N-[2-(2-Chloro­phen­yl)-2-hydroxy­ethyl]propan-2-aminium nitrate

In the title compound, C11H17ClNO+·NO3 −, the side chain of the ethyl­ammonium group is orientated approximately perpendicular to the benzene ring, the dihedral angle between the C/C/N plane of the ethyl­ammonium group and the benzene ring being 79.40 (18)°. In the crystal structure, inter­molecular O—H⋯O and N—H⋯O hydrogen bonds are observed between the cation and the anion

Insulin deficiency exacerbates cerebral amyloidosis and behavioral deficits in an Alzheimer transgenic mouse model

<p>Abstract</p> <p>Background</p> <p>Although increasing evidence has indicated that brain insulin dysfunction is a risk factor for Alzheimer disease (AD), the underlying mechanisms by which insulin deficiency may impact the development of AD are still obscure. Using a streptozotocin (STZ)-induced insulin deficient diabetic AD transgenic mouse model, we evaluated the effect of insulin deficiency on AD-like behavior and neuropathology.</p> <p>Results</p> <p>Our data showed that administration of STZ increased the level of blood glucose and reduced the level of serum insulin, and further decreased the phosphorylation levels of insulin receptors, and increased the activities of glycogen synthase kinase-3α/β and c-Jun N-terminal kinase in the APP/PS1 mouse brain. We further showed that STZ treatment promoted the processing of amyloid-β (Aβ) precursor protein resulting in increased Aβ generation, neuritic plaque formation, and spatial memory deficits in transgenic mice.</p> <p>Conclusions</p> <p>Our present data indicate that there is a close link between insulin deficient diabetes and cerebral amyloidosis in the pathogenesis of AD.</p

Inferring causal genomic alterations in breast cancer using gene expression data

<p>Abstract</p> <p>Background</p> <p>One of the primary objectives in cancer research is to identify causal genomic alterations, such as somatic copy number variation (CNV) and somatic mutations, during tumor development. Many valuable studies lack genomic data to detect CNV; therefore, methods that are able to infer CNVs from gene expression data would help maximize the value of these studies.</p> <p>Results</p> <p>We developed a framework for identifying recurrent regions of CNV and distinguishing the cancer driver genes from the passenger genes in the regions. By inferring CNV regions across many datasets we were able to identify 109 recurrent amplified/deleted CNV regions. Many of these regions are enriched for genes involved in many important processes associated with tumorigenesis and cancer progression. Genes in these recurrent CNV regions were then examined in the context of gene regulatory networks to prioritize putative cancer driver genes. The cancer driver genes uncovered by the framework include not only well-known oncogenes but also a number of novel cancer susceptibility genes validated via siRNA experiments.</p> <p>Conclusions</p> <p>To our knowledge, this is the first effort to systematically identify and validate drivers for expression based CNV regions in breast cancer. The framework where the wavelet analysis of copy number alteration based on expression coupled with the gene regulatory network analysis, provides a blueprint for leveraging genomic data to identify key regulatory components and gene targets. This integrative approach can be applied to many other large-scale gene expression studies and other novel types of cancer data such as next-generation sequencing based expression (RNA-Seq) as well as CNV data.</p

The fast light of CsI(Na) crystals

The responds of different common alkali halide crystals to alpha-rays and gamma-rays are tested in our research. It is found that only CsI(Na) crystals have significantly different waveforms between alpha and gamma scintillations, while others have not this phenomena. It is suggested that the fast light of CsI(Na) crystals arises from the recombination of free electrons with self-trapped holes of the host crystal CsI. Self-absorption limits the emission of fast light of CsI(Tl) and NaI(Tl) crystals.Comment: 5 pages, 11 figures Submit to Chinese Physics

Using e-learning to support international students' dissertation preparation

Purpose: A research paper on the design and implementation of an e-learning resource responding to the globalisation of education. The purpose of this paper is to focus on the challenges presented in learning and teaching on how to support international postgraduate (PG) students undertaking the specific task of a dissertation. Design/methodology/approach: Using findings from 250 PG students, 40 supervisors and two module tutors the research identified the content and language issues faced by students and recognised the need to design an enabler supporting the latter as independent learners and the academic staff delivering support. Findings: The e-learning tool provides an independent learning tool which addresses student concerns relating to the process and content of structuring a dissertation and the function of language. Initial responses have been positive from both staff and students in respect to providing a source of student support and feedback. Originality/value: The research shows how the Dissertation Game Model (DGM), evolved into an e-learning resource supporting student understanding of the content, structure, planning and writing of a dissertation. The e-learning tool focuses on helping international students understand what the generic contents of each chapter of a dissertation should contain and supports them in engaging in research as a transferable skill

Clinical characteristics and prognosis of basaloid squamous cell carcinoma of the lung: a population-based analysis

Background This study analyzed the clinical features and prognosis of basaloid squamous cell carcinoma of the lung (BSC), and constructed a nomogram to predict the prognoses of patients. Methods The information of pure BSC patients was obtained in the Surveillance, Epidemiology, and End Results database between 2004 and 2015. Then, it was evaluated, and compared with the data of lung squamous cell carcinoma (SCC), lung large cell carcinoma (LCC) and lung adenocarcinoma (LAC) patients. Subsequently, we used univariate and multivariate analyses to investigate the independent factors related to the prognoses of patients with BSC and constructed a nomogram to verify the prognoses. Results A total of 425 patients diagnosed with BSC were enrolled. Compared with patients with SCC, LCC and LAC, the mean survival time of BSC patients was better than all of them. Compared with SCC, there were significant differences between the characteristics of grade (P < 0.001), total stage (P < 0.001), T stage (P < 0.001), N stage (P < 0.001), M stage (P < 0.001), surgery (P < 0.001), radiotherapy (P < 0.001), and chemotherapy (P < 0.001), while BSC also had significantly different clinical characteristics from LCC and LAC. Univariate and multivariate survival analyses showed that age (P < 0.001), T stage (P < 0.001), N stage (P = 0.009), M stage (P < 0.001), and surgery (P < 0.001) were independent prognostic factors of BSC. The survival of patients undergoing lobectomy was significantly better than sublobar resection, with an OR of 0.389 (0.263–0.578). We constructed a nomogram with a C-index of 0.750 (95% confidence interval) based on the results of multivariate analysis. The calibration curves based on nomogram scores indicated that the nomogram could accurately predict the prognosis of patients. Conclusions BSC had unique clinical and prognostic features. T stage, N stage, M stage, age, and surgery were independently associated with overall survival (OS). Lobectomy was a relative ideal choice for patients with BSC. The nomogram effectively predicted the OS at 1-, 3-, and 5-years

OmniForce: On Human-Centered, Large Model Empowered and Cloud-Edge Collaborative AutoML System

Automated machine learning (AutoML) seeks to build ML models with minimal human effort. While considerable research has been conducted in the area of AutoML in general, aiming to take humans out of the loop when building artificial intelligence (AI) applications, scant literature has focused on how AutoML works well in open-environment scenarios such as the process of training and updating large models, industrial supply chains or the industrial metaverse, where people often face open-loop problems during the search process: they must continuously collect data, update data and models, satisfy the requirements of the development and deployment environment, support massive devices, modify evaluation metrics, etc. Addressing the open-environment issue with pure data-driven approaches requires considerable data, computing resources, and effort from dedicated data engineers, making current AutoML systems and platforms inefficient and computationally intractable. Human-computer interaction is a practical and feasible way to tackle the problem of open-environment AI. In this paper, we introduce OmniForce, a human-centered AutoML (HAML) system that yields both human-assisted ML and ML-assisted human techniques, to put an AutoML system into practice and build adaptive AI in open-environment scenarios. Specifically, we present OmniForce in terms of ML version management; pipeline-driven development and deployment collaborations; a flexible search strategy framework; and widely provisioned and crowdsourced application algorithms, including large models. Furthermore, the (large) models constructed by OmniForce can be automatically turned into remote services in a few minutes; this process is dubbed model as a service (MaaS). Experimental results obtained in multiple search spaces and real-world use cases demonstrate the efficacy and efficiency of OmniForce