Tetrad stage transient cold stress skews auxin-mediated energy metabolism balance in Chinese cabbage pollen.

Changing ambient temperature often impairs plant development and sexual reproduction, particularly pollen ontogenesis. However, mechanisms underlying cold stress-induced male sterility are not well understood. Here, we exposed Chinese cabbage (Brassica campestris) to different cold conditions during flowering and demonstrated that the tetrad stage was the most sensitive. After completion of pollen development at optimal conditions, transient cold stress at the tetrad stage still impacted auxin levels, starch and lipid accumulation, and pollen germination, ultimately resulting in partial male sterility. Transcriptome and metabolome analyses and histochemical staining indicated that the reduced pollen germination rate was due to the imbalance of energy metabolism during pollen maturation. The investigation of ß-glucuronidase (GUS)-overexpressing transgenic plants driven by the promoter of DR5 (DR5::GUS report system) combined with cell tissue staining and metabolome analysis further validated that cold stress during the tetrad stage reduced auxin levels in mature pollen grains. Low-concentration auxin treatment on floral buds at the tetrad stage before cold exposure improved the cold tolerance of mature pollen grains. Artificially changing the content of endogenous auxin during pollen maturation by spraying chemical reagents and loss-of-function investigation of the auxin biosynthesis gene YUCCA6 by artificial microRNA technology showed that starch overaccumulation severely reduced the pollen germination rate. In summary, we revealed that transient cold stress at the tetrad stage of pollen development in Chinese cabbage causes auxin-mediated starch-related energy metabolism imbalance that contributes to the decline in pollen germination rate and ultimately seed set.

Microwave ablation for recurrent primary hyperparathyroidism in four patients with multiple endocrine neoplasia type 1: a case series report.

Multiple endocrine neoplasia type 1 (MEN1), a rare tumor syndrome, is inherited in an autosomal dominant pattern, mainly manifested as primary hyperparathyroidism (PHPT). Surgery is preferred for patients with MEN1 and PHPT. Thermal ablation has been widely applied for PHPT but rarely for postoperative recurrent PHPT in MEN1 patients. Based on a series of cases, we aimed to investigate the clinical efficacy and safety of ultrasound-guided percutaneous microwave ablation in the treatment of MEN1 patients with postoperative recurrence of PHPT.

Extreme environments and human health: From the immune microenvironments to immune cells.

Exposure to extreme environments causes specific acute and chronic physiological responses in humans. The adaptation and the physiological processes under extreme environments predominantly affect multiple functional systems of the organism, in particular, the immune system. Dysfunction of the immune system affected by several extreme environments (including hyperbaric environment, hypoxia, blast shock, microgravity, hypergravity, radiation exposure, and magnetic environment) has been observed from clinical macroscopic symptoms to intracorporal immune microenvironments. Therefore, simulated extreme conditions are engineered for verifying the main influenced characteristics and factors in the immune microenvironments. This review summarizes the responses of immune microenvironments to these extreme environments during in vivo or in vitro exposure, and the approaches of engineering simulated extreme environments in recent decades. The related microenvironment engineering, signaling pathways, molecular mechanisms, clinical therapy, and prevention strategies are also discussed.

DeePMD-kit v2: A software package for deep potential models.

DeePMD-kit is a powerful open-source software package that facilitates molecular dynamics simulations using machine learning potentials known as Deep Potential (DP) models. This package, which was released in 2017, has been widely used in the fields of physics, chemistry, biology, and material science for studying atomistic systems. The current version of DeePMD-kit offers numerous advanced features, such as DeepPot-SE, attention-based and hybrid descriptors, the ability to fit tensile properties, type embedding, model deviation, DP-range correction, DP long range, graphics processing unit support for customized operators, model compression, non-von Neumann molecular dynamics, and improved usability, including documentation, compiled binary packages, graphical user interfaces, and application programming interfaces. This article presents an overview of the current major version of the DeePMD-kit package, highlighting its features and technical details. Additionally, this article presents a comprehensive procedure for conducting molecular dynamics as a representative application, benchmarks the accuracy and efficiency of different models, and discusses ongoing developments.

Finite-Length Analysis for Spatially Coupled LDPC Codes Based on Base Matrix.

Spatially coupled low density parity check (SC-LDPC) are prominent candidates for future communication standards due to their "threshold saturation" properties. To evaluate the finite-length performance of SC-LDPC codes, a general and efficient finite-length analysis from the perspective of the base matrix is proposed. We analyze the evolution of the residual graphs resulting at each iteration during the decoding process based on the base matrix and then derive the expression for the error probability. To verify the effectiveness of the proposed finite-length analysis, we consider the SC-LDPC code ensembles constructed by parallelly connecting multiple chains (PC-MSC-LDPC). The analysis results show that the predicted error probabilities obtained by using the derived expression for the error probability match the simulated error probabilities. The proposed finite-length analysis provides a useful engineering tool for practical SC-LDPC code design and for analyzing the effects of the code parameters on the performances.

Endothelial TNF receptor 2 induces IRF1 transcription factor-dependent interferon-ß autocrine signaling to promote monocyte recruitment.

Endothelial-dependent mechanisms of mononuclear cell influx are not well understood. We showed that acute stimulation of murine microvascular endothelial cells expressing the tumor necrosis factor receptors TNFR1 and TNFR2 with the soluble cytokine TNF led to CXCR3 chemokine generation. The TNF receptors signaled through interferon regulatory factor-1 (IRF1) to induce interferon-ß (IFN-ß) and subsequent autocrine signaling via the type I IFN receptor and the transcription factor STAT1. Both TNFR2 and TNFR1 were required for IRF1-IFNß signaling and, in human endothelial cells TNFR2 expression alone induced IFN-ß signaling and monocyte recruitment. In vivo, TNFR1 was required for acute renal neutrophil and monocyte influx after systemic TNF treatment, whereas the TNFR2-IRF1-IFN-ß autocrine loop was essential only for macrophage accumulation. In a chronic model of proliferative nephritis, IRF1 and renal-expressed TNFR2 were essential for sustained macrophage accumulation. Thus, our data identify a pathway in endothelial cells that selectively recruits monocytes during a TNF-induced inflammatory response.

Anti-Inflammatory Effects of Opuntia Milpa Alta Polysaccharides Fermented by Lactic Acid Bacteria in Human Keratinocyte HaCaT Cells.

Opuntia milpa alta polysaccharides (ODP) are bioactive compounds extracted from Opuntia milpa alta and widely used in the treatment of diseases, but the therapeutic mechanism of ODP on inflammatory injury remains unclear. Therefore, this study explores the effects and mechanisms of ODP in lipopolysaccharide (LPS)-induced inflammation of human keratinocytes (HaCaT). In this experiment, ODP was extracted via the water extraction and fermentation methods, respectively. LPS was then used to induce inflammatory damage in HaCaT cells, and the stimulated cells were treated with different concentrations of ODP. Cell viability was detected by MTT assay, and the concentrations of COX-2, iNOS, IL-6, IL-8, IL-10 and TNF-α were determined by enzyme-linked immunosorbent assay (ELISA). Changes in inflammatory cytokines and related mRNA expression were observed to assess LPS-induced cell damage. In the experiment, it was found that the LPS stimulation of HaCaT cells can induce cellular inflammatory response, reduce cell viability, increase cell apoptosis and increase the expression of COX-2, iNOS, IL-6, IL-8, IL-10 and TNF-α. However, the experimental data shows that ODP can reverse the above results by increasing cell viability, inhibiting cell apoptosis, reducing the expression of the above genes and inactivating antioxidant pathways, which revealed the specific mechanism of ODP repairing LPS-induced inflammatory damage to Hacat cell. In addition, The experimental results showed that fermentation could improve the anti-inflammatory effect of ODP. In conclusion, our experimental results indicate that ODP fermented by lactic acid bacteria can be used as an anti-inflammatory agent.

Increased WD40 motifs in Planctomycete bacteria and their evolutionary relevance.

Species of the family Planctomycetes have a complex intracellular structure, which is distinct from that of the majority of non-Planctomycetes bacteria. At present, genomic evidence of the evolution of intracellular complexity is lacking, cognitions of Planctomycetes's intracellular structure mainly rely on electron microscope observation. As the presence of WD40 motifs in eukaryotic proteins probably links to intracellular complexity, bioinformatic studies were conducted to detect and enumerate WD40 motifs, WD40 domains, and WD40 motif-bearing proteins in the genomes of 11 Planctomycetes species, 2775 non-Planctomycetes bacteria, and 63 representative eukaryotes. Compared to non-Planctomycetes bacteria (average 5 WD40 motifs and 1 WD40 motif-bearing protein per genome), a large increase in the number of WD40 motifs in Planctomycetes species (average 116 WD40 motifs and 26 WD40 motif-bearing proteins per genome) was observed. However, the average number of WD40 motifs in Planctomycetes species was significantly lower than that of eukaryotes (average 584 WD40 motifs and 193 WD40 motif-bearing proteins per genome). The number of WD40 motif-bearing proteins was found to correlate with genome size and gene number. Most WD40 motif-bearing proteins of Planctomycetes species belonged to the categories of 'ribosome assembly protein 4' and 'eukaryotic-like serine/threonine protein kinase.' Collinearity analysis of amino acid compositions of Planctomycetes and eukaryotic WD40 motifs revealed that the sequences of the four anti-parallel ß-sheets of WD40 motifs were conserved. However, a number of Planctomycetes WD40 motifs had increased size of the interval region of ß-sheets D and A. Taken together, results of this study suggest a positive correlation between the number of WD40 motif-bearing proteins and the evolution of Planctomycetes species toward a complex intracellular structure similar to that of eukaryotes.

Molecular adaptation and convergent evolution of frugivory in Old World and neotropical fruit bats.

Although cases of independent adaptation to the same dietary niche have been documented in mammalian ecology, the molecular correlates of such shifts are seldom known. Here, we used genomewide analyses of molecular evolution to examine two lineages of bats that, from an insectivorous ancestor, have both independently evolved obligate frugivory: the Old World family Pteropodidae and the neotropical subfamily Stenodermatinae. New genome assemblies from two neotropical fruit bats (Artibeus jamaicensis and Sturnira hondurensis) provide a framework for comparisons with Old World fruit bats. Comparative genomics of 10 bat species encompassing dietary diversity across the phylogeny revealed convergent molecular signatures of frugivory in both multigene family evolution and single-copy genes. Evidence for convergent molecular adaptations associated with frugivorous diets includes the composition of three subfamilies of olfactory receptor genes, losses of three bitter taste receptor genes, losses of two digestive enzyme genes and convergent amino acid substitutions in several metabolic genes. By identifying suites of adaptations associated with the convergent evolution of frugivory, our analyses both reveal the extent of molecular mechanisms under selection in dietary shifts and will facilitate future studies of molecular ecology in mammals.

Comparative transcriptome analysis and ChIP-sequencing reveals stage-specific gene expression and regulation profiles associated with pollen wall formation in Brassica rapa.

BACKGROUND: Genic male sterility (GMS) line is an important approach to utilize heterosis in Brassica rapa, one of the most widely cultivated vegetable crops in Northeast Asia. However, the molecular genetic mechanisms of GMS remain to be largely unknown. RESULTS: Detailed phenotypic observation of 'Bcajh97-01A/B', a B. rapa genic male sterile AB line in this study revealed that the aberrant meiotic cytokinesis and premature tapetal programmed cell death occurring in the sterile line ultimately resulted in microspore degeneration and pollen wall defect. Further gene expression profile of the sterile and fertile floral buds of 'Bcajh97-01A/B' at five typical developmental stages during pollen development supported the result of phenotypic observation and identified stage-specific genes associated with the main events associated with pollen wall development, including tapetum development or functioning, callose metabolism, pollen exine formation and cell wall modification. Additionally, by using ChIP-sequencing, the genomic and gene-level distribution of trimethylated histone H3 lysine 4 (H3K4) and H3K27 were mapped on the fertile floral buds, and a great deal of pollen development-associated genes that were covalently modified by H3K4me3 and H3K27me3 were identified. CONCLUSIONS: Our study provids a deeper understanding into the gene expression and regulation network during pollen development and pollen wall formation in B. rapa, and enabled the identification of a set of candidate genes for further functional annotation.

CircRNA Expression Pattern and ceRNA and miRNA-mRNA Networks Involved in Anther Development in the CMS Line of Brassica campestris.

Male-sterile plants provide an important breeding tool for the heterosis of hybrid crops, such as Brassicaceae. In the last decade, circular RNAs (circRNAs), as a novel class of covalently closed and single-stranded endogenous non-coding RNAs (ncRNAs), have received much attention because of their functions as "microRNA (miRNA) sponges" and "competing endogenous RNAs" (ceRNAs). However, the information about circRNAs in the regulation of male-sterility and anther development is limited. In this study, we established the Polima cytoplasm male sterility (CMS) line "Bcpol97-05A", and the fertile line, "Bcajh97-01B", in Brassica campestris L. ssp. chinensis Makino, syn. B. rapa ssp. chinensis, and performed RNA expression profiling comparisons between the flower buds of the sterile line and fertile line by whole-transcriptome sequencing. A total of 31 differentially expressed (DE) circRNAs, 47 DE miRNAs, and 4779 DE mRNAs were identified. By using Cytoscape, the miRNA-mediated regulatory network and ceRNA network were constructed, and the circRNA A02:23507399|23531438 was hypothesized to be an important circRNA regulating anther development at the post-transcriptional level. The gene ontology (GO) analysis demonstrated that miRNAs and circRNAs could regulate the orderly secretion and deposition of cellulose, sporopollenin, pectin, and tryphine; the timely degradation of lipids; and the programmed cell death (PCD) of tapetum cells, which play key roles in anther development. Our study revealed a new circRNA-miRNA-mRNA network, which is involved in the anther development of B. campestris, which enriched the understanding of CMS in flowering plants, and laid a foundation for further study on the functions of circRNAs and miRNAs during anther development.

Receiver-Initiated Handshaking MAC Based on Traffic Estimation for Underwater Sensor Networks ‡.

In underwater sensor networks (UWSNs), the unique characteristics of acoustic channels have posed great challenges for the design of medium access control (MAC) protocols. The long propagation delay problem has been widely explored in recent literature. However, the long preamble problem with acoustic modems revealed in real experiments brings new challenges to underwater MAC design. The overhead of control messages in handshaking-based protocols becomes significant due to the long preamble in underwater acoustic modems. To address this problem, we advocate the receiver-initiated handshaking method with parallel reservation to improve the handshaking efficiency. Despite some existing works along this direction, the data polling problem is still an open issue. Without knowing the status of senders, the receiver faces two challenges for efficient data polling: when to poll data from the sender and how much data to request. In this paper, we propose a traffic estimation-based receiver-initiated MAC (TERI-MAC) to solve this problem with an adaptive approach. Data polling in TERI-MAC depends on an online approximation of traffic distribution. It estimates the energy efficiency and network latency and starts the data request only when the preferred performance can be achieved. TERI-MAC can achieve a stable energy efficiency with arbitrary network traffic patterns. For traffic estimation, we employ a resampling technique to keep a small computation and memory overhead. The performance of TERI-MAC in terms of energy efficiency, channel utilization, and communication latency is verified in simulations. Our results show that, compared with existing receiver-initiated-based underwater MAC protocols, TERI-MAC can achieve higher energy efficiency at the price of a delay penalty. This confirms the strength of TERI-MAC for delay-tolerant applications.

Clinical and epidemiologic characteristics of respiratory syncytial virus infection among children aged <5 years, Jingzhou City, China, 2011.

Severe acute respiratory illness (SARI) surveillance began in Jingzhou City, China, in 2010. A subset of 511 children aged <5 years enrolled in the SARI study during 2011 were tested for influenza and noninfluenza respiratory viral infection by real-time reverse-transcription polymerase chain reaction. Respiratory syncytial virus (RSV) was most commonly detected. Children aged 12-23 and 24-60 months were equally likely to test positive for RSV. Although cases of RSV infection could be detected throughout the year, the greatest numbers were detected from autumn to early winter.

Respiratory syncytial virus circulation in seven countries with Global Disease Detection Regional Centers.

BACKGROUND: Respiratory syncytial virus (RSV) is the leading cause of lower respiratory tract infections in young children globally, with the highest burden in low- and middle-income countries where the association between RSV activity and climate remains unclear. METHODS: Monthly laboratory-confirmed RSV cases and associations with climate data were assessed for respiratory surveillance sites in tropical and subtropical areas (Bangladesh, China, Egypt, Guatemala, Kenya, South Africa, and Thailand) during 2004-2012. Average monthly minimum and maximum temperatures, relative humidity, and precipitation were calculated using daily local weather data from the US National Climatic Data Center. RESULTS: RSV circulated with 1-2 epidemic periods each year in site areas. RSV seasonal timing and duration were generally consistent within country from year to year. Associations between RSV and weather varied across years and geographic locations. RSV usually peaked in climates with high annual precipitation (Bangladesh, Guatemala, and Thailand) during wet months, whereas RSV peaked during cooler months in moderately hot (China) and arid (Egypt) regions. In South Africa, RSV peaked in autumn, whereas no associations with seasonal weather trends were observed in Kenya. CONCLUSIONS: Further understanding of RSV seasonality in developing countries and various climate regions will be important to better understand the epidemiology of RSV and for timing the use of future RSV vaccines and immunoprophylaxis in low- and middle-income countries.

Machine-Learning-Based Interatomic Potentials for Group IIB to VIA Semiconductors: Toward a Universal Model.

Rapid advancements in machine-learning methods have led to the emergence of machine-learning-based interatomic potentials as a new cutting-edge tool for simulating large systems with ab initio accuracy. Still, the community awaits universal interatomic models that can be applied to a wide range of materials without tuning neural network parameters. We develop a unified deep-learning interatomic potential (the DPA-Semi model) for 19 semiconductors ranging from group IIB to VIA, including Si, Ge, SiC, BAs, BN, AlN, AlP, AlAs, InP, InAs, InSb, GaN, GaP, GaAs, CdTe, InTe, CdSe, ZnS, and CdS. In addition, independent deep potential models for each semiconductor are prepared for detailed comparison. The training data are obtained by performing density functional theory calculations with numerical atomic orbitals basis sets to reduce the computational costs. We systematically compare various properties of the solid and liquid phases of semiconductors between different machine-learning models. We conclude that the DPA-Semi model achieves GGA exchange-correlation functional quality accuracy and can be regarded as a pretrained model toward a universal model to study group IIB to VIA semiconductors.

Enhancing adversarial attacks with resize-invariant and logical ensemble.

In black-box scenarios, most transfer-based attacks usually improve the transferability of adversarial examples by optimizing the gradient calculation of the input image. Unfortunately, since the gradient information is only calculated and optimized for each pixel point in the image individually, the generated adversarial examples tend to overfit the local model and have poor transferability to the target model. To tackle the issue, we propose a resize-invariant method (RIM) and a logical ensemble transformation method (LETM) to enhance the transferability of adversarial examples. Specifically, RIM is inspired by the resize-invariant property of Deep Neural Networks (DNNs). The range of resizable pixel is first divided into multiple intervals, and then the input image is randomly resized and padded within each interval. Finally, LETM performs logical ensemble of multiple images after RIM transformation to calculate the final gradient update direction. The proposed method adequately considers the information of each pixel in the image and the surrounding pixels. The probability of duplication of image transformations is minimized and the overfitting effect of adversarial examples is effectively mitigated. Numerous experiments on the ImageNet dataset show that our approach outperforms other advanced methods and is capable of generating more transferable adversarial examples.

Terpene synthases GhTPS6 and GhTPS47 participate in resistance to Verticillium dahliae in upland cotton.

Terpene synthases (TPSs) are enzymes responsible for catalyzing the production of diverse terpenes, the largest class of secondary metabolites in plants. Here, we identified 107 TPS gene loci encompassing 92 full-length TPS genes in upland cotton (Gossypium hirsutum L.). Phylogenetic analysis showed they were divided into six subfamilies. Segmental duplication and tandem duplication events contributed greatly to the expansion of TPS gene family, particularly the TPS-a and TPS-b subfamilies. Expression profile analysis screened out that GhTPSs may mediate the interaction between cotton and Verticillium dahliae. Three-dimensional structures and subcellular localizations of the two selected GhTPSs, GhTPS6 and GhTPS47, which belong to the TPS-a subfamily, demonstrated similarity in protein structures and nucleus and cytoplasm localization. Virus-induced gene silencing (VIGS) of the two GhTPSs yielded plants characterized by increased wilting and chlorosis, more severe vascular browning, and higher disease index than control plants. Additionally, knockdown of GhTPS6 and GhTPS47 led to the down-regulation of cotton terpene synthesis following V. dahliae infection, indicating that these two genes may positively regulate resistance to V. dahliae through the modulation of disease-resistant terpene biosynthesis. Overall, our study represents a comprehensive analysis of the G. hirsutum TPS gene family, revealing their potential roles in defense responses against Verticillium wilt.

Engineering microvascular networks using a KLF2 reporter to probe flow-dependent endothelial cell function.

Shear stress generated by the flow of blood in the vasculature is a potent regulator of endothelial cell function and vascular structure. While vascular responses to flow are complex and context-dependent, endothelial cell signaling in response to shear stress induced by laminar flows is coordinated by the transcription factor KLF2. The flow-dependent expression of KLF2 in endothelial cells is associated with a quiescent, anti-inflammatory phenotype and has been well characterized in two-dimensional systems but has not been studied in three-dimensional in vitro systems. Here we develop engineered microvascular networks (MVNs) that incorporate a KLF2-based endothelial cell flow sensor within a microfluidic chip, apply continuous flow using an attached microfluidic pump, and study the effects of this flow on vascular structure and function. We found that application of flow to MVNs for 48 h resulted in increased expression of the KLF2 reporter, larger vessel diameters, and decreased vascular branching and resistance. Notably, vessel diameters after the application of flow were independent of initial MVN morphologies. Finally, we found that MVNs exposed to flow have improved vascular barrier function and decreased platelet adhesion. MVNs with KLF2-based flow sensors represent a novel, powerful tool for evaluating the structural and functional effects of flow on engineered three-dimensional vascular systems.

Efficient spatial and channel net for lane marker detection based on self-attention and row anchor.

Lane detection is an important component of advanced driving aided system (ADAS). It is a combined component of the planning and control algorithms. Therefore, it has high standards for the detection accuracy and speed. Recently several researchers have worked extensively on this topic. An increasing number of researchers have been interested in self-attention-based lane detection. In difficult situations such as shadows, bright lights, and nights extracting global information is effective. Regardless of channel or spatial attention, it cannot independently extract all global information until a complicated model is used. Furthermore, it affects the run-time. However trading in this contradiction is challenging. In this study, a new lane identification model that combines channel and spatial self-attention was developed. Conv1d and Conv2d were introduced to extract the global information. The model is lightweight and efficient avoiding difficult model calculations and massive matrices, In particular obstacles can be overcome under certain difficult conditions. We used the Tusimple and CULane datasets as verification standards. The accuracy of the Tusimple benchmark was the highest at 95.49%. In the CULane dataset, the proposed model achieved 75.32% in F1, which is the highest result, particularly in difficult scenarios. For the Tusimple and CULane datasets, the proposed model achieved the best performance in terms of accuracy and speed.

ADDNS: An asymmetric dual deep network with sharing mechanism for medical image fusion of CT and MR-T2.

Medical images with different modalities have different semantic characteristics. Medical image fusion aiming to promotion of the visual quality and practical value has become important in medical diagnostics. However, the previous methods do not fully represent semantic and visual features, and the model generalization ability needs to be improved. Furthermore, the brightness-stacking phenomenon is easy to occur during the fusion process. In this paper, we propose an asymmetric dual deep network with sharing mechanism (ADDNS) for medical image fusion. In our asymmetric model-level dual framework, primal Unet part learns to fuse medical images of different modality into a fusion image, while dual Unet part learns to invert the fusion task for multi-modal image reconstruction. This asymmetry of network settings not only enables the ADDNS to fully extract semantic and visual features, but also reduces the model complexity and accelerates the convergence. Furthermore, the sharing mechanism designed according to task relevance also reduces the model complexity and improves the generalization ability of our model. In the end, we use the intermediate supervision method to minimize the difference between fusion image and source images so as to prevent the brightness-stacking problem. Experimental results show that our algorithm achieves better results on both quantitative and qualitative experiments than several state-of-the-art methods.