Prediction of regulatory motifs from human Chip-sequencing data using a deep learning framework

The identification of transcription factor binding sites and cis-regulatory motifs is a frontier whereupon the rules governing protein-DNA binding are being revealed. Here, we developed a new method (DEep Sequence and Shape mOtif or DESSO) for cis-regulatory motif prediction using deep neural networks and the binomial distribution model. DESSO outperformed existing tools, including DeepBind, in predicting motifs in 690 human ENCODE ChIP-sequencing datasets. Furthermore, the deep-learning framework of DESSO expanded motif discovery beyond the state-of-the-art by allowing the identification of known and new protein-protein-DNA tethering interactions in human transcription factors (TFs). Specifically, 61 putative tethering interactions were identified among the 100 TFs expressed in the K562 cell line. In this work, the power of DESSO was further expanded by integrating the detection of DNA shape features. We found that shape information has strong predictive power for TF-DNA binding and provides new putative shape motif information for human TFs. Thus, DESSO improves in the identification and structural analysis of TF binding sites, by integrating the complexities of DNA binding into a deep-learning framework

Hierarchical Graph Capsule Network

Graph Neural Networks (GNNs) draw their strength from explicitly modeling the topological information of structured data. However, existing GNNs suffer from limited capability in capturing the hierarchical graph representation which plays an important role in graph classification. In this paper, we innovatively propose hierarchical graph capsule network (HGCN) that can jointly learn node embeddings and extract graph hierarchies. Specifically, disentangled graph capsules are established by identifying heterogeneous factors underlying each node, such that their instantiation parameters represent different properties of the same entity. To learn the hierarchical representation, HGCN characterizes the part-whole relationship between lower-level capsules (part) and higher-level capsules (whole) by explicitly considering the structure information among the parts. Experimental studies demonstrate the effectiveness of HGCN and the contribution of each component.Comment: AAAI 2021; Code: https://github.com/uta-smile/HGC

Tag2Text: Guiding Vision-Language Model via Image Tagging

This paper presents Tag2Text, a vision language pre-training (VLP) framework, which introduces image tagging into vision-language models to guide the learning of visual-linguistic features. In contrast to prior works which utilize object tags either manually labeled or automatically detected with a limited detector, our approach utilizes tags parsed from its paired text to learn an image tagger and meanwhile provides guidance to vision-language models. Given that, Tag2Text can utilize large-scale annotation-free image tags in accordance with image-text pairs, and provides more diverse tag categories beyond objects. As a result, Tag2Text achieves a superior image tag recognition ability by exploiting fine-grained text information. Moreover, by leveraging tagging guidance, Tag2Text effectively enhances the performance of vision-language models on both generation-based and alignment-based tasks. Across a wide range of downstream benchmarks, Tag2Text achieves state-of-the-art or competitive results with similar model sizes and data scales, demonstrating the efficacy of the proposed tagging guidance

Effects of brain–Computer interface combined with mindfulness therapy on rehabilitation of hemiplegic patients with stroke: a randomized controlled trial

AimTo explore the effects of brain–computer interface training combined with mindfulness therapy on Hemiplegic Patients with Stroke.BackgroundThe prevention and treatment of stroke still faces great challenges. Maximizing the improvement of patients’ ability to perform activities of daily living, limb motor function, and reducing anxiety, depression, and other social and psychological problems to improve patients’ overall quality of life is the focus and difficulty of clinical rehabilitation work.MethodsPatients were recruited from December 2021 to November 2022, and assigned to either the intervention or control group following a simple randomization procedure (computer-generated random numbers). Both groups received conventional rehabilitation treatment, while patients in the intervention group additionally received brain–computer interface training and mindfulness therapy. The continuous treatment duration was 5 days per week for 8 weeks. Limb motor function, activities of daily living, mindfulness attention awareness level, sleep quality, and quality of life of the patients were measured (in T0, T1, and T2). Generalized estimated equation (GEE) were used to evaluate the effects. The trial was registered with the Chinese Clinical Trial Registry (ChiCTR2300070382).ResultsA total of 128 participants were randomized and 64 each were assigned to the intervention and control groups (of these, eight patients were lost to follow-up). At 6 months, compared with the control group, intervention group showed statistically significant improvements in limb motor function, mindful attention awareness, activities of daily living, sleep quality, and quality of life.ConclusionBrain–computer interface combined with mindfulness therapy training can improve limb motor function, activities of daily living, mindful attention awareness, sleep quality, and quality of life in hemiplegic patients with stroke.ImpactThis study provides valuable insights into post-stroke care. It may help improve the effect of rehabilitation nursing to improve the comprehensive ability and quality of life of patients after stroke.Clinical review registrationhttps://www.chictr.org.cn/, identifier ChiCTR2300070382

The Representation of Mosuo People and Mosuo Culture in Chinese Tourism Websites

Past research has shown that because tourism itself is a product of a gendered society, its processes are gendered in terms of construction, presentation, and consumption. This study examines how these websites shape the image of the Mosuo people and the Mosuo culture by analyzing texts in Chinese tourism websites. Ten representative Chinese tourism websites were selected for this study, and all relevant texts that could be retrieved were analyzed manually. All samples selected were officially published and represent only the attitudes of the tourism websites. The results of the study show that there are a large number of feminized or sexualized descriptions in the texts about the Mosuo people and the Mosuo culture provided by Chinese tourism websites. The language used on tourism websites is shaped by discourses of patriarchy and sexuality and is intended for heterosexual male tourists

Recognize Anything: A Strong Image Tagging Model

We present the Recognize Anything Model (RAM): a strong foundation model for image tagging. RAM can recognize any common category with high accuracy. RAM introduces a new paradigm for image tagging, leveraging large-scale image-text pairs for training instead of manual annotations. The development of RAM comprises four key steps. Firstly, annotation-free image tags are obtained at scale through automatic text semantic parsing. Subsequently, a preliminary model is trained for automatic annotation by unifying the caption and tagging tasks, supervised by the original texts and parsed tags, respectively. Thirdly, a data engine is employed to generate additional annotations and clean incorrect ones. Lastly, the model is retrained with the processed data and fine-tuned using a smaller but higher-quality dataset. We evaluate the tagging capabilities of RAM on numerous benchmarks and observe impressive zero-shot performance, significantly outperforming CLIP and BLIP. Remarkably, RAM even surpasses the fully supervised manners and exhibits competitive performance with the Google API. We are releasing the RAM at \url{https://recognize-anything.github.io/} to foster the advancements of large models in computer vision

Enhancing Vibrio vulnificus infection diagnosis for negative culture patients with metagenomic next-generation sequencing

ObjectiveTo evaluate the diagnostic value of metagenomic next-generation sequencing (mNGS) in Vibrio vulnificus (V. vulnificus) infection.MethodsA retrospective analysis of patients with V. vulnificus infection at the Fifth Affiliated Hospital of Sun Yat-Sen University from January 1, 2020 to April 23, 2023 was conducted. 14 enrolled patients were diagnosed by culture or mNGS. The corresponding medical records were reviewed, and the clinical data analyzed included demographics, epidemiology laboratory findings, physical examination, symptoms at presentation, antibiotic and surgical treatment, and outcome.ResultsIn this study, 78.6% (11/14) patients had a history of marine trauma (including fish stab, shrimp stab, crab splints and fish hook wounds), 7.1% (1/14) had eaten seafood, and the remaining 14.3% (2/14) had no definite cause. Isolation of V. vulnificus from clinical samples including blood, tissue, fester and secreta. 9 cases were positive for culture, 5 cases were detected synchronously by mNGS and got positive for V. vulnificus. 85.7% (12/14) cases accepted surgical treatment, with 1 patient suffering finger amputated. 14 enrolled patients received appropriate antibiotic therapy, and all of them had recovered and discharged. 9 strains V. vulnificus isolated in this study were sensitive to most beta-lactam antibiotics, aminoglycosides, quinolones, etc.ConclusionVibrio vulnificus infection is a common water-exposed disease in Zhuhai, which requires identification of a number of pathogens. Of severe infections with unknown pathogen, mNGS can be used simultaneously, and the potential to detect multiple pathogens is of great help in guiding treatment

Effect of megarectum on postoperative defecation of female patients with congenital rectovestibular fistula or rectoperineal fistula

BackgroundTo assess the effect of megarectum on postoperative defecation of female patients with congenital rectovestibular fistula or rectoperineal fistula.MethodsFrom March 2013 to February 2021, 74 female patients with congenital rectovestibular fistula or rectoperineal fistula were treated. The age of patients ranged from 3 months to 1 year. Barium enema and spinal cord MRI were performed in all children. 4 patients were removed from the study because of spinal cord and sacral agenesis. Finally, 70 patients underwent one-stage anterior sagittal anorectoplasty (ASARP). Anal endoscopy and anorectal pressure measurement were performed 1 year after surgery. All patients were divided into two groups depending on the presence of megarectum (+) and (−) and observed for constipation and anal sphincter function.Results16 patients (4 months to 1 year) were complicated with megarectum, and 5 patients (3 months to 9 months) were without megarectum. The incision infection was seen in 3 patients. All patients were followed up for 1 year to 5 years. Fecal soiling was seen in 2 patients and constipation in 14 patients. Among 16 patients with megarectum, soiling was seen in 1 patient and the constipation in 12 patients. Among 54 patients without megarectum, soiling was seen in 1 patient and constipation in 2 patients. There was a significant difference in the incidence of postoperative constipation between the two groups (megarectum (+) 75% vs. megarectum (−) 3.7% (P < 0.05)). However, there was no significant difference in the score of anal sphincters between the two groups (P < 0.05). And there was no significant difference in anal resting pressure (P = 0.49) and length of anal high pressure area (P = 0.76). 7 patients with constipation and megarectum acquired normal anal function after the dilated rectum was resected.ConclusionMegarectum increases the possibility of difficult postoperative defecation in the patients with congenital rectovestibular fistula or rectoperineal fistula. However, constipation was not associated with ASARP postoperative effects on sphincter function. Resection of megarectum is helpful to the improvement of constipation

Electrocatalytic reduction of CO2 to ethylene and ethanol through hydrogen-assisted C-C coupling over fluorine-modified copper

精准控制C1分子C-C偶联合成特定C2+化合物是C1化学中极具挑战性的难题。由于C2+化合物(如乙烯和乙醇)在化工和能源领域具有重要用途，将CO2直接转化为C2+产物极具吸引力。发展高效催化剂，实现高电流密度、高C2+选择性、高稳定性的“三高”性能，是推进电催化还原CO2走向实际应用的关键。研究团队针对电催化还原CO2中高CO2还原法拉第效率的催化剂常常活性低的问题，提出了适当提高催化剂活化水的能力对增加CO2还原活性的重要性，发展出氢助碳碳偶联(hydrogen-assisted C-C coupling)的新策略，在氟修饰的铜(F-Cu)催化剂上实现了CO2电催化还原制乙烯和乙醇的新突破。该研究工作实验部分主要由王野、张庆红教授指导，能源材料协同创新中心iChEM2016级博士生马文超、固体表面物理化学国家重点实验室高级工程师谢顺吉(共同第一作者)完成；理论计算部分由程俊教授指导，2017级硕士生刘彤彤(共同第一作者)、2016级博士生樊祺源完成。叶进裕博士为原位红外测试提供了支持。上海光源姜政研究员、孙凡飞博士、杨若欧为同步辐射表征提供了支持。 这是投稿的最终版本，正式出版的论文版本请访问官方链接（https://doi.org/10.1038/s41929-020-0450-0）。Electrocatalytic reduction of CO2 into multi-carbon (C2+) products is a highly attractive route for CO2 utilization. However, the yield of C2+ products remains low because of the limited C2+ selectivity at high CO2 conversion rate. Here, we report a fluorine-modified copper catalyst that exhibits an ultrahigh current density of 1.6 A cm−2 at C2+ (mainly ethylene and ethanol) Faradaic efficiency of 80% for electrocatalytic CO2 reduction in a flow cell. The C2-4 selectivity reaches 85.8% at a single-pass yield of 16.5%. We show a hydrogen-assisted C−C coupling mechanism between adsorbed formyl (CHO) intermediates for C2+ formation. Fluorine enhances water activation, CO adsorption and hydrogenation of adsorbed CO to CHO intermediate that can readily undergo coupling. Our findings offer an opportunity to design highly active and selective CO2 electroreduction catalysts with potential for practical applicationThis work was supported by the National Key Research and Development Program of the Ministry of Science and Technology of China (No. 2017YFB0602201), the National Natural Science Foundation of China (Nos. 21690082, 91545203, 21503176 and 21802110), We thank staffs at the BL14W1 beamline of the Shanghai Synchrotron Radiation Facilities (SSRF) for assistance with the EXAFS measurements.研究工作得到科技部重点研发计划（批准号：2017YFB0602201）和国家自然科学基金（批准号：21690082、91545203、21503176、21802110）项目的资助