Blinding and blurring the multi-object tracker with adversarial perturbations.

Adversarial attack reveals a potential imperfection in deep models that they are susceptible to being tricked by imperceptible perturbations added to images. Recent deep multi-object trackers combine the functionalities of detection and association, rendering attacks on either the detector or the association component an effective means of deception. Existing attacks focus on increasing the frequency of ID switching, which greatly damages tracking stability, but is not enough to make the tracker completely ineffective. To fully explore the potential of adversarial attacks, we propose Blind-Blur Attack (BBA), a novel attack method based on spatio-temporal motion information to fool multi-object trackers. Specifically, a simple but efficient perturbation generator is trained with the blind-blur loss, simultaneously making the target invisible to the tracker and letting the background be regarded as moving targets. We take TraDeS as our main research tracker, and verify our attack method on other excellent algorithms (i.e., CenterTrack, FairMOT, and ByteTrack) on MOT-Challenge benchmark datasets (i.e., MOT16, MOT17, and MOT20). BBA attack reduced the MOTA of TraDeS and ByteTrack from 69.1 and 80.3 to -238.1 and -357.0, respectively, indicating that it is an efficient method with a high degrees of transferability.

Extracellular vesicles of Bifidobacterium longum reverse the acquired carboplatin resistance in ovarian cancer cells via p53 phosphorylation on Ser15.

We previously found that the relative abundance of Bifidobacterium was increased after chemotherapy; however, the role of Bifidobacterium longum in chemotherapeutic drug resistance in ovarian cancer (OVC) remains unclear. This study aimed to understand the potential effects and mechanism of B. longum extracellular vesicles (B. longum-EVs) on carboplatin (CBP) resistance in OVC. Eight normal and 11 ovarian tissues were collected and the expression of B. longum genomic DNA and its association with acquired CBP resistance in OVC patients was determined. After isolating EVs by ultracentrifugation from B. longum (ATCC 15707), CBP-resistant A2780 cells were treated with PBS, CBP, B. longum-EVs, or CBP + B. longum-EVs, and subsequently analyzed by CCK-8, Edu staining, Annexin V/PI double staining, wound healing, and Transwell assays to detect cell viability, proliferation, apoptosis, migration, and invasion, respectively. MRP1, ATP7A, ATP7B, and p53 expression as well as p53 phosphorylation were measured by western blot analysis. S15A mutation of p53 was assessed to examine the potential role of p53 Ser15 phosphorylation in CBP-resistant OVC. B. longum levels were elevated and positively associated with CBP resistance in OVC patients. Only high concentrations of B. longum-EVs attenuated A2780 cell proliferation, apoptosis, migration, and invasion. B. longum-EVs exposure significantly enhanced the sensitivity of CBP-resistant A2780 cells to CBP and decreased the expression of drug resistance-related proteins. The effect of B. longum-EVs on reversing CBP resistance was completely inhibited by S15A mutation of p53. B. longum-EVs enhanced the sensitivity of OVC cells to CBP through p53 phosphorylation on Ser15.

Reconstructed Graph Neural Network With Knowledge Distillation for Lightweight Anomaly Detection.

The proliferation of Internet-of-Things (IoT) technologies in modern smart society enables massive data exchange for offering intelligent services. It becomes essential to ensure secure communications while exchanging highly sensitive IoT data efficiently, which leads to high demands for lightweight models or algorithms with limited computation capability provided by individual IoT devices. In this study, a graph representation learning model, which seamlessly incorporates graph neural network (GNN) and knowledge distillation (KD) techniques, named reconstructed graph with global-local distillation (RG-GLD), is designed to realize the lightweight anomaly detection across IoT communication networks. In particular, a new graph network reconstruction strategy, which treats data communications as nodes in a directed graph while edges are then connected according to two specifically defined rules, is devised and applied to facilitate the graph representation learning in secure and efficient IoT communications. Both the structural and traffic features are then extracted from the graph data and flow data respectively, based on the graph attention network (GAT) and multilayer perceptron (MLP) techniques. These can benefit the GNN-based KD process in accordance with the more effective feature fusion and representation, considering both structural and data levels across the dynamic IoT networks. Furthermore, a lightweight local subgraph preservation mechanism improved by the graph attention mechanism and downsampling scheme to better utilize the topological information, and a so-called global information alignment defined based on the self-attention mechanism to effectively preserve the global information, are developed and incorporated in a refined graph attention based KD scheme. Compared with four different baseline methods, experiments and evaluations conducted based on two public datasets demonstrate the usefulness and effectiveness of our proposed model in improving the efficiency of knowledge transfer with higher classification accuracy but lower computational load, which can be deployed for lightweight anomaly detection in sustainable IoT computing environments.

Evaluating the predictive value of initial lactate/albumin ratios in determining prognosis of sepsis patients.

Sepsis remains a significant clinical challenge owing to its complex pathophysiology and variable prognosis. The early identification of patients at a higher risk of poor outcomes can be crucial for improving treatment strategies. This study aimed to evaluate the predictive value of early serum lactate and albumin levels and the lactate/albumin (L/A) ratio for 28-day prognosis in patients with sepsis. Patients diagnosed with sepsis between January 2021 and December 2022 were evaluated using a retrospective cohort methodology. Inclusion followed the International Consensus on sepsis and septic shock (Sepsis-3) guidelines and patients were selected based on well-defined criteria. Variables such as lactate, albumin, and the L/A ratio were documented within the first 24 hours of admission. Statistical analyses were performed using various tools, including the nonparametric Mann-Whitney U test and receiver operating characteristic curves. A total of 301 patients were divided into the survival (n = 167) and death (n = 134) groups. Notable differences were detected in the incidence of pulmonary infection, shock, lactate, albumin, and the L/A ratio. The L/A ratio was identified as a key predictor with an area under the curve of 0.868, an optimal cutoff value of >0.17, a sensitivity of 56.21%, and a specificity of 94.18%. Significant disparities in mortality rates and survival times were observed for the lactate, albumin, and L/A levels. This study underscores the predictive value of early serum lactate and albumin levels and the L/A ratio for 28-day prognosis in patients with sepsis, with the L/A ratio showing a superior predictive capability. These findings highlight the importance of L/A ratio as a robust and precise marker for evaluating the future clinical course of patients with sepsis, potentially aiding early detection and management.

On-Chip Micro Temperature Controllers Based on Freestanding Thermoelectric Nano Films for Low-Power Electronics.

Multidimensional integration and multifunctional component assembly have been greatly explored in recent years to extend Moore's Law of modern microelectronics. However, this inevitably exacerbates the inhomogeneity of temperature distribution in microsystems, making precise temperature control for electronic components extremely challenging. Herein, we report an on-chip micro temperature controller including a pair of thermoelectric legs with a total area of 50 × 50 µm2, which are fabricated from dense and flat freestanding Bi2Te3-based thermoelectric nano films deposited on a newly developed nano graphene oxide membrane substrate. Its tunable equivalent thermal resistance is controlled by electrical currents to achieve energy-efficient temperature control for low-power electronics. A large cooling temperature difference of 44.5 K at 380 K is achieved with a power consumption of only 445 µW, resulting in an ultrahigh temperature control capability over 100 K mW-1. Moreover, an ultra-fast cooling rate exceeding 2000 K s-1 and excellent reliability of up to 1 million cycles are observed. Our proposed on-chip temperature controller is expected to enable further miniaturization and multifunctional integration on a single chip for microelectronics.

Information Theoretic Learning-Enhanced Dual-Generative Adversarial Networks With Causal Representation for Robust OOD Generalization.

Recently, machine/deep learning techniques are achieving remarkable success in a variety of intelligent control and management systems, promising to change the future of artificial intelligence (AI) scenarios. However, they still suffer from some intractable difficulty or limitations for model training, such as the out-of-distribution (OOD) issue, in modern smart manufacturing or intelligent transportation systems (ITSs). In this study, we newly design and introduce a deep generative model framework, which seamlessly incorporates the information theoretic learning (ITL) and causal representation learning (CRL) in a dual-generative adversarial network (Dual-GAN) architecture, aiming to enhance the robust OOD generalization in modern machine learning (ML) paradigms. In particular, an ITL-and CRL-enhanced Dual-GAN (ITCRL-DGAN) model is presented, which includes an autoencoder with CRL (AE-CRL) structure to aid the dual-adversarial training with causality-inspired feature representations and a Dual-GAN structure to improve the data augmentation in both feature and data levels. Following a newly designed feature separation strategy, a causal graph is built and improved based on the information theory, which can enhance the causally related factors among the separated core features and further enrich the feature representation with the counterfactual features via interventions based on the refined causal relationships. The ITL is incorporated to improve the extraction of low-dimensional feature representations and learn the optimized causal representations based on the idea of "information flow." A dual-adversarial training mechanism is then developed, which not only enables the generator to expand the boundary of feature distribution in accordance with the optimized feature representation from AE-CRL, but also allows the discriminator to further verify and improve the quality of the augmented data for OOD generalization. Experiment and evaluation results based on an open-source dataset demonstrate the outstanding learning efficiency and classification performance of our proposed model for robust OOD generalization in modern smart applications compared with three baseline methods.

AffinityVAE: A multi-objective model for protein-ligand affinity prediction and drug design.

In the prediction of protein-ligand affinity, the traditional methods require a large amount of computing resources, and have certain limitations in predicting and simulating the structural changes. Although employing data-driven approaches can yield favorable outcomes in deep learning, it entails a lack of interpretability. Some methods may require additional structural information or domain knowledge to support the interpretation, which may limit their applicability. This paper proposes an affinity variational autoencoder (AffinityVAE) using interaction feature mapping and a variational autoencoder, which consists of a multi-objective model capable of end-to-end affinity prediction and drug discovery. In this study, the limitations of affinity prediction in terms of interpretability are tackled by proposing the concept of a protein-ligand interaction feature map. This increases the diversity and quantity of protein-ligand binding data by designing an adaptive autoencoder of target chemical properties to generate new ligands similar to known ligands and adding them to the original training set. AffinityVAE is then retrained using this extended training set to further validate the protein-ligand binding affinity prediction. Comparisons were conducted between the AffinityVAE and recent methods to demonstrate the high efficiency of the proposed model. The experimental results show that AffinityVAE has very high prediction performance, and it has the potential to enhance the diversity and the amount of protein-ligand binding data, which promotes the drug development.

Serum­derived exosomal hsa­let­7b­5p as a biomarker for predicting the severity of coronary stenosis in patients with coronary heart disease and hyperglycemia.

Exosomal microRNAs (miRNAs/miRs) are potential biomarkers for the diagnosis and treatment of cardiovascular disease, and hyperglycemia serves an important role in the development of atherosclerosis. The present study aimed to investigate the expression profile of serum­derived exosomal miRNAs in coronary heart disease (CHD) with hyperglycemia, and to identify effective biomarkers for predicting coronary artery lesions. Serum samples were collected from eight patients with CHD and hyperglycemia and eight patients with CHD and normoglycemia, exosomes were isolated and differentially expressed miRNAs (DEMIs) were filtered using a human miRNA microarray. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were performed using standard enrichment computational methods for the target genes of DEMIs. Receiver operating characteristic (ROC) curve analysis was applied to evaluate the values of the selected DEMIs in predicting the severity of coronary stenosis. A total of 10 DEMIs, including four upregulated miRNAs (hsa­let­7b­5p, hsa­miR­4313, hsa­miR­4665­3p and hsa­miR­940) and six downregulated miRNAs (hsa­miR­4459, hsa­miR­4687­3p, hsa­miR­6087, hsa­miR­6089, hsa­miR­6740­5p and hsa­miR­6800­5p), were screened in patients with CHD and hyperglycemia. GO analysis showed that the 'cellular process', 'single­organism process' and 'biological regulation' were significantly enriched. KEGG pathway analysis revealed that the 'mTOR signaling pathway', 'FoxO signaling pathway' and 'neurotrophin signaling pathway' were significantly enriched. Among these DEMIs, only hsa­let­7b­5p expression was positively correlated with both hemoglobin A1C levels and Synergy between Percutaneous Coronary Intervention with Taxus and Cardiac Surgery score. ROC curves showed that hsa­let­7b­5p could serve as an effective biomarker for differentiating the severity of coronary stenosis. In conclusion, the present study demonstrated that serum­derived exosomal hsa­let­7b­5p is upregulated in patients with CHD and hyperglycemia, and may serve as a noninvasive biomarker for the severity of coronary stenosis.

Flexible Carbon Nanotube-Epitaxially Grown Nanocrystals for Micro-Thermoelectric Modules.

Flexible thermoelectric materials have attracted increasing interest because of their potential use in thermal energy harvesting and high-spatial-resolution thermal management. However, a high-performance flexible micro-thermoelectric device (TED) compatible with the microelectronics fabrication process has not yet been developed. Here a universal epitaxial growth strategy is reported guided by 1D van der Waals-coupling, to fabricate freestanding and flexible hybrids comprised of single-wall carbon nanotubes and ordered (Bi,Sb)2 Te3 nanocrystals. High power factors ranging from ≈1680 to ≈1020 µW m-1 K-2 in the temperature range of 300-480 K, combined with a low thermal conductivity yield a high average figure of merit of ≈0.81. The fabricated flexible micro-TED module consisting of two p-n couples of freestanding thermoelectric hybrids has an unprecedented open circuit voltage of ≈22.7 mV and a power density of ≈0.36 W cm-2 under ≈30 K temperature difference, and a net cooling temperature of ≈22.4 K and a heat absorption density of ≈92.5 W cm-2 .

CDNet: Contrastive Disentangled Network for Fine-Grained Image Categorization of Ocular B-Scan Ultrasound.

Precise and rapid categorization of images in the B-scan ultrasound modality is vital for diagnosing ocular diseases. Nevertheless, distinguishing various diseases in ultrasound still challenges experienced ophthalmologists. Thus a novel contrastive disentangled network (CDNet) is developed in this work, aiming to tackle the fine-grained image categorization (FGIC) challenges of ocular abnormalities in ultrasound images, including intraocular tumor (IOT), retinal detachment (RD), posterior scleral staphyloma (PSS), and vitreous hemorrhage (VH). Three essential components of CDNet are the weakly-supervised lesion localization module (WSLL), contrastive multi-zoom (CMZ) strategy, and hyperspherical contrastive disentangled loss (HCD-Loss), respectively. These components facilitate feature disentanglement for fine-grained recognition in both the input and output aspects. The proposed CDNet is validated on our ZJU Ocular Ultrasound Dataset (ZJUOUSD), consisting of 5213 samples. Furthermore, the generalization ability of CDNet is validated on two public and widely-used chest X-ray FGIC benchmarks. Quantitative and qualitative results demonstrate the efficacy of our proposed CDNet, which achieves state-of-the-art performance in the FGIC task.

Effectiveness and safety of acupuncture for insulin resistance in women with polycystic ovary syndrome: A systematic review and meta-analysis.

Objective: To perform a systematic review and meta-analysis of randomized controlled trials (RCTs) to evaluate acupuncture's clinical effect on insulin resistance (IR) in women with polycystic ovary syndrome (PCOS). Methods: PubMed, Cochrane Library, Embase databases, and Chinese databases, including China National Knowledge Infrastructure, Technology Journal Database, and Wanfang Database, were searched without language restrictions from inception to December 20, 2021. Only RCTs in which acupuncture had been examined as the sole or adjunctive PCOS-IR treatment were included. Our primary endpoint was the homeostasis model assessment of insulin resistance (HOMA-IR). The secondary outcomes were fasting blood glucose (FBG), fasting insulin (FINS), body mass index (BMI), and adverse events. Results: Our analysis included 17 eligible RCTs (N = 1511 participants). Compared with other treatments, acupuncture therapy yielded a greater mean reduction in HOMA-IR (MD = -0.15; 95% CI, -0.27 to -0.03; P = 0.01) and BMI (MD = -1.47; 95% CI, -2.46 to -0.47; P = 0.004). Besides acupuncture was associated with a lower risk of adverse events than other treatments (RR, 0.15; 95% CI, 0.10 to 0.22; P < 0.01). Additionally, the combination treatment of acupuncture and medicine is more effective in improving HOMA-IR (MD = -0.91; 95% CI, -1.11 to -0.71; P < 0.01), FBG (MD = -0.30; 95% CI, -0.56 to -0.04; P = 0.02), FINS (MD = -2.33; 95% CI, -2.60 to -2.06; P < 0.01) and BMI (MD = -1.63; 95% CI, -1.94 to -1.33; P < 0.01) than medicine alone. Conclusions: Acupuncture is relatively effective in improving HOMA-IR and BMI in PCOS-IR. Besides, it's safer than other treatments and could be an adjuvant strategy for improving PCOS-IR. Further large-scale, long-term RCTs with strict methodological standards are justified.

Effectiveness, Policy, and User Acceptance of COVID-19 Contact-Tracing Apps in the Post-COVID-19 Pandemic Era: Experience and Comparative Study.

BACKGROUND: In the post-COVID-19 pandemic era, many countries have launched apps to trace contacts of COVID-19 infections. Each contact-tracing app (CTA) faces a variety of issues owing to different national policies or technologies for tracing contacts. OBJECTIVE: In this study, we aimed to investigate all the CTAs used to trace contacts in various countries worldwide, including the technology used by each CTA, the availability of knowledge about the CTA from official websites, the interoperability of CTAs in various countries, and the infection detection rates and policies of the specific country that launched the CTA, and to summarize the current problems of the apps based on the information collected. METHODS: We investigated CTAs launched in all countries through Google, Google Scholar, and PubMed. We experimented with all apps that could be installed and compiled information about apps that could not be installed or used by consulting official websites and previous literature. We compared the information collected by us on CTAs with relevant previous literature to understand and analyze the data. RESULTS: After screening 166 COVID-19 apps developed in 197 countries worldwide, we selected 98 (59%) apps from 95 (48.2%) countries, of which 63 (66.3%) apps were usable. The methods of contact tracing are divided into 3 main categories: Bluetooth, geolocation, and QR codes. At the technical level, CTAs face 3 major problems. First, the distance and time for Bluetooth- and geolocation-based CTAs to record contact are generally set to 2 meters and 15 minutes; however, this distance should be lengthened, and the time should be shortened for more infectious variants. Second, Bluetooth- or geolocation-based CTAs also face the problem of lack of accuracy. For example, individuals in 2 adjacent vehicles during traffic jams may be at a distance of ≤2 meters to make the CTA trace contact, but the 2 users may actually be separated by car doors, which could prevent transmission and infection. In addition, we investigated infection detection rates in 33 countries, 16 (48.5%) of which had significantly low infection detection rates, wherein CTAs could have lacked effectiveness in reducing virus propagation. Regarding policy, CTAs in most countries can only be used in their own countries and lack interoperability among other countries. In addition, 7 countries have already discontinued CTAs, but we believe that it was too early to discontinue them. Regarding user acceptance, 28.6% (28/98) of CTAs had no official source of information that could reduce user acceptance. CONCLUSIONS: We surveyed all CTAs worldwide, identified their technological policy and acceptance issues, and provided solutions for each of the issues we identified. This study aimed to provide useful guidance and suggestions for updating the existing CTAs and the subsequent development of new CTAs.

A Self-Supervised Learning Based Framework for Eyelid Malignant Melanoma Diagnosis in Whole Slide Images.

Eyelid malignant melanoma (MM) is a rare disease with high mortality. Accurate diagnosis of such disease is important but challenging. In clinical practice, the diagnosis of MM is currently performed manually by pathologists, which is subjective and biased. Since the heavy manual annotation workload, most pathological whole slide image (WSI) datasets are only partially labeled (without region annotations), which cannot be directly used in supervised deep learning. For these reasons, it is of great practical significance to design a laborsaving and high data utilization diagnosis method. In this paper, a self-supervised learning (SSL) based framework for automatically detecting eyelid MM is proposed. The framework consists of a self-supervised model for detecting MM areas at the patch-level and a second model for classifying lesion types at the slide level. A squeeze-excitation (SE) attention structure and a feature-projection (FP) structure are integrated to boost learning on details of pathological images and improve model performance. In addition, this framework also provides visual heatmaps with high quality and reliability to highlight the likely areas of the lesion to assist the evaluation and diagnosis of the eyelid MM. Extensive experimental results on different datasets show that our proposed method outperforms other state-of-the-art SSL and fully supervised methods at both patch and slide levels when only a subset of WSIs are annotated. It should be noted that our method is even comparable to supervised methods when all WSIs are fully annotated. To the best of our knowledge, our work is the first SSL method for automatic diagnosis of MM at the eyelid and has a great potential impact on reducing the workload of human annotations in clinical practice.

Recombinant EGFL7 Mitigated Pressure Overload-Induced Cardiac Remodeling by Blocking PI3K γ /AKT/ NFκB Signaling in Macrophages.

Inflammation and endothelial dysfunction play an essential role in heart failure (HF). Epidermal growth factor-like protein 7 (EGFL7) is upregulated during pathological hypoxia and exerts a protective role. However, it is unclear whether there is a link between abnormal EGFL7 expression and inflammation in overload stress-induced heart failure. Our results showed that EGFL7 transiently increased during the early 4 weeks of TAC and in hypertensive patients without heart failure. However, it decreased to the basal line in the heart tissue 8 weeks post-transverse aortic constriction (TAC) or hypertensive patients with heart failure. Knockdown of EGFL7 with siRNA in vivo accelerated cardiac dysfunction, fibrosis, and macrophage infiltration 4 weeks after TAC. Deletion of macrophages in siRNA-EGFL7-TAC mice rescued that pathological phenotype. In vitro research revealed the mechanism. PI3K γ /AKT/N FκB signaling in macrophages was activated by the supernatant from endothelial cells stimulated by siRNA-EGFL7+phenylephrine. More macrophages adhered to endothelial cells, but pretreatment of macrophages with PI3Kγ inhibitors decreased the adhesion of macrophages to endothelial cells. Ultimately, treatment with recombinant rmEGFL7 rescued cardiac dysfunction and macrophage infiltration in siRNA-EGFL7-TAC mice. In conclusion, EGFL7 is a potential inhibitor of macrophage adhesion to mouse aortic endothelial cells. The downregulation of EGFL7 combined with increased macrophage infiltration further promoted cardiac dysfunction under pressure overload stress. Mechanistically, EGFL7 reduced endothelial cell adhesion molecule expression and inhibited the PI3K γ /AKT/NF κ B signaling pathway in macrophages.

Research progress on multicomponent physical exercise for patients with neurocognitive impairment.

Neurocognitive impairment is a group of clinical syndromes characterized by impaired cognitive function and decreased motor ability. Non-pharmacological interventions such as physical exercise have advantages in the treatment of patients with neurocognitive impairment. Multicomponent exercise is a combination of various physical exercises, including strength training, endurance training, balance training and flexibility training, that can improve gait, balance and cardiopulmonary function by increasing muscle mass, strength and endurance in people with neurocognitive impairment, while also reducing the risk of falls in elders. This article reviews the benefits of multicomponent exercise for patients with neurocognitive impairment and its evaluation methods; also describes 4 intervention programs and their clinical application, to provide evidence for clinical practice and promote the application of multicomponent exercise in patients with neurocognitive impairment.

Analysis on the Subdivision of Skilled Mowing Movements on Slopes.

Owing to the aging of the rural population in the hilly and mountainous areas of Japan, mowing on narrow ridges and steep slopes is done manually by the elderly-individuals over 65 years of age. Studies have shown that many accidents that occurred during mowing were caused by workers' unstable posture, especially when mowing on steep surfaces where there is a high risk of falling. It is necessary to analyze the body movements of mowing workers to elucidate the elements related to the risk of falls. Therefore, in this study, based on a high-precision motion-capture device and a series of experiments with elderly, skilled mowing workers, we focused on the movements of mowing. We sought to identify effective and safe mowing patterns and the factors that lead to the risk of falls. In various mowing styles, compared to the stride (S) and downward (D) mowing patterns, the basic (B) and moving (M) patterns were the most efficient; however, the risk of falls was also the highest among these patterns. While mowing, workers need to pay more attention to their arm strength and take appropriate measures to reduce the risk of falls according to their age and physique. The results can be used as data for the development of fall-detection systems and offer useful insights for the training of new mowing workers.

Combined assembly of long and short sequencing reads improve the efficiency of exploring the soil metagenome.

BACKGROUND: Advances in DNA sequencing technologies have transformed our capacity to perform life science research, decipher the dynamics of complex soil microbial communities and exploit them for plant disease management. However, soil is a complex conglomerate, which makes functional metagenomics studies very challenging. RESULTS: Metagenomes were assembled by long-read (PacBio, PB), short-read (Illumina, IL), and mixture of PB and IL (PI) sequencing of soil DNA samples were compared. Ortholog analyses and functional annotation revealed that the PI approach significantly increased the contig length of the metagenomic sequences compared to IL and enlarged the gene pool compared to PB. The PI approach also offered comparable or higher species abundance than either PB or IL alone, and showed significant advantages for studying natural product biosynthetic genes in the soil microbiomes. CONCLUSION: Our results provide an effective strategy for combining long and short-read DNA sequencing data to explore and distill the maximum information out of soil metagenomics.

Isolation and functional characteristics of the fungus Hypoxylon spp. Sj18 with biocontrol potential.

A fungus with biocontrol potential was isolated from the roots of hickory trees. The strain named sj18 was classified as a member of the genus Hypoxylon (Hypoxylaceae) after multigene phylogenetic analysis (beta-tubulin gene, internal transcribed spacer, 28S large subunit ribosomal RNA gene, and RNA polymerase II subunit gene). The strain grew well on a PDA with an optimum temperature range between 32 and 34 °C. The fungus had obvious inhibitory effects on Botryosphaeria dothidea, Colletotrichum gloeosporioides, and Gibberella moniliformis in fumigation experiments on solid agar plates. In an inoculation experiment of Chinese cabbage, the fungus was also found to have an obvious repellent effect on cabbage caterpillars. In vitro experiments on Petri dishes showed that the fermentation broth of the sj18 strain could kill 100% of Bursaphelenchus xylophilus within 8 h even if the fermentation broth was diluted 8 times. The inoculation test of Arabidopsis thaliana showed that the fungus could promote the lateral root formation of plants and significantly increase their aboveground biomass. Through the analysis of solid phase microextraction (SPME), it was found that the main volatile components of the fermentation products were azulene 65.39% (61.77% + 3.62%), caryophyllene 7.41%, and eucalyptol 6.83% according to the peak area ratio. Therefore, sj18 can be used as a candidate for the further research and development of biocontrol agents.

AGMB-Transformer: Anatomy-Guided Multi-Branch Transformer Network for Automated Evaluation of Root Canal Therapy.

Accurate evaluation of the treatment result on X-ray images is a significant and challenging step in root canal therapy since the incorrect interpretation of the therapy results will hamper timely follow-up which is crucial to the patients' treatment outcome. Nowadays, the evaluation is performed in a manual manner, which is time-consuming, subjective, and error-prone. In this article, we aim to automate this process by leveraging the advances in computer vision and artificial intelligence, to provide an objective and accurate method for root canal therapy result assessment. A novel anatomy-guided multi-branch Transformer (AGMB-Transformer) network is proposed, which first extracts a set of anatomy features and then uses them to guide a multi-branch Transformer network for evaluation. Specifically, we design a polynomial curve fitting segmentation strategy with the help of landmark detection to extract the anatomy features. Moreover, a branch fusion module and a multi-branch structure including our progressive Transformer and Group Multi-Head Self-Attention (GMHSA) are designed to focus on both global and local features for an accurate diagnosis. To facilitate the research, we have collected a large-scale root canal therapy evaluation dataset with 245 root canal therapy X-ray images, and the experiment results show that our AGMB-Transformer can improve the diagnosis accuracy from 57.96% to 90.20% compared with the baseline network. The proposed AGMB-Transformer can achieve a highly accurate evaluation of root canal therapy. To our best knowledge, our work is the first to perform automatic root canal therapy evaluation and has important clinical value to reduce the workload of endodontists.

Functional Genomics and Comparative Lineage-Specific Region Analyses Reveal Novel Insights into Race Divergence in Verticillium dahliae.

Verticillium dahliae is a widespread soilborne fungus that causes Verticillium wilt on numerous economically important plant species. In tomato, until now, three races have been characterized based on the response of differential cultivars to V. dahliae, but the genetic basis of race divergence in V. dahliae remains undetermined. To investigate the genetic basis of race divergence, we sequenced the genomes of two race 2 strains and four race 3 strains for comparative analyses with two known race 1 genomes. The genetic basis of race divergence was described by the pathogenicity-related genes among the three races, orthologue analyses, and genomic structural variations. Global comparative genomics showed that chromosomal rearrangements are not the only source of race divergence and that race 3 should be split into two genotypes based on orthologue clustering. Lineage-specific regions (LSRs), frequently observed between genomes of the three races, encode several predicted secreted proteins that potentially function as suppressors of immunity triggered by known effectors. These likely contribute to the virulence of the three races. Two genes in particular that can act as markers for race 2 and race 3 (VdR2e and VdR3e, respectively) contribute to virulence on tomato, and the latter acts as an avirulence factor of race 3. We elucidated the genetic basis of race divergence through global comparative genomics and identified secreted proteins in LSRs that could potentially play critical roles in the differential virulence among the races in V. dahliae. IMPORTANCE Deciphering the gene-for-gene relationships during host-pathogen interactions is the basis of modern plant resistance breeding. In the Verticillium dahliae-tomato pathosystem, two races (races 1 and 2) and their corresponding avirulence (Avr) genes have been identified, but strains that lack these two Avr genes exist in nature. In this system, race 3 has been described, but the corresponding Avr gene has not been identified. We de novo-sequenced genomes of six strains and identified secreted proteins within the lineage-specific regions (LSRs) distributed among the genomes of the three races that could potentially function as manipulators of host immunity. One of the LSR genes, VdR3e, was confirmed as the Avr gene for race 3. The results indicate that differences in transcriptional regulation may contribute to race differentiation. This is the first study to describe these differences and elucidate roles of secreted proteins in LSRs that play roles in race differentiation.