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

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Using Multivariate Statistics

Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

Attachment--and loss.

Emotion and Adaptation

Category: Ankle; Ankle Arthritis Introduction/Purpose: Total ankle arthroplasty (TAA) is an increasingly-utilized treatment for ankle arthritis, and opioids are commonly used as part of perioperative pain control. However, many states have enacted opioid-limiting legislation to reduce perioperative opioid prescribing. The aim of this study was to evaluate the impact of time and state legislation on perioperative opioid prescribing in TAA. Methods: This study is a retrospective, observational review of 90-day perioperative opioid prescribing in 1,829 patients undergoing TAA throughout the United States using a large insurance database. Initial and cumulative volumes and rates of opioid prescription filling were recorded along with baseline patient and operative characteristics. Dates of state legislation enactment were also recorded. Student t-tests, analysis of variance (ANOVA), and multivariable linear and logistic regression were utilized to analyze the impact of time and state legislation on opioid prescription filling. Results: In the 90-day perioperative time period, initial and cumulative opioid prescription filling in oxycodone 5-mg equivalents has decreased significantly from 2010 (63.8 initial and 163.3 cumulative oxycodone 5-mg equivalents filled) to 2019 (41.1 initial and 67.2 cumulative oxycodone 5-mg equivalents filled). States with opioid-limiting legislation saw larger and more significant reductions in initial and cumulative opioid prescription filling pre-act to post-act (63.3 to 50.6 oxycodone 5-mg equivalents filled with legislation vs 61.4 to 51.9 oxycodone 5-mg equivalents filled without legislation initial and 146.4 to 93.3 oxycodone 5-mg equivalents filled with legislation vs 125.1 to 108.6 oxycodone 5-mg equivalents filled without legislation cumulative). Conclusion: This study demonstrates that orthopaedic surgeons in states with opioid-limiting legislation have responded by significantly reducing 90-day perioperative opioid prescribing in TAA. These results encourage states without legislation to enact opioid-specific laws to reduce opioid prescribing.

https://journals.sagepub.com/doi/pdf/10.1177/2473011421S00159

How Has Opioid Prescribing in Total Ankle Arthroplasty Changed with Time and State Legislation? A National and State-Level Analysis

Mental health conditions remain underdiagnosed even in countries with common access to advanced medical care. The ability to accurately and efficiently predict mood from easily collectible data has several important implications for the early detection, intervention, and treatment of mental health disorders. One promising data source to help monitor human behavior is daily smartphone usage. However, care must be taken to summarize behaviors without identifying the user through personal (e.g., personally identifiable information) or protected (e.g., race, gender) attributes. In this paper, we study behavioral markers of daily mood using a recent dataset of mobile behaviors from adolescent populations at high risk of suicidal behaviors. Using computational models, we find that language and multimodal representations of mobile typed text (spanning typed characters, words, keystroke timings, and app usage) are predictive of daily mood. However, we find that models trained to predict mood often also capture private user identities in their intermediate representations. To tackle this problem, we evaluate approaches that obfuscate user identity while remaining predictive. By combining multimodal representations with privacy-preserving learning, we are able to push forward the performance-privacy frontier.

Nicholas B. Allen

Papers

How Has Opioid Prescribing in Total Ankle Arthroplasty Changed with Time and State Legislation? A National and State-Level Analysis

Learning Language and Multimodal Privacy-Preserving Markers of Mood from Mobile Data