There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

抗原变异可使得多种致病微生物易于逃避宿主免疫应答。表达在感染红细胞表面的恶性疟原虫红细胞表面蛋白1（PfPMP1）与感染红细胞、内皮细胞、树突状细胞以及胎盘的单个或多个受体作用，在黏附及免疫逃避中起关键的作用。每个单倍体基因组var基因家族编码约60种成员，通过启动转录不同的var基因变异体为抗原变异提供了分子基础。

疟原虫var基因转换速率变化导致抗原变异[英]／Paul H, Robert P, Christodoulou Z, et al//Proc Natl Acad Sci U S A

Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

Data Analysis Using Regression and Multilevel/Hierarchical Models is a comprehensive manual for the applied researcher who wants to perform data analysis using linear and nonlinear regression and multilevel models. The book introduces a wide variety of models, whilst at the same time instructing the reader in how to fit these models using available software packages. The book illustrates the concepts by working through scores of real data examples that have arisen from the authors' own applied research, with programming codes provided for each one. Topics covered include causal inference, including regression, poststratification, matching, regression discontinuity, and instrumental variables, as well as multilevel logistic regression and missing-data imputation. Practical tips regarding building, fitting, and understanding are provided throughout.

/pdf/data-analysis-using-regression-and-multilevel-hierarchical-31gve8l12s.pdf

Data Analysis Using Regression and Multilevel/Hierarchical Models

XI. STRATEGIES FOR IMPROVING DIABETES CARE D iabetes is a chronic illness that requires continuing medical care and patient self-management education to prevent acute complications and to reduce the risk of long-term complications. Diabetes care is complex and requires that many issues, beyond glycemic control, be addressed. A large body of evidence exists that supports a range of interventions to improve diabetes outcomes. These standards of care are intended to provide clinicians, patients, researchers, payors, and other interested individuals with the components of diabetes care, treatment goals, and tools to evaluate the quality of care. While individual preferences, comorbidities, and other patient factors may require modification of goals, targets that are desirable for most patients with diabetes are provided. These standards are not intended to preclude more extensive evaluation and management of the patient by other specialists as needed. For more detailed information, refer to Bode (Ed.): Medical Management of Type 1 Diabetes (1), Burant (Ed): Medical Management of Type 2 Diabetes (2), and Klingensmith (Ed): Intensive Diabetes Management (3). The recommendations included are diagnostic and therapeutic actions that are known or believed to favorably affect health outcomes of patients with diabetes. A grading system (Table 1), developed by the American Diabetes Association (ADA) and modeled after existing methods, was utilized to clarify and codify the evidence that forms the basis for the recommendations. The level of evidence that supports each recommendation is listed after each recommendation using the letters A, B, C, or E.

Standards of Medical Care in Diabetes

Bladder cancer risk variants and overall and disease-specific survival in two independent cohorts.

Objective: This study examined the association between the prevalence of cardiometabolic disease and fat-enlarged lymph nodes (LNs), identified on digital screening mammography.
Methods: A cross-sectional study on 834 women presenting for full-field digital screening mammography was conducted. The status of fat-enlarged LNs was assessed based on the size and morphology of axillary LNs from the mammograms. The prevalence of cardiometabolic disease, including type 2 diabetes mellitus (T2DM), hypertension, dyslipidemia, high blood glucose, cardiovascular disease, stroke, and non-alcoholic fatty liver disease, were retrieved from the electronic medical records. 
Results: Fat-enlarged axillary LNs were associated with a high prevalence of T2DM among all women (adjusted odds ratio: 3.92, 95% CI: [2.40, 6.60], p-value < 0.001), and in subgroups of women with and without obesity. Utilizing the status of fatty LNs improved the classification of T2DM status in addition to age and BMI (1.4% improvement in the area under the receiver operating characteristic curve).
Conclusion: Fat-enlarge axillary LNs visualized on screening mammograms were associated with the prevalence of T2DM. If further validated with larger datasets from external institutions, fat-enlarged axillary LNs may become a novel imaging biomarker in clinical practice to improve the risk assessment of T2DM for women with screening mammography.

https://medrxiv.org/content/10.1101/2021.11.16.21266413v1.full.pdf

Association between Fat-enlarged Axillary Lymph Nodes on Screening Mammography and Cardiometabolic Disease

Exposure to toxic metals can harm fetal growth (how much a baby grows during pregnancy), while nutritionally essential metals can support fetal growth. Previous research studying the effects of metals during pregnancy on fetal growth has usually focused on one metal at a time. However, most people are exposed to multiple metals, which may have complex effects on fetal growth. Therefore, combining data from three ECHO cohorts representing different geographic regions in the U.S., the research team looked at how maternal exposure to multiple metals affects fetal growth, an important indicator of future health.

Effects of Metal Mixture Exposure During Pregnancy on Fetal Growth


 Background: Genome-wide association studies (GWAS) have identified several common germline variants associated with bladder cancer risk. However, the genetic architecture of bladder cancer risk is still largely unknown. Therefore, we conducted a transcriptome-wide association study (TWAS) to identify gene expression patterns associated with bladder cancer risk. Methods: Individual-level genotype data from bladder cancer cases and controls was accessed from dbGaP (phs000346.v2.p2). We included 3,630 muscle invasive and non-muscle invasive bladder cancer cases and 3,356 controls from the Spanish Bladder Cancer Study (1,145 cases and 1,083 controls) and the New England Bladder Cancer Study (Maine and Vermont, 2,485 cases and 2,273 controls) with imputed genotype data for 22 million single-nucleotide polymorphisms (SNPs). We used PrediXcan to predict gene expression in whole blood using reference data from the Depression Genes and Networks (DGN). Covariate-adjusted logistic regression was used to estimate the associations between gene expression and bladder cancer risk. Results were validated using data from 280 non-muscle invasive bladder cancer cases and 353 controls of the New Hampshire Bladder Cancer Study. Results: We identified five genes associated with bladder cancer risk, including a previously reported locus (1p13.3: GSTM1) and four other loci (4p16.3: FAM53A, 7q22.3: RP11-325F22.2, 8p23.1: RP11-297N6.4, 11q24.1: JHY). Among them, higher predicted expression of GSTM1 in whole blood was associated with lower risk of bladder cancer (Discovery dataset OR = 0.78, 95% CI = 0.67, 0.92; Validation dataset OR = 0.58, 95% CI = 0.34, 0.98). Higher predicted expression of FAM53A (Discovery OR = 1.34, 95% CI = 1.08, 1.68; Validation OR = 3.79, 95% CI = 1.76, 8.29), RP11-325F22.2 (Discovery OR = 1.34, 95% CI = 1.09, 1.65; Validation OR = 2.11, 95% CI = 1.05, 4.30), RP11-297N6.4 (Discovery OR = 1.78, 95% CI = 1.22, 2.60; Validation OR = 6.32, 95% CI = 1.74, 23.34) and JHY (Discovery OR = 1.94, 95% CI = 1.30, 2.91; Validation OR = 6.78, 95% CI = 1.85, 25.76) in whole blood was associated with higher risk of bladder cancer. Conclusion: Our TWAS identified five genes associated with bladder cancer risk: GSTM1, a previously reported locus associated with bladder cancer risk, is related to metabolic detoxification. Four novel loci were identified: FAM53A (related to neural tube development and was previously reported to be related to breast cancer progression), JHY (related to axoneme structure), RP11-325F22.2 (lincRNA), and RP11-297N6.4 (lncRNA). These loci require further validation and may help to reveal the underlying mechanism of the relationship between gene expression and bladder cancer risk.
 Citation Format: Siting Li, Jiang Gui, Margaret R. Karagas, Michael N. Passarelli. Transcriptome-wide association study identifies novel genes associated with bladder cancer risk. [abstract]. In: Proceedings of the AACR Special Conference: Precision Prevention, Early Detection, and Interception of Cancer; 2022 Nov 17-19; Austin, TX. Philadelphia (PA): AACR; Can Prev Res 2023;16(1 Suppl): Abstract nr P038.

Abstract P038: Transcriptome-wide association study identifies novel genes associated with bladder cancer risk

Background: Cohort studies are often used to answer clinical and etiologic questions. Objective: This article is to provide an overview of the basic concepts of cohort studies that are pertinent to the evaluation of the published literature. Methods: Fundamental principles, methods of assessing exposure and outcome status, choice of a comparison group, and statistical methods used in cohort studies are reviewed. Studies focusing on skin neoplasms are used as examples. Results: Methods used in cohort studies are similar to those used in clinical trials. One of the distinguishing features is that the exposure (e.g., treatment) is observed rather than assigned by the study investigators. Therefore, an effort must be made to ensure comparability between exposed and unexposed groups. Conclusion: Cohort studies have provided valuable clinical and etiologic information, but the strengths and limitations of this approach need to be recognized when evaluating the literature.

When Subjects are Not Randomly Assigned: The Cohort Approach

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