Miracle-dz/UniFAR-FTUs huggingface dataset:
Effectiveness of Subcutaneous Casirivimab and Imdevimab Relative to no COVID-19 Antibody Treatment Among Patients Diagnosed With COVID-19 in the Ambulatory Setting

Importance: Data on real-world effectiveness of subcutaneous (SC) administration of casirivimab and imdevimab (CAS+IMD) for treatment of COVID-19 are limited. Objective: To assess effectiveness of SC CAS+IMD vs no COVID-19 antibody treatment among patients diagnosed with COVID-19 in ambulatory settings during the Delta-dominant period prior to Omicron emergence. Design: Retrospective cohort study. Setting: Encrypted linked data between Komodo Health closed claims database and CDR Maguire Health & Medical database. Participants: Patients with COVID-19 in ambulatory settings between August 1, 2021 and October 30, 2021 treated with SC CAS+IMD were exact- and propensity score-matched to up to 5 untreated patients who were treatment-eligible under the Emergency Use Authorization (EUA). Exposure: Subcutaneous CAS+IMD. Main Outcomes and Measures: Composite endpoint of 30-day all-cause mortality or COVID-19-related hospitalization. Kaplan-Meier estimators were used to calculate composite risk overall and across subgroups including age, COVID-19 vaccination status, immunocompromised, and elevated risk defined as age [≥] 65 years or 55-64 years with body mass index [≥] 35 kg/m2, type 2 diabetes, chronic obstructive pulmonary disease, or chronic kidney disease. Cox proportional-hazards models were used to estimate adjusted hazard ratios (aHR) and 95% confidence intervals (CI). Results: Among 13 522 patients treated with SC CAS+IMD, 12 972 (95.9%) were matched to 41 848 EUA-eligible untreated patients; patients were 57-58% female, with mean age between 50 and 52 years. The 30-day composite outcome risk was 1.9% (95% CI, 1.7-2.2; 247 events) and 4.4% (95% CI, 4.2-4.6; 1822 events) in the CAS+IMD-treated and untreated cohorts, respectively; CAS+IMD treatment was associated with a 49% lower risk (aHR 0.51; 95% CI, 0.46-0.58). Treatment was also associated with a 67% lower 30-day risk of all-cause mortality (aHR 0.33, 95% CI, 0.18-0.60). Treatment effectiveness was consistent regardless of vaccination status and across subgroups, including those at elevated risk (aHR 0.51, 95% CI 0.42-0.60) or immunocompromised (aHR 0.34, 95% CI 0.17-0.66). Conclusions and Relevance: Subcutaneous treatment with CAS+IMD is effective for reducing all-cause mortality or COVID-19-related hospitalization in patients diagnosed with COVID-19 and managed in real-world outpatient settings during the Delta-dominant period. Effectiveness is maintained among immunocompromised, vaccinated, and elevated risk patients.
Induces Type 1 Regulatory T Cells Smith Proteins Postpones Murine Lupus and Intravenous Injection of a D1 Protein of the

T cells that recognize nucleoproteins are required for the production of anti-dsDNA Abs involved in lupus development. SmD1 83–119 (a D1 protein of the Smith (Sm) proteins, part of small nuclear ribonucleoprotein) was recently shown to provide T cell help to anti-dsDNA Abs in the NZB/NZW model of lupus. Using this model in the present study, we showed that high dose tolerance to SmD1 (600–1000 (cid:1) g i.v. of SmD1 83–119 peptide/mo) delays the production of autoantibodies, postpones the onset of lupus nephritis as conﬁrmed by histology, and prolongs survival. Tolerance to SmD1 83–119 was adoptively transferred by CD90 (cid:2) T cells, which also reduce T cell help for autoreactive B cells in vitro. One week after SmD1 83–119 tolerance induction in prenephritic mice, we detected cytokine changes in cultures of CD90 (cid:2) T and B220 (cid:2) B cells with decreased IFN- (cid:3) and IL-4 expression and an increase in TGF (cid:4) . Increased frequencies of regulatory IFN- (cid:3) (cid:2) and IL10 (cid:2) CD4 (cid:2) T cells were later detected. Such regulatory IL-10 (cid:2) / IFN- (cid:3) (cid:2) type 1 regulatory T cells prevented autoantibody generation and anti-CD3-induced proliferation of naive T cells. In conclusion, these results indicate that SmD1
CONTRIBUTIONS TO THE MORPHOLOGY

The rhizome of Dennstaedtia appendiculata var. elwesii and D. scabra is creeping, slender, sparsely branched and covered by multicellular, uniseriate, acicular, gland-tipped, non-chlorophyllous hairs. The ground tissue is parenchymatous. The vascular cylinder is siphonostelic, pierced by two alternate, closely placed, dorsal rows of small non-overlapping leaf gaps. Leaf trace is solitary, broad, gutter-shaped and placed lateral to the leaf gap. Branching is dichotomous and the branch trace is siphonostelic; there are no branch gaps. The leaf is 2-3-pinnate with the stipe and rachis cylindrical and adaxially grooved; the adaxial groove of the branches of the rachises are interrupted at the base. Vascular con-nection to the secondary rachis is extramarginal. The ultimate segments of the leaf are small and bear multiceUular, uniseriate hairs on both the surfaces. The soft are terminal on the apices of the veinlets (mostly on the apex of the superior basal veinlet so that only one sorus per segment is present), and protected by cup-shaped indusia which are glabrous. The sporangial stalk is 3--4 cells long and 3 cells thick at the apex. The spores are trilete with the exine smooth or bearing small circular pro-tuberances. Spore germination is of the Vittaria-type and prothallial development of the Adiantum-type. Adult cordate-thaUoid leptosporangiate-type of sex Morphologically Dennstaedtia is very, close to Microlepia.
Selective Effects on Neutrophil Functions Rac1 Deletion in Mouse Neutrophils Has

Defects in myeloid cell function in Rac2 knockout mice underline the importance of this isoform in activation of NADPH oxidase and cell motility. However, the speciﬁc role of Rac1 in neutrophil function has been difﬁcult to assess since deletion of Rac1 results in embryonic lethality in mice. To elucidate the speciﬁc role of Rac1 in neutrophils, we generated mice with a conditional Rac1 deﬁciency restricted to cells of the granulocyte/monocyte lineage. As observed in Rac2-deﬁcient neutrophils, Rac1-deﬁcient neutrophils demonstrated profound defects in inﬂammatory recruitment in vivo, migration to chemotactic stimuli, and chemoattrac-tant-mediated actin assembly. In contrast, superoxide production is normal in Rac1-deﬁcient neutrophils but markedly diminished in Rac2 null cells. These data demonstrate that although Rac1 and Rac2 are both required for actin-mediated functions, Rac2 is speciﬁcally required for activation of the neutrophil NADPH oxidase. The Journal of Immunology, 2003, 170: 5652–5657.
LADRC Based Digital Control DC-DC ASIC Design

The Internet of Things (IoT) systems require power management modules to have the characteristics of low power consumption, high conversion efficiency, high integration, stable output, and good dynamic response. Due to its ease of tuning and superior control performance, linear active disturbance rejection control (LADRC) is an attractive design option. Yet, to date, due to its high complexity, LADRC has been used in software systems, while little effort has been made in its hardware implementation. In this work, we investigate and explore efficient hardware implementation of the LADRC algorithm. A fully integrated digital LADRC controller for DC-DC Buck converters is synthesized with UMC 55 nm CMOS process. Its input voltage is 5V, stable output voltage is 2V, and rated operating current is 100mA. To our best knowledge, this is the first work to implement LADRC algorithms in hardware. Simulation results show that the LADRC controller is superior to the PID controller in the terms of start-up time, line regulation, load regulation.
Development of an electronic stopping power model based on deep learning and its application in ion range prediction

Deep learning algorithm emerges as a new method to take the raw features from large dataset and mine their deep implicit relations, which is promising for solving traditional physical challenges. A particularly intricate and difficult challenge is the energy loss mechanism of energetic ions in solid, where accurate prediction of stopping power is a long-time problem. In this work, we develop a deep-learning-based stopping power model with high overall accuracy, and overcome the long-standing deficiency of the existing classical models by improving the predictive accuracy of stopping power for ultra-heavy ion with low energy, and the corresponding projected range. This electronic stopping power model, based on deep learning algorithm, could be hopefully applied for the study of ion-solid interaction mechanism and enormous relevant applications.
Fabrication of Polyvinylpyrrolidone (PVP) Nanofibrous Membranes using Mushroom-Spinneret Needleless Electrospinning

Mushroom-spinneret is an improved spinneret design for needleless electrospinning apparatus, which has a bowl-shaped base with a mushroom-shaped cover. The mushroom spinneret can overcome some limitations that is often associated with the needleless electrospinning technique, i.e., high excitation voltages, poor stability of free liquid, and difficulty of controlling the spatial motion of multiple jets. In this study, we fabricate polyvinylpyrrolidone (PVP) nanofibrous membranes using mushroom-spinneret needleless electrospinning and determine the effect of solution concentration and applied voltage on the fiber morphology and diameter. The solution concentration had significant effect on the fibers diameter. When the solution concentration was increased from 8 wt% to 12 wt%, the average fibers diameter increased from 0.99 μm to 1.31 μm and the fibers diameter distribution became wider. Moreover, the average fiber diameter decreased from 1.31 μm to 1.16 μm when the applied voltage was increased from 30 kV to 45 kV. The coefficient of variations (CV) for all membrane samples ranged from 0.12 - 0.26, indicated uniform fibers.
Application of LSTM-LightGBM Nonlinear Combined Model to Power Load Forecasting

The accurate prediction of power system load is extremely important for the operation of the power market and the safe operation of the power grid. In order to improve the accuracy of short-term load forecasting of power systems, a combination model based on long short term memory network (LSTM) and light gradient boosting machine (LightGBM) is proposed. The experiment first decomposes historical load data by EMD, uses historical weather data and load data decomposed by EMD to establish LSTM prediction model and LightGBM prediction model respectively, and then these two predicted values are linearly combined to obtain the final predicted value. The electrical load data of the 2016 Electrician Mathematical Contest in Modeling is used as an example to verify. The experimental results show that the LSTM-LightGBM combined model has higher forecasting accuracy and application prospects for power load forecasting than traditional load forecasting methods and standard LSTM and LightGBM load forecasting methods.
Multi-Interval Discretization of Continuous-Valued Attributes for Constructing Incremental Decision Tree

Since most real-world application data involve continuous-valued attributes, properly addressing the discretization process for constructing a decision tree is an important problem. A continuous-valued attribute is typically discretized during decision tree generation by partitioning its range into two intervals recursively. In this paper, by removing the restriction to the binary discretization, we present a hybrid multi-interval discretization algorithm for discretizing the range of continuous-valued attribute into multiple intervals. On the basis of experiment using semiconductor etching machine, it has been verified that our discretization algorithm constructs a more efficient incremental decision tree compared to previously proposed discretization algorithms.
An optimization method for the inverted pendulum problem based on deep reinforcement learning

The inverted pendulum problem is a classical problem. The inverted pendulum starting at a random position keeps moving upwards and aims to reach an upright position. The problem has been solved through some methods based on deep reinforcement learning (DRL) such as Deep Deterministic Policy Gradient (DDPG). However, DDPG also has disadvantages. Deterministic policy is not conducive to action exploration. Moreover, the Q value needs to be estimated reasonably accurately for the policy to be accurate. Nevertheless, at the beginning of the learning, there is a certain difference in the Q value estimation, and the parameters learned at this time are easy to deviate. Therefore, this paper combining AdaBound with DDPG algorithm proposes an optimization method for the inverted pendulum problem, and compares the performance with that of four published baselines. The experimental results show that for the inverted pendulum problem, the proposed method outperforms the above four baselines to a certain extent.
Development of COVID-19 Detection Using SPR Sensors: A Preliminary Result

Coronavirus disease or COVID-19 is a virus from the Coronaviridae family that has caused pandemics throughout the world since the end of 2019. The virus spreads ten times faster through human interaction than SARS-CoV. The RNA sequence of COVID-19 has a 79.5% similarity with SARS-CoV. Fast and specific detection of COVID-19 is needed so that patient detection can be done quickly and accurately. One method that can be developed as a COVID-19 biosensor is aptamers-based biosensors. The aptamer is an artificial oligo nucleic acid that can specifically bind to target molecules. The aptamer is easily and chemically modifiable for increasing stability and reducing toxicity. It shows a comparable affinity for the target virus and better thermal stability than monoclonal antibodies. This advantage makes aptamer a promising candidate in diagnostic and detection applications. The goal of this research is to use an RNA aptamer as the specific recognition element in a portable surface plasmon resonance (SPR) biosensor for the detection of COVID-19 in humans. An aptamer RNA 1 COVID-19 was designed using the COVID-19 sequence from GISAID using the in silico method. End of 3’ aptamer RNA 1 was modified with dithiol. And Then, the aptamer was immobilized on the gold nanoparticle sensor surface via Cysteine-dithiol binding. The RNA solution, that had been extracted from swab samples, was diluted ten times before being used as a sample. The immobilized aptamer RNA 1 captured COVID-19 in RNA solution, causing an increase in refraction index (r.u). An aptamer RNA 1 was found to bind RNA virus of COVID-19 where the positive sample of COVID-19 has refraction index (r.u) between 3 r.u – 10 r.u for various Ct values.
MFUC: Mitigating Transient Congestion for Multi-flow Updating in SDN

Through the logically centralized control plane, software-defined network (SDN) provides giant convenience for the design of the network strategies, like load balancing, fault recovery, etc. However, when deploying those strategies on the data plane, transient link congestions may occur since the updating process among the distributed switches is asynchronous. To address this issue, we propose a congestion-aware mechanism for multi-flow updating in SDN. We first obtain the real-time traffic size of each flow to be updated through the link monitoring model. Then we use the congestion-aware algorithm to find the potential congested link and construct the dynamic dependency graph of the updating process, to generate the final updating sequence. Finally, the meter table is configured to limit the flow of crucial predecessor nodes that may have congestion deadlocks to mitigate the transient congestion.
Mask Wearing Detection Based on Inceptionv4 and Multi-Scale Retinex Image Enhancement Algorithm

The novel coronavirus is a contagious virus with a high mortality rate, and the international health emergency due to COVID-19 has never stopped since the outbreak, due to this situation, wearing masks has become a basic recommended public epidemic prevention approach for many countries. In the current shortage of medical human resources, we urgently need a non-artificial mask-wearing detection method. In this paper, the multi-scale Retinex algorithm is involved as the preprocessing step of the input image. The mask-wearing detection is based on the InceptionV4 convolutional neural network model. During the experiment, we compared and verified the superiority of the Inception part of the InceptionV4 model compared with the Stem structure of the GoogLenet model, and from LFW (Labeled Faces in the Wild), RMFD (Real-World-Masked-Face-Dataset) more than 10,000 samples were selected from the public datasets for model training. Finally, the precise rate reaches 97.3 %.
Usage of machine-learning algorithms in inverse problem of light self-focusing in isotropic chiral medium with cubic nonlinearity

Efficiency of convolutional artificial neural networks in the problem of finding nonlinearity parameters proportional to the local and non-local cubic dielectric susceptibilities of a medium and intensity of the incident radiation, fully describing the self-focusing character of elliptically polarized laser beams, is demonstrated. It is shown that realization of the predictive algorithm by the neural network can be improved by using complex structured light so that the error is lowered down to percent units.
Development of a real-time wireless sensor network-based information system for efficient irrigation of upland and lowland crop production systems

This study developed a real-time web-and WSN-based information system for efficient irrigation water management and automation of drip-irrigated upland crop and intermittently-irrigated lowland crop production systems. The web-based system uses Flutter and DART to accommodate multiple end user platforms, while the WSN-based system uses state-of-the-art hardware and sensors for real-time monitoring of soil moisture, water level and weather conditions. The sensors are wirelessly connected in a low-power mesh network that sends data to a central server. The sensor readings are uploaded to the web application via MQTT, which generates charts and graphs for data analysis. The sensor readings compared well with measurements from conventional instruments. The system in this study provides a sustainable solution for improving irrigation efficiencies under both upland and lowland crop production systems, in minimizing water losses and in improving the overall agricultural crop productivity.
Prediction of RBPs from sequence by means of genetic algorithm and nu-SVR

Post-transcriptional regulation occurs at every moment in human’s body, so it makes the identification of RNA-binding proteins (RBPs) very important, because the RBPs are indispensable accessories to post-transcriptional regulation. Although many computational methods have been developed to replace the high-costly experimental methods, most of them run slowly and the result not well enough. Based on above factors, in this study, we propose a new method namely GASVR-RBP. Firstly, we extract features from protein sequences based on physicochemical properties and Pre-in-One web server, after the feature vector space constructed, we trained eight classifiers on 9857 protein sequences with the combination of genetic algorithm (GA) and nu-SVR, and by employing the ensemble strategy, we obtained an improved performance in three test set, the accuracy are 89.3%, 84.3% and 88.8%, which higher than Naive Bayes (NB) and Random Forest (RF). These results show that our method is effective for RBPs prediction.
A facile approach for preparing Zr-BDC and Zr-BDC-NH2 MOFs using solvothermal method

In the last two decades, researchers have developed new compounds made of central metals that bond in coordination with organic ligands as linkers (for example, benzene-dicarboxylic acid or BDC). These compounds are called metal-organic frameworks (MOFs) which are also known as coordination polymer compounds. MOFs have large surface area and porosity so they can be used for various purposes, such as for adsorption and removal of hazardous materials of CO2 and Hg. Here, we proposed a facile solvothermal method for preparing two kinds of MOFs with Zr as a central metal, i.e. Zr-BDC and Zr-BDC-NH2. The synthesized MOFs were characterized using XRD, FTIR, SEM, TEM, and BET. The results show that both MOFs have a crystalline structure with cubic form on the surface of MOFs. Results of the FTIR analysis show the peak at 3400 1/cm that is due to the stretching of O-H bonds of both MOFs. In the Zr-BDC-NH2, peak at 1378 1/cm represents the stretching of N-H bonds in the amino benzene-dicarboxylic or terephthalic acid linker. The surface areas of Zr-BDC and Zr-BDC-NH2 MOFs are 714 and 757 m2/g, respectively. We will utilize these MOFs as a sensor for HER2 protein, a biomarker of breast cancer.
Rainfall study based on ARIMA-RBF combined model

The traditional differential integrated moving average autoregressive model (ARIMA) has some deviations in the prediction accuracy of monthly rainfall. In this paper, we propose to combine the ARIMA model with the radial basis function neural network (RBF) neural network model to predict the monthly rainfall in Nanchang, Jiangxi Province, using the ARIMA-RBF model. Firstly, the ARIMA model is used to predict the monthly rainfall and calculate its residuals, and then the RBF neural network model is used to approximate and compensate the prediction results of the ARIMA model to correct the final prediction results. The results show that the prediction results of the combined model are better than those of the single ARIMA model and the single RBF neural network model with good accuracy.
Fault Diagnosis of Transformer Winding Looseness Based on Vibration Signal and GOA-KELM Model

Aiming at the problem that the latent fault of transformer winding looseness is difficult to identify and the diagnosis accuracy is low, a fault diagnosis method based on vibration signal and grasshopper optimization algorithm (GOA) optimization Kernel Extreme Learning Machine (KELM) is proposed in this paper. The method uses the time-domain and frequency-domain characteristics of the vibration signal. First, the time-frequency features of the vibration signals of multiple sensors are extracted, and a multi-sensor time-frequency feature matrix is constructed. Second, the feature matrix is identified and diagnosed by using GOA to optimize the KELM model. Finally, the feasibility and superiority of the proposed method are verified by the experimental data of 110kV transformer. The experimental results show that the method in this paper can successfully identify the looseness of different degrees of windings and the looseness of different phases, and has a higher diagnostic accuracy than the existing algorithms.
DADDE:deep anomaly detection with density estimation

The problem of abnormal nodes detection in attributed network is widely used in daily life, such as social networks, cyberspace security, financial fields and so on. Most existing detection methods ignore the relationship between structure information and attribute information in attributed network. Although some methods consider the relationship between them, but it can’t distinguish the types of abnormal nodes well, that is, attribute exceptions or structural exceptions. Aiming at the shortcomings of existing methods, this paper proposes a deep anomaly detection model combining density estimation. The idea of detecting abnormal nodes by reconstruction error is used, the structure information and attribute information of attributed network are reconstructed respectively, and abnormal nodes are detected by density estimation based on reconstruction error and embedding vector of nodes. In this paper, the validity of DADDE model is proved by experiments on three commonly used data sets in this field.