. We applied six evaluation metrics and several baseline segmentation approaches to our novel kidney US dataset. Among the evaluated models, DeepLabv3+ performed well and achieved the highest dice, Hausdorff distance 95, accuracy, specificity, average symmetric surface distance, and recall scores of 89.76%, 9.91, 98.14%, 98.83%, 3.03, and 90.68%, respectively. The proposed training strategy aids state-of-the-art segmentation models, resulting in better-segmented predictions. Furthermore, the large, well-annotated kidney US public dataset will serve as a valuable baseline source for future medical image analysis research. The graphic abstract for this research study visually encapsulates the key contributions and innovations: Dataset creation Developed WD-KUS dataset (44,880 US images). Aims to standardize US segmentation benchmarks and simplify US interpretation efforts. Automatic kidney segmentation framework Demonstrated a practical framework for segmenting whole kidneys from low-quality US images. Integrated various encoder–decoder models and a unique training strategy. Addressed challenges like shadows, internal fluid, and blurring. Training and post-processing strategies Introduced effective training strategies (pre-processing, networks, learning rate). Utilized post-processing techniques, including concave curvature assessment. Improved segmentation accuracy and generalizability. Auxiliary function for abnormal segmentation Proposed an auxiliary function to distinguish normal and abnormal kidney segmentation. Based on the concept that normal segmentation has few concave corners, while abnormal segmentation has many. Quantitative and qualitative enhancement Enhanced segmentation results quantitatively (six metrics). Showcased qualitative improvement over baseline methods. Validation with state-of-the-art networks Validated the approach using modified state-of-the-art segmentation neural networks. Demonstrated the effectiveness of the WD-KUS dataset in validation. The research significantly contributes to the field by providing a comprehensive dataset, an advanced segmentation framework, and innovative strategies for training and post-processing, resulting in improved kidney segmentation accuracy and applicability. [ABSTRACT FROM AUTHOR]

Copper-doped carbon dots and aminated carbon nanotubes (Cu-CDs/NH2-CNTs) nanocomposites were synthesized by a one-step growth method, and the composites were characterized for their performance. An electrochemical sensor for sensitive detection of bisphenol A (BPA) was developed for using Cu-CDs/NH2-CNTs nanocomposites modified with glassy carbon electrodes (GCE). The sensor exhibited an excellent electrochemical response to BPA in 0.2 M PBS (pH 7.0) under optimally selected conditions. The linear range of the sensor for BPA detection was 0.5–160 μM, and the detection limit (S/N = 3) was 0.13 μM. Moreover, the sensor has good interference immunity, stability and reproducibility. In addition, the feasibility of the practical application of the sensor was demonstrated by the detection of BPA in bottled drinking water and Liu Yang River water. [ABSTRACT FROM AUTHOR]

Atherosclerosis is a cardiovascular disease, accounting for the most common mortality cause worldwide. Notoginsenoside R1 (NGR1) is a characteristic saponin of Radix notoginseng that exhibits anti-inflammatory and antioxidant effects while modulating lipid metabolism. Evidence suggests that NGR1 exerts cardioprotective, neuroprotective, and anti-atherosclerosis effects. However, underlying NGR1 mechanisms alleviating atherosclerosis (AS) have not been examined. This study used a network pharmacology approach to construct the drug-target-disease correlation and protein–protein interaction (PPI) network of NGR1 and AS. Moreover, functional annotation and pathway enrichment analyses deciphered the critical biological processes and signaling pathways potentially regulated by NGR1. The protective effect of NGR1 against AS and the underlying mechanism(s) was assessed in an atherogenic apolipoprotein E-deficient (ApoE−/−) mice in vivo and an oxidized low-density lipoprotein (ox-LDL)-induced macrophage model in vitro. The network pharmacology and molecular docking analyses revealed that NGR1 protects against AS by targeting the NLRP3/caspase-1/IL-1β pathway. NGR1 reduced foam cell formation in ox-LDL-induced macrophages and decreased atherosclerotic lesion formation, serum lipid metabolism, and inflammatory cytokines in AS mice in vivo. Therefore, NGR1 downregulates the NLRP3 inflammasome complex gene expression of NLRP3, caspase-1, ASC, IL-1β, and IL-18, in vivo and in vitro. [ABSTRACT FROM AUTHOR]

The k-set tree connectivity, as a natural extension of classical connectivity, is an extremely significant index to measure the fault-tolerance of interconnection networks. For a connected graph G = (V , E) and a subset S ⊆ V , an S-tree T = (V ′ , E ′) of graph G is a tree that contains the subset S. Any two S-trees T and T ′ are internally disjoint if and only if E (T) ∩ E (T ′) = ∅ and V (T) ∩ V (T ′) = S . The cardinality of maximum internally disjoint S-trees is defined as κ G (S) , and the k-set tree connectivity is denoted by κ k (G) = min { κ G (S) | S ⊆ V (G) and | S | = k } . In this note, we determine the 4-set tree connectivity of H F Q n . That is, κ 4 (H F Q n) = n + 1 for n ≥ 7 , where H F Q n is hierarchical folded hypercube. [ABSTRACT FROM AUTHOR]

There are some attacks on the network, such as botnet attack, DDoS attack and Local Area Network Denial attack, which are attacked on certain group of clustered nodes in the network. At present, the existing connectivity has certain defects in reflecting the fault-tolerant ability of the network under these network attacks. In order to measure the fault tolerance and reliability of a network which is attacked on certain group of clustered nodes in the network by attacker, Lin et al. (IEEE Trans Comput 70:1719–1731, 2021) proposed the g-faulty-block connectivity. A subset F ⊆ V (G) is called a g-faulty-block of a graph G if G - F is disconnected, each component of it has at least g + 1 vertices and the subgraph induced by F is connected. The cardinality of a minimum g-faulty-block of G, denoted by FB κ g (G) , is the g-faulty-block connectivity of G. Larger h-fault block connectivity means that an attacker must launch an attack on a larger block of connected nodes so that each remaining component is not too small, which in turn limits the size of the larger components. The larger the h-fault block, the more difficult it is for an attacker to accomplish this goal. In this paper, we obtain FB κ 0 (FQ n) = 2 n + 1 , FB κ 1 (FQ n) = 3 n - 1 and FB κ g (FQ n) = (g + 2) n - 3 g + 3 for 2 ≤ g ≤ n - 4 and n ≥ 7 , where FQ n is n-dimension folded hypercube. [ABSTRACT FROM AUTHOR]

Purpose: Accurately and early detection of intestinal fibrosis in Crohn's disease (CD) is crucial for clinical management yet remains an unmet need. Fibroblast activation protein inhibitor (FAPI) PET/CT has emerged as a promising tool to assess fibrosis. We aimed to investigate the diagnostic capability of [18F]F-FAPI PET/CT in detecting intestinal fibrosis and compared it with[18F]F-FDG PET/CT and magnetization transfer MR imaging (MTI). Methods: Twenty-two rats underwent TNBS treatment to simulate fibrosis development, followed by three quantitative imaging sessions within one week. Mean and maximum standardized uptake values (SUVmean and SUVmax) were calculated on[18F]F-FAPI and [18F]F-FDG PET/CT, along with normalized magnetization transfer ratio on MTI. Intestinal fibrosis was assessed pathologically, with MTI serving as imaging standard for fibrosis. The diagnostic efficacy of imaging parameters in fibrosis was compared using pathological and imaging standards. Ten patients with 34 bowel strictures were prospectively recruited to validate their diagnostic performance, using the identical imaging protocol. Results: In CD patients, the accuracy of FAPI uptake (both AUCs = 0.87, both P ≤ 0.01) in distinguishing non-to-mild from moderate-to-severe fibrosis was higher than FDG uptake (both AUCs = 0.82, P ≤ 0.01) and comparable to MTI (AUCs = 0.90, P ≤ 0.001). In rats, FAPI uptake responded earlier to fibrosis development than FDG and MTI; consistently, during early phase, FAPI uptake showed a stronger correlation (SUVmean: R = 0.69) with pathological fibrosis than FDG (SUVmean: R = 0.17) and MTI (R = 0.52). Conclusion: The diagnostic efficacy of [18F]F-FAPI PET/CT in detecting CD fibrosis is superior to [18F]F-FDG PET/CT and comparable to MTI, exhibiting great potential for early detection of intestinal fibrosis. [ABSTRACT FROM AUTHOR]

Strong orthogonal arrays (SOAs) were recently introduced and studied as a class of spacefilling designs for computer experiments. To surely realize better space-filling properties, SOAs of strength three or higher are desirable. In addition, orthogonality is also an important property for designs of computer experiments, because it guarantees that the estimates of the main effects are uncorrelated. This paper first provides a systematic study on the construction of (nearly) orthogonal strength-three SOAs with better space-filling properties. The newly proposed strength-three SOAs enjoy almost the same space-filling properties of strength-four SOAs, and can accommodate much more columns than the latter. Moreover, they are (nearly) orthogonal and flexible in run sizes. The construction methods are straightforward to implement, and their theoretical supports are well established. In addition to the theoretical results, many designs are tabulated for practical needs. [ABSTRACT FROM AUTHOR]

As the complexity and quantity of network data continue to increase, accurate and efficient anomaly detection methods become critical. Deep learning-based methods are suitable for real-time detection because they leverage neural networks to efficiently process massive amounts of data. However, for complex network environments and unknown threats, it is difficult to acquire balanced datasets for training, resulting in low model accuracy. Moreover, in a large-scale network environment, the model training process is complicated and resource-consuming, ignoring the important information hidden behind the data and poor scalability. To address these issues, we develop a novel network anomaly detection method that integrates fused feature imageization with an enhanced extreme learning machine, termed GMD-DELM. The network data stream features are transformed into images by an adaptive transformation method, generating a feature representation with highly enhanced data recognition capability. In addition, a ResNeXt network embedded with an attention mechanism is used to extract high-level features from images, enhancing the ability of deep learning networks to extract important features from network streams. Finally, we implement a network anomaly detection method established on an improved adaptive differential evolution kernel extreme learning machine. The experimental results demonstrate that the proposed model achieves notable enhancements achieved by the proposed model in reducing feature redundancy and improving accuracy compared to existing network anomaly detection models. Furthermore, our model exhibits improved stability and robustness in detecting corrupted network data containing noise. [ABSTRACT FROM AUTHOR]

In this paper, we introduce an anisotropic geometric quantity W p , q ; k which involves the weighted integral of k-th elementary symmetric function. We first show the monotonicity of W p , 1 ; k and W 0 , q ; k along a class of inverse anisotropic curvature flows, and then prove the generalization of anisotropic Alexandrov–Fenchel type inequalities. On the other hand, an extension of anisotropic Hsiung–Minkowski formula is derived. Therefore, we at last obtain an extension of the Alexandrov–Fenchel type inequality, which involve the general W p , q ; k . In terms of the above inequalities, we have also demonstrated some other meaningful conclusions on convex body geometry, such as generalized L p -Minkowski inequality and estimates of anisotropic p-affine surface area. [ABSTRACT FROM AUTHOR]

Differentiated thyroid cancer (DTC) is the most common endocrine malignancy. Patients who receive systematic care typically have a better prognosis. RAI treatment plays a key role in eradicating any remaining thyroid lesions in DTC patients, hence decreasing the risk of distant metastases and cancer recurrence. As research continues to advance, RAI treatment is becoming more and more individualized. Because of the excellent prognosis for DTC patients, there is a relatively broad window for RAI treatment, making it easy to overlook when to receive RAI treatment. However, research on this issue can help patients with varying recurrence risk stratification make better decisions about when to begin RAI treatment following surgery, and physicians can schedule patients based on the severity of their disease. This will improve patient prognosis and lessen needless anxiety in addition to helping solve the problems of unjust healthcare resource distribution. In this review, we will mainly discuss the target population of RAI treatment as well as studies that examine the impact of RAI treatment timing on patient outcomes. In an effort to discourage DTC patients and physicians from selecting RAI therapy at random, we also review the possible negative effects of this treatment. [ABSTRACT FROM AUTHOR]

As a nonlinear dynamic index, hierarchical permutation entropy (HPE) can effectively represent the complexity change of time series. However, HPE only focuses on extracting time domain information and ignoring the rich information in frequency domain. Meanwhile, HPE is greatly influenced by the length of time series and has poor stability. To address these limitations, a two-dimensional hierarchical time–frequency permutation entropy (HTFPE2D) is proposed based on the definition of two-dimensional permutation entropy, and its purpose is to combine time-domain and frequency-domain information. To consider the time–frequency information of the multi-scale low-frequency sequences, the two-dimensional multi-scale hierarchical time–frequency permutation entropy (MHTFPE2D) is further established. MHTFPE2D allows for the synthesis of multidimensional effective information and leads to better feature extraction. Based on the advantages of the MHTFPE2D, a new fault diagnosis method of rolling bearing is developed by combining the MHTFPE2D and GOA-SVM. The proposed fault diagnosis method is validated by using the public rolling bearing datasets of CRWU and our rolling bearing datasets of Anhui University of Technology. The comparison results demonstrate that the proposed method achieves high fault identification accuracy, stability and robustness. [ABSTRACT FROM AUTHOR]

Efficient allocation of tasks and resources is crucial for the performance of heterogeneous cloud computing platforms. To achieve harmony between task completion time, device power consumption, and load balance, we propose a Graph neural network-enhanced Elite Particle Swarm Optimization (EPSO) model for collaborative scheduling, namely GraphEPSO. Specifically, we first construct a Directed Acyclic Graph (DAG) to model the complicated tasks, thereby using Graph Neural Network (GNN) to encode the information of task sets and heterogeneous resources. Then, we treat subtasks and independent tasks as basic task units while considering virtual or physical devices as resource units. Based on this, we exploit the performance adaptation principle and conditional probability to derive the solution space for resource allocation. Besides, we employ EPSO to consider multiple optimization objectives, providing fine-grained perception and utilization of task and resource information. It also increases the diversity of particle swarms, allowing GraphEPSO to adaptively search for the global optimal solution with the highest probability. Experimental results demonstrate the superiority of our proposed GraphEPSO compared to several state-of-the-art baseline methods on all evaluation metrics. [ABSTRACT FROM AUTHOR]

Uncontrollable zinc (Zn) plating and hydrogen evolution greatly undermine Zn anode reversibility. Previous electrolyte designs focus on suppressing H2O reactivity, however, the accumulation of alkaline byproducts during battery calendar aging and cycling still deteriorates the battery performance. Here, we present a direct strategy to tackle such problems using a strong Brønsted acid, bis(trifluoromethanesulfonyl)imide (HTFSI), as the electrolyte additive. This approach reformulates battery interfacial chemistry on both electrodes, suppresses continuous corrosion reactions and promotes uniform Zn deposition. The enrichment of hydrophobic TFSI– anions at the Zn|electrolyte interface creates an H2O-deficient micro-environment, thus inhibiting Zn corrosion reactions and inducing a ZnS-rich interphase. This highly acidic electrolyte demonstrates high Zn plating/stripping Coulombic efficiency up to 99.7% at 1 mA cm–2 (> 99.8% under higher current density and areal capacity). Additionally, Zn | |ZnV6O9 full cells exhibit a high capacity retention of 76.8% after 2000 cycles. Trace amounts of strong acid can suppress Zn corrosion and promote uniform Zn deposition. Here, the authors use HTFSI to create a hydrophobic micro-environment at the Zn-electrolyte interface, enabling high efficiency and cycling stability. [ABSTRACT FROM AUTHOR]

Accurately predicting the future trajectory of pedestrians is critical for tasks such as autonomous driving and robot navigation. Previous methods for pedestrian trajectory prediction dealt with social interaction and pedestrian movement factors either concurrently or sequentially, neglecting the link between them. Therefore, a spatial–temporal interaction-aware graph convolution network (STIGCN) is proposed for pedestrian trajectory prediction. STIGCN considers the correlation between social interaction and pedestrian movement factors to achieve more accurate interaction modeling. Specifically, we first constructed spatial and temporal graphs to model social interactions and movement factors. Then, we designed the spatial–temporal interaction-aware learning to utilize the spatial interaction features of each moment to assist the temporal interaction modeling and utilize the temporal interaction features of each pedestrian to assist the spatial interaction modeling, resulting in more accurate interaction modeling. Finally, a time-extrapolator pyramid convolution neural network (TEP-CNN) is designed to jointly estimate the two-dimensional Gaussian distribution parameters of future trajectories by combining the prediction features from multiple layers. Experimental results on two benchmark pedestrian trajectory prediction datasets show that our proposed method outperforms existing methods in terms of average displacement error and final displacement error and achieves more accurate predictions for pedestrian motions such as convergence and encounter. [ABSTRACT FROM AUTHOR]

In this study, arsenic (As) speciation was investigated in the freshwater alga Chlamydomonas reinhardtii treated with 20 μg/L arsenate using fractionation as well as ICP-MS/ESI-MS analyses and was compared with the known As metabolite profile of wild-grown Saccharina latissima. While the total As accumulation in C. reinhardtii was about 85% lower than in S. latissima, the relative percentage of arsenolipids was significantly higher in C. reinhardtii (57.0% vs. 5.01%). As-containing hydrocarbons and phospholipids dominated the hydrophobic As profile in S. latissima, but no As-containing hydrocarbons were detectable in C. reinhardtii. Instead for the first time, an arsenoriboside-containing phytol (AsSugPhytol) was found to dominate the hydrophobic arsenicals of C. reinhardtii. Interestingly, this compound and its relatives had so far been only found in green marine microalgae, open sea plankton (mixed assemblage), and sediments but not in brown or red macroalgae. This compound family might therefore relate to differences in the arsenic metabolism between the algae phyla. [ABSTRACT FROM AUTHOR]

Introduction: Immune checkpoint inhibitors have made tremendous progress over the last decade in the treatment of cutaneous melanoma, but their application in uveal melanoma treatment is less successful, owing in part to the immunological privilege of the eye and the liver, the most frequent site of metastasis. Nevertheless, the therapeutic outcomes reported currently are less pessimistic. Methods: In this review, we provide an overview of recent studies of immune checkpoint inhibitors in uveal melanoma and its metastasis and classify studies in this field into three groups: monotherapy of immune checkpoint inhibitors, dual-agent immune checkpoint inhibitors, and immune checkpoint inhibitors combined with other systemic or regional therapies. Results: Briefly, monotherapy with immune checkpoint inhibitors performed poorly. Dual-agent immune checkpoint inhibitors had slightly better outcomes than traditional treatments, especially in specific patient populations. As for the combination therapy, the combination with other systemic therapies did not show superiority over dual-agent immune checkpoint inhibitors, but combination with hepatic regional therapies was quite promising. Moreover, research on emerging checkpoints is currently limited to the stage of mechanistic studies. Conclusion: We propose that immune checkpoint inhibitors remain alternative treatments for patients with uveal melanoma, but factors such as cost-effectiveness should also be taken into account. The combination therapy with immune checkpoint inhibitors deserves to be further explored. [ABSTRACT FROM AUTHOR]

Geometric deviations and assembly deformations are inevitable in compliant assembly processes and have a significant impact on assembly quality. This paper proposes a variation propagation model based on isogeometric analysis (IGA), which unifies geometric deviations and compliant deformations in one framework by embedding exact geometry into assembly analysis. First, geometric deviations are modeled via offsets along control points using non-uniform rational basis spline (NURBS). Then, the elastic force induced by geometric deviation is calculated using Kirchhoff–Love shell elements. Further, clamping constraints, such as fixture positioning deviation and connection matching deviation, are transformed into displacement boundary conditions using the Lagrange multiplier method. Finally, several examples including the practical project cases are provided to demonstrate the accuracy and efficiency of the developed method in obtaining assembly deviation distributions. [ABSTRACT FROM AUTHOR]

Background: Congestive heart failure (CHF) demonstrates a heightened prevalence in individuals with diabetes mellitus within Intensive Care Units. The occurrence of abnormal chloride levels is frequently observed in critically ill patients, yet its clinical significance remains subject to debate. This study endeavors to explore the relationship between serum chloride levels and in-hospital mortality among patients affected by both congestive heart failure and diabetes. Methods: A retrospective cohort study was conducted, utilizing data from the Medical Information Mart for Intensive Care-IV (MIMIC-IV) database, focusing on adult patients in the United States. The impact of serum chloride levels upon ICU admission on in-hospital mortality was analyzed using multivariable logistic regression models, generalized additive models and subgroup analysis. Results: The study encompassed 7,063 patients with coexisting diabetes and congestive heart failure. The fully adjusted model revealed an inverse association between serum chloride levels and in-hospital mortality. As a tertile variable (Q3 vs Q1), the odds ratio (OR) was 0.73 with a 95% confidence interval (CI) of 0.54–0.98 (p = 0.039). As a continuous variable, per 1 mmol/L increment, the OR (95% CI) was 0.97 (0.96–0.99, p = 0.01). The relationship between serum chloride and in-hospital mortality demonstrated linearity (non-linear p = 0.958). Stratified analyses further validated the robustness of this correlation. Conclusions: Serum chloride levels exhibited a negative association with in-hospital mortality in patients with both congestive heart failure and diabetes. Nevertheless, prospective, randomized, controlled studies are warranted to corroborate and validate the findings presented in this investigation. [ABSTRACT FROM AUTHOR]

Purpose: To compare machine learning (ML) models with logistic regression model in order to identify the optimal factors associated with mammography-occult (i.e. false-negative mammographic findings) magnetic resonance imaging (MRI)-detected newly diagnosed breast cancer (BC). Material and methods: The present single-centre retrospective study included consecutive women with BC who underwent mammography and MRI (no more than 45 days apart) for breast cancer between January 2018 and May 2023. Various ML algorithms and binary logistic regression analysis were utilized to extract features linked to mammography-occult BC. These features were subsequently employed to create different models. The predictive value of these models was assessed using receiver operating characteristic curve analysis. Results: This study included 1957 malignant lesions from 1914 patients, with an average age of 51.64 ± 9.92 years and a range of 20–86 years. Among these lesions, there were 485 mammography-occult BCs. The optimal features of mammography-occult BC included calcification status, tumour size, mammographic density, age, lesion enhancement type on MRI, and histological type. Among the different ML models (ANN, L1-LR, RF, and SVM) and the LR-based combined model, the ANN model with RF features was found to be the optimal model. It demonstrated the best discriminative performance in predicting mammography false- negative findings, with an AUC of 0.912, an accuracy of 86.90%, a sensitivity of 85.85%, and a specificity of 84.18%. Conclusion: Mammography-occult MRI-detected breast cancers have features that should be considered when performing breast MRI to improve the detection rate for breast cancer and aid in clinician management. [ABSTRACT FROM AUTHOR]