mTOR Activation Underlies Enhanced B Cell Proliferation and Autoimmunity in PrkcdG510S/G510S Mice.

Autosomal recessive PRKCD deficiency has previously been associated with the development of systemic lupus erythematosus in human patients, but the mechanisms underlying autoimmunity remain poorly understood. We introduced the Prkcd G510S mutation that we previously associated to a Mendelian cause of systemic lupus erythematosus in the mouse genome, using CRISPR-Cas9 gene editing. PrkcdG510S/G510S mice recapitulated the human phenotype and had reduced lifespan. We demonstrate that this phenotype is linked to a B cell-autonomous role of Prkcd. A detailed analysis of B cell activation in PrkcdG510S/G510S mice shows an upregulation of the PI3K/mTOR pathway after the engagement of the BCR in these cells, leading to lymphoproliferation. Treatment of mice with rapamycin, an mTORC1 inhibitor, significantly improves autoimmune symptoms, demonstrating in vivo the deleterious effect of mTOR pathway activation in PrkcdG510S/G510S mice. Additional defects in PrkcdG510S/G510S mice include a decrease in peripheral mature NK cells that might contribute to the known susceptibility to viral infections of patients with PRKCD mutations.

Mapping individual structural covariance network in development brain with dynamic time warping.

A conspicuous property of brain development or maturity is coupled with coordinated or synchronized brain structural co-variation. However, there is still a lack of effective approach to map individual structural covariance network. Here, we developed a novel individual structural covariance network method using dynamic time warping algorithm and applied it to delineate developmental trajectories of topological organizations of structural covariance network from childhood to early adulthood with a large sample of 655 individuals from Human Connectome Project-Development dataset. We found that the individual structural covariance network exhibited small-worldness property and the network global topological characteristics including small-worldness, global efficiency, local efficiency, and modularity linearly increase with age while the shortest path length linearly decreases with age. The nodal topological properties including betweenness and degree increased with age in language and emotion regulation related brain areas, while it decreased with age mainly in visual cortex, sensorimotor area, and hippocampus. Moreover, the topological attributes of structural covariance network as features could predict the age of each individual. Taken together, our results demonstrate that dynamic time warping can effectively map individual structural covariance network to uncover the developmental trajectories of network topology, which may facilitate future investigations to establish the links of structural co-variations with respect to cognition and disease vulnerability.

Efficient control of western flower thrips by plastid-mediated RNA interference.

Plastid-mediated RNA interference (PM-RNAi) has emerged as a promising strategy for pest control. Expression from the plastid genome of stable double-stranded RNAs (dsRNAs) targeted against essential insect genes can effectively control some herbivorous beetles, but little is known about the efficacy of the transplastomic approach in other groups of pest insects, especially nonchewing insects that do not consume large amounts of leaf material. Here we have investigated the susceptibility of the western flower thrip (WFT, Frankliniella occidentalis), a notorious pest in greenhouses and open fields, to PM-RNAi. We show that WFTs ingest chloroplasts and take up plastid-expressed dsRNAs. We generated a series of transplastomic tobacco plants expressing dsRNAs and hairpin RNAs (hpRNAs) targeted against four essential WFT genes. Unexpectedly, we discovered plastid genome instability in transplastomic plants expressing hpRNAs, suggesting that dsRNA cassettes are preferable over hpRNA cassettes when designing PM-RNAi strategies. Feeding studies revealed that, unlike nuclear transgenic plants, transplastomic plants induced a potent RNAi response in WFTs, causing efficient suppression of the targeted genes and high insect mortality. Our study extends the application range of PM-RNAi technology to an important group of nonchewing insects, reveals design principles for the construction of dsRNA-expressing transplastomic plants, and provides an efficient approach to control one of the toughest insect pests in agriculture and horticulture.

InClust+: the deep generative framework with mask modules for multimodal data integration, imputation, and cross-modal generation.

BACKGROUND: With the development of single-cell technology, many cell traits can be measured. Furthermore, the multi-omics profiling technology could jointly measure two or more traits in a single cell simultaneously. In order to process the various data accumulated rapidly, computational methods for multimodal data integration are needed. RESULTS: Here, we present inClust+, a deep generative framework for the multi-omics. It's built on previous inClust that is specific for transcriptome data, and augmented with two mask modules designed for multimodal data processing: an input-mask module in front of the encoder and an output-mask module behind the decoder. InClust+ was first used to integrate scRNA-seq and MERFISH data from similar cell populations, and to impute MERFISH data based on scRNA-seq data. Then, inClust+ was shown to have the capability to integrate the multimodal data (e.g. tri-modal data with gene expression, chromatin accessibility and protein abundance) with batch effect. Finally, inClust+ was used to integrate an unlabeled monomodal scRNA-seq dataset and two labeled multimodal CITE-seq datasets, transfer labels from CITE-seq datasets to scRNA-seq dataset, and generate the missing modality of protein abundance in monomodal scRNA-seq data. In the above examples, the performance of inClust+ is better than or comparable to the most recent tools in the corresponding task. CONCLUSIONS: The inClust+ is a suitable framework for handling multimodal data. Meanwhile, the successful implementation of mask in inClust+ means that it can be applied to other deep learning methods with similar encoder-decoder architecture to broaden the application scope of these models.

Enhancing pathological complete response prediction in breast cancer: the role of dynamic characterization of DCE-MRI and its association with tumor heterogeneity.

BACKGROUND: Early prediction of pathological complete response (pCR) is important for deciding appropriate treatment strategies for patients. In this study, we aimed to quantify the dynamic characteristics of dynamic contrast-enhanced magnetic resonance images (DCE-MRI) and investigate its value to improve pCR prediction as well as its association with tumor heterogeneity in breast cancer patients. METHODS: The DCE-MRI, clinicopathologic record, and full transcriptomic data of 785 breast cancer patients receiving neoadjuvant chemotherapy were retrospectively included from a public dataset. Dynamic features of DCE-MRI were computed from extracted phase-varying radiomic feature series using 22 CAnonical Time-sereis CHaracteristics. Dynamic model and radiomic model were developed by logistic regression using dynamic features and traditional radiomic features respectively. Various combined models with clinical factors were also developed to find the optimal combination and the significance of each components was evaluated. All the models were evaluated in independent test set in terms of area under receiver operating characteristic curve (AUC). To explore the potential underlying biological mechanisms, radiogenomic analysis was implemented on patient subgroups stratified by dynamic model to identify differentially expressed genes (DEGs) and enriched pathways. RESULTS: A 10-feature dynamic model and a 4-feature radiomic model were developed (AUC = 0.688, 95%CI: 0.635-0.741 and AUC = 0.650, 95%CI: 0.595-0.705) and tested (AUC = 0.686, 95%CI: 0.594-0.778 and AUC = 0.626, 95%CI: 0.529-0.722), with the dynamic model showing slightly higher AUC (train p = 0.181, test p = 0.222). The combined model of clinical, radiomic, and dynamic achieved the highest AUC in pCR prediction (train: 0.769, 95%CI: 0.722-0.816 and test: 0.762, 95%CI: 0.679-0.845). Compared with clinical-radiomic combined model (train AUC = 0.716, 95%CI: 0.665-0.767 and test AUC = 0.695, 95%CI: 0.656-0.714), adding the dynamic component brought significant improvement in model performance (train p < 0.001 and test p = 0.005). Radiogenomic analysis identified 297 DEGs, including CXCL9, CCL18, and HLA-DPB1 which are known to be associated with breast cancer prognosis or angiogenesis. Gene set enrichment analysis further revealed enrichment of gene ontology terms and pathways related to immune system. CONCLUSION: Dynamic characteristics of DCE-MRI were quantified and used to develop dynamic model for improving pCR prediction in breast cancer patients. The dynamic model was associated with tumor heterogeniety in prognostic-related gene expression and immune-related pathways.

Plasma ofCS-modified CD44 predicts the survival of patients with lung cancer.

Plasma levels of oncofetal chondroitin sulfate (ofCS)-modified CD44 have emerged as a promising biomarker for multi-cancer detection. Here, we explored its potential to predict the survival of patients with lung cancer. A prospective observational cohort was conducted involving 274 newly diagnosed patients with lung cancer at the Sun Yat-sen University Cancer Center from 2013 to 2015. The plasma levels of ofCS-modified CD44 were measured, and Cox regression analysis was performed to assess the association between plasma-modified CD44 levels and overall survival (OS) as well as other prognostic outcomes. Prognostic nomograms were constructed based on plasma ofCS-modified CD44 levels to predict survival outcomes for patients with lung cancer. Patients with high expression ofCS-modified CD44 exhibited significantly worse outcomes in terms of OS (HR = 1.61, 95%CI = 1.13-2.29, p = 0.009) and progression-free survival (PFS). These findings were consistent across various analyses. The concordance index of the prognostic nomogram for predicting OS in both the training set and validation set were 0.723 and 0.737, respectively. Additionally, time-dependent receiver operating characteristic (ROC) curves showed that the nomogram could serve as a useful tool for predicting OS in patients with lung cancer. Plasma ofCS-modified CD44 may serve as an independent prognosis marker for patients with lung cancer. Further validation of its predictive value could enhance prognostic assessment and guide personalized treatment strategies for patients with lung cancer.

Magnetic Properties of Self-Assemble Naphthalene Diimide Radical Aggregates.

The concept of creating room-temperature ferromagnets from organic radicals proposed nearly sixty years ago, has recently experienced a resurgence due to advances in organic radical chemistry and materials. However, the lack of definitive design paradigms for achieving stable long-range ferromagnetic coupling between organic radicals presents an uncertain future for this research. Here, an innovative strategy is presented to achieve room-temperature ferromagnets by assembling π-conjugated radicals into π-π stacking aggregates. These aggregates, with ultra-close π-π distances and optimal π-π overlap, provide a platform for strong ferromagnetic (FM) interaction. The planar aromatic naphthalene diimide (NDI) anion radicals form nanorod aggregates with a π-π distance of just 3.26 Å, shorter than typical van der Waals distances. The suppressed electron paramagnetic resonance (EPR) signal and emergent near-infrared (NIR) absorption of the aggregates confirm strong interactions between the radicals. Magnetic measurements of NDI anion radical aggregates demonstrate room-temperature ferromagnetism with a saturated magnetization of 1.1 emu g-1, the highest among pure organic ferromagnets. Theoretical calculations reveal that π-stacks of NDI anion radicals with specific interlayer translational slippage favor ferromagnetic coupling over antiferromagnetic coupling.

A heat-inducible expression system for external control of gene expression in plastids.

Inducible expression systems can overcome the trade-off between high-level transgene expression and its pleiotropic effects on plant growth. In addition, they can facilitate the expression of biochemical pathways that produce toxic metabolites. Although a few inducible expression systems for the control of transgene expression in plastids have been developed, they all depend on chemical inducers and/or nuclear transgenes. Here we report a temperature-inducible expression system for plastids that is based on the bacteriophage λ leftward and rightward promoters (pL/pR) and the temperature-sensitive repressor cI857. We show that the expression of green fluorescent protein (GFP) in plastids can be efficiently repressed by cI857 under normal growth conditions, and becomes induced over time upon exposure to elevated temperatures in a light-dependent process. We further demonstrate that by introducing into plastids an expression system based on the bacteriophage T7 RNA polymerase, the temperature-dependent accumulation of GFP increased further and was ~24 times higher than expression driven by the pL/pR promoter alone, reaching ~0.48% of the total soluble protein. In conclusion, our heat-inducible expression system provides a new tool for the external control of plastid (trans) gene expression that is cost-effective and does not depend on chemical inducers.

Control of two insect pests by expression of a mismatch corrected double-stranded RNA in plants.

RNA interference (RNAi) has emerged as an efficient technology for pest control by silencing the essential genes of targeted insects. Owing to its nucleotide sequence-guided working mechanism, RNAi has a high degree of species-specificity without impacts on non-target organisms. However, as plants are inevitably under threat by two or more insect pests in nature, the species-specific mode of RNAi-based technology restricts its wide application for pest control. In this study, we artificially designed an intermediate dsRNA (iACT) targeting two ß-Actin (ACT) genes of sap-sucking pests Bemisia tabaci and Myzus persicae by mutual correction of their mismatches. When expressing hairpin iACT (hpiACT) from tobacco nuclear genome, transgenic plants are well protected from both B. tabaci and M. persicae, either individually or simultaneously, as evidenced by reduced fecundity and suppressed ACT gene expression, whereas expression of hpRNA targeting BtACT or MpACT in transgenic tobacco plants could only confer specific resistance to either B. tabaci or M. persicae, respectively. In sum, our data provide a novel proof-of-concept that two different insect species could be simultaneously controlled by artificial synthesis of dsRNA with sequence optimization, which expands the range of transgenic RNAi methods for crop protection.

CD276 enhances sunitinib resistance in clear cell renal cell carcinoma by promoting DNA damage repair and activation of FAK-MAPK signaling pathway.

OBJECTIVE: This study aimed to explore the effect of CD276 expression on the sunitinib sensitivity of clear cell renal cell carcinoma (ccRCC) cell and animal models and the potential mechanisms involved. METHODS: CD276 expression levels of ccRCC and normal samples were analyzed via online databases and real-time quantitative PCR (RT-qPCR). CD276 was knocked down in ccRCC cell models (sunitinib-resistant 786-O/R cells and sunitinib-sensitive 786-O cells) using shRNA transfection, and the cells were exposed to a sunitinib (2 µM) environment. Cells proliferation was then analyzed using MTT assay and colony formation experiment. Alkaline comet assay, immunofluorescent staining, and western blot experiments were conducted to assess the DNA damage repair ability of the cells. Western blot was also used to observe the activation of FAK-MAPK pathway within the cells. Finally, a nude mouse xenograft model was established and the nude mice were orally administered sunitinib (40 mg/kg/d) to evaluate the in vivo effects of CD276 knockdown on the therapeutic efficacy of sunitinib against ccRCC. RESULTS: CD276 was significantly upregulated in both ccRCC clinical tissue samples and cell models. In vitro experiments showed that knocking down CD276 reduced the survival rate, IC50 value, and colony-forming ability of ccRCC cells. Knocking down CD276 increased the comet tail moment (TM) values and γH2AX foci number, and reduced BRCA1 and RAD51 protein levels. Knocking down CD276 also decreased the levels of p-FAK, p-MEK, and p-ERK proteins. CONCLUSION: Knocking down CD276 effectively improved the sensitivity of ccRCC cell and animal models to sunitinib treatment.

Astrocyte PERK and IRE1 Signaling Contributes to Morphine Tolerance and Hyperalgesia through Upregulation of Lipocalin-2 and NLRP3 Inflammasome in the Rodent Spinal Cord.

BACKGROUND: Endoplasmic reticulum stress plays a crucial role in the pathogenesis of neuroinflammation and chronic pain. This study hypothesized that PRKR-like endoplasmic reticulum kinase (PERK) and inositol-requiring enzyme type 1 (IRE1) regulate lipocalin-2 (LCN2) and Nod-like receptor family pyrin domain containing 3 (NLRP3) expression in astrocytes, thereby contributing to morphine tolerance and hyperalgesia. METHODS: The study was performed in Sprague-Dawley rats and C57/Bl6 mice of both sexes. The expression of LCN2 and NLRP3 was assessed by Western blotting. The tail-flick, von Frey, and Hargreaves tests were used to evaluate nociceptive behaviors. Chromatin immunoprecipitation was conducted to analyze the binding of activating transcription factor 4 (ATF4) to the promoters of LCN2 and TXNIP. Whole-cell patch-clamp recordings were used to evaluate neuronal excitability. RESULTS: Pharmacologic inhibition of PERK and IRE1 attenuated the development of morphine tolerance and hyperalgesia in male (tail latency on day 7, 8.0 ± 1.13 s in the morphine + GSK2656157 [10 µg] group vs. 5.8 ± 0.65 s in the morphine group; P = 0.04; n = 6 rats/group) and female (tail latency on day 7, 6.0 ± 0.84 s in the morphine + GSK2656157 [10 µg] group vs. 3.1 ± 1.09 s in the morphine group; P = 0.0005; n = 6 rats/group) rats. Activation of PERK and IRE1 upregulated expression of LCN2 and NLRP3 in vivo and in vitro. Chromatin immunoprecipitation analysis showed that ATF4 directly bound to the promoters of the LCN2 and TXNIP. Lipocalin-2 induced neuronal hyperexcitability in the spinal cord and dorsal root ganglia via melanocortin-4 receptor. CONCLUSIONS: Astrocyte endoplasmic reticulum stress sensors PERK and IRE1 facilitated morphine tolerance and hyperalgesia through upregulation of LCN2 and NLRP3 in the spinal cord.

Novel drimane-type sesquiterpenoids and nucleosides from the Helicoma septoconstrictum suppress the growth of ovarian cancer cells.

Four new drimane-type sesquiterpenoids and two new nucleoside derivatives (1-6), were isolated from the fungus Helicoma septoconstrictum. Their structures were determined based on the combination of the analysis of their HR-ESI-MS, NMR, ECD calculations data and acid hydrolysis. All the isolated compounds were detected for their bio-activities against MDA-MB-231, A549/DDP, A2780 and HepG2 cell lines. Helicoside C (4) exhibited superior cytotoxicity against the A2780 cell line with IC50 7.5 ± 1.5 µM. The analysis of reactive oxygen species (ROS) revealed that Helicoside C induced an increase in intracellular ROS. Furthermore, the flow cytometry and mitochondrial membrane potential (MMP) analyses unveiled that Helicoside C mediated mitochondrial-dependent apoptosis in A2780 cells. The western blotting test showed that Helicoside C could suppress the STAT3's phosphorylation. These findings offered crucial support for development of H. septoconstrictum and highlighted the potential application of drimane-type sesquiterpenoids in pharmaceuticals.

Global responses of wetland methane emissions to extreme temperature and precipitation.

As global warming continues, events of extreme heat or heavy precipitation will become more frequent, while events of extreme cold will become less so. How wetlands around the globe will react to these extreme events is unclear yet critical, because they are among the greatest natural sources of methane(CH4). Here we use seven indices of extreme climate and the rate of methane emission from global wetlands(WME) during 2000-2019 simulated by 12 published models as input data. Our analyses suggest that extreme cold (particularly extreme low temperatures) inhibits WME, whereas extreme heat (particularly extreme high temperatures) accelerates WME. Our results also suggest that daily precipitation >10 mm accelerates WME, while much higher daily precipitation levels can slow WME. The correlation of extreme high temperature and precipitation with rate of WME became stronger during the study period, while the correlation between extreme low temperature and WME rate became weaker.

Eomes and T-bet, a dynamic duo regulating NK cell differentiation.

T-bet and Eomes are two related transcription factors (TFs) that regulate the differentiation of cytotoxic lymphocytes such as Natural Killer (NK) cells and CD8 T cells. Recent genome-wide analyses suggest they have complementary roles in instructing the transcriptional program of NK cells, although their DNA binding sites appear to be very similar. In this essay, we discuss the mechanisms that could specify their action, addressing their expression profile, the cofactors they interact with, as well as their roles in the epigenetic regulation of chromatin accessibility. Based on the recent literature on these TFs, we propose different models to describe how they regulate gene expression in NK cells at steady state, or in the context of activation or exhaustion. We also discuss recent findings in the field of CD8 T cell differentiation and residency, where Eomes and T-bet appear to be major regulators, and the parallels that can be drawn between mechanisms of NK and CD8 T cell differentiation and trafficking.

Telehealth-Supported Exercise or Physical Activity Programs for Knee Osteoarthritis: Systematic Review and Meta-Analysis.

BACKGROUND: The integration of telehealth-supported programs in chronic disease management has become increasingly common. However, its effectiveness for individuals with knee osteoarthritis (KOA) remains unclear. OBJECTIVE: This study aimed to assess the effectiveness of telehealth-supported exercise or physical activity programs for individuals with KOA. METHODS: A comprehensive literature search encompassing Embase, MEDLINE, CENTRAL, Web of Science, PubMed, Scopus, PEDro, GreyNet, and medRxiv from inception to September 2023 was conducted to identify randomized controlled trials comparing telehealth-supported exercise or physical activity programs to a control condition for KOA. Data were extracted and qualitatively synthesized across eligible studies, and a meta-analysis was performed to evaluate the effects. The study was reported according to PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 2020. RESULTS: In total, 23 studies met eligibility criteria, with 20 included in the meta-analysis. Results showed that telehealth-supported exercise or physical activity programs reduced pain (g=-0.39; 95% CI -0.67 to -0.11; P<.001), improved physical activity (g=0.13; 95% CI 0.03-0.23; P=.01), and enhanced physical function (g=-0.51; 95% CI -0.98 to -0.05; P=.03). Moreover, significant improvements in quality of life (g=0.25; 95% CI 0.14-0.36; P<.001), self-efficacy for pain (g=0.72; 95% CI 0.53-0.91; P<.001), and global improvement (odds ratio 2.69, 95% CI 1.41-5.15; P<.001) were observed. However, self-efficacy for physical function (g=0.14; 95% CI -0.26 to 0.53; P=.50) showed insignificant improvements. Subgroup analyses based on the World Health Organization classification of digital health (pain: χ22=6.5; P=.04 and physical function: χ22=6.4; P=.04), the type of teletechnology in the intervention group (pain: χ24=4.8; P=.31 and function: χ24=13.0; P=.01), and active or inactive controls (pain: χ21=5.3; P=.02 and physical function: χ21=3.4; P=.07) showed significant subgroup differences. CONCLUSIONS: Telehealth-supported exercise or physical activity programs might reduce knee pain and improve physical activity, physical function, quality of life, self-efficacy, and global improvement in individuals with KOA. Future research should consider longer implementation durations and assess the feasibility of incorporating wearables and standardized components into large-scale interventions to evaluate the effects. TRIAL REGISTRATION: PROSPERO CRD42022359658; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=359658.

Model predictive complex system control from observational and interventional data.

Complex systems, characterized by intricate interactions among numerous entities, give rise to emergent behaviors whose data-driven modeling and control are of utmost significance, especially when there is abundant observational data but the intervention cost is high. Traditional methods rely on precise dynamical models or require extensive intervention data, often falling short in real-world applications. To bridge this gap, we consider a specific setting of the complex systems control problem: how to control complex systems through a few online interactions on some intervenable nodes when abundant observational data from natural evolution is available. We introduce a two-stage model predictive complex system control framework, comprising an offline pre-training phase that leverages rich observational data to capture spontaneous evolutionary dynamics and an online fine-tuning phase that uses a variant of model predictive control to implement intervention actions. To address the high-dimensional nature of the state-action space in complex systems, we propose a novel approach employing action-extended graph neural networks to model the Markov decision process of complex systems and design a hierarchical action space for learning intervention actions. This approach performs well in three complex system control environments: Boids, Kuramoto, and Susceptible-Infectious-Susceptible (SIS) metapopulation. It offers accelerated convergence, robust generalization, and reduced intervention costs compared to the baseline algorithm. This work provides valuable insights into controlling complex systems with high-dimensional state-action spaces and limited intervention data, presenting promising applications for real-world challenges.

Drought stress enhances plastid-mediated RNA interference for efficient the willow leaf beetle management.

Plastid-mediated RNA interference has emerged as a promising and effective approach for pest management. By expressing high levels of double-stranded RNAs (dsRNAs) in plastid that target essential pest genes, it has been demonstrated to effectively control certain herbivorous beetles and spider mites. However, as plants are sessile organisms, they frequently experience a combination of biotic and abiotic stresses. It remains unclear whether abiotic stress, such as drought stress, influences the accumulation of dsRNAs produced in plastids and its effectiveness in controlling pests. In this study, we aimed to investigate the effects of drought stress on dsACT expression in transplastomic poplar plants and its control efficiency against the willow leaf beetle (Plagiodera versicolora). Our findings revealed that drought stress did not significantly affect the dsRNA contents in transplastomic poplar plants, but it did lead to higher mortality of insect larvae. This increased mortality may be attributed to increased levels of jasmonic acid and cysteine proteinase inhibitor induced by water deficit. These results contribute to understanding of the mechanisms linking water deficit in plants to insect performance and provide valuable insights for implementing appropriate pest control strategies under drought stress conditions.

Nonlinear Regularization Decoding Method for Speech Recognition.

Existing end-to-end speech recognition methods typically employ hybrid decoders based on CTC and Transformer. However, the issue of error accumulation in these hybrid decoders hinders further improvements in accuracy. Additionally, most existing models are built upon Transformer architecture, which tends to be complex and unfriendly to small datasets. Hence, we propose a Nonlinear Regularization Decoding Method for Speech Recognition. Firstly, we introduce the nonlinear Transformer decoder, breaking away from traditional left-to-right or right-to-left decoding orders and enabling associations between any characters, mitigating the limitations of Transformer architectures on small datasets. Secondly, we propose a novel regularization attention module to optimize the attention score matrix, reducing the impact of early errors on later outputs. Finally, we introduce the tiny model to address the challenge of overly large model parameters. The experimental results indicate that our model demonstrates good performance. Compared to the baseline, our model achieves recognition improvements of 0.12%, 0.54%, 0.51%, and 1.2% on the Aishell1, Primewords, Free ST Chinese Corpus, and Common Voice 16.1 datasets of Uyghur, respectively.

AaLaeA targets AaFla1 to mediate the production of antitumor compound in Alternaria alstroemeria.

Endophytic fungi are an important source of novel antitumor substances. Previously, we isolated an endophytic fungus, Alternaria alstroemeria, from the medicinal plant Artemisia artemisia, whose crude extracts strongly inhibited A549 tumor cells. We obtained a transformant, namely AaLaeAOE26 , which completely loses its antitumor activity due to overexpression of the global regulator AaLaeA. Re-sequencing analysis of the genome revealed that the insertion site was in the noncoding region and did not destroy any other genes. Metabolomics analysis revealed that the level of secondary antitumor metabolic substances was significantly lower in AaLaeAOE26 compared with the wild strain, in particular flavonoids were more downregulated according to the metabolomics analysis. A further comparative transcriptome analysis revealed that a gene encoding FAD-binding domain protein (Fla1) was significantly downregulated. On the other hand, overexpression of AaFla1 led to significant enhancement of antitumor activity against A549 with a sevenfold higher inhibition ratio than the wild strain. At the same time, we also found a significant increase in the accumulation of antitumor metabolites including quercetin, gitogenin, rhodioloside, liensinine, ginsenoside Rg2 and cinobufagin. Our data suggest that the global regulator AaLaeA negatively affects the production of antitumor compounds via controlling the transcription of AaFla1 in endophytic A. alstroemeria.

Optimal intraventricular hemorrhage volume cutoff for predicting poor outcome in patients with intracerebral hemorrhage.

BACKGROUND AND OBJECTIVES: The prognosis of patients with spontaneous intracerebral hemorrhage (ICH) is often influenced by hematoma volume, a well-established predictor of poor outcome. However, the optimal intraventricular hemorrhage (IVH) volume cutoff for predicting poor outcome remains unknown. METHODS: We analyzed 313 patients with spontaneous ICH not undergoing evacuation, including 7 cases with external ventricular drainage (EVD). These patients underwent a baseline CT scan, followed by a 24-hour CT scan for measurement of both hematoma and IVH volume. We defined hematoma growth as hematoma growth > 33 % or 6 mL at follow-up CT, and poor outcome as modified Rankin Scale score≥3 at three months. Cutoffs with optimal sensitivity and specificity for predicting poor outcome were identified using receiver operating curves. RESULTS: The receiver operating characteristic analysis identified 6 mL as the optimal cutoff for predicting poor outcome. IVH volume> 6 mL was observed in 53 (16.9 %) of 313 patients. Patients with IVH volume>6 mL were more likely to be older and had higher NIHSS score and lower GCS score than those without. IVH volume>6 mL (adjusted OR 2.43, 95 % CI 1.13-5.30; P = 0.026) was found to be an independent predictor of poor clinical outcome at three months in multivariable regression analysis. CONCLUSIONS: Optimal IVH volume cutoff represents a powerful tool for improving the prediction of poor outcome in patients with ICH, particularly in the absence of clot evacuation or common use of EVD. Small amounts of intraventricular blood are not independently associated with poor outcome in patients with intracerebral hemorrhage. The utilization of optimal IVH volume cutoffs may improve the clinical trial design by targeting ICH patients that will obtain maximal benefit from therapies.