Deep Residual Shrinkage Networks for EMG-based Gesture Identification

This work introduces a method for high-accuracy EMG based gesture identification. A newly developed deep learning method, namely, deep residual shrinkage network is applied to perform gesture identification. Based on the feature of EMG signal resulting from gestures, optimizations are made to improve the identification accuracy. Finally, three different algorithms are applied to compare the accuracy of EMG signal recognition with that of DRSN. The result shows that DRSN excel traditional neural networks in terms of EMG recognition accuracy. This paper provides a reliable way to classify EMG signals, as well as exploring possible applications of DRSN

Enhanced Group Delay of the Pulse Reflection with Graphene Surface Plasmon via Modified Otto Configuration

In this paper, the group delay of the transverse magnetic (TM) polarized wave reflected from a modified Otto configuration with graphene surface plasmon is investigated theoretically. The findings show that the optical group delay in this structure can be enhanced negatively and can be switched from negative to positive due to the excitation of surface plasmon by graphene. It is clear that the negative group delay can be actively tuned through the Fermi energy of the graphene. Furthermore, the delay properties can also be manipulated by changing either the relaxation time of graphene or the distance between the coupling prism and the graphene. These tunable delay characteristics are promising for fabricating grapheme-based optical delay devices and other applications in the terahertz regime

The effectiveness of exercise on the symptoms in breast cancer patients undergoing adjuvant treatment: an umbrella review of systematic reviews and meta-analyses

BackgroundExercise has the potential to reduce symptoms for breast cancer patients during adjuvant treatment, and high-quality systematic reviews are essential for guiding clinical practice. The objective of this umbrella review is to examine current research evidence concerning the effectiveness of exercise on symptom management in breast cancer patients undergoing adjuvant treatment.MethodsAn umbrella review was conducted. We searched for eligible systematic reviews through 11 databases until August 13rd, 2023. Two authors independently screened titles and abstracts, assessing the full-text studies based on inclusion criteria. We used AMSTAR-2 to appraise the quality of the meta-analyses. The results would be presented with narrative summaries if the replication rate of the original study for a symptom was higher than 5% (calculated via the Corrected Covered Area, CCA). The protocol was documented in the PROSPERO registry (CRD42023403990).ResultsOf the 807 systematic reviews identified, 15 met the inclusion criteria, and 7 symptoms were the main focus. The main form of exercise mentioned was aerobic combined resistance exercise. The results of the quality assessment were mostly critically low (10/15). The repetition rate calculated by CCA showed moderate to very high repetition rates (10% to 18.6%). The findings of the included reviews indicated that the effects of exercise on relieving symptoms during breast cancer adjuvant treatment were mixed.ConclusionsResearch is still needed to confirm the majority of studies’ recommendations for exercise during adjuvant treatment for breast cancer patients, as it is crucial for managing symptoms in the rehabilitation process. To increase the efficiency of exercise in symptom management, future studies may focus more on the application of bridge symptoms, symptom networks, and ecological instantaneous assessment

Depletion of TRRAP induces p53-independent senescence in liver cancer by downregulating mitotic genes

Hepatocellular carcinoma (HCC) is an aggressive subtype of liver cancer with few effective treatments and the underlying mechanisms that drive HCC pathogenesis remain poorly characterized. Identifying genes and pathways essential for HCC cell growth will aid the development of new targeted therapies for HCC. Using a kinome CRISPR screen in three human HCC cell lines, we identified transformation/transcription domain-associated protein (TRRAP) as an essential gene for HCC cell proliferation. TRRAP has been implicated in oncogenic transformation, but how it functions in cancer cell proliferation is not established. Here, we show that depletion of TRRAP or its co-factor, histone acetyltransferase KAT5, inhibits HCC cell growth via induction of p53- and p21-independent senescence. Integrated cancer genomics analyses using patient data and RNA-sequencing identified mitotic genes as key TRRAP/KAT5 targets in HCC, and subsequent cell cycle analyses revealed that TRRAP- and KAT5-depleted cells are arrested at G2/M phase. Depletion of TOP2A, a mitotic gene and TRRAP/KAT5 target, was sufficient to recapitulate the senescent phenotype of TRRAP/KAT5 knockdown. CONCLUSION: Our results uncover a role for TRRAP/KAT5 in promoting HCC cell proliferation via activation of mitotic genes. Targeting the TRRAP/KAT5 complex is a potential therapeutic strategy for HCC

Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States

Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub (https://covid19forecasthub.org/) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multimodel ensemble forecast that combined predictions from dozens of groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naïve baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-wk horizon three to five times larger than when predicting at a 1-wk horizon. This project underscores the role that collaboration and active coordination between governmental public-health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks

The United States COVID-19 Forecast Hub dataset

Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hospitalizations, incident deaths, and cumulative deaths due to COVID-19 at county, state, and national, levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages

The Enlightenment of Indian Institute of Technology: Autonomy, Selection and Practical Education

Indian Institute of Technology (IIT) is one of the most famous universities in the world, and its history witnessed the development of Indian higher education. The unique management mode and educational philosophy have played an important role in its success story and given a strong boost to become one of the world-class universities. This paper explores the successful experience of IIT, which can be summarized in the following three aspects. Firstly, a high degree of autonomy and academic freedom ensure a good academic reputation. Secondly, based on rigorous selection and assessment, the IIT students work their way to the top of their profession. Lastly, the emphasis on practical education and the system of industry-university-research integration work together to promote the productivity transformation of academic achievements. These distinctive characteristics of management and talent cultivation system boost the development of IIT and make it outstanding. The success of IIT can be used as a reference and provide enlightenment for the construction of world-class universities in China. It inspires universities in China to make changes in the management system, entrance selection and talent cultivation