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Self-efficacy or perceived ability refers to the confidence an individual has in their ability to successfully perform a specific task. Previous researchers have demonstrated the importance of self-efficacy beliefs and its impact on predicting students' mathematics performances. Specifically, gender has been regarded as an important moderator variable in the prediction of achievement from measures of affect. To this end, using data from High School Longitudinal Study of 2009, this study utilized the multi-group analysis under the framework of structural equation modeling to examine gender differences in the reciprocal relationship between mathematics self-efficacy and mathematics achievement. The results showed that there were statistically significant differences between male students and female students in the reciprocal relationship between mathematics self-efficacy and mathematics achievement; the impact of prior mathematics achievement on later mathematics achievement was stronger than the impact of prior mathematics self-efficacy on later mathematics self-efficacy. Significance of these findings were discussed in this paper.

Autonomic nerve dysfunction is a common non-motor symptom of Parkinson's disease (PD). It has a high incidence and involves multiple organ system disorders with various manifestations, seriously affecting the quality of life. Autonomic nerve dysfunction can be a major symptom of advanced PD and a major cause of disability. Subthalamic nucleus deep brain stimulation (STN-DBS) is an effective treatment for advanced PD, and its improvement of motor symptoms in PD patients has been widely recognized. However, the effect of STN - DBS on the symptoms of autonomic nerve dysfunction in PD patients is still unclear. Here, the efficacy of STN - DBS on PD autonomic nerve dysfunction is briefly reviewed. Therefore, we should pay more attention to the symptoms of autonomic nerve dysfunction in PD patients, and conduct preoperative evaluation of DBS in PD patients, so as to comprehensively consider the postoperative efficacy, target selection, stimulus parameter setting, drug adjustment, develop an individualized treatment plan.

Abstract Dielectric capacitors offer great potential for advanced electronics due to their high power densities, but their energy density still needs to be further improved. High-entropy strategy has emerged as an effective method for improving energy storage performance, however, discovering new high-entropy systems within a high-dimensional composition space is a daunting challenge for traditional trial-and-error experiments. Here, based on phase-field simulations and limited experimental data, we propose a generative learning approach to accelerate the discovery of high-entropy dielectrics in a practically infinite exploration space of over 1011 combinations. By encoding-decoding latent space regularities to facilitate data sampling and forward inference, we employ inverse design to screen out the most promising combinations via a ranking strategy. Through only 5 sets of targeted experiments, we successfully obtain a Bi(Mg0.5Ti0.5)O3-based high-entropy dielectric film with a significantly improved energy density of 156 J cm−3 at an electric field of 5104 kV cm−1, surpassing the pristine film by more than eight-fold. This work introduces an effective and innovative avenue for designing high-entropy dielectrics with drastically reduced experimental cycles, which could be also extended to expedite the design of other multicomponent material systems with desired properties.

Abstract Background The parallel evolution of similar traits or species provides strong evidence for the role of natural selection in evolution. Traits or species that evolved repeatedly can be driven by separate de novo mutations or interspecific gene flow. Although parallel evolution has been reported in many studies, documented cases of parallel evolution caused by gene flow are scarce by comparison. Aquilegia ecalcarata and A. kansuensis belong to the genus of Aquilegia, and are the closest related sister species. Mutiple origins of A. ecalcarata have been reported in previous studies, but whether they have been driven by separate de novo mutations or gene flow remains unclear. Results In this study, We conducted genomic analysis from 158 individuals of two repeatedly evolving pairs of A. ecalcarata and A. kansuensis. All samples were divided into two distinct clades with obvious geographical distribution based on phylogeny and population structure. Demographic modeling revealed that the origin of the A. ecalcarata in the Eastern of China was caused by gene flow, and the Eastern A. ecalcarata occurred following introgression from Western A. ecalcarata population. Analysis of Treemix and D-statistic also revealed that a strong signal of gene flow was detected from Western A. ecalcarata to Eastern A. ecalcarata. Genetic divergence and selective sweep analyses inferred parallel regions of genomic divergence and identified many candidate genes associated with ecologically adaptive divergence between species pair. Comparative analysis of parallel diverged regions and gene introgression confirms that gene flow contributed to the parallel evolution of A. ecalcarata. Conclusions Our results further confirmed the multiple origins of A. ecalcarata and highlighted the roles of gene flow. These findings provide new evidence for parallel origin after hybridization as well as insights into the ecological adaptation mechanisms underlying the parallel origins of species.

Abstract Objective This cross‐sectional study aimed to determine the epidemiology of olfactory and gustatory dysfunctions related to COVID‐19 in China. Methods This study was conducted by 45 tertiary Grade‐A hospitals in China. Online and offline questionnaire data were obtained from patients infected with COVID‐19 between December 28, 2022, and February 21, 2023. The collected information included basic demographics, medical history, smoking and drinking history, vaccination history, changes in olfactory and gustatory functions before and after infection, and other postinfection symptoms, as well as the duration and improvement status of olfactory and gustatory disorders. Results Complete questionnaires were obtained from 35,566 subjects. The overall incidence of olfactory and taste dysfunction was 67.75%. Being female or being a cigarette smoker increased the likelihood of developing olfactory and taste dysfunction. Having received four doses of the vaccine or having good oral health or being a alcohol drinker decreased the risk of such dysfunction. Before infection, the average olfactory and taste VAS scores were 8.41 and 8.51, respectively; after infection, they decreased to 3.69 and 4.29 and recovered to 5.83 and 6.55 by the time of the survey. The median duration of dysosmia and dysgeusia was 15 and 12 days, respectively, with 0.5% of patients having symptoms lasting for more than 28 days. The overall self‐reported improvement rate was 59.16%. Recovery was higher in males, never smokers, those who received two or three vaccine doses, and those that had never experienced dental health issues, or chronic accompanying symptoms. Conclusions The incidence of dysosmia and dysgeusia following infection with the SARS‐CoV‐2 virus is high in China. Incidence and prognosis are influenced by several factors, including sex, SARS‐CoV‐2 vaccination, history of head‐facial trauma, nasal and oral health status, smoking and drinking history, and the persistence of accompanying symptoms.

Abstract Cortical spreading depression (CSD) is a slow wave of cortical depolarization closely associated with migraines with an aura. Previously, it was thought that CSD depolarization was mainly driven by neurons, with characteristic changes in neuronal swelling and increased extracellular potassium (K+) and glutamate. However, the role of astrocytes, a member of the neurovascular unit, in migraine with CSD has recently received increasing attention. In the early stages of CSD, astrocytes provide neurons with energy support and clear K+ and glutamate from synaptic gaps. However, in the late stages of CSD, astrocytes release large amounts of lactic acid to exacerbate hypoxia when the energy demand exceeds the astrocytes’ compensatory capacity. Astrocyte endfoot swelling is a characteristic of CSD, and neurons are not similarly altered. It is primarily due to K+ influx and abnormally active calcium (Ca2+) signaling. Aquaporin 4 (AQP-4) only mediates K+ influx and has little role as an aquaporin. Astrocytes endfoot swelling causes perivascular space closure, slowing the glymphatic system flow and exacerbating neuroinflammation, leading to persistent CSD. Astrocytes are double-edged swords in migraine with CSD and may be potential targets for CSD interventions.

Abstract Zinc (Zn) is a crucial trace element essential for human growth and development, particularly for reproductive health. Previous research has shown a decrease in serum zinc concentration with age and individuals with conditions such as polycystic ovary syndrome (PCOS) and diabetes mellitus. However, the specific effects of zinc deficiency on the female reproductive system, especially ovarian function, are not fully understood. In our study, we observed a significant reduction in the total number of follicles and mature follicles in the zinc deficiency group. This reduction correlated with decreased level of anti-Mullerian hormone (AMH) and abnormal gene expression affecting hormone secretion regulation. Furthermore, we found that zinc deficiency disrupted mitochondrial dynamics, leading to oxidative stress in the ovaries, which further inhibited autophagy and increased ovarian apoptosis. These changes ultimately resulted in the failure of germinal vesicle breakdown (GVBD) and reduced oocyte quality. Meanwhile, administration of zinc glycine effectively alleviated the oocyte meiotic arrest caused by dietary zinc deficiency. In conclusion, our findings demonstrated that dietary zinc deficiency can affect hormone secretion and follicle maturation by impairing mitochondrial function and autophagy.

Deep brain stimulation (DBS) has been widely used for Parkinson's disease, essential tremor, dystonia, drug - resistant epilepsy and other diseases. The operation procedure of this surgical method is complicated, and it also has certain complications in clinical application, including surgical complications such as intracranial hemorrhage and infection, hardware complications such as lead fracture and displacement, and stimulation complications such as paresthesia and muscle spasm, in which lead fracture and erosion and infection of the skin behind the ear are common and troublesome complications. In clinical practice, we found that lead fracture and erosion and infection of the skin were mainly located behind the ear. Since 2012, Beijing Tiantan Hospital, Capital Medical University had applied the retroauricular bone grinding groove technology in DBS, which significantly reduced the incidence of lead fracture and erosion and infection of the skin. Therefore, the retroauricular bone grinding groove technology can effectively solve the above problems. This paper mainly introduces the retroauricular bone grinding groove technology and the clinical application attention matters, providing references for colleagues.

Stroke can lead to long - term motor disorders and reduced quality of life. Despite advances in conventional rehabilitation, many patients are still faced with limited efficacy of rehabilitation. Neuromodulation techniques have shown breakthrough therapeutic potential. The purpose of this article is to provide an overview of the mechanism of poststroke neuroplasticity, to explore the mechanism of dentate nucleus deep brain stimulation (DN - DBS) to improve poststroke motor function, and to summarize the progress of the study, in order to provide a scientific basis and a new therapeutic perspective for clinical practice of DN-DBS.

Abstract Artificial Intelligence (AI) is currently experiencing a bloom driven by deep learning (DL) techniques, which rely on networks of connected simple computing units operating in parallel. The low communication bandwidth between memory and processing units in conventional von Neumann machines does not support the requirements of emerging applications that rely extensively on large sets of data. More recent computing paradigms, such as high parallelization and near-memory computing, help alleviate the data communication bottleneck to some extent, but paradigm- shifting concepts are required. Memristors, a novel beyond-complementary metal-oxide-semiconductor (CMOS) technology, are a promising choice for memory devices due to their unique intrinsic device-level properties, enabling both storing and computing with a small, massively-parallel footprint at low power. Theoretically, this directly translates to a major boost in energy efficiency and computational throughput, but various practical challenges remain. In this work we review the latest efforts for achieving hardware-based memristive artificial neural networks (ANNs), describing with detail the working principia of each block and the different design alternatives with their own advantages and disadvantages, as well as the tools required for accurate estimation of performance metrics. Ultimately, we aim to provide a comprehensive protocol of the materials and methods involved in memristive neural networks to those aiming to start working in this field and the experts looking for a holistic approach.