Unravelling spatial gene associations with SEAGAL: a Python package for spatial transcriptomics data analysis and visualization.

SUMMARY: In the era where transcriptome profiling moves toward single-cell and spatial resolutions, the traditional co-expression analysis lacks the power to fully utilize such rich information to unravel spatial gene associations. Here, we present a Python package called Spatial Enrichment Analysis of Gene Associations using L-index (SEAGAL) to detect and visualize spatial gene correlations at both single-gene and gene-set levels. Our package takes spatial transcriptomics datasets with gene expression and the aligned spatial coordinates as input. It allows for analyzing and visualizing genes' spatial correlations and cell types' colocalization within the precise spatial context. The output could be visualized as volcano plots and heatmaps with a few lines of code, thus providing an easy-yet-comprehensive tool for mining spatial gene associations. AVAILABILITY AND IMPLEMENTATION: The Python package SEAGAL can be installed using pip: https://pypi.org/project/seagal/. The source code and step-by-step tutorials are available at: https://github.com/linhuawang/SEAGAL.

Synthesis of 2,2-difluoro-1,3-diketone and 2,2-difluoro-1,3-ketoester derivatives using fluorine gas.

Solutions of 1,3-diketones and 1,3-ketoester derivatives react with fluorine to give the corresponding 2,2-difluoro-1,3-dicarbonyl derivatives in the presence of quinuclidine. Quinuclidine reacts with fluorine in situ to generate a fluoride ion that facilitates limiting enolization processes, and an electrophilic N-F fluorinating agent that is reactive towards neutral enol species.

Single-cell multi-omics integration for unpaired data by a siamese network with graph-based contrastive loss.

BACKGROUND: Single-cell omics technology is rapidly developing to measure the epigenome, genome, and transcriptome across a range of cell types. However, it is still challenging to integrate omics data from different modalities. Here, we propose a variation of the Siamese neural network framework called MinNet, which is trained to integrate multi-omics data on the single-cell resolution by using graph-based contrastive loss. RESULTS: By training the model and testing it on several benchmark datasets, we showed its accuracy and generalizability in integrating scRNA-seq with scATAC-seq, and scRNA-seq with epitope data. Further evaluation demonstrated our model's unique ability to remove the batch effect, a common problem in actual practice. To show how the integration impacts downstream analysis, we established model-based smoothing and cis-regulatory element-inferring method and validated it with external pcHi-C evidence. Finally, we applied the framework to a COVID-19 dataset to bolster the original work with integration-based analysis, showing its necessity in single-cell multi-omics research. CONCLUSIONS: MinNet is a novel deep-learning framework for single-cell multi-omics sequencing data integration. It ranked top among other methods in benchmarking and is especially suitable for integrating datasets with batch and biological variances. With the single-cell resolution integration results, analysis of the interplay between genome and transcriptome can be done to help researchers understand their data and question.

The NCOA1-CBP-NF-κB transcriptional complex induces inflammation response and triggers endotoxin-induced myocardial dysfunction.

Inflammatory pathways represented by TLR4/NF-κB (Toll-like receptor 4/Nuclear factor-κB) axis signaling are activated in the pathogenesis of endotoxin-induced myocardial dysfunction (EIMD). However, the underlying mechanism by which NF-κB coordinates with other transcriptional coactivators/corepressors to regulate the expression of proinflammatory cytokine genes remains unclear. We established an EIMD-mouse model by intraperitoneal injection of lipopolysaccharides (LPS), and we discovered that NCOA1 (nuclear receptor coactivator 1) assembled with CBP (CREB binding protein) and NF-κB subunits to form a transcriptional complex that specifically bound to promoters of proinflammatory cytokine genes to activate their expression. LPS treatment also inhibited DNMT1 (DNA methyltransferase 1) expression, thereby decreasing DNA methylation of a CpG island located on the promoter of NCOA1 and causing NCOA1 overexpression. Screening small molecules that abolished NCOA1-CBP interaction in a yeast system identified a compound PSSM2126 that effectively blocked the NCOA1-CBP interaction in vitro and in vivo. Administration of PSSM2126 to EIMD mice significantly alleviated the inflammation response and improved cardiac function. Collectively, our results reveal that an NCOA1-dependent transactivation mechanism can regulate proinflammatory cytokine expression, thereby improving our understanding of the activation of NF-κB targets. The promising inhibition of the NCOA1-CBP interaction by PSSM2126 may provide a new therapeutic option for EIMD.

Automatic Classification of Mass Shape and Margin on Mammography with Artificial Intelligence: Deep CNN Versus Radiomics.

The purpose of this study is to test the feasibility for deep CNN-based artificial intelligence methods for automatic classification of the mass margin and shape, while radiomic feature-based machine learning methods were also implemented in this study as baseline and for comparison study. In this retrospective study, 596 patients with breast mass that underwent mammography from 4 hospitals were enrolled from January 2012 to October 2019. Margin and shape of each mass were annotated according to BI-RADS by 2 experienced radiologists. Deep CNN-based AI was implemented for the classification task based on Resnet50. Balanced sampler and CBAM were also used to improve the performance of the Deep CNNs. As comparison, image texture features were extracted and then dimensionality reduction methods (such as PCA, ICA) and classical classifiers (such as SVM, DT, KNN) were used for classification task. Based on Python programming software, accuracy (ACC) was used to evaluate the performance of the model, and the model with the highest ACC value was selected. Deep CNN based on Resnet50 with balanced sampler and CBAM achieved the best performance for both margin and shape classification, with ACC of 0.838 and 0.874, respectively. For the radiomics-based machine learning, the best performance for margin was achieved as 0.676 by the combination of FA + RF, while the best performance for shape was 0.802 by the combination of PCA + MLP. The feasibility for automatic classification with coarse labeling of the mass shape and margin was testified with the deep CNN-based AI methods, while radiomic feature-based machine learning methods achieved inferior classification results.

Targeting the TXNIP-NLRP3 interaction with PSSM1443 to suppress inflammation in sepsis-induced myocardial dysfunction.

Sepsis-induced myocardial dysfunction (SIMD), a deadly symptom in sepsis patients, is mainly caused by cardiovascular inflammation. However, it remains unclear how systemic inflammation triggers and aggravates cardiovascular inflammation in the pathogenesis of SIMD. This study found that proinflammatory cytokines and H2 O2 concentrations were significantly induced in SIMD-mice. In particular, a microarray analysis of CD63+ exosomes isolated from sham- and SIMD-monocytes revealed a significant induction of thioredoxin-interacting protein (TXNIP) and NLR family pyrin domain-containing 3 (NLRP3). We proved that oxidative stress caused the disassociation of the TXNIP-TRX2 (thioredoxin 2) complex and the assembly of the TXNIP-NLRP3 complex. In addition, this finding showed that the latter complex could be embedded into CD63+ exosomes and traffic from monocytes to the resident heart macrophages, where it activated caspase-1 and cleaved inactive interleukin 1ß (IL-1ß) and IL-18. Furthermore, using an amplified luminescent proximity homogeneous assay (Alpha) with GST-TXNIP and His-NLRP3, we obtained a small molecule named PSSM1443 that could disrupt the TXNIP-NLRP3 interaction in vitro, impairing NLRP3 downstream events. Of note, after administering PSSM1443 to the SIMD-mice, we found the small molecule could significantly suppress the activation of caspase-1 and the cleavage of pro-IL-1ß and pro-IL-18, reducing inflammation in the SIMD-mice. Collectively, our results reveal that monocyte-derived exosomes harbor the overexpressed TXNIP-NLRP3 complex, which traffics from circulating monocytes to local macrophages and promotes the cleavage of inactive IL-1ß and IL-18 in the macrophages, aggravating cardiovascular inflammation. PSSM1443 functions as an inhibitor of the TXNIP-NLRP3 complex and its administration can decrease inflammation in SIMD-mice.

[Variation of polysaccharides and alcohol-soluble extracts content of Dendrobium officinale].

To reveal the variation of polysaccharides and alcohol-soluble extract contents of Dendrobium officinale, the polysaccharides and alcohol-soluble extracts contents of three D. officinale strains were determined by phenol-sulfuric acid method and hot-dip method, respectively. The results showed that the contents of polysaccharides and alcohol-soluble extracts and their total content were significantly different among D. officinale samples collected in different periods, and the variations were closely related to the phenology of D. officinale. Additionally, the quality variation of polysaccharides was closely related to the flowering of D. officinale, while the alcohol-soluble extracts was closely associated to the formation and germination of buds. According to the dynamic variation of these two compounds, it is more reasonable to harvest D. officinale at biennials pre-bloom than at specific harvesting month considering polysaccharides content. It is better to harvest before the germination of buds considering alcohol-soluble extracts. While with regards to both polysaccharides and alcohol-soluble extract, it is better to harvest this plant at the period from the sprouting to pre-bloom next year.

Quantification and visualization of cis-regulatory dynamics in single-cell multi-omics data with TREASMO.

Recent advances in single-cell multi-omics technologies have provided unprecedented insights into regulatory processes. We introduce TREASMO, a versatile Python package designed to quantify and visualize transcriptional regulatory dynamics in single-cell multi-omics datasets. TREASMO has four modules, spanning data preparation, correlation quantification, downstream analysis and visualization, enabling comprehensive dataset exploration. By introducing a novel single-cell gene-peak correlation strength index, TREASMO facilitates accurate identification of regulatory changes at single-cell resolution. Validation on a hematopoietic stem and progenitor cell dataset showcases TREASMO's capacity in quantifying the gene-peak correlation strength at the single-cell level, identifying regulatory markers and discovering temporal regulatory patterns along the trajectory.

nBu4N+[AgI(CF3)2]-: Trifluoromethylated Argentate Derived from Fluoroform and Its Reaction with (Hetero)Aryl Diazonium Salts.

The preparation of a well-defined trifluoromethylated argentate nBu4N+[Ag(CF3)2]- 1 from fluoroform was described. The complex was stable in the solid state and in solution under an inert atmosphere. Treatment of a variety of (hetero)aryl diazonium tetrafluoroborates with nBu4N+[Ag(CF3)2]- 1 generated trifluoromethylated (hetero)arenes in good to excellent yields. Preliminary experiments were conducted, and a reasonable mechanism of the reaction was proposed.

A Psychometric Development and Evaluation of the Flow State Scale for Caregiving Tasks Among Clinical Nurses.

BACKGROUND: The extensive research already conducted in numerous non-healthcare disciplines has consistently demonstrated the effectiveness of flow in alleviating work-related stress and boosting employee engagement in the workplace. Despite the potential benefits to nursing of improving flow, no valid instrument is currently available to measure the work-related flow experience of clinical nurses. PURPOSE: This study was designed to develop the Flow State Scale for Caregiving Tasks (FSS-CT) and to evaluate its psychometric properties in the context of clinical nurses. METHODS: This study, guided by the flow theory of Mihaly Csikszentmihalyi (1975), employed a three-phase approach to developing and evaluating the psychometric properties of the FSS-CT. Five hundred thirty-six full-time clinical nurses working in hospitals of various levels in Taiwan were recruited via cluster random sampling to participate in an anonymous online survey. The content validity of the developed scale was established through expert panel verification, criterion-related validity was assessed by correlating the scale with the Work-related Flow Inventory, and construct validity was evaluated via explanatory and confirmatory factor analyses. Scale reliability was determined using Cronbach's α coefficient. RESULTS: The FSS-CT, a 12-item Likert scale comprising four domains, was developed. These domains capture the elements of nurses' work-related flow, including professional confidence, merging of action and awareness, balance between skill and challenge, and autotelic experience. The FSS-CT demonstrated high content validity (content validity index > .90) and satisfactory criterion-related validity (coefficient = .55, p < .001). Furthermore, the scale was found to have excellent internal consistency (Cronbach's α = .91). Factor analyses yielded a four-factor structure accounting for 69.34% of the total variance, indicating a good fit for the final model based on recommended goodness-of-fit indices. CONCLUSIONS: The FSS-CT is a viable and dependable instrument for assessing the work-related flow experiences of nurses, with the measured outcomes offering to nursing administrators valuable insights into or directions for personnel development and work allocation initiatives.

On-site monitoring of nandrolone in cattle farming samples by portable atmospheric pressure chemical ionization mass spectrometry with ambient sampling.

Nandrolone (NT) is a type of androgen anabolic steroid that is often illegally used in cattle farming, leading to unpredictable harm to human health via the food chain. In this study, a rapid detection method for NT in the samples of cattle farming was established using a portable mass spectrometer. The instrument parameters were optimized, including a thermal desorption temperature of 220 °C, a pump speed of 30 %, an APCI ionization voltage of 3900 v, and an injection volume of 6 µL. The samples of bovine urine, feed, sewage, and tissue were selected, and extracted using a solution of methanol:acetonitrile (1:1, v/v), followed by spiking a NT standard solution (1000 ng·mL-1) and ionization through the APCI ion source for detection. The results showed that NT could not be detected in beef and feed due to the complexity of the matrix, while clear signals of NT ions were observed in bovine urine and sewage samples, with LODs of 1000 and 100 ng·mL-1, respectively. Furthermore, quantitative analysis was attempted, and a good linear relationship (R2 = 0.9952) was observed for NT in sewage within the range of 100 to 1000 ng·mL-1. At spiked levels of 100, 500, 1000 and 2000 ng mL-1, the recovery rates ranged from 74.3 % to 92.8 %, with a relative standard deviation (n = 6) of less than 15 %. In conclusion, this detection method offers the advantages of simplicity, rapidity, strong timeliness, and specificity, making it suitable for on-site detection. It can be used for qualitative screening of nandrolone in bovine urine and quantitative analysis of nandrolone in sewage.

AI-MARRVEL - A Knowledge-Driven AI System for Diagnosing Mendelian Disorders.

BACKGROUND: Diagnosing genetic disorders requires extensive manual curation and interpretation of candidate variants, a labor-intensive task even for trained geneticists. Although artificial intelligence (AI) shows promise in aiding these diagnoses, existing AI tools have only achieved moderate success for primary diagnosis. METHODS: AI-MARRVEL (AIM) uses a random-forest machine-learning classifier trained on over 3.5 million variants from thousands of diagnosed cases. AIM additionally incorporates expert-engineered features into training to recapitulate the intricate decision-making processes in molecular diagnosis. The online version of AIM is available at https://ai.marrvel.org. To evaluate AIM, we benchmarked it with diagnosed patients from three independent cohorts. RESULTS: AIM improved the rate of accurate genetic diagnosis, doubling the number of solved cases as compared with benchmarked methods, across three distinct real-world cohorts. To better identify diagnosable cases from the unsolved pools accumulated over time, we designed a confidence metric on which AIM achieved a precision rate of 98% and identified 57% of diagnosable cases out of a collection of 871 cases. Furthermore, AIM's performance improved after being fine-tuned for targeted settings including recessive disorders and trio analysis. Finally, AIM demonstrated potential for novel disease gene discovery by correctly predicting two newly reported disease genes from the Undiagnosed Diseases Network. CONCLUSIONS: AIM achieved superior accuracy compared with existing methods for genetic diagnosis. We anticipate that this tool may aid in primary diagnosis, reanalysis of unsolved cases, and the discovery of novel disease genes. (Funded by the NIH Common Fund and others.).

[Special culture mediums of breeding clones of Dendrobium officinale].

The explants were obtained from stem segments of hybrid combination of Dendrobium officinale germplasms (Zhejiang Yandang Mountain x Yunnan Guangnan). The screened culture mediums for buds induction and multiplication, which were 1/2MS + IBA 1.0 mg x L(-1) +6-BA 1.0 mg x L(-1) and 1/2MS + IBA 1.5 mg x L(-1) +6-BA 0.5 mg x L(-1) respectively, were applied to 159 germplasms of D. officinale from Zhejiang, Yunnan, Guangxi, Hunan, etc. The medium for axillary buds induction had universality with 94.3% induction rate. During buds multiplication, there were significant differences in proliferation effect among germplasms, and three proliferation forms i.e. single bud, multiple buds and protocorm were differentiated from different germplasms. The results showed that different germplasms of D. officinale had specific requirements for culture medium. Therefore, developing special culture medium for breeding clones of D. officinale is urgent and has important application values.

[Field experiment of F1 generation and superior families selection of Dendrobium officinale].

Based on randomized block design of experiment, agronomic traits and yields of 14 F1 generations of Dendrobium officinale were determined. The results showed that the differences in agronomic traits and yields among families were significant, and the hybrid vigor was obvious. Families of 6b x 2a, 9 x 66 and 78 x 69 were selected with the remarkable superiority of yields, agronomic traits and product customization. Correlation analysis between agronomic traits and yields showed that plant height, stem diameter, leaf number, blade length and blade width were all significantly correlated with biological yields and economic yields. Among which, stem diameter, leaf number and blade length were the most significant, and an optimal linear regression model could be established. When the number of shoots was fewer than 4.5, both biological yields and economic yields increased with the increasing number of shoots, but it could not much affect yields when the number of shoots was larger than 4.5. Shoots number, stem diameter and leaf index were basic stability when compared biennial traits to annual, which could be used for early selection.

[Quantitive variation of polysaccharides and alcohol-soluble extracts in F1 generation of Dendrobium officinale].

Using phenol-sulfuric acid method and hot-dip method of alcohol-soluble extracts, the contents of polysaccharides and alcohol-soluble extracts in 11 F1 generations of Dendrobium officinale were determined. The results showed that the polysaccharides contents in samples collected in May and February were 32.89%-43.07% and 25.77%-35.25%, respectively, while the extracts contents were 2.81%-4.85% and 7.90%-17.40%, respectively. They were significantly different among families. The content of polysaccharides in offspring could be significantly improved by hybridization between parents with low and high polysaccharides contents, and the hybrid vigor was obvious. Cross breeding was an effective way for breeding new varieties with higher polysaccharides contents. Harvest time would significantly affect the contents of polysaccharides and alcohol-soluble extracts. The contents of polysaccharides in families collected in May were higher than those of polysaccharides in families collected in February, but the extracts content had the opposite variation. The extents of quantitative variation of polysaccharides and alcohol-soluble extracts were different among families, and each family had its own rules. It would be significant in giving full play to their role as the excellent varieties and increasing effectiveness by studying on the quantitative accumulation regularity of polysaccharides and alcohol-soluble extracts in superior families (varieties) of D. officinale to determine the best harvesting time.

Unraveling Spatial Gene Associations with SEAGAL: a Python Package for Spatial Transcriptomics Data Analysis and Visualization.

Summary: In the era where transcriptome profiling moves towards single-cell and spatial resolutions, the traditional co-expression analysis lacks the power to fully utilize such rich information to unravel spatial gene associations. Here we present a Python package called Spatial Enrichment Analysis of Gene Associations using L-index (SEAGAL) to detect and visualize spatial gene correlations at both single-gene and gene-set levels. Our package takes spatial transcriptomics data sets with gene expression and the aligned spatial coordinates as input. It allows for analyzing and visualizing spatial correlations at both single-gene and gene-set levels. The output could be visualized as volcano plots and heatmaps with a few lines of code, thus providing an easy-yet-comprehensive tool for mining spatial gene associations. Availability and Implementation: The Python package SEAGAL can be installed using pip: https://pypi.org/project/seagal/ . The source code and step-by-step tutorials are available at: https://github.com/linhuawang/SEAGAL . Contact: linhuaw@bcm.edu.

scGREAT: Graph-based regulatory element analysis tool for single-cell multi-omics data.

Motivation: With the development in single-cell multi-omics sequencing technology and data integration algorithms, we have entered the single-cell multi-omics era. Current multi-omics analysis algorithms failed to systematically dissect the heterogeneity within the datasets when inferring cis-regulatory events. Thus, there is a need for cis-regulatory element inferring algorithms that considers the cellular heterogeneity. Results: Here, we propose scGREAT, a single-cell multi-omics regulatory state analysis Python package with a rapid graph-based correlation measurement L. The graph-based correlation method assigns each cell a local L index, pinpointing specific cell groups of certain regulatory states. Such single-cell resolved regulatory state information enables the heterogeneity analysis equipped in the package. Applying scGREAT to the 10X Multiome PBMC dataset, we demonstrated how it could help subcluster cell types, infer regulation-based pseudo-time trajectory, discover feature modules, and find cluster-specific regulatory gene-peak pairs. Besides, we showed that global L index, which is the average of all local L values, is a better replacement for Pearson's r in ruling out confounding regulatory relationships that are not of research interests. Availability: https://github.com/ChaozhongLiu/scGREAT.

Clinical application of Mycobacterium RT-PCR assay using various specimens for the rapid detection of lymph node tuberculosis: A diagnostic accuracy study.

To evaluate the diagnostic accuracy of the Capital Bio Mycobacterium real-time polymerase chain reaction assay Capital Bio assay for lymph node (LN) tuberculosis (LNTB), and to further compare the effect of different types of LN specimens on the detection capability of the test. We retrospectively analyzed the medical records of LNTB patients who met the inclusion criteria. The sensitivity, specificity, positive predictive value, negative predictive value, and area under the curve of Capital Bio assay were calculated to evaluate its diagnostic accuracy compared with the final clinical diagnosis as reference standard. Three hundred sixty-four patients were included in the study. The overall sensitivity, specificity, positive predictive value, negative predictive value, and area under the curve of the Capital Bio assay for LNTB were 74.4%, 100.0%, 100.0%, 34.9%, and 0.87, respectively. For the pus specimens, these values for Capital Bio assay were 93.2%, 100.0%, 100.0%, 27.3%, 0.97, respectively. For the core needle biopsy specimens, these values were 65.9%, 100.0%, 100.0%, 33.3%, and 0.83, respectively. For the fine-needle aspiration specimens, these values were 60.0%, 100.0%, 100.0%, 53.9%, and 0.80, respectively. For the tissue, these values were 59.3%, 100.0%, 100.0%, 33.3%, 0.80, respectively. The Capital Bio assay had good effective for the diagnosis of LNTB. Compared to LN fine-needle aspiration and core needle biopsy specimens and tissue specimens, pus specimens were more suitable for molecular testing and had the best diagnostic efficacy.

From Diabetes to Diabetic Complications: Role of Autophagy.

Diabetes and its complications reduce quality of life and are life-limiting. At present, diabetes treatment consists of hypoglycemic agents to control blood glucose and the use of insulin-sensitizing drugs to overcome insulin resistance. In diabetes, autophagy is impaired and thus there is poor intracellular environment homeostasis. Pancreatic ß-cells and insulin target tissues are protected by enhancing autophagy. Autophagy decreases ß-cell apoptosis, promotes ß-cell proliferation, and alleviates insulin resistance. Autophagy in diabetes is regulated by the mammalian target of rapamycin (mTOR)/adenosine 5'-monophosphate (AMP)-activated protein kinase (AMPK) pathway and others. Autophagy enhancers can likely be used as a treatment for diabetes and its complications. This review examines the evidence linking autophagy to diabetes.