This handbook presents a thorough examination of the intricate interplay of race, ethnicity, and culture in mental health – historical origins, subsequent transformations, and the discourses ...generated from past and present mental health and wellness practices.
The text demonstrates how socio-cultural identities including race, gender, class, sexual orientation, disability, religion, and age intersect with clinical work in a range of settings. Case vignettes and recommendations for best practice help ground each in a clinical focus, guiding practitioners and educators to actively increase their understanding of non-Western and indigenous healing techniques, as well as their awareness of contemporary mental health theories as a product of Western culture with a particular historical and cultural perspective. The international contributors also discuss ways in which global mental health practices transcend racial, cultural, ethnic, linguistic, and political boundaries.
The Routledge International Handbook of Race, Culture and Mental Health is an essential resource for students, researchers, and professionals alike as it addresses the complexity of mental health issues from a critical, global perspective.
Internet-connected devices, especially mobile devices such as smartphones, have become widely accessible in the past decade. Interaction with such devices has evolved into frequent and short-duration ...usage, and this phenomenon has resulted in a pervasive popularity of casual games in the game sector. On the other hand, development of casual games has become easier than ever as a result of the advancement of development tools. With the resulting fierce competition, now both acquisition and retention of users are the prime concerns in the field. In this study, we focus on churn prediction of mobile and online casual games. While churn prediction and analysis can provide important insights and action cues on retention, its application using play log data has been primitive or very limited in the casual game area. Most of the existing methods cannot be applied to casual games because casual game players tend to churn very quickly and they do not pay periodic subscription fees. Therefore, we focus on the new players and formally define churn using observation period (OP) and churn prediction period (CP). Using the definition, we develop a standard churn analysis process for casual games. We cover essential topics such as pre-processing of raw data, feature engineering including feature analysis, churn prediction modeling using traditional machine learning algorithms (logistic regression, gradient boosting, and random forests) and two deep learning algorithms (CNN and LSTM), and sensitivity analysis for OP and CP. Play log data of three different casual games are considered by analyzing a total of 193,443 unique player records and 10,874,958 play log records. While the analysis results provide useful insights, the overall results indicate that a small number of well-chosen features used as performance metrics might be sufficient for making important action decisions and that OP and CP should be properly chosen depending on the analysis goal.
Compared to the traditional machine learning models, deep neural networks (DNN) are known to be highly sensitive to the choice of hyperparameters. While the required time and effort for manual tuning ...has been rapidly decreasing for the well developed and commonly used DNN architectures, undoubtedly DNN hyperparameter optimization will continue to be a major burden whenever a new DNN architecture needs to be designed, a new task needs to be solved, a new dataset needs to be addressed, or an existing DNN needs to be improved further. For hyperparameter optimization of general machine learning problems, numerous automated solutions have been developed where some of the most popular solutions are based on Bayesian Optimization (BO). In this work, we analyze four fundamental strategies for enhancing BO when it is used for DNN hyperparameter optimization. Specifically, diversification, early termination, parallelization, and cost function transformation are investigated. Based on the analysis, we provide a simple yet robust algorithm for DNN hyperparameter optimization - DEEP-BO (Diversified, Early-termination-Enabled, and Parallel Bayesian Optimization). When evaluated over six DNN benchmarks, DEEP-BO mostly outperformed well-known solutions including GP-Hedge, BOHB, and the speed-up variants that use Median Stopping Rule or Learning Curve Extrapolation. In fact, DEEP-BO consistently provided the top, or at least close to the top, performance over all the benchmark types that we have tested. This indicates that DEEP-BO is a robust solution compared to the existing solutions. The DEEP-BO code is publicly available at https://github.com/snu-adsl/DEEP-BO.
Probiotics are microorganisms that can benefit host health when ingested in a live state, and lactic acid bacteria are the most common type. Among fungi,
(SB) is the only strain known to have a ...probiotic function with beneficial effects on colitis; however, information on other probiotic yeast strains is limited. Therefore, this study aimed to discover yeast strains expressing intestinal anti-inflammatory activities by exhibiting probiotic properties in dextran sodium sulfate (DSS)-induced colitis mice model. Nuruk (Korean traditional fermentation starter) containing various microbial strains was used as a source for yeast strains, and
28-7 (SC28-7) strain was selected with in vitro and in vivo characteristics to enable survival in the intestines. After 14 days of pretreatment with the yeast strains, DSS was co-administered for six days to induce colitis in mice. The results revealed that the disease activity index score was lowered by SC28-7 treatment compared to the DSS group, and the colon length and weight/length ratio were recovered in a pattern similar to that of the normal group. SC28-7 administration significantly reduced the secretion of pro-inflammatory cytokines in the serum and modified the mRNA expression of inflammatory cytokines (interleukin-1β, transforming growth factor-β, and interferon-γ) and proteins involved in gut barrier functions (mucin 2, mucin 3, zonula occludens-1, and occludin) in colon tissues. These results indicate that SC28-7 attenuates DSS-induced colon damage and inflammation, supporting its future use as a probiotic yeast for treating and preventing intestinal inflammatory diseases such as inflammatory bowel disease.
A substantial fraction of disease-causing mutations are pathogenic through aberrant splicing. Although genome profiling studies have identified somatic single-nucleotide variants (SNVs) in cancer, ...the extent to which these variants trigger abnormal splicing has not been systematically examined. Here we analyzed RNA sequencing and exome data from 1,812 patients with cancer and identified ∼900 somatic exonic SNVs that disrupt splicing. At least 163 SNVs, including 31 synonymous ones, were shown to cause intron retention or exon skipping in an allele-specific manner, with ∼70% of the SNVs occurring on the last base of exons. Notably, SNVs causing intron retention were enriched in tumor suppressors, and 97% of these SNVs generated a premature termination codon, leading to loss of function through nonsense-mediated decay or truncated protein. We also characterized the genomic features predictive of such splicing defects. Overall, this work demonstrates that intron retention is a common mechanism of tumor-suppressor inactivation.
While the general belief is that the best way to predict electric load is through individualized models, the existing studies have focused on one-for-all models because the individual models are ...difficult to train and require a significantly larger data accumulation time per individual. In recent years, applying deep learning for forecasting electric load has become an important research topic but still one-for-all has been the main approach. In this work, we adopt transfer learning and meta learning that can be smoothly integrated into deep neural networks, and show how a high-performance individualized model can be formed using the individual's data collected over just several days. This is made possible by extracting the common patterns of many individuals using a sufficiently large dataset, and then customizing each individual model using the specific individual's small dataset. The proposed methods are evaluated over residential and non-residential datasets. When compared to the conventional methods, the meta learning model shows 7.84% and 15.07% RMSE improvements over the residential and non-residential datasets, respectively. Our results suggest that the individualized models can be used as effective tools for many short-term load forecasting tasks.
In many next-generation sequencing (NGS) studies, multiple samples or data types are profiled for each individual. An important quality control (QC) step in these studies is to ensure that datasets ...from the same subject are properly paired. Given the heterogeneity of data types, file types and sequencing depths in a multi-dimensional study, a robust program that provides a standardized metric for genotype comparisons would be useful. Here, we describe NGSCheckMate, a user-friendly software package for verifying sample identities from FASTQ, BAM or VCF files. This tool uses a model-based method to compare allele read fractions at known single-nucleotide polymorphisms, considering depth-dependent behavior of similarity metrics for identical and unrelated samples. Our evaluation shows that NGSCheckMate is effective for a variety of data types, including exome sequencing, whole-genome sequencing, RNA-seq, ChIP-seq, targeted sequencing and single-cell whole-genome sequencing, with a minimal requirement for sequencing depth (>0.5X). An alignment-free module can be run directly on FASTQ files for a quick initial check. We recommend using this software as a QC step in NGS studies.
https://github.com/parklab/NGSCheckMate.
Autophagy is an essential catabolic program that forms part of the stress response and enables cells to break down their own intracellular components within lysosomes for recycling. Accumulating ...evidence suggests that autophagy plays vital roles in determining pathological outcomes of immune responses and tumorigenesis. Autophagy regulates innate and adaptive immunity affecting the pathologies of infectious, inflammatory, and autoimmune diseases. In cancer, autophagy appears to play distinct roles depending on the context of the malignancy by either promoting or suppressing key determinants of cancer cell survival. This review covers recent developments in the understanding of autophagy and discusses potential therapeutic interventions that may alter the outcomes of certain diseases.
This study investigates how the introduction of compulsory long-term care insurance has affected the wages of eldercare workers in South Korea. I obtained 94,457 observations from 12,600 employees ...using the Korean Labor and Income Panel Study data. Using a difference-in-differences approach, I compare eldercare workers with other workers in terms of their wage growth rates before and after the policy introduction. The evidence indicates that the policy increased the wages of eldercare workers. More interestingly, eldercare workers, mostly women aged 50 or older, are given lower wage returns for job experience than other occupations, and these returns have further decreased after the policy introduction. This study challenges the notion that welfare-state expansion guarantees women’s improved status at work and calls for more attention to care workers’ working conditions in the defamilialization of care. KCI Citation Count: 0
Neurons live for decades in a postmitotic state, their genomes susceptible to DNA damage. Here we survey the landscape of somatic single-nucleotide variants (SNVs) in the human brain. We identified ...thousands of somatic SNVs by single-cell sequencing of 36 neurons from the cerebral cortex of three normal individuals. Unlike germline and cancer SNVs, which are often caused by errors in DNA replication, neuronal mutations appear to reflect damage during active transcription. Somatic mutations create nested lineage trees, allowing them to be dated relative to developmental landmarks and revealing a polyclonal architecture of the human cerebral cortex. Thus, somatic mutations in the brain represent a durable and ongoing record of neuronal life history, from development through postmitotic function.
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