Local zones of endoplasmic reticulum complexity confine cargo in neuronal dendrites.

Following synthesis, integral membrane proteins dwell in the endoplasmic reticulum (ER) for variable periods that are typically rate limiting for plasma membrane delivery. In neurons, the ER extends for hundreds of microns as an anastomosing network throughout highly branched dendrites. However, little is known about the mobility, spatial scales, or dynamic regulation of cargo in the dendritic ER. Here, we show that membrane proteins, including AMPA-type glutamate receptors, rapidly diffuse within the continuous network of dendritic ER but are confined by increased ER complexity at dendritic branch points and near dendritic spines. The spatial range of receptor mobility is rapidly restricted by type I mGluR signaling through a mechanism involving protein kinase C (PKC) and the ER protein CLIMP63. Moreover, local zones of ER complexity compartmentalize ER export and correspond to sites of new dendritic branches. Thus, local control of ER complexity spatially scales secretory trafficking within elaborate dendritic arbors.

A comprehensive assessment of hurdle and zero-inflated models for single cell RNA-sequencing analysis.

Single cell RNA-sequencing (scRNA-seq) technology has significantly advanced the understanding of transcriptomic signatures. Although various statistical models have been used to describe the distribution of gene expression across cells, a comprehensive assessment of the different models is missing. Moreover, the growing number of features associated with scRNA-seq datasets creates new challenges for analytical accuracy and computing speed. Here, we developed a Python-based package (TensorZINB) to solve the zero-inflated negative binomial (ZINB) model using the TensorFlow deep learning framework. We used a sequential initialization method to solve the numerical stability issues associated with hurdle and zero-inflated models. A recursive feature selection protocol was used to optimize feature selections for data processing and downstream differentially expressed gene (DEG) analysis. We proposed a class of hybrid models combining nested models to further improve the model's performance. Additionally, we developed a new method to convert a continuous distribution to its equivalent discrete form, so that statistical models can be fairly compared. Finally, we showed that the proposed TensorFlow algorithm (TensorZINB) was numerically stable and that its computing speed and performance were superior to those of existing ZINB solvers. Moreover, we implemented seven hurdle and zero-inflated statistical models in Python and systematically assessed their performance using a real scRNA-seq dataset. We demonstrated that the ZINB model achieved the lowest Akaike information criterion compared with other models tested. Taken together, TensorZINB was accurate, efficient and scalable for the implementation of ZINB and for large-scale scRNA-seq data analysis with DEG identification.

Glial Sphingosine-Mediated Epigenetic Regulation Stabilizes Synaptic Function in Drosophila Models of Alzheimer's Disease.

Destabilization of neural activity caused by failures of homeostatic regulation has been hypothesized to drive the progression of Alzheimer's Disease (AD). However, the underpinning mechanisms that connect synaptic homeostasis and the disease etiology are yet to be fully understood. Here, we demonstrated that neuronal overexpression of amyloid ß (Aß) causes abnormal histone acetylation in peripheral glia and completely blocks presynaptic homeostatic potentiation (PHP) at the neuromuscular junction in Drosophila The synaptic deficits caused by Aß overexpression in motoneurons are associated with motor function impairment at the adult stage. Moreover, we found that a sphingosine analog drug, Fingolimod, ameliorates synaptic homeostatic plasticity impairment, abnormal glial histone acetylation, and motor behavior defects in the Aß models. We further demonstrated that perineurial glial sphingosine kinase 2 (Sk2) is not only required for PHP, but also plays a beneficial role in modulating PHP in the Aß models. Glial overexpression of Sk2 rescues PHP, glial histone acetylation, and motor function deficits that are associated with Aß in Drosophila Finally, we showed that glial overexpression of Sk2 restores PHP and glial histone acetylation in a genetic loss-of-function mutant of the Spt-Ada-Gcn5 Acetyltransferase complex, strongly suggesting that Sk2 modulates PHP through epigenetic regulation. Both male and female animals were used in the experiments and analyses in this study. Collectively, we provided genetic evidence demonstrating that abnormal glial epigenetic alterations in Aß models in Drosophila are associated with the impairment of PHP and that the sphingosine signaling pathway displays protective activities in stabilizing synaptic physiology.SIGNIFICANCE STATEMENT Fingolimod, an oral drug to treat multiple sclerosis, is phosphorylated by sphingosine kinases to generate its active form. It is known that Fingolimod enhances the cognitive function in mouse models of Alzheimer's disease (AD), but the role of sphingosine kinases in AD is not clear. We bridge this knowledge gap by demonstrating the relationship between impaired homeostatic plasticity and AD. We show that sphingosine kinase 2 (Sk2) in glial cells is necessary for homeostatic plasticity and that glial Sk2-mediated epigenetic signaling has a protective role in synapse stabilization. Our findings demonstrate the potential of the glial sphingosine signaling as a key player in glia-neuron interactions during homeostatic plasticity, suggesting it could be a promising target for sustaining synaptic function in AD.

Image-based remote evaluation of PASI scores with psoriasis by the YOLO-v4 algorithm.

As a chronic relapsing disease, psoriasis is characterized by widespread skin lesions. The Psoriasis Area and Severity Index (PASI) is the most frequently utilized tool for evaluating the severity of psoriasis in clinical practice. Nevertheless, long-term monitoring and precise evaluation pose difficulties for dermatologists and patients, which is time-consuming, subjective and prone to evaluation bias. To develop a deep learning system with high accuracy and speed to assist PASI evaluation, we collected 2657 high-quality images from 1486 psoriasis patients, and images were segmented and annotated. Then, we utilized the YOLO-v4 algorithm to establish the model via four modules, we also conducted a human-computer comparison through quadratic weighted Kappa (QWK) coefficients and intra-class correlation coefficients (ICC). The YOLO-v4 algorithm was selected for model training and optimization compared with the YOLOv3, RetinaNet, EfficientDet and Faster_rcnn. The model evaluation results of mean average precision (mAP) for various lesion features were as follows: erythema, mAP = 0.903; scale, mAP = 0.908; and induration, mAP = 0.882. In addition, the results of human-computer comparison also showed a median consistency for the skin lesion severity and an excellent consistency for the area and PASI score. Finally, an intelligent PASI app was established for remote disease assessment and course management, with a pleasurable agreement with dermatologists. Taken together, we proposed an intelligent PASI app based on the image YOLO-v4 algorithm that can assist dermatologists in long-term and objective PASI scoring, shedding light on similar clinical assessments that can be assisted by computers in a time-saving and objective manner.

Validation of GPT-4 for clinical event classification: A comparative analysis with ICD codes and human reviewers.

BACKGROUND AND AIM: Effective clinical event classification is essential for clinical research and quality improvement. The validation of artificial intelligence (AI) models like Generative Pre-trained Transformer 4 (GPT-4) for this task and comparison with conventional methods remains unexplored. METHODS: We evaluated the performance of the GPT-4 model for classifying gastrointestinal (GI) bleeding episodes from 200 medical discharge summaries and compared the results with human review and an International Classification of Diseases (ICD) code-based system. The analysis included accuracy, sensitivity, and specificity evaluation, using ground truth determined by physician reviewers. RESULTS: GPT-4 exhibited an accuracy of 94.4% in identifying GI bleeding occurrences, outperforming ICD codes (accuracy 63.5%, P < 0.001). GPT-4's accuracy was either slightly lower or statistically similar to individual human reviewers (Reviewer 1: 98.5%, P < 0.001; Reviewer 2: 90.8%, P = 0.170). For location classification, GPT-4 achieved accuracies of 81.7% and 83.5% for confirmed and probable GI bleeding locations, respectively, with figures that were either slightly lower or comparable with those of human reviewers. GPT-4 was highly efficient, analyzing the dataset in 12.7 min at a cost of 21.2 USD, whereas human reviewers required 8-9 h each. CONCLUSION: Our study indicates GPT-4 offers a reliable, cost-efficient, and faster alternative to current clinical event classification methods, outperforming the conventional ICD coding system and performing comparably to individual expert human reviewers. Its implementation could facilitate more accurate and granular clinical research and quality audits. Future research should explore scalability, prompt and model tuning, and ethical implications of high-performance AI models in clinical data processing.

A novel RyR2 mutation associated with co-morbid catecholaminergic polymorphic ventricular tachycardia (CPVT) and benign epilepsy with centrotemporal spikes (BECTS).

In this case report, we describe a 14-year-old patient with a novel RyR2 gene mutation (c.6577G > T/p.Val2193Leu), identified through a comprehensive review of medical history, examination findings, and follow-up data. The pathogenic potential of this mutation, which results in the loss of some interatomic forces and compromises the closure of the RyR2 protein pore leading to calcium leakage, was analyzed using the I-TASSER Suite to predict the structural changes in the protein. This mutation manifested clinically as co-morbid catecholaminergic polymorphic ventricular tachycardia (CPVT) and benign epilepsy with centrotemporal spikes (BECTS), a combination not previously documented in the same patient. While seizures were successfully managed with levetiracetam, the patient's exercise-induced syncope episodes could not be controlled with metoprolol, highlighting the complexity and challenge in managing CPVT associated with this novel RyR2 variation.

Prediction of moisture content for a single maize kernel based on viscoelastic properties.

BACKGROUND: The rapid and accurate detection of moisture content is important to ensure maize quality. However, existing technologies for rapidly detecting moisture content often suffer from the use of costly equipment, stringent environmental requirements, or limited accuracy. This study proposes a simple and effective method for detecting the moisture content of single maize kernels based on viscoelastic properties. RESULTS: Two types of viscoelastic experiments were conducted involving three different parameters: relaxation tests (initial loads: 60, 80, 100 N) and frequency-sweep tests (frequencies: 0.6, 0.8, 1 Hz). These experiments generated corresponding force-time graphs and viscoelastic parameters were extracted based on the four-element Maxwell model. Then, viscoelastic parameters and data of force-time graphs were employed as input variables to explore the relationships with moisture content separately. The impact of different preprocessing methods and feature time variables on model accuracy was explored based on force-time graphs. The results indicate that models utilizing the force-time data were more accurate than those utilizing viscoelastic parameters. The best model was established by partial least squares regression based on S-G smoothing data from relaxation tests conducted with initial force of 100 N. The correlation coefficient and the root mean square error of the calibration set were 0.954 and 0.021, respectively. The corresponding values of the prediction set were 0.905 and 0.029, respectively. CONCLUSIONS: This study confirms the potential for accurate and fast detection of moisture content in single maize kernels using viscoelastic properties, which provides a novel approach for the detection of various components in cereals. © 2024 Society of Chemical Industry.

Pancreatic Cancer and its Attributable Risk Factors in East Asia, Now and Future.

BACKGROUND: The disease burden of pancreatic cancer in East Asia is at a high level, but the epidemiological characteristics of pancreatic cancer in the region have not been systematically studied. METHOD: Joinpoint analysis was used to identify average annual percentage change (AAPC) and annual percentage change (APC) in mortality. Age-period-cohort models were used to analyze age-period cohort effects across countries. Bayesian age-period-cohort (BAPC) analysis was used to project the burden of disease for 2020-2030. RESULTS: Pancreatic cancer mortality in males in Japan (2012-2019, APC = -0.97) and Korea (2012-2019, APC = -0.91) has shown a decreasing trend since 2012 (P < .05). However, China (2016-2019, APC = 3.21), Mongolia (2015-2.019, APC = 2.37), and North Korea (2012-2019, APC = 0.47) showed a significant increase in pancreatic cancer in both genders (P < .05). Risk factors for pancreatic cancer in East Asia remained largely stable between 2010 and 2019. Mortality of pancreatic cancer due to smoking began to decline in areas with high socio-demographic index (SDI), and mortality of pancreatic cancer due to high body mass index and high fasting plasma glucose increased with SDI. The age-standardized mortality for pancreatic cancer in Chinese males is expected to exceed that of Japan and South Korea by 2030, but the disease burden of pancreatic cancer in Japan and South Korea remains at extremely high levels. CONCLUSION: Economically developed countries are beginning to show a decreasing trend in the burden of pancreatic cancer disease, and developing countries are experiencing a rapid increase in the age-standardized death rate (ASDR) of pancreatic cancer.

Typical metabolic pattern of 18F-FDG PET in Anti-NMDAR encephalitis in the acute and subacute phases and its correlation with T2 FLAIR-MRI features.

BACKGROUND/AIMS: Early diagnosis of Anti-N-methyl-D-aspartate receptor (anti-NMDAR) encephalitis with non-invasive imaging modalities benefiting is crucial to guarantee prompt treatments decision-making and good prognosis for patients. The present study aimed to explore the correlation of MRI features with brain metabolism characteristics of 18F-fluorodeoxyglucose positron emission tomography (18F-FDG PET) and to describe the metabolic patterns in Anti-N-methyl-D-aspartate receptor (anti-NMDAR) encephalitis at acute and subacute phases. Twenty-four patients with anti-NMDAR encephalitis confirmed by serum and/or CSF tests at acute and subacute phases, 9 females and 15 males, with an age range of 6-80 years, were enrolled in this retrospective study as encephalitis group. 18F-FDG PET and MRI findings of all patients were investigated and interpreted with visual analysis. Chi-square test was performed to compare the diagnostic sensitivity between MRI and PET. Independent sample t-test was used to compare the standardized uptake value ratio (SUVR) of each ROI between the encephalitis group and control group, which consisted of 24 healthy volunteers of the same age and gender. RESULTS: There was no statistical difference in the diagnostic sensitivity between FDG PET (23/24, 95.83%) and MRI (18/24, 75.00%) in anti-NMDAR encephalitis patients (P > 0.05). Three categories of abnormalities shown on T2 FLAIR, including shallow of sulci and swelling of brain tissue, increased signal in the sulci, increased signal on brain gray matter or adjacent white matter presented hypermetabolism on PET, excepting increased signal in brain linear structure with hypometabolism of the basal ganglia on PET. We identified 19 brain regions with hypermetabolism and 16 brain regions with hypometabolism that exhibited statistically significant changes in SUVRs between anti-NMDAR encephalitis group and control group (FDR P < 0.05). CONCLUSION: Anteroposterior glucose metabolism gradient (frontal-temporal/parietal-occipital) is proved to be a typical pattern of anti-NMDAR encephalitis at the acute and subacute phases in both visual and statistical testing. Interestingly, the pattern is also commonly found in the anterior and posterior portions of the parietal lobe and cingular cortex, which may be a potential indicator for the diagnosis of this disorder. In addition, MRI is an important and reliable neuroimaging modality to assist in the correct evaluation of activity changes on individual 18F-FDG PET.

Missing data imputation, prediction, and feature selection in diagnosis of vaginal prolapse.

BACKGROUND: Data loss often occurs in the collection of clinical data. Directly discarding the incomplete sample may lead to low accuracy of medical diagnosis. A suitable data imputation method can help researchers make better use of valuable medical data. METHODS: In this paper, five popular imputation methods including mean imputation, expectation-maximization (EM) imputation, K-nearest neighbors (KNN) imputation, denoising autoencoders (DAE) and generative adversarial imputation nets (GAIN) are employed on an incomplete clinical data with 28,274 cases for vaginal prolapse prediction. A comprehensive comparison study for the performance of these methods has been conducted through certain classification criteria. It is shown that the prediction accuracy can be greatly improved by using the imputed data, especially by GAIN. To find out the important risk factors to this disease among a large number of candidate features, three variable selection methods: the least absolute shrinkage and selection operator (LASSO), the smoothly clipped absolute deviation (SCAD) and the broken adaptive ridge (BAR) are implemented in logistic regression for feature selection on the imputed datasets. In pursuit of our primary objective, which is accurate diagnosis, we employed diagnostic accuracy (classification accuracy) as a pivotal metric to assess both imputation and feature selection techniques. This assessment encompassed seven classifiers (logistic regression (LR) classifier, random forest (RF) classifier, support machine classifier (SVC), extreme gradient boosting (XGBoost) , LASSO classifier, SCAD classifier and Elastic Net classifier)enhancing the comprehensiveness of our evaluation. RESULTS: The proposed framework imputation-variable selection-prediction is quite suitable to the collected vaginal prolapse datasets. It is observed that the original dataset is well imputed by GAIN first, and then 9 most significant features were selected using BAR from the original 67 features in GAIN imputed dataset, with only negligible loss in model prediction. BAR is superior to the other two variable selection methods in our tests. CONCLUDES: Overall, combining the imputation, classification and variable selection, we achieve good interpretability while maintaining high accuracy in computer-aided medical diagnosis.

Temperature Compensation of Wind Tunnel Balance Signal Detection System Based on IGWO-ELM.

The wind tunnel balance signal detection system is widely employed in aerospace applications for the accurate and automated measurement of aerodynamic forces and moments. However, measurement errors arise under different environmental temperature. This paper addresses the issue of measurement accuracy under different temperature conditions by proposing a temperature compensation method based on an improved gray wolf optimization (IGWO) algorithm and optimized extreme learning machine (ELM). The IGWO algorithm is enhanced by improving the initial population position, convergence factor, and iteration weights of the gray wolf optimization algorithm. Subsequently, the IGWO algorithm is employed to determine the optimal network parameters for the ELM. The calibration decoupling experiment and high-low temperature experiment are designed and carried out. On this basis, ELM, GWO-ELM, PSO-ELM, GWO-RBFNN and IGWO-ELM are used for temperature compensation experiments. The experimental results show that IGWO-ELM has a good temperature compensation effect, reducing the measurement error from 20%FS to within 0.04%FS. Consequently, the accuracy and stability of the wind tunnel balance signal detection system under different temperature environments are enhanced.

Dysbiosis of vaginal microbiota associated with persistent high-risk human papilloma virus infection.

BACKGROUND: The status of vaginal microbiota in persistent high-risk human papilloma virus (HR-HPV) infection is unclear. The present work aimed to identify the vaginal microbiota of persistent HPV infection and explore the possible underlying microbiota factors. METHODS: A total of 100 women were recruited in this study, of which 28 presented HR-HPV persistent infection (P group), 30 showed clearance of any subtype of HR-HPV (C group), and 42 had no history of any HR-HPV infection (NC group). The vaginal microbiota and the community structure of the three groups were compared based on the 16S rRNA sequencing of the V3-V4 region. The microbiota diversity and differential analysis were carried out to detect the potential factors associated with HR-HPV infection. RESULTS: P and C groups showed an increase of Firmicutes and Actinobacteriota but a decrease in Proteobacteria compared to the NC group. The Chao1 index indicated that the microbial richness of the NC group was greater than C group (P < 0.05).The principal co-ordinate analysis(PCoA) revealed differences between the NC and P/C groups.The linear discriminant analysis effect size (LEfSe) method indicated that Proteobacteria phylum was significantly different in the mean relative abundance in the NC group,but the P and C groups did not show such indicative taxa. The Wilcox rank-sum test indicated that the Bifidobacterium (P = 0.002) and Lactobacillus (P = 0.005) of the C group were in a high mean relative abundance compared to the NC group. CONCLUSIONS: The persistent HR-HPV infection is associated with dysbiosis of the vaginal microbiota. Microbiome regulation with Bifidobacteria and Lactobacillus may affect the clearance of HPV.

Whole-brain metabolic pattern analysis in patients with anti-leucine-rich glioma-inactivated 1 (LGI1) encephalitis.

BACKGROUND AND PURPOSE: Faciobrachial dystonic seizures (FBDS) and hyponatremia are the distinct clinical features of autoimmune encephalitis (AE) caused by antibodies against leucine-rich glioma-inactivated 1 (LGI1). The present study aims to explore the pathophysiological patterns and neural mechanisms underlying these symptoms. METHODS: We included 30 patients with anti-LGI1 AE and 30 controls from a retrospective observational cohort. Whole-brain metabolic pattern analysis was performed to assess the pathological network of anti-LGI1 AE, as well as the symptom networks associated with FBDS. Logistic regression was applied to explore independent predictors of FBDS. Finally, we used a multiple regression model to investigate the hyponatremia-associated brain network and its effect on serum sodium levels. RESULTS: The pathological network of anti-LGI1 AE involved hypermetabolism in the cerebellum, subcortical structures and Rolandic area, as well as hypometabolism in the medial prefrontal cortex. The symptom network of FBDS included hypometabolism in the cerebellum and Rolandic area (pFDR <0.05). Hypometabolism in the cerebellum was an independent predictor of FBDS (p < 0.001). Hyponatremia-associated network highlighted a negative effect on the caudate nucleus, frontal and temporal white matter. The metabolism of the hypothalamus was negatively associated with (Pearson's R = -0.180, p = 0.342), while not the independent predictor for serum sodium level (path c' = -7.238, 95% confidence interval = -30.947 to 16.472). CONCLUSIONS: Our results provide insights into the whole-brain metabolic patterns of patients with anti-LGI1 AE, including the symptom network associated with FBDS and the hyponatremia-associated brain network. The findings help us to understand the neural mechanisms underlying anti-LGI1 AE and to evaluate the progress of this disease.

Safety and economics of an enhanced recovery after surgery protocol in pelvic reconstructive surgery.

INTRODUCTION AND HYPOTHESIS: We hypothesized that an enhanced recovery after surgery (ERAS) protocol for patients undergoing female pelvic reconstructive surgery would conserve hospital resources without compromising patient safety. METHODS: In June 2020, an ERAS protocol designed to promote same-day discharge was initiated that included pre-operative hydration, a urinary anesthetic, non-narcotic analgesia, perineal ice, a bowel regimen, enrollment of the family to assist with care, and communication regarding planned same-day discharge. We compared demographic, operative, hospital stay, complications, and cost data in patients undergoing pelvic organ prolapse or incontinence surgery over 4 sequential months pre (PRE; N = 82) and post (POST; N = 91) ERAS implementation using univariate statistics. RESULTS: There were no differences in demographics, operative details, or complications (p > 0.05). There were no significant differences in overall revenues or expenses (p > 0.05), but bed unit cost was significantly lower in the POST group ($210 vs $533, p < 0.0001). There was a trend toward an increased operating margin in POST patients ($4,554 vs $2,151, p = 0.1163). Significantly more POST surgeries were performed in an ambulatory setting (73.6% vs 48.8%, p = 0.0008) and resulted in same-day discharge (80.2% vs 50.0%, p = 0.0003). There were no differences in the rates of emergency room or unexpected clinic visits (p > 0.05). Prescribed post-operative opiate dose was significantly reduced in POST patients (p < 0.0001). CONCLUSIONS: In patients undergoing female pelvic reconstructive surgery, an ERAS protocol facilitated transfer of procedures to an ambulatory surgical site and permitted same-day discharge without increasing complications, clinic visits, or emergency room visits. It also reduced bed unit cost and may improve operating margins.

Determination of skeletal muscle mass by aspartate aminotransferase / alanine aminotransferase ratio, insulin and FSH in Chinese women with sarcopenia.

BACKGROUND: Sarcopenia is an age-related skeletal muscle disorder that involves a loss of muscle mass or strength and physiological function. Skeletal muscle deteriorates in both quantity and quality. The endocrine system is an important regulator of muscle metabolism. Therefore, we aimed to explore the relationship between biochemical markers and muscle mass in sarcopenia. METHODS: We used the AWGS 2014 as the diagnostic criteria for sarcopenia, considering both the loss in muscle mass, muscle strength and physical performance. A total of 2837 elderly female participants over 50 years of age from the West China Health and Aging Trend (WCHAT) study were included. Insulin, glucose, 25(OH)VD, procalcitonin, alanine aminotransferase, aspartate aminotransferase, total protein, prealbumin, albumin, thyroid-stimulating hormone, free triiodothyronine, free tetraiodothyronine, triglycerides, cholesterol, high-density lipoprotein, very low-density lipoprotein, cortisol, and follicle-stimulating hormone were measured. Based on the findings of univariate analysis, multivariate regression and receiver operating characteristic (ROC) curves were established. RESULTS: Participants with sarcopenia had significantly lower free triiodothyronine, insulin, total protein, albumin, prealbumin, albumin/prealbumin ratio (A/G), alanine aminotransferase, triglycerides, and very low-density lipoprotein concentrations (P < 0.05). Compared with those without sarcopenia, those with sarcopenia had significantly higher free tetraiodothyronine, cortisol, follicle-stimulating hormone (FSH), aspartate aminotransferase/alanine aminotransferase ratio (AST/ALT), and high-density lipoprotein concentrations (P < 0.05). Insulin (OR = 0.854), FSH (OR = 1.016), and the AST/ALT ratio (OR = 1.819) were independent risk factors for low muscle mass (P < 0.001). The AUC of insulin was the highest, followed by the AST/ALT ratio and FSH (0.691, 0.671, and 0.634, respectively), and the AUC of the mixture of the above three reached 0.736. CONCLUSION: In this cross-sectional study of elderly Chinese females aged over 50 years from the WCHAT, FSH, insulin, and AST/ALT ratio were associated with sarcopenia and risk factors for low muscle mass.

The inhibitory effects of Ulva prolifera extracts on early growth of Spartina alterniflora and the underlying mechanisms.

Spartina alterniflora, a highly invasive plant, has caused a serious threat to ecosystem biodiversity and economic development in coastal areas of many countries. In this study, the allelopathic effect of Ulva prolifera extracts on seed germination and seedling growth of S. alterniflora was studied. The results showed that three different treatments (water, methanol and ethyl acetate extract) could inhibit the seed germination and seedling growth of S. alterniflora by reducing the germination proportion and germination index of seeds, decreasing the seedling length and root length of seedlings, and affecting the lipid peroxidation and enzyme activity. The higher the concentration of the extracts, the higher the inhibition effect. When the aqueous extract concentration reached 0.20 g/mL, the germination proportion of S. alterniflora decreased to 49.53% of the control. RNA-seq analysis showed that the expression of genes related to amino acid metabolism and photosynthesis were both upregulated, and genes related to energy generation and metabolism were both downregulated after adding the extracts. GC-MS analysis indicated that the U. prolifera extract was rich in organic acids, alcohols and esters, among which butanoic acid, butyl ester, Valine and Hexanedioic acid, bis (2-ethylhexyl) ester might be the dominant allelochemicals. In order to facilitate field dosing, prolong action time and control release effect, PVA/SA hydrogel embedded U. prolifera extract was used to obtain a sustained-release agent. In addition, the survival rate of S. alterniflora was significantly reduced, which was only 21.67% at the salinity of 30 ppt. The results of this study provide a feasible method for controlling the invasion of S. alterniflora and achieving the waste utilization of U. prolifera.

Sources, Transformations, Syntheses, and Bioactivities of Monoterpene Pyridine Alkaloids and Cyclopenta[c]pyridine Derivatives.

Monoterpene pyridine alkaloids (MTPAs) are alkaloids derived from iridoid glycosides (IGs). The common molecular structure of MTPAs is the pyridine ring, while some of them have a cyclopenta[c]pyridine skeleton. Some compounds containing this structure are potentially bioactive medicinal agents. In this paper, seven drug candidates (A-G), ninety natural source products (1-90), thirty-seven synthesized compounds (91-127), as well as twenty-six key intermediates (S1-S26) were summarized. We categorized five types of MTPAs and one type of cyclopenta[c]pyridine alkaloids in all. Additionally, their possible genetic pathways were proposed. Then, the chemical transformation, biotransformation, chemical synthesis, as well as the bioactivity of MTPAs and cyclopenta[c]pyridine derivatives were analyzed and summarized. Cyclopenta[c]pyridine derivatives can be concisely and chirally synthesized, and they have shown potentials with antibacterial, insecticidal, antiviral, anti-inflammatory, and neuropharmacological activities.

JOINT for large-scale single-cell RNA-sequencing analysis via soft-clustering and parallel computing.

BACKGROUND: Single-cell RNA-Sequencing (scRNA-Seq) has provided single-cell level insights into complex biological processes. However, the high frequency of gene expression detection failures in scRNA-Seq data make it challenging to achieve reliable identification of cell-types and Differentially Expressed Genes (DEG). Moreover, with the explosive growth of single-cell data using 10x genomics protocol, existing methods will soon reach the computation limit due to scalability issues. The single-cell transcriptomics field desperately need new tools and framework to facilitate large-scale single-cell analysis. RESULTS: In order to improve the accuracy, robustness, and speed of scRNA-Seq data processing, we propose a generalized zero-inflated negative binomial mixture model, "JOINT," that can perform probability-based cell-type discovery and DEG analysis simultaneously without the need for imputation. JOINT performs soft-clustering for cell-type identification by computing the probability of individual cells, i.e. each cell can belong to multiple cell types with different probabilities. This is drastically different from existing hard-clustering methods where each cell can only belong to one cell type. The soft-clustering component of the algorithm significantly facilitates the accuracy and robustness of single-cell analysis, especially when the scRNA-Seq datasets are noisy and contain a large number of dropout events. Moreover, JOINT is able to determine the optimal number of cell-types automatically rather than specifying it empirically. The proposed model is an unsupervised learning problem which is solved by using the Expectation and Maximization (EM) algorithm. The EM algorithm is implemented using the TensorFlow deep learning framework, dramatically accelerating the speed for data analysis through parallel GPU computing. CONCLUSIONS: Taken together, the JOINT algorithm is accurate and efficient for large-scale scRNA-Seq data analysis via parallel computing. The Python package that we have developed can be readily applied to aid future advances in parallel computing-based single-cell algorithms and research in various biological and biomedical fields.

Drift correction in localization microscopy using entropy minimization.

Localization microscopy offers resolutions down to a single nanometer but currently requires additional dedicated hardware or fiducial markers to reduce resolution loss from the drift of the sample. Drift estimation without fiducial markers is typically implemented using redundant cross correlation (RCC). We show that RCC has sub-optimal precision and bias, which leaves room for improvement. Here, we minimize a bound on the entropy of the obtained localizations to efficiently compute a precise drift estimate. Within practical compute-time constraints, simulations show a 5x improvement in drift estimation precision over the widely used RCC algorithm. The algorithm operates directly on fluorophore localizations and is tested on simulated and experimental datasets in 2D and 3D. An open source implementation is provided, implemented in Python and C++, and can utilize a GPU if available.

CRNDE mediates the viability and epithelial-mesenchymal transition of renal cell carcinoma via miR-136-5p.

Long non-coding RNAs (lncRNAs) regulate epithelial-mesenchymal transition (EMT) and the mutual adhesion and development of renal cell carcinoma (RCC). The underlying molecular mechanism of EMT and RCC cells in the treatment of RCC was less reported. In this study, the related functional lncRNA and miRNA in RCC tissues were predicted by bioinformatics analysis and verified by quantitative real-time PCR (qRT-PCR). The RNA interference technology was applied to measure the effects of the predicted lncRNAs and miRNAs on RCC cells. The expressions of EMT-related mRNAs and proteins were determined using qRT-PCR and Western-blot experiments. CRNDE was overexpressed and miR-136-5p was low-expressed in RCC. Upregulation of CRNDE could promote the viability, migration, invasion of RCC, while downregulation of CRNDE produced the opposite effects. Both the upregulation and downregulation of CRNDE alternated the protein expressions related to EMT, while miR-136-5p resulted in the opposite effects on CRNDE. Moreover, the promotive effect of overexpressed CRNDE on RCC cells could be blocked by miR-136-5p mimic. CRNDE can mediate miR-136-5p, promote the development of EMT and RCC cells, showing the potential to serve as a novel biomarker and therapeutic target in RCC treatment.