Does Collaboration Always Enhance Work Efficiency? Investigating Collective IS Use from a Process Perspective

Previous studies have focused mainly on individual IS use, while empirical evidence on collective IS use remains limited. Collective IS use involves interdependent instances of individual IS use within a common work process to fulfill collaborative work. This paper investigates the impact of collective IS use on collaboration performance, what form of collective IS use is efficient, and how to improve work efficiency. Drawing on coordination theory and taking a process perspective, we conceptualize two forms of collective IS use: asynchronous use and synchronous use. Objective data from a high-tech company reveals that asynchronous use improves work efficiency in terms of the time to complete a workflow, while synchronous use prolongs the time resulting in lower work efficiency. We further investigate the moderating role of worker repetitiveness, manager involvement, and task routineness. This study contributes to understanding collective IS use and offers guidance for optimizing collaboration process design

Alteration of Brain Structure With Long-Term Abstinence of Methamphetamine by Voxel-Based Morphometry

Background: A large portion of previous studies that have demonstrated brain gray matter reduction in individuals who use methamphetamine (MA) have focused on short-term abstinence, but few studies have focused on the effects of long-term abstinence of methamphetamine on brain structures.Materials and Methods: Our study includes 40 healthy controls and 44 abstinent methamphetamine-dependent (AMD) subjects who have abstained for at least 14 months. For every AMD subject, the age when they first used MA, the total time of MA use, the frequency of MA use in the last month before abstinence, the duration of abstinence and the craving score were recorded. Here we used magnetic resonance imaging (MRI) to measure the gray matter volume (GMV) of each subject with voxel-based morphometry method. Two-sample t-test (AlphaSim corrected) was performed to obtain brain regions with different gray matter volume (GMV) between groups. In addition, partial correlation coefficients adjusted for age, years of education, smoking, and drinking were calculated in the AMD group to assess associations between the mean GMV values in significant clusters and variables of MA use and abstinence.Results: Compared with the healthy control group, AMD group showed increased gray matter volumes in the bilateral cerebellum and decreased volumes in the right calcarine and right cuneus. Moreover, GMV of left cerebellum are positively correlated with the duration of abstinence in the AMD group (p = 0.040, r = 0.626).Conclusions: The present study showed that the gray matter volume in some brain regions is abnormal in the AMD subjects with long-term abstinence. Changes in gray matter volume of visual and cognitive function regions suggested that these areas play important roles in the progress of MA addiction and abstinence. In addition, positive correlation between GMV of the left cerebellum crus and duration of abstinence suggested that prolonged abstinence is beneficial to cognitive function recovery

The History and Outlook of Animal Drugs Treating Asthma, Chronic Bronchitis, and Haze Episode-induced Respiratory Diseases

Animal drugs have been historically applied in Chinese remedies for more than two thousands. It was reported that Chinese medical animals consisting of 1,590 species took up 12.5% of the total number of all TCM resources. Those animal drugs such as, earthworm, gecko, periostracum cicadae, and scorpios, of commonly used in China, are very remarkable and traditional for the treatment of asthma or chronic bronchitis. This review presents research advance of animal drugs possessing significant implications for the development of novel anti-asthma or chronic bronchitis drugs. The experimental studies and clinical efficacies against asthma and chronic bronchitis of animal drugs were summarized herein. Moreover, the potential utilization of animal drugs on inhibiting haze/fog induced respiratory diseases was also discusse

Research on stress sensitivity of fractured carbonate reservoirs based on CT technology

Fracture aperture change under stress has long been considered as one of primary causes of stress sensitivity of fractured gas reservoirs. However, little is known about the evolution of the morphology of fracture apertures on flow property in loading and unloading cycles. This paper reports a stress sensitivity experiment on carbonate core plugs in which Computed Tomography (CT) technology is applied to visualize and quantitatively evaluate morphological changes to the fracture aperture with respect to confining pressure. Fracture models were obtained at selected confining pressures on which pore-scale flow simulations were performed to estimate the equivalent absolute permeability. The results showed that with the increase of confining pressure from 0 to 0.6 MPa, the fracture aperture and equivalent permeability decreased at a greater gradient than their counterparts after 0.6 MPa. This meant that the rock sample is more stress-sensitive at low effective stress than at high effective stress. On the loading path, an exponential fitting was found to fit well between the effective confining pressure and the calculated permeability. On the unloading path, the relationship is found partially reversible, which can evidently be attributed to plastic deformation of the fracture as observed in CT images

NanoSIMS analysis of water content in bridgmanite at the micron scale: An experimental approach to probe water in Earth’s deep mantle

Water, in trace amounts, can greatly alter chemical and physical properties of mantle minerals and exert primary control on Earth’s dynamics. Quantifying how water is retained and distributed in Earth’s deep interior is essential to our understanding of Earth’s origin and evolution. While directly sampling Earth’s deep interior remains challenging, the experimental technique using laser-heated diamond anvil cell (LH-DAC) is likely the only method available to synthesize and recover analog specimens throughout Earth’s lower mantle conditions. The recovered samples, however, are typically of micron sizes and require high spatial resolution to analyze their water abundance. Here we use nano-scale secondary ion mass spectrometry (NanoSIMS) to characterize water content in bridgmanite, the most abundant mineral in Earth’s lower mantle. We have established two working standards of natural orthopyroxene that are likely suitable for calibrating water concentration in bridgmanite, i.e., A119(H2O) = 99 ± 13 μg/g (1SD) and A158(H2O) = 293 ± 23 μg/g (1SD). We find that matrix effect among orthopyroxene, olivine, and glass is less than 10%, while that between orthopyroxene and clinopyroxene can be up to 20%. Using our calibration, a bridgmanite synthesized by LH-DAC at 33 ± 1 GPa and 3,690 ± 120 K is measured to contain 1,099 ± 14 μg/g water, with partition coefficient of water between bridgmanite and silicate melt ∼0.025, providing the first measurement at such condition. Applying the unique analytical capability of NanoSIMS to minute samples recovered from LH-DAC opens a new window to probe water and other volatiles in Earth’s deep mantle

Multicentre, prospective, randomised controlled trial to evaluate hexaminolevulinate photodynamic therapy (Cevira) as a novel treatment in patients with high-grade squamous intraepithelial lesion: APRICITY phase 3 study protocol

INTRODUCTION: High-risk human papilloma virus (HPV)-associated cervical cancer is the fourth most common cancer in women worldwide. Current treatments of high-grade squamous intraepithelial lesion (HSIL) of the cervix are based on invasive surgical interventions, compromising cervical competence and functionality. APRICITY is a multicentre, prospective, double-blind, randomised controlled phase 3 study further evaluating the efficacy and safety of Cevira, an integrated drug-delivery and light-delivery device for hexaminolevulinate photodynamic therapy, which shows promise as a novel, non-invasive outpatient therapy for women with HSIL. METHODS AND ANALYSIS: Patients with biopsy-confirmed HSIL histology are invited to participate in the study planned to be conducted at 47 sites in China and 25 sites in Ukraine, Russia and the European Union. The aim is to include at least 384 patients, which will be randomised to either Cevira or placebo group (2:1). All patients will be assessed 3 months after first treatment and a second treatment will be administered in patients who are HPV positive or have at least low-grade squamous intraepithelial lesion. Primary endpoint is the proportion of the responders 6 months after first treatment. Secondary efficacy and safety endpoints will be assessed at 6 months, and data for secondary performance endpoints of the Cevira device will be collected at 3 months and 6 months, in case second treatment was administered. All patients in the Cevira group will be enrolled in an open, long-term extension study for 6 months to collect additional efficacy and safety data (study extension endpoints). ETHICS AND DISSEMINATION: The study was approved by the ethics committee of the Peking Union Medical College Hospital and Hannover Medical University, Germany. Findings will be disseminated through peer review publications and conference presentations. TRIAL REGISTRATION NUMBER: NCT04484415; clinicaltrials.gov

ChatRadio-Valuer: A Chat Large Language Model for Generalizable Radiology Report Generation Based on Multi-institution and Multi-system Data

Radiology report generation, as a key step in medical image analysis, is critical to the quantitative analysis of clinically informed decision-making levels. However, complex and diverse radiology reports with cross-source heterogeneity pose a huge generalizability challenge to the current methods under massive data volume, mainly because the style and normativity of radiology reports are obviously distinctive among institutions, body regions inspected and radiologists. Recently, the advent of large language models (LLM) offers great potential for recognizing signs of health conditions. To resolve the above problem, we collaborate with the Second Xiangya Hospital in China and propose ChatRadio-Valuer based on the LLM, a tailored model for automatic radiology report generation that learns generalizable representations and provides a basis pattern for model adaptation in sophisticated analysts' cases. Specifically, ChatRadio-Valuer is trained based on the radiology reports from a single institution by means of supervised fine-tuning, and then adapted to disease diagnosis tasks for human multi-system evaluation (i.e., chest, abdomen, muscle-skeleton, head, and maxillofacial $\&$ neck) from six different institutions in clinical-level events. The clinical dataset utilized in this study encompasses a remarkable total of \textbf{332,673} observations. From the comprehensive results on engineering indicators, clinical efficacy and deployment cost metrics, it can be shown that ChatRadio-Valuer consistently outperforms state-of-the-art models, especially ChatGPT (GPT-3.5-Turbo) and GPT-4 et al., in terms of the diseases diagnosis from radiology reports. ChatRadio-Valuer provides an effective avenue to boost model generalization performance and alleviate the annotation workload of experts to enable the promotion of clinical AI applications in radiology reports

Underground Mine Road Detection Using Deep Learning Technique

Semantic segmentation of underground mine roads is very important to efficiently obtain road information from images. The boundary of underground mine roads is not obvious, the environment is complex, and road identification is difficult. In order to effectively realize the accurate identification of underground mine roads, a network identification model using a deep learning technique is proposed. Choosing BiSeNet as the basic framework, adopting a unified attention fusion module, and using channel and spatial attention to enrich the fusion feature representation can effectively obtain feature information and reduce the loss of feature information. In addition, the lightweight network STDC is integrated into the backbone network to reduce computational complexity. Finally, experiments were carried out on underground mine roads. The experimental results show that the mean intersection over union and pixel accuracy of the proposed method reached 89.34% and 98.34%, respectively, and the recognition speed reached 23 f/s when identifying underground mine roads. In this study, the underground mine road recognition model trained by deep learning technology can solve the problem of underground mine road recognition with high accuracy