Regulation of CLC-1 chloride channel biosynthesis by FKBP8 and Hsp90β.

Mutations in human CLC-1 chloride channel are associated with the skeletal muscle disorder myotonia congenita. The disease-causing mutant A531V manifests enhanced proteasomal degradation of CLC-1. We recently found that CLC-1 degradation is mediated by cullin 4 ubiquitin ligase complex. It is currently unclear how quality control and protein degradation systems coordinate with each other to process the biosynthesis of CLC-1. Herein we aim to ascertain the molecular nature of the protein quality control system for CLC-1. We identified three CLC-1-interacting proteins that are well-known heat shock protein 90 (Hsp90)-associated co-chaperones: FK506-binding protein 8 (FKBP8), activator of Hsp90 ATPase homolog 1 (Aha1), and Hsp70/Hsp90 organizing protein (HOP). These co-chaperones promote both the protein level and the functional expression of CLC-1 wild-type and A531V mutant. CLC-1 biosynthesis is also facilitated by the molecular chaperones Hsc70 and Hsp90β. The protein stability of CLC-1 is notably increased by FKBP8 and the Hsp90β inhibitor 17-allylamino-17-demethoxygeldanamycin (17-AAG) that substantially suppresses cullin 4 expression. We further confirmed that cullin 4 may interact with Hsp90β and FKBP8. Our data are consistent with the idea that FKBP8 and Hsp90β play an essential role in the late phase of CLC-1 quality control by dynamically coordinating protein folding and degradation

Pneumonia Detection on Chest X-ray using Radiomic Features and Contrastive Learning

Chest X-ray becomes one of the most common medical diagnoses due to its noninvasiveness. The number of chest X-ray images has skyrocketed, but reading chest X-rays still have been manually performed by radiologists, which creates huge burnouts and delays. Traditionally, radiomics, as a subfield of radiology that can extract a large number of quantitative features from medical images, demonstrates its potential to facilitate medical imaging diagnosis before the deep learning era. With the rise of deep learning, the explainability of deep neural networks on chest X-ray diagnosis remains opaque. In this study, we proposed a novel framework that leverages radiomics features and contrastive learning to detect pneumonia in chest X-ray. Experiments on the RSNA Pneumonia Detection Challenge dataset show that our model achieves superior results to several state-of-the-art models (> 10% in F1-score) and increases the model's interpretability.Comment: Accepted for ISBI 202

Geometric-Process-Based Battery Management Optimizing Policy for the Electric Bus

With the rapid development of the electric vehicle industry and promotive policies worldwide, the electric bus (E-bus) has been adopted in many major cities around the world. One of the most important factors that restrain the widespread application of the E-bus is the high operating cost due to the deficient battery management. This paper proposes a geometric-process-based (GP-based) battery management optimizing policy which aims to minimize the average cost of the operation on the premise of meeting the required sufficient battery availability. Considering the deterioration of the battery after repeated charging and discharging, this paper constructs the model of the operation of the E-bus battery as a geometric process, and the premaintenance time has been considered with the failure repairment time to enhance the GP-based battery operation model considering the battery cannot be as good as new after the two processes. The computer simulation is carried out by adopting the proposed optimizing policy, and the result verifies the effectiveness of the policy, denoting its significant performance on the application of the E-bus battery management

Knowledge-Augmented Contrastive Learning for Abnormality Classification and Localization in Chest X-rays with Radiomics using a Feedback Loop

Building a highly accurate predictive model for classification and localization of abnormalities in chest X-rays usually requires a large number of manually annotated labels and pixel regions (bounding boxes) of abnormalities. However, it is expensive to acquire such annotations, especially the bounding boxes. Recently, contrastive learning has shown strong promise in leveraging unlabeled natural images to produce highly generalizable and discriminative features. However, extending its power to the medical image domain is under-explored and highly non-trivial, since medical images are much less amendable to data augmentations. In contrast, their prior knowledge, as well as radiomic features, is often crucial. To bridge this gap, we propose an end-to-end semi-supervised knowledge-augmented contrastive learning framework, that simultaneously performs disease classification and localization tasks. The key knob of our framework is a unique positive sampling approach tailored for the medical images, by seamlessly integrating radiomic features as a knowledge augmentation. Specifically, we first apply an image encoder to classify the chest X-rays and to generate the image features. We next leverage Grad-CAM to highlight the crucial (abnormal) regions for chest X-rays (even when unannotated), from which we extract radiomic features. The radiomic features are then passed through another dedicated encoder to act as the positive sample for the image features generated from the same chest X-ray. In this way, our framework constitutes a feedback loop for image and radiomic modality features to mutually reinforce each other. Their contrasting yields knowledge-augmented representations that are both robust and interpretable. Extensive experiments on the NIH Chest X-ray dataset demonstrate that our approach outperforms existing baselines in both classification and localization tasks.Comment: Accepted by WACV 202

Identification and analysis of the germin-like gene family in soybean

In line 12 of page 1, replace "GmGER 9" with "GmGER 15"

Superconductivity at 22.3 K in SrFe2-xIrxAs2

By substituting the Fe with the 5d-transition metal Ir in SrFe2As2, we have successfully synthesized the superconductor SrFe2-xIrxAs2 with Tc = 22.3 K at x = 0.5. X-ray diffraction indicates that the material has formed the ThCr2Si2-type structure with a space group I4/mmm. The temperature dependence of resistivity and dc magnetization both reveal sharp superconducting transitions at around 22 K. An estimate on the diamagnetization signal reveals a high Meissner shielding volume. Interestingly, the normal state resistivity exhibits a roughly linear behavior up to 300 K. The superconducting transitions at different magnetic fields were also measured yielding a slope of -dHc2/dT = 3.8 T/K near Tc. Using the Werthamer-Helfand-Hohenberg (WHH) formula, the upper critical field at zero K is found to be about 58 T. Counting the possible number of electrons doped into the system in SrFe2-xIrxAs2, we argue that the superconductivity in the Ir-doped system is different from the Co-doped case, which should add more ingredients to the underlying physics of the iron pnictide superconductors.Comment: 4 pages, 4 figure

Uncovering Misattributed Suicide Causes through Annotation Inconsistency Detection in Death Investigation Notes

Data accuracy is essential for scientific research and policy development. The National Violent Death Reporting System (NVDRS) data is widely used for discovering the patterns and causes of death. Recent studies suggested the annotation inconsistencies within the NVDRS and the potential impact on erroneous suicide-cause attributions. We present an empirical Natural Language Processing (NLP) approach to detect annotation inconsistencies and adopt a cross-validation-like paradigm to identify problematic instances. We analyzed 267,804 suicide death incidents between 2003 and 2020 from the NVDRS. Our results showed that incorporating the target state's data into training the suicide-crisis classifier brought an increase of 5.4% to the F-1 score on the target state's test set and a decrease of 1.1% on other states' test set. To conclude, we demonstrated the annotation inconsistencies in NVDRS's death investigation notes, identified problematic instances, evaluated the effectiveness of correcting problematic instances, and eventually proposed an NLP improvement solution.Comment: 19 pages, 6 figure

Colorectal cancer screening with fecal occult blood test: A 22-year cohort study.

The aim of the present study was to investigate the efficacy of colorectal cancer (CRC) screening with a three-tier fecal occult blood test (FOBT) in the Chinese population. The study was performed between 1987 and 2008 at the Beijing Military General Hospital, in a cohort of army service males and females aged >50 years. Between 1987 and 2005, a three-tier screening program, comprising guaiac-based FOBTs (gFOBTs), followed by immunochemical FOBTs for positive guaiac test samples and then colonoscopy for positive immunochemical test subjects, was performed annually. The cohort was followed up until 2008. The cohort included 5,104 subjects, of which, 3,863 subjects participated in screening (screening group) and 1,241 did not (non-screening group). The two groups did not differ in age, gender or other major risk factors for colon cancer. Overall, 36 CRCs occurred in the screening group and 21 in the non-screening group. Compared with the non-screening group, the relative risk for the incidence and mortality of CRC was 0.51 [95% confidence interval (CI), 0.30-0.87] and 0.36 (95% CI, 0.18-0.71), respectively, in the screening group. The general sensitivity of this three-tier FOBT was 80.6% (95% CI, 65.3-91.1). Thus, annual screening using the three-tier FOBT program may reduce the CRC incidence and mortality rate