Prevalence and Associated Factors of Elder Mistreatment in a Rural Community in People's Republic of China: A Cross-Sectional Study

Background: Current knowledge about elder mistreatment is mainly derived from studies done in Western countries, which indicate that this problem is related to risk factors such as a shared living situation, social isolation, disease burden, and caregiver strain. We know little about prevalence and risk factors for elder mistreatment and mistreatment subtypes in rural China where the elder population is the most vulnerable. Methods: In 2010, we conducted a cross-sectional survey among older adults aged 60 or older in three rural communities in Macheng, a city in Hubei province, China. Of 2245 people initially identified, 2039 were available for interview and this was completed in 2000. A structured questionnaire was used to collect data regarding mistreatment and covariates. Logistic regression analysis was used to identify factors related to elder mistreatment and subtypes of mistreatment. Results: Elder mistreatment was reported by 36.2 % (95 % CI: 34.1%–38.3%) of the participants. Prevalence rates of psychological mistreatment, caregiver neglect, physical mistreatment, and financial mistreatment were 27.3 % (95 % CI

Ferroelectric memristor based on Pt/BiFeO3/Nb-doped SrTiO3 heterostructure

We report a continuously tunable resistive switching behavior in Pt/BiFeO₃/Nb-doped SrTiO₃ heterostructure for ferroelectric memristor application. The resistance of this memristor can be tuned up to 5 × 10⁵% by applying voltage pulses at room temperature, which exhibits excellent retention and anti-fatigue characteristics. The observed memristive behavior is attributed to the modulation effect of the ferroelectric polarization reversal on the width of depletion region and the height of potential barrier of the p-n junction formed at the BiFeO₃/Nb-doped SrTiO₃ interface.This work was supported by the National Natural Science Foundation of China (Grant Nos. 11074193 and 51132001). Q.L. and Y.L. acknowledge the support of the Australian Research Council (ARC) in the form of ARC Discovery Grants

Analysis of Global Sagittal Postural Patterns in Asymptomatic Chinese Adults

Study DesignA prospective imaging study.PurposeTo characterize the distribution of the global sagittal postural patterns in asymptomatic Chinese adults using Roussouly classification.Overview of LiteratureThe norms of sagittal parameters in asymptomatic Chinese population have been previously described, but no report described their global sagittal postural patterns as characterized by Roussouly classification.MethodsA cohort of 272 asymptomatic Chinese adults was recruited. Data was assimilated by reviewing the films for each subject. Sagittal parameters were measured and sagittal postural patterns were then determined according to Roussouly classification. The pattern distributions were compared across genders within the study cohort. We also compared the data across different ethnicities from our study and a previous study to further characterize Chinese sagittal postures.ResultsThe cohort included 161 males and 111 females, with mean age of 23.2±4.4 years. The average descriptive results were as below: pelvic incidence (PI) 46.4°±9.6°, thoracic kyphosis (TK) 24.2°±9.0°, lumbar lordosis (LL) 50.6°±10.6°, sacral slope (SS) 37.2°±7.6°, pelvic tilt (PT) 9.4°±6.8°, spinosacral angle (SSA) 131.1°±7.5° and sagittal vertical axis (SVA) 17.24±32.36 mm. Despite a significant difference between two genders in LL, PI, SSA, and SVA, no difference was found in the distribution of Roussouly types among them. 47.8% of our cohort belonged to Roussouly type 3, while type 1, 2 and 4 comprised 23.2%, 14.0% and 15.1% of the subjects, respectively. Roussouly classification was capable of categorizing sagittal parameters except for the PT. This study also found that 4.4% of the recruited subjects belonged to the C7-anterior subgroup.ConclusionsFrom a characterization of the sagittal postural patterns of asymptomatic Chinese adults using Roussouly classification, the distribution was similar between Chinese males and females; however, from a cross-study comparison, it was different between asymptomatic Chinese and Caucasian adults, with a higher proportion of Roussouly type 3 in Chinese adults

Monitoring Prevalence and Persistence of Environmental Contamination by SARS-CoV-2 RNA in a Makeshift Hospital for Asymptomatic and Very Mild COVID-19 Patients

Objective: To investigate the details of environmental contamination status by SARS-CoV-2 in a makeshift COVID-19 hospital.Methods: Environmental samples were collected from a makeshift hospital. The extent of contamination was assessed by quantitative reverse transcription polymerase chain reaction (RT-qPCR) for SARS-CoV-2 RNA from various samples.Results: There was a wide range of total collected samples contaminated with SARS-CoV-2 RNA, ranging from 8.47% to 100%. Results revealed that 70.00% of sewage from the bathroom and 48.19% of air samples were positive. The highest rate of contamination was found from the no-touch surfaces (73.07%) and the lowest from frequently touched surfaces (33.40%). The most contaminated objects were the top surfaces of patient cubic partitions (100%). The median Ct values among strongly positive samples were 33.38 (IQR, 31.69–35.07) and 33.24 (IQR, 31.33–34.34) for ORF1ab and N genes, respectively. SARS-CoV-2 relic RNA can be detected on indoor surfaces for up to 20 days.Conclusion: The findings show a higher prevalence and persistence in detecting the presence of SARS-CoV-2 in the makeshift COVID-19 hospital setting. The contamination mode of droplet deposition may be more common than contaminated touches

PyPose: A Library for Robot Learning with Physics-based Optimization

Deep learning has had remarkable success in robotic perception, but its data-centric nature suffers when it comes to generalizing to ever-changing environments. By contrast, physics-based optimization generalizes better, but it does not perform as well in complicated tasks due to the lack of high-level semantic information and the reliance on manual parametric tuning. To take advantage of these two complementary worlds, we present PyPose: a robotics-oriented, PyTorch-based library that combines deep perceptual models with physics-based optimization techniques. Our design goal for PyPose is to make it user-friendly, efficient, and interpretable with a tidy and well-organized architecture. Using an imperative style interface, it can be easily integrated into real-world robotic applications. Besides, it supports parallel computing of any order gradients of Lie groups and Lie algebras and $2^{\text{nd}}$-order optimizers, such as trust region methods. Experiments show that PyPose achieves 3-20$\times$ speedup in computation compared to state-of-the-art libraries. To boost future research, we provide concrete examples across several fields of robotics, including SLAM, inertial navigation, planning, and control

In Silico Identification of Specialized Secretory-Organelle Proteins in Apicomplexan Parasites and In Vivo Validation in Toxoplasma gondii

Apicomplexan parasites, including the human pathogens Toxoplasma gondii and Plasmodium falciparum, employ specialized secretory organelles (micronemes, rhoptries, dense granules) to invade and survive within host cells. Because molecules secreted from these organelles function at the host/parasite interface, their identification is important for understanding invasion mechanisms, and central to the development of therapeutic strategies. Using a computational approach based on predicted functional domains, we have identified more than 600 candidate secretory organelle proteins in twelve apicomplexan parasites. Expression in transgenic T. gondii of eight proteins identified in silico confirms that all enter into the secretory pathway, and seven target to apical organelles associated with invasion. An in silico approach intended to identify possible host interacting proteins yields a dataset enriched in secretory/transmembrane proteins, including most of the antigens known to be engaged by apicomplexan parasites during infection. These domain pattern and projected interactome approaches significantly expand the repertoire of proteins that may be involved in host parasite interactions

A Review of Research on Signal Modulation Recognition Based on Deep Learning

Since the emergence of 5G technology, the wireless communication system has had a huge data throughput, so the joint development of artificial intelligence technology and wireless communication technology is one of the current mainstream development directions. In particular the combination of deep learning technology and communication physical layer technology is the future research hotspot. The purpose of this research paper is to summarize the related algorithms of the combination of Automatic Modulation Recognition (AMR) technology and deep learning technology in the communication physical layer. In order to elicit the advantages of the modulation recognition algorithm based on deep learning, this paper firstly introduces the traditional AMR method, and then summarizes the advantages and disadvantages of the traditional algorithm. Then, the application of the deep learning algorithm in AMR is described, and the identification method based on a typical deep learning network is emphatically described. Afterwards, the existing Deep Learning (DL) modulation identification algorithm in a small sample environment is summarized. Finally, DL modulation is discussed, identifying field challenges, and future research directions

Tunable Magnonic Chern Bands and Chiral Spin Currents in Magnetic Multilayers

Realization of novel topological phases in magnonic band structures represents a new opportunity for the development of spintronics and magnonics with low power consumption. In this work, we show that in antiparallelly aligned magnetic multilayers, the long-range, chiral dipolar interaction between propagating magnons generates bulk bands with nonzero Chern integers and magnonic surface states carrying chiral spin currents. The surface states are highly localized and can be easily toggled between nontrivial and trivial phases through an external magnetic field. The realization of chiral surface spin currents in this dipolarly coupled heterostructure represents a magnonic implementation of the coupled wire model that has been extensively explored in electronic systems. Our work presents an easy-to-implement system for realizing topological magnonic surface states and low-dissipation spin current transport in a tunable manner