A New Measurement Method of Relative Volume Wear Ratio Based on Discharge Debris Composition Analysis in Micro-EDM

In microelectrical discharge machining (micro-EDM) milling process, due to the unavoidability of electrode wear, selection of electrode with high electrical erosion resistance and accurate electrode compensation is entitled to be conducted to ensure high precision and high quality. The RVWR is used as criterion for electrode wear characteristics and is fundamental to achieve accurate electrode compensation; however, it is hardly measured accurately with conventional methods. In this paper, firstly, the error of RVWR measured by conventional measurement method is analyzed. Thereafter, for accurately measuring RVWR, a new measurement method is proposed based on electrical debris composition analysis. The RVWR of widely used tungsten, molybdenum, and copper electrode in machining different materials is measured, respectively, and the optimum electrode is selected based on the measuring results. Finally, microgrooves on different materials are machined with tungsten electrode, and the experiment results show that the microstructures have good bottom surface profiles, which indicates that the proposed method is effective to precisely measure the RVWR and guarantee accurate electrode compensation in micro-EDM process

Genomic regions, cellular components and gene regulatory basis underlying pod length variations in cowpea (V. unguiculata L. Walp).

Cowpea (V. unguiculata L. Walp) is a climate resilient legume crop important for food security. Cultivated cowpea (V. unguiculata L) generally comprises the bushy, short-podded grain cowpea dominant in Africa and the climbing, long-podded vegetable cowpea popular in Asia. How selection has contributed to the diversification of the two types of cowpea remains largely unknown. In the current study, a novel genotyping assay for over 50 000 SNPs was employed to delineate genomic regions governing pod length. Major, minor and epistatic QTLs were identified through QTL mapping. Seventy-two SNPs associated with pod length were detected by genome-wide association studies (GWAS). Population stratification analysis revealed subdivision among a cowpea germplasm collection consisting of 299 accessions, which is consistent with pod length groups. Genomic scan for selective signals suggested that domestication of vegetable cowpea was accompanied by selection of multiple traits including pod length, while the further improvement process was featured by selection of pod length primarily. Pod growth kinetics assay demonstrated that more durable cell proliferation rather than cell elongation or enlargement was the main reason for longer pods. Transcriptomic analysis suggested the involvement of sugar, gibberellin and nutritional signalling in regulation of pod length. This study establishes the basis for map-based cloning of pod length genes in cowpea and for marker-assisted selection of this trait in breeding programmes

Compound Kushen Injection suppresses human breast cancer stem-like cells by down-regulating the canonical Wnt/β-catenin pathway

<p>Abstract</p> <p>Background</p> <p>Cancer stem cells (CSCs) play an important role in cancer initiation, relapse and metastasis. To date, no specific medicine has been found to target CSCs as they are resistant to most conventional therapies and proliferate indefinitely. Compound Kushen Injection (CKI) has been widely used for cancer patients with remarkable therapeutic effects in Chinese clinical settings for many years. This study focused on whether CKI could inhibit MCF-7 SP cells in vitro and in vivo.</p> <p>Methods</p> <p>The analysis of CKI on SP population and the main genes of Wnt signaling pathway were studied first. Then we studied the tumorigenicity of SP cells and the effects of CKI on SP cells in vivo. The mice inoculated with 10,000 SP cells were randomly divided into three groups (6 in each group) and treated with CKI, cisplatin and saline (as a control) respectively for 7 weeks. The tumor formation rates of each group were compared. The main genes and proteins of the Wnt signaling pathway were analyzed by RT-PCR and western blot.</p> <p>Results</p> <p>CKI suppressed the size of SP population (approximately 90%), and down-regulated the main genes of Wnt signaling pathway. We also determined that MCF-7 SP cells were more tumorigenic than non-SP and unsorted cells. The Wnt signaling pathway was up-regulated in tumors derived from SP cells compared with that in tumors from non-SP cells. The tumor formation rate of the CKI Group was 33% (2/6, <it>P </it>< 0.05), and that of Cisplatin Group was 50%(3/6, <it>P </it>< 0.05), whereas that of the Control Group was 100% (6/6).The RT-PCR and western blot results indicated that CKI suppressed tumor growth by down-regulating the Wnt/β-catenin pathway, while cisplatin activated the Wnt/β-catenin pathway and might spare SP cells.</p> <p>Conclusions</p> <p>It suggested that CKI may serve as a novel drug targeting cancer stem-like cells, though further studies are recommended.</p

Picornavirus security proteins promote the release of extracellular vesicle enclosed viruses via the modulation of host kinases

The discovery that extracellular vesicles (EVs) serve as carriers of virus particles calls for a reevaluation of the release strategies of non-enveloped viruses. Little is currently known about the molecular mechanisms that determine the release and composition of EVs produced by virus-infected cells, as well as conservation of these mechanisms among viruses. We previously described an important role for the Leader protein of the picornavirus encephalomyocarditis virus (EMCV) in the induction of virus-carrying EV subsets with distinct molecular and physical properties. EMCV L acts as a 'viral security protein' by suppressing host antiviral stress and type-I interferon (IFN) responses. Here, we tested the ability of functionally related picornavirus proteins of Theilers murine encephalitis virus (TMEV L), Saffold virus (SAFV L), and coxsackievirus B3 (CVB3 2Apro), to rescue EV and EV-enclosed virus release when introduced in Leader-deficient EMCV. We show that all viral security proteins tested were able to promote virus packaging in EVs, but that only the expression of EMCV L and CVB3 2Apro increased overall EV production. We provide evidence that one of the main antiviral pathways counteracted by this class of picornaviral proteins, i.e. the inhibition of PKR-mediated stress responses, affected EV and EV-enclosed virus release during infection. Moreover, we show that the enhanced capacity of the viral proteins EMCV L and CVB3 2Apro to promote EV-enclosed virus release is linked to their ability to simultaneously promote the activation of the stress kinase P38 MAPK. Taken together, we demonstrate that cellular stress pathways involving the kinases PKR and P38 are modulated by the activity of non-structural viral proteins to increase the release EV-enclosed viruses during picornavirus infections. These data shed new light on the molecular regulation of EV production in response to virus infection

A three-dimensional network of graphene/silicon/graphene sandwich sheets as anode for Li-ion battery

Abstract(#br)A freestanding porous three-dimensional (3D) network composed of graphene/silicon/graphene sandwich sheets is proposed to prevent the expansion induced pulverization for Si-based anode in a lithium-ion battery. The architecture ensures the attachment of Si active material, improves the conductivity, and absorbs the Si volume expansions. The 3D Graphene and Si in this architecture work synergistically to contribute to the capacity, while the nanoscale of Si lowers the expansion during lithiation. And the 3D graphene with an interconnected skeleton, in addition to active material, also acts as the current collector as well as a stable support for Si

Highlights from the 2019 International Myopia Summit on 'controversies in myopia'.

Myopia is an emerging public health issue with potentially significant economic and social impact, especially in East Asia. However, many uncertainties about myopia and its clinical management remain. The International Myopia Summit workgroup was convened by the Singapore Eye Research Institute, the WHO Regional Office for the Western Pacific and the International Agency for the Prevention of Blindness in 2019. The aim of this workgroup was to summarise available evidence, identify gaps or unmet needs and provide consensus on future directions for clinical research in myopia. In this review, among the many 'controversies in myopia' discussed, we highlight three main areas of consensus. First, development of interventions for the prevention of axial elongation and pathologic myopia is needed, which may require a multifaceted approach targeting the Bruch's membrane, choroid and/or sclera. Second, clinical myopia management requires co-operation between optometrists and ophthalmologists to provide patients with holistic care and a tailored approach that balances risks and benefits of treatment by using optical and pharmacological interventions. Third, current diagnostic technologies to detect myopic complications may be improved through collaboration between clinicians, researchers and industry. There is an unmet need to develop new imaging modalities for both structural and functional analyses and to establish normative databases for myopic eyes. In conclusion, the workgroup's call to action advocated for a paradigm shift towards a collaborative approach in the holistic clinical management of myopia

Fetal Brain Tissue Annotation and Segmentation Challenge Results

In-utero fetal MRI is emerging as an important tool in the diagnosis and analysis of the developing human brain. Automatic segmentation of the developing fetal brain is a vital step in the quantitative analysis of prenatal neurodevelopment both in the research and clinical context. However, manual segmentation of cerebral structures is time-consuming and prone to error and inter-observer variability. Therefore, we organized the Fetal Tissue Annotation (FeTA) Challenge in 2021 in order to encourage the development of automatic segmentation algorithms on an international level. The challenge utilized FeTA Dataset, an open dataset of fetal brain MRI reconstructions segmented into seven different tissues (external cerebrospinal fluid, grey matter, white matter, ventricles, cerebellum, brainstem, deep grey matter). 20 international teams participated in this challenge, submitting a total of 21 algorithms for evaluation. In this paper, we provide a detailed analysis of the results from both a technical and clinical perspective. All participants relied on deep learning methods, mainly U-Nets, with some variability present in the network architecture, optimization, and image pre- and post-processing. The majority of teams used existing medical imaging deep learning frameworks. The main differences between the submissions were the fine tuning done during training, and the specific pre- and post-processing steps performed. The challenge results showed that almost all submissions performed similarly. Four of the top five teams used ensemble learning methods. However, one team's algorithm performed significantly superior to the other submissions, and consisted of an asymmetrical U-Net network architecture. This paper provides a first of its kind benchmark for future automatic multi-tissue segmentation algorithms for the developing human brain in utero.Comment: Results from FeTA Challenge 2021, held at MICCAI; Manuscript submitte

Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States

Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub (https://covid19forecasthub.org/) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multimodel ensemble forecast that combined predictions from dozens of groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naïve baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-wk horizon three to five times larger than when predicting at a 1-wk horizon. This project underscores the role that collaboration and active coordination between governmental public-health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks

The United States COVID-19 Forecast Hub dataset

Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hospitalizations, incident deaths, and cumulative deaths due to COVID-19 at county, state, and national, levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals &lt;1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data