Mining literature and pathway data to explore the relations of ketamine with neurotransmitters and gut microbiota using a knowledge-graph.

MOTIVATION: Up-to-date pathway knowledge is usually presented in scientific publications for human reading, making it difficult to utilize these resources for semantic integration and computational analysis of biological pathways. We here present an approach to mining knowledge graphs by combining manual curation with automated named entity recognition and automated relation extraction. This approach allows us to study pathway-related questions in detail, which we here show using the ketamine pathway, aiming to help improve understanding of the role of gut microbiota in the antidepressant effects of ketamine. RESULTS: The thus devised ketamine pathway 'KetPath' knowledge graph comprises five parts: (i) manually curated pathway facts from images; (ii) recognized named entities in biomedical texts; (iii) identified relations between named entities; (iv) our previously constructed microbiota and pre-/probiotics knowledge bases; and (v) multiple community-accepted public databases. We first assessed the performance of automated extraction of relations between named entities using the specially designed state-of-the-art tool BioKetBERT. The query results show that we can retrieve drug actions, pathway relations, co-occurring entities, and their relations. These results uncover several biological findings, such as various gut microbes leading to increased expression of BDNF, which may contribute to the sustained antidepressant effects of ketamine. We envision that the methods and findings from this research will aid researchers who wish to integrate and query data and knowledge from multiple biomedical databases and literature simultaneously. AVAILABILITY AND IMPLEMENTATION: Data and query protocols are available in the KetPath repository at https://dx.doi.org/10.5281/zenodo.8398941 and https://github.com/tingcosmos/KetPath.

Cycling and activated CD8+ T lymphocytes and their association with disease severity in influenza patients.

BACKGROUND: T cell lymphopenia was a significant characteristic of severe influenza infection and it was associated with the functional changes of T cells. It is necessary to clarify the T cells characteristics of kinetic changes and their correlation with disease severity. METHODS: In a cohort of hospitalized influenza patients with varying degrees of severity, we characterized lymphocyte populations using flow cytometry. RESULTS: The numbers of cycling (Ki67+) T cells at the acute phase of severe influenza were higher, especially in the memory (CD45RO+) T cell subsets. T cells from hospitalized influenza patients also had significantly higher levels of the exhausted marker PD-1. Cycling status of T cells was associated with T cell activation during the acute phase of influenza infection. The recruitment of cycling and activated (CD38+HLA-DR+) CD8+ T cells subset is delayed in severe influenza patients. CONCLUSIONS: The increased numbers of cycling memory (Ki67+CD45RO+) T cells subsets and delayed kinetics of activated (CD38+HLA-DR+) CD8+ T cells, could serve as possible biological markers for disease severity.

Smartphone-Based Electrochemical Immunoassay for Point-of-Care Detection of SARS-CoV-2 Nucleocapsid Protein.

Large-scale, rapid, and inexpensive serological diagnoses of severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) are of great interest in reducing virus transmission at the population level; however, their development is greatly plagued by the lack of available point-of-care methods, leading to low detection efficiency. Herein, an ultrasensitive smartphone-based electrochemical immunoassay is reported for rapid (less than 5 min), low-cost, easy-to-implement detection of the SARS-CoV-2 nucleocapsid protein (SARS-CoV-2 N protein). Specifically, the electrochemical immunoassay was fabricated on a screen-printed carbon electrode coated with electrodeposited gold nanoparticles, followed by incubation of anti-N antibody (Ab) and bovine serum albumin as the working electrode. Accompanied by the antigen-antibody reaction between the SARS-CoV-2 N protein and the Ab, the electron transfer between the electroactive species [Fe(CN)6]3-/4- and the electrode surface is disturbed, resulting in reduced square-wave voltammetry currents at 0.075 V versus the Ag/AgCl reference electrode. The proposed immunoassay provided a good linear range with SARS-CoV-2 N protein concentrations within the scope of 0.01-1000 ng/mL (R2 = 0.9992) and the limit of detection down to 2.6 pg/mL. Moreover, the detection data are wirelessly transmitted to the interface of the smartphone, and the corresponding SARS-CoV-2 N protein concentration value is calculated and displayed. Therefore, the proposed portable detection mode offers great potential for self-differential diagnosis of residents, which will greatly facilitate the effective control and large-scale screening of virus transmission in resource-limited areas.

Identification of key candidate biomarkers for severe influenza infection by integrated bioinformatical analysis and initial clinical validation.

One of the key barriers for early identification and intervention of severe influenza cases is a lack of reliable immunologic indicators. In this study, we utilized differentially expressed genes screening incorporating weighted gene co-expression network analysis in one eligible influenza GEO data set (GSE111368) to identify hub genes associated with clinical severity. A total of 10 genes (PBI, MMP8, TCN1, RETN, OLFM4, ELANE, LTF, LCN2, DEFA4 and HP) were identified. Gene set enrichment analysis (GSEA) for single hub gene revealed that these genes had a close association with antimicrobial response and neutrophils activity. To further evaluate these genes' ability for diagnosis/prognosis of disease developments, we adopted double validation with (a) another new independent data set (GSE101702); and (b) plasma samples collected from hospitalized influenza patients. We found that 10 hub genes presented highly correlation with disease severity. In particular, BPI and MMP8 encoding proteins in plasma achieved higher expression in severe and dead cases, which indicated an adverse disease development and suggested a frustrating prognosis. These findings provide new insight into severe influenza pathogenesis and identify two significant candidate genes that were superior to the conventional clinical indicators. These candidate genes or encoding proteins could be biomarker for clinical diagnosis and therapeutic targets for severe influenza infection.

Correction: Distant Supervision for Mental Health Management in Social Media: Suicide Risk Classification System Development Study.

[This corrects the article DOI: 10.2196/26119.].

Distant Supervision for Mental Health Management in Social Media: Suicide Risk Classification System Development Study.

BACKGROUND: Web-based social media provides common people with a platform to express their emotions conveniently and anonymously. There have been nearly 2 million messages in a particular Chinese social media data source, and several thousands more are generated each day. Therefore, it has become impossible to analyze these messages manually. However, these messages have been identified as an important data source for the prevention of suicide related to depression disorder. OBJECTIVE: We proposed in this paper a distant supervision approach to developing a system that can automatically identify textual comments that are indicative of a high suicide risk. METHODS: To avoid expensive manual data annotations, we used a knowledge graph method to produce approximate annotations for distant supervision, which provided a basis for a deep learning architecture that was built and refined by interactions with psychology experts. There were three annotation levels, as follows: free annotations (zero cost), easy annotations (by psychology students), and hard annotations (by psychology experts). RESULTS: Our system was evaluated accordingly and showed that its performance at each level was promising. By combining our system with several important psychology features from user blogs, we obtained a precision of 80.75%, a recall of 75.41%, and an F1 score of 77.98% for the hardest test data. CONCLUSIONS: In this paper, we proposed a distant supervision approach to develop an automatic system that can classify high and low suicide risk based on social media comments. The model can therefore provide volunteers with early warnings to prevent social media users from committing suicide.

Silence of lncRNA UCA1 rescues drug resistance of cisplatin to non-small-cell lung cancer cells.

The aim of this study was to investigate the effect of long noncoding RNA (lncRNA) urogenital carcinoma antigen 1 (UCA1) on drug resistance in A549/DDP cell and explore its underlying mechanism. The inhibition rate and IC 50 of DDP were detected in A549 and A549/DDP cells by 3-(4,5-dimethylthiazol-2-yl)-2,5 diphenyl tetrazolium bromide assay. The expression of lncRNA UCA1 was measured in A549 and A549/DDP cells by quantitative real-time polymerase chain reaction. The expressions of N-cadherin, E-cadherin, vimentin, and Snail were detected in A549 and A549/DDP cells by Western blot analysis. Results showed that the IC 50 of DDP was 16.20 ± 2.27 µmol/L and 69.72 ± 4.83 µmol/L in A549 and A549/ DDP cells, respectively. Compared with the A549 group, the expressions of N-cadherin, vimentin, and Snail was significantly upregulated in A549/DDP group, but E-cadherin was significantly downregulated. Compared with the shCon group, the abundance of N-cadherin, vimentin, and Snail was significantly downregulated in short hairpin RNA UCA1 (shUCA1) group, while E-cadherin was significantly upregulated. Cell migration and invasion were significantly suppressed and IC 50 was reversed to 16.20 ± 2.27 µmol/L in the shUCA1 group. Silencing lncRNA UCA1 inhibited the migration and invasion of A549/DDP cells and reversed the resistance of A549/DDP cells to DDP. The mechanism might be related to downregulation of epithelial-mesenchymal transition, which will provide a new direction for the treatment of non-small-cell lung cancer with cisplatin.

Relationship between melatonin receptor 1B (rs10830963 and rs1387153) with gestational diabetes mellitus: a case-control study and meta-analysis.

PURPOSE: Variants rs10830963 (C/G) and rs1387153 (C/T) in MTNR1B have been shown with an increased risk of developing type 2 diabetes and gestational diabetes mellitus. However, the results are still controversial, and evidence was not satisfied. Hence, a case-control study and a further meta-analysis will be performed in this study. METHODS: We recruited 674 GDM patients and 690 controls from Jan 2010 and Jan 2014. The SNPs were genotyped by ABI TaqMan SNP Genotyping Assays. MTNR1B rs10830963 and rs1387153 single nucleotide polymorphisms (SNPs) were performed for association analysis. Then a systematic search of all relevant studies was conducted. A meta-analysis was performed to prove the relationship between melatonin receptor 1B (rs10830963 and rs1387153) with GDM. RESULTS: The case-control study presented that G allele of the rs10830963 and T allele of rs1387153 were significantly associated with increased risk of GDM. The further meta-analysis included other five studies showed that the frequency of MTNR1B rs10830963 G allele and rs1387153 T allele are higher in GDM patients. CONCLUSION: The case-control study proved that the risk allele (G allele) of rs10830963 and (T allele) of rs1387153 lead to a higher risk for GDM. The further meta-analysis provides additional evidence supporting the above results. Due to the limited data currently available in different race population, further studies with large sample sizes are required.

Identification of characteristics and construction of nomogram to predict the survival probability of mesonephric carcinoma patients: A population-based analysis and a case report.

BACKGROUND: Mesonephric carcinoma (MC) is a very rare tumor with less than 70 cases had been reported. The rarity of MC has restricted its research, resulting in the lack of published guidelines. OBJECTIVE: To summarize the characteristics and construct an external-validated nomogram to predict the survival of MC patients. METHOD: Sixty-four qualified patients derived from the Surveillance, Epidemiology, and End Results Plus database, and one patient from the Guangzhou Red Cross Hospital were enrolled. The entire cohort was randomly divided into a development (70%) and a validation cohort (30%). The Kaplan-Meier method and univariate and multivariate Cox regression analyses were applied. Two nomograms were established to predict the 3-to-8-year survival probability of MC patients, which were evaluated by C-index, ROC curves, DCA curves, and calibration plots. RESULTS: The average survival time of MC patients was 84.22 ± 50.66 months. No significant difference was shown among different groups of race, primary site, tumor differentiated grade, and FIGO stages, while different SEER stages did distinguish patients' survival time, which indicated that the SEER stage standards might be a better staging system in the MC patients than FIGO stage (p = .0835). Additional survival analyses showed that MC patients benefited from shorter waiting times to begin treatment, accepting surgery, regional lymph node examination, radiotherapy, and chemotherapy. Two nomograms were established, both of which got satisfied scores in C-index, ROC curves, DCA curves, and calibration plots. CONCLUSION: Sufficient regional lymph nodes examined, and applying radiotherapy in high-risk patients are recommended in MC patients. Nomograms established in the present study had good predicting and discriminating capabilities, which would be helpful in patients' individual risk estimation, management, counseling, and follow-up.

Integrated Bioinformatics Exploration and Preliminary Clinical Verification for the Identification of Crucial Biomarkers in Severe Cases of COVID-19.

Background: Coronavirus disease 2019 (COVID-19) is a respiratory infectious illness caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The objective of this study is to identify reliable and accurate biomarkers for the early stratification of disease severity, a crucial aspect that is currently lacking for the impending phases of the next COVID-19 pandemic. Methods: In this study, we identified important module and hub genes related to clinical severe COVID-19 using differentially expressed genes (DEGs) screening combing weighted gene co-expression network analysis (WGCNA) in dataset GSE213313. We further screened and confirmed these hub genes in another two new independent datasets (GSE172114 and GSE157103). In order to evaluate these key genes' stability and robustness for diagnosing or predicting the progression of illness, we used RT-PCR validation of selected genes in blood samples obtained from hospitalized COVID-19 patients. Results: A total of 968 and 52 DEGs were identified between COVID-19 patients and normal people, critical and non-critical patients, respectively. Then, using WGCNA, 10 modules were constructed. Among them, the blue module positively associated with clinic disease severity of COVID-19. From overlapped section between DEGs and blue module, 12 intersected common differential genes were obtained. Subsequently, these hub genes were validated in another two new independent datasets as well and 9 genes that overlapped showed a highly correlation with disease severity. Finally, the mRNA expression levels of these hub genes were tested in blood samples from COVID-19 patients. In severe cases, there was increased expression of MCEMP1, ANXA3, CD177, and SCN9A. In particular, MCEMP1 increased with disease severity, which suggested an unfavorable development and a frustrating prognosis. Conclusion: Using comprehensive bioinformatical analysis and the validation of clinical samples, we identified four major candidate genes, MCEMP1, ANXA3, CD177, and SCN9A, which are essential for diagnosis or development of COVID-19.

Comprehensive immune landscape of lung-resident memory CD8+ T cells after influenza infection and reinfection in a mouse model.

Background: Resident phenotypic memory CD8+ T cells are crucial for immune defense against pathogens. However, little is known about the potential transitions and regulation mechanisms of their function after influenza virus infection and reinfection. In this study, we utilized integrated transcriptome data and in vivo experiments to investigate the key characteristics behind it. Methods: Two single-cell RNA sequencing (scRNA-seq) datasets of lung CD8+ T cells and one RNA-seq dataset of lung tissue after infection or reinfection were included. After Seurat procedures classifying CD8+ T subsets, the scCODE algorithm was used to identify the differentially expressed genes for GSVA, GO, and KEGG pathway enrichment. Monocle 3 and CellChat were used to infer pseudotime cell trajectory and cell interactions. The ssGSEA method was used to estimate the relative proportions of immune cells. The findings were confirmed with a mouse model via flow cytometry and RT-PCR analysis. Results: Our study refined the landscape of CD8+ T-cell subsets in the lung, showing that CD8+ Trm cells accumulated in the lung within 14 days after influenza infection. The classical CD8+ Trm cells co-expressed a high level of CD49a and even maintained 90 days after primary infection. The ratio of CD8+ Trm cells decreased 1 day after influenza reinfection, which may be parallel with their potential transition into effector types, as observed in trajectory inference analysis. KEGG analysis suggested that PD-L1 expression and PD-1 checkpoint pathway were upregulated in CD8+ Trm cells on day 14 after infection. GO and GSVA analyses revealed that PI3K-Akt-mTOR and type I interferon signaling pathways were enriched in CD8+ Tem and Trm cells after reinfection. Additionally, CCL signaling pathways were involved in cell interaction between CD8+ Trm cells and other cells, with Ccl4-Ccr5 and Ccl5-Ccr5 ligand/receptor pairs being important between CD8+ Trm and other memory subsets after infection and reinfection. Conclusion: Our data suggest that resident memory CD8+ T cells with CD49a co-expression account for a large proportion after influenza infection, and they can be rapidly reactivated against reinfection. Function differences exist in CD8+ Trm and Tem cells after influenza infection and reinfection. Ccl5-Ccr5 ligand/receptor pair is important in cell interactions between CD8+ Trm and other subsets.

Food4healthKG: Knowledge graphs for food recommendations based on gut microbiota and mental health.

Food is increasingly acknowledged as a powerful means to promote and maintain mental health. The introduction of the gut-brain axis has been instrumental in understanding the impact of food on mental health. It is widely reported that food can significantly influence gut microbiota metabolism, thereby playing a pivotal role in maintaining mental health. However, the vast amount of heterogeneous data published in recent research lacks systematic integration and application development. To remedy this, we construct a comprehensive knowledge graph, named Food4healthKG, focusing on food, gut microbiota, and mental diseases. The constructed workflow includes the integration of numerous heterogeneous data, entity linking to a normalized format, and the well-designed representation of the acquired knowledge. To illustrate the availability of Food4healthKG, we design two case studies: the knowledge query and the food recommendation based on Food4healthKG. Furthermore, we propose two evaluation methods to validate the quality of the results obtained from Food4healthKG. The results demonstrate the system's effectiveness in practical applications, particularly in providing convincing food recommendations based on gut microbiota and mental health. Food4healthKG is accessible at https://github.com/ccszbd/Food4healthKG.

ADHD-KG: a knowledge graph of attention deficit hyperactivity disorder.

Purpose: Attention Deficit Hyperactivity Disorder (ADHD) is a widespread condition that affects human behaviour and can interfere with daily activities and relationships. Medication or medical information about ADHD can be found in several data sources on the Web. Such distribution of knowledge raises notable obstacles since researchers and clinicians must manually combine various sources to deeply explore aspects of ADHD. Knowledge graphs have been widely used in medical applications due to their data integration capabilities, offering rich data stores of information built from heterogeneous sources; however, general purpose knowledge graphs cannot represent knowledge in sufficient detail, thus there is an increasing interest in domain-specific knowledge graphs. Methods: In this work we propose a Knowledge Graph of ADHD. In particular, we introduce an automated procedure enabling the construction of a knowledge graph that covers knowledge from a wide range of data sources primarily focusing on adult ADHD. These include relevant literature and clinical trials, prescribed medication and their known side-effects. Data integration between these data sources is accomplished by employing a suite of information linking procedures, which aim to connect resources by relating them to common concepts found in medical thesauri. Results: The usability and appropriateness of the developed knowledge graph is evaluated through a series of use cases that illustrate its ability to enhance and accelerate information retrieval. Conclusion: The Knowledge Graph of ADHD can provide valuable assistance to researchers and clinicians in the research, training, diagnostic and treatment processes for ADHD.

Development and validation of nomograms to predict the survival probability and occurrence of a second primary malignancy of male breast cancer patients: a population-based analysis.

Background: Male breast cancer (MBC) is rare, which has restricted prospective research among MBC patients. With effective treatments, the prognosis of MBC patients has improved and developing a second primary malignancy (SPM) has become a life-threatening event for MBC survivors. However, few studies have focused on the prognosis of MBC patients and looked into the SPM issue in MBC survivors. Method: We reviewed MBC patients diagnosed between 1990 and 2016 from the latest Surveillance, Epidemiology, and End Results (SEER) Plus database. Competing risk models and nomograms were conducted for predicting the risk of cancer-specific death and SPM occurrence. C-indexes, calibration curves, ROC curves, and decision curve analysis (DCA) curves were applied for validation. Result: A total of 1,843 MBC patients with complete information were finally enrolled and 60 (3.26%) had developed an SPM. Prostate cancer (40%) was the most common SPM. The median OS of all the enrolled patients was 102.41 months, while the median latency from the initial MBC diagnosis to the subsequent diagnosis of SPM was 67.2 months. The patients who suffered from an SPM shared a longer OS than those patients with only one MBC (p = 0.027). The patients were randomly divided into the development cohort and the validation cohort (at a ratio of 7:3). The Fine and Gray competing risk model was used to identify the risk factors. Two nomograms were constructed and validated to predict the 5-year, 8-year, and 10-year survival probability of MBC patients, both of which had good performance in the C-index, ROC curves, calibration plots, and DCA curves, showing the ideal discrimination capability and predictive value clinically. Furthermore, we, for the first time, constructed a nomogram based on the competing risk model to predict the 5-year, 8-year, and 10-year probability of developing an SPM in MBC survivors, which also showed good discrimination, calibration, and clinical effectiveness. Conclusion: We, for the first time, included treatment information and clinical parameters to construct a nomogram to predict not only the survival probability of MBC patients but also the probability of developing an SPM in MBC survivors, which were helpful in individual risk estimation, patient follow-up, and counseling in MBC patients.

Consistent 3D Hand Reconstruction in Video via Self-Supervised Learning.

We present a method for reconstructing accurate and consistent 3D hands from a monocular video. We observe that the detected 2D hand keypoints and the image texture provide important cues about the geometry and texture of the 3D hand, which can reduce or even eliminate the requirement on 3D hand annotation. Accordingly, in this work, we propose S2HAND, a self-supervised 3D hand reconstruction model, that can jointly estimate pose, shape, texture, and the camera viewpoint from a single RGB input through the supervision of easily accessible 2D detected keypoints. We leverage the continuous hand motion information contained in the unlabeled video data and explore S2HAND(V), which uses a set of weights shared S2HAND to process each frame and exploits additional motion, texture, and shape consistency constrains to obtain more accurate hand poses, and more consistent shapes and textures. Experiments on benchmark datasets demonstrate that our self-supervised method produces comparable hand reconstruction performance compared with the recent full-supervised methods in single-frame as input setup, and notably improves the reconstruction accuracy and consistency when using the video training data.

TREM-1, TREM-2 and their association with disease severity in patients with COVID-19.

BACKGROUND: Delayed diagnosis and inadequate treatment caused by limited biomarkers are associated with the outcomes of COVID-19 patients. It is necessary to identify other promising biomarkers and candidate targets for defining dysregulated inflammatory states. METHODS: The triggering receptors expressed on myeloid cell (TREM)-1 and TREM-2 expression from hospitalized COVID-19 patients were characterized using ELISA and flow cytometry, respectively. Their correlation with disease severity and contrast with the main clinical indicators were evaluated. RESULTS: Increased expression of soluble TREM-1 and TREM-2 in the plasma of COVID-19 patients was found compared to the control group. Moreover, membrane-bound TREM-1 and TREM-2 expression was upregulated on the cell surface of circulating blood T cells from COVID-19 patients. Correlation analysis showed that sTREM-2 levels were negatively correlated with PaO2/FiO2, but positively correlated with C-reactive protein (CRP), procalcitonin (PCT) and interleukin (IL)-6 levels. Receiver operating characteristic curve analysis indicated that the predictive efficacy of sTREM-1 and sTREM-2 was equivalent to CRP and IL-6, and a little better than absolute leukocyte or neutrophil count and PCT in distinguishing disease severity. CONCLUSION: TREM-2 and TREM-1 are critical host immune factors that response to SARS-COV-2 infection and could serve as potential diagnostic biomarkers and therapeutic targets for COVID-19.

The expression of soluble TREM-1 and TREM-2 in plasma and membrane-bound TREM-1 and TREM-2 on the cell surface was upregulated in COVID-19 patients.sTREM-2 level was negatively correlated with PaO₂/FiO₂, but positively correlated with CRP, PCT and IL-6 level, respectively.sTREM-1 and sTREM-2 exhibited potential predictive abilities, and their expression was equivalent to CRP and IL-6, and better than the absolute leukocytes or neutrophil counts and PCT in distinguishing disease severity.

KG4NH: A Comprehensive Knowledge Graph for Question Answering in Dietary Nutrition and Human Health.

It is commonly known that food nutrition is closely related to human health. The complex interactions between food nutrients and diseases, influenced by gut microbial metabolism, present challenges in systematizing and practically applying knowledge. To address this, we propose a method for extracting triples from a vast amount of literature, which is used to construct a comprehensive knowledge graph on nutrition and human health. Concurrently, we develop a query-based question answering system over our knowledge graph, proficiently addressing three types of questions. The results show that our proposed model outperforms other state-of-art methods, achieving a precision of 0.92, a recall of 0.81, and an F1 score of 0.86 in the nutrition and disease relation extraction task. Meanwhile, our question answering system achieves an accuracy of 0.68 and an F1 score of 0.61 on our benchmark dataset, showcasing competitiveness in practical scenarios. Furthermore, we design five independent experiments to assess the quality of the data structure in the knowledge graph, ensuring results characterized by high accuracy and interpretability. In conclusion, the construction of our knowledge graph shows significant promise in facilitating diet recommendations, enhancing patient care applications, and informing decision-making in clinical research.

A 1.25-GHz multi-amplitude modulator driver in 0.18 µm SiGe BiCOMOS technology for high speed quantum key distribution.

Quantum key distribution (QKD) research has yielded highly fruitful results and is currently undergoing an industrialization transformation. In QKD systems, electro-optic modulators are typically employed to prepare the required quantum states. While various QKD systems operating at GHz repetition frequency have demonstrated exceptional performance, they predominantly rely on instruments or printed circuit boards to fulfill the driving circuit function of the electro-optic modulator. Consequently, these systems tend to be complex with low integration levels. To address this challenge, we have introduced a modulator driver integrated circuit in 0.18 µm SiGe BiCMOS technology. The circuit can generate multiple-level driving signals with a clock frequency of 1.25 GHz and a rising edge of ∼50 ps. Each voltage amplitude can be independently adjusted, ensuring the precise preparation of quantum states. The measured signal-to-noise ratio was more than 17 dB, resulting in a low quantum bit error rate of 0.24% in our polarization-encoding system. This work will contribute to the advancement of QKD system integration and promote the industrialization process in this field.

Analysis of sentiment changes in online messages of depression patients before and during the COVID-19 epidemic based on BERT+BiLSTM.

With the development of the Internet, more and more people prefer to confide their sentiments in the virtual world, especially those with depression. The social media where people with depression collectively leave messages is called the "Tree Hole". The purpose of this article is to support the "Tree Hole" rescue volunteers to help patients with depression, especially after the outbreak of COVID-19 and other major events, to guide the crisis intervention of patients with depression. Based on the message data of "Tree Hole" named "Zou Fan", this paper used a deep learning model and sentiment scoring algorithm to analyze the fluctuation characteristics sentiment of user's message in different time dimensions. Through detailed investigation of the research results, we found that the number of "Tree Hole" messages in multiple time dimensions is positively correlated to emotion. The longer the "Tree Hole" is formed, the more negative the emotion is, and the outbreak of COVID-19 and other major events have obvious effects on the emotion of the messages. In order to improve the efficiency of "Tree Hole" rescue, volunteers should focus on the long-formed "Tree Hole" and the user groups that are active in the early morning. This research is of great significance for the emotional guidance of online mental health patients, especially the crisis intervention for depression patients after the outbreak of COVID-19 and other major events.

A semantic web technology index.

Semantic web (SW) technology has been widely applied to many domains such as medicine, health care, finance, geology. At present, researchers mainly rely on their experience and preferences to develop and evaluate the work of SW technology. Although the general architecture (e.g., Tim Berners-Lee's Semantic Web Layer Cake) of SW technology was proposed many years ago and has been well-known, it still lacks a concrete guideline for standardizing the development of SW technology. In this paper, we propose an SW technology index to standardize the development for ensuring that the work of SW technology is designed well and to quantitatively evaluate the quality of the work in SW technology. This index consists of 10 criteria that quantify the quality as a score of [Formula: see text]. We address each criterion in detail for a clear explanation from three aspects: (1) what is the criterion? (2) why do we consider this criterion and (3) how do the current studies meet this criterion? Finally, we present the validation of this index by providing some examples of how to apply the index to the validation cases. We conclude that the index is a useful standard to guide and evaluate the work in SW technology.