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With the extension of coal mining in China, fault water inrush has become one of major disasters threatening the safety of coal mine production. Based on the research results related to the mining-induced fault water inrush, this paper proposes a conceptual model of water inrush caused by the erosion synergy of mining-induced rock mass damage rupture and fractured rock mass (fault), derives the permeability evolution equation of the two media, and systematically constructs the cooperative disaster causing mechanism model between mining failure and particle erosion inside faults. The numerical simulation is conducted to study the deformation and failure of rock mass, the particle transport in faults and the evolution characteristics of seepage channel, and systematically explain the temporal and spatial evolution mechanism of seepage catastrophe caused by mining-induced fault inrush. The results show that: ① With the continuous advancement of working face, the damage field of mine floor rock mass is connected with the fault erosion fracture, forming a seepage path of aquifer-fault-mining fracture-working face, and with the increase of erosion time, it finally develops into several dominant water diversion channels, resulting in a sharp increase in water inflow at the working face and a lagging water inrush. ② With the increase of seepage time, the water inflow and fracture opening degree inside faults all show three stages: slow change, sudden increase and stable, and the erosion particle concentration shows a trend of first increasing and then decreasing. ③ Under the geological conditions of the mining area studied in this paper, in order to prevent the occurrence of fault water inrush, the methods such as advanced grouting or leaving water prevention coal pillars can be adopted, and the advance grouting time should be before the bottom plate fracture zone connects faults, if grouting is not applied, the width of the reasonable water prevention coal pillar should not be less than 20 m.

Abstract Background Cotton is a major world cash crop and an important source of natural fiber, oil, and protein. Drought stress is becoming a restrictive factor affecting cotton production. To facilitate the development of drought-tolerant cotton varieties, it is necessary to study the molecular mechanism of drought stress response by exploring key drought-resistant genes and related regulatory factors. Results In this study, two cotton varieties, ZY007 (drought-sensitive) and ZY168 (drought-tolerant), showing obvious phenotypic differences under drought stress, were selected. A total of 25,898 drought-induced genes were identified, exhibiting significant enrichment in pathways related to plant stress responses. Under drought induction, At subgenome expression bias was observed at the whole-genome level, which may be due to stronger inhibition of Dt subgenome expression. A gene co-expression module that was significantly associated with drought resistance was identified. About 90% of topologically associating domain (TAD) boundaries were stable, and 6613 TAD variation events were identified between the two varieties under drought. We identified 92 genes in ZY007 and 98 in ZY168 related to chromatin 3D structural variation and induced by drought stress. These genes are closely linked to the cotton response to drought stress through canonical hormone-responsive pathways, modulation of kinase and phosphatase activities, facilitation of calcium ion transport, and other related molecular mechanisms. Conclusions These results lay a foundation for elucidating the molecular mechanism of the cotton drought response and provide important regulatory locus and gene resources for the future molecular breeding of drought-resistant cotton varieties.

Abstract Three‐way concept analysis is an important tool for information processing, and rule acquisition is one of the research hotspots of three‐way concept analysis. However, compared with three‐way concept lattices, three‐way semi‐concept lattices have three‐way operators with weaker constraints, which can generate more concepts. In this article, the problem of rule acquisition for three‐way semi‐concept lattices is discussed in general. The authors construct the finer relation of three‐way semi‐concept lattices, and propose a method of rule acquisition for three‐way semi‐concept lattices. The authors also discuss the set of decision rules and the relationships of decision rules among object‐induced three‐way semi‐concept lattices, object‐induced three‐way concept lattices, classical concept lattices and semi‐concept lattices. Finally, examples are provided to illustrate the validity of our conclusions.

As the highest altitude and the longest plateau railway line, the Qinghai–Tibet railway passes through a large area of continuous permafrost. As the key position of the combination of roadbed and bridge, the stability and strength of the bridge approach directly affect the smoothness of the whole line. Through an indoor model test, the distribution pattern of space temperature field and the change law of temperature measuring points in the bridge approach of gravel sandwich are studied. With the increase in the depth from the subgrade surface, the influence of the temperature change in the upper atmosphere decreases gradually at the 2.5-m platform back of the subgrade center section, resulting in an obvious lag in the subgrade. By comparing the temperature measurement data of different bridge approaches of the gravel interlayer road bridge and the bridge approach of the U-shaped block gravel road bridge, it is shown that the U-shaped structure not only has a better overall cooling effect, but also the cooling effect gradually increases with the increase in the distance from the abutment back. The influence range of the road–bridge transition section on the spatial temperature field is approximately 14 m in the back of the abutment, and the influence range at 7.5 m is the most severe. The research results have important practical significance and engineering application value for the operation and maintenance of the Qinghai–Tibet railway.

Forward-looking imaging and target detection are highly desirable in many military and civilian fields, such as search and rescue, sea surface surveillance, airport surveillance, and guidance. However, during the processing procedure, imaging and target detection are usually regarded as two independent parts, which means that the imaging result will directly affect the detection performance. In this article, the LRSD-ADMM-net is proposed to achieve simultaneous super-resolution imaging and target detection for forward-looking scanning radar. First, low-rank and sparse constraints as regularization norms are incorporated to establish objective function, and the alternating direction multiplier method is used to solve simultaneous super-resolution and target detection problems. Then, the solving process is expanded into a neural network, where the weight parameters of each level are obtained through adaptive learning. At last, experiments are conducted to verify the effectiveness of the proposed method.

Abstract Methane (CH4) emissions from ruminants are of a significant environmental concern, necessitating accurate prediction for emission inventories. Existing models rely solely on dietary and host animal-related data, ignoring the predicting power of rumen microbiota, the source of CH4. To address this limitation, we developed novel CH4 prediction models incorporating rumen microbes as predictors, alongside animal- and feed-related predictors using four statistical/machine learning (ML) methods. These include random forest combined with boosting (RF-B), least absolute shrinkage and selection operator (LASSO), generalized linear mixed model with LASSO (glmmLasso), and smoothly clipped absolute deviation (SCAD) implemented on linear mixed models. With a sheep dataset (218 observations) of both animal data and rumen microbiota data (relative sequence abundance of 330 genera of rumen bacteria, archaea, protozoa, and fungi), we developed linear mixed models to predict CH4 production (g CH4/animal·d, ANIM-B models) and CH4 yield (g CH4/kg of dry matter intake, DMI-B models). We also developed models solely based on animal-related data. Prediction performance was evaluated 200 times with random data splits, while fitting performance was assessed without data splitting. The inclusion of microbial predictors improved the models, as indicated by decreased root mean square prediction error (RMSPE) and mean absolute error (MAE), and increased Lin’s concordance correlation coefficient (CCC). Both glmmLasso and SCAD reduced the Akaike information criterion (AIC) and Bayesian information criterion (BIC) for both the ANIM-B and the DMI-B models, while the other two ML methods had mixed outcomes. By balancing prediction performance and fitting performance, we obtained one ANIM-B model (containing 10 genera of bacteria and 3 animal data) fitted using glmmLasso and one DMI-B model (5 genera of bacteria and 1 animal datum) fitted using SCAD. This study highlights the importance of incorporating rumen microbiota data in CH4 prediction models to enhance accuracy and robustness. Additionally, ML methods facilitate the selection of microbial predictors from high-dimensional metataxonomic data of the rumen microbiota without overfitting. Moreover, the identified microbial predictors can serve as biomarkers of CH4 emissions from sheep, providing valuable insights for future research and mitigation strategies.

Abstract Due to its complicated pathophysiology, propensity for metastasis, and poor prognosis, colon cancer is challenging to treat and must be managed with a combination of therapy. Using rolling circle transcription (RCT), this work created a nanosponge therapeutic medication system (AS1411@antimiR-21@Dox). Using the AS1411 aptamer, this approach accomplished targeted delivery to cancer cells. Furthermore, analysis of cell viability, cell apoptosis, cell cycle arrest, reactive oxygen species (ROS) content, and mitochondrial membrane potential (MMP) levels revealed that functional nucleic acid nanosponge drug (FND) can kill cancer cells. Moreover, transcriptomics uncovered a putative mechanism for the FND anti-tumor effect. These pathways, which included mitotic metaphase and anaphase as well as the SMAC-mediated dissociation of the IAP: caspase complexes, were principally linked to the cell cycle and cell death. In conclusion, by triggering cell cycle arrest and apoptosis, the nano-synergistic therapeutic system allowed for the intelligent and effective targeted administration of RNA and chemotherapeutic medicines for colon cancer treatment. The system allowed for payload efficiency while being customizable, targeted, reliable, stable, and affordable.

Pluripotent stem cells (PSCs) can differentiate into three germ layers and diverse autologous cell lines. Since cattle are the most commonly used large domesticated animals, an important food source, and bioreactors, great efforts have been made to establish bovine PSCs (bPSCs). bPSCs have great potential in bovine breeding and reproduction, modeling in vitro differentiation, imitating cancer development, and modeling diseases. Currently, bPSCs mainly include bovine embryonic stem cells (bESCs), bovine induced pluripotent stem cells (biPSCs), and bovine expanded potential stem cells (bEPSCs). Establishing stable bPSCs in vitro is a critical scientific challenge, and researchers have made numerous efforts to this end. In this review, the category of PSC pluripotency; the establishment of bESCs, biPSCs, and bEPSCs and its challenges; and the application outlook of bPSCs are discussed, aiming to provide references for future research.

Abstract Background In utero exposure to diabetes has been shown to contribute to preterm birth, though the underlying biological mechanisms are yet to be fully elucidated. Fetal epigenetic variations established in utero may be a possible pathway. This study aimed to investigate whether in utero exposure to diabetes was associated with a change in newborn DNA methylation, and whether the identified CpG sites mediate the association between diabetes and preterm birth in a racially diverse birth cohort population. Methods This study included 954 mother–newborn pairs. Methylation levels in the cord blood were determined using the Illumina Infinium MethylationEPIC BeadChip 850 K array platform. In utero exposure to diabetes was defined by the presence of maternal pregestational or gestational diabetes. Preterm birth was defined as gestational age at birth less than 37 weeks. Linear regression analysis was employed to identify differentially methylated CpG sites. Differentially methylated regions were identified using the DMRcate Package. Results 126 (13%) newborns were born to mothers with diabetes in pregnancy and 173 (18%) newborns were born preterm, while 41 newborns were born both preterm and to mothers with diabetes in pregnancy. Genomic-wide CpG analysis found that eighteen CpG sites in cord blood were differentially methylated by maternal diabetes status at an FDR threshold of 5%. These significant CpG sites were mapped to 12 known genes, one of which was annotated to gene Major Histocompatibility Complex, Class II, DM Beta (HLA-DMB). Consistently, one of the two identified significant methylated regions overlapped with HLA-DMB. The identified differentially methylated CpG sites mediated the association between diabetes in pregnancy and preterm birth by 61%. Conclusions In this US birth cohort, we found that maternal diabetes was associated with altered fetal DNA methylation patterns, which substantially explained the link between diabetes and preterm birth.

Abstract Background Although the relationship between type 2 diabetes (T2D) and the increased risk of colorectal carcinogenesis is widely defined in clinical studies, the therapeutic methods and molecular mechanism of T2D-induced colon cancer and how does hyperglycemia affect the progression is still unknown. Here, we studied the function of lactoferrin (LF) in suppressing the progression of colon cancer in T2D mice, and uncovered the related molecular mechanisms in DNA 5mC and RNA m6A levels. Methods We examined the effects of LF (50% iron saturation) on the migration and invasion of colon tumor cells under high concentration of glucose. Then, transcriptomics and DNA methylation profilings of colon tumor cells was co-analyzed to screen out the special gene (NT5DC3), and the expression level of NT5DC3 in 75 clinical blood samples was detected by q-PCR and western blot, to investigate whether NT5DC3 was a biomarker to distinguish T2D patients and T2D-induced colon cancer patients from healthy volunteers. Futhermore, in T2D mouse with xenografted colon tumor models, the inhibitory effects of LF and NT5DC3 protein on colon tumors were investigated. In addition, epigenetic alterations were measured to examine the 5mC/m6A modification sites of NT5DC3 regulated by LF. Utilizing siRNA fragments of eight m6A-related genes, the special gene (WTAP) regulating m6A of NT5DC was proved, and the effect of LF on WTAP/NT5DC3/HKDC1 axis was finally evaluated. Results A special gene NT5DC3 was screened out through co-analysis of transcriptomics and DNA methylation profiling, and HKDC1 might be a downstream sensor of NT5DC3. Mechanistically, LF-dependent cellular DNA 5mC and RNA m6A profiling remodeling transcriptionally regulate NT5DC3 expression. WTAP plays a key role in regulating NT5DC3 m6A modification and subsequently controls NT5DC3 downstream target HKDC1 expression. Moreover, co-treatment of lactoferrin and NT5DC3 protein restrains the growth of colon tumors by altering the aberrant epigenetic markers. Strikingly, clinical blood samples analysis demonstrates NT5DC3 protein expression is required to direct the distinction of T2D or T2D-induced colon cancer with healthy humans. Conclusions Together, this study reveals that lactoferrin acts as a major factor to repress the progression of colon cancer under hyperglycemia, thus, significantly expanding the landscape of natural dietary mediated tumor suppression. Graphical Abstract

As an important part of the industrial internet, identity analysis data are growing with the expansion of the field involved in the industrial internet. The management of industrial internet identity analysis data faces many problems, such as complex types, a wide range of information, rapid growth, reduced security, etc. In view of the above problems, a trusted management model of industrial internet identity analysis data based on blockchain is first designed. Meanwhile, the identity analysis data information is analyzed and classified, and industrial data are divided into three levels according to the degree of privacy for hierarchical encryption. Secondly, the “on-chain + off-chain” storage model combining the blockchain main-slave chain and the off-chain database is designed to improve the efficiency of the whole model. Then, a collaborative consensus mechanism suitable for the main-slave multi-chain of the industrial internet is also designed, including slave-chain CIPBFT consensus, inter-chain cross-chain transmission protocol and main chain KZKP consensus. Finally, a prototype system is built to analyze the correctness, security, scalability and consensus efficiency of the model proposed in this study. The results show that the model proposed in this study can be applied to trusted management of data information for industrial internet identity analysis, and also provides an optimized solution for the same problem in fields of the industrial internet.

Unmanned aerial vehicle (UAV) swarms offer unique advantages for area search and environmental monitoring applications. For practical deployments, determining the optimal number of UAVs required for a given task and defining key performance metrics for the platforms and payloads are crucial challenges. This study aims to address mission planning and performance optimization for cooperative UAV swarm search scenarios. A new clustering algorithm is proposed, integrating enhanced clustering techniques with ant colony optimization, particle swarm optimization, and crow search optimization. This jointly optimizes and validates the UAV numbers and coordinated trajectories. Sensitivity analysis and indicator optimization further examine specific scenarios to quantify platform and sensor factors influencing search efficiency. Lastly, sensitivity analysis and performance indicator optimization are conducted in specific scenarios. The modular algorithmic components and modeling techniques established in this work lay a foundation for continued research into real−world mission−based swarm optimization.

BackgroundThe occurrence of ischemic stroke (IS) is associated with nonalcoholic fatty liver disease (NAFLD). The cancer burden of NAFLD complicated by IS also warrants attention. This study aimed to identify candidate immune biomarkers linked to NAFLD and IS and analyze their association with cancer.MethodsTwo of each of the NAFLD and IS datasets were downloaded, differentially expressed genes (DEGs) were identified, and module genes were screened via weighted gene coexpression network analysis (WGCNA). Subsequently, utilizing machine learning (least absolute shrinkage and selection operator regression, random forest and support vector machine-recursive feature elimination) and immune cell infiltration analysis, immune-related candidate biomarkers for NAFLD with IS were determined. Simultaneously, a nomogram was established, the diagnostic efficacy was assessed, and the role of candidate biomarkers in cancer was ascertained through pan-cancer analyses.ResultsIn this study, 117 and 98 DEGs were identified from the combined NAFLD and IS datasets, respectively, and 279 genes were obtained from the most significant modules of NAFLD. NAFLD module genes and IS DEGs were intersected to obtain nine genes, which were enriched in the inflammatory response and immune regulation. After overlapping the results of the three machine learning algorithms, six candidate genes were obtained, based on which a nomogram was constructed. The calibration curve demonstrated good accuracy, and the candidate genes had high diagnostic values. The genes were found to be related to the immune dysregulation of stroke, and RRS1 was strongly associated with the prognosis, immune cell infiltration, microsatellite instability (MSI), and tumor mutation burden (TMB).ConclusionSix common candidate immune-related genes (PTGS2, FCGR1A, MMP9, VNN3, S100A12, and RRS1) of NAFLD and IS were identified, and a nomogram for diagnosing NAFLD with IS was established. RRS1 may serve as a candidate gene for predicting the prognosis of patients with cancer who have NAFLD complicated by IS, which could aid in their diagnosis and treatment.

The hidden collapse column has the characteristics of concealment, suddenness and connection with karst water, which pose a serious threat to safe production in coal mines. In this study, a numerical model of collapse columns with a random distribution of pores was constructed by a finite difference calculation program. Numerical simulation analyses of the coal mining face advance were carried out to explore the response characteristics of rock in the collapse column area under the influence of four factors: mining impact, lateral pressure coefficient, pore water and confined groundwater. The results show that the abutment pressure reaches its maximum when the working face advances to the boundary of the elastic stress elevated zone of the collapse column. The plastic zone above the collapse column shows a "Λ" shape and keeps growing during the advance of the working face. The height of the plastic zone in the top and bottom of the coal seam increases with the increasing lateral pressure coefficient. An increase in the lateral pressure coefficient can amplify the effect of mining on the vertical displacement and plastic zone distribution of the collapse column. When the collapse column is connected to the confined groundwater, the pore water pressure will increase significantly with the advancement of the working face. Under the joint action of confined groundwater and pore water, the extent of the plastic zone around the collapse column will be larger, and the development of the plastic zone inside the collapse column is especially obvious. This study will provide a basis for revealing the rock response rules in the area of the hidden collapse column during mining.

Al-Zn-Mg-Cu alloy, recognized for its heat-treatable and super-high strength, is a pivotal material for critical structural components in aviation. Consequently, grasping the intricate relationship between heat treatment processes and the microstructural characteristics and properties of the Al-Zn-Mg-Cu alloy is of paramount practical significance. This study encompasses a spectrum of heat treatment methodologies, delving into the diverse effects of distinct aging treatments on the microstructure and performance of thin Al-Zn-Mg-Cu alloy sheets. The findings illuminate that short natural aging treatments bestow heightened strength and hardness upon the alloy, albeit at the expense of ductility. Nevertheless, the alloy retains commendable malleability, enabling significant cold deformation. In contrast, artificial aging markedly elevates the alloy’s strength and hardness, albeit concurrently inducing a precipitous decline in ductility, thereby attenuating the alloy’s cold formability.

Stress field analysis is an essential part of umbilical component layout design. The stress field analysis of an umbilical, via numerical simulation, has commonly been applied in practical engineering. The high economic and time cost associated with numerical simulation and analysis of the stress field in an umbilical has been replaced by data-driven, deep-learning-based, real-time computational methods. In this study, a novel Pyramidal Efficient U-Net (PyEf-U-Net) network is proposed to predict the stress field distribution of the umbilical. The input dataset is obtained via the Differential Evolution-Generalized Lagrange Multiplier (DE-GLM) method, which is entered into the network for training, with a detailed discussion of the effects of hyperparameters such as optimizer, learning rate, and loss function on the performance of the network. The experimental research demonstrates that the proposed PyEf-U-Net can accurately predict the stress field of the umbilical in real time with a prediction accuracy of 94.2%, which is superior to other deep learning networks. The proposed method can provide an effective way for rapid mechanical analysis and design of the umbilical in practical engineering, while the method can be extended to the mechanical analysis and design of other similar marine engineering equipment structures.

The Dingqing ophiolite represents a significant allochthonous ophiolite nappe in the eastern segment of the Bangong–Nujiang suture zone in southeastern Tibet. The microanalytical data of associated podiform chromitites classify them into two distinct varieties: high-Al and high-Cr. The coexistence of both high-Cr and high-Al chromitites in the Dingqing ophiolite suggests a complex or multistage evolutionary history of the host rocks. New petrological and geochemical analyses are used herein to unravel the interrelationships between the chromitite ores and host rocks and assess the mechanism of formation. The Dingqing ophiolitic nappe is made up mainly of harzburgite, dunite, and less abundant pyroxenite and gabbro. Several small lens-shaped bodies of chromitite ore are mostly confined to the harzburgite rocks, with ore textures varying from massive to sparsely disseminated chromite. In addition to magnesiochromite, the orebodies contain minor amounts of olivine, amphibole, and serpentine. The textural relationships provide compelling evidence of plastic deformation and partial melting of the associated peridotites. Detailed examination of the Cr-spinel grains reveals a wide range of composition, spanning from high-Al (Cr# = 3.18–59.5) to high-Cr (Cr# 60.3–87.32). The abundances of the platinum-group element (PGE) in chromitites are significantly variable (93 to 274 ppb). Formation of the Dingqing peridotites most likely took place in a mid-ocean ridge (MOR) setting, and subsequent modifications by supra-subduction zone (SSZ) melts resulted in heterogenous or mixed geochemical characteristics of these rocks. Chemistry of the spinel–olivine–clinopyroxene assemblage demonstrates multiple stages of partial melting of the source mantle rocks, including an early phase of restricted partial melting (~20%–30%) and a later phase of extensive partial melting (>40%). The formation of the high-Al chromitite type was associated with the early phase (constrained melting), whereas extensive partial melting in the late stages likely led to the accumulation of high-Cr podiform chromitite bodies.

Shearography is a coherent optical technique that allows the identification of the first derivative of deformation in the shearing direction. Due to direct measuring strain information, shearography is suited for non-destructive testing and evaluation (NDT/NDE). However, if there is a small defect parallel to the shearing direction, the first derivative of deformation in the direction has no noticeable change, and the defect is not visible. Therefore, the development of a shearography system with dual-directional simultaneous measurement of the first derivatives of deformation both in x- and y-directions is highly demanded in the field of NDT/NDE. It is suited to inspect complicated defects, such as long and narrow slots, microcracks, etc. This paper presents a review of shearography for different dual-directional systems developed in the last two decades. After a brief overview of shearography, the paper will display two dual-directional shearographic techniques—temporal phase-shift (TPS) and spatial phase-shift (SPS) methods. TPS dual-shearing systems are suited for static measurements, while the SPS dual-shearing systems are useful for dynamic measurements. The basic theories, optical layouts, and comparisons are presented. The advantages and disadvantages of practical applications are discussed.

This study is focused on coal mass fracture grouting sealing and seepage reduction with coal-based grouting sealing materials. First, a new type of coal-based grouting sealing materials was developed and applied to the fractured coal sample in the grouting test. Then, the coal sample before and after grouting was scanned and reconstructed with the aid of industrial computed tomography (CT). Based on CT images and reconstructed data, a theoretical model of grouting sealing for fractured coal mass was constructed by taking the migration and sedimentation of grouting slurry particles into consideration. Furthermore, on the ground of the theoretical model, a numerical calculation model of grouting sealing for the fractured coal sample was established, and the mechanism of grouting sealing and seepage reduction with coal-based sealing materials was simulated. Finally, a field test of gas extraction drilling and sealing was performed using the coal-based grouting sealing technology. The results show that: 1) The new type of coal-based grouting materials is effective in sealing the coal mass fracture, as the fracture is tightly filled with slurry particles after grouting. 2) The numerical simulation reproduces the whole process of grouting slurry flow and fracture sealing. After the permeability of the grouting coal sample decreases rapidly, it gradually tends to stabilize. The permeability of the whole coal sample goes down by 96% on average, and that of the fracture drops by more than 99.9% on average. The law of slurry diffusion and permeability variation calculated by numerical simulation is in line with the test results, which verifies the reasonableness of the model. 3) The application of the coal-based grouting sealing technology achieves an excellent sealing effect, promoting the gas extraction concentration by 1.43 times. The research results provide guidance for revealing mechanism of coal mass fracture grouting sealing with coal-based materials and bolstering the effect of extraction borehole sealing.

Spermatogonial stem cells (SSCs) are the only primitive spermatogonial cells in males that can naturally transmit genetic information to their offspring and replicate throughout their lives. Phospholipase D family member 6 (PLD6) has recently been found to be a surface marker for SSCs in mice and boars; however, it has not been validated in cattle. The results of reversed transcription-polymerase chain reaction (RT-PCR) and quantitative real-time PCR (qRT-PCR) found that the relative expression of the PLD6 gene in the testicular tissues of two-year-old Simmental calves was significantly higher than that of six-month-old calves. Immunofluorescent staining further verified the expression of PLD6 protein in bovine spermatogenic cells like germ cell marker DEAD box helicase 4 (DDX4, also known as VASA). Based on multiple bioinformatic databases, PLD6 is a conservative protein which has high homology with mouse Q5SWZ9 protein. It is closely involved in the normal functioning of the reproductive system. Molecular dynamics simulation analyzed the binding of PLD6 as a phospholipase to cardiolipin (CL), and the PLD6-CL complex showed high stability. The protein interaction network analysis showed that there is a significant relationship between PLD6 and piwi-interacting RNA (piRNA) binding protein. PLD6 acts as an endonuclease and participates in piRNA production. In addition, PLD6 in bovine and mouse testes has a similar expression pattern with the spermatogonium-related genes VASA and piwi like RNA-mediated gene silencing 2 (PIWIL2). In conclusion, these analyses imply that PLD6 has a relatively high expression in bovine testes and could be used as a biomarker for spermatogenic cells including SSCs.

Classification tasks are pivotal across diverse applications, yet the burgeoning amount of data, coupled with complicating factors such as noise, exacerbates the challenge of classifying complex data. For algorithms that require a large amount of data, the annotation work for datasets is also exceptionally complex and tedious. Drawing upon existing research, this paper first introduces a novel semi-supervised category dictionary model based on transfer learning (SSDT). This model is designed to construct a more representative category dictionary and to delineate the associations of information across different domains, utilizing the lens of conditional probability distribution. This approach is particularly apt for semi-supervised transfer learning scenarios. Subsequently, the proposed method is applied to the domain of bearing fault diagnosis. This model is suitable for transfer scenarios; moreover, its semi-supervised characteristic eliminates the need for labeling the entire input dataset, significantly reducing manual workload. Experimental results attest to the model’s practical utility. When benchmarked against other 6 models, the SSDT model demonstrates enhanced generalization performance.

1,5-Anhydro-D-fructose (1,5-AF) is a bioactive monosaccharide that is produced by the glycogenolysis in mammalians and is metabolized to 1,5-anhydro-D-glucitol (1,5-AG). 1,5-AG is used as a marker of glycemic control in diabetes patients. 1,5-AF has a variety of physiological activities, but its effects on energy metabolism, including feeding behavior, are unclarified. The present study examined whether 1,5-AF possesses the effect of satiety. Peroral administration of 1,5-AF, and not of 1,5-AG, suppressed daily food intake. Intracerebroventricular (ICV) administration of 1,5-AF also suppressed feeding. To investigate the neurons targeted by 1,5-AF, we investigated c-Fos expression in the hypothalamus and brain stem. ICV injection of 1,5-AF significantly increased c-Fos positive oxytocin neurons and mRNA expression of oxytocin in the paraventricular nucleus (PVN). Moreover, 1,5-AF increased cytosolic Ca2+ concentration of oxytocin neurons in the PVN. Furthermore, the satiety effect of 1,5-AF was abolished in oxytocin knockout mice. These findings reveal that 1,5-AF activates PVN oxytocin neurons to suppress feeding, indicating its potential as the energy storage monitoring messenger to the hypothalamus for integrative regulation of energy metabolism.

Integration of mechanical cues in conventional 2D or 3D cell culture platforms is an important consideration for in vivo and ex vivo models of lung health and disease. Available commercial and published custom-made devices are frequently limited in breadth of applications, scalability, and customization. Herein we present a technical report on an open-source, cell and tissue (CaT) stretcher, with modularity for different in vitro and ex vivo systems, that includes the following features: 1) Programmability for modeling different breathing patterns, 2) scalability to support low to high-throughput experimentation, and 3) modularity for submerged cell culture, organ-on-chips, hydrogels, and live tissues. The strategy for connecting the experimental cell or tissue samples to the stretching device were designed to ensure that traditional biomedical outcome measurements including, but not limited to microscopy, soluble mediator measurement, and gene and protein expression remained possible. Lastly, to increase the uptake of the device within the community, the system was built with economically feasible and available components. To accommodate diverse in vitro and ex vivo model systems we developed a variety of chips made of compliant polydimethylsiloxane (PDMS) and optimized coating strategies to increase cell adherence and viability during stretch. The CaT stretcher was validated for studying mechanotransduction pathways in lung cells and tissues, with an increase in alpha smooth muscle actin protein following stretch for 24 h observed in independent submerged monolayer, 3D hydrogel, and live lung tissue experiments. We anticipate that the open-source CaT stretcher design will increase accessibility to studies of the dynamic lung microenvironment through direct implementation by other research groups or custom iterations on our designs.

Geological structures of Sima coal mine in Shanxi Province were analyzed to understand the control effect of the geological structures on the occurrence of coalbed methane (CBM) in coal seam #3 of Sima coal mine. The CBM contents in the districts #2 and #3 of Sima coal mine were tested, and the effects of buried depth, fault and collapse column on the distribution of coalbed methane content are studied. The research results showed that: 1) The average content of CBM has a linear relationship with buried depth and overburden thickness, but in the smallscale range of buried depth, the dispersion between CBM and buried depth is very large. 2) Faults and collapse columns significantly affect the content of local CBM nearby, but from the largescale range such as the whole mining area, the average value of CBM content at a certain buried depth will not be affected by faults and collapse columns. 3) In the hanging wall of F29 normal fault, it is roughly estimated that the average escape rate of CBM near the fault is 13.9%, while in the footwall of F29 normal fault, this value is 0.7%–1.1%. The results show that there is a significant difference in the influence of the fault on the CBM content in the hanging wall and footwall. 4) The control effect of collapse column on CBM occurrence is related to the development height of collapse column, the cementation degree of collapse column, groundwater runoff conditions and other factors. It can be divided into three categories: aggregation action, escape action (such as collapse column X8) and no obvious effect (such as collapse column DX7).

The study of flow behaviour of water-sand mixtures in fractured rocks is of great necessity to understand the producing mechanism and prevention of water inrush and sand gushing accidents. A self-developed seepage test system is used in this paper to conduct laboratory experiments in order to study the influence of the particle size distribution, the void ratio, and the initial mass of Aeolian sand on the flow behavior. It is concluded that the water flow velocity is insensitive to the initial mass of the Aeolian sand but increases with the power exponent in the Talbot formula and the specimen height. The outflow of the Aeolian sand increases with the power exponent in the Talbot formula, the specimen height, and the initial mass of the Aeolian sand. Besides, the outflow of the Aeolian sand changes exponentially with the water flow velocity. Finally, it is found that the fractured specimen has a maximum sand filtration capacity beyond which the outflow of the Aeolian sand significantly increases with the initial mass of the Aeolian sand.

Abstract Outside the explosive successful applications of deep learning (DL) in natural language processing, computer vision, and information retrieval, there have been numerous Deep Neural Networks (DNNs) based alternatives for common security-related scenarios with malware detection among more popular. Recently, adversarial learning has gained much focus. However, unlike computer vision applications, malware adversarial attack is expected to guarantee malwares’ original maliciousness semantics. This paper proposes a novel adversarial instruction learning technique, DeepMal, based on an adversarial instruction learning approach for static malware detection. So far as we know, DeepMal is the first practical and systematical adversarial learning method, which could directly produce adversarial samples and effectively bypass static malware detectors powered by DL and machine learning (ML) models while preserving attack functionality in the real world. Moreover, our method conducts small-scale attacks, which could evade typical malware variants analysis (e.g., duplication check). We evaluate DeepMal on two real-world datasets, six typical DL models, and three typical ML models. Experimental results demonstrate that, on both datasets, DeepMal can attack typical malware detectors with the mean F1-score and F1-score decreasing maximal 93.94% and 82.86% respectively. Besides, three typical types of malware samples (Trojan horses, Backdoors, Ransomware) prove to preserve original attack functionality, and the mean duplication check ratio of malware adversarial samples is below 2.0%. Besides, DeepMal can evade dynamic detectors and be easily enhanced by learning more dynamic features with specific constraints.

Summary: Background: COVID-19 mRNA vaccines elicit strong T and B cell responses to the SARS-CoV-2 spike glycoprotein in both SARS-CoV-2 naïve and experienced patients. However, it is unknown whether the post-vaccine CD4+ T cell responses seen in patients with a history of COVID-19 are due to restimulation of T cell clonotypes that were first activated during natural infection or if they are the result of new clones activated by the vaccine. Methods: To address this question, we analyzed the SARS-CoV-2 spike glycoprotein-specific CD4+ T cell receptor repertoire before and after vaccination in 10 COVID-19 convalescent patients and 4 SARS-CoV-2 naïve healthy donor vaccine recipients. We used the viral Functional Expansion of Specific T cells (ViraFEST) assay to quantitatively identify specific SARS-CoV-2 and common cold coronavirus CD4+ T cell clonotypes post COVID-19 disease resolution and post mRNA SARS-CoV-2 vaccination. Findings: We found that while some preexisting T cell receptor clonotypes persisted, the post-vaccine repertoire consisted mainly of vaccine-induced clones and was largely distinct from the repertoire induced by natural infection. Vaccination-induced clones led to an overall maintenance of the total number of SARS-CoV-2 reactive clonotypes over time through expansion of novel clonotypes only stimulated through vaccination. Additionally, we demonstrated that the vaccine preferentially induces T cells that are only specific for SARS-CoV-2 antigens, rather than T cells that cross-recognize SARS-CoV-2/common cold coronaviruses. Interpretation: These data demonstrate that SARS-CoV-2 vaccination in patients with prior SARS-CoV-2 infection induces a new antigen-specific repertoire and sheds light on the differential immune responses induced by vaccination versus natural infection. Funding: Bloomberg∼Kimmel Institute for Cancer Immunotherapy, The Johns Hopkins University, The Bill and Melinda Gates Foundation, NCI U54CA260492, NIH.

As the main food source of the world’s population, grain quality safety is of great significance to the healthy development of human beings. The grain food supply chain is characterized by its long life cycle, numerous and complex business data, difficulty defining private information, and difficult managing and sharing. In order to strengthen the ability of information application processing and coordination of the grain food supply chain under many risk factors, an information management model suitable for the grain food supply chain is studied based on the blockchain multi-chain technology. First, the information on key links in the grain food supply chain is analyzed to obtain privacy data classifications. Second, a multi-chain network model of the grain food supply chain is constructed, and based on this model, the hierarchical encryption and storage mode of private data as well as the relay cross-chain communication mode, are designed. In addition, a complete consensus process, including CPBFT, ZKP, and KZKP algorithms, is designed for the global information collaborative consensus under the multi-chain architecture. Finally, the model is verified through performance simulation, theory analysis, and prototype system verification in terms of its correctness, security, scalability, and consensus efficiency. The results show that this research model effectively reduces the storage redundancy and deals with problems of data differential sharing in traditional single-chain research, as well as provides a secure data protection mechanism, a credible data interaction mechanism, and an efficient multi-chain collaborative consensus mechanism. By attempting to apply blockchain multi-chain technology to the grain food supply chain, this study provides new research ideas for the trusted protection of data and information collaborative consensus in this field.

Through inductive coupling, AC transmission lines can generate large amounts of voltage to buried oil and gas pipelines in areas with common corridors, posing a threat to the cathodic protection effect of pipelines. Therefore, this paper investigates the effect of AC transmission lines on the cathodic protection of long-distance pipelines through inductive coupling. COMSOL Multiphysics finite element simulation software is used to calculate the distribution of cathodic protection potential of long-distance pipelines under different voltage levels, parallel spacing, conductor-to-ground height, conductor arrangement and pipeline burial depth for normal operation of AC transmission lines. Comparison and analysis of the AC transmission line on the pipeline cathodic protection potential interference law is conducted. The results show that: 1. AC transmission lines cause serious electromagnetic interference with the pipeline cathodic protection system, which will cause the pipeline cathodic protection potential to shift out of the effective protection area. 2. The maximum value of the induced voltage of the pipeline will appear at the two ends of the pipeline, and the induced voltage of the pipeline in the middle position is 0. 3. The shift of the pipeline cathodic protection potential increases with the increase of voltage level and decreases with the increase of parallel spacing, conductor height and burial depth. The pipeline cathodic protection potential shift is highest when the wires are arranged horizontally and lowest when they are arranged in an umbrella shape.

Minimally invasive surgical robots have the advantages of high positioning accuracy, good stability, and flexible operation, which can effectively improve the quality of surgery and reduce the difficulty for doctors to operate. However, in order to realize the translation of the existing RCM mechanism, it is often necessary to add a mobile unit, which is often bulky and occupies most space above the patient’s body, thus causing interference to the operation. In this paper, a new type of planar RCM mechanism is proposed. Based on this mechanism, a 3-DOF robotic arm is designed, which can complete the required motion for surgery without adding a mobile unit. In this paper, the geometric model of the mechanism is first introduced, and the RCM point of the mechanism is proven during the motion process. Then, based on the establishment of the geometric model of the mechanism, a kinematics analysis of the mechanism is carried out. The singularity, the Jacobian matrix, and the kinematic performance of the mechanism are analyzed, and the working space of the mechanism is verified according to the kinematic equations. Finally, a prototype of the RCM mechanism was built, and its functionality was tested using a master–slave control strategy.

Abstract Background Millennia of directional human selection has reshaped the genomic architecture of cultivated cotton relative to wild counterparts, but we have limited understanding of the selective retention and fractionation of genomic components. Results We construct a comprehensive genomic variome based on 1961 cottons and identify 456 Mb and 357 Mb of sequence with domestication and improvement selection signals and 162 loci, 84 of which are novel, including 47 loci associated with 16 agronomic traits. Using pan-genome analyses, we identify 32,569 and 8851 non-reference genes lost from Gossypium hirsutum and Gossypium barbadense reference genomes respectively, of which 38.2% (39,278) and 14.2% (11,359) of genes exhibit presence/absence variation (PAV). We document the landscape of PAV selection accompanied by asymmetric gene gain and loss and identify 124 PAVs linked to favorable fiber quality and yield loci. Conclusions This variation repertoire points to genomic divergence during cotton domestication and improvement, which informs the characterization of favorable gene alleles for improved breeding practice using a pan-genome-based approach.

Abstract Introduction Angiotensin Converting Enzyme Inhibitors (ACEI) are a common cause of Emergency Room presentation for angioedema. Although no treatment guidelines exist, C1 esterase inhibitor concentrate (C1-INH) is used on an off label basis for management of ACEI acquired angioedema (ACEI AAE). Objective To evaluate the efficacy of C1-INH in management of ACEI AAE at our local centers. Results Nine patients, from 3 academic sites, were identified through Allergy Service consultation data and records from Diagnostic Services Manitoba, Canada from 2010–2020. The majority of the patients (n = 8/9) required endotracheal intubation prior to the initiation of C1-INH. Overall, approximately 56% of patients (n = 5/9) had resolution of angioedema ranging between 12 and 17 h, with a median time of 13.5 h, and no recurrence after the administration of C1-INH concentrate. One patient had transient symptom resolution in 14 h, however, recurrence of angioedema required re-intubation. The remainder of patients (n = 4/9), had resolution of angioedema between 22 and 72 h, with a median time of 33.75 h. Conclusion Our findings demonstrate continued ambivalence of the efficacy and role of C1-INH concentrate in the treatment of ACEI AAE, secondary to multiple uncontrolled confounding factors. Further research into characterizing a subgroup of intubated patients in our study that responded to C1-INH concentrate needs to be completed.

Soft actuators have received extensive attention in robotics and smart device applications due to their distinctive dexterity and compliance. Among them, hydraulic soft actuators play an important role in the area because they have much higher specific power and power density than other types such as pneumatic soft actuators. Nevertheless, the deformation of flexible materials in soft actuators brings about inherent hysteresis and nonlinearity, which severely hinders them from producing the desired movement in the presence of advanced control strategies. In this paper, previous research efforts made to enhance the driving capability and actuation efficiency of hydraulic soft actuators are illustrated and analyzed from the three aspects of architecture, materials, and control strategy. Meanwhile, the issues and challenges that have emerged when developing hydraulic soft actuators are discussed. Finally, the potential future development of hydraulic powered soft actuators is discussed.

The basic leucine zipper (bZIP) transcription factors, which harbor a conserved bZIP domain composed of two regions, a DNA-binding basic region and a Leu Zipper region, operate as important switches of transcription networks in eukaryotes. However, this gene family has not been systematically characterized in cotton (Gossypium hirsutum). Here, we identified 197 bZIP family members in cotton. The chromosome distribution pattern indicates that the GhbZIP genes have undergone 53 genome-wide segmental and 7 tandem duplication events which contribute to the expansion of the cotton bZIP family. Phylogenetic analysis showed that cotton GhbZIP proteins cluster into 13 subfamilies, and homologous protein pairs showed similar characteristics. Inspection of the DNA-binding basic region and leucine repeat heptads within the bZIP domains indicated different DNA-binding site specificities as well as dimerization properties among different groups. Comprehensive expression analysis indicated the most highly and differentially expressed genes in root and leaf that might play significant roles in cotton response to drought stress. GhABF3D was identified as a highly and differentially expressed bZIP family gene in cotton leaf and root under drought stress treatments that likely controls drought stress responses in cotton. These data provide useful information for further functional analysis of the GhbZIP gene family and its potential application in crop improvement.

Abstract Background Preeclampsia and preterm delivery (PTD) are believed to affect women’s long-term health including cardiovascular disease (CVD), but the biological underpinnings are largely unknown. We aimed to test whether maternal postpartum metabolomic profiles, especially CVD-related metabolites, varied according to PTD subtypes with and without preeclampsia, in a US urban, low-income multi-ethnic population. Methods This study, from the Boston Birth Cohort, included 980 women with term delivery, 79 with medically indicated PTD (mPTD) and preeclampsia, 52 with mPTD only, and 219 with spontaneous PTD (sPTD). Metabolomic profiling in postpartum plasma was conducted by liquid chromatography-mass spectrometry. Linear regression models were used to assess the associations of each metabolite with mPTD with preeclampsia, mPTD only, and sPTD, respectively, adjusting for pertinent covariates. Weighted gene coexpression network analysis was applied to investigate interconnected metabolites associated with the PTD/preeclampsia subgroups. Bonferroni correction was applied to account for multiple testing. Results A total of 380 known metabolites were analyzed. Compared to term controls, women with mPTD and preeclampsia showed a significant increase in 36 metabolites, mainly representing acylcarnitines and multiple classes of lipids (diacylglycerols, triacylglycerols, phosphocholines, and lysophosphocholines), as well as a decrease in 11 metabolites including nucleotides, steroids, and cholesteryl esters (CEs) (P

Background. This study is aimed at to establish an effective prognostic nomogram for patients with atrial fibrillation (AF) and acute coronary syndrome (ACS) underwent percutaneous coronary intervention (PCI). Methods. The nomogram was based on a retrospective study of 977 patients with AF and ACS who underwent PCI who were admitted to any of the 11 tertiary hospitals in the Beijing area between 2009 and 2015. The predictive accuracy and discriminative ability of the nomogram were determined by a concordance index (C-index) and calibration curve and were compared using current risk scores such as GRACE, CRUSADE, CHA2DS2-VASc, and HAS-BLED. The results were validated using bootstrap resampling and a retrospective cohort study of 409 patients enrolled in Fuwai Hospital at the same institution. Results. Independent factors derived from multivariable analysis of the primary cohort to predict all-cause mortality were age, pattern of ACS, red blood cell distribution width, N-terminal proBNP, and serum creatinine, all of which were assembled into the nomogram. The calibration curve for the probability of recurrence showed that the nomogram-based predictions were in good agreement with actual observations. The C-index of the nomogram for predicting mortality was 0.764 (95% CI, 0.718-0.810), which was statistically higher than the C-index values for the current risk scores (from 0.573 to 0.681). In the validation cohort, the C-index of the nomogram for predicting all-cause death was 0.706 (95% CI 0.601-0.811), with no significant differences compared with GRACE and CRUSADE, but better than that of CHA2DS2-VASc and HAS-BLED. Conclusions. The nomogram has good prognostic prediction for patients with AF and ACS who underwent PCI.

Precision toxicology evaluates the toxicity of certain substances by isolating a small group of cells with a typical phenotype of interest followed by a single cell sequencing-based analysis. In this in vitro attempt, ochratoxin A (OTA), a typical mycotoxin and food contaminant, is found to induce G0/G1 phase cell cycle arrest in human renal proximal tubular HKC cells at a concentration of 20 μM after a 24h-treatment. A small number of G0/G1 phase HKC cells are evaluated in both the presence and absence of OTA. These cells are sorted with a flow cytometer and subjected to mRNA and DNA methylation sequencing using Smart-Seq2 and single-cell reduced-representation bisulfite sequencing (scRRBS) technology, respectively. Integrated analysis of the transcriptome and methylome profiles reveals that OTA causes abnormal expression of the essential genes that regulate G1/S phase transition, act as signal transductors in G1 DNA damage checkpoints, and associate with the anaphase-promoting complex/cyclosome. The alteration of their DNA methylation status is a significant underlying epigenetic mechanism. Furthermore, Notch signaling and Ras/MAPK/CREB pathways are found to be suppressed by OTA. This attempt at precision toxicology paves the way for a deeper understanding of OTA toxicity and provides an innovative strategy to researchers in the toxicology and pharmacology field.

The single-cell RNA sequencing provides a way to obtain marker genes of different cells, which lays the foundation for discovering new cell types. The general strategy of achieving this goal is to build a clustering pipeline and derive differentially expressed genes, followed by the cell type enrichment analysis and driving force analysis. Throughout the entire analysis process, clustering models and appropriate methods of dimension reduction are two vital and challenging tasks. In this study, we present a novel method LAK (a computational pipeline for single-cell RNA-seq data clustering analysis using Lasso and K-means based feature selection method) that can be applied to single-cell RNA-seq data by selecting the candidate genes. To deal with the sparse high-dimensional data, we integrated Lasso penalty into clustering method for single-cell RNA-seq data as the feature selection method, which extracts out the genes that have an actual effect on clustering. We also improved the parameter selection algorithm to search the appropriate parameters automatically by binary search according to the size of the data. Compared with other computational approaches, LAK obtains a better performance in reliability, stability, convenience and accuracy applied to the real datasets, the simulation data, and the datasets with a large number of dropout events.

This study proposes a new technology for reducing residual stress using a combined magnetic–vibration treatment. To study the combined reduction effect of magnetic and vibration treatments, the independent reduction effects of vibration and magnetic technology were compared. To study the effect of vibration, magnetic, and combined magnetic-vibration treatments on the residual stress in silicon steel, the change in the mesoscopic structure was studied using electron backscatter diffraction (EBSD). The microstructures of the materials were studied using residual stress measurements, transmission electron microscopy (TEM) observations, X-ray diffraction (XRD) analysis, and magnetic domain observations. The experimental results reveal that all three methods reduced the residual stress, among which the combined magnetic-vibration treatment was the best, and the effect of the composite treatment was greater than the sum of the effects of the individual treatments. All three methods reduced the residual stress by providing energy to increase the movement of dislocations in the material and cause dislocation slip. Compared with the other two methods, the combined magnetic-vibration treatment promoted the transformation of magnetic domains into uniform layered domains and excited dislocation movement in more regions to reduce residual stress. However, after all three types of reduction techniques, the mesoscopic structure, such as grain size and texture, was not significantly affected.

Cooperative group formation control of manned/unmanned aircraft vehicles (MAV/UAVs) using a hierarchical framework can be more efficient and flexible than centralized control strategies. In this paper, a two-level hierarchical-interaction-based cooperative control strategy is proposed for the MAV/UAVs group formation. At the upper level, combined with the nonlinear disturbance observer (NDO) and dynamic surface control (DSC) algorithm, a trajectory tracking problem with external disturbances for MAV is formulated. At the lower level, the leader-following formation controller is utilized to deal with the sub-formation keeping control problem for UAVs, based on the sliding mode disturbance observer and fast terminal sliding mode control law, and the robust performance and control accuracy are effectively improved. Moreover, the overall stability of the MAV/UAVs system is demonstrated using Lyapunov theory. The proposed approach is evaluated by simulation under the ground penetration combat mission for MAV/UAVs, and the performance is compared with that of other control strategies.

Nanomotor systems have attracted extensive attention due to their applications in nanorobots and nanodevices. The control of their response is crucial but presents a great challenge. In this work, the rotating and braking processes of a carbon nanotube (CNT)-based rotor system have been studied using molecular dynamics simulation. The speed of response can be tuned by controlling the ratio of hydroxyl groups on the edges. The ratio of hydroxyl groups is positively correlated with the speed of response. The mechanism involved is that the strong hydrogen bonds formed between interfaces increase the interface interaction. Incremental increase in the hydroxyl group concentration causes more hydrogen bonds and thus strengthens the interconnection, resulting in the enhancement of the speed of response. The phonon density of states analysis reveals that the vibration of hydroxyl groups plays the key role in energy dissipation. Our results suggest a novel routine to remotely control the nanomotors by modulating the chemical environment, including tuning the hydroxyl groups concentration and pH chemistry.

T-cell receptor sequencing (TCRseq) enables tracking of T-cell clonotypes recognizing the same antigen over time and across biological compartments. TCRseq has been used to test if cross-reactive antitumor T cells are responsible for development of immune-related adverse events (irAEs) following immune checkpoint blockade. Prior studies have interpreted T-cell clones shared among the tumor and irAE as evidence supporting this, but interpretations of these findings are challenging, given the constraints of TCRseq. Here we capitalize on a rare opportunity to understand the impact of potential confounders, such as sample size, tissue compartment, and collection batch/timepoint, on the relative proportion of shared T-cell clones between an irAE and tumor specimens. TCRseq was performed on tumor-involved and -uninvolved tissues, including an irAE, that were obtained throughout disease progression and at the time of rapid autopsy from a patient with renal cell carcinoma treated with programmed death-1 (PD-1) blockade. Our analyses show significant effects of these confounders on our ability to understand T-cell receptor overlap, and we present mitigation strategies and study design recommendations to reduce these errors. Implementation of these strategies will enable more rigorous TCRseq-based studies of immune responses in human tissues, particularly as they relate to antitumor T-cell cross-reactivity in irAEs following checkpoint blockade.

The central melanocortin system conducted by anorexigenic pro-opiomelanocortin (POMC) neurons and orexigenic agouti-related peptide (AgRP) neurons in the arcuate nucleus of the hypothalamus (ARC) not only regulates feeding behavior but also blood pressure. Excessive salt intake raises the Na+ concentration ([Na+]) in the cerebrospinal fluid (CSF) and worsens hypertension. The blood–brain barrier is immature in the ARC. Therefore, both AgRP and POMC neurons in the ARC have easy access to the electrolytes in the blood and can sense changes in their concentrations. However, the sensitivity of AgRP and POMC neurons to Na+ remains unclear. This study aimed to explore how the changes in the extracellular Na+ concentration ([Na+]) influence these neurons by measuring the cytosolic Ca2+ concentration ([Ca2+]i) in the single neurons isolated from the ARC that were subsequently immunocytochemically identified as AgRP or POMC neurons. Both AgRP and POMC neurons responded to increases in both [Na+] and osmolarity in C57BL/6 mice. In contrast, in transient receptor potential vanilloid 1 (TRPV1) knockout (KO) mice, POMC neurons failed to respond to increases in both [Na+] and osmolarity, while they responded to high glucose and angiotensin II levels with increases in [Ca2+]i. Moreover, in KO mice fed a high-salt diet, the expression of POMC was lower than that in wild-type mice. These results demonstrate that changes in [Na+] and osmolarity are sensed by the ARC POMC neurons via the TRPV1-dependent mechanism.

Recrystallization can affect the mechanical properties of aluminum alloys by changing the grain structure, and even the secondary recrystallization will cause a sudden change in the grain size in the alloy. In this work, by choosing different annealing treatments on the cold-drawn 5056 aluminum wire, the microstructure evolution in the alloy homogenized at different annealing processes was discussed, and its influence on the mechanical properties was tested. The results demonstrated that the different annealing treatments had a great effect on the recrystallized structure in the 5056 aluminum alloy. During the annealing, it was observed that the recrystallization started at 250 °C and completed at 310 °C, leading to a significant decrease in the mechanical properties. When the temperature was further increased to 530 °C, the secondary recrystallization occurred, and the grain size of the secondary recrystallization was larger than that when the annealing temperature was 560 °C. However, there was only a minor decrease in the mechanical properties. The reasons and laws of the secondary recrystallization are analyzed and discussed in this paper.