Single-nucleotide polymorphism-based genetic risk score and patient age at prostate cancer diagnosis

Importance: Few studies have evaluated the association between a single-nucleotide polymorphism-based genetic risk score (GRS) and patient age at prostate cancer (PCa) diagnosis. Objectives: To test the association between a GRS and patient age at PCa diagnosis and to compare the performance of a GRS with that of family history (FH) in PCa risk stratification. Design, Setting, and Participants: A cohort study of 3225 white men was conducted as a secondary analysis of the Reduction by Dutasteride of Prostate Cancer Events (REDUCE) chemoprevention trial, a 4-year, randomized, double-blind, placebo-controlled multicenter study conducted from March 2003 to April 2009 to evaluate the safety and efficacy of dutasteride in reducing PCa events. Participants were confirmed to be cancer free by prostate biopsy (6-12 cores) within 6 months prior to the study and underwent 10 core biopsies every 2 years per protocol. The dates for performing data analysis were from July 2016 to October 2019. Interventions: A well-established, population-standardized GRS was calculated for each participant based on 110 known PCa risk-associated single-nucleotide polymorphisms, which is a relative risk compared with the general population. Men were classified into 3 GRS risk groups based on predetermined cutoff values: low (\u3c0.50), average (0.50-1.49), and high (≥1.50). Main Outcomes and Measures: Prostate cancer diagnosis-free survival among men of different risk groups. Results: Among 3225 men (median age, 63 years [interquartile range, 58-67 years]) in the study, 683 (21%) were classified as low risk, 1937 (60%) as average risk, and 605 (19%) as high risk based on GRS alone. In comparison, 2789 (86%) were classified as low or average risk and 436 (14%) as high risk based on FH alone. Men in higher GRS risk groups had a PCa diagnosis-free survival rate that was worse than that of those in the lower GRS risk group (χ2 = 53.3; P \u3c .001 for trend) and in participants with a negative FH of PCa (χ2 = 45.5; P \u3c .001 for trend). Combining GRS and FH further stratified overall genetic risk, indicating that 957 men (30%) were at high genetic risk (either high GRS or positive FH), 1667 men (52%) were at average genetic risk (average GRS and negative FH), and 601 men (19%) were at low genetic risk (low GRS and negative FH). The median PCa diagnosis-free survival was 74 years (95% CI, 73-75 years) for men at high genetic risk, 77 years (95% CI, 75 to \u3e80 years) for men at average genetic risk, and more than 80 years (95% CI, \u3e80 to \u3e80 years) for men at low genetic risk. In contrast, the median PCa diagnosis-free survival was 73 years (95% CI, 71-76 years) for men with a positive FH and 77 years (95% CI, 76-79 years) for men with a negative FH. Conclusions and Relevance: This study suggests that a GRS is significantly associated with patient age at PCa diagnosis. Combining FH and GRS may better stratify inherited risk than FH alone for developing personalized PCa screening strategies

Rechargeable Li/Cl2_2 battery down to -80 {\deg}C

Low temperature rechargeable batteries are important to life in cold climates, polar/deep-sea expeditions and space explorations. Here, we report ~ 3.5 - 4 V rechargeable lithium/chlorine (Li/Cl2) batteries operating down to -80 {\deg}C, employing Li metal negative electrode, a novel CO2 activated porous carbon (KJCO2) as the positive electrode, and a high ionic conductivity (~ 5 to 20 mS cm-1 from -80 {\deg}C to 25 {\deg}C) electrolyte comprised of 1 M aluminum chloride (AlCl3), 0.95 M lithium chloride (LiCl), and 0.05 M lithium bis(fluorosulfonyl)imide (LiFSI) in low melting point (-104.5 {\deg}C) thionyl chloride (SOCl2). Between room-temperature and -80 {\deg}C, the Li/Cl2 battery delivered up to ~ 30,000 - 4,500 mAh g-1 first discharge capacity and a 1,200 - 5,000 mAh g-1 reversible capacity (discharge voltages in ~ 3.5 to 3.1 V) over up to 130 charge-discharge cycles. Mass spectrometry and X-ray photoelectron spectroscopy (XPS) probed Cl2 trapped in the porous carbon upon LiCl electro-oxidation during charging. At lower temperature down to -80 {\deg}C, SCl2/S2Cl2 and Cl2 generated by electro-oxidation in the charging step were trapped in porous KJCO2 carbon, allowing for reversible reduction to afford a high discharge voltage plateau near ~ 4 V with up to ~ 1000 mAh g-1 capacity for SCl2/S2Cl2 reduction and up to ~ 4000 mAh g-1 capacity at ~ 3.1 V plateau for Cl2 reduction. Towards practical use, we made CR2032 Li/Cl2 battery cells to drive digital watches at -40 {\deg}C and light emitting diode at -80 {\deg}C, opening Li/Cl2 secondary batteries for ultra-cold conditions

Real-time power system dispatch scheme using grid expert strategy-based imitation learning

With large-scale grid integration of renewable energy sources (RES), power grid operations gradually exhibit the new characteristics of high-order uncertainty, leading to significant challenges for system operational security. Traditional model-driven generation dispatch methods require large computational resources, whereas the widely concerned Reinforcement Learning (RL)-based methods lead to issues such as slow training speed due to the high complexity and dimension of processed grid state information. For this reason, this paper proposes a novel Grid Expert Strategy Imitation Learning (GESIL)-based real-time (5 min intervals in this paper) dispatch method. Firstly, a grid model is established based on the graph theory. Secondly, a pure rule-based grid expert strategy (GES) considering detailed power grid operations is proposed. Then, the GES is combined with the established model to obtain a GESIL agent using imitation learning by offline–online training, which can produce specific grid dispatch decisions for real-time. By designing a graph theory-based grid model, a model-driven purely rule-based GES, and embedding a penalty factor-based loss function into IL offline–online training, GESIL ultimately achieves high training speed, high solution speed, and strong generalization capability. A modified IEEE 118-node system is employed to compare the proposed GESIL to traditional dispatch method and RL method. Results show that GESIL has significantly improved computational efficiency by approximately 17 times and training speed by 14.5 times. GESIL can more stably and efficiently compute real-time dispatch decisions of grid operations, enhancing the optimization effect in terms of transmission overloading mitigation, transmission loading optimization, and power balancing control

Bilateral inertia and damping emulation control scheme of VSC-HVDC transmission systems for asynchronous grid interconnections

This paper proposes a novel bilateral inertia and damping emulation (BIDE) control scheme for VSC-HVDC transmission systems that can autonomously provide inertial and damping responses to two VSC-HVDC interconnected asynchronous AC grids in a similar fashion of synchronous generators. For each VSC station, the energy for inertia emulation comes from augmented DC link capacitance whereas the energy for damping emulation comes from the interconnected grid on the other side. This proposed approach is communication-free as the essential information of two grid frequencies for inertia and damping emulation can be obtained from the locally measured variables dictated by the BIDE control algorithms. Modal analysis is carried out to investigate the impacts of BIDE-emulated inertia time constants and damping factors on system small-signal stability, and to obtain the optimal control parameters. The effectiveness of the proposed BIDE scheme is verified through controller hardware-in-the-loop experiments, in the presence of load changes and grid faults. The results show that the BIDE scheme can effectively enhance the stability and damp the frequency oscillations for both AC grids

Open X-Embodiment:Robotic learning datasets and RT-X models

Large, high-capacity models trained on diverse datasets have shown remarkable successes on efficiently tackling downstream applications. In domains from NLP to Computer Vision, this has led to a consolidation of pretrained models, with general pretrained backbones serving as a starting point for many applications. Can such a consolidation happen in robotics? Conventionally, robotic learning methods train a separate model for every application, every robot, and even every environment. Can we instead train "generalist" X-robot policy that can be adapted efficiently to new robots, tasks, and environments? In this paper, we provide datasets in standardized data formats and models to make it possible to explore this possibility in the context of robotic manipulation, alongside experimental results that provide an example of effective X-robot policies. We assemble a dataset from 22 different robots collected through a collaboration between 21 institutions, demonstrating 527 skills (160266 tasks). We show that a high-capacity model trained on this data, which we call RT-X, exhibits positive transfer and improves the capabilities of multiple robots by leveraging experience from other platforms. The project website is robotics-transformer-x.github.io

Multilingual Bottle-Neck Feature Learning from Untranscribed data for track 1 in zerospeech2017 (system 1 -- without VTLN)

We investigate the extraction of bottle-neck features (BNFs) for multiple languages without access to manual transcription. Multilingual BNFs are derived from a multi-task learning deep neural network which is trained with unsupervised phoneme-like labels. The unsupervised phoneme-like labels are obtained from language-dependent Dirichlet process Gaussian mixture models separately trained on untranscribed speech of multiple languages

Multilingual bottle-neck feature learning from untranscribed speech for track 1 in zerospeech2017 (system 2 -- with VTLN)

We investigate the extraction of bottle-neck features (BNFs) for multiple languages without access to manual transcription. Multilingual BNFs are derived from a multi-task learning deep neural network which is trained with unsupervised phoneme-like labels. The unsupervised phoneme-like labels are obtained from language-dependent Dirichlet process Gaussian mixture models separately trained on untranscribed speech of multiple languages. In this version, the input MFCC for DPGMM is processed with VTLN

Positive Expression of Human Cytomegalovirus Phosphoprotein 65 in Atherosclerosis

Previous studies showed that human cytomegalovirus (HCMV) is associated with atherosclerosis. However, local vascular atherosclerosis related HCMV infection and protein expression remain unclear. This study aimed to assess the relationship between HCMV infection and atherosclerosis. Formalin-fixed, paraffin-embedded peripheral artery specimens were obtained from 15 patients with atherosclerosis undergoing vascular surgery from 2008 to 2010 at Zhongnan Hospital, Wuhan University. Pathological analyses were carried out after hematoxylin and eosin (H&E) and Masson trichrome staining. In situ hybridization and immunohistochemistry with two different monoclonal antibodies were employed to detect HCMV nucleic acids and proteins, respectively. H&E and Masson trichrome staining showed homogeneous extracellular matrix in femoral artery, while smooth muscle fibers were interlaced with collagen fibers; in carotid artery, inflammatory cell infiltration, foam cell vascular change, cholesterol crystals, and layered collagen fibers were observed. In situ hybridization showed no expression of HCMV nucleic acids in all 15 cases. Immunohistochemical staining for protein immediate-early protein (IE1 72) was negative in all cases, while phosphoprotein 65 (pp65) expression was detected in 14 cases. A high rate of positive pp65 signals was found in patients with atherosclerosis, suggesting that local HCMV infection may be associated with the pathogenesis of atherosclerosis. Further studies on this relationship are warranted