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Testis-specifically expressed genes are important for male reproduction according to their unique expression patterns. However, the functions of most of these genes in reproduction are unclear. Here, we showed that mouse 4930590J08Rik was a testis-specifically expressed gene. 4930590J08Rik knockout mice exhibited a delay in the first wave of spermatogenesis and a reduction of cauda epididymal sperm. Furthermore, knockout spermatozoa exhibited defective acrosome reactions and decreased progressive motility, which led to impaired in vivo fertilization. Transcriptome analysis of testes revealed that most of the differentially expressed genes in knockout testes were associated with metabolic processes. 4930590J08Rik knockout sperm exhibited oxidative phosphorylation deficiency and were highly dependent on increased anaerobic glycolysis to compensate for ATP demands. Taken together, the 4930590J08Rik-disrupted mouse partially mimics the phenotypes of human asthenospermia and oligozoospermia, which provides a new model for further understanding the pathogenesis of idiopathic male infertility.

Insect endosymbionts are widespread in nature and known to play key roles in regulating host biology. As a secondary endosymbiont, bacteria in the genus Hamiltonella help cotton aphids (Aphis gossypii) defend against parasitism by parasitoid wasps, however, the potential negative impacts of these bacteria on cotton aphid biology remain largely unclear.This study aims to evaluate the potential impacts of Hamiltonella on the growth and development of cotton aphids based on life table parameters and RNA sequencing. The results showed that infection with Hamiltonella resulted in smaller body type and lower body weight in aphids. Compared to the control group, there were significant differences in the finite and intrinsic rates of increase and mean generation time. Furthermore, the RNA sequencing data revealed that the genes related to energy synthesis and nutrient metabolism pathways were significantly downregulated and genes related to molting and nervous system pathways were significantly upregulated in the Hamiltonella population.Our results confirm that Hamiltonella retarded the growth and development of cotton aphids accompanied by the downregulation of genes related to energy synthesis and nutrient metabolism, which provides new insights into aphid-symbiont interactions and may support the development of improved aphid management strategies. © 2022 Society of Chemical Industry.

Propylea japonica has been regarded as one of the most remarkable natural enemies against aphid in China. However, the mechanism of juvenile hormone (JH) regulation of reproduction in P. japonica is still unclear. In this study, we investigated the JH titers of P. japonica and the development of the ovaries. We selected the six different developmental stages of ladybeetle females for transcriptome sequencing. We identified 583 genes involved in insect reproduction regulation, including 107 insect hormone synthesis signaling pathway-related genes and 476 nutrition-sensing signaling pathway-related genes. Transcriptome analysis indicated that a large number JH synthesis- and metabolism-related enzyme genes and some potential nutrient signal sensing- and transduction-related genes were significantly differentially expressed during P. japonica development. We investigated the effects of Met gene silencing on the reproduction of female adults and found that the ovarian maturation, vitellogenesis, and follicular epithelium development in the dsMet treatment group were significantly inhibited.

Background Aphis gossypii, a polyphagous and recurrent pest induced by pesticides, causes tremendous loss in the yield of crops each year. Previous studies of pesticide-induced sublethal effects mechanism mainly focus on gene level. The symbiotic bacteria are also important participants of this mechanism, but their roles in hormesis are still unclear. Results In this study, life table parameters and 16S rRNA sequencing were applied to evaluate the sublethal and transgenerational effects of sulfoxaflor on adult A. gossypii after 24-h LC20 (6.96 mg L-1 ) concentration exposure. The results indicated that the LC20 of sulfoxaflor significantly reduced the finite rate of increase (λ) and net reproductive rate (R0 ) of parent generation (G0), and significantly increased mean generation time (T) of G1 and G2, but not of G3 and G4. Both reproductive period and fecundity of G1 and G2 were significantly higher than those of the control. Furthermore, our sequencing data revealed that more than 95% bacterial communities were dominated by the phylum Proteobacteria, in which the maximum proportion genus was the primary symbiont Buchnera and the facultative symbiont Arsenophonus. Compared to those of the control, the abundance and composition of symbiotic bacteria of A. gossypii for three successive generations (G0-G2) were changed after G0 A. gossypii was exposed to sulfoxaflor: the diversity of the bacterial community was decreased, but the abundance of Buchnera was increased (G0), while the abundance of Arsenophonus was decreased. Contrary to G0, G1 and G2 cotton aphid exhibited an increased relative abundance of Arsenophonus in sublethal treatment group. Conclusion Taken together, our results provide an insight into the interactions among pesticide resistance, aphids, and symbionts, which will eventually help to better manage the resurgence of A. gossypii. This article is protected by copyright. All rights reserved.

Pilomatricoma, a benign skin appendage tumor, also known as calcifying epithelioma, consists of islands of epithelial cells histologically that contain anucleated cells in the center surrounded by basophilic cells and partial calcification. Sporadic pilomatricomas commonly have somatic mutations in the gene CTNNB1, but causative genes from germline and the underlying pathophysiology are unclear. In this study, we identified a germline missense variant of PLCD1 encoding PLCδ1, c.1186G>A (p.Glu396Lys), in a large Chinese family with autosomal dominant multiple pilomatricomas. Phospholipase C, a key enzyme playing critical roles in intracellular signal transduction, is essential for epidermal barrier integrity. The p.Glu396Lys variant increased the enzymatic activity of PLCδ1, leading to protein kinase C/protein kinase D/extracellular signal–regulated kinase1/2 pathway activation and TPRV6 channel closure, which not only resulted in excessive proliferation of keratinocytes in vitro and in vivo but also induced local accumulation of calcium in the pilomatricoma-like tumor that developed spontaneously in the skin of Plcd1E396K/E396K mice. Our results implicate this p.Glu396Lys variant of PLCD1 from germline leading to gain-of-function of PLCδ1 as a causative genetic defect in familial multiple pilomatricomas.

To explore the mechanisms of TLR9 from macrophages on mitochondrial apoptosis in cardiomyocytes at early stage of sepsis.TheThe expressions of p-PI3K, p-AKT, exosome markers (CD9, CD63, and TSG101), p-ERK1/2, TNF-TLR9

Primary familial brain calcification (PFBC) is an inherited neurodegenerative disorder mainly characterized by progressive calcium deposition bilaterally in the brain, accompanied by various symptoms, such as dystonia, ataxia, parkinsonism, dementia, depression, headaches, and epilepsy. Currently, the etiology of PFBC is largely unknown, and no specific prevention or treatment is available. During the past 10 years, six causative genes (SLC20A2, PDGFRB, PDGFB, XPR1, MYORG, and JAM2) have been identified in PFBC. In this review, considering mechanistic studies of these genes at the cellular level and in animals, we summarize the pathogenesis and potential preventive and therapeutic strategies for PFBC patients. Our systematic analysis suggests a classification for PFBC genetic etiology based on several characteristics, provides a summary of the known composition of brain calcification, and identifies some potential therapeutic targets for PFBC.

PiT2 is an inorganic phosphate (Pi) transporter whose mutations are linked to primary familial brain calcification (PFBC). PiT2 mainly consists of two ProDom (PD) domains and a large intracellular loop region (loop7). The PD domains are crucial for the Pi transport, but the role of PiT2-loop7 remains unclear. In PFBC patients, mutations in PiT2-loop7 are mainly nonsense or frameshift mutations that probably cause PFBC due to C-PD1131 deletion. To date, six missense mutations have been identified in PiT2-loop7; however, the mechanisms by which these mutations cause PFBC are poorly understood. Here, we found that the p.T390A and p.S434W mutations in PiT2-loop7 decreased the Pi transport activity and cell surface levels of PiT2. Furthermore, we showed that these two mutations attenuated its membrane localization by affecting adenosine monophosphate-activated protein kinase (AMPK)- or protein kinase B (AKT)-mediated PiT2 phosphorylation. In contrast, the p.S121C and p.S601W mutations in the PD domains did not affect PiT2 phosphorylation but rather impaired its substrate-binding abilities. These results suggested that missense mutations in PiT2-loop7 can cause Pi dyshomeostasis by affecting the phosphorylation-regulated cell-surface localization of PiT2. This study helps understand the pathogenesis of PFBC caused by PiT2-loop7 missense mutations and indicates that increasing the phosphorylation levels of PiT2-loop7 could be a promising strategy for developing PFBC therapies.

As microplastics became the focus of global attention, the hazards of plastic plasticizers (PAEs) have gradually attracted people's attention. Agricultural soil is one of its hardest hit areas. However, current research of its impact on soil ecology only stops at the microorganism itself, and there is still lack of conclusion on the impact of soil metabolism. To this end, three most common PAEs (Dimethyl phthalate: DMP, Dibutyl phthalate: DBP and Bis (2-ethylhexyl) phthalate: DEHP) were selected and based on high-throughput sequencing and metabolomics platforms, the influence of PAEs residues on soil metabolic functions were revealed for the first time. PAEs did not significantly changed the alpha diversity of soil bacteria in the short term, but changed their community structure and interfered with the complexity of community symbiosis network. Metabolomics indicated that exposure to DBP can significantly change the soil metabolite profile. A total of 172 differential metabolites were found, of which 100 were up-regulated and 72 were down-regulated. DBP treatment interfered with 43 metabolic pathways including basic metabolic processes. In particular, it interfered with the metabolism of residual steroids and promoted the metabolism of various plasticizers. In addition, through differential labeling and collinear analysis, some bacteria with the degradation potential of PAEs, such as Gordonia, were excavated.

The complete mitochondrial genome of Aphis gossypii Glover cucumber biotype was sequenced using traditional PCR amplification coupled with Sanger sequencing. The genome is 15,870 bp long, with 83.7...

In insects, cytochrome P450 monooxygenases (P450s or CYPs) play an important role in the detoxification and metabolism of exogenous plant allelochemicals. In this study, a P450 gene CYP6AB12 was identified and characterized from Spodoptera litura. The cDNA contains an open reading frame (ORF) encoding 511 amino acid residues. CYP6AB12 was expressed at different ages of S. litura, with the highest levels found in the third and fourth instar larvae. Its highest expression was found in the midgut and fat body of fourth instar larvae fed with gossypol. Moreover, these expression levels were substantially increased compared with those from larvae fed with control diet. Gene silencing was then conducted by smearing dsRNA mixed with nanomaterials onto the cuticle. CYP6AB12 expression was significantly decreased in the midgut and fat body, and the net weight increase was substantially lower than that of the control group, indicating that the treatment group had more sensitivity to gossypol than the control. These results reveal that CYP6AB12 plays an important role in the detoxification and metabolism of gossypol, thus further confirming that P450s have a broad ability to detoxify and metabolize plant allelochemicals. It provides an important molecular basis for the exploration of detoxification metabolism and pest control of S. litura.

The broad adoption of electronic health record (EHR) systems and the advances of deep learning technology have motivated the development of health risk prediction models, which mainly depend on the expressiveness and temporal modeling capacity of deep neural networks (DNNs) to improve prediction performance. Some further augment the prediction by using external knowledge, however, a great deal of EHR information inevitably loses during the knowledge mapping. In addition, prediction made by existing models usually lacks reliable interpretation, which undermines their reliability in guiding clinical decision-making. To solve these challenges, we propose MedRetriever, an effective and flexible framework that leverages unstructured medical text collected from authoritative websites to augment health risk prediction as well as to provide understandable interpretation. Besides, MedRetriever explicitly takes the target disease documents into consideration, which provide key guidance for the model to learn in a target-driven direction, i.e., from the target disease to the input EHR. To specify, MedRetriever can flexibly choose its backbone from major predictive models to learn the EHR embedding for each visit. After that, the EHR embedding and features of target disease documents are aggregated into a query by self-attention to retrieve highly relevant text segments from the medical text pool, which is stored in the dynamically updated text memory. Finally, the comprehensive EHR embedding and the text memory are used for prediction and interpretation. We evaluate MedRetriever against nine state-of-the-art approaches across three real-world EHR datasets, which consistently achieves the best performance in AUC and recall metrics and outperforms the best baseline by at least 4.8% in recall on three test datasets. Furthermore, we conduct case studies to show the easy-to-understand interpretation by MedRetriever.

Federated learning aims to protect users' privacy while performing data analysis from different participants. However, it is challenging to guarantee the training efficiency on heterogeneous systems due to the various computational capabilities and communication bottlenecks. In this work, we propose FedSkel to enable computation-efficient and communication-efficient federated learning on edge devices by only updating the model's essential parts, named skeleton networks. FedSkel is evaluated on real edge devices with imbalanced datasets. Experimental results show that it could achieve up to 5.52$\times$ speedups for CONV layers' back-propagation, 1.82$\times$ speedups for the whole training process, and reduce 64.8% communication cost, with negligible accuracy loss., Comment: CIKM 2021

Thanks to the explosion of heterogeneous healthcare data and advanced machine learning and data mining techniques, specifically deep learning methods, we now have an opportunity to make difference in healthcare. In this tutorial, we will present state-of-the-art deep learning methods and their real-world applications, specifically focusing on exploring the unique characteristics of different types of healthcare data. The first half will be spent on introducing recent advances in mining structured healthcare data, including computational phenotyping, disease early detection/risk prediction and treatment recommendation. In the second half, we will focus on challenges specific to the unstructured healthcare data, and introduce advanced deep learning methods in automated ICD coding, understandable medical language translation, clinical trial mining, and medical report generation. This tutorial is intended for students, engineers and researchers who are interested in applying deep learning methods to healthcare, and prerequisite knowledge will be minimal. The tutorial will be concluded with open problems and a Q&A session.

Recently proposed fine-grained 3D visual grounding is an essential and challenging task, whose goal is to identify the 3D object referred by a natural language sentence from other distractive objects of the same category. Existing works usually adopt dynamic graph networks to indirectly model the intra/inter-modal interactions, making the model difficult to distinguish the referred object from distractors due to the monolithic representations of visual and linguistic contents. In this work, we exploit Transformer for its natural suitability on permutation-invariant 3D point clouds data and propose a TransRefer3D network to extract entity-and-relation aware multimodal context among objects for more discriminative feature learning. Concretely, we devise an Entity-aware Attention (EA) module and a Relation-aware Attention (RA) module to conduct fine-grained cross-modal feature matching. Facilitated by co-attention operation, our EA module matches visual entity features with linguistic entity features while RA module matches pair-wise visual relation features with linguistic relation features, respectively. We further integrate EA and RA modules into an Entity-and-Relation aware Contextual Block (ERCB) and stack several ERCBs to form our TransRefer3D for hierarchical multimodal context modeling. Extensive experiments on both Nr3D and Sr3D datasets demonstrate that our proposed model significantly outperforms existing approaches by up to 10.6% and claims the new state-of-the-art. To the best of our knowledge, this is the first work investigating Transformer architecture for fine-grained 3D visual grounding task., ACM MM2021

Aims: Cleft lip with or without cleft palate (CL/P) is a common birth defect with an average prevalence of 1/700 to 1/1000. Almost 70% of CL/P cases are nonsyndromic CL/P (NSCL/P). The aim of this study was to identify the underlying cause of a four-generation Chinese family with autosomal dominant NSCL/P. Methods: Genomic DNA was extracted from peripheral blood leukocytes, and whole-exome sequencing was carried out to identify the underlying genetic cause of the disorder. The mutation was confirmed by Sanger sequencing and polymerase chain reaction-restriction fragment length polymorphism methods. Western blotting and coimmunoprecipitation were used to analyze the protein expression level and adhesive dimerization of the CDH1 mutants. Slow aggregation assays were conducted to investigate the cell-cell adhesion ability. Results: A novel missense mutation (c.468G>C/p.Trp156Cys) of CDH1 was identified in the proband and the mutation was shown to cosegregate with the phenotype in the family. Furthermore, we found that the p.Trp156Cys mutation led to decreased E-cadherin dimerization and cell-cell adhesion ability. Conclusions: Our findings identified a novel CDH1 variant (c.468G>C/p.Trp156Cys) responsible for NSCL/P in a Chinese family, which expanded the mutational spectrum of the CDH1 gene and may contribute to understanding the molecular basis of NSCL/P.

The ladybird beetle Propylea japonica is an important natural enemy in agro-ecological systems. Studies on the strong tolerance of P. japonica to high temperatures and insecticides, and its population and phenotype diversity have recently increased. However, abundant genome resources for obtaining insights into stress-resistance mechanisms and genetic intra-species diversity for P. japonica are lacking. Here, we constructed the P. japonica genome maps using Pacific Bioscience (PacBio) and Illumina sequencing technologies. The genome size was 850.90 Mb with a contig N50 of 813.13 kb. The Hi-C sequence data were used to upgrade draft genome assemblies; 4,777 contigs were assembled to 10 chromosomes; and the final draft genome assembly was 803.93 Mb with a contig N50 of 813.98 kb and a scaffold N50 of 100.34 Mb. Approximately 495.38 Mb of repeated sequences was annotated. The 18,018 protein-coding genes were predicted, of which 95.78% were functionally annotated, and 1,407 genes were species-specific. The phylogenetic analysis showed that P. japonica diverged from the ancestor of Anoplophora glabripennis and Tribolium castaneum ~ 236.21 million years ago. We detected that some important gene families involved in detoxification of pesticides and tolerance to heat stress were expanded in P. japonica, especially cytochrome P450 and Hsp70 genes. Overall, the high-quality draft genome sequence of P. japonica will provide invaluable resource for understanding the molecular mechanisms of stress resistance and will facilitate the research on population genetics, evolution and phylogeny of Coccinellidae. This genome will also provide new avenues for conserving the diversity of predator insects.

Parasitism usually causes considerable changes in lipids and fatty acids by redirecting the development of the host. In this study, changes in weight and in free fatty acid content of cotton aphids were recorded after aphids had been parasitized. Results showed that the weight of parasitized Aphis gossypii was increased compared to nonparasitized aphids, and significantly increased weights were detected at 1, 2, and 3 instars after parasitization by Lysiphlebia japonica. Free fatty acid test kits and GC-MS showed that the fatty acid content increased in the early stage of parasitization but decreased after 3 days of parasitization. Seven genes related to the fatty acid synthesis pathway were significantly upregulated in the parasitized aphids, where they were 1.96-10.97 times greater. Our data described the change that occurs in the fatty acid content of parasitized A. gossypii.

The broad adoption of electronic health records (EHR) data and the availability of biomedical knowledge graphs (KGs) on the web have provided clinicians and researchers unprecedented resources and opportunities for conducting health risk predictions to improve healthcare quality and medical resource allocation. Existing methods have focused on improving the EHR feature representations using attention mechanisms, time-aware models, or external knowledge. However, they ignore the importance of using personalized information to make predictions. Besides, the reliability of their prediction interpretations needs to be improved since their interpretable attention scores are not explicitly reasoned from disease progression paths. In this paper, we propose MedPath to solve these challenges and augment existing risk prediction models with the ability to use personalized information and provide reliable interpretations inferring from disease progression paths. Firstly, MedPath extracts personalized knowledge graphs (PKGs) containing all possible disease progression paths from observed symptoms to target diseases from a large-scale online medical knowledge graph. Next, to augment existing EHR encoders for achieving better predictions, MedPath learns a PKG embedding by conducting multi-hop message passing from symptom nodes to target disease nodes through a graph neural network encoder. Since MedPath reasons disease progression by paths existing in PKGs, it can provide explicit explanations for the prediction by pointing out how observed symptoms can finally lead to target diseases. Experimental results on three real-world medical datasets show that MedPath is effective in improving the performance of eight state-of-the-art methods with higher F1 scores and AUCs. Our case study also demonstrates that MedPath can greatly improve the explicitness of the risk prediction interpretation.1

The complete mitochondrial genome of Aphis gossypii Glover cucumber biotype was sequenced using traditional PCR amplification coupled with Sanger sequencing. The genome is 15,870 bp long, with 83.7% AT content (MW048625). The genome encodes 37 typical mitochondrial genes, including 13 protein-coding genes, 2 ribosomal RNA genes, 22 transfer RNAs, a repeat region of 784 bp, and a control region of 627 bp. The base composition of the genome is A (45.4%), T (38.3%), C (10.5%), and G (5.8%). An analysis of two biotypes A. gossypii mitogenomes identified 77 single nucleotide polymorphisms and 1 insertion and deletion.

The predatory ladybird beetle, Harmonia axyridis, is a predominant natural enemy of pest insects in cotton fields. Commercialization of genetically modified crops has promoted the increased use of the herbicide glyphosate. In this study, to assess potential negative effects of glyphosate on beneficial non-target organisms in cotton fields, we first examined how glyphosate exposure affected the development and endosymbiotic bacterial community of H. axyridis. The results showed that the survival rate, development duration, pupation rate and emergence rate of H. axyridis under low and high concentrations of glyphosate exposure were not significantly changed, but glyphosate did significantly reduce the body weight of H. axyridis. Based on 16S rRNA sequencing, there were no significant differences in the diversity or richness of the endosymbiotic bacteria of H. axyridis before and after glyphosate exposure. The dominant bacterial phyla Firmicutes and Proteobacteria and genera Staphylococcus and Enterobacter remained the same regardless of treatment with glyphosate, however the abundance and copy number of these bacteria were altered. Glyphosate treatment significantly reduced the abundance and gene copy number of Staphylococcus and increased the abundance and gene copy number of Enterobacter. This is the first report demonstrating that glyphosate can reduce the body weight H. axyridis and alter the bacterial endosymbiont community by affecting the abundance and gene copy number of dominant bacteria.

Radio frequency connectors are widely used in microwave equipment and radio frequency circuits. Connector degradation in harsh environment may lead to the deterioration of signal integrity and communication quality. In this paper, the effects of connector dielectric degradation on signal transmission in humid environment were presented by the way of experimental tests and theoretical analysis. The equivalent dielectric constant of the degraded dielectric was calculated and a circuit model was developed to explain these effects. The simulation results of the circuit model were in good agreements with the measurement results. In addition, the impacts of dielectric degradation on digital signal waveform and eye diagram were predicted using the model. The results of this research provided a better understanding of the influence of connector dielectric degradation on signal transmission in humid environment.

In the communication systems, passive intermodulation (PIM) induced by the nonlinear effect of coaxial connectors may impact the transmission quality of analog modulated signals, when the analog modulated signals are transmitted through coaxial connectors. In this paper, based on the traditional polynomial model of PIM products, an empirical expression of PIM products in coaxial connectors was deduced from a series of PIM two-tone tests, curve fitting and coefficient extraction. For different signal modulated modes, the simulation results of PIM characteristics of analog modulated signals in coaxial connectors were respectively obtained using the empirical expression applied to ADS simulator. In addition, the Adjacent Channel Power Ratio (ACPR) metric was introduced to quantify the effect of PIM interference in the modulated signal frequency band, providing a theoretical basis for evaluating the performance of analog modulated signals in coaxial connectors against PIM interference.

RF connectors generally have slight nonlinear characteristics. The nonlinearity of connectors can cause passive intermodulation (PIM) interference. The RF connectors will degrade due to the vibration environment, which will aggravate the PIM interference and affect the transmission quality of communication systems. In this paper, a vibration device is designed for a series of accelerated degradation tests of N-type RF connectors. The contact stress is selected as the index to quantify the degradation degree of the connectors. The main findings of the test are that passive intermodulation is greatly affected by vibration contact pressure. The parameters of the theoretical model are modified through the test results of a series of vibration comparative experiments, and a developed high-order power series model is proposed to obtain a higher accuracy prediction of harmonic power.

Background Endosymbiotic bacteria have been reported to mediate interactions between parasitoids and their insect hosts. How parasitic wasps influence changes in host microbial communities and the relationship between them are of great importance to the study of host-parasitoid co-evolutionary and ecological interactions. However, these interactions remain largely unreported for interactions between in Aphis gossypii and Lysiphlebia japonica. Results In this study, we characterize the bacterial microbiota of L. japonica wasps at different developmental stages and monitor changes over time in the bacterial microbiota of their parasitized and non-parasitized aphid hosts, using metagenomic analysis of 16S rDNA sequencing data. Proteobacteria, Firmicutes, and Actinobacteria were the three most abundant bacterial phyla identified in L. japonica. We found that parasitism was associated with an increased abundance of Buchnera nutritional endosymbionts, but decreased abundance of Acinetobacter, Arsenophonus, Candidatus_Hamiltonella, and Pseudomonas facultative symbionts in aphid hosts. Functional analysis of enriched pathways of parasitized aphids showed significant differences in the 'transport and metabolism of carbohydrates' and 'amino acid, lipid, and coenzyme biosynthesis' pathways. Notably, the composition of symbiotic bacteria in wasp larvae was highly similar to that of their aphid hosts, especially the high abundance of Buchnera. Conclusion The results provide a conceptual framework for L. japonica interactions with A. gossypii in which the exchange of symbiotic microbes provides a means by which microbiota can potentially serve as evolutionary drivers of complex, multilevel interactions underlying the ecology and co-evolution of these hosts and parasites. This article is protected by copyright. All rights reserved.

Bar charts are commonly used for data visualization. One common form of chart distribution is in its image form. To enable machine comprehension of chart images, precise detection of chart components in chart images is a critical step. Existing image object detection methods do not perform well in chart component detection which requires high boundary detection precision. And traditional rule-based approaches lack enough generalization ability. In order to address this problem, we design a novel two-stage component detection framework for bar charts that combines point-based and region-based ideas, by simulating the process that human creating bounding boxes for objects. The experiment on our labeled ChartDet dataset shows our method greatly improves the performance of chart object detection. We further extend our method to a general object detection task and get comparable performance.

Simple Summary The Aphis gossypii is a global problem for its pesticide resistance with substantial economic and ecological cost and a wide host range, including cotton and cucurbits. The development of insecticide resistance is rapid and widespread and threatens crop productivity. Biopesticides have emerged as a better alternative for pest control. Cucurbitacin B (CucB) and epigallocatechin gallate (EGCG) are the major secondary metabolites of host plants cucurbits and cotton. In this study, we used cotton- and cucurbit-specialized aphids (CO and CU) as a study system to better understand the effects of CucB and EGCG on cotton aphid. Our study showed that CucB and EGCG can significantly reduce the population-level fitness of A. gossypii, affect their ability to adapt to nonhost plants and alter the levels of some detoxifying enzymes, which showed a potential to be developed into new biopesticides against the notorious aphids. Abstract Aphis gossypii (Glover) is distributed worldwide and causes substantial economic and ecological problems owing to its rapid reproduction and high pesticide resistance. Plant-derived cucurbitacin B (CucB) and epigallocatechin gallate (EGCG) are known to have insecticidal and repellent activities. However, their insecticidal activity on cotton- and cucurbit-specialized aphids (CO and CU), the two important host biotypes of A. gossypii, remains to be investigated. In the present study, we characterized, for the first time, the effects of these two plant extracts on the two host biotypes of A. gossypii. CucB and EGCG significantly reduced the A. gossypii population-level fitness and affected their ability to adapt to nonhost plants. Activities of important detoxification enzymes were also altered, indicating that pesticide resistance is weakened in the tested aphids. Our results suggest that CucB and EGCG have unique properties and may be developed as potential biopesticides for aphid control in agriculture.

Additional file 5: Table S1. Primers used in RT-qPCR.

Additional file 7: Table S3. Fold changes of selected DEGs validated by using RT-qPCR.

Risk prediction using electronic health records (EHR) is a challenging data mining task due to the two-level hierarchical structure of EHR data. EHR data consist of a set of time-ordered visits, and within each visit, there is a set of unordered diagnosis codes. Existing approaches focus on modeling temporal visits with deep neural network (DNN) techniques. However, they ignore the importance of modeling diagnosis codes within visits, and a lot of task-unrelated information within visits usually leads to unsatisfactory performance of existing approaches. To minimize the effect caused by noise information of EHR data, in this paper, we propose a novel DNN for risk prediction termed as LSAN, which consists of a Hierarchical Attention Module (HAM) and a Temporal Aggregation Module (TAM). Particularly, LSAN applies HAM to model the hierarchical structure of EHR data. Using the attention mechanism in the hierarchy of diagnosis code, HAM is able to retain diagnosis details and assign flexible attention weights to different diagnosis codes by their relevance to corresponding diseases. Moreover, the attention mechanism in the hierarchy of visit learns a comprehensive feature throughout the visit history by paying greater attention to visits with higher relevance. Based on the foundation laying by HAM, TAM uses a two-pathway structure to learn a robust temporal aggregation mechanism among all visits for LSAN. It extracts long-term dependencies by a Transformer encoder and short-term correlations by a parallel convolutional layer among different visits. With the construction of HAM and TAM, LSAN achieves the state-of-the-art performance on three real-world datasets with larger AUCs, recalls and F1 scores. Furthermore, the model analysis results demonstrate the effectiveness of the network construction with good interpretability and robustness of decision making by LSAN.

Deep learning methods especially recurrent neural network based models have demonstrated early success in disease risk prediction on longitudinal patient data. Existing works follow a strong assumption to implicitly assume the stationary disease progression during each time period, and thus, take a homogeneous way to decay the information from previous time steps for all patients. However,in reality, disease progression is non-stationary. Besides, the key time steps for a target disease vary among patients. To leverage time information for risk prediction in a more reasonable way, we propose a new hierarchical time-aware attention network, named HiTANet, which imitates the decision making process of doctors inrisk prediction. Particularly, HiTANet models time information in local and global stages. The local evaluation stage has a time aware Transformer that embeds time information into visit-level embed-ding and generates local attention weight for each visit. The global synthesis stage further adopts a time-aware key-query attention mechanism to assign global weights to different time steps. Finally, the two types of attention weights are dynamically combined to generate the patient representations for further risk prediction. We evaluate HiTANet on three real-world datasets. Compared with the best results among twelve competing baselines, HiTANet achieves over 7% in terms of F1 score on all datasets, which demonstrates the effectiveness of the proposed model and the necessity of modeling time information in risk prediction task.

Understanding of neonicotinoid insecticides toxicity on non-target organisms, such as bees, has indirectly promoted their soil treatment use. However, their effect on soil ecosystems haven’t fully understood. Here, based on 16S rRNA high-throughput sequencing and metagenomics, the effects of neonicotinoid insecticide thiamethoxam on bacterial communities and metabolic functions in two types of soils were studied. Thiamethoxam treatment significantly affected soil bacterial abundance, reduced microbial diversity, and changed the bacterial community structure in the short term, and the structure soon returned to a stable state. Soil type and time were important factors affecting bacterial community structure. Some plant growth-promoting rhizosphere bacteria (PGPR) including Actinobacteria were found, and their populations were reduced, while pollutant-degrading bacteria including Firmicutes were also found, and their populations were increased. Based on metagenomics analysis, thiamethoxam treatment insignificantly promoted or inhibited multiple metabolic processes, but gene abundance of some key processes significantly changed. Subtypes of 18 biodegradation genes (BDGs) and 5 pesticide degradation genes (PDGs) were identified. Thiamethoxam treatment significantly increased the abundance of BDGs and PDGs, including cytochrome P450. Potential hosts of P450 degradation genes, including the genus Rhodococcus, were discovered. Conclusions of this study will promote safety evaluation and degradation-related research on neonicotinoid insecticides in soil.

Spodoptera litura is one of the most devastating agricultural pests with a wide range of host plants. To study larval performance on different diets and midgut adaptation at transcriptional levels, feeding assay and RNA-Seq experiments were conducted. RNA interference technology was used to explore the detoxification and metabolism of two cytochrome P450 genes. The bioassay data showed that S. litura larvae developed more quickly when fed on cabbage than when fed on soybean, corn and cotton, tannin can inhibit the growth of S. litura . The result of RNA-Seq indicated that S. litura midgut modified gene expression levels to accommodate different diets, and the most differentially expressed genes were detoxification-related and digestion-related genes . Further analysis showed that the glutathione metabolism pathway was the common detoxification pathway in S. litura. The expression of cytochrome P450 genes showed a clear response to different plant hosts, and these differences may play key functions in primary detoxification of secondary metabolites from host plants. Meanwhile, the digestive enzymes of proteinases, lipases, and carbohydrases in midgut showed special responses to different plant hosts. After injection of dsRNA of CYP321A19 and CYP6AB60 , the expression level of target gene were decreased, and the sensitivity of insect to plant allelochemicals increased and the weight increase significantly slowed. In this study, genes involved in detoxification were identified, and the results demonstrate the genes and pathways S. litura utlize to detoxify specific plant-host allelochemicals. These results may also provide a theoretical basis for S. litura management.

Lysiphlebia japonica Ashmead (Hymenoptera, Braconidae) is an endophagous parasitoid and Aphis gossypii Glover (Hemiptera, Aphididae) is a major pest in cotton. The relationship between insect host-parasitoids and their hosts involves complex physiological, biochemical and genetic interactions. This study examines changes in the development and physiological metabolism of A. gossypii regulated by L. japonica. Our results demonstrated that both the body length and width increased compared to non-parasitized aphids. We detected significantly increases in the developmental period as well as severe reproductive castration following parasitization by L. japonica. We then used proteomics to characterize these biological changes, and when combined with transcriptomes, this analysis demonstrated that the differential expression of mRNA (up or downregulation) captured a maximum of 48.7% of the variations of protein expression. We assigned these proteins to functional categories that included immunity, energy metabolism and transport, lipid metabolism, and reproduction. We then verified the contents of glycogen and 6-phosphate glucose, which demonstrated that these important energy sources were significantly altered following parasitization. These results uncover the effects on A. gossypii following parasitization by L. japonica, additional insight into the mechanisms behind insect-insect parasitism, and a better understanding of host-parasite interactions.

The widespread commercialization of genetically modified (GM) cotton makes it important to assess the potential impact of this recombinant crop on non-target organisms. As important natural enemies of cotton field predators, green lacewing Chrysoperla sinica larvae are exposed to Bt insecticidal proteins expressed by GM cotton by feeding on herbivorous pests, and adults are directly exposed to Bt proteins by cotton pollen consumption. However, potential impacts of transgenic Bt cotton on C. sinica remain unclear. In this study, we evaluated the effects of two transgenic cotton varieties, CCRI41 and CCRI45, which express Cry1Ac (Bt toxin) and CpTI (Cowpea Trypsin Inhibitor), on C. sinica larvae and adults. After being fed with cotton aphids Aphis gossypii reared on transgenic cotton, the survival rate, developmental duration, pupation rate, and emergence rate of larvae were not adversely affected. After being fed two types of transgenic cotton pollen, the 7-day weight of adults and the preoviposition period and the cumulative oviposition of females were not significantly different from control specimen. Taken together, these results indicate that the potential risks of the two tested GM cotton varieties for the predator C. sinica are negligible. Capsule: Our study indicated that GM cotton varieties CCRI41 and CCRI45 have no adverse effects on insect predator C. sinica.

Mosaic variants resulting from postzygotic mutations are prevalent in the human genome and play important roles in human diseases. However, except for cancer-related variants, there is no collection of postzygotic mosaic variants in noncancer disease-related and healthy individuals. Here, we present MosaicBase, a comprehensive database that includes 6698 mosaic variants related to 266 noncancer diseases and 27,991 mosaic variants identified in 422 healthy individuals. Genomic and phenotypic information of each variant was manually extracted and curated from 383 publications. MosaicBase supports the query of variants with Online Mendelian Inheritance in Man (OMIM) entries, genomic coordinates, gene symbols, or Entrez IDs. We also provide an integrated genome browser for users to easily access mosaic variants and their related annotations for any genomic region. By analyzing the variants collected in MosaicBase, we find that mosaic variants that directly contribute to disease phenotype show features distinct from those of variants in individuals with mild or no phenotypes, in terms of their genomic distribution, mutation signatures, and fraction of mutant cells. MosaicBase will not only assist clinicians in genetic counseling and diagnosis but also provide a useful resource to understand the genomic baseline of postzygotic mutations in the general human population. MosaicBase is publicly available at http://mosaicbase.com/ or http://49.4.21.8:8000.

Mosaic variants resulting from postzygotic mutations are prevalent in the human genome and play important roles in human diseases. However, except for cancer-related variant collections, there are no collections of mosaic variants in noncancer diseases and asymptomatic individuals. Here, we present MosaicBase (http://mosaicbase.cbi.pku.edu.cn/ or http://49.4.21.8:8000/), a comprehensive database that includes 6,698 mosaic variants related to 269 noncancer diseases and 27,991 mosaic variants identified in 422 asymptomatic individuals. The genomic and phenotypic information for each variant was manually extracted and curated from 383 publications. MosaicBase supports the query of variants with Online Mendelian Inheritance in Man (OMIM) entries, genomic coordinates, gene symbols, or Entrez IDs. We also provide an integrated genome browser for users to easily access mosaic variants and their related annotations within any genomic region. By analyzing the variants collected in MosaicBase, we found that mosaic variants that directly contribute to disease phenotype showed features distinct from those of variants in individuals with a mild or no phenotype in terms of their genomic distribution, mutation signatures, and fraction of mutant cells. MosaicBase will not only assist clinicians in genetic counseling and diagnosis but also provide a useful resource to understand the genomic baseline of postzygotic mutations in the general human population.

Background: Three wing morphs exists in the life cycle of the worldwide pest Aphis gossypii, i.e., wing parthenogenetic female (WPF), gynopara (GP) and male, which were produced mostly by crowding and host quality, photoperiod, loss of X chromosome, respectively. However, the shared molecular mechanism underlying their wing differentiation remains an enigma. Here we firstly induced gynoparae and males indoors and compared the characters of these wing morphs in body, internal genitals and fecundity. Then we identified the shared and separate differentially expressed genes (DEGs) and signaling pathways potentially involved in the wing morphs regulation in WPF, GP and male compared to wingless parthenogenetic female (WLPF). Results: Newly-born nymphs reared in short photoperiod condition exclusively produce gynoparae and males in adulthood successively, in which the sex ratio is gynoparae biased. Compared with WLPF, three wing morphs have similar morphology in bodies but is obviously discriminated in the reproductive system and fecundity. Built upon our previous study, 37 090 annotated unigenes were obtained from libraries constructed by the four morphs above through RNA-sequencing, in which 10 867, 19 334 DEGs were identified in pairwise comparison of GP vs. WLPF, Male vs. WLPF, respectively. Furthermore, 2 335 shared DEGs including 1 658 up- and 677 downregulated were obtained in these wing morphs compared to WLPF. The 1 658 shared up-regulated DEGs were enriched in multiple signaling pathways including insulin, FoxO, MAPK, strarch and sucrose metabolism, fatty acid biosynthesis and degradation which hint their key roles in the regulation of wing plasticity in cotton aphid. Gene expression levels were validated by using Pearson’s correlation (r) and potential roles of 15 DEGs related to the insulin signaling pathway in cotton wing dimorphism were discussed. Conclusions: The results of this study establish a solid foundation for deciphering molecular mechanisms underlying the switch between wingless and wing morphs in the cotton aphid and provide valuable resources for future research on the host-alternating aphid species.

Adult neural stem cells (NSCs) reside in specialized niches, which hold a balanced number of NSCs, their progeny, and other cells. How niche capacity is regulated to contain a specific number of NSCs remains unclear. Here, we show that ependyma‐derived matricellular protein CCN1 (cellular communication network factor 1) negatively regulates niche capacity and NSC number in the adult ventricular–subventricular zone (V‐SVZ). Adult ependyma‐specific deletion of Ccn1 transiently enhanced NSC proliferation and reduced neuronal differentiation in mice, increasing the numbers of NSCs and NSC units. Although proliferation of NSCs and neurogenesis seen in Ccn1 knockout mice eventually returned to normal, the expanded NSC pool was maintained in the V‐SVZ until old age. Inhibition of EGFR signaling prevented expansion of the NSC population observed in CCN1 deficient mice. Thus, ependyma‐derived CCN1 restricts NSC expansion in the adult brain to maintain the proper niche capacity of the V‐SVZ.

Increasing evidence shows that mechanical stresses are critical in regulating cell functions, fate, and diseases. However, no methods exist that can quantify isotropic compressive stresses. Here we describe fluorescent nanoparticle-labeled, monodisperse elastic microspheres made of Arg-Gly-Asp-conjugated alginate hydrogels (elastic round microgels, ERMGs). We generate 3D displacements and calculate strains and tractions exerted on an ERMG. Average compressive tractions on an ERMG are 570 Pa within cell layers and 360 Pa in tumor-repopulating cell (TRC) colonies grown in 400-Pa matrices. 3D compressive tractions on a 1.4-kPa ERMG are applied by surrounding cells via endogenous actomyosin forces but not via mature focal adhesions. Compressive stresses are substantially heterogeneous on ERMGs within a uniform cell colony and do not increase with TRC colony sizes. Early-stage zebrafish embryos generate spatial and temporal differences in local normal and shear stresses. This ERMG method could be useful for quantifying stresses in vitro and in vivo., Increasing importance is placed upon the effect of mechanical forces on cell regulation, fate and disease states. Here, the authors describe a deformable fluorescent nanoparticle labeled elastic microsphere which can be used to calculate strain and traction forces in vitro and in vivo.

Research on feature selection in text classification is usually limited to propose various techniques to select a set of features with highest scores based on different metrics. The selected features are usually determined by using a separate validation dataset with a fixed threshold. Obviously, it may not generalize well to new data as the best number for selected features is various on different datasets. In this paper, we first conduct a deep analysis, and find that simply extracting the features based on the score calculated by a metric may not always be the best strategy as it may turn many documents into zero length, which make them not suitable for training. We then model the feature selection process as a multiple objectives optimization problem to gain the best number of selected features rationally and automatically. In addition, as the optimization process costs a lot of resources, we design a parallel algorithm to improve the running time using dynamic programming. Extensive experiments are performed on several popular datasets, and the results indicate that our proposed approach is effective and feasible.