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BackgroundCerebral small vessel disease (CSVD) is a cluster of microvascular disorders with unclear pathological mechanisms. The microbiota-gut-brain axis is an essential regulatory mechanism between gut microbes and their host. Therefore, the compositional and functional gut microbiota alterations lead to cerebrovascular disease pathogenesis. The current study aims to determine the alteration and clinical value of the gut microbiota in CSVD patients.MethodsSixty-four CSVD patients and 18 matched healthy controls (HCs) were included in our study. All the participants underwent neuropsychological tests, and the multi-modal magnetic resonance imaging depicted the changes in brain structure and function. Plasma samples were collected, and the fecal samples were analyzed with 16S rRNA gene sequencing.ResultsBased on the alpha diversity analysis, the CSVD group had significantly decreased Shannon and enhanced Simpson compared to the HC group. At the genus level, there was a significant increase in the relative abundances of Parasutterella, Anaeroglobus, Megasphaera, Akkermansia, Collinsella, and Veillonella in the CSVD group. Moreover, these genera with significant differences in CSVD patients revealed significant correlations with cognitive assessments, plasma levels of the blood-brain barrier-/inflammation-related indexes, and structural/functional magnetic resonance imaging changes. Functional prediction demonstrated that lipoic acid metabolism was significantly higher in CSVD patients than HCs. Additionally, a composite biomarker depending on six gut microbiota at the genus level displayed an area under the curve of 0.834 to distinguish CSVD patients from HCs using the least absolute shrinkage and selection operator (LASSO) algorithm.ConclusionThe evident changes in gut microbiota composition in CSVD patients were correlated with clinical features and pathological changes of CSVD. Combining these gut microbiota using the LASSO algorithm helped identify CSVD accurately.

BackgroundNon-valvular atrial fibrillation (NVAF) is the most common cause of cardiogenic cerebral embolism (CCE). However, the underlying mechanism between cerebral embolism and NVAF is indefinite, and there is no effective and convenient biomarker to identify potential risk of CCE in patients with NVAF in clinic. The present study aims to identify risk factors for interpreting the potential association of CCE with NVAF and providing valuable biomarkers to predict the risk of CCE for NVAF patients.Methods641 NVAF patients diagnosed with CCE and 284 NVAF patients without any history of stroke were recruited in the present study. Clinical data including demographic characteristics, medical history, and clinical assessments, were recorded. Meanwhile, Blood cell counts, lipid profiles, high-sensitivity C-reactive protein, and coagulation function-related indicators were measured. Least absolute shrinkage and selection operator (LASSO) regression analysis was utilized to build a composite indicator model based on the blood risk factors.Results(1) CCE patients had significantly increased neutrophil-to-lymphocyte ratio, platelet-to-lymphocyte ratio (PLR), and D-dimer levels as compared with patients in the NVAF group, and these three indicators can distinguish CCE patients from ones in the NVAF group with an area under the curve (AUC) value of over 0.750, respectively. (2) Using the LASSO model, a composite indicator, i.e., the risk score, was determined based on PLR and D-dimer and displayed differential power for distinguishing CCE patients from NVAF patients with an AUC value of over 0.934. (3) The risk score was positively correlated with the National Institutes of Health Stroke Scale and CHADS2 scores in CCE patients. (4) There was a significant association between the change value of the risk score and the recurrence time of stroke in initial CCE patients.ConclusionsThe PLR and D-dimer represent an aggravated process of inflammation and thrombosis in the occurrence of CCE after NVAF. The combination of these two risk factors can contribute to identifying the risk of CCE for patients with NVAF with an accuracy of 93.4%, and the greater in change of composite indicator, the shorter in the recurrence of CCE for NVAF patients.

BackgroundReliable and individualized biomarkers are crucial for identifying early cognitive impairment in subcortical small-vessel disease (SSVD) patients. Personalized brain age prediction can effectively reflect cognitive impairment. Thus, the present study aimed to investigate the association of brain age with cognitive function in SSVD patients and assess the potential value of brain age in clinical assessment of SSVD.Materials and methodsA prediction model for brain age using the relevance vector regression algorithm was developed using 35 healthy controls. Subsequently, the prediction model was tested using 51 SSVD patients [24 subjective cognitive impairment (SCI) patients and 27 mild cognitive impairment (MCI) patients] to identify brain age-related imaging features. A support vector machine (SVM)-based classification model was constructed to differentiate MCI from SCI patients. The neurobiological basis of brain age-related imaging features was also investigated based on cognitive assessments and oxidative stress biomarkers.ResultsThe gray matter volume (GMV) imaging features accurately predicted brain age in individual patients with SSVD (R2 = 0.535, p < 0.001). The GMV features were primarily distributed across the subcortical system (e.g., thalamus) and dorsal attention network. SSVD patients with age acceleration showed significantly poorer Mini-Mental State Examination and Montreal Cognitive Assessment (MoCA) scores. The classification model based on GMV features could accurately distinguish MCI patients from SCI patients (area under the curve = 0.883). The classification outputs of the classification model exhibited significant associations with MoCA scores, Trail Making Tests A and B scores, Stroop Color and Word Test C scores, information processing speed total scores, and plasma levels of total antioxidant capacity in SSVD patients.ConclusionBrain age can be accurately quantified using GMV imaging data and shows potential clinical value for identifying early cognitive impairment in SSVD patients.

BackgroundFluid-attenuated inversion recovery vascular hyperintensity (FVH) can reflect the collateral status, which may be a valuable indicator to predict the functional outcome of acute stroke (AS) patients.MethodsA total of 190 AS patients with large vessel occlusion (LVO) were retrospectively investigated. All patients completed a 6-month follow-up and their modified Rankin Scale (mRS) scores were recorded at 1, 3, and 6 months after intravenous thrombolysis (IVT). Based on their mRS at 3 months, patients were divided into two groups: poor prognosis (131 patients; 68.9% of all subjects) and favorable prognosis (59 patients; 31.1% of all subjects). The death records of 28 patients were also analyzed in the poor prognosis group.Results(1) Univariate and multivariate analyses showed that the higher National Institutes of Health Stroke Scale (NIHSS) score at admission, higher fasting blood glucose, and lower FVH score were independent risk factors to predict the poor prognosis of IVT. (2) Survival analysis indicated that FVH score was the only baseline factor to predict the 6-month survival after IVT. (3) Baseline FVH score had great prediction performance for the prognosis of IVT (area under the curve = 0.853). (4) Baseline FVH score were negatively correlated with the NIHSS score at discharge and mRS score at 1, 3, and 6 months.ConclusionAmong various baseline clinical factors, only the FVH score might have implications for 3-month outcome and 6-month survival of AS patients after IVT. Baseline FVH score showed great potential to predict the prognosis of the AS patients.

It is generally accepted that cognitive processes, such as learning and memory, are affected in depression. The present study used a rat model of depression, chronic unpredictable mild stress (CUMS), to determine whether hippocampal volume and neurochemical changes were involved in learning and memory alterations. A further aim was to determine whether these effects could be ameliorated by escitalopram treatment, as assessed with the non-invasive techniques of structural magnetic resonance imaging (MRI) and magnetic resonance spectroscopy (MRS). Our results demonstrated that CUMS had a dramatic influence on spatial cognitive performance in the Morris water maze task, and CUMS reduced the concentration of neuronal marker N-acetylaspartate (NAA) in the hippocampus. These effects could be significantly reversed by repeated administration of escitalopram. However, neither chronic stress nor escitalopram treatment influenced hippocampal volume. Of note, the learning and memory alterations of the rats were associated with right hippocampal NAA concentration. Our results indicate that in depression, NAA may be a more sensitive measure of cognitive function than hippocampal volume.

To investigate the efficacy and safety of intravenous thrombolytic (IVT) therapy and endovascular (EV) therapy in patients with acute posterior large vessel occlusion stroke (PLVOS). A total of fifty patients with acute PLVOS were randomly divided into the IVT group and EV group. The general baseline data, post-treatment vascular recanalization rate, National Institute of Health Stroke Scale (NIHSS) score before and after intervention, prognosis, and perioperative complications were compared between the two groups. There were no significant differences in the overall baseline data between the two groups (p > 0.05). The post-treatment recanalization rate was significantly higher in the EV group (88.00%) than in the IVT group (64.00%, p < 0.05). The NIHSS score at 24 h post-admission was significantly lower and prognosis was significantly better in the EV group than in the IVT group (both p < 0.05). The EV group had significantly lower overall complication rate (16.00%) than the IVT group (48.00%, p < 0.05). Compared with IVT therapy, EV therapy can effectively improve neurological damage, vascular recanalization rate and prognosis as well as reduce the incidence of perioperative complications in patients with acute PLVOS. [ABSTRACT FROM AUTHOR]

Objective MicroRNAs (miRNAs) are endogenous non-coding RNAs that can play a role in the post-transcriptional regulation of mammalian preadipocyte differentiation. However, the precise functional mechanism of its regulation of fat metabolism is not fully understood. Methods We identified bta-miR-365-3p, which specifically targets the 3′ untranslated region (3′UTR) of the FK506-binding protein 5 (FKBP5), and verified its mechanisms for regulating expression and involvement in adipogenesis. Results In this study, we found that the overexpression of bta-miR-365-3p significantly decreased the lipid accumulation and triglyceride content in the adipocytes. Compared to inhibiting bta-miR-36 5-3p group, overexpression of bta-miR-365-3p can inhibit the expression of adipocyte differentiation-related genes C/EBPα and PPARγ. The dual-luciferase reporter system further validated the targeting relationship between bta-miR-365-3p and FKBP5. FKBP5 mRNA and protein expression were detected by quantitative real-time polymerase chain reaction and Western blot. Overexpression of bta-miR-365-3p significantly down-regulated FKBP5 expression, while inhibition of bta-miR-365-3p showed the opposite, indicating that bta-miR-365-3p negatively regulates FKBP5. Adenosine 5′-monophosphate (AMP)-activated protein kinase/mammalian target of rapamycin (AMPK/mTOR) signaling pathway is closely related to the regulation of cell growth and is involved in the development of bovine adipocytes. In this study, overexpression of bta-miR-365-3p significantly inhibited mRNA and protein expression of AMPK, mTOR, and SREBP1 genes, while the inhibition of bta-miR-365-3p expression was contrary to these results. Overexpression of FKBP5 significantly upregulated AMPK, mTOR, and SREBP1 gene expression, while inhibition of FKBP5 expression was contrary to the above experimental results. Conclusion In conclusion, these results indicate that bta-miR-365-3p may be involved in the AMPK/mTOR signaling pathway in regulating Yanbian yellow cattle preadipocytes differentiation by targeting the FKBP5 gene.

The intelligent monitoring of cutting tools used in the manufacturing industry is steadily becoming more convenient. To accurately predict the state of tools and tool breakages, this study proposes a tool wear prediction technique based on multi-sensor information fusion. First, the vibrational, current, and cutting force signals transmitted during the machining process were collected, and the features were extracted. Next, the Kalman filtering algorithm was used for feature fusion, and a predictive model for tool wear was constructed by combining the ResNet and long short-term memory (LSTM) models (called ResNet-LSTM). Experimental data for thin-walled parts obtained under various machining conditions were utilized to monitor the changes in tool conditions. A comparison between the ResNet and LSTM tool wear prediction models indicated that the proposed ResNet-LSTM model significantly improved the prediction accuracy compared to the individual LSTM and ResNet models. Moreover, ResNet-LSTM exhibited adaptive noise reduction capabilities at the front end of the network for signal feature extraction, thereby enhancing the signal feature extraction capability. The ResNet-LSTM model yielded an average prediction error of 0.0085 mm and a tool wear prediction accuracy of 98.25%. These results validate the feasibility of the tool wear prediction method proposed in this study.

Abstract MicroRNAs (miRNAs) play a vital role in improving meat quality by binding to messenger RNAs (mRNAs). We performed an integrated analysis of miRNA and mRNA expression profiling between bulls and steers based on the differences in meat quality traits. Fat and fatty acids are the major phenotypic indices of meat quality traits to estimate between-group variance. In the present study, 90 differentially expressed mRNAs (DEGs) and 18 differentially expressed miRNAs (DEMs) were identified. Eighty-three potential DEG targets and 18 DEMs were used to structure a negative interaction network, and 75 matching target genes were shown in this network. Twenty-six target genes were designated as intersection genes, screened from 18 DEMs, and overlapped with the DEGs. Seventeen of these genes enriched to 19 terms involved in lipid metabolism. Subsequently, 13 DEGs and nine DEMs were validated using quantitative real-time PCR, and seven critical genes were selected to explore the influence of fat and fatty acids through hub genes and predict functional association. A dual-luciferase reporter and Western blot assays confirmed a predicted miRNA target (bta-miR-409a and PLIN5). These findings provide substantial evidence for molecular genetic controls and interaction among genes in cattle.

Fibroblast growth factor 9 (FGF9) is crucial for the growth and development of hair follicles (HFs); however, its role in sheep wool growth is unknown. Here, we clarified the role of FGF9 in HF growth in the small-tailed Han sheep by quantifying FGF9 expression in skin tissue sections collected at different periods. Moreover, we evaluated the effects of FGF9 protein supplementation on hair shaft growth in vitro and FGF9 knockdown on cultured dermal papilla cells (DPCs). The relationship between FGF9 and the Wnt/β-catenin signaling pathway was examined, and the underlying mechanisms of FGF9-mediated DPC proliferation were investigated. The results show that FGF9 expression varies throughout the HF cycle and participates in wool growth. The proliferation rate and cell cycle of FGF9-treated DPCs substantially increase compared to that of the control group, and the mRNA and protein expression of CTNNB1, a Wnt/β-catenin signaling pathway marker gene, is considerably lower than that in the control group. The opposite occurs in FGF9-knockdown DPCs. Moreover, other signaling pathways are enriched in the FGF9-treated group. In conclusion, FGF9 accelerates the proliferation and cell cycle of DPCs and may regulate HF growth and development through the Wnt/β-catenin signaling pathway.

Though polymer memristors are promising for low‐power flexible edge computing applications, realizing efficient nanometer‐scale arrays remains a challenge. Here, the authors report a record high 90% production yield in nm‐scale 2D conjugated polymer memristors with homogeneous resistive switching.

As the 2019 novel coronavirus disease (COVID-19) rapidly spread across China and to more than 70 countries, an increasing number of pregnant women were affected. The vertical transmission potential of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is of great concern to the obstetrics, neonatologists, and public health agencies. Though some studies indicated the risk of vertical transmission is low, few cases have been reported with comprehensive serial tests from multiple specimens. In this case, a female preterm infant was born to a mother with confirmed COVID-19. She presented with mild respiratory distress and received general management and a short period of nasal continuous positive airway pressure support. During her stay at the hospital, a series of SARS-CoV-2 nucleic test from her throat and anal swab, serum, bronchoalveolar lavage fluid, and urine were negative. The nucleic acid test from the mother's amniotic fluid, vaginal secretions, cord blood, placenta, serum, anal swab, and breast milk were also negative. The most comprehensively tested case reported to date confirmed that the vertical transmission of COVID is unlikely, but still, more evidence is needed. Keywords: COVID-19, SARS-CoV-2, Vertical transmission, Neonate

Background subtraction is an important task in computer vision. Traditional approaches usually utilize low-level visual features like color, texture, or edge to build background models. Due to the lack of deep features, they often achieve poor performance when facing complex video scenes such as illumination changes, background, or camera motions, camouflage effects and shadows. Recently, deep learning has shown to perform well in extracting deep features. To improve the robustness of background subtraction, in this paper, we propose an end-to-end multi-scale spatio-temporal (MS-ST) method which is able to extract deep features from video sequences. First, a video clip is input into a convolutional neural network for extracting multi-scale spatial features. Subsequently, to exploit the temporal information, we combine temporal sampling operations and ConvLSTM modules to extract the multi-scale temporal contextual information. Finally, the segmentation result is generated by fusing multi-scale spatio-temporal features. The experimental results on the CDnet-2014 dataset and the LASIESTA dataset demonstrate the effectiveness and superiority of the proposed method.

In this study, the effects of vitamin A and its metabolite, all-trans retinoic acid (ATRA), on the proliferation and differentiation of preadipocytes and the intestinal microbiome in Yanbian yellow cattle were investigated. Preadipocytes collected from Yanbian yellow cattle treated with different concentrations of ATRA remained in the G1/G0 phase, as determined by flow cytometry. Quantitative reverse-transcription polymerase chain reaction and western blotting analyses showed that the mRNA and protein expression levels of key adipogenic factors, peroxisome proliferator- activated receptor gamma (PPARγ), CCAAT enhancer-binding protein α (C/EBPα), and extracellular signal-regulated kinase 2 (ERK2), decreased. ATRA was found to regulate the mTOR signaling pathway, which is involved in lipid metabolism, by inhibiting the expression of AKT2 and the adipogenic transcription factors SREBP1, ACC, and FAS; the protein and mRNA expression levels showed consistent trends. In addition, 16S rRNA sequencing results showed that a low concentration of vitamin A promoted the growth of intestinal microflora beneficial to lipid metabolism and maintained intestinal health. The results indicated that ATRA inhibited the adipogenic differentiation of preadipocytes from Yanbian yellow cattle through the AKT/mTOR signaling pathway, and that low concentrations of vitamin A may help maintain the intestinal microbes involved in lipid metabolism in cattle.

Background: It is generally accepted that chronic treatment with antidepressants increases hippocampal neurogenesis, but the molecular mechanisms underlying their effects are unknown. Recently, glycogen synthase kinase-3 beta (GSK-3β)/β-catenin signaling was shown to be involved in the mechanism of how antidepressants might influence hippocampal neurogenesis. Methods: The aim of this study was to determine whether GSK-3β/β-catenin signaling is involved in the alteration of neurogenesis as a result of treatment with fluoxetine, a selective serotonin reuptake inhibitor. The mechanisms involved in fluoxetine's regulation of GSK-3β/β-catenin signaling pathway were also examined. Results: Our results demonstrated that fluoxetine increased the proliferation of embryonic neural precursor cells (NPCs) by up-regulating the phosphorylation of Ser9 on GSK-3β and increasing the level of nuclear β-catenin. The overexpression of a stabilized β-catenin protein (ΔN89 β-catenin) significantly increased NPC proliferation, while inhibition of β-catenin expression in NPCs led to a significant decrease in the proliferation and reduced the proliferative effects induced by fluoxetine. The effects of fluoxetine-induced up-regulation of both phosphorylation of Ser9 on GSK-3β and nuclear β-catenin were significantly prevented by the 5-hydroxytryptamine-1A (5-HT1A) receptor antagonist WAY-100635. Conclusions: The results demonstrate that fluoxetine may increase neurogenesis via the GSK-3β/β-catenin signaling pathway that links postsynaptic 5-HT1A receptor activation. [ABSTRACT FROM AUTHOR]

It is generally accepted that cognitive processes, such as learning and memory, are affected in depression. The present study used a rat model of depression, chronic unpredictable mild stress (CUMS), to determine whether hippocampal volume and neurochemical changes were involved in learning and memory alterations. A further aim was to determine whether these effects could be ameliorated by escitalopram treatment, as assessed with the non-invasive techniques of structural magnetic resonance imaging (MRI) and magnetic resonance spectroscopy (MRS). Our results demonstrated that CUMS had a dramatic influence on spatial cognitive performance in the Morris water maze task, and CUMS reduced the concentration of neuronal marker N-acetylaspartate (NAA) in the hippocampus. These effects could be significantly reversed by repeated administration of escitalopram. However, neither chronic stress nor escitalopram treatment influenced hippocampal volume. Of note, the learning and memory alterations of the rats were associated with right hippocampal NAA concentration. Our results indicate that in depression, NAA may be a more sensitive measure of cognitive function than hippocampal volume. [ABSTRACT FROM AUTHOR]

Quantum-dot cellular automata (QCA) is a burgeoning technology at the nano-scale range, with the potential for lower power consumption, smaller size and faster speed than conventional complementary metal–oxide semiconductor-based technology. Because of its ultra-density integration and its inherent physical properties, fault-tolerance is an important property to consider in the research and manufacture of QCA. In this paper, one type of defect, in which displacement and misalignment occur coinstantaneously, is investigated in detail on rudimentary QCA devices (majority voter (MV), inverter, wire) with QCADesigner. Another MV with rotated cells is also proposed, and it is more robust than the original one. Simulation results present the defect-tolerance of these devices, that is, the maximum precise region the defective cell can be moved moreover, with correct logical function. These conclusions have a meaningful guiding significance for QCA physical implementation and fault-tolerance research.