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Abstract The assessment of mutagenicity is essential in drug discovery, as it may lead to cancer and germ cells damage. Although in silico methods have been proposed for mutagenicity prediction, their performance is hindered by the scarcity of labeled molecules. However, experimental mutagenicity testing can be time-consuming and costly. One solution to reduce the annotation cost is active learning, where the algorithm actively selects the most valuable molecules from a vast chemical space and presents them to the oracle (e.g., a human expert) for annotation, thereby rapidly improving the model’s predictive performance with a smaller annotation cost. In this paper, we propose muTOX-AL, a deep active learning framework, which can actively explore the chemical space and identify the most valuable molecules, resulting in competitive performance with a small number of labeled samples. The experimental results show that, compared to the random sampling strategy, muTOX-AL can reduce the number of training molecules by about 57%. Additionally, muTOX-AL exhibits outstanding molecular structural discriminability, allowing it to pick molecules with high structural similarity but opposite properties.

Spaceborne photon-counting LiDAR holds significant potential for shallow-water bathymetry. However, the received photon data often contain substantial noise, complicating the extraction of elevation information. Currently, a denoising algorithm named ordering points to identify the clustering structure (OPTICS) draws people’s attention because of its strong performance under high background noise. However, this algorithm’s fixed input variables can lead to inaccurate photon distribution parameters in areas near the water bottom, which results in inadequate denoising in these areas, affecting bathymetric accuracy. To address this issue, an Adaptive Variable OPTICS (AV-OPTICS) model is proposed in this paper. Unlike the traditional OPTICS model with fixed input variables, the proposed model dynamically adjusts input variables based on point cloud distribution. This adjustment ensures accurate measurement of photon distribution parameters near the water bottom, thereby enhancing denoising effects in these areas and improving bathymetric accuracy. The findings indicate that, compared to traditional OPTICS methods, AV-OPTICS achieves higher F1-values and lower cohesions, demonstrating better denoising performance near the water bottom. Furthermore, this method achieves an average MAE of 0.28 m and RMSE of 0.31 m, indicating better bathymetric accuracy than traditional OPTICS methods. This study provides a promising solution for shallow-water bathymetry based on photon-counting LiDAR data.

Predicting protein–ligand binding sites is an integral part of structural biology and drug design. A comprehensive understanding of these binding sites is essential for advancing drug innovation, elucidating mechanisms of biological function, and exploring the nature of disease. However, accurately identifying protein–ligand binding sites remains a challenging task. To address this, we propose PGpocket, a geometric deep learning-based framework to improve protein–ligand binding site prediction. Initially, the protein surface is converted into a point cloud, and then the geometric and chemical properties of each point are calculated. Subsequently, the point cloud graph is constructed based on the inter-point distances, and the point cloud graph neural network (GNN) is applied to extract and analyze the protein surface information to predict potential binding sites. PGpocket is trained on the scPDB dataset, and its performance is verified on two independent test sets, Coach420 and HOLO4K. The results show that PGpocket achieves a 58% success rate on the Coach420 dataset and a 56% success rate on the HOLO4K dataset. These results surpass competing algorithms, demonstrating PGpocket’s advancement and practicality for protein–ligand binding site prediction.

This article presents a LiDAR system that utilizes a Fizeau interferometer and photomultiplier tube array to detect the water Rayleigh–Brillouin spectrum, utilized to obtain underwater temperature and salinity synchronizing measurements based on the Brillouin spectral linewidth and shift. Temperature and salinity measurements were conducted in the laboratory to verify the efficiency of the system. The results demonstrate that the LiDAR system can accurately obtain the Rayleigh–Brillouin spectral backscattering profiles of water. Following linear fitting and reconstruction, the retrieved temperature accuracy is ±0.13 °C and salinity accuracy is ±0.16‰. By effectively leveraging the multiparameter information contained in the Rayleigh–Brillouin spectrum, the system achieved precise temperature and salinity measurements. This study provides a reference for marine remote sensing applications

Abstract Aims Growing clinical evidence suggests that not all patients with rheumatoid arthritis (RA) benefit to the same extent by treatment with tripterygium glycoside (TG), which highlights the need to identify RA‐related genes that can be used to predict drug responses. In addition, single genes as markers of RA are not sufficiently accurate for use as predictors. Therefore, there is a need to identify paired expression genes that can serve as biomarkers for predicting the therapeutic effects of TG tablets in RA. Methods A total of 17 pairs of co‐expressed genes were identified as candidates for predicting an RA patient's response to TG therapy, and genes involved in the Lnc‐ENST00000602558/GF1 axis were selected for that purpose. A partial‐least‐squares (PLS)‐based model was constructed based on the expression levels of Lnc‐ENST00000602558/IGF1 in peripheral blood. The model showed high efficiency for predicting an RA patient's response to TG tablets. Results Our data confirmed that genes co‐expressed in the Lnc‐ENST00000602558/IGF1 axis mediate the efficacy of TG in RA treatment, reduce tumor necrosis factor‐α induced IGF1 expression, and decrease the inflammatory response of MH7a cells. Conclusion We found that genes expressed in the Lnc‐ENST00000602558/IGF1 axis may be useful for identifying RA patients who will not respond to TG treatment. Our findings provide a rationale for the individualized treatment of RA in clinical settings.

Objective To improve the treatment of the acetabular transverse posterior wall fracture (ATPWF), a novel anatomical locking guidance plate (NALGP) was designed and compared with traditional fixations using finite element analysis. Methods The ATPWF model was constructed using the three‐dimensional finite element model of the half pelvis via the Mimics software and three internal devices were used to fix this model: the posterior‐column locking plate with anterior‐column screws (PCLP), double‐column locking plates (DCLP), and NALGP. Next, mesh division was conducted by solid 187 tetrahedral elements in the workbench software. After defining the boundary condition and material properties, each assembly model was loaded in an increasing manner with a downward vertical force of 200, 400, and 600 N, respectively. The loading force was directed at 45 degrees upward in the coronal plane and 25 degrees backward in the sagittal plane. Finally, the stress distribution and stress peak of plates and screws were measured and evaluated, and the displacement of fracture fragments under different loading force was assessed among the three groups. Results For stress distribution, it was found that the stress mainly acted on the posterior‐column plate, especially concentrated at the middle and lower section of the plate in all three groups after fixation on the ATPWF. In addition, most stresses of screws appeared on the lag screws instead of the common screws. The common screws in the NALGP group experienced larger stresses under all loading force, while those in the DCLP group withstood less stresses compared to those in the PCLP group. For the displacement of fracture fragments, the NALGP group were found to have less fracture fragment displacements than the PCLP group, but had comparable results to DCLP at both the transverse fracture and the posterior wall fracture sites. Conclusion The newly‐designed fixation device showed superiorities on fracture stabilization over PCLP, but had comparable stability to DCLP. This suggests that the DCLP might be unnecessary for treating ATPWF in some instances because it might cause bigger surgical trauma and blood loss.

Southeast Asia has great potential for mineral exploration, and this region is well-known to host huge economic ore deposits located in complex tectonic terranes. Amongst these ore deposits, the Ni(Co) laterite deposits are mainly distributed in Indonesia, the Philippines, and Myanmar. There are two main types of Ni(Co) laterite deposits consisting of hydrous Mg silicate (or garnierite) and oxide ores, with limited development of clay silicate type. These deposits are influenced and controlled by the lithology of ultramafic bedrock, topography, climate, weathering, structures, and tectonic environment. The degree of bedrock serpentinization has an important influence on the grade of Ni laterite ore. Given the growing demand of modern society for Ni(Co) ore resources, deep research should be focused on a better understanding of the genesis of this laterite deposit and geological features of Ni(Co) ore, as well as its exploration applications in southeastern Asia. Improving current research and exploration methods by means of cutting-edge technologies can enhance the understanding of the Ni(Co) enrichment mechanism in weathered laterite and lead to the discovery of new deposits in Southeast Asia. Ni(Co) laterite deposits from this region, especially Indonesia and the Philippines, have the potential to be a source of scandium, rare earth elements, and platinum group elements.

Abstract Pyroptosis and related gasdermin family proteins play an important role in the tumorigenesis of colorectal cancer (CRC). However, the prognostic roles of pyroptosis-related genes (PRGs) and their relation to infiltrates of immune cells in the pathogenesis of CRC remain unclear. Using this study, we set up a prognostic gene pattern on the basis of 13 PRGs (AIM2, CASP1, CASP5, CASP6, CASP8, CASP9, ELANE, GPX4, GSDMD, NLRP7, NOD2, PJVK, and PRKACA) for CRC patients. A comprehensive bioinformatics analysis based on these genes was then performed. With the good AUC prediction value of the ROC curves, the group with high hazard first had a poorer survival prognosis than the group with low hazard. Second, we found that PRGs were significantly related to inflammation-associated genes and immune-associated genes in CRC. Then, we identified a correlation of PRGs with immune infiltrations in CRC. For instance, the abundances of resting NK cells resting and neutrophils were higher in the low hazard group than in the high hazard group. Overall, this work indicated that PRGs contributed to generate heterogeneity of the tumor microenvironment (TME) in CRC. This prognostic PRG model may provide a starting point for the early diagnosis and medication use of CRC.

Cotton is widely grown in many countries around the world due to the huge economic value of the total natural fiber. Verticillium wilt, caused by the soil-borne pathogen Verticillium dahliae, is the most devastating disease that led to extensive yield losses and fiber quality reduction in cotton crops. Developing resistant cotton varieties through genetic engineering is an effective, economical, and durable strategy to control Verticillium wilt. However, there are few resistance gene resources in the currently planted cotton varieties, which has brought great challenges and difficulties for breeding through genetic engineering. Further revealing the molecular mechanism between V. dahliae and cotton interaction is crucial to discovering genes related to disease resistance. In this review, we elaborated on the pathogenic mechanism of V. dahliae and the resistance mechanism of cotton to Verticillium wilt. V. dahliae has evolved complex mechanisms to achieve pathogenicity in cotton, mainly including five aspects: (1) germination and growth of microsclerotia; (2) infection and successful colonization; (3) adaptation to the nutrient-deficient environment and competition of nutrients; (4) suppression and manipulation of cotton immune responses; (5) rapid reproduction and secretion of toxins. Cotton has evolved multiple physiological and biochemical responses to cope with V. dahliae infection, including modification of tissue structures, accumulation of antifungal substances, homeostasis of reactive oxygen species (ROS), induction of Ca2+ signaling, the mitogen-activated protein kinase (MAPK) cascades, hormone signaling, and PAMPs/effectors-triggered immune response (PTI/ETI). This review will provide an important reference for the breeding of new cotton germplasm resistant to Verticillium wilt through genetic engineering.

The mechanical properties of biological soft tissues are inextricably linked to the field of health care, and their mechanical properties can be important indicators for diagnosing and detecting diseases; they can also be used to analyze the causes of organ diseases from a pathological point of view and thus guide the deployment of medical solutions. As an effective method to characterize the mechanical properties of materials, mechanical loading experiments have been successfully applied to the mechanical properties of materials, including tension, compression, pure shear, and so on. Under quasi-static loading, when the material is a biological soft tissue material between a solid and an ideal fluid, its viscoelastic properties strongly respond to the force stimulus, and the stress-strain-time in the elastic phase will have obvious disturbance characteristics. Therefore, the existing statistical methods are often difficult to quantitatively describe the mechanical properties of materials. Therefore, this study proposes an Interval Capture Point based on the principle of integration. The experimental data based on this method can characterize its nonlinear mechanical properties well, especially when the loading speed is extremely low and the soft materials show strong disturbance characteristics. The proposed method can still accurately characterize the hyperelastic and viscoelastic properties of the mechanical properties of biological soft tissues under quasi-static loading.

Combustion behaviors of propellant depend heavily on the architecture of the pocket bounded by coarse AP particles. In order to achieve a better control of the meso-scale architecture of the pocket, overcoming random mixing of the fine particles inside propellant, two kinds of cross-linked fluorinated polymer supported Aluminum/Oxidizer microspheres containing fine Al and oxidizer particles are fabricated by emulsion solvent evaporation and in-situ polymerization method (ESV-ISP), which are used to replace the fine Al and oxidizer particles of the Al/AP/HMX/HTPB propellant. The morphologies of the Al/Oxidizer microspheres and the corresponding propellants are studied, it shows that the fine Al and oxidizer particles are well dispersed and arranged in the microspheres. Besides, the average sizes of the microspheres are ranging from 113.9 ± 4.3 μm to 183.0 ± 5.9 μm, and equivalent to that of the Al-rich clusters located inside the pocket. The energy property and combustion performance of the propellants indicated that the combustion heat of the propellants containing Al/Oxidizer microsphere is increased by about 200 J/g, while the fraction of residual active aluminum is decreased by 77.8%, additionally, the D50 and D(4,3) of the combustion residue are both reduced significantly. Large Al aggregate nearby the burning surface is observed clearly in the blank propellant, while it could not be observed in the propellants containing Al/Oxidizer microsphere. Furthermore, compared to the pressure exponent (n), 0.34, of the B-Propellant, the n of the Al/AP-Propellant and the Al/HMX-Propellant significantly drop to 0.24 and 0.25, respectively, and the burning rate (r) could be adjusted and controlled by the Al/Oxidizer microsphere consisting of different fine oxidizer particles. It demonstrates that using the structurally ordered Al/Oxidizer microsphere could successfully achieve a better control of the meso-scale architecture of the pocket in typical Al/AP/HMX/HTPB propellant.

Verticillium wilt is a fungal disease in upland cotton and exerts a significant effect on growth and potential productivity. This disease is mainly caused by V. dahliae Kleb. Ethylene response factor (ERF) is one of the superfamilies of transcription factors that is involved in the development and environmental adaption of crops. A total of 30 ERF.B4 group members were detected in upland cotton and divided into 6 subgroups. Gene structures, conserved motifs, and domain analysis revealed that members in each subgroup are highly conserved. Further, the 30 GhERF.B4 group members were distributed on 18 chromosomes, and 36 gene synteny relationships were found among them. GhERF.B4 genes were ubiquitously expressed in various tissues and developmental stages of cotton. Amongst them, GhERF.B4-15D was predominantly expressed in roots, and its expression was induced by V. dahliae infection. In addition, GhERF.B4-15D responded to methyl jasmonate (MeJA), methyl salicylate (MeSA), and ethylene (ET) phytohormones. It was also found that the V. dahliae resistance was enhanced due to overexpression of GhERF.B4-15D in Arabidopsis thaliana. On the contrary, interference of GhERF.B4-15D by virus-induced gene silencing (VIGS) technology decreased the V. dahliae resistance level in upland cotton. The subcellular localization experiment showed that GhERF.B4-15D was located in the nucleus. Yeast two-hybrid (Y2H) and luciferase complementation (LUC) approaches demonstrated that GhERF.B4-15D interacted with GhDREB1B. Additionally, the V. dahliae resistance was significantly decreased in GhDREB1B knockdowns. Our results showed that GhERF.B4-15D plays a role during V. dahliae infection in cotton.

Abstract Delayed luminescence (DL) is gradually used in various detection of biological systems as a rapid detection technique, however, its biological mechanism was still not clear. In this study, a new model of DL detection system for liquid biological samples is established to investigate the DL emission of Saccharomyces cerevisiae cells cultured in different glucose concentrations. We analyzed the relationship between the DL emission and cell growth, cell vitality, mitochondrial morphology, mitochondrial DNA (mtDNA) copy number, adenosine triphosphate (ATP), oxygen consumption rate (OCR), as well as mitochondria membrane potential (MMP) in S. cerevisiae cells cultured with 0.01, 0.05, 0.15, 3, 10 and 20 g/L glucose respectively. It was found that the DL emission had strong correlation with mitochondrial morphology, OCR, and MMP. The results suggested that DL is an indicator of mitochondria status under different glucose supply conditions, and may be an effective method to detect mitochondrial metabolism related disorders.

Abstract As a clean and renewable energy, wave energy has abundant reserves, and its reasonable development and utilization can effectively alleviate the problems of energy shortage and environmental pollution. This paper proposes a mechanical rectification wave energy converter. The reciprocating linear motion of the buoy is directly converted into the rotary motion through the ball screw. The magnetic coupling force transmission system is adopted to ensure the sealing of the device. Using mechanical rectification mechanism to ensure the service life of the generator. In addition, a dynamic model of the mechanical rectification wave energy converter is established. Through simulation analysis, the curve of power with load resistance is obtained. And there is an optimal load resistance value corresponding to the maximum power in different wave conditions. In order to improve the power generation efficiency, the maximum power point tracking control algorithm is designed based on the admittance differentiation method. By controlling the duty cycle of the switch tube in the Buck‐Boost chopper circuit, the variable load is realized, and then the maximum power point tracking control is realized. Finally, simulation analysis verifies the feasibility of the algorithm under different wave conditions.

Abstract Background Non-coding RNA (ncRNA) and protein interactions play essential roles in various physiological and pathological processes. The experimental methods used for predicting ncRNA–protein interactions are time-consuming and labor-intensive. Therefore, there is an increasing demand for computational methods to accurately and efficiently predict ncRNA–protein interactions. Results In this work, we presented an ensemble deep learning-based method, EDLMFC, to predict ncRNA–protein interactions using the combination of multi-scale features, including primary sequence features, secondary structure sequence features, and tertiary structure features. Conjoint k-mer was used to extract protein/ncRNA sequence features, integrating tertiary structure features, then fed into an ensemble deep learning model, which combined convolutional neural network (CNN) to learn dominating biological information with bi-directional long short-term memory network (BLSTM) to capture long-range dependencies among the features identified by the CNN. Compared with other state-of-the-art methods under five-fold cross-validation, EDLMFC shows the best performance with accuracy of 93.8%, 89.7%, and 86.1% on RPI1807, NPInter v2.0, and RPI488 datasets, respectively. The results of the independent test demonstrated that EDLMFC can effectively predict potential ncRNA–protein interactions from different organisms. Furtherly, EDLMFC is also shown to predict hub ncRNAs and proteins presented in ncRNA–protein networks of Mus musculus successfully. Conclusions In general, our proposed method EDLMFC improved the accuracy of ncRNA–protein interaction predictions and anticipated providing some helpful guidance on ncRNA functions research. The source code of EDLMFC and the datasets used in this work are available at https://github.com/JingjingWang-87/EDLMFC .

In this investigation, non-spontaneous combustion coal gangue was activated by two methods: (1) low-temperature calcination and (2) calcium addition. Differences in the activity of the activated coal gangue were studied at various calcination temperatures and amounts of calcium addition. Meanwhile, the cementation activity of the activated coal gangue was evaluated according to the activity effect analysis. Furthermore, the influences of the activated coal gangue on the cementation activity of cement were investigated. The results indicated that the activities of the activated coal gangue increased at a temperature between 500 °C and 700 °C. The calcium addition method can also increase the activity of coal gangue, with the effect being better when the gangue is mixed with slag. The addition of calcium and the calcination of coal gangue can promote the production of active minerals such as metakaolin, which is the main reason for the increased cementation activity.

The age-related loss of skeletal muscle, sarcopenia, is characterized by progressive loss of muscle mass, reduction in muscle strength, and dysfunction of physical performance. It has become a global health problem leading to several adverse outcomes in the ageing population. Research on skeletal muscle loss prevention and treatment is developing quickly. However, the current clinical approaches to sarcopenia are limited. Recently, novel drug delivery systems offer new possibilities for treating aged muscle loss. Herein, we briefly recapitulate the potential therapeutic targets of aged skeletal muscle and provide a concise advance in the drug delivery systems, mainly focus on the use of nano-carriers. Furthermore, we elaborately discuss the prospect of aged skeletal muscle treatment by nanotechnology approaches.

Cotton fiber is renewable natural fiber source for textile. Improving fiber quality is an essential goal for cotton breeding project. In present study, F14 recombinant inbred line (RIL) population was backcrossed by the maternal parent to obtain a backcross (BC) population, derived from one Upland cotton hybrid. Three repetitive field trials were performed by randomized complete block design with two replicates in three locations in 2015, together with the BC population, common male parent and the RIL population. Totally, 26 QTLs in BC population explained 5.00–14.17% of phenotype variation (PV) and 37 quantitative trait loci (QTL) were detected in RIL population explaining 5.13–34.00% of PV. Seven common QTLs detected simultaneously in two populations explained PV from 7.69 to 23.05%. A total of 20 QTLs in present study verified the previous results across three environments in 2012. Particularly, qFL-Chr5-2 controlling fiber length on chromosome 5 explained 34.00% of PV, while qFL-Chr5-3 only within a 0.8 cM interval explained 13.93% of PV on average in multiple environments. These stable QTLs explaining great variation offered essential information for marker-assisted selection (MAS) to improve fiber quality traits. Lots of epistasis being detected in both populations acted as one of important genetic compositions of fiber quality traits.

Acetyl-CoA carboxylase is an important enzyme, which catalyzes acetyl-CoA’s carboxylation to produce malonyl-CoA and to serve as a committed step for de novo fatty acid biosynthesis in plastids. In this study, 24 putative cotton BCCP genes were identified based on the lately published genome data in Gossypium. Among them, 4, 4, 8, and 8 BCCP homologs were identified in Gossypium raimondii, G. arboreum, G. hirsutum, and G. barbadense, respectively. These genes were divided into two classes based on a phylogenetic analysis. In each class, these homologs were relatively conserved in gene structure and motifs. The chromosomal distribution pattern revealed that all the BCCP genes were distributed equally on corresponding chromosomes or scaffold in the four cotton species. Segmental duplication was a predominant duplication event in both of G. hirsutum and G. barbadense. The analysis of the expression profile showed that 8 GhBCCP genes expressed in all the tested tissues with changed expression levels, and GhBCCP genes belonging to class II were predominantly expressed in developing ovules. Meanwhile, the expression analysis for the 16 cotton BCCP genes from G. raimondii, G. arboreum and G. hirsutum showed that they were induced or suppressed by cold or salt stress, and their expression patterns varied among different tissues. These findings will help to determine the functional and evolutionary characteristics of the BCCP genes in Gossypium species.