Beneficial or adverse? Exploring the association between Internet content consumption and sexual identity among single gay men who use the Internet in Guangdong, China

The purpose of this research is to explore the relationship between homosexual identity and the Internet, using an online sample from Southern China. The goals of this research are (1) to document the nature of participants\u27 homosexual identity (including their history of sexual experiences, their level of self-acceptance, and their fears of disclosure); (2) to describe participants\u27 Internet use and attitudes (including the functions they most frequently use, and how much they value the anonymity provided by the Internet); and (3) to explore the relationships between these two sets of factors. Using secondary data from an LGBT oriented website in Guangdong, China, I conducted correlational and mediation analyses. Results reveal support for most of the hypotheses, except that Internet use had little to do with participants\u27 level of sexual identity. A strong mediation effect was found between low self-acceptance and high perceived importance of Internet anonymity, with disclosure comfort mediating the relationship. Findings suggest that more qualitative research is needed in order to better understand the function of the Internet in sexual identity formation among gay men in Southern China

Uncertainty-Aware Unlikelihood Learning Improves Generative Aspect Sentiment Quad Prediction

Recently, aspect sentiment quad prediction has received widespread attention in the field of aspect-based sentiment analysis. Existing studies extract quadruplets via pre-trained generative language models to paraphrase the original sentence into a templated target sequence. However, previous works only focus on what to generate but ignore what not to generate. We argue that considering the negative samples also leads to potential benefits. In this work, we propose a template-agnostic method to control the token-level generation, which boosts original learning and reduces mistakes simultaneously. Specifically, we introduce Monte Carlo dropout to understand the built-in uncertainty of pre-trained language models, acquiring the noises and errors. We further propose marginalized unlikelihood learning to suppress the uncertainty-aware mistake tokens. Finally, we introduce minimization entropy to balance the effects of marginalized unlikelihood learning. Extensive experiments on four public datasets demonstrate the effectiveness of our approach on various generation templates1

Three-dimensional electron ptychography of organic-inorganic hybrid nanostructures

Three dimensional scaffolded DNA origami with inorganic nanoparticles has been used to create tailored multidimensional nanostructures. However, the image contrast of DNA is poorer than those of the heavy nanoparticles in conventional transmission electron microscopy at high defocus so that the biological and non-biological components in 3D scaffolds cannot be simultaneously resolved using tomography of samples in a native state. We demonstrate the use of electron ptychography to recover high contrast phase information from all components in a DNA origami scaffold without staining. We further quantitatively evaluate the enhancement of contrast in comparison with conventional transmission electron microscopy. In addition, We show that for ptychography post-reconstruction focusing simplifies the workflow and reduces electron dose and beam damage

Combination treatment with telitacicept, cyclophosphamide and glucocorticoids for severe Granulomatous polyangiitis: a case report and literature review

Granulomatous polyangiitis (GPA) is a rare autoimmune disease that can involve multiple systems throughout the body, including the ear, nose, upper and lower respiratory tracts. It is classified as an antineutrophil cytoplasmic antibody (ANCA)-associated vasculitis. Telitacicept is a novel recombinant fusion protein targeting B-lymphocyte stimulator (BLyS). Telitacicept can inhibit the development and maturation of abnormal B cells by blocking BLyS, and inhibit the production of antibodies by abnormal plasma cells by blocking APRIL (A proliferation-inducing ligand), which is expected to become a new drug for the treatment of GPA. We report a 64-year-old man diagnosed at our hospital with GPA involving multiple systems including kidneys, lungs, nose and ears. Renal involvement was severe, with a clinical characteristic of rapidly progressive glomerulonephritis and a pathologic manifestation of crescentic nephritis with plasma cell infiltration. The patient was treated with hormones, immunoglobulins and cyclophosphamide (CYC) with the addition of telitacicept and a rapid reduction in hormone dosage. The patient’s renal function improved significantly within a short period of time, and his hearing and lung lesions improved significantly. At the same time, he did not develop serious infections and other related complications. Our report suggests that short-term control of the patient’s conditions is necessary in GPA patients with organ-threatening disease. Telitacicept combined with CYC and glucocorticoids may be an induction therapy with safety and feasibility. However, more clinical trials are needed to validate the efficacy and safety of the therapeutic regimen

Energy-Efficient Boarder Node Medium Access Control Protocol for Wireless Sensor Networks

This paper introduces the design, implementation, and performance analysis of the scalable and mobility-aware hybrid protocol named boarder node medium access control (BN-MAC) for wireless sensor networks (WSNs), which leverages the characteristics of scheduled and contention-based MAC protocols. Like contention-based MAC protocols, BN-MAC achieves high channel utilization, network adaptability under heavy traffic and mobility, and low latency and overhead. Like schedule-based MAC protocols, BN-MAC reduces idle listening time, emissions, and collision handling at low cost at one-hop neighbor nodes and achieves high channel utilization under heavy network loads. BN-MAC is particularly designed for region-wise WSNs. Each region is controlled by a boarder node (BN), which is of paramount importance. The BN coordinates with the remaining nodes within and beyond the region. Unlike other hybrid MAC protocols, BN-MAC incorporates three promising models that further reduce the energy consumption, idle listening time, overhearing, and congestion to improve the throughput and reduce the latency. One of the models used with BN-MAC is automatic active and sleep (AAS), which reduces the ideal listening time. When nodes finish their monitoring process, AAS lets them automatically go into the sleep state to avoid the idle listening state. Another model used in BN-MAC is the intelligent decision-making (IDM) model, which helps the nodes sense the nature of the environment. Based on the nature of the environment, the nodes decide whether to use the active or passive mode. This decision power of the nodes further reduces energy consumption because the nodes turn off the radio of the transceiver in the passive mode. The third model is the least-distance smart neighboring search (LDSNS), which determines the shortest efficient path to the one-hop neighbor and also provides cross-layering support to handle the mobility of the nodes. The BN-MAC also incorporates a semi-synchronous feature with a low duty cycle, which is advantageous for reducing the latency and energy consumption for several WSN application areas to improve the throughput. BN-MAC uses a unique window slot size to enhance the contention resolution issue for improved throughput. BN-MAC also prefers to communicate within a one-hop destination using Anycast, which maintains load balancing to maintain network reliability. BN-MAC is introduced with the goal of supporting four major application areas: monitoring and behavioral areas, controlling natural disasters, human-centric applications, and tracking mobility and static home automation devices from remote places. These application areas require a congestion-free mobility-supported MAC protocol to guarantee reliable data delivery. BN-MAC was evaluated using network simulator-2 (ns2) and compared with other hybrid MAC protocols, such as Zebra medium access control (Z-MAC), advertisement-based MAC (A-MAC), Speck-MAC, adaptive duty cycle SMAC (ADC-SMAC), and low-power real-time medium access control (LPR-MAC). The simulation results indicate that BN-MAC is a robust and energy-efficient protocol that outperforms other hybrid MAC protocols in the context of quality of service (QoS) parameters, such as energy consumption, latency, throughput, channel access time, successful delivery rate, coverage efficiency, and average duty cycle.https://doi.org/10.3390/s14030507

Sequence-based identification of recombination spots using pseudo nucleic acid representation and recursive feature extraction by linear kernel SVM

BackgroundIdentification of the recombination hot/cold spots is critical for understanding the mechanism of recombination as well as the genome evolution process. However, experimental identification of recombination spots is both time-consuming and costly. Developing an accurate and automated method for reliably and quickly identifying recombination spots is thus urgently needed.ResultsHere we proposed a novel approach by fusing features from pseudo nucleic acid composition (PseNAC), including NAC, n-tier NAC and pseudo dinucleotide composition (PseDNC). A recursive feature extraction by linear kernel support vector machine (SVM) was then used to rank the integrated feature vectors and extract optimal features. SVM was adopted for identifying recombination spots based on these optimal features. To evaluate the performance of the proposed method, jackknife cross-validation test was employed on a benchmark dataset. The overall accuracy of this approach was 84.09%, which was higher (from 0.37% to 3.79%) than those of state-of-the-art tools.ConclusionsComparison results suggested that linear kernel SVM is a useful vehicle for identifying recombination hot/cold spots