The Impact of a New App Channel on Physicians’ Performance: Evidence From Online Healthcare Natural Experiment

Besides the web browser, the introduction of the mobile app in online healthcare systems has resulted in an additional touchpoint for users. Drawing on the Media Richness theory, we aim to reveal the effect of the mobile app channel on physicians’ performance in the online health communities (OHCs). We provide direct empirical evidence on a large-scale dataset from one of the largest Chinese OHCs, Haodf, and propose a natural experiment to show the casual effect. Our results demonstrate that the introduction of the app channel to OHCs for patients has a positive impact on physicians’ responses and rating performance on the online platforms, especially for male physicians from high-ranking hospitals

Skip DETR: end-to-end Skip connection model for small object detection in forestry pest dataset

Object detection has a wide range of applications in forestry pest control. However, forest pest detection faces the challenges of a lack of datasets and low accuracy of small target detection. DETR is an end-to-end object detection model based on the transformer, which has the advantages of simple structure and easy migration. However, the object query initialization of DETR is random, and random initialization will cause the model convergence to be slow and unstable. At the same time, the correlation between different network layers is not strong, resulting in DETR is not very ideal in small object training, optimization, and performance. In order to alleviate these problems, we propose Skip DETR, which improves the feature fusion between different network layers through skip connection and the introduction of spatial pyramid pooling layers so as to improve the detection results of small objects. We performed experiments on Forestry Pest Datasets, and the experimental results showed significant AP improvements in our method. When the value of IoU is 0.5, our method is 7.7% higher than the baseline and 6.1% higher than the detection result of small objects. Experimental results show that the application of skip connection and spatial pyramid pooling layer in the detection framework can effectively improve the effect of small-sample obiect detection

Prognostication of chronic disorders of consciousness using brain functional networks and clinical characteristics

Disorders of consciousness are a heterogeneous mixture of different diseases or injuries. Although some indicators and models have been proposed for prognostication, any single method when used alone carries a high risk of false prediction. This study aimed to develop a multidomain prognostic model that combines resting state functional MRI with three clinical characteristics to predict one year outcomes at the single-subject level. The model discriminated between patients who would later recover consciousness and those who would not with an accuracy of around 90% on three datasets from two medical centers. It was also able to identify the prognostic importance of different predictors, including brain functions and clinical characteristics. To our knowledge, this is the first implementation reported of a multidomain prognostic model based on resting state functional MRI and clinical characteristics in chronic disorders of consciousness. We therefore suggest that this novel prognostic model is accurate, robust, and interpretable.Comment: Although some prognostic indicators and models have been proposed for disorders of consciousness, each single method when used alone carries risks of false prediction. Song et al. report that a model combining resting state functional MRI with clinical characteristics provided accurate, robust, and interpretable prognostications. 52 pages, 1 table, 7 figure

Spatio-Temporal Evolution of Sandy Land and its Impact on Soil Wind Erosion in the Kubuqi Desert in Recent 30 Years

Continuous remote-sensing monitoring of sand in desert areas and the exploration of the spatio–temporal evolution characteristics of soil–wind erosion has an important scientific value for desertification prevention and ecological restoration. In this study, the Kubuqi Desert was selected as the study area, and the Landsat series satellite remote sensing data, supplemented by satellite remote sensing data such as GE images, SPOT-5, ZY-3, GF-1/2/6, etc., integrated object-oriented, decision tree, and auxiliary human–computer interaction interpretation methods, developed the Kubuqi Desert area dataset from 1990 to 2020, and established a soil erosion intensity database of the past 30 years based on the soil–wind erosion correction equation. The results show that the application of the training samples obtained by a high-score collaborative ground sampling to land use/cover classification in desert areas can effectively improve the efficiency of remote-sensing mapping of sand changes and the accuracy of change information identification, and the overall accuracy of the classification results is 95%. In general, the sandy area of the Kubuqi Desert area has decreased year by year, during which the mobile sand in the hinterland of the desert has expanded in a scattered distribution. The overall soil–wind erosion intensity showed a downward trend, especially since 2000; the ecological improvement trend after the implementation of desertification control projects is obvious. Changes in the sand type contributed the most to the reduction of soil–wind erosion intensity (contribution 81.14%), ecological restoration played a key role in reducing the soil–wind erosion intensity (contribution 14.42%), and the increase of forest and grass vegetation covers and agricultural oases played a positive role in solidifying the soil- and wind-proof sand fixation. The pattern of sandy land changes in desert areas is closely related to the national ecological civilization construction policy and the impact of climate change

Effects of Long-Lasting High-Definition Transcranial Direct Current Stimulation in Chronic Disorders of Consciousness: A Pilot Study

Transcranial direct current stimulation (tDCS) recently was shown to benefit rehabilitation of patients with disorders of consciousness (DOC). However, high-Definition tDCS (HD-tDCS) has not been applied in DOC. In this study, we tried to use HD-tDCS protocol (2 mA, 20 min, the precuneus, and sustaining 14 days) to rehabilitate 11 patients with DOC. Electroencephalography (EEG) and Coma Recovery Scale–Revised (CRS-R) scores were recorded at before (T0), after a single session (T1), after 7 days’ (T2), and 14 days’ HD-tDCS (T3) to assess the modulation effects. EEG coherence was measured to evaluate functional connectivity during the experiment. It showed that 9 patients’ scores increased compared with the baseline. The central-parietal coherence significantly decreased in the delta band in patients with DOC. EEG coherence might be useful for assessing the effect of HD-tDCS in patients with DOC. Long-lasting HD-tDCS over the precuneus is promising for the treatment of patients with DOC

Finishing the euchromatic sequence of the human genome

The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead

DCTransformer: A Channel Attention Combined Discrete Cosine Transform to Extract Spatial–Spectral Feature for Hyperspectral Image Classification

Hyperspectral image (HSI) classification tasks have been adopted in huge applications of remote sensing recently. With the rise of deep learning development, it becomes crucial to investigate how to exploit spatial–spectral features. The traditional approach is to stack models that can encode spatial–spectral features, coupling sufficient information as much as possible, before the classification model. However, this sequential stacking tends to cause information redundancy. In this paper, a novel network utilizing the channel attention combined discrete cosine transform (DCTransformer) to extract spatial–spectral features has been proposed to address this issue. It consists of a detail spatial feature extractor (DFE) with CNN blocks and a base spectral feature extractor (BFE) utilizing the channel attention mechanism (CAM) with a discrete cosine transform (DCT). Firstly, the DFE can extract detailed context information using a series of layers of a CNN. Further, the BFE captures spectral features using channel attention and stores the wider frequency information by utilizing the DCT. Ultimately, the dynamic fusion mechanism has been adopted to fuse the detail and base features. Comprehensive experiments show that the DCTransformer achieves a state-of-the-art (SOTA) performance in the HSI classification task, compared to other methods on four datasets, the University of Houston (UH), Indian Pines (IP), MUUFL, and Trento datasets. On the UH dataset, the DCTransformer achieves an OA of 94.40%, AA of 94.89%, and kappa of 93.92