Modeling single cell trajectory using forward-backward stochastic differential equations.

Recent advances in single-cell sequencing technology have provided opportunities for mathematical modeling of dynamic developmental processes at the single-cell level, such as inferring developmental trajectories. Optimal transport has emerged as a promising theoretical framework for this task by computing pairings between cells from different time points. However, optimal transport methods have limitations in capturing nonlinear trajectories, as they are static and can only infer linear paths between endpoints. In contrast, stochastic differential equations (SDEs) offer a dynamic and flexible approach that can model non-linear trajectories, including the shape of the path. Nevertheless, existing SDE methods often rely on numerical approximations that can lead to inaccurate inferences, deviating from true trajectories. To address this challenge, we propose a novel approach combining forward-backward stochastic differential equations (FBSDE) with a refined approximation procedure. Our FBSDE model integrates the forward and backward movements of two SDEs in time, aiming to capture the underlying dynamics of single-cell developmental trajectories. Through comprehensive benchmarking on multiple scRNA-seq datasets, we demonstrate the superior performance of FBSDE compared to other methods, highlighting its efficacy in accurately inferring developmental trajectories.

Health Impacts of Fine Particulate Matter Shift Due to Urbanization in China.

Rapid urbanization and industrialization have resulted in diverse anthropogenic activities and emissions between urban and non-urban regions, leading to varying levels of exposure to air pollutants and associated health risks. However, endeavors to mitigate air pollution and health benefits have displayed considerable heterogeneity across different regions. Therefore, comprehending the changes in air pollutant concentrations and health impacts within an urbanization context is imperative for promoting environmental equity. This paper uses gross domestic product (GDP)- and population-weighted methods to distinguish anthropogenic emissions from urban and non-urban areas in China and quantified their contributions to fine particulate matter (PM2.5) using the Community Multiscale Air Quality (CMAQ) model in 2010 and 2019. Anthropogenic emissions from urban and non-urban (outside urban) regions decreased by 26 and 44% from 2010 to 2019, respectively, resulting in 31 and 28% reductions of PM2.5 in China. PM2.5-related premature mortality attributed to non-urban and urban anthropogenic emission decreases by 8%. Non-urban anthropogenic activities are the main contributor to PM2.5 (56% in 2010 and 2019) and its associated premature mortality (59%), which also predominantly affects non-urban premature mortality (37-42% in 2010-2019). Population changes increase the proportion of premature mortality in urban populations (7-19%) from 2010 to 2019. This study emphasizes the shift of affected populations due to urbanization and population changes.

An Effective and Promising Strategy for Plant Protection: Synthesis of L-Carvone-Based Thiazolinone-Hydrazone/Nanochitosan Complexes with Antifungal Activity and Sustained Releasing Performance.

The development of novel natural product-derived nano-pesticide systems with loading capacity and sustained releasing performance of bioactive compounds is considered an effective and promising plant protection strategy. In this work, 25 L-carvone-based thiazolinone-hydrazone compounds 4a~4y were synthesized by the multi-step modification of L-carvone and structurally confirmed. Compound 4h was found to show favorable and broad-spectrum antifungal activity through the in vitro antifungal activity evaluation of compounds 4a~4y against eight phytopathogenic fungi. Thus, it could serve as a leading compound for new antifungal agents in agriculture. Moreover, the L-carvone-based nanochitosan carrier 7 bearing the 1,3,4-thiadiazole-amide group was rationally designed for the loading and sustained releasing applications of compound 4h, synthesized, and characterized. It was proven that carrier 7 had good thermal stability below 200 °C, dispersed well in the aqueous phase to form numerous nanoparticles with a size of~20 nm, and exhibited an unconsolidated and multi-aperture micro-structure. Finally, L-carvone-based thiazolinone-hydrazone/nanochitosan complexes were fabricated and investigated for their sustained releasing behaviors. Among them, complex 7/4h-2 with a well-distributed, compact, and columnar micro-structure displayed the highest encapsulation efficiency and desirable sustained releasing property for compound 4h and thus showed great potential as an antifungal nano-pesticide for further studies.

[Tonglong Kaibi Prescription for the treatment of severe benign prostate hyperplasia: A clinical study].

OBJECTIVE: To evaluate the clinical effect of the traditional Chinese medicine (TCM) Tonglong Kaibi Prescription (TKP) in the treatment of severe BPH with kidney deficiency and blood stasis combined with damp heat syndrome. METHODS: We randomly divided 120 cases of severe BPH with kidney deficiency and blood stasis combined with damp heat syndrome into three groups of equal number, treated with TKP, doxazosin mesylate sustained-release tablets (the DM control), and TKP + DM, all for 8 weeks. We obtained the IPSS, TCM symptoms scores, quality of life (QOL) scores, maximum urinary flow rate (Qmax) and postvoid residual urine volume (PVR) from the patients before and after treatment and compared them among the three groups. RESULTS: After 8 weeks of treatment, the effectiveness rate was significantly higher in the TKP + DM than in the DM control group (P < 0.05). The IPSS, TCM symptoms scores, QOL scores and PVR decreased (P < 0.01), while the Qmax increased dramatically (P < 0.01) in all the three groups. Pairwise comparison showed that the IPSS and QOL scores were lower in the TKP + DM than in the TKP and DM control groups (P < 0.05 or 0.01), and so were the TCM syndrome scores in the TKP + DM and TKP groups than in the DM control (P < 0.01). There were no statistically significant differences in PVR and Qmax among the three groups after treatment (P> 0.05), and no serious adverse events during the treatment. CONCLUSION: TKP is safe and effective in the treatment of severe BPH, which can improve the TCM symptoms, reduce the IPSS, QOL scores and PVR and increase the Qmax of the patients. TKP is evidently superior to DM alone in improving TCM symptoms of BPH and combined medication of TKP and DM produces even better clinical efficacy.

Deciphering 3'UTR Mediated Gene Regulation Using Interpretable Deep Representation Learning.

The 3' untranslated regions (3'UTRs) of messenger RNAs contain many important cis-regulatory elements that are under functional and evolutionary constraints. It is hypothesized that these constraints are similar to grammars and syntaxes in human languages and can be modeled by advanced natural language techniques such as Transformers, which has been very effective in modeling complex protein sequence and structures. Here 3UTRBERT is described, which implements an attention-based language model, i.e., Bidirectional Encoder Representations from Transformers (BERT). 3UTRBERT is pre-trained on aggregated 3'UTR sequences of human mRNAs in a task-agnostic manner; the pre-trained model is then fine-tuned for specific downstream tasks such as identifying RBP binding sites, m6A RNA modification sites, and predicting RNA sub-cellular localizations. Benchmark results show that 3UTRBERT generally outperformed other contemporary methods in each of these tasks. More importantly, the self-attention mechanism within 3UTRBERT allows direct visualization of the semantic relationship between sequence elements and effectively identifies regions with important regulatory potential. It is expected that 3UTRBERT model can serve as the foundational tool to analyze various sequence labeling tasks within the 3'UTR fields, thus enhancing the decipherability of post-transcriptional regulatory mechanisms.

The first complete chloroplast genome of Cyclamen persicum and its phylogenetic position within Primulaceae.

Cyclamen persicum Mill., 1768, is a perennial herb of Primulaceae and is native to the Mediterranean region. In this study, we sequenced the chloroplast genome of Cyclamen persicum. Its complete chloroplast contained 151,911 nucleotides, including a large single-copy (LSC) region with a length of 83,191 bp, a small single-copy (SSC) region with a length of 17,922 bp, and a pair of reverse repeat IR regions (25,399 bp). The C. persicum chloroplast genome encoded 112 unique genes, including 78 protein-coding genes, 30 tRNA genes, and four rRNA genes. The GC content of the entire genome was 37.25%, lower than that of many angiosperm plastome. The phylogenetic result indicated that C. persicum exhibited the closest relationship with Cyclamen rohlfsianum, and provided new information for the phylogeny relationship of genus Cyclamen.

scGREAT: Transformer-based deep-language model for gene regulatory network inference from single-cell transcriptomics.

Gene regulatory networks (GRNs) involve complex and multi-layer regulatory interactions between regulators and their target genes. Precise knowledge of GRNs is important in understanding cellular processes and molecular functions. Recent breakthroughs in single-cell sequencing technology made it possible to infer GRNs at single-cell level. Existing methods, however, are limited by expensive computations, and sometimes simplistic assumptions. To overcome these obstacles, we propose scGREAT, a framework to infer GRN using gene embeddings and transformer from single-cell transcriptomics. scGREAT starts by constructing gene expression and gene biotext dictionaries from scRNA-seq data and gene text information. The representation of TF gene pairs is learned through optimizing embedding space by transformer-based engine. Results illustrated scGREAT outperformed other contemporary methods on benchmarks. Besides, gene representations from scGREAT provide valuable gene regulation insights, and external validation on spatial transcriptomics illuminated the mechanism behind scGREAT annotation. Moreover, scGREAT identified several TF target regulations corroborated in studies.

Complete mitochondrial DNA sequence of Alboglossiphonia lata Oka, 1910 (Rhynchobdellida: Glossiphoniidae) and its phylogenetic analysis.

The complete mitochondrial genome of Alboglossiphonia lata (basionym: Glossiphonia lata), sourced from a biodiversity hotspot of China, has been determined and reported in this study. It was 15,236 bp in length and consisted of 13 protein-coding genes, 22 transfer RNA genes, 2 ribosomal RNA genes and three control regions. The mitogenome was deposited GenBank under the accession number PP165800. A. lata and other species within the Glossiphoniidae family were clustered together with high bootstrap values. The mitochondrial genome of A. lata provides valuable molecular data for further phylogenetic research on the Glossiphoniidae family.

Long-term variations of air pollutants and public exposure in China during 2000-2020.

Since 2000, China has faced severe air pollution challenges，prompting the initiation of comprehensive emission control measures post-2013. The subsequent implementation of these measures has led to remarkable enhancements in air quality. This study aims to enhance our understanding of the long-term trends in fine particulate matter (PM2.5) and gaseous pollutants of ozone (O3) and nitrogen dioxide (NO2) across China from 2000 to 2020. Utilizing the Community Multiscale Air Quality (CMAQ) model, we conducted a nationwide analysis of air quality, systematically quantifying model predictions against observations for pollutants. The CMAQ model effectively captured the trends of air pollutants, meeting recommended performance benchmarks. The findings reveal variations in pollutant concentrations, with initial increases in PM2.5 followed by a decline after 2013. The proportion of the population living in high PM2.5 concentrations (>75 µg/m3) decreased to <5 % after 2015. However, during the period from 2017 to 2020, around 40 % of the population continued to live in regions that did not meet the criteria for Chinese air quality standards (35 µg/m3). From 2000 to 2019, fewer than 20 % of the population met the WHO standard (100 µg/m3) for MDA8 O3. In 2000, 77 % of the population met the NO2 standard (<20 µg/m3), a figure that declined to 60 % between 2005 and 2014, nearly reaching 70 % in 2020. This study offers a comprehensive analysis of the changes in pollutants and public exposure in 2000-2020. It serves as a foundational resource for future efforts in air pollution control and health research.

The energy metabolism of the freshwater leech Whitmania pigra in response to fasting.

Non-blood-feeding leeches, Whitmania pigra, have evolved unique digestive structures and physiological mechanisms to cope with fasting. However, the metabolic changes and molecular mechanisms induced by fasting remain unclear. Therefore, this study recorded the weights of leeches during the fasting process. The weight changes were divided into two stages: a rapid decline period (1-9 weeks) and a fluctuating decline period (9-24 weeks). Leeches fasted for 4 (H4), 11 (H11), and 24 (H24) weeks were selected for transcriptome sequencing. Compared to the control group (H0), 436, 1157, and 337 differentially expressed genes (DEGs) were identified, which were mainly related to glycolysis/gluconeogenesis, amino acid metabolism, and the lipid metabolism pathway. The 6-phosphofructokinase (Pfk), pyruvate kinase (PK), and phosphoenolpyruvate carboxykinase (Pck) transcription levels revealed glycolysis/gluconeogenesis activation during the early stage of fasting and peaked at 11 weeks. Decreased expression of the rate-limiting enzyme acetyl-CoA carboxylase (ACC) in fatty acid synthesis during fasting may impede fatty acid synthesis. These results indicated that the nutrient storage and energy-supplying pathways in W. pigra were modified to improve fasting resistance. The findings of this study provided guidance for exploring the mechanism underlying fasting metabolism and laid a foundation for artificial breeding to improve the resistance of leeches.

Mutations in myeloid transcription factors and activated signaling genes predict chronic myeloid leukemia outcomes.

ABSTRACT: Advancements in genomics are transforming the clinical management of chronic myeloid leukemia (CML) toward precision medicine. The impact of somatic mutations on treatment outcomes is still under debate. We studied the association of somatic mutations in epigenetic modifier genes and activated signaling/myeloid transcription factors (AS/MTFs) with disease progression and treatment failure in patients with CML after tyrosine kinase inhibitor (TKI) therapy. A total of 394 CML samples were sequenced, including 254 samples collected at initial diagnosis and 140 samples taken during follow-up. Single-molecule molecular inversion probe (smMIP)-based next-generation sequencing (NGS) was conducted targeting recurrently mutated loci in 40 genes, with a limit of detection of 0.2%. Seventy mutations were detected in 57 diagnostic samples (22.4%), whereas 64 mutations were detected in 39 of the follow-up samples (27.9%). Carrying any mutation at initial diagnosis was associated with worse outcomes after TKI therapy, particularly in AS/MTF genes. Patients having these mutations at initial diagnosis and treated with imatinib showed higher risks of treatment failure (hazard ratio, 2.53; 95% confidence interval, 1.13-5.66; P = .0239). The adverse prognostic impact of the mutations was not clear for patients treated with second-generation TKIs. The multivariate analysis affirmed that mutations in AS/MTF genes independently serve as adverse prognostic factors for molecular response, failure-free survival, and progression risk. Additionally, there was an observable nonsignificant trend indicating a heightened risk of progression to advanced disease and worse overall survival. In conclusion, mutations in the AS/MTF genes using smMIP-based NGS can help identify patients with a potential risk of both treatment failure and progression and may help upfront TKI selection.