Research Progress on Bioactive Components and Aroma Quality of Mulberry Wine

Mulberry has a long history of use as an edible fruit and medicine. Studies have shown that mulberry possesses several functional characteristics including antioxidation, anti-inflammation and anticancer. In recent years, mulberry wine has gained much attention from consumers due to its abundant bioactive components and distinctive sensory attributes. Based on the latest research, this review summarizes the health effects of mulberry wine from the perspective of bioactive components, clarifies the aroma composition of mulberry wine, discusses the main factors affecting the aroma quality of mulberry wine and prospected the future research trends. It is expected to provide new ideas for theoretical study and product development of mulberry wine, which will be helpful to promote the quality standardization of mulberry wine and enhance the economic value of mulberry wine industry

Robo360: A 3D Omnispective Multi-Material Robotic Manipulation Dataset

Building robots that can automate labor-intensive tasks has long been the core motivation behind the advancements in computer vision and the robotics community. Recent interest in leveraging 3D algorithms, particularly neural fields, has led to advancements in robot perception and physical understanding in manipulation scenarios. However, the real world's complexity poses significant challenges. To tackle these challenges, we present Robo360, a dataset that features robotic manipulation with a dense view coverage, which enables high-quality 3D neural representation learning, and a diverse set of objects with various physical and optical properties and facilitates research in various object manipulation and physical world modeling tasks. We confirm the effectiveness of our dataset using existing dynamic NeRF and evaluate its potential in learning multi-view policies. We hope that Robo360 can open new research directions yet to be explored at the intersection of understanding the physical world in 3D and robot control

Identification of apoptosis-related gene signatures as potential biomarkers for differentiating active from latent tuberculosis via bioinformatics analysis

BackgroundApoptosis is associated with the pathogenesis of Mycobacterium tuberculosis infection. This study aims to identify apoptosis-related genes as biomarkers for differentiating active tuberculosis (ATB) from latent tuberculosis infection (LTBI).MethodsThe tuberculosis (TB) datasets (GSE19491, GSE62525, and GSE28623) were downloaded from the Gene Expression Omnibus (GEO) database. The diagnostic biomarkers differentiating ATB from LTBI were identified by combining the data of protein-protein interaction network, differentially expressed gene, Weighted Gene Co-Expression Network Analysis (WGCNA), and receiver operating characteristic (ROC) analyses. Machine learning algorithms were employed to validate the diagnostic ability of the biomarkers. Enrichment analysis for biomarkers was established, and potential drugs were predicted. The association between biomarkers and N6-methyladenosine (m6A) or 5-methylated cytosine (m5C) regulators was evaluated.ResultsSix biomarkers including CASP1, TNFSF10, CASP4, CASP5, IFI16, and ATF3 were obtained for differentiating ATB from LTBI. They showed strong diagnostic performances, with area under ROC (AUC) values > 0.7. Enrichment analysis demonstrated that the biomarkers were involved in immune and inflammation responses. Furthermore, 24 drugs, including progesterone and emricasan, were predicted. The correlation analysis revealed that biomarkers were positively correlated with most m6A or m5C regulators.ConclusionThe six ARGs can serve as effective biomarkers differentiating ATB from LTBI and provide insight into the pathogenesis of Mycobacterium tuberculosis infection

Abrupt elevation of tumor marker levels in a huge splenic epidermoid cyst, a case report

Epidermoid cyst of the spleen is a rare disease, and relatively few cases were reported by literatures. Most published case reports provided inadequate information on the impact of splenic epidermoid cyst on tumor markers. A 32-year-old woman with a giant splenic epidermoid cyst was reported, for whom the serum concentration of a collection of tumor markers (CA19–9, CEA, CA125, CA242, and CA50) increased abruptly accompanied by left upper abdominal pain for 5 days. After comprehensive preoperative examination and multidisciplinary team discussion, we ruled out any concurrent malignancy and a laparoscopic total splenectomy was performed, during which the splenic cyst spontaneously ruptured unexpectedly. After surgery, the elevated serum tumor marker levels decreased sharply until reaching normal range 3 months later. Learning from the case, we conclude that interval monitoring of serum tumor markers is of critical value for patients with splenic epidermoid cyst. Abrupt elevation of tumor marker levels and abdominal pain may serve as signs of cyst rupture, which is strongly indicative of surgical intervention as soon as possible. Total removal of the splenic cyst is strongly suggested considering the recurrence and malignant potential of the splenic epidermoid cyst

Implementering av Transformer-algoritm på DRRA-2

The Transformer algorithm has become a widely discussed topic in recent times. It is a method used in fields such as natural language processing (NLP) and computer vision to manage sequential data and capture long-range dependencies within text. By utilizing a self-attention mechanism, it significantly improves the performance and accuracy of language models. While the Transformer algorithm can be implemented on various platforms, this thesis focuses on its implementation specifically on the DRRA-2 fabric. Multi-head attention and feed-forward network functions of the Transformer were built with fixed input size and evaluated on the Silago environment with accuracy verification conducted via C++ functions. Thus, as a result of this project, data evaluation based on output matching, overhead and number of instructions all led to the conclusion that the implementation of the mapped functions were mostly effective except for the cases where the input size was relatively small. The mapped components can serve as a basis for implementing functions with variable input sizes in the future, specifically by taking the base function as sample instructions.Transformer-algoritmen har blivit ett mycket diskuterat ämne på senare tid. Det är en metod som används inom NLP för att hantera sekventiella data och fånga långväga beroenden inom text. Genom att använda en självuppmärksamhetsmekanism förbättrar den betydligt prestanda och noggrannhet hos språkmodeller. Medan Transformer-algoritmen kan implementeras på olika plattformar, fokuserar den här avhandlingen på dess implementering specifikt på DRRA-2-fabriken. Multi-head uppmärksamhet och feed-forward nätverksfunktioner hos Transformer byggdes med en fast inmatningsstorlek och utvärderades på Silago-miljön med noggrannhetsverifiering genomförda via C++-funktioner. Således, som ett resultat av detta projekt, bekräftade omfattande tester att alla mappade komponenter integrerades effektivt, och en diskussion om overhead och instruktionsräkning genomfördes baserat på dessa komponenter. De kartlagda komponenterna kan utgöra en grund för att implementera funktioner med variabla inmatningsstorlekar i framtiden, specifikt genom att ta basfunktionen som provinstruktion

RELIABILITY PREDICTION METHOD BASED ON THE 3CAW DISTRIBUTION (MT)

The distribution of failures is crucial in reliability engineering, and the applicability of a single distribution is limited. In complex systems, a mixture distribution is more appropriate. This article introduces the 3CAW distribution, which provides a more flexible failure rate function, especially a bathtub-shaped failure rate with a long constant region, which is suitable for more complex systems. This article discusses the use of maximum likelihood estimation to estimate the unknown parameters of the 3CAW distribution, and calculate reliability and failure rate as reliability indicators. The cross-entropy method is used to find the global optimum of the logarithmic likelihood function and compare it with single distributions such as the Weibull distribution. The results show that although the model is complex, it has the highest logarithmic likelihood function value and the highest fitting accuracy. The cross-entropy method reduces the difficulty of parameter estimation, making it worth further promotion in engineering practice

RELIABILITY ANALYSIS OF MEDICAL EQUIPMENT BASED ON q-WEIBULL DISTRIBUTION (MT)

A reliability analysis of medical equipment is implemented based on the q-Weibull distribution to provide a basis for healthcare organizations to revise their operational maintenance management strategies. The Weibull distribution failure rate function is monotonic and cannot fully describe the full life cycle operation of complex medical equipment. Therefore, this study introduces the q-Weibull distribution to predict the remaining life of medical equipment, uses a method based on the contour error function to simplify the q-Weibull distribution parameter estimation process, and verifies the validity and feasibility of the method using a hemofiltration apparatus and a lamp-holder of surgical shadowless lamp as examples, and compares the advantages and disadvantages of the two distributions by mean square error（MSE）, the Akaike information criterion（AIC） and the coefficient of determination R~2. Both distributions for the hemofiltration apparatus and lamp-holder of surgical shadowless lamp show the same predictive trend, but the R~2 and MSE comparisons show that the q-Weibull distribution had a better fit accuracy, especially the MSE(2.818 1×10-3)for the hemofiltration apparatus based on the q-Weibull distribution is much smaller than the MSE（9.465） for the Weibull distribution. When the hemofiltration apparatus and lamp-holder of surgical shadowless lamp are operated for 50 days, their estimated remaining life is 254.390 9 days and 291.011 1 days respectively. The above data verifies the validity and fitting accuracy of the q-Weibull distribution, which is worthy of further research and promotion in the reliability research of medical equipment

The object as the unit of interaction between visual working memory and visual attention

This study aimed to determine the unit of interaction between visual working memory (VWM) and attention by examining two opposing hypotheses: (a) the unit of interaction is a Boolean map, a data format that can contain only one within-dimension feature (e.g., “red” or “circle”; Boolean-map-unit hypothesis) and (b) the unit of interaction is an object (object-unit hypothesis). In Experiments 1 and 2, participants held in their VWM one color, or multiple colors (two or three) that either came from one integrated object or separate objects, and then performed a visual search task that sometimes contained a distractor with a memory-matching color. Results showed that the VWM-driven attentional-capture effect triggered by colors from an integrated object was larger than that from separate objects. Moreover, surprisingly, the magnitude of the attentional guidance effect of multiple color representations from the integrated object was almost equivalent to that of a single-color object. Experiment 3 replicated the modulation effect of objecthood and demonstrated that it was not driven by different memory fidelity. Experiments 4 and 5 extended these findings by generalizing the effect to two features from different dimensions (i.e., color and orientation), and from different modules (i.e., visual and verbal color representations). These results suggest the objecthood of multiple representations could modulate their ability to guide attention, supporting the object-unit hypothesis. These findings have crucial implications for understanding the architecture of interactions between VWM and attention

Early childhood SARS experience leads to long-lasting impacts on adulthood mental health in China

Abstract The association between pandemic experience and immediate mental health risks, such as depression, is well-documented, yet the long-term effects remain unclear. This study examines the impact of early childhood exposure to the 2003 SARS pandemic on adulthood mental health after 17 years in China, using data from the 2020 China Family Panel Studies (CFPS). The analysis included 6289 participants, aged 3 to 30 years during the SARS outbreak, with an average age of 35.3 years at the time of survey. Adulthood mental health was assessed using Center for Epidemiologic Studies Depression Scale (CESD) and an indicator of clinical depression. The severity of local SARS outbreaks was assessed by cumulative cases per 10,000 population. Results show that each additional case per 10,000 population was linked to a 1.617-fold (95% confidence interval (CI): 1.425–1.836) increase in odds of depression after 17 years for younger children (aged 3–12 years in 2003) relative to older cohorts (aged 13-30). This risk was higher in children from rural areas (adjusted odds ratio (aOR) 3.64; 95% CI 2.92–4.55), with poor physical health (1.98; 1.59–2.48), and from low-income families (2.87; 2.03–4.05). The childhood pandemic experience elevated the probability of developing depression-prone personality traits, which contributes to the enduring impact of childhood pandemic experiences on adulthood mental health. These findings highlight the long-lasting psychological impact of early-childhood pandemic exposure, underscoring the need for targeted interventions to mitigate its effects on the younger generation and emphasizing the importance of monitoring long-term mental health and personality development in children post-pandemics, particularly in light of COVID-19