Knowledge-based inductive bias and domain adaptation for cell type annotation.

Measurement techniques often result in domain gaps among batches of cellular data from a specific modality. The effectiveness of cross-batch annotation methods is influenced by inductive bias, which refers to a set of assumptions that describe the behavior of model predictions. Different annotation methods possess distinct inductive biases, leading to varying degrees of generalizability and interpretability. Given that certain cell types exhibit unique functional patterns, we hypothesize that the inductive biases of cell annotation methods should align with these biological patterns to produce meaningful predictions. In this study, we propose KIDA, Knowledge-based Inductive bias and Domain Adaptation. The knowledge-based inductive bias constrains the prediction rules learned from the reference dataset, composed of multiple batches, to functional patterns relevant to biology, thereby enhancing the generalization of the model to unseen batches. Since the query dataset also contains gaps from multiple batches, KIDA's domain adaptation employs pseudo labels for self-knowledge distillation, effectively narrowing the distribution gap between model predictions and the query dataset. Benchmark experiments demonstrate that KIDA is capable of achieving accurate cross-batch cell type annotation.

Modal-nexus auto-encoder for multi-modality cellular data integration and imputation.

Heterogeneous feature spaces and technical noise hinder the cellular data integration and imputation. The high cost of obtaining matched data across modalities further restricts analysis. Thus, there's a critical need for deep learning approaches to effectively integrate and impute unpaired multi-modality single-cell data, enabling deeper insights into cellular behaviors. To address these issues, we introduce the Modal-Nexus Auto-Encoder (Monae). Leveraging regulatory relationships between modalities and employing contrastive learning within modality-specific auto-encoders, Monae enhances cell representations in the unified space. The integration capability of Monae furnishes it with modality-complementary cellular representations, enabling the generation of precise intra-modal and cross-modal imputation counts for extensive and complex downstream tasks. In addition, we develop Monae-E (Monae-Extension), a variant of Monae that can converge rapidly and support biological discoveries. Evaluations on various datasets have validated Monae and Monae-E's accuracy and robustness in multi-modality cellular data integration and imputation.

Toward a personalized autonomous transportation system: Vision, challenges, and solutions.

The fragmented design of intelligent transportation systems creates isolated intelligent systems. Resource competition and information gaps are fierce and widespread, worsening traffic issues and degrading overall service levels. Therefore, empowered by advanced technologies, an evolution toward an autonomous transportation system (ATS) is observed. This evolution aims to develop a collaborative and sustainable ecosystem, prompting interoperability within the cloud-edge-device continuum. It can, accordingly, dismantle internal resource barriers and achieve a systematic balance between demand and supply with less human intervention. Despite the promising vision of an ATS, it encounters three key challenges: disparate data, deficient models, and conflicting interests in supporting autonomous and personalized mobility. Hence, as an innovative solution, a trustworthy, private, and equal-serving framework called TPE is designed. It seamlessly integrates blockchain, federated learning, and large-scale models to deploy a trustworthy operating environment, process private data for globally shareable knowledge, and develop a foundation model for personalized adaptation, respectively. Consequently, ATSs empowered by TPE can serve diverse user groups both privately and equally.

[Construction of risk factor assessment table for hyperoxemia in patients after cardiopulmonary bypass heart surgery].

OBJECTIVE: To construct Risk factor assessment table for hyperoxemia in patients after cardiopulmonary bypass heart surgery based on Delphi method, providing a basis for early prediction and assessment of the risk of hyperoxemia in patients after cardiac surgery. METHODS: A research team was established. Based on the characteristics of extracorporeal circulation cardiac surgery, the Chinese and English literature published by each database until October 2022 was retrieved and the opinions of relevant professional clinicians were combined to screen the risk factors of hyperoxemia in patients after cardiopulmonary bypass heart surgery, and the preliminary draft of the Risk factor assessment table for hyperoxemia in patients after cardiopulmonary bypass heart surgery was drawn up. The Delphi method was used to conduct two rounds of expert letter consultation to supplement and improve the initial draft and finally established the final draft of the Risk factor assessment table for hyperoxemia in patients after cardiopulmonary bypass heart surgery. RESULTS: The preliminary draft of the Risk factor assessment table for hyperoxemia in patients after cardiopulmonary bypass heart surgery was constructed according to the literature review and the opinions of relevant professional clinicians, which contained 4 dimensions and 21 items. A total of 14 experts were consulted by letter, including 5 senior titles and 9 associate senior titles. Six of them major in critical care and the other eight major in cardiovascular surgery. The effective response rates for the two rounds of questionnaire surveys were 100% and 85.71%, expert familiarity levels were 0.81 and 0.80, judgment coefficients were 0.94 and 0.92, respectively. Expert authority coefficients were both 0.86. Coefficients of variation for the importance and feasibility items in the two rounds ranged from 0.13 to 0.32 and 0.11 to 0.32, 0.06 to 0.26 and 0.06 to 0.35, respectively. The Kendall's W for importance and feasibility in the two rounds were 0.264 and 0.162, and 0.258 and 0.144 respectively, indicating statistically significant (all P < 0.05). After two rounds of expert consultations, a comprehensive evaluation and selection process resulted in the final establishment of the Risk factor assessment table for hyperoxemia in patients after cardiopulmonary bypass heart surgery, consisting of 4 dimensions and 23 items, which included general data, past history, operation-related data and postoperative data. CONCLUSIONS: The Risk factor assessment table for hyperoxemia in patients after cardiopulmonary bypass heart surgery based on the Delphi method is highly scientific and feasible, which can provide reference for clinical assessments of the risk of hyperoxemia in such patients.

Structural basis of archaeal FttA-dependent transcription termination.

The ribonuclease FttA (also known as aCPSF and aCPSF1) mediates factor-dependent transcription termination in archaea1-3. Here we report the structure of a Thermococcus kodakarensis transcription pre-termination complex comprising FttA, Spt4, Spt5 and a transcription elongation complex (TEC). The structure shows that FttA interacts with the TEC in a manner that enables RNA to proceed directly from the TEC RNA-exit channel to the FttA catalytic centre and that enables endonucleolytic cleavage of RNA by FttA, followed by 5'→3' exonucleolytic cleavage of RNA by FttA and concomitant 5'→3' translocation of FttA on RNA, to apply mechanical force to the TEC and trigger termination. The structure further reveals that Spt5 bridges FttA and the TEC, explaining how Spt5 stimulates FttA-dependent termination. The results reveal functional analogy between bacterial and archaeal factor-dependent termination, functional homology between archaeal and eukaryotic factor-dependent termination, and fundamental mechanistic similarities in factor-dependent termination in bacteria, archaea, and eukaryotes.

GMMID: genetically modified mice information database.

Genetically engineered mouse models (GEMMs) are vital for elucidating gene function and disease mechanisms. An overwhelming number of GEMM lines have been generated, but endeavors to collect and organize the information of these GEMMs are seriously lagging behind. Only a few databases are developed for the information of current GEMMs, and these databases lack biological descriptions of allele compositions, which poses a challenge for nonexperts in mouse genetics to interpret the genetic information of these mice. Moreover, these databases usually do not provide information on human diseases related to the GEMM, which hinders the dissemination of the insights the GEMM provides as a human disease model. To address these issues, we developed an algorithm to annotate all the allele compositions that have been reported with Python programming and have developed the genetically modified mice information database (GMMID; http://www.gmmid.cn), a user-friendly database that integrates information on GEMMs and related diseases from various databases, including National Center for Biotechnology Information, Mouse Genome Informatics, Online Mendelian Inheritance in Man, International Mouse Phenotyping Consortium, and Jax lab. GMMID provides comprehensive genetic information on >70 055 alleles, 65 520 allele compositions, and ∼4000 diseases, along with biologically meaningful descriptions of alleles and allele combinations. Furthermore, it provides spatiotemporal visualization of anatomical tissues mentioned in these descriptions, shown alongside the allele compositions. Compared to existing mouse databases, GMMID considers the needs of researchers across different disciplines and presents obscure genetic information in an intuitive and easy-to-understand format. It facilitates users in obtaining complete genetic information more efficiently, making it an essential resource for cross-disciplinary researchers. Database URL: http://www.gmmid.cn.

Abnormal Insula Network Characteristics in Panic Disorder.

BACKGROUND: Panic disorder (PD) is a common disabling condition characterized by recurrent panic attacks. Emotional and behavioral impairments are associated with functional connectivity (FC) and network abnormalities. We used the whole brain FC, modular networks, and graph-theory analysis to investigate extensive network profiles in PD. METHOD: The functional MRI data from 82 PD and 97 controls were included. Intrinsic FC between each pair of 160 regions, 6 intra-networks, and 15 inter-networks were analyzed. The topological properties were explored. RESULTS: PD patients showed altered FCs within the right insula, between frontal cortex-posterior cingulate cortex (PCC), frontal cortex-cerebellum, and PCC-occipital cortex (corrected P values < 0.001). Lower connections within the Sensorimotor Network (SMN) and SMN-Occipital Network (OCN) were detected (P values < 0.05). Various decreased global and local network features were found in PD (P values < 0.05). In addition, significant correlations were found between PD symptoms and nodal efficiency (Ne) in the insula (r = -0.273, P = 0.016), and the FC of the intra-insula (r = -0.226, P = 0.041). CONCLUSIONS: PD patients present with abnormal functional brain networks, especially the decreased FC and Ne within insula, suggesting that dysfunction of information integration plays an important role in PD.

Distinguishing bipolar depression, bipolar mania, and major depressive disorder by gut microbial characteristics.

BACKGROUND: Gut microbial disturbance has been widely confirmed in mood disorders. However, little is known about whether gut microbial characteristics can distinguish major depressive disorder (MDD), bipolar depression (BP-D), and bipolar mania (BP-M). METHODS: This was a prospective case-control study. The composition of gut microbiota was profiled using 16S ribosomal RNA (rRNA) gene sequencing of fecal samples and compared between healthy controls (HC; n = 46), MDD (n = 51), BP-D (n = 44), and patients with BP-M (n = 45). RESULTS: Gut microbial compositions were remarkably changed in the patients with MDD, BP-D, and BP-M. Compared to HC, distinct gut microbiome signatures were found in MDD, BP-D, and BP-M, and some gut microbial changes were overlapping between the three mood disorders. Furthermore, we identified a signature of 7 operational taxonomic units (OUT; Prevotellaceae-related OUT22, Prevotellaceae-related OUT31, Prevotellaceae-related OTU770, Ruminococcaceae-related OUT70, Bacteroidaceae-related OTU1536, Propionibacteriaceae-related OTU97, Acidaminococcaceae-related OTU34) that can distinguish patients with MDD from those with BP-D, BP-M, or HC, with area under the curve (AUC) values ranging from 0.910 to 0.996. CONCLUSION: Our results provide the clinical rationale for the discriminative diagnosis of MDD, BP-D, and BP-M by characteristic gut microbial features.

DNA looping mediates cooperative transcription activation.

Transcription factors respond to multilevel stimuli and co-occupy promoter regions of target genes to activate RNA polymerase (RNAP) in a cooperative manner. To decipher the molecular mechanism, here we report two cryo-electron microscopy structures of Anabaena transcription activation complexes (TACs): NtcA-TAC composed of RNAP holoenzyme, promoter and a global activator NtcA, and NtcA-NtcB-TAC comprising an extra context-specific regulator, NtcB. Structural analysis showed that NtcA binding makes the promoter DNA bend by ∼50°, which facilitates RNAP to contact NtcB at the distal upstream NtcB box. The sequential binding of NtcA and NtcB induces looping back of promoter DNA towards RNAP, enabling the assembly of a fully activated TAC bound with two activators. Together with biochemical assays, we propose a 'DNA looping' mechanism of cooperative transcription activation in bacteria.

Childhood trauma and social support affect symptom profiles through cortical thickness abnormalities in major depressive disorder: A structural equation modeling analysis.

BACKGROUND: Childhood trauma, low social support, and alexithymia are recognized as risk factors for major depressive disorder (MDD). However, the mechanisms of risk factors, symptoms, and corresponding structural brain abnormalities in MDD are not fully understood. Structural equation modeling (SEM) has advantages in studying multivariate interrelationships. We aim to illustrate their relationships using SEM. METHODS: 313 MDD patients (213 female; mean age 42.49 years) underwent magnetic resonance imaging and completed assessments. We integrated childhood trauma, alexithymia, social support, anhedonia, depression, anxiety, suicidal ideation and cortical thickness into a multivariate SEM. RESULTS: We first established the risk factors-clinical phenotype SEM with an adequate fit. Cortical thickness results show a negative correlation of childhood trauma with the left middle temporal gyrus (MTG) (p = 0.012), and social support was negatively correlated with the left posterior cingulate cortex (PCC) (p < 0.001). The final good fit SEM (χ2 = 32.92, df = 21, χ2/df = 1.57, CFI = 0.962, GFI = 0.978, RMSEA = 0.043) suggested two pathways, with left PCC thickness mediating the relationship between social support and suicidal ideation, and left MTG thickness mediating between childhood trauma and anhedonia/anxiety. CONCLUSION: Our findings provide evidence for the impact of risk factor variables on the brain structure and clinical phenotype of MDD patients. Insufficient social support and childhood trauma might lead to corresponding cortical abnormalities in PCC and MTG, affecting the patient's mood and suicidal ideation. Future interventions should aim at these nodes.

An SI3-σ arch stabilizes cyanobacteria transcription initiation complex.

Multisubunit RNA polymerases (RNAPs) associate with initiation factors (σ in bacteria) to start transcription. The σ factors are responsible for recognizing and unwinding promoter DNA in all bacterial RNAPs. Here, we report two cryo-EM structures of cyanobacterial transcription initiation complexes at near-atomic resolutions. The structures show that cyanobacterial RNAP forms an "SI3-σ" arch interaction between domain 2 of σA (σ2) and sequence insertion 3 (SI3) in the mobile catalytic domain Trigger Loop (TL). The "SI3-σ" arch facilitates transcription initiation from promoters of different classes through sealing the main cleft and thereby stabilizing the RNAP-promoter DNA open complex. Disruption of the "SI3-σ" arch disturbs cyanobacteria growth and stress response. Our study reports the structure of cyanobacterial RNAP and a unique mechanism for its transcription initiation. Our data suggest functional plasticity of SI3 and provide the foundation for further research into cyanobacterial and chloroplast transcription.

Common susceptibility variants of KDR and IGF-1R are associated with poststroke depression in the Chinese population.

Background: Depression, one of the most frequent complications after stroke, increases the disease's burden and physical disability. Poststroke depression (PSD) is a multifactorial disease with genetic, environmental and biological factors involved in its occurrence. Genetic studies on PSD to date have mainly focused on the monoamine system and brain-derived neurotrophic factors. However, understanding is still limited about the influence of the single nucleotide polymorphism (SNP) of other neurotrophic factors on PSD. Aims: The present study aimed to investigate the relationship between seven vascular endothelial growth factor (VEGF) family gene variants that occur with PSD. Methods: A multicentre candidate gene study from five hospitals in Jiangsu Province from June 2013 to December 2014 involved 121 patients with PSD and 131 patients with non-PSD. Demographic characteristics and neuropsychological assessments were collected. The χ2 test was used to evaluate categorical variables, while the independent t-test was applied to continuous variables. SNPs in seven genes (VEGFA, VEGFB, KDR, FLT-1, IGF-1, IGF-1R and PlGF) were genotyped. Single-marker association for PSD was analysed by χ2 tests and logistic regression using SPSS and PLINK software. Results: Patients with PSD included more women and those with lower education levels, lower body mass indexes, lower Mini-Mental State Examination scores, and higher scores on the 17-item Hamilton Depression Rating Scale than non-PSD patients. Ninety-two SNPs with seven genes were genotyped and passed quality control. The rs7692791 CC genotypes, the C allele of KDR and the rs9282715 T allele of IGF-1R increased the risk for PSD (χ2=7.881, p=0.019; χ2=4.259, p=0.039; χ2=4.222, p=0.040, respectively). In addition, the SNP rs7692791 of KDR was significantly associated with PSD by the logistic regression of an additive model (p=0.015, OR=9.584, 95% CI: 1.549 to 59.31). Conclusions: Patients with rs7692791 C allele carriers or the CC genotype of KDR and the rs9282715 T allele of IGF-1R may have PSD susceptibility. Findings such as these may help clinicians to identify the high-risk population for PSD earlier and, thus, enable them to provide more timely interventions. Trial registration number: ChiCTR-OCH-13003133.

Structural basis for intrinsic transcription termination.

Efficient and accurate termination is required for gene transcription in all living organisms1,2. Cellular RNA polymerases in both bacteria and eukaryotes can terminate their transcription through a factor-independent termination pathway3,4-called intrinsic termination transcription in bacteria-in which RNA polymerase recognizes terminator sequences, stops nucleotide addition and releases nascent RNA spontaneously. Here we report a set of single-particle cryo-electron microscopy structures of Escherichia coli transcription intrinsic termination complexes representing key intermediate states of the event. The structures show how RNA polymerase pauses at terminator sequences, how the terminator RNA hairpin folds inside RNA polymerase, and how RNA polymerase rewinds the transcription bubble to release RNA and then DNA. These macromolecular snapshots define a structural mechanism for bacterial intrinsic termination and a pathway for RNA release and DNA collapse that is relevant for factor-independent termination by all RNA polymerases.

Serum metabolomic profiling revealed potential diagnostic biomarkers in patients with panic disorder.

BACKGROUND: Currently, specific metabolites and diagnostic biomarkers of panic disorder (PD) patients have not been identified in clinical practice. The aim of this study was to explore metabolites and metabolic pathways in serum through a metabolomics method. METHODS: Fifty-five PD patients who completed 2 weeks of inpatient treatment and 55 healthy control subjects (HCs) matched for age, sex and BMI were recruited. Ultra-high performance liquid chromatography tandem mass spectrometry (UHPLC-MS/MS) was used to detect metabolites in serum. Multivariate Statistical Analysis was used to identify differential metabolites. The relevant biometabolic pathways were further identified by the online tool MetaboAnalyst 5.0. RESULTS: 43 different metabolites in PD patients compared to HCs (P < 0.05) were screened. Pathway analysis showed that these small molecules were mainly associated with amino acid metabolism. 14 metabolites were significantly changed after 2 weeks of drug treatment (P < 0.05), which were mainly associated with tryptophan metabolism. CONCLUSION: In conclusion, our analysis of metabolomics of PD patients at baseline and two weeks after treatment screened for differential metabolites that could be potential diagnostic biomarkers involved in PD pathogenesis and influence some biometabolic pathways such as phenylalanine metabolism and tryptophan metabolism. In the future, we can summarize and observe the dynamic changes of differential metabolites that appear more frequently in similar studies to further explore the underlying mechanisms of PD evolution.

[Research progress in the evaluation of post-intensive care syndrome].

Post-intensive care syndrome (PICS) is the most common complication in patients discharged from intensive care unit (ICU), which seriously affects the life quality of the patients. At present, there is still lack of standardevaluation methods for PICS. Continuous and dynamic assessment can earlyidentify PICS, moreover, early identification and intervention of PICS can improve the life quality of patients those patients, which is critical to improve the long-term outcome of the patients. In this paper, we reviewed the current research states of evaluation timing, contents, tools and modalities of PICS domestic and abroad, analyzed the problems and prospects of the existing evaluation methods, aiming to provide a reference for clinical staff to effectively and comprehensively evaluate PICS.

Pseudomonas aeruginosa SutA wedges RNAP lobe domain open to facilitate promoter DNA unwinding.

Pseudomonas aeruginosa (Pae) SutA adapts bacteria to hypoxia and nutrition-limited environment during chronic infection by increasing transcription activity of an RNA polymerase (RNAP) holoenzyme comprising the stress-responsive σ factor σS (RNAP-σS). SutA shows no homology to previously characterized RNAP-binding proteins. The structure and mode of action of SutA remain unclear. Here we determined cryo-EM structures of Pae RNAP-σS holoenzyme, Pae RNAP-σS holoenzyme complexed with SutA, and Pae RNAP-σS transcription initiation complex comprising SutA. The structures show SutA pinches RNAP-ß protrusion and facilitates promoter unwinding by wedging RNAP-ß lobe open. Our results demonstrate that SutA clears an energetic barrier to facilitate promoter unwinding of RNAP-σS holoenzyme.

Clinical Efficacy of the Chinese Herbal Medicine Shumian Capsule for Insomnia: A Randomized, Double-Blind, Placebo-Controlled Trial.

Purpose: Shumian capsule (SMC) is a patent Chinese herbal medicine that can soothe the liver and relieves depression, quiet the spirit. Here, we aimed to investigate the efficacy of SMC for treating insomnia using both scales and polysomnography (PSG). Patients and Methods: A randomized, double-blind, placebo-controlled trial was performed. Twenty-six insomnia patients randomly received SMC (n = 11) or placebo (n = 15) for four weeks. Pittsburgh Sleep Quality Inventory (PSQI), Insomnia Severity Index (ISI), 9-items Patient Health Questionnaire (PHQ-9), 7-items Generalized Anxiety Disorder (GAD-7), 17-item Hamilton Depression Rating Scale (HAMD-17), and Hamilton Anxiety Rating Scale (HAMA) were applied at the baseline and the 2nd, 4th week after treatment. Treatment Emergent Symptom Scale was used to assess adverse reactions. We used PSG to record and analyze sleep features at baseline and after four weeks. Results: PSQI, ISI, PHQ-9, HAMD-17, and HAMA scores decreased significantly after SMC treatment. Also, the total sleep time, rapid-eye-movement (REM) sleep latency, stage 2 sleep, deep sleep, REM sleep, and sleep efficiency improved significantly after SMC treatment. In the placebo group, the only significant change was the decrease of PHQ-9 at week-2. Furthermore, both SMC and placebo reported no adverse events. Conclusion: SMC could safely improve sleep quality with depression and anxiety remission in insomnia patients.

Pol IV and RDR2: A two-RNA-polymerase machine that produces double-stranded RNA.

DNA methylation affects gene expression and maintains genome integrity. The DNA-dependent RNA polymerase IV (Pol IV), together with the RNA-dependent RNA polymerase RDR2, produces double-stranded small interfering RNA precursors essential for establishing and maintaining DNA methylation in plants. We determined the cryo­electron microscopy structures of the Pol IV­RDR2 holoenzyme and the backtracked transcription elongation complex. These structures reveal that Pol IV and RDR2 form a complex with their active sites connected by an interpolymerase channel, through which the Pol IV­generated transcript is handed over to the RDR2 active site after being backtracked, where it is used as the template for double-stranded RNA (dsRNA) synthesis. Our results describe a 'backtracking-triggered RNA channeling' mechanism underlying dsRNA synthesis and also shed light on the evolutionary trajectory of eukaryotic RNA polymerases.