Ferromagnetic Property of Co and Ni Doped TiO 2

The Co and Ni doped diluted magnetic semiconductor nanoparticle TiO2 is prepared by sol-gel method. Ti0.97Co0.03O2, Ti0.97Ni0.03O2, Ti0.97Co0.06O2, and Ti0.97Ni0.06O2 samples were characterized by X-ray scattering techniques and high resolution transmission electron microscope. The results show that there are no other phases existing in TiO2. As to the sample of high-concentration dopant, the X-ray scattering techniques have explored the existing of CoTiO3 and NiTiO3. The ferromagnetic measurement shows that the magnetization of the sample of high-concentration dopant increases in the same external magnetic field. However, the relatively higher dopant Co and Ni may form more interstitial ions and paramagnet matters, reducing the oxygen vacancy concentration and finally leading to the decrease of remanent magnetization and coercivity of the materials

Porcine transcriptome analysis based on 97 non-normalized cDNA libraries and assembly of 1,021,891 expressed sequence tags.

RIGHTS : This article is licensed under the BioMed Central licence at http://www.biomedcentral.com/about/license which is similar to the 'Creative Commons Attribution Licence'. In brief you may : copy, distribute, and display the work; make derivative works; or make commercial use of the work - under the following conditions: the original author must be given credit; for any reuse or distribution, it must be made clear to others what the license terms of this work are.BACKGROUND: Knowledge of the structure of gene expression is essential for mammalian transcriptomics research. We analyzed a collection of more than one million porcine expressed sequence tags (ESTs), of which two-thirds were generated in the Sino-Danish Pig Genome Project and one-third are from public databases. The Sino-Danish ESTs were generated from one normalized and 97 non-normalized cDNA libraries representing 35 different tissues and three developmental stages. RESULTS: Using the Distiller package, the ESTs were assembled to roughly 48,000 contigs and 73,000 singletons, of which approximately 25% have a high confidence match to UniProt. Approximately 6,000 new porcine gene clusters were identified. Expression analysis based on the non-normalized libraries resulted in the following findings. The distribution of cluster sizes is scaling invariant. Brain and testes are among the tissues with the greatest number of different expressed genes, whereas tissues with more specialized function, such as developing liver, have fewer expressed genes. There are at least 65 high confidence housekeeping gene candidates and 876 cDNA library-specific gene candidates. We identified differential expression of genes between different tissues, in particular brain/spinal cord, and found patterns of correlation between genes that share expression in pairs of libraries. Finally, there was remarkable agreement in expression between specialized tissues according to Gene Ontology categories. CONCLUSION: This EST collection, the largest to date in pig, represents an essential resource for annotation, comparative genomics, assembly of the pig genome sequence, and further porcine transcription studies.Published versio

Mapping topology-disorder phase diagram with a quantum simulator

We explore the topology-disorder phase diagram by simulating one-dimensional Su-Schrieffer-Heeger (SSH) model with quasiperiodic disorder using a programmable superconducting simulator. We experimentally map out and identify various trivial and topological phases with extended and localized bulk states. We find that in the topological phase the bulk states can be critically localized without mobility edge or contain both critically and completely localized states. In addition, there exist trivial and topological intermediate phases with mobility edge and coexistence of extended and completely localized states. The presence of the surprisingly rich phases in the simple SSH model with quasiperiodic disorder sheds new light on the investigation of the topological and localization phenomena in condensed-matter physics.Comment: 5 pages, 4 figure

Two novel TSC2 mutations in renal epithelioid angiomyolipoma sensitive to everolimus.

People who suffers renal angiomyolipoma (AML) has a low quality of life. It is widely known that genetic factors including TSC2 mutation contribute to certain populations of renal AML-bearing patients. In this study, we are the first to identify novel TSC2 mutations in one Chinese renal epithelioid AML patient: c.2652C>A; c.2688G>A based on sequencing result from biopsy tissue. These two somatic mutations cause a translational stop of TSC2, which leads to mTORC1 activation. Given the fact that activation of mTORC1 ensures cell growth and survival, we applied its inhibitor, FDA-approved everolimus, to this woman. After months of treatment with everolimus, Computer-Tomography (CT) scan results showed that everolimus successfully reduced tumor growth and distal metastasis and achieved partial response (PR) to everolimu according to Response Evaluation Criteria in Solid Tumors (RECIST version 1.1). Further Blood Routine Examination results showed the concentration of red cell mass, hemoglobin, white blood cell (WBC), platelets and hematocrit (HCT) significantly returned to normal levels indicating patients with these two TSC2 mutations could be effectively treated by everolimus

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

Finishing the euchromatic sequence of the human genome

The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead