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To address challenges related to the inadequate representation and inaccurate discrimination of pedestrian attributes, we propose a novel method for person re-identification, which leverages global feature learning and classification optimization. Specifically, this approach integrates a Normalization-based Channel Attention Module into the fundamental ResNet50 backbone, utilizing a scaling factor to prioritize and enhance key pedestrian feature information. Furthermore, dynamic activation functions are employed to adaptively modulate the parameters of ReLU based on the input convolutional feature maps, thereby bolstering the nonlinear expression capabilities of the network model. By incorporating Arcface loss into the cross-entropy loss, the supervised model is trained to learn pedestrian features that exhibit significant inter-class variance while maintaining tight intra-class coherence. The evaluation of the enhanced model on two popular datasets, Market1501 and DukeMTMC-ReID, reveals improvements in Rank-1 accuracy by 1.28% and 1.4%, respectively, along with corresponding gains in the mean average precision (mAP) of 1.93% and 1.84%. These findings indicate that the proposed model is capable of extracting more robust pedestrian features, enhancing feature discriminability, and ultimately achieving superior recognition accuracy. [ABSTRACT FROM AUTHOR]

The chiral H8-BINOL derivatives R-1 and R-2 were efficiently synthesized via a Suzuki coupling reaction, and they can be used as novel dialdehyde fluorescent probes for the enantioselective recognition of R/S-2-amino-1-phenylethanol. In addition, R-1 is much more effective than R-2. Scanning electron microscope images and X-ray analyses show that R-1 can form supramolecular vesicles through the self-assembly effect of the π-π force and strong hydrogen bonding. As determined via analysis, the fluorescence of the probe was significantly enhanced by mixing a small amount of S-2-amino-1-phenylethanol into R-1, with a redshift of 38 nm, whereas no significant fluorescence response was observed in R-2-amino-1-phenylethanol. The enantioselective identification of S-2-amino-1-phenylethanol by the probe R-1 was further investigated through nuclear magnetic titration and fluorescence kinetic experiments and DFT calculations. The results showed that this mechanism was not only a simple reactive probe but also realized object recognition through an ICT mechanism. As the intramolecular hydrogen bond activated the carbonyl group on the probe R-1, the carbonyl carbon atom became positively charged. As a strong nucleophile, the amino group of S-2-amino-1-phenylethanol first transferred the amino electrons to a carbonyl carbocation, resulting in a significantly enhanced fluorescence of the probe R-1 and a 38 nm redshift. Similarly, S-2-amino-1-phenylethanol alone caused severe damage to the self-assembled vesicle structure of the probe molecule itself due to its spatial structure, which made R-1 highly enantioselective towards it. [ABSTRACT FROM AUTHOR]

Inductive Logic Programming (ILP) is a research field at the intersection between machine learning and logic programming, focusing on developing a formal framework for inductively learning relational descriptions in the form of logic programs from examples and background knowledge. As an emerging method of ILP, Meta-Interpretive Learning (MIL) leverages the specialization of a set of higher-order metarules to learn logic programs. In MIL, the input includes a set of examples, background knowledge, and a set of metarules, while the output is a logic program. MIL executes a depth-first traversal search, where its program search space expands polynomially with the number of predicates in the provided background knowledge and exponentially with the number of clauses in the program, sometimes even leading to search collapse. To address this challenge, this study introduces a strategy that employs the concept of reuse, specifically through the integration of auxiliary predicates, to reduce the number of clauses in programs and improve the learning efficiency. This approach focuses on the proactive identification and reuse of common program patterns. To operationalize this strategy, we introduce MILER, a novel method integrating a predicate generator, program learner, and program evaluator. MILER leverages frequent subgraph mining techniques to detect common patterns from a limited dataset of training samples, subsequently embedding these patterns as auxiliary predicates into the background knowledge. In our experiments involving two Visual Question Answering (VQA) tasks and one program synthesis task, we assessed MILER's approach to utilizing reusable program patterns as auxiliary predicates. The results indicate that, by incorporating these patterns, MILER identifies reusable program patterns, reduces program clauses, and directly decreases the likelihood of timeouts compared to traditional MIL. This leads to improved learning success rates by optimizing computational efforts. [ABSTRACT FROM AUTHOR]

This article aims to explore the impact of HLB (Hydrophile-Lipophile Balance) values on two key properties, namely the thermoelectric conductivities and the stability of the suspension, of a hybrid nanofluid composed of TiO2 and CuO nanoparticles. The present study employed a two-step synthesis method to prepare the polymeric nanofluid, which meant that the nanoparticles were mixed with the base fluid using an ultrasonic oscillator, which was easier and cheaper than the one-step synthesis method. To ensure that the nanoparticles remain evenly dispersed in the base fluid, two distinct polymer-emulsifier combinations with different HLB values were employed as the dispersing agents. The first pair of polymeric emulsifiers consisted of Span#20 and Tween#20, and the second pair was Span#80 and Tween#80 composed to four HLB values of 12, 13, 14, and 15. The experiment measured the properties of the nanofluid, including the particle size, Zeta potential, and thermoelectric conductivities at different temperatures from 20 °C to 50 °C. The experimental outcomes indicated that an HLB value of 13 was the best for the two sets of polymeric emulsifiers tested. This value corresponded to the most reduced particle size, measured at 170 nm, alongside the most elevated Zeta potential, recorded at −30 mV. Additionally, this HLB value was associated with the peak thermoelectric conductivity, which was 1.46 W/m∙K. This suggests that there may be some variation in the best HLB value depending on the type of polymeric emulsifiers and the temperature of the hybrid nanofluid. [ABSTRACT FROM AUTHOR]

Investigating the dynamic evolution process of the ocean and ionosphere in sudden sea conditions poses a challenging problem. To address this objective, this study utilizes actual data from high-frequency surface wave radar (HFSWR) to analyze, validate, summarize, and characterize the echo properties of the ocean and ionosphere during the severe Typhoon Muifa. By employing the short-time Fourier transform (STFT) method, the HFSWR ocean and ionosphere echoes stimulated by typhoon-induced gravity waves are observed, and the joint gravity wave features of the ocean and ionosphere echoes at different time scales are extracted. Additionally, the phase-space reconstruction method is employed to characterize the dynamical evolution of the joint gravity wave features in higher-dimensional space. Furthermore, the chaotic dynamics behavior of the joint gravity wave features is analyzed using the largest Lyapunov exponents. By combining the gravity wave features with chaotic dynamics, this study introduces a method to characterize the joint gravity wave features. The extraction of joint gravity wave features in HFSWR echoes stimulated by typhoons, along with the construction of a chaotic characterization scheme for the gravity wave features, provides an innovative approach and a solid technical foundation for studying the ocean and ionosphere using HFSWR under sudden sea conditions. [ABSTRACT FROM AUTHOR]

Liver disease accounts for millions of deaths per year all over the world due to complications from cirrhosis and liver injury. In this study, a novel compound, dimethyl bisphenolate (DMB), was synthesized to investigate its role in ameliorating carbon tetrachloride (CCl4)-induced liver injury through the regulation of oxidative stress-related genes. The structure of DMB was confirmed based on its hydrogen spectrum and mass spectrometry. DMB significantly reduced the high levels of ALT, AST, DBIL, TBIL, ALP, and LDH in a dose-dependent manner in the sera of CCl4-treated rats. The protective effects of DMB on biochemical indicators were similar to those of silymarin. The ROS fluorescence intensity increased in CCl4-treated cells but significantly weakened in DMB-treated cells compared with the controls. DMB significantly increased the content of oxidative stress-related GSH, Nrf2, and GCLC dose-dependently but reduced MDA levels in CCl4-treated cells or the liver tissues of CCl4-treated rats. Moreover, DMB treatment decreased the expression levels of P53 and Bax but increased those of Bcl2. In summary, DMB demonstrated protective effects on CCl4-induced liver injury by regulating oxidative stress-related genes. [ABSTRACT FROM AUTHOR]

Technological learning curve models have been continuously used to estimate the cost development of solar photovoltaics (PV) for climate mitigation targets over time. They can integrate several technical sources that influence the learning process. Yet, the accurate and realistic learning curve that reflects the cost estimations of PV development is still challenging to determine. To address this question, we develop four hypothetical-alternative learning curve models by proposing different combinations of technological learning sources, including both local and global technological experience and knowledge stock. We specifically adopt the system dynamics approach to focus on the non-linear relationship and dynamic interaction between the cost development and technological learning source. By applying this approach to Chinese PV systems, the results reveal that the suitability and accuracy of learning curve models for cost estimation are dependent on the development stages of PV systems. At each stage, different models exhibit different levels of closure in cost estimation. Furthermore, our analysis underscores the critical role of incorporating global technical sources into learning curve models. [ABSTRACT FROM AUTHOR]

Large-scale short-term monitoring and prediction of surface deformation are of great significance for the prevention and control of geohazards in rapidly urbanizing developing cities. Most studies focus on individual cities, but it would be more meaningful to address large urban agglomerations and consider the relevance of the regions within them. In addition, the commonly used linear fitting prediction methods cannot accurately capture the dynamic mechanisms of deformation. In this study, we proposed an automatic PS extraction method (named PS-SBAS-InSAR) that improves SBAS-InSAR to extract surface deformation and an Informer-based short-term surface deformation prediction method for case studies in 16 typical cities of the Yangtze River Delta (YRD). The results show that PS-SBAS-InSAR successfully extracted accurate surface deformation sequences of the YRD. During the period from January 2019 to January 2021, the YRD experienced a slight deformation with an average deformation rate within [−4, 4] mm/year. Geographically neighboring cities may have associated deformation distributions and similar deformation trends, as indicated by average deformation rate maps and landscape metrics. Both types of deformation (i.e., subsidence/uplift) tend to occur simultaneously, with specific areas of subsidence/uplift occurring in close proximity to areas of concentrated deformation. The Informer model effectively captured the time-series variation in surface deformation, suggesting a slowdown of deformation over the next two months (February 2021–March 2021). Our work contributes to a better understanding of changes and trends in large-scale surface deformation and provides useful methods for monitoring and predicting surface deformation in coastal areas. [ABSTRACT FROM AUTHOR]

Transmission gear is a key component of vehicles and its surface integrity affects the safety of the transmission system as well as the entire mechanical system. The design and optimization of allowances in form grinding are important for improving dimensional accuracy and machining efficiency during the manufacturing of heavy-duty gears. This work aims to investigate the effects of grinding allowance allocation on surface morphology, grinding temperature, microstructure, surface roughness, and microhardness fluctuation during the form grinding of 20Cr2Ni4A gears. Results indicated that grinding temperature was primarily influenced by rough grinding involving significant grinding depths exceeding 0.02 mm. The ground surface exhibited slight work hardening, while thermal softening led to a reduction in microhardness of around 40 HV. Ground surface roughness Ra varied from 0.930 μm to 1.636 μm, with an allowance allocation of the last two passes exerting the most significant influence. Analysis of surface and subsurface microstructures indicated that a removal thickness of 0.02 mm during fine grinding was insufficient to eliminate the roughness obtained from rough grinding. Evident ridges, gullies, and surface defects such as material extraction, adhesion, and plastic deformation were also observed. The proposed grinding strategy was validated in practical manufacturing with good surface quality and geometrical accuracy. [ABSTRACT FROM AUTHOR]

Person re-identification is a technology used to identify individuals across different cameras. Existing methods involve extracting features from an input image and using a single feature for matching. However, these features often provide a biased description of the person. To address this limitation, this paper introduces a new method called the Dual Descriptor Feature Enhancement (DDFE) network, which aims to emulate the multi-perspective observation abilities of humans. The DDFE network uses two independent sub-networks to extract descriptors from the same person image. These descriptors are subsequently combined to create a comprehensive multi-view representation, resulting in a significant improvement in recognition performance. To further enhance the discriminative capability of the DDFE network, a carefully designed training strategy is employed. Firstly, the CurricularFace loss is introduced to enhance the recognition accuracy of each sub-network. Secondly, the DropPath operation is incorporated to introduce randomness during sub-network training, promoting difference between the descriptors. Additionally, an Integration Training Module (ITM) is devised to enhance the discriminability of the integrated features. Extensive experiments are conducted on the Market1501 and MSMT17 datasets. On the Market1501 dataset, the DDFE network achieves an mAP of 91.6% and a Rank1 of 96.1%; on the MSMT17 dataset, the network achieves an mAP of 69.9% and a Rank1 of 87.5%. These outcomes outperform most SOTA methods, highlighting the significant advancement and effectiveness of the DDFE network. [ABSTRACT FROM AUTHOR]

Marine natural products have been recognized as the most promising source of bioactive substances for drug discovery research. This review illustrates the diversity of culturable actinobacteria associated with marine algae, their bioactivity and metabolites, and approaches to their isolation and determination of their biological properties. Furthermore, actinobacteria associated with marine algae are presented as a new subject for an extensive investigation to find novel and active natural products, which make them a potentially rich and innovative source for new drug development deserving more attention and exploration. [ABSTRACT FROM AUTHOR]

This paper aims to propose a new approach to calculate the quantitative relations between morphostructural similarity degree and map scale change in multi-scale contour clusters for automatic contour generalization. Terrain lines were extracted by pre-processing of unclosed contour lines, and an indirect quantitative expression method of morphostructural similarity relation was proposed based on terrain line hierarchical trees. Thirteen groups of multi-scale contour clusters with different drainage areas of loess geomorphy were employed to explore the changing regularity of morphostructural similarity indices with map scale. Finally, the quantitative relations between morphostructural similarity degree and map scale change were calculated using 52 groups of points. The results show that power function is the best fit among the candidate functions, and the quantitative relations between the morphostructural similarity degree and map scale change can be expressed using the same power function, which facilitates the automation of contour generalization. [ABSTRACT FROM AUTHOR]

Visible-infrared person re-identification aims to solve the matching problem between cross-camera and cross-modal person images. Existing methods strive to perform better cross-modal alignment, but often neglect the critical importance of feature enhancement for achieving better performance. Therefore, we proposed an effective method that combines both modal alignment and feature enhancement. Specifically, we introduced Visible-Infrared Modal Data Augmentation (VIMDA) for visible images to improve modal alignment. Margin MMD-ID Loss was also used to further enhance modal alignment and optimize model convergence. Then, we proposed Multi-Grain Feature Extraction (MGFE) Structure for feature enhancement to further improve recognition performance. Extensive experiments have been carried out on SYSY-MM01 and RegDB. The result indicates that our method outperforms the current state-of-the-art method for visible-infrared person re-identification. Ablation experiments verified the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Aiming at the problem of fast divergence of pure inertial navigation system without correction under the condition of GNSS restricted environment, this paper proposes a multi-mode navigation method with an intelligent virtual sensor based on long short-term memory (LSTM). The training mode, predicting mode, and validation mode for the intelligent virtual sensor are designed. The modes are switching flexibly according to GNSS rejecting situation and the status of the LSTM network of the intelligent virtual sensor. Then the inertial navigation system (INS) is corrected, and the availability of the LSTM network is also maintained. Meanwhile, the fireworks algorithm is adopted to optimize the learning rate and the number of hidden layers of LSTM hyperparameters to improve the estimation performance. The simulation results show that the proposed method can maintain the prediction accuracy of the intelligent virtual sensor online and shorten the training time according to the performance requirements adaptively. Under small sample conditions, the training efficiency and availability ratio of the proposed intelligent virtual sensor are improved significantly more than the neural network (BP) as well as the conventional LSTM network, improving the navigation performance in GNSS restricted environment effectively and efficiently. [ABSTRACT FROM AUTHOR]

Simple Summary: The history of the treatment of APL has shown remarkable success. However, it is not clear whether it is necessary to monitor long-term toxicity in "cured" patients who survive for more than five years, which is critical to ensuring maximum survival in APL patients. To answer this question, we performed a comprehensive analysis of 1952 APL 5-year survivors and 5973 non-APL acute myeloid leukemia (AML) 5-year survivors from the Surveillance, Epidemiology, and End Results (SEER) database. We found that the overall mortality risk in >5-year survivors of APL was 1.41 times than that of the general population due to a significant increase in mortality from AML (SMR, 87.67) and other second malignant neoplasms (SMR, 1.56) only at 60–119 months. This result indicated long-term survivors of APL are safe after 10 years, which may help clinicians make future risk assessment, health guidance, and follow-up plans for APL long-term survivors. Background: Acute promyelocytic leukemia (APL) is a highly curable cancer, but it is not clear whether it is also necessary to monitor long-term toxicity in "cured" patients who survive for more than five years, which is critical to ensuring maximum survival in APL patients. Methods: A total of 1952 APL 5-year survivors and 5973 non-APL acute myeloid leukemia (AML) 5-year survivors were included from the Surveillance, Epidemiology, and End Results (SEER) database. The standardized mortality ratio (SMR) was calculated to measure the risk of death. Cumulative mortality is calculated as the incidence of specific causes of death under competing risk events. Results: The SMR of all causes of death in >5-year survivors of APL was higher than that of the general population only at 60–119 months (SMR, 1.41). This was mainly because a significant increase in mortality from AML (SMR, 87.67) and second malignant neoplasms (SMNs) (SMR, 1.56) was found only at 60–119 months. However, there was no higher risk of death from non-cancer-related disease in >5-year survivors of APL than that of the general population (SMR, 0.89). The SMR of all-cause deaths in >5-year survivors of non-APL AML decreased year by year and was no higher than that of the general population until after 216 months. The cumulative incidence of AML-related death, SMN-related death, and non-cancer-related death was significantly lower in APL patients than in non-APL AML patients throughout the follow-up period. Conclusions: Compared with the general population, the risk of death of patients with APL was higher within 5 to 10 years but not higher over 10 years. Therefore, we believe that long-term survivors of APL are safe after 10 years. [ABSTRACT FROM AUTHOR]

There are five closed types of piezo actuators (closed type of PA, closed PA) as a cooling fan relative to those different PAJs of the previous work (open type of PAJ, open PAJ) for analysis in the present study. Closed PA was composed of circular piezoelectric ceramics (PCs) and acrylic (PMMA) plates and investigated on five different types at operating conditions. The results show that the noise of the closed PA is quieter than that of the open PAJ by about 10 dB. When the closed PA is deposed at a suitable distance of 10 to 20 mm from the heat source, averting sucking back the high-temperature fluids around that, the thermal convection coefficient is above 120% more than that of the conventional rotary fan. The cooling performances of these five closed PAs were evaluated by thermal analysis technique, and the convection thermal resistance of the best closed PA can be decreased by over 15%. In terms of energy consumption, a monolithic closed PA was less than 10% than that of a rotary fan. Among these five closed PAs, the best one has the essential qualities that the diameter of the piezoelectric sheet is 41 mm, the opening length is 4 mm, and the outer opening length is 10 mm. Moreover, the best operating conditions are a voltage frequency of 300 Hz and a release distance of 15 mm in the present study. [ABSTRACT FROM AUTHOR]

An electrochemical sensor enabled by electropolymerization (EP) of β-cyclodextrin on glassy carbon electrode (β-CDP/GCE) is built for the determination of fenitrothion (FNT). The effects of the EP cycles, pH value, and enrichment time on the electrochemical response of FNT were studied. With the optimum conditions, good linear relationships between the current of the reduction peak of the nitroso derivative of FNT and the concentration are obtained in the range of 10–150 and 150–4000 ng/mL, with a detection limit of 6 ng/mL (S/N = 3). β-CDP/GCE also exhibits a satisfactory applicability in cabbage and tap water, with recovery values between 98.43% and 112%. These outstanding results suggest that β-CDP/GCE could be a new effective alternative for the determination of FNT in real samples. [ABSTRACT FROM AUTHOR]

Bombyx batryticatus (BB) is an anticonvulsant animal medicine in traditional Chinese medicine (TCM) and acts on the central nervous system. This research aimed to study the anticonvulsant effects of different polarity fractions of extracts from BB and to explore the components conferring anticonvulsant activity. Materials and methods: Crude extracts of BB at 20 g/kg were divided into different polarity fractions (petroleum ether, chloroform, ethyl acetate, water) and were administered to groups of mice before injecting pentylenetetrazol (PTZ) to induce convulsions. The animals were placed in chambers, and their behaviors were recorded for 30 min following the injection. Latency time, percent of protection, convulsion, convulsion rate, and convulsion score were determined for these mice. The compounds present in the different fractions were analyzed, and those from the fraction that conferred anticonvulsant activity were identified by high-performance liquid chromatography-quadrupole-time-of-flight mass spectrometry (UHPLC-Q-TOF MS) and molecular networking (MN). The chloroform extract fractions (B-C) clearly increased the seizure latency time and protection percentage and decreased the convulsion percentage compared to the control group. The anticonvulsant effect of other extract fractions was not significant. Our study shows that the chloroform extract fractions (B-C) of BB have a significant anticonvulsant effect. We also identified 17 compounds including lumichrome, pheophorbide A, and episyringaresinol 4′-O-beta-d-glucopyranose that were found for the first time. The results of this study may lay the groundwork for studying compounds derived from Bombyx batryticatus and their anticonvulsant effect. [ABSTRACT FROM AUTHOR]

Rice black-streaked dwarf virus (RBSDV) is the main pathogen causing maize rough dwarf disease (MRDD) in China. Typical enation symptoms along the abaxial leaf veins prevail in RBSDV-infected maize inbred line B73 (susceptible to RBSDV), but not in X178 (resistant to RBSDV). Observation of the microstructures of epidermal cells and cross section of enations from RBSDV-infected maize leaves found that the increase of epidermal cell and phloem cell numbers is associated with enation formation. To identify proteins associated with enation formation and candidate proteins against RBSDV infection, comparative proteomics between B73 and X178 plants were conducted using isobaric tags for relative and absolute quantitation (iTRAQ) with leaf samples at the enation forming stage. The proteomics data showed that 260 and 316 differentially expressed proteins (DEPs) were identified in B73 and X178, respectively. We found that the majority of DEPs are located in the chloroplast and cytoplasm. Moreover, RBSDV infection resulted in dramatic changes of DEPs enriched by the metabolic process, response to stress and the biosynthetic process. Strikingly, a cell number regulator 10 was significantly down-regulated in RBSDV-infected B73 plants. Altogether, these data will provide value information for future studies to analyze molecular events during both enation formation and resistance mechanism to RBSDV infection. [ABSTRACT FROM AUTHOR]

With more stringent IEC energy efficiency standards, electrical machine industry increasingly focuses on new motor technologies. Amongst others, the line-start permanent magnet synchronous machine (LSPMSM) is considered as an attractive alternative to induction machine, especially for low power and fixed-speed applications. However, the design of LSPMSMs is rather complex as both steady-state and transient synchronization performances need to be considered. The synchronization capability determination of a LSPMSM design usually relies on time-consuming transient finite-element simulations, which is impractical for use in an iterative design optimization process. This paper compares and evaluates various existing analytical synchronization analysis methods in an attempt to identify most suitable equations and methods for fast synchronization analysis. Using the selected methods, a software tool is developed that can seamlessly work with ANSYS Electronics Desktop to perform rapid transient synchronization analysis. Given its ability to quickly determine the critical inertia factor of a LSPMSM design, the software tool is further adapted for use in a highly iterative, multi-objective design optimization procedure. It shows that the developed software tool can be successfully used in the design of LSPMSMs. [ABSTRACT FROM AUTHOR]

Ovarian cancer is one of the most lethal gynecological malignancies, because of metastatic dissemination with poor late clinical therapy. Maggots have been used in traditional Chinese medicine, where they are also known as 'Wu Gu Chong'. Previous studies have indicated that maggot extract (ME) was beneficial for the treatment of gastric cancer when combined with other drugs, but the effect on anti-ovarian cancer and the underlying mechanism remains unclear. The aim of this study was to investigate the effects of ME on suppressing the proliferation and migration of ovarian cancer cells, and to clarify the underlying mechanism. In this research, Cell Counting Kit-8 (CCK-8), colony formation assay, and luciferase-positive cell quantification assay were employed to identify the inhibitory effects of ME on cell proliferation. Then, the pro-apoptosis and anti-metastasis effects of ME were explored by Western blot, dual annexin V-fluorescein isothiocyanate/propidium iodide (FITC/PI) assay, immunofluorescent staining, and wound-healing assay. We further established a xenograft model by subcutaneously or intraperitoneally injecting BALB/c nude mice with SKOV3 cells stably expressing luciferase, and the mice were treated with ME. The results showed that ME therapy effectively restrained the growth and metastasis of ovarian tumors in vivo. Furthermore, the mRNA levels of cancer factors including heat shock protein 90 alpha family class B member 1 (HSP90AB1), MYC, and insulin like growth factor 1 receptor (IGF1R) were analyzed by quantitative real-time PCR assay to explore the possible antitumor mechanisms of ME. Next, HSP90 ATPase activity was inhibited by geldanamycin in A2780, and the cell viability was shown to be dramatically reduced, decreasing further with the combination of ME and cisplatin. In turn, HSP90AB1 overexpression effectively inhibited the effect of ME in suppressing capability for cell viability and migration. In addition, HSP90AB1 overexpression limited the ability of ME to inhibit expression of MYC and IGF1R, while the opposite effect was observed for expression of pro-apoptosis protein caspase3 and BAX. Therefore, this study confirmed the potential roles and mechanisms of ME in inhibiting the growth and metastasis of ovarian tumors and promoting apoptosis of ovarian cancer cells by inhibiting overexpression of HSP90AB1. [ABSTRACT FROM AUTHOR]

Objective: To investigate the angioarchitectural factors associated with postoperative cerebral infarction in ischemic moyamoya disease. Methods: Data on patients who underwent surgery for ischemic MMD from 1 October 2015 to 31 October 2020, at Peking University International Hospital were collected and retrospectively analyzed. General conditions such as patient sex, age, site of surgery, preoperative manifestations such as TIA attack and old cerebral infarction, and seven angioarchitectural factors of the MMD based on DSA were selected and measured. Statistical analysis was performed by the Pearson chi-square statistic, analysis of variance (ANOVA), and multifactor logistic regression analysis. Results: Age (OR, 0.969; 95%CI, 0.939–1.000; p = 0.049), A1stenosis (OR, 5.843; 95%CI, 1.730–19.732; p = 0.004), M1stenosis (OR, 6.206; 95%CI, 2.079–18.526; p = 0.001), PCA anomalies (OR, 4.367; 95%CI, 1.452–13.129; p = 0.049), Unstable compensation (OR, 5.335; 95%CI, 1.427–19.948; p = 0.013), TIA (OR, 4.264; 95%CI, 1.844–9.863; p = 0.001), Old cerebral infarction (OR, 2.972; 95%CI, 1.194–7.397; p = 0.019). The above seven factors can be used in the regression equation to predict the probability of postoperative cerebral infarction. The prediction accuracy is 90.2%. Conclusions: Age, TIA attack, old cerebral infarction, and five angioarchitectural factors of MMD are strongly associated with postoperative cerebral infarction. Seven factors, including age, TIA attack, old infarction, and four angioarchitectural factors, can be taken to quantify the probability of surgical cerebral infarction in MMD. [ABSTRACT FROM AUTHOR]

Atrial fibrillation (AF) is a form of sustained cardiac arrhythmia and microRNAs (miRs) play crucial roles in the pathophysiology of AF. To identify novel miR–mRNA pairs, we performed RNA-seq from atrial biopsies of persistent AF patients and non-AF patients with normal sinus rhythm (SR). Differentially expressed miRs (11 down and 9 up) and mRNAs (95 up and 82 down) were identified and hierarchically clustered in a heat map. Subsequently, GO, KEGG, and GSEA analyses were run to identify deregulated pathways. Then, miR targets were predicted in the miRDB database, and a regulatory network of negatively correlated miR–mRNA pairs was constructed using Cytoscape. To select potential candidate genes from GSEA analysis, the top-50 enriched genes in GSEA were overlaid with predicted targets of differentially deregulated miRs. Further, the protein–protein interaction (PPI) network of enriched genes in GSEA was constructed, and subsequently, GO and canonical pathway analyses were run for genes in the PPI network. Our analyses showed that TNF-α, p53, EMT, and SYDECAN1 signaling were among the highly affected pathways in AF samples. SDC-1 (SYNDECAN-1) was the top-enriched gene in p53, EMT, and SYDECAN1 signaling. Consistently, SDC-1 mRNA and protein levels were significantly higher in atrial samples of AF patients. Among negatively correlated miRs, miR-302b-3p was experimentally validated to suppress SDC-1 transcript levels. Overall, our results suggested that the miR-302b-3p/SDC-1 axis may be involved in the pathogenesis of AF. [ABSTRACT FROM AUTHOR]

This paper investigates the prospect of permanent magnet vernier machine (PMVM) technology for wind power applications. Two types of PMVMs are defined based on the winding arrangements and resultant gear ratio ranges. A comprehensive design study of the selected PMVM topologies is conducted at 1 and 3 MW power levels. The optimized candidate designs of the PMVMs are then evaluated and also compared against the equivalent permanent magnet synchronous machine (PMSM) in terms of performance, costs, size and mass. While the existing research publications mainly focused on the PMVM designs of ( G r = 5 ), this study reveals that the pole/slot combinations of PMVMs with ( G r ≤ 5 ) are more appealing as there is a good trade-off between a reasonable power factor and high power density in these designs. It shows, in this paper, that the PMVM is a promising alternative to common PMSM technology for utility-scale wind-turbine drive-train applications. [ABSTRACT FROM AUTHOR]

(1) Background: This study introduces a novel computational approach to examine government capabilities in information intervention for risk management, influential agents in a global information network, and the socioeconomic factors of information-sharing behaviors of the public across regions during the COVID-19 pandemic. (2) Methods: Citation network analysis was employed to gauge the online visibility of governmental health institutions across regions. A bipartite exponential random graph modeling (ERGM) procedure was conducted to measure network dynamics. (3) Results: COVID-19 response agencies in Europe had the highest web impact, whereas health agencies in North America had the lowest. Various stakeholders, such as businesses, non-profit organizations, governments, and educational institutions played a key role in sharing the COVID-19 response by agencies' information given on their websites. Income inequality and GDP per capita were associated with the high online visibility of governmental health agencies. Other factors, such as population size, an aging population, death rate, and case percentage, did not contribute to the agencies' online visibility, suggesting that demographic characteristics and health status are not predictors of sharing government resources. (4) Conclusions: A combination of citation network analysis and ERGM helps reveal information flow dynamics and understand the socioeconomic consequences of sharing the government's COVID-19 information during the pandemic. [ABSTRACT FROM AUTHOR]

Simple Summary: Corn stover is an important agricultural byproduct, and represents an animal feedstuff. Waxy corn is harvested at milk stage or dough stage (20 d difference in harvest time) to meet the market demand for fresh corn and ripe corn in China, resulting in plenty of byproducts of corn stover after fresh- (CF) or ripe-corn (CR) harvested. This study was conducted to investigate the digestibility and rumen fermentation of these two corn stover silages in growing beef cattle. We observed that CR silage had greater dry matter and fiber contents, but with similar substrate degradation through in vitro ruminal fermentation in comparison with the CF silage. Further feeding trials indicated that feeding a CR diet (i.e., replaced 50% forage of CF silage with CR silage on a dry matter basis) increased feed intake and decreased fiber digestibility with reduction in the abundance of ruminal fiber degraders, together with similar growth performance in beef cattle, when comparing with CF diet. Both waxy corn stover after fresh- (CF) and ripe-corn (CR) harvested are important byproducts of corn cropping system and have 20 d difference in harvest time. The study aimed to investigate the effects of prolonging harvest time on the nutritive value of corn stover silage by comparing CF with CR silages. In vitro ruminal experiment was firstly performed to investigate substrate degradation and fermentation of CF and CR silages. The CR diet was formulated by replacing 50% forage of CF silage with CR silage on a dry matter (DM) basis. Fourteen crossbred steers (Simmental × Limousin × local Chinese) aged 13 months with an average weight of 318.1 ± 37.1 kg were selected and randomly allocated into two dietary treatment groups. Although the CR silage had greater DM and fiber contents than CF silage, it did not alter in vitro degradation (p > 0.05), but with lower molar percentage of propionate and acetate to propionate ratio (p < 0.05). The cattle fed CR diet had a higher DM intake and lower fiber digestibility with reduction in 18S rRNA gene copies of protozoa and fungi and 16S rRNA gene copies of Fibrobacter succinogenes (p < 0.05). Further 16S rRNA gene amplicon analysis indicated a similar diversity of bacteria community between CR and CF treatments (p > 0.05). Few differences were observed in the abundance of genera larger than 1% (p > 0.05), except for the reduction in abundance of genera Ruminococcaceae_NK4A214_group in CR treatment (p < 0.05). In summary, prolonging 20 d harvest time of corn stover silage increases the forage fiber and DM content, which promotes feed intake with decreased fiber degradation, although rumen fermentation and growth performance are not changed in growing beef cattle. [ABSTRACT FROM AUTHOR]

Porcine reproductive and respiratory syndrome virus (PRRSV) induces secretion of high mobility group box 1 (HMGB1) to mediate inflammatory response that is involved in the pulmonary injury of infected pigs. Our previous study indicates that protein kinase C-delta (PKC-delta) is essential for HMGB1 secretion in PRRSV-infected cells. However, the underlying mechanism in HMGB1 secretion induced by PRRSV infection is still unclear. Here, we discovered that the phosphorylation level of HMGB1 in threonine residues increased in PRRSV-infected cells. A site-directed mutagenesis study showed that HMGB1 phosphorylation at threonine-51 was associated with HMGB1 secretion induced by PRRSV infection. Co-immunoprecipitation (co-IP) of HMGB1 failed to precipitate PKC-delta, but interestingly, mass spectrometry analysis of the HMGB1 co-IP product showed that PRRSV infection enhanced HMGB1 binding to ribosomal protein S3 (RPS3), which has various extra-ribosomal functions. The silencing of RPS3 by siRNA blocked HMGB1 secretion induced by PRRSV infection. Moreover, the phosphorylation of HMGB1 at threonine-51 was correlated with the interaction between HMGB1 and RPS3. In vivo, PRRSV infection also increased RPS3 levels and nuclear accumulation in pulmonary alveolar macrophages. These results demonstrate that PRRSV may induce HMGB1 phosphorylation at threonine-51 and increase its interaction with RPS3 to enhance HMGB1 secretion. This finding provides insights into the pathogenesis of PRRSV infection. [ABSTRACT FROM AUTHOR]

Quantitative relations between topological similarity degree and map scale change of multi-scale contour clusters are vital to the automation of map generalization. However, no method has been proposed to calculate the relations. This paper aims at filling the gap by proposing a new approach. It firstly constructed a directed contour tree by pre-processing of unclosed contours, and then developed a quantitative expression of topological relations of contour cluster based on directed contour tree. After this, it employed 108 groups of multi-scale contour clusters with different geomorphological types to explore the changing regularity of topological indices with map scale. Last, it used 416 points to calculate the quantitative relations between topological similarity degree and map scale change by curve fitting method. The results show that the quantitative expression of multi-scale topological indexes is closely related to the contour interval change, and power function is the best fit among the candidate functions. [ABSTRACT FROM AUTHOR]

Simple Summary: RNA pseudouridine modifications are present in various RNAs across different organisms and play crucial roles in regulating gene expression during biological processes. The accurate identification of pseudouridine sites within RNA sequences is essential for understanding their functional mechanisms. This study proposes a novel ensemble learning predictor named PseUpred-ELPSO, which accurately predicts RNA pseudouridine sites. The predictor demonstrates excellent performance in both cross-validation and independent testing. A user-friendly web server has been established, making it a powerful tool for pseudouridine site identification. RNA pseudouridine modification exists in different RNA types of many species, and it has a significant role in regulating the expression of biological processes. To understand the functional mechanisms for RNA pseudouridine sites, the accurate identification of pseudouridine sites in RNA sequences is essential. Although several fast and inexpensive computational methods have been proposed, the challenge of improving recognition accuracy and generalization still exists. This study proposed a novel ensemble predictor called PseUpred-ELPSO for improved RNA pseudouridine site prediction. After analyzing the nucleotide composition preferences between RNA pseudouridine site sequences, two feature representations were determined and fed into the stacking ensemble framework. Then, using five tree-based machine learning classifiers as base classifiers, 30-dimensional RNA profiles are constructed to represent RNA sequences, and using the PSO algorithm, the weights of the RNA profiles were searched to further enhance the representation. A logistic regression classifier was used as a meta-classifier to complete the final predictions. Compared to the most advanced predictors, the performance of PseUpred-ELPSO is superior in both cross-validation and the independent test. Based on the PseUpred-ELPSO predictor, a free and easy-to-operate web server has been established, which will be a powerful tool for pseudouridine site identification. [ABSTRACT FROM AUTHOR]

Antimicrobial peptides (AMPs) are vital components of innate immunotherapy. Existing approaches mainly rely on either deep learning for the automatic extraction of sequence features or traditional manual amino acid features combined with machine learning. The peptide sequence contains symmetrical sequence motifs or repetitive amino acid patterns, which may be related to the function and structure of the peptide. Recently, the advent of large language models has significantly boosted the representational power of sequence pattern features. In light of this, we present a novel AMP predictor called UniproLcad, which integrates three prominent protein language models—ESM-2, ProtBert, and UniRep—to obtain a more comprehensive representation of protein features. UniproLcad utilizes deep learning networks, encompassing the bidirectional long and short memory network (Bi-LSTM) and one-dimensional convolutional neural networks (1D-CNN), while also integrating an attention mechanism to enhance its capabilities. These deep learning frameworks, coupled with pre-trained language models, efficiently extract multi-view features from antimicrobial peptide sequences and assign attention weights to them. Through ten-fold cross-validation and independent testing, UniproLcad demonstrates competitive performance in the field of antimicrobial peptide identification. This integration of diverse language models and deep learning architectures enhances the accuracy and reliability of predicting antimicrobial peptides, contributing to the advancement of computational methods in this field. [ABSTRACT FROM AUTHOR]

Background: Normal tension glaucoma (NTG) is considered a neurodegenerative disease with glaucomatous damage extending to diffuse brain areas. Therefore, this study aims to explore the abnormalities in the NTG structural network to help in the early diagnosis and course evaluation of NTG. Methods: The structural networks of 46 NTG patients and 19 age- and sex-matched healthy controls were constructed using diffusion tensor imaging, followed by graph theory analysis and correlation analysis of small-world properties with glaucoma clinical indicators. In addition, the network-based statistical analysis (NBS) method was used to compare structural network connectivity differences between NTG patients and healthy controls. Results: Structural brain networks in both NTG and NC groups exhibited small-world properties. However, the small-world index in the severe NTG group was reduced and correlated with a mean deviation of the visual field (MDVF) and retinal nerve fiber layer (RNFL) thickness. When compared to healthy controls, degree centrality and nodal efficiency in visual brain areas were significantly decreased, and betweenness centrality and nodal local efficiency in both visual and nonvisual brain areas were also significantly altered in NTG patients (all p < 0.05, FDR corrected). Furthermore, NTG patients exhibited increased structural connectivity in the occipitotemporal area, with the left fusiform gyrus (FFG.L) as the hub (p < 0.05). Conclusions: NTG exhibited altered global properties and local properties of visual and cognitive-emotional brain areas, with enhanced structural connections within the occipitotemporal area. Moreover, the disrupted small-world properties of white matter might be imaging biomarkers for assessing NTG progression. [ABSTRACT FROM AUTHOR]

Soil moisture (SM) is a vital climate variable in the interaction process between the Earth's atmosphere and land. However, global soil moisture products from various satellite missions and land surface models are affected by inherently discontinuous observations and coarse spatial resolution, which limits their application at fine spatial scales. To address this problem, this paper integrates three diverse types of datasets from in situ, satellites, and models through Spherical cap harmonic analysis (SCHA) and Helmert variance component estimation (HVCE) to produce 1 km of spatio-temporally continuous SM products with high accuracy. First, this paper eliminates the bias between different datasets and in situ sites and resamples the datasets before data fusion. Then, multi-source SM data fusion is performed based on the SCHA and HVCE methods. Finally, this paper evaluates the fused products from three aspects, including the performance of representative sites under different climate types, the overall performance of validation sites, and the comparison with other products. The results show that the fused products have better performance than other SM products. In the representative sites, the minimal correlation coefficient (R) of the fused products is above 0.85, and the largest root mean square error (RMSE) is below 0.040 m3 m−3. For all validation sites, the R and RMSE of the fused products are 0.889 and 0.036 m3 m−3, respectively, while the R for other products is below 0.75 and the RMSE is above 0.06 m3 m−3. In comparison to other SM products, the fused products exhibit superior performance, generally align more closely with in situ measurements, and possess the ability to accurately and finely capture the spatial and temporal variability of surface SM. [ABSTRACT FROM AUTHOR]

Objective: Sound, natural dentin collagen can be stabilized against enzymatic degradation through exogenous crosslinking treatment for durable bonding; however, the effect on denatured dentin (DD) collagen is unknown. Hence, the ability of different crosslinkers to enhance/restore the properties of DD collagen was assessed. Methods: Demineralized natural and DD collagen films (7 mm × 7 mm × 7 µm) and beams (0.8 mm × 0.8 mm × 7 mm) were prepared. DD collagen was experimentally produced by heat or acid exposure, which was then assessed by various techniques. All specimens were then treated with 1 wt% of chemical crosslinker 1-ethyl-3-(3-dimethylaminopropyl) carbodiimide/n-hydroxysuccinimide (EDC/NHS) and two structurally different flavonoids—theaflavins (TF) from black tea and type-A proanthocyanidins from cranberry juice (CR) for either 30 s or 1 h. The controls were untreated. Dentin films were assessed for chemical interaction and cross-linking effect by FTIR, biostability against exogenous collagenase by weight loss (WL) and hydroxyproline release (HYP), and endogenous matrix metalloproteinases (MMPs) activity by confocal laser microscopy. Dentin beams were evaluated for tensile properties. Data were analyzed using ANOVA and Tukey's test (α = 0.05). Results: Compared with natural collagen, DD collagen showed pronounced structural changes, altered biostability and decreased mechanical properties, which were then improved to various degrees that were dependent on the crosslinkers used, with EDC/NHS being the least effective. Surprisingly, the well-known MMP inhibitor EDC/NHS showed negligible effect on or even increased MMP activity in DD collagen. As compared with control, cross-linking induced by TF and CR significantly increased collagen biostability (reduced WL and HYP release, p < 0.05), MMP inhibition (p < 0.001) and mechanical properties (p < 0.05), regardless of denaturation. Conclusions: DD collagen cannot or can only minimally be stabilized via EDC/NHS crosslinking; however, the challenging substrate of DD collagen can be enhanced or restored using the promising flavonoids TF and CR. [ABSTRACT FROM AUTHOR]

Cultivated land is the basis of food security and an important component of the construction of ecological civilization. The sustainable use of cultivated land is an important issue in land resource management, and it is also an inevitable factor when addressing the contradiction between food demands and resource and environmental constraints. Cultivated land use is both a food production process and a water- and energy-intensive process. Therefore, sustainable use of cultivated land is important not only for cultivated land itself but also for the associated social, economic, and ecological impacts of water and energy input. Therefore, based on the water–land–energy–food nexus, this paper carries out a theoretical analysis of cultivated land use following the element–structure–function framework and builds an evaluation framework of the sustainable use of cultivated land. Finally, this paper selects appropriate evaluation indicators to evaluate the changes in element coordination and function trade-offs of cultivated land use in Quzhou County from 2000 to 2020; analyzes the key influencing factors in detail; and proposes future development directions. The results reflect the fact that the element coordination degree showed obvious continuous decline three times in a row, then a brief rise, and it finally stabilized at a high level, whereas the synergies between the functions decreased and then increased. This means that the sustainable use level of cultivated land in Quzhou County basically presents a good trend. At present, the obstacle that is restricting the efficient use of cultivated land and sustainable development is water, which should be improved by some measures in the future. The results of this evaluation have important theoretical and practical significance for identifying the characteristics of changes in cultivated land use and for guiding future sustainable use in Quzhou County and other regions. [ABSTRACT FROM AUTHOR]

Hypertension is the main risk factor of cardiovascular and cerebrovascular diseases. In this paper, a novel compound known as 221s (2,9), which includes tanshinol, borneol and a mother nucleus of ACEI, was synthesized by condensation esterification, deprotection, amidation, deprotection, and amidation, with borneol as the initial raw material, using the strategy of combinatorial molecular chemistry. The structure of the compound was confirmed by 1H NMR, 13C NMR, and high-resolution mass spectrometry, with a purity of more than 99.5%. The compound 221s (2,9) can significantly reduce the systolic and diastolic blood pressure of SHR rats by about 50 mmHg and 35 mmHg after 4 weeks of administration. The antihypertensive effect of 221s (2,9) is equivalent to that of captopril. The use of 221s (2,9) can reduce the content of Ren, Ang II and ACE in the serum of SHR rats, inhibit the RAAS and enhance the vascular endothelial function by upregulating the level of NO. Pathological studies in this area have shown that high dosage of 221s (2,9) can notably protect myocardial fibrosis in rats and reduce the degeneration and necrosis of myocardial fibers, inflammatory cell infiltration, and proliferation of fibrous tissue in the heart of rat. Therefore, the existing work provided a foundation for preclinical research and follow-up clinical research of 221s (2,9) as a new drug. [ABSTRACT FROM AUTHOR]

Post-translational modifications regulate diverse activities of a colossal number of proteins. For example, various types of lipids can be covalently linked to proteins enzymatically or non-enzymatically. Protein lipidation is perhaps not as extensively studied as protein phosphorylation, ubiquitination, or glycosylation although it is no less significant than these modifications. Evidence suggests that proteins can be attached by at least seven types of lipids, including fatty acids, lipoic acids, isoprenoids, sterols, phospholipids, glycosylphosphatidylinositol anchors, and lipid-derived electrophiles. In this review, we summarize types of protein lipidation and methods used for their detection, with an emphasis on the conjugation of proteins with polyunsaturated fatty acids (PUFAs). We discuss possible reasons for the scarcity of reports on PUFA-modified proteins, limitations in current methodology, and potential approaches in detecting PUFA modifications. [ABSTRACT FROM AUTHOR]

The existing federated learning framework is based on the centralized model coordinator, which still faces serious security challenges such as device differentiated computing power, single point of failure, poor privacy, and lack of Byzantine fault tolerance. In this paper, we propose an asynchronous federated learning system based on permissioned blockchains, using permissioned blockchains as the federated learning server, which is composed of a main-blockchain and multiple sub-blockchains, with each sub-blockchain responsible for partial model parameter updates and the main-blockchain responsible for global model parameter updates. Based on this architecture, a federated learning asynchronous aggregation protocol based on permissioned blockchain is proposed that can effectively alleviate the synchronous federated learning algorithm by integrating the learned model into the blockchain and performing two-order aggregation calculations. Therefore, the overhead of synchronization problems and the reliability of shared data is also guaranteed. We conducted some simulation experiments and the experimental results showed that the proposed architecture could maintain good training performances when dealing with a small number of malicious nodes and differentiated data quality, which has good fault tolerance, and can be applied to edge computing scenarios. [ABSTRACT FROM AUTHOR]

As a kind of Amaryllidaceae alkaloid which is accumulated in the species of Lycoris plants, lycorine has a range of physiological effects. The biosynthesis pathway of lycorine has been partly revealed, but the transport and accumulation mechanisms of lycorine have rarely been studied. In this study, an ATP-binding cassette (ABC) transporter from Lycoris aurea (L'Hér) Herb., namely LaABCB11, was cloned and functionally characterized. Heterologous expression showed that LaABCB11 transported lycorine in an outward direction, increased the tolerance of yeast cells to lycorine, and caused a lower lycorine accumulation in transformants than control or mutant in yeast. LaABCB11 is associated with the plasma membrane, and in situ hybridization indicated that LaABCB11 was mainly expressed in the phloem of leaves and bulbs, as well as in the cortical cells of roots. These findings suggest that LaABCB11 functions as a lycorine transport and it might be related to the translocation and accumulation of lycorine from the leaves and bulbs to the roots. [ABSTRACT FROM AUTHOR]

The Fourier transform infrared (FTIR) imaging technique was used in a transmission model for the evaluation of twelve oral hyperkeratosis (HK), eleven oral epithelial dysplasia (OED), and eleven oral squamous cell carcinoma (OSCC) biopsy samples in the fingerprint region of 1800–950 cm−1. A series of 100 µm × 100 µm FTIR imaging areas were defined in each sample section in reference to the hematoxylin and eosin staining image of an adjacent section of the same sample. After outlier removal, signal preprocessing, and cluster analysis, a representative spectrum was generated for only the epithelial tissue in each area. Two representative spectra were selected from each sample to reflect intra-sample heterogeneity, which resulted in a total of 68 representative spectra from 34 samples for further analysis. Exploratory analyses using Principal component analysis and hierarchical cluster analysis showed good separation between the HK and OSCC spectra and overlaps of OED spectra with either HK or OSCC spectra. Three machine learning discriminant models based on partial least squares discriminant analysis (PLSDA), support vector machines discriminant analysis (SVMDA), and extreme gradient boosting discriminant analysis (XGBDA) were trained using 46 representative spectra from 12 HK and 11 OSCC samples. The PLSDA model achieved 100% sensitivity and 100% specificity, while both SVM and XGBDA models generated 95% sensitivity and 96% specificity, respectively. The PLSDA discriminant model was further used to classify the 11 OED samples into HK-grade (6), OSCC-grade (4), or borderline case (1) based on their FTIR spectral similarity to either HK or OSCC cases, providing a potential risk stratification strategy for the precancerous OED samples. The results of the current study support the application of the FTIR-machine learning technique in early oral cancer detection. [ABSTRACT FROM AUTHOR]

The crop leaf color is tightly connected with its meteorological environment. Color gradation skewness-distribution (CGSD) parameters can describe the information of leaf color more accurately, systematically, and comprehensively from five dimensions. We took photographs of pepper growing in the greenhouse at a fixed time every day and observed the meteorological factors. The results showed that the CGSD parameters were significantly correlated with meteorological factors, especially with the accumulated temperature, which showed the strongest correlation. Since the relationship between canopy leaf color and accumulated temperature is nonlinear, the piecewise inversion models were constructed by taking the stationary point of the high-order response model of Gskewness to accumulated temperature as the point of demarcation. The rate of outliers had decreased by 57.72%; moreover, the overall inversion accuracy had increased by 3.31% compared with the linear model directly constructed by the stepwise regression. It was observed that the pepper in the greenhouse had a different response to the same meteorological environmental stimulus before and after the stationary point. This study will provide a new method for constructing crop growth models in future research. [ABSTRACT FROM AUTHOR]

This work studied the regulation of hole concentration and mobility in p-InGaN layers grown by metalorganic chemical vapor deposition (MOCVD) under an N-rich environment. By adjusting the growth temperature, the hole concentration can be controlled between 6 × 1017/cm3 and 3 × 1019/cm3 with adjustable hole mobility from 3 to 16 cm2/V.s. These p-InGaN layers can meet different requirements of devices for hole concentration and mobility. First-principles defect calculations indicate that the p-type doping of InGaN at the N-rich limiting condition mainly originated from Mg substituting In (MgIn). In contrast with the compensation of nitrogen vacancy in p-type InGaN grown in a Ga-rich environment, the holes in p-type InGaN grown in an N-rich environment were mainly compensated by interstitial Mg (Mgi), which has very low formation energy. [ABSTRACT FROM AUTHOR]

Lowering the interface charge transfer, ohmic and diffusion impedances are the main considerations to achieve an intermediate temperature solid oxide fuel cell (ITSOFC). Those are determined by the electrode materials selection and manipulating the microstructures of electrodes. The composite electrodes are utilized by a variety of mixed and impregnation or infiltration methods to develop an efficient electrocatalytic anode and cathode. The progress of our proposed core-shell structure pre-formed during the preparation of electrode particles compared with functional layer and repeated impregnation by capillary action. The core-shell process possibly prevented the electrocatalysis decrease, hindering and even blocking the fuel gas path through the porous electrode structure due to the serious agglomeration of impregnated particles. A small amount of shell nanoparticles can form a continuous charge transport pathway and increase the electronic and ionic conductivity of the electrode. The triple-phase boundaries (TPBs) area and electrode electrocatalytic activity are then improved. The core-shell anode SLTN-LSBC and cathode BSF-LC configuration of the present report effectively improve the thermal stability by avoiding further sintering and thermomechanical stress due to the thermal expansion coefficient matching with the electrolyte. Only the half-cell consisting of 2.75 μm thickness thin electrolyte iLSBC with pseudo-core-shell anode LST could provide a peak power of 325 mW/cm2 at 700 °C, which is comparable to other reference full cells' performance at 650 °C. Then, the core-shell electrodes preparation by simple chelating solution and cost-effective one process has a potential enhancement of full cell electrochemical performance. Additionally, it is expected to apply for double ions (H+ and O2−) conducting cells at low temperature. [ABSTRACT FROM AUTHOR]

The present study utilizes an acrylic (PMMA) plate with circular piezoelectric ceramics (PC) as an actuator to design and investigate five different types of piezo actuation jets (PAJs) with operating conditions. The results show that the heat transfer coefficient of a device of PAJ is 200% greater than that of a traditional rotary fan when PAJ is placed at the proper distance of 10 to 20 mm from the heat source, avoiding the suck back of surrounding fluids. The cooling effect of these five PAJs was calculated by employing the thermal analysis method and the convection thermal resistance of the optimal PAJ can be reduced by about 36%, while the voltage frequency, wind speed, and noise were all positively correlated. When the supplied piezoelectric frequency is 300 Hz, the decibel level of the noise is similar to that of a commercial rotary fan. The piezoelectric sheets had one of two diameters of 31 mm or 41 mm depending on the size of the tested PAJs. The power consumption of a single PAJ was less than 10% of that of a rotary fan. Among the five types of PAJ, the optimal one has the characteristics that the diameter of the piezoelectric sheet is 41 mm, the piezoelectric spacing is 2 mm, and the length of the opening is 4 mm. Furthermore, the optimal operating conditions are a voltage frequency of 300 Hz and a placement distance of 20 mm in the present study. [ABSTRACT FROM AUTHOR]

This study applies relative entropy in naturalistic large-scale corpus to calculate the difference among L2 (second language) learners at different levels. We chose lemma, token, POS-trigram, conjunction to represent lexicon and grammar to detect the patterns of language proficiency development among different L2 groups using relative entropy. The results show that information distribution discrimination regarding lexical and grammatical differences continues to increase from L2 learners at a lower level to those at a higher level. This result is consistent with the assumption that in the course of second language acquisition, L2 learners develop towards a more complex and diverse use of language. Meanwhile, this study uses the statistics method of time series to process the data on L2 differences yielded by traditional frequency-based methods processing the same L2 corpus to compare with the results of relative entropy. However, the results from the traditional methods rarely show regularity. As compared to the algorithms in traditional approaches, relative entropy performs much better in detecting L2 proficiency development. In this sense, we have developed an effective and practical algorithm for stably detecting and predicting the developments in L2 learners' language proficiency. [ABSTRACT FROM AUTHOR]

From a global perspective, carbon emissions are a global problem that needs to be solved urgently. At present, 61% of countries have committed to achieving net zero emissions. Compared with industry and construction, the transportation sector has become the focus and challenge for countries to achieve carbon neutrality due to the characteristics of strong mobility, scattered emission sources, and complex social behaviors. Therefore, the issue of carbon emissions in the transportation industry has become the focus of academic attention. This paper first calculates the carbon emission efficiency (CEE) of the regional transportation industry through the super-efficiency SBM model and then evaluates its regional differentiation characteristics through the Theil index, which has important practical significance for reducing regional carbon emissions. The results show that the national transportation CEE average value is 0.612, a relatively low level. The spatial distribution of China's transportation CEE shows an obvious characteristic of "east highest and west lowest". The regional differences in the transportation industry CEE are larger than those between regions. The differences in the transportation industry CEE among the eastern, central, and western regions are on the downward trend as a whole, and intra-regional differences are greater than inter-regional. The intra-regional differences cause the overall differences in transportation industry CEE; the eastern region contributed the most to the Theil index, while the central contributed the least. The biggest factor affecting the transportation industry CEE is the regional energy structure, and the smallest factor is the per capita GDP. This research has important reference significance on the target of carbon neutrality. [ABSTRACT FROM AUTHOR]

Within the current process of large-scale dairy-cattle breeding, to address the problems of low recognition-accuracy and significant recognition-error associated with existing visual methods, we propose a method for recognizing the feeding behavior of dairy cows, one based on an improved RefineMask instance-segmentation model, and using high-quality detection and segmentation results to realize the recognition of the feeding behavior of dairy cows. Firstly, the input features are better extracted by incorporating the convolutional block attention module into the residual module of the feature extraction network. Secondly, an efficient channel attention module is incorporated into the neck design to achieve efficient integration of feature extraction while avoiding the surge of parameter volume computation. Subsequently, the GIoU loss function is used to increase the area of the prediction frame to optimize the convergence speed of the loss function, thus improving the regression accuracy. Finally, the logic of using mask information to recognize foraging behavior was designed, and the accurate recognition of foraging behavior was achieved according to the segmentation results of the model. We constructed, trained, and tested a cow dataset consisting of 1000 images from 50 different individual cows at peak feeding times. The method's effectiveness, robustness, and accuracy were verified by comparing it with example segmentation algorithms such as MSRCNN, Point_Rend, Cascade_Mask, and ConvNet_V2. The experimental results show that the accuracy of the improved RefineMask algorithm in recognizing the bounding box and accurately determining the segmentation mask is 98.3%, which is higher than that of the benchmark model by 0.7 percentage points; for this, the model parameter count size was 49.96 M, which meets the practical needs of local deployment. In addition, the technologies under study performed well in a variety of scenarios and adapted to various light environments; this research can provide technical support for the analysis of the relationship between cow feeding behavior and feed intake during peak feeding periods. [ABSTRACT FROM AUTHOR]

Hydrogen Sulfide (H2S), an endogenously produced gasotransmitter, is involved in various important physiological and disease conditions, including vasodilation, stimulation of cellular bioenergetics, anti-inflammation, and pro-angiogenesis. In cancer, aberrant up-regulation of H2S-producing enzymes is frequently observed in different cancer types. The recognition that tumor-derived H2S plays various roles during cancer development reveals opportunities to target H2S-mediated signaling pathways in cancer therapy. In this review, we will focus on the mechanism of H2S-mediated protein persulfidation and the detailed information about the dysregulation of H2S-producing enzymes and metabolism in different cancer types. We will also provide an update on mechanisms of H2S-mediated cancer progression and summarize current options to modulate H2S production for cancer therapy. [ABSTRACT FROM AUTHOR]

A thermoelectric pipe (TEP) is constructed by tubular graphite electrodes, Teflon material, and stainless-steel tube containing polymeric nanofluids as electrolytes in this study. Heat dissipation and power generation (generating capacity) are both fulfilled with temperature difference via the thermal-electrochemistry and redox reaction effects of polymeric nanofluids. The notion of TEP is to recover the dissipative heat from the heat capacity generated by the relevant machine systems. The thermal conductivity and power density empirical formulas of the novel TEP were derived through the intelligent dimensional analysis with thermoelectric experiments and evaluated at temperatures between 25 and 100 °C and vacuum pressures between 400 and 760 torr. The results revealed that the polymeric nanofluids composed of titanium dioxide (TiO2) nanoparticles with 0.2 wt.% sodium hydroxide (NaOH) of the novel TEP have the best thermoelectric performance among these electrolytes, including TiO2 nanofluid, TiO2 nanofluid with 0.2 wt.% NaOH, deionized water, and seawater. Furthermore, the thermal conductivity and power density of the novel TEP are 203.1 W/(m·K) and 21.16 W/m3, respectively. [ABSTRACT FROM AUTHOR]

Fusarium head blight (FHB) causes wheat yield loss and mycotoxin (deoxynivalenol, DON) accumulation in wheat kernel. Developing wheat cultivars with overall resistance to both FHB spread within a spike and DON accumulation in kernels is crucial for ensuring food security and food safety. Here, two relatively novel inoculation methods, bilateral floret inoculation (BFI) and basal rachis internode injection (BRII), were simultaneously employed to evaluate disease severity and DON content in kernels in a segregating population of recombinant inbred lines (RILs) developed from Ning 7840 (carrying Fhb1) and Clark (without Fhb1). Under both inoculation methods, four contrasting combinations of disease severity and DON content were identified: high severity/high DON (HSHD), high severity/low DON (HSLD), low severity/high DON (LSHD) and low severity/low DON (LSLD). Unexpectedly, the BRII method clearly indicated that disease severity was not necessarily relevant to DON concentration. The effects of Fhb1 on disease severity, and on DON concentrations, agreed very well across the two methods. Several lines carrying Fhb1 showed extremely higher severity and (or) DON content under both inoculation methods. The "Mahalanobis distance" (MD) method was used to rate overall resistance of a line by inclusion of both disease severity and DON content over both methods to select LSLD lines. [ABSTRACT FROM AUTHOR]

We evaluate the potential of using a process-based ecosystem model (BEPS) for crop biomass mapping at 20 m resolution over the research site in Manitoba, western Canada driven by spatially explicit leaf area index (LAI) retrieved from Sentinel-2 spectral reflectance throughout the entire growing season. We find that overall, the BEPS-simulated crop gross primary production (GPP), net primary production (NPP), and LAI time-series can explain 82%, 83%, and 85%, respectively, of the variation in the above-ground biomass (AGB) for six selected annual crops, while an application of individual crop LAI explains only 50% of the variation in AGB. The linear relationships between the AGB and these three indicators (GPP, NPP and LAI time-series) are rather high for the six crops, while the slopes of the regression models vary for individual crop type, indicating the need for calibration of key photosynthetic parameters and carbon allocation coefficients. This study demonstrates that accumulated GPP and NPP derived from an ecosystem model, driven by Sentinel-2 LAI data and abiotic data, can be effectively used for crop AGB mapping; the temporal information from LAI is also effective in AGB mapping for some crop types. [ABSTRACT FROM AUTHOR]