Texture of cooked germinated brown rice subjected to freeze-thaw treatment and its improvement by magnetic field treatment.

This study aimed to investigate the textural changes of cooked germinated brown rice (GBR) during freeze-thaw treatment and propose a strategy for enhancing its texture using magnetic field (MF). Seven freeze-thaw cycles exhibited more pronounced effects compared to 7 days of freezing, resulting in increases in GBR hardness by 85.59 %-164.36 % and decreases in stickiness by 10.34 %-43.55 %. Water loss, structural damage of GBR flour, and starch retrogradation contributed to the deterioration of texture. MF mitigated these effects by inhibiting the transformation of bound water into free water, reducing water loss by 0.39 %-0.57 %, and shortening the phase transition period by 2.0-21.5 min, thereby diminishing structural damage to GBR flour and hindering starch retrogradation. Following MF treatment (5 mT), GBR hardness decreased by 21.00 %, while stickiness increased by 45.71 %. This study elucidates the mechanisms through which MF enhances the texture, offering theoretical insights for the industrial production of high-quality frozen rice products.

Leveraging imitation learning in agricultural robotics: a comprehensive survey and comparative analysis.

Imitation learning (IL), a burgeoning frontier in machine learning, holds immense promise across diverse domains. In recent years, its integration into robotics has sparked significant interest, offering substantial advancements in autonomous control processes. This paper presents an exhaustive insight focusing on the implementation of imitation learning techniques in agricultural robotics. The survey rigorously examines varied research endeavors utilizing imitation learning to address pivotal agricultural challenges. Methodologically, this survey comprehensively investigates multifaceted aspects of imitation learning applications in agricultural robotics. The survey encompasses the identification of agricultural tasks that can potentially be addressed through imitation learning, detailed analysis of specific models and frameworks, and a thorough assessment of performance metrics employed in the surveyed studies. Additionally, it includes a comparative analysis between imitation learning techniques and conventional control methodologies in the realm of robotics. The findings derived from this survey unveil profound insights into the applications of imitation learning in agricultural robotics. These methods are highlighted for their potential to significantly improve task execution in dynamic and high-dimensional action spaces prevalent in agricultural settings, such as precision farming. Despite promising advancements, the survey discusses considerable challenges in data quality, environmental variability, and computational constraints that IL must overcome. The survey also addresses the ethical and social implications of implementing such technologies, emphasizing the need for robust policy frameworks to manage the societal impacts of automation. These findings hold substantial implications, showcasing the potential of imitation learning to revolutionize processes in agricultural robotics. This research significantly contributes to envisioning innovative applications and tools within the agricultural robotics domain, promising heightened productivity and efficiency in robotic agricultural systems. It underscores the potential for remarkable enhancements in various agricultural processes, signaling a transformative trajectory for the sector, particularly in the realm of robotics and autonomous systems.

Discriminating bronchiolar adenoma from peripheral lung cancer by thin-section computed tomography (CT): a 2-center study.

Background: Bronchiolar adenoma (BA) is frequently misdiagnosed as peripheral lung cancer (PLC) because it resembles PLC. Computed tomography (CT) examination is an effective tool for detecting and diagnosing lung diseases. To date, there has been no comprehensive study on the differential diagnosis of BAs and PLCs using thin-section computed tomography (TSCT) based on a large sample, and the efficiency of CT in diagnosing BAs has not been verified. The goal of this study was to distinguish BA from PLC by summarizing their clinical and TSCT characteristics. Methods: A retrospective cross-sectional study on 71 cases with BAs and 218 matched controls with PLCs (from March 2020 to May 2023) within 2 centers (The First Affiliated Hospital of Chongqing Medical University and the Second Affiliated Hospital of Army Medical University) was conducted to investigate their clinical and radiological differences. The clinical characteristics and TSCT features of BAs and PLCs were summarized and compared. A multivariate logistic regression analysis was performed to reveal the key predictors of BAs. Results: The BAs and PLCs exhibited significant differences in TSCT features. Multivariate analysis revealed that the lesion being located in basal segments [odds ratio (OR), 17.835; 95% confidence interval (CI): 6.977-45.588; P<0.001], irregular shape (OR, 4.765; 95% CI: 1.877-12.099; P=0.001), negative of spiculation sign (OR, 7.436; 95% CI: 2.063-26.809; P=0.002), central vessel sign with pulmonary artery (OR, 3.576; 95% CI: 1.557-8.211; P=0.003), peripheral vessel sign with pulmonary vein (OR, 12.444; 95% CI: 4.934-31.383; P<0.001), and distance from lesion edge to pleura (D-ETP) ≤5 mm (OR, 5.535; 95% CI: 2.346-13.057; P<0.001) were independent predictors of BAs, and the area under the curve (AUC) of this model was 0.935; 95% CI: 0.901-0.960 (sensitivity: 88.0%, specificity: 86.03%, P<0.001). Conclusions: Peripheral pulmonary nodules locating in the basal segment of lower lobe with irregular shape, central vessel sign with pulmonary artery, peripheral vessel sign with pulmonary vein and D-ETP ≤5 mm, but without spiculation sign, should be highly suspected of BAs.

The VDR/FFAR2 axis mitigates sepsis-induced lung injury by suppressing macrophage lipid peroxidation.

Sepsis-induced lung injury is a common critical condition in clinical practice, characterized by the accumulation of peroxides and inflammatory damage caused by excessive macrophage activation. Currently, effective treatments for sepsis-induced lung injury are lacking. Short-chain fatty acid receptor FFAR2 serves as an anti-inflammatory biomarker, but its role and mechanism in sepsis-induced lung injury remain unclear. To elucidate the influence and mechanism of FFAR2 on macrophage lipid peroxidation levels in sepsis-induced lung injury, this study conducted bioinformatics analysis and cellular experiments using the THP-1 macrophage cell line. By dual luciferase reporter and chromatin immunoprecipitation-quantitative PCR assays, it is confirmed that the transcription factor VDR upregulates FFAR2 expression in macrophages by binding to the promoter region -1695 âˆ¼ 1525, thereby increasing the expression of iron death negative regulatory molecules and lowering macrophage lipid peroxidation levels. Moreover, both in vitro using THP-1 cells and bone marrow-derived macrophages (BMDMs) and in vivo using an LPS-induced septic mice model experiments revealed that activating the VDR/FFAR2 axis could reduce inflammation-induced macrophage lipid peroxide accumulation and alleviate lung injury in septic mice. This finding highlights the potential of FFAR2 as an immunotherapeutic target for mitigating sepsis-related lung injury.

The application of metagenomics, radiomics and machine learning for diagnosis of sepsis.

Introduction: Sepsis poses a serious threat to individual life and health. Early and accessible diagnosis and targeted treatment are crucial. This study aims to explore the relationship between microbes, metabolic pathways, and blood test indicators in sepsis patients and develop a machine learning model for clinical diagnosis. Methods: Blood samples from sepsis patients were sequenced. α-diversity and ß-diversity analyses were performed to compare the microbial diversity between the sepsis group and the normal group. Correlation analysis was conducted on microbes, metabolic pathways, and blood test indicators. In addition, a model was developed based on medical records and radiomic features using machine learning algorithms. Results: The results of α-diversity and ß-diversity analyses showed that the microbial diversity of sepsis group was significantly higher than that of normal group (p < 0.05). The top 10 microbial abundances in the sepsis and normal groups were Vitis vinifera, Mycobacterium canettii, Solanum pennellii, Ralstonia insidiosa, Ananas comosus, Moraxella osloensis, Escherichia coli, Staphylococcus hominis, Camelina sativa, and Cutibacterium acnes. The enriched metabolic pathways mainly included Protein families: genetic information processing, Translation, Protein families: signaling and cellular processes, and Unclassified: genetic information processing. The correlation analysis revealed a significant positive correlation (p < 0.05) between IL-6 and Membrane transport. Metabolism of other amino acids showed a significant positive correlation (p < 0.05) with Cutibacterium acnes, Ralstonia insidiosa, Moraxella osloensis, and Staphylococcus hominis. Ananas comosus showed a significant positive correlation (p < 0.05) with Poorly characterized and Unclassified: metabolism. Blood test-related indicators showed a significant negative correlation (p < 0.05) with microorganisms. Logistic regression (LR) was used as the optimal model in six machine learning models based on medical records and radiomic features. The nomogram, calibration curves, and AUC values demonstrated that LR performed best for prediction. Discussion: This study provides insights into the relationship between microbes, metabolic pathways, and blood test indicators in sepsis. The developed machine learning model shows potential for aiding in clinical diagnosis. However, further research is needed to validate and improve the model.

Parents' rearing styles and adolescents' math achievement: the multiple mediating effect of self-control and math anxiety.

Introduction: This cross-sectional study examined the mechanisms underlying adolescent math achievement by investigating the relationship between parents' rearing styles (including different dimensions of rearing style) and adolescent self-control, math anxiety, and math achievement based on the ecological systems theory. Method: A total of 584 junior high school students (M age = 12.52) completed the Parenting Style Questionnaire, Self-control Scale, and Math Anxiety Rating Scale and provided their math test scores. Results: The rearing styles of both fathers and mothers directly predicted adolescents' math achievement. Maternal rearing style indirectly predicted adolescents' math achievement through their self-control and math anxiety; however, the indirect effect of paternal rearing style on adolescents' math achievement was not significant. After distinguishing the three dimensions of rearing styles, we found that paternal emotional warmth can increase adolescents' self-control, while maternal emotional warmth can reduce adolescents' self-control. Further, paternal overprotectiveness can directly and positively predict adolescents' math achievement, while maternal rejection and overprotectiveness can positively predict adolescents' math achievement. None of the three dimensions of rearing styles can predict math achievement through adolescents' self-control; however, they can predict math achievement indirectly through adolescents' math anxiety and the chain-mediation of adolescents' self-control and math anxiety. Discussion: Our results suggest both commonalities and differences in how paternal and maternal rearing styles, along with their three dimensions (emotional warmth, rejection, overprotection), predict adolescent math achievement. These findings highlight the importance of paternal and maternal rearing styles on adolescents' math achievement and underscore the need to examine them separately to better understand their impact.

Enhanced Extracellular Electron Transfer in Magnetite-Mediated Anaerobic Oxidation of Methane Coupled to Humic Substances Reduction: The Pivotal Role of Membrane-Bound Electron Transfer Proteins.

Humic substances are organic substances prevalent in various natural environments, such as wetlands, which are globally important sources of methane (CH4) emissions. Extracellular electron transfer (EET)-mediated anaerobic oxidation of methane (AOM)-coupled with humic substances reduction plays an important role in the reduction of methane emissions from wetlands, where magnetite is prevalent. However, little is known about the magnetite-mediated EET mechanisms in AOM-coupled humic substances reduction. This study shows that magnetite promotes the reduction of the AOM-coupled humic substances model compound, anthraquinone-2,6-disulfonate (AQDS). 13CH4 labeling experiments further indicated that AOM-coupled AQDS reduction occurred, and acetate was an intermediate product of AOM. Moreover, 13CH313COONa labeling experiments showed that AOM-generated acetate can be continuously reduced to methane in a state of dynamic equilibrium. In the presence of magnetite, the EET capacity of the microbial community increased, and Methanosarcina played a key role in the AOM-coupled AQDS reduction. Pure culture experiments showed that Methanosarcina barkeri can independently perform AOM-coupled AQDS reduction and that magnetite increased its surface protein redox activity. The metatranscriptomic results indicated that magnetite increased the expression of membrane-bound proteins involved in energy metabolism and electron transfer in M. barkeri, thereby increasing the EET capacity. This phenomenon potentially elucidates the rationale as to why magnetite promoted AOM-coupled AQDS reduction.

Analysis and calculation of scour critical velocity of suspended particles in a storm sewer.

The suspended particles in storm sewer can be easily washed away and migrated. However, few studies analyzed the scouring state of suspended particles in pipelines, and also, there was a lack of quantitative calculation. This study simulated the scouring process of suspended particles in a storm sewer with different pipe materials, and mathematical models were built for the scour critical velocity. The results showed that with the increase of particle size, density and the roughness of the pipe wall, the scour resistance of suspended particles increased, and the scouring rate decreased; therefore, the corresponding scour critical velocity increased. In accordance with the scouring rates of quartz sand and zeolite at different flow velocities in the storm sewer, the scouring state of the suspended particles could be divided into three types: no scouring, minor scouring, and massive scouring. The scour critical velocity ranges of quartz sand and zeolite with two densities in four kinds of pipes were determined, and mathematical models for the scour critical velocity of suspended particles were established. After verification, the difference rate between the calculated values and measured values was in the range of -10.56% to 6.63%, and the two values had good consistency. PRACTITIONER POINTS: Scour resistance of suspended particles increases with particle size or density. The smaller the roughness of the pipe wall, the higher the scouring rate. Higher flow velocity leads to a higher scouring rate. As scouring rate rises, no scouring, minor or massive scouring occur in sequence. Difference between the calculated and measured values is from -10.56% to 6.63%.

Analysis of the 5' Untranslated Region Length-Dependent Control of Gene Expression in Maize: A Case Study with the ZmLAZ1 Gene Family.

The untranslated regions (UTRs) within plant mRNAs play crucial roles in regulating gene expression and the functionality of post-translationally modified proteins by various mechanisms. These regions are vital for plants' ability to sense to multiple developmental and environmental stimuli. In this study, we conducted a genome-wide analysis of UTRs and UTR-containing genes in maize (Zea mays). Using the ZmLAZ1 family as a case study, we demonstrated that the length of 5' UTRs could influence gene expression levels by employing GUS reporter gene assays. Although maize and arabidopsis (Arabidopsis thaliana), as well as rice (Oryza sativa), have distinct functional categories of UTR-containing genes, we observed a similar lengthwise distribution of UTRs and a recurring appearance of certain gene ontology (GO) terms between maize and rice. These suggest a potentially conserved mechanism within the Poaceae species. Furthermore, the analysis of cis-acting elements in these 5' UTRs of the ZmLAZ1 gene family further supports the hypothesis that UTRs confer functional specificity to genes in a length-dependent manner. Our findings offer novel insights into the role of UTRs in maize, contributing to the broader understanding of gene expression regulation in plants.

Correlation between intracellular electron transfer and gene expression for electrically conductive pili in electroactive bacteria during anaerobic digestion with ethanol.

Ethanol feeding has been widely documented as an economical and effective strategy for establishing direct interspecies electron transfer (DIET) during anaerobic digestion. However, the mechanisms involved are still unclear, especially on correlation between intracellular electron transfer in electroactive bacteria and their gene expression for electrically conductive pili (e-pili), the most essential electrical connection component for DIET. Upon cooling from room temperature, the conductivity of digester aggregates with ethanol exponentially increased by an order of magnitude (from 45.5 to 125.4 µS/cm), whereas which with its metabolites (acetaldehyde [from 40.5 to 54.4 µS/cm] or acetate [from 32.1 to 50.4 µS/cm]) did not increase significantly. In addition, the digester aggregates only with ethanol were observed with a strong dependence of conductivity on pH. Metagenomic and metatranscriptomic analysis showed that Desulfovibrio desulfuricans was the most dominant and metabolically active bacterium that contained and highly expressed the genes for e-pili. Abundance of genes encoding the total type IV pilus assembly proteins (6.72E-04 vs 1.24E-03, P < 0.05), PilA that determined the conductive properties (2.22E-04 vs 2.44E-04, P > 0.05), and PilB that proceeded the polymerization of pilin (1.56E-04 vs 3.52E-03, P < 0.05) with ethanol was lower than that with acetaldehyde. However, transcript abundance of these genes with ethanol was generally higher than that with acetaldehyde. In comparison to acetaldehyde, ethanol increased the transcript abundance of genes encoding the key enzymes involved in NADH/NAD+ transformation on complex I and ATP synthesis on complex V in intracellular electron transport chain. The improvement of intracellular electron transfer in D. desulfuricans suggested that electrons were intracellularly energized with high energy to activate e-pili during DIET.

Mechanisms of Puerariae Lobatae Radix in regulating sebaceous gland secretion: insights from network pharmacology and experimental validation.

Objective: This research aims to explore how Puerariae Lobatae Radix regulates sebaceous gland secretion using network pharmacology, and validate its effects on important targets through animal studies. Methods: This study utilized UPLC-EQ-MS to analyze Puerariae Lobatae Radix extract and identify potential bioactive compounds. Predicted targets of these compounds were obtained from the Swiss Target Prediction database, while targets associated with sebaceous gland secretion were obtained from the GeneCards database. Common targets between the databases were identified and a protein-protein interaction (PPI) network was established using the STRING platform. The PPI network was further analyzed using Cytoscape software. Pathway enrichment analysis was performed using Reactome, and molecular docking experiments targeted pivotal pathway proteins. Animal experiments were then conducted to validate the regulatory effects of the primary active compounds of Puerariae Lobatae Radix on key pathway proteins. Results: This research identified 17 active compounds in Puerariae Lobatae Radix and 163 potential targets associated with the regulation of sebum secretion. Pathway enrichment analysis indicates that these targets may modulate lipid metabolism pathways through involvement in peroxisome proliferator-activated receptor α, SREB, steroid metabolism, and arachidonic acid metabolism pathways. Molecular docking analysis demonstrates that puerarin and daidzein show favorable binding interactions with key targets in these pathways. Animal experiments demonstrated that the administration of Puerariae Lobatae Radix resulted in a significant reduction in the area of sebaceous gland patches compared to the control group. Histological analysis revealed notable alterations in the structure of sebaceous glands, including reductions in size, thickness, and density. Furthermore, the expression levels of TG, DHT, and IL-6 were significantly decreased in the Puerariae Lobatae Radix group (p < 0.05), and immunoblotting indicated a significant decrease in the expression of PPARG and ACC1 (p < 0.05). Conclusion: This study demonstrates that Puerariae Lobatae Radix can regulate skin lipid metabolism by targeting multiple pathways. The primary mechanism involves inhibiting sebaceous gland growth and reducing TG secretion by modulating the expression of PPARG and ACC1. Puerarin and Daidzein are identified as key bioactive compounds responsible for this regulatory effect. These findings highlight the therapeutic potential of Puerariae Lobatae Radix in addressing sebaceous gland-related conditions.

Automatic mapping of winter wheat planting structure and phenological phases using time-series sentinel data.

The precise extraction of winter wheat planting structure holds significant importance for food security risk assessment, agricultural resource management, and governmental decision-making. This study proposed a method for extracting the winter wheat planting structure by taking into account the growth phenology of winter wheat. Utilizing the fitting effect index, the optimal Savitzky-Golay (S-G) filtering parameter combination was determined automatically to achieve automated filtering and reconstruction of NDVI time series data. The phenological phases of winter wheat growth was identified automatically using a threshold method, and subsequently, a model for extracting the winter wheat planting structure was constructed based on three key phenological stages, including seeding, heading, and harvesting, with the combination of hierarchical classification principles. A priori sample library was constructed using historical data on winter wheat distribution to verify the accuracy of the extracted results. The validation of fitting effect on different surfaces demonstrated that the optimal filtering parameters for S-G filtering could be obtained automatically by using the fitting effect index. The extracted winter wheat phenological phases showed good consistency with ground-based observational results and MOD12Q2 phenological products. Validation against statistical yearbook data and the proposed priori knowledge base exhibited high statistical accuracy and spatial precision, with an extracting accuracy of 94.92%, a spatial positioning accuracy of 93.26%, and a kappa coefficient of 0.9228. The results indicated that the proposed method for winter wheat planting structure extracting can identify winter wheat areas rapidly and significantly. Furthermore, this method does not require training samples or manual experience, and exhibits strong transferability.

Three-dimensional mass transfer modeling and phenolic chemistry exploration for ultrasound-assisted and microwave drying of goji berry.

Herein, goji berries were pretreated with sodium carbonate (Na2CO3) and then dried via ultrasound-assisted air drying or microwave drying. Water migration and phenolic chemistry of goji berries were studied under drying. A three-dimensional ellipsoid water transport model, accounting for porosity and temperature fluctuations, was established to explore the intricacies of the drying mechanism. Generally, microwave drying promoted interior water transport compared to ultrasound drying. Among all the drying methods, microwave drying at 240 W (MW-240 W) exhibited the highest De (from 7.34 × 10-9 to 9.61 × 10-9 m2/s) and kc (6.78 × 10-4 m/s) values. The goji berries received a considerably high water content gradient between its surface and center within the first 2 s of all the drying treatments. Microwave drying diminished the water content gradient earlier than air drying and ultrasound-assisted air drying treatments. Furthermore, most correlations observed among phenolics, oxidase activity, and cell wall pectin did not align with the established theories, highlighting the highly nonlinear nature of phenolic chemistry during goji berry drying. This study provides a three-dimensional model to study the mass transfer mechanism of goji berries and analyzes the evolution of polyphenols during the drying process.

MEMO: dataset and methods for robust multimodal retinal image registration with large or small vessel density differences.

The measurement of retinal blood flow (RBF) in capillaries can provide a powerful biomarker for the early diagnosis and treatment of ocular diseases. However, no single modality can determine capillary flowrates with high precision. Combining erythrocyte-mediated angiography (EMA) with optical coherence tomography angiography (OCTA) has the potential to achieve this goal, as EMA can measure the absolute RBF of retinal microvasculature and OCTA can provide the structural images of capillaries. However, multimodal retinal image registration between these two modalities remains largely unexplored. To fill this gap, we establish MEMO, the first public multimodal EMA and OCTA retinal image dataset. A unique challenge in multimodal retinal image registration between these modalities is the relatively large difference in vessel density (VD). To address this challenge, we propose a segmentation-based deep-learning framework (VDD-Reg), which provides robust results despite differences in vessel density. VDD-Reg consists of a vessel segmentation module and a registration module. To train the vessel segmentation module, we further designed a two-stage semi-supervised learning framework (LVD-Seg) combining supervised and unsupervised losses. We demonstrate that VDD-Reg outperforms existing methods quantitatively and qualitatively for cases of both small VD differences (using the CF-FA dataset) and large VD differences (using our MEMO dataset). Moreover, VDD-Reg requires as few as three annotated vessel segmentation masks to maintain its accuracy, demonstrating its feasibility.

Severe necrotizing tracheobronchitis caused by influenza B and methicillin-resistant Staphylococcus aureus co-infection in an immunocompetent patient.

PURPOSE AND METHOD: Necrotizing tracheobronchitis is a rare clinical entity presented as a necrotic inflammation involving the mainstem trachea and distal bronchi. We reported a case of severe necrotizing tracheobronchitis caused by influenza B and methicillin-resistant Staphylococcus aureus (MRSA) co-infection in an immunocompetent patient. CASE PRESENTATION: We described a 36-year-old man with initial symptoms of cough, rigors, muscle soreness and fever. His status rapidly deteriorated two days later and he was intubated. Bronchoscopy demonstrated severe necrotizing tracheobronchitis, and CT imaging demonstrated multiple patchy and cavitation formation in both lungs. Next-generation sequencing (NGS) and bronchoalveolar lavage fluid (BALF) culture supported the co-infection of influenza B and MRSA. We also found T lymphocyte and NK lymphocyte functions were extremely suppressed during illness exacerbation. The patient was treated with antivirals and antibiotics including vancomycin. Subsequent bronchoscopy and CT scans revealed significant improvement of the airway and pulmonary lesions, and the lymphocyte functions were restored. Finally, this patient was discharged successfully. CONCLUSION: Necrotizing tracheobronchitis should be suspected in patients with rapid deterioration after influenza B infection. The timely diagnosis of co-infection and accurate antibiotics are important to effective treatment.

miR-101a-3p/ROCK2 axis regulates neuronal injury in Parkinson's disease models.

BACKGROUND: Parkinson's disease (PD) is a neurodegenerative disease characterized by the loss of dopaminergic neurons in substantia nigra pars compacta (SNpc). This study focuses on deciphering the role of microRNA (miR)-101a-3p in the neuronal injury of PD and its regulatory mechanism. METHODS: We constructed a mouse model of PD by intraperitoneal injection of 1-methyl 4-phenyl 1, 2, 3, 6-tetrahydropyridine hydrochloride (MPTP), and used 1-methyl-4-phenylpyridinium (MPP+) to treat Neuro-2a cells to construct an in-vitro PD model. Neurological dysfunction in mice was evaluated by swimming test and traction test. qRT-PCR was utilized to examine miR-101a-3p expression and ROCK2 expression in mouse brain tissues and Neuro-2a cells. Western blot was conducted to detect the expression of α-synuclein protein and ROCK2 in mouse brain tissues and Neuro-2a cells. The targeting relationship between miR-101a-3p and ROCK2 was determined by dual-luciferase reporter gene assay. The apoptosis of neuro-2a cells was assessed by flow cytometry. RESULTS: Low miR-101a-3p expression and high ROCK2 expression were found in the brain tissues of PD mice and MPP+-treated Neuro-2a cells; PD mice showed decreased neurological disorders, and apoptosis of Neuro-2a cells was increased after MPP+ treatment, both of which were accompanied by increased accumulation of α-synuclein protein. After miR-101a-3p was overexpressed, the neurological function of PD mice was improved, and the apoptosis of Neuro-2a cells induced by MPP+ was alleviated, and the accumulation of α-synuclein protein was reduced; ROCK2 overexpression counteracted the protective effect of miR-101a-3p. Additionally, ROCK2 was identified as the direct target of miR-101a-3p. CONCLUSION: MiR-101a-3p can reduce neuronal apoptosis and neurological deficit in PD mice by inhibiting ROCK2 expression, suggesting that miR-101a-3p is a promising therapeutic target for PD.

The first diagnosis of Severe Fever with Thrombocytopenia Syndrome caused by tick-borne Severe Fever with Thrombocytopenia Syndrome virus in Chongqing, China: A case report and literature review.

BACKGROUND: Severe Fever with Thrombocytopenia Syndrome (SFTS) is a tick-borne disease caused by the SFTS virus (SFTSV) which has the potential to become a pandemic and is currently a major public health concern. CASE PRESENTATION: We present the case of a 74-year-old female from an urban area of Chongqing, with leukocytopenia, thrombocytopenia, organ function, inflammatory, blood coagulation, and immune abnormalities. SFTSV infection was confirmed through molecular detection and metagenomic next-generation sequencing (mNGS) analysis, indicating a diagnosis of SFTS due to the patient's history of tick bites. The patient received symptomatic and supportive therapy, including antibiotics, antiviral treatment, and antifungal therapy, and finally discharged from the hospital on day 18. CONCLUSIONS: This study highlights the need for increased awareness, early diagnosis, and prompt treatment for tick-borne SFTS. It also provides a comprehensive understanding of the disease's characteristics, pathogenesis, detection methods, and available treatments.

An exploration on the machine-learning-based stroke prediction model.

Introduction: With the rapid development of artificial intelligence technology, machine learning algorithms have been widely applied at various stages of stroke diagnosis, treatment, and prognosis, demonstrating significant potential. A correlation between stroke and cytokine levels in the human body has recently been reported. Our study aimed to establish machine-learning models based on cytokine features to enhance the decision-making capabilities of clinical physicians. Methods: This study recruited 2346 stroke patients and 2128 healthy control subjects from Chongqing University Central Hospital. A predictive model was established through clinical experiments and collection of clinical laboratory tests and demographic variables at admission. Three classification algorithms, namely Random Forest, Gradient Boosting, and Support Vector Machine, were employed. The models were evaluated using methods such as ROC curves, AUC values, and calibration curves. Results: Through univariate feature selection, we selected 14 features and constructed three machine-learning models: Support Vector Machine (SVM), Random Forest (RF), and Gradient Boosting Machine (GBM). Our results indicated that in the training set, the RF model outperformed the GBM and SVM models in terms of both the AUC value and sensitivity. We ranked the features using the RF algorithm, and the results showed that IL-6, IL-5, IL-10, and IL-2 had high importance scores and ranked at the top. In the test set, the stroke model demonstrated a good generalization ability, as evidenced by the ROC curve, confusion matrix, and calibration curve, confirming its reliability as a predictive model for stroke. Discussion: We focused on utilizing cytokines as features to establish stroke prediction models. Analyses of the ROC curve, confusion matrix, and calibration curve of the test set demonstrated that our models exhibited a strong generalization ability, which could be applied in stroke prediction.

Investigation on the exopolysaccharide production from blueberry juice fermented with lactic acid bacteria: Optimization, fermentation characteristics and Vis-NIR spectral model.

The exopolysaccharide production from blueberry juice fermented were investigated. The highest exopolysaccharide yield of 2.2 ± 0.1 g/L (increase by 32.5 %) was reached under the conditions of temperature 26.5 °C, pH 5.5, inoculated quantity 5.4 %, and glucose addition 9.1 % using the artificial neural network and genetic algorithm. Under the optimal conditions, the viable cell counts and total acids were increased by 2.0 log CFU/mL and 1.6 times, respectively, while the content of phenolics and anthocyanin was decreased by 9.26 % and 7.86 %, respectively. The changes of these components affected the exopolysaccharide biosynthesis. The absorption bands of -OH and -CH associated with the main functional groups of exopolysaccharide were detected by Visible near-infrared spectroscopy. The prediction model based on spectrum results was constructed. Competitive adaptive reweighted sampling and the random forest were used to enhance the model's prediction performance with the value of RC = 0.936 and RP = 0.835, indicating a good predictability of exopolysaccharides content during fermentation.

Active Dual Line-Laser Scanning for Depth Imaging of Piled Agricultural Commodities for Itemized Processing Lines.

The accurate depth imaging of piled products provides essential perception for the automated selection of individual objects that require itemized food processing, such as fish, crabs, or fruit. Traditional depth imaging techniques, such as Time-of-Flight and stereoscopy, lack the necessary depth resolution for imaging small items, such as food commodities. Although structured light methods such as laser triangulation have high depth resolution, they depend on conveyor motion for depth scanning. This manuscript introduces an active dual line-laser scanning system for depth imaging static piled items, such as a pile of crabs on a table, eliminating the need for conveyor motion to generate high-resolution 3D images. This advancement benefits robotic perception for loading individual items from a pile for itemized food processing. Leveraging a unique geometrical configuration and laser redundancy, the dual-laser strategy overcomes occlusions while reconstructing a large field of view (FOV) from a long working distance. We achieved a depth reconstruction MSE of 0.3 mm and an STD of 0.5 mm on a symmetrical pyramid stage. The proposed system demonstrates that laser scanners can produce depth maps of complex items, such as piled Chesapeake Blue Crab and White Button mushrooms. This technology enables 3D perception for automated processing lines and offers broad applicability for quality inspection, sorting, and handling of piled products.