Thermoelectric Skutterudite Compositions and Methods for Producing the Same

Compositions related to skutterudite-based thermoelectric materials are disclosed. Such compositions can result in materials that have enhanced ZT values relative to one or more bulk materials from which the compositions are derived. Thermoelectric materials such as n-type and p-type skutterudites with high thermoelectric figures-of-merit can include materials with filler atoms and/or materials formed by compacting particles (e.g., nanoparticles) into a material with a plurality of grains each having a portion having a skutterudite-based structure. Methods of forming thermoelectric skutterudites, which can include the use of hot press processes to consolidate particles, are also disclosed. The particles to be consolidated can be derived from (e.g., grinded from), skutterudite-based bulk materials, elemental materials, other non-Skutterudite-based materials, or combinations of such materials

Association Study between BDNF Gene Polymorphisms and Autism by Three-Dimensional Gel-Based Microarray

Single nucleotide polymorphisms (SNPs) are important markers which can be used in association studies searching for susceptible genes of complex diseases. High-throughput methods are needed for SNP genotyping in a large number of samples. In this study, we applied polyacrylamide gel-based microarray combined with dual-color hybridization for association study of four BDNF polymorphisms with autism. All the SNPs in both patients and controls could be analyzed quickly and correctly. Among four SNPs, only C270T polymorphism showed significant differences in the frequency of the allele (χ2 = 7.809, p = 0.005) and genotype (χ2 = 7.800, p = 0.020). In the haplotype association analysis, there was significant difference in global haplotype distribution between the groups (χ2 = 28.19, p = 3.44e-005). We suggest that BDNF has a possible role in the pathogenesis of autism. The study also show that the polyacrylamide gel-based microarray combined with dual-color hybridization is a rapid, simple and high-throughput method for SNPs genotyping, and can be used for association study of susceptible gene with disorders in large samples

The Chinese Open Science Network (COSN): Building an Open Science Community From Scratch

Open Science is becoming a mainstream scientific ideology in psychology and related fields. However, researchers, especially early-career researchers (ECRs) in developing countries, are facing significant hurdles in engaging in Open Science and moving it forward. In China, various societal and cultural factors discourage ECRs from participating in Open Science, such as the lack of dedicated communication channels and the norm of modesty. To make the voice of Open Science heard by Chinese-speaking ECRs and scholars at large, the Chinese Open Science Network (COSN) was initiated in 2016. With its core values being grassroots-oriented, diversity, and inclusivity, COSN has grown from a small Open Science interest group to a recognized network both in the Chinese-speaking research community and the international Open Science community. So far, COSN has organized three in-person workshops, 12 tutorials, 48 talks, and 55 journal club sessions and translated 15 Open Science-related articles and blogs from English to Chinese. Currently, the main social media account of COSN (i.e., the WeChat Official Account) has more than 23,000 subscribers, and more than 1,000 researchers/students actively participate in the discussions on Open Science. In this article, we share our experience in building such a network to encourage ECRs in developing countries to start their own Open Science initiatives and engage in the global Open Science movement. We foresee great collaborative efforts of COSN together with all other local and international networks to further accelerate the Open Science movement

6G Network AI Architecture for Everyone-Centric Customized Services

Mobile communication standards were developed for enhancing transmission and network performance by using more radio resources and improving spectrum and energy efficiency. How to effectively address diverse user requirements and guarantee everyone's Quality of Experience (QoE) remains an open problem. The Sixth Generation (6G) mobile systems will solve this problem by utilizing heterogenous network resources and pervasive intelligence to support everyone-centric customized services anywhere and anytime. In this article, we first coin the concept of Service Requirement Zone (SRZ) on the user side to characterize and visualize the integrated service requirements and preferences of specific tasks of individual users. On the system side, we further introduce the concept of User Satisfaction Ratio (USR) to evaluate the system's overall service ability of satisfying a variety of tasks with different SRZs. Then, we propose a network Artificial Intelligence (AI) architecture with integrated network resources and pervasive AI capabilities for supporting customized services with guaranteed QoEs. Finally, extensive simulations show that the proposed network AI architecture can consistently offer a higher USR performance than the cloud AI and edge AI architectures with respect to different task scheduling algorithms, random service requirements, and dynamic network conditions

Rickettsiae Induce Microvascular Hyperpermeability via Phosphorylation of VE-Cadherins: Evidence from Atomic Force Microscopy and Biochemical Studies

The most prominent pathophysiological effect of spotted fever group (SFG) rickettsial infection of microvascular endothelial cells (ECs) is an enhanced vascular permeability, promoting vasogenic cerebral edema and non-cardiogenic pulmonary edema, which are responsible for most of the morbidity and mortality in severe cases. To date, the cellular and molecular mechanisms by which SFG Rickettsia increase EC permeability are largely unknown. In the present study we used atomic force microscopy (AFM) to study the interactive forces between vascular endothelial (VE)-cadherin and human cerebral microvascular EC infected with R. montanensis, which is genetically similar to R. rickettsii and R. conorii, and displays a similar ability to invade cells, but is non-pathogenic and can be experimentally manipulated under Biosafety Level 2 (BSL2) conditions. We found that infected ECs show a significant decrease in VE-cadherin-EC interactions. In addition, we applied immunofluorescent staining, immunoprecipitation phosphorylation assay, and an in vitro endothelial permeability assay to study the biochemical mechanisms that may participate in the enhanced vascular permeability as an underlying pathologic alteration of SFG rickettsial infection. A major finding is that infection of R. montanensis significantly activated tyrosine phosphorylation of VE-cadherin beginning at 48 hr and reaching a peak at 72 hr p.i. In vitro permeability assay showed an enhanced microvascular permeability at 72 hr p.i. On the other hand, AFM experiments showed a dramatic reduction in VE-cadherin-EC interactive forces at 48 hr p.i. We conclude that upon infection by SFG rickettsiae, phosphorylation of VE-cadherin directly attenuates homophilic protein–protein interactions at the endothelial adherens junctions, and may lead to endothelial paracellular barrier dysfunction causing microvascular hyperpermeability. These new approaches should prove useful in characterizing the antigenically related SFG rickettsiae R. conorii and R. rickettsii in a BSL3 environment. Future studies may lead to the development of new therapeutic strategies to inhibit the VE-cadherin-associated microvascular hyperpermeability in SFG rickettsioses

Machine learning for the prediction of all-cause mortality in patients with sepsis-associated acute kidney injury during hospitalization

BackgroundSepsis-associated acute kidney injury (S-AKI) is considered to be associated with high morbidity and mortality, a commonly accepted model to predict mortality is urged consequently. This study used a machine learning model to identify vital variables associated with mortality in S-AKI patients in the hospital and predict the risk of death in the hospital. We hope that this model can help identify high-risk patients early and reasonably allocate medical resources in the intensive care unit (ICU).MethodsA total of 16,154 S-AKI patients from the Medical Information Mart for Intensive Care IV database were examined as the training set (80%) and the validation set (20%). Variables (129 in total) were collected, including basic patient information, diagnosis, clinical data, and medication records. We developed and validated machine learning models using 11 different algorithms and selected the one that performed the best. Afterward, recursive feature elimination was used to select key variables. Different indicators were used to compare the prediction performance of each model. The SHapley Additive exPlanations package was applied to interpret the best machine learning model in a web tool for clinicians to use. Finally, we collected clinical data of S-AKI patients from two hospitals for external validation.ResultsIn this study, 15 critical variables were finally selected, namely, urine output, maximum blood urea nitrogen, rate of injection of norepinephrine, maximum anion gap, maximum creatinine, maximum red blood cell volume distribution width, minimum international normalized ratio, maximum heart rate, maximum temperature, maximum respiratory rate, minimum fraction of inspired O2, minimum creatinine, minimum Glasgow Coma Scale, and diagnosis of diabetes and stroke. The categorical boosting algorithm model presented significantly better predictive performance [receiver operating characteristic (ROC): 0.83] than other models [accuracy (ACC): 75%, Youden index: 50%, sensitivity: 75%, specificity: 75%, F1 score: 0.56, positive predictive value (PPV): 44%, and negative predictive value (NPV): 92%]. External validation data from two hospitals in China were also well validated (ROC: 0.75).ConclusionsAfter selecting 15 crucial variables, a machine learning-based model for predicting the mortality of S-AKI patients was successfully established and the CatBoost model demonstrated best predictive performance

Creative destruction in science

Drawing on the concept of a gale of creative destruction in a capitalistic economy, we argue that initiatives to assess the robustness of findings in the organizational literature should aim to simultaneously test competing ideas operating in the same theoretical space. In other words, replication efforts should seek not just to support or question the original findings, but also to replace them with revised, stronger theories with greater explanatory power. Achieving this will typically require adding new measures, conditions, and subject populations to research designs, in order to carry out conceptual tests of multiple theories in addition to directly replicating the original findings. To illustrate the value of the creative destruction approach for theory pruning in organizational scholarship, we describe recent replication initiatives re-examining culture and work morality, working parents\u2019 reasoning about day care options, and gender discrimination in hiring decisions. Significance statement It is becoming increasingly clear that many, if not most, published research findings across scientific fields are not readily replicable when the same method is repeated. Although extremely valuable, failed replications risk leaving a theoretical void\u2014 reducing confidence the original theoretical prediction is true, but not replacing it with positive evidence in favor of an alternative theory. We introduce the creative destruction approach to replication, which combines theory pruning methods from the field of management with emerging best practices from the open science movement, with the aim of making replications as generative as possible. In effect, we advocate for a Replication 2.0 movement in which the goal shifts from checking on the reliability of past findings to actively engaging in competitive theory testing and theory building. Scientific transparency statement The materials, code, and data for this article are posted publicly on the Open Science Framework, with links provided in the article