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Abstract Parkinson’s disease (PD) is a multifactorial neurodegenerative disorder with high prevalence among the elderly, primarily manifested by progressive decline in motor function. The aging global demographic and increased life expectancy have led to a rapid surge in PD cases, imposing a significant societal burden. PD along with other neurodegenerative diseases has garnered increasing attention from the scientific community. In PD, motor symptoms are recognized when approximately 60% of dopaminergic neurons have been damaged. The irreversible feature of PD and benefits of early intervention underscore the importance of disease onset prediction and prompt diagnosis. The advent of digital health technology in recent years has elevated the role of digital biomarkers in precisely and sensitively detecting early PD clinical symptoms, evaluating treatment effectiveness, and guiding clinical medication, focusing especially on motor function, responsiveness and sleep quality assessments. This review examines prevalent digital biomarkers for PD and highlights the latest advancements.

Abstract Reaumuria soongarica is a xerophytic shrub belonging to the Tamaricaceae family. The species is widely distributed in the deserts of Central Asia and is characterized by its remarkable adaptability to saline and barren desert environments. Using PacBio long-read sequencing and Hi-C technologies, we assembled a chromosome-level genome of R. soongarica. The genome assembly has a size of 1.28 Gb with a scaffold N50 of 116.15 Mb, and approximately 1.25 Gb sequences were anchored in 11 pseudo-chromosomes. A completeness assessment of the assembled genome revealed a BUSCO score of 97.5% and an LTR Assembly Index of 12.37. R. soongarica genome had approximately 60.07% repeat sequences. In total, 21,791 protein-coding genes were predicted, of which 95.64% were functionally annotated. This high-quality genome will serve as a foundation for studying the genomic evolution and adaptive mechanisms to arid-saline environments in R. soongarica, facilitating the exploration and utilization of its unique genetic resources.

Abstract Force transmission through adherens junctions (AJs) is crucial for multicellular organization, wound healing and tissue regeneration. Recent studies shed light on the molecular mechanisms of mechanotransduction at the AJs. However, the canonical model fails to explain force transmission when essential proteins of the mechanotransduction module are mutated or missing. Here, we demonstrate that, in absence of α-catenin, β-catenin can directly and functionally interact with vinculin in its open conformation, bearing physiological forces. Furthermore, we found that β-catenin can prevent vinculin autoinhibition in the presence of α-catenin by occupying vinculin´s head-tail interaction site, thus preserving force transmission capability. Taken together, our findings suggest a multi-step force transmission process at AJs, where α-catenin and β-catenin can alternatively and cooperatively interact with vinculin. This can explain the graded responses needed to maintain tissue mechanical homeostasis and, importantly, unveils a force-bearing mechanism involving β-catenin and extended vinculin that can potentially explain the underlying process enabling collective invasion of metastatic cells lacking α-catenin.

Abstract The primary objective of this study was to compare short-term outcomes between Intracorporeal ileocolic anastomosis (IIA) and extracorporeal ileocolic anastomosis (EIA) after laparoscopic right hemicolectomy in patients with visceral obesity. The secondary objective was to identify risk factors associated with prolonged postoperative ileus (PPOI) after laparoscopic right hemicolectomy. This single-center retrospective study analyzed visceral obesity patients who underwent laparoscopic right hemicolectomy for primary bowel cancer between January 2020 and June 2023. Patients were categorized into IIA and EIA groups based on the type of anastomosis, and a 1:1 propensity score-matched analysis was performed. A total of 129 patients were initially included in this study, with 45 patients in each group following propensity score matching. The IIA group had significantly longer anastomosis times (p

Abstract The authors propose a novel object detection algorithm for identifying bird nests in medium voltage power line aerial images, which is crucial for ensuring the safe operation of the power grid. The algorithm utilises an improved Swin Transformer as the main feature extraction network of Fast R‐CNN, further enhanced with a channel attention and modified binary self‐attention mechanism to improve the feature representation ability. The proposed algorithm is evaluated on a newly constructed image dataset of medium voltage transmission lines containing bird nests, which are annotated and classified. Experimental results show that the proposed algorithm achieves satisfied accuracy and robustness in recognising bird nests compared to traditional algorithms.

Drought, excessive use of nitrogen fertilizer, and decline in soil organic matter threaten corn production. This study investigated the potential of water retaining agents, inhibitors, and corn stalks in enhancing soil physicochemical properties to bolster corn yield in semi-arid farmlands. In our study, polyacrylamide addition increased the content of ammonium nitrogen (NH4+-N) and nitrate nitrogen (NO3--N) in the seedling stage, exchangeable potassium (K) in the mature stage but decreased the content of available phosphorus (P) in the seedling stage. Potassium polyacrylate addition increased the content of NH4+-N and decreased the content of available P in the seedling stage. The addition of inhibitors decreased the content of NH4+-N and available P in the seedling stage, NO3--N and available P in the jointing stage, and NH4+-N in the mature stage, respectively. Corn stalks returning could maintain soil moisture, decrease the content of NH4+-N in the seedling stage and exchangeable K in the mature stage, and increase the content of available P and exchangeable K in the seedling stage. Combined application of inhibitors and corn stalks could increase soil organic carbon (SOC) and ensure corn yield, which was the best fertilization mode in semi-arid cropland.

Abstract Purpose Luminal breast cancer (BC) is a prevalent subtype associated with an increased risk of late disease recurrence and mortality. Long noncoding RNAs (lncRNAs) likely play significant roles in regulating tissue-specific gene expression during tumorigenesis. However, the biological function and underlying mechanisms of specific dysregulated lncRNAs in luminal BC remain largely unknown, which has drawn our attention. Methods The expression pattern of lncRNA NCALD in luminal BC was predicted and validated in collected tissue samples. Following cell transfection with knockdown of lncRNA NCALD and ESR1 and overexpression of GRHL2 and ESR1, we investigated the interactions among lncRNA NCALD, ESR1, and GRHL2. Additionally, their regulatory functions in luminal BC cell biological processes were studied. Subsequently, a xenograft tumor model was prepared for validation. Results Our study identified a specific overexpression of the lncRNA NCALD in luminal BC, which correlated with an unfavorable prognosis. Suppression of lncRNA NCALD or ESR1 led to inhibition of GRHL2 expression, while concurrent overexpression of ESR1 and lncRNA NCALD potentially elevated GRHL2 expression. Mechanistically, ERα may drive the expression of lncRNA NCALD. Furthermore, the 1–151 nt fragment of lncRNA NCALD was found to recruit ERα and interact with its oest-Recep domain located in the promoter region of GRHL2, ultimately inducing GRHL2 transcription. Conclusions These findings reveal the involvement of lncRNA NCALD and its specific expression pattern in luminal BC. Targeting lncRNA NCALD could be a potential therapeutic strategy for delaying the progression of BC.

A wide width is one of the fundamental requirements for magnesium alloy sheets to fulfill the lightweight needs of the aerospace and rail transportation industries. Nevertheless, ensuring the dimensional integrity of wide-format magnesium alloy sheets is a challenge in most manufacturing facilities. Simultaneously, processing operations are susceptible to encountering production issues, including but not limited to significant loss of raw materials, elevated costs, environmental contamination, and safety risks. Therefore, to fulfill the operational demands of the hot rolling finishing line for wide sheets made of magnesium alloy, the present study employed ultrasonic-assisted shearing (UAS) to carry out a high-temperature shearing test investigation on the AZ31B magnesium alloy sheet. In this research, we employed scanning electron microscopy, optical microscopy, and a micro-hardness tester to investigate the relationship between ultrasonic amplitude and fracture behavior, deformation mechanism, and mechanical properties of magnesium alloy sheets following exposure to ultrasonic vibration. The fracture morphology of the sheet fracture demonstrates that the behavior of the magnesium alloy sheet fractures under ultrasonic vibration changes from ductile fracture under conventional shearing to brittle fracture under ultrasonic-assisted shearing. The primary cause of this transformation between ductile and brittle is the effect of ultrasonic vibration on the material's internal energy. High-energy ultrasonic amplitude action causes magnesium to absorb a significant amount of ultrasonic energy. This results in an increase in the number of dynamic re-crystallization grains in the material organization, a decrease in the number of twins, and significant refinement of the grains. The sheet's hardness also changes along the rolling and normal directions, with a uniform distribution. The magnesium alloy sheet's fracture quality is optimal when the ultrasonic amplitude is 10 μm in the UAS process. This treatment results in notable enhancements in the section's straightness and brightness. Our study aims to advance the mass manufacturing of wide-format magnesium alloy sheets by investigating a cost-effective and high-yield technique for shearing magnesium alloy sheets.

Despite the rapid growth of the eSports industry with respect to its economic scale and the demand for eSports players, the career transformation experiences and well-being of eSports athletes have been largely overlooked. This study aims to explore eSports athletes’ personal development in terms of career continuity, associated skill transformation, and well-being after retirement. Employing non-participatory observations and semi-structured interviews, we unpack two different pedagogical contexts of retiring eSports athletes: one is “continuity paths” (three transformation options and associated skill sets), and the other is “desire for continuity” (psychological strengths and explicit tacit knowledge). Our findings provide practical guidance for the career transition experiences of eSports athletes and contribute to understanding the sustainable framework required for the self-development of talent within eSports education and the eSports industry. The findings suggest that psychological consultants and athletic rehabilitation therapists should be employed to provide professional advice, interventions, and treatment to eSports athletes before and after retirement.

Soil microbial carbon use efficiency (CUE) is a key parameter controlling the short-term carbon (C) cycle in terrestrial ecosystems. The effect of urea application (156 kg N ha-1) and corn stalks returning (9.0 tons ha-1) on soil microbial CUE and corn yield in semi-arid cropland was studied using the 18O-labeled water approach during a one-year experiment. In semi-arid cropland, applying urea reduced soil microbial CUE by 44%, while the soil microbial CUE was increased significantly by 34% after returning corn stalks to the field. The application of urea increased the total nitrogen content of soil by 23%, and corn stalks returning further increased nitrate nitrogen (NO3--N) by 45%, dissolved organic carbon (DOC) by 53%, and dissolved organic nitrogen (DON) by 122%. Compared with no fertilization, urea application increased the corn height by 4% and the corn yield by 21%. Corn stalks returning combined with urea reduced the corn stalks by 37% compared with no fertilizer. There was no significant difference in corn yield between corn stalks returning combined with urea and single urea application. Therefore, corn stalks returning combined with urea may be an effective agronomic measure to increase soil carbon sequestration, improve soil fertility, maintain corn yield, restore soil fertility, and improve production capacity.

Objective To establish the GC-MS qualitative and quantitative analysis methods for the synthetic cannabinoids, its main matrix and additives in suspicious electronic cigarette （e-cigarette） oil samples. Methods The e-cigarette oil samples were analyzed by GC-MS after diluted with methanol. Synthetic cannabinoids, its main matrix and additives in e-cigarette oil samples were qualitatively analyzed by the characteristic fragment ions and retention time. The synthetic cannabinoids were quantitatively analyzed by using the selective ion monitoring mode. Results The linear range of each compound in GC-MS quantitative method was 0.025-1 mg/mL, the matrix recovery rate was 94%-103%, the intra-day precision relative standard deviations （RSD） was less than 2.5%, and inter-day precision RSD was less than 4.0%. Five indoles or indazole amide synthetic cannabinoids were detected in 25 e-cigarette samples. The main matrixes of e-cigarette samples were propylene glycol and glycerol. Additives such as N,2,3-trimethyl-2-isopropyl butanamide （WS-23）, glycerol triacetate and nicotine were detected in some samples. The content range of synthetic cannabinoids in 25 e-cigarette samples was 0.05%-2.74%. Conclusion The GC-MS method for synthesizing cannabinoid, matrix and additive in e-cigarette oil samples has good selectivity, high resolution, low detection limit, and can be used for simultaneous qualitative and quantitative analysis of multiple components; The explored fragment ion fragmentation mechanism of the electron bombardment ion source of indole or indoxamide compounds helps to identify such substances or other compounds with similar structures in cases.

BackgroundTotal mesorectal excision (TME), represents a key technique in radical surgery for rectal cancer. This study aimed to construct a preoperative nomogram for predicting the surgical difficulty of laparoscopic total mesorectal excision (L-TME) and to investigate whether there were potential benefits of robotic TME (R-TME) for patients with technically challenging rectal cancer.MethodsConsecutive mid­low rectal cancer patients receiving total mesorectal excision were included. A preoperative nomogram to predict the surgical difficulty of L-TME was established and validated. Patients with technically challenging rectal cancer were screened by calculating the prediction score of the nomogram. Then patients with technically challenging rectal cancer who underwent different types of surgery, R-TME or L-TME, were analyzed for comparison.ResultsA total of 533 consecutive patients with mid­low rectal cancer who underwent TME at a single tertiary medical center between January 2018 and January 2021 were retrospectively enrolled. Multivariable analysis demonstrated that mesorectal fat area, intertuberous distance, tumor size, and tumor height were independent risk factors for surgical difficulty. Subsequently, these variables were used to construct the nomogram model to predict the surgical difficulty of L-TME. The area under the receiver operating characteristic curve of the nomogram was 0.827 (95% CI 0.745 - 0.909) and 0.809 (95% CI 0.674- 0.944) in the training and validation cohort, respectively. For patients with technically challenging rectal cancer, R-TME was associated with a lower diverting ileostomy rate (p = 0.003), less estimated blood loss (p < 0.043), shorter procedure time (p = 0.009) and shorter postoperative hospital stay (p = 0.037).ConclusionIn this study, we established a preoperative nomogram to predict the surgical difficulty of L-TME. Furthermore, this study also indicated that R-TME has potential technical advantages for patients with technically challenging rectal cancer.

Study region: Andun river basin of Southern China and 273 watersheds across the continental United States. Study focus: Owing to the data incompleteness and the changing environment, there is bias existing in the data driven models for streamflow prediction, greatly limiting their application in actual practice. In this paper, we incorporate the bias learning components into the data driven models to establish the mapping-bias-learning models, and design two groups of experiments, one in the Andun River basin in China and the other in 273 watersheds in the continental United States (CONUS). In the first group of experiments, we respectively apply three machine learning algorithms and one traditional statistical method to generate sixteen mapping-bias-learning models ae well as four mapping-learning-alone models. We also explore the effectiveness of different bias learning strategies, including multiple bias learning, stacking bias learning and incremental updating bias learning. In the second group of experiments, we apply the mapping-bias-learning models to the streamflow prediction in 273 watersheds of CONUS to verify the universality and robustness of the bias learning method. New hydrological insights for the region: The mapping-bias-learning models significantly outperform the mapping-learning-alone models, and the machine learning methods are superior to the traditional statistical method in term of the ability of bias learning. In addition, adopting the appropriate bias learning strategies can further improve the streamflow forecast performance of data-driven models. The bias learning method is applicable to various watersheds with different hydrological conditions and play efficient roles at different streamflow levels. The median of MRE drops from 0.156 for mapping-learning-alone models to 0.131 for mapping-bias-learning models, while median of NSE increases from 0.918 for mapping-learning-alone models to 0.932 for mapping-bias-learning models. More inspiringly, the bias learning method can still play roles in data driven models under the environmental conditions that never occurred before, suggesting its remarkable superiority. This study highlights the power of the bias learning method for data driven models in streamflow prediction, and the promising prospect of the combination between bias learning and machine learning methods in hydrological modelling.