15,324 research outputs found
Machine Learning Applications in Studying Mental Health Among Immigrants and Racial and Ethnic Minorities: A Systematic Review
Background: The use of machine learning (ML) in mental health (MH) research
is increasing, especially as new, more complex data types become available to
analyze. By systematically examining the published literature, this review aims
to uncover potential gaps in the current use of ML to study MH in vulnerable
populations of immigrants, refugees, migrants, and racial and ethnic
minorities.
Methods: In this systematic review, we queried Google Scholar for ML-related
terms, MH-related terms, and a population of a focus search term strung
together with Boolean operators. Backward reference searching was also
conducted. Included peer-reviewed studies reported using a method or
application of ML in an MH context and focused on the populations of interest.
We did not have date cutoffs. Publications were excluded if they were narrative
or did not exclusively focus on a minority population from the respective
country. Data including study context, the focus of mental healthcare, sample,
data type, type of ML algorithm used, and algorithm performance was extracted
from each.
Results: Our search strategies resulted in 67,410 listed articles from Google
Scholar. Ultimately, 12 were included. All the articles were published within
the last 6 years, and half of them studied populations within the US. Most
reviewed studies used supervised learning to explain or predict MH outcomes.
Some publications used up to 16 models to determine the best predictive power.
Almost half of the included publications did not discuss their cross-validation
method.
Conclusions: The included studies provide proof-of-concept for the potential
use of ML algorithms to address MH concerns in these special populations, few
as they may be. Our systematic review finds that the clinical application of
these models for classifying and predicting MH disorders is still under
development
Grasping nothing: a study of minimal ontologies and the sense of music
If music were to have a proper sense – one in which it is truly given – one might reasonably place this in sound and aurality. I contend, however, that no such sense exists; rather, the sense of music takes place, and it does so with the impossible. To this end, this thesis – which is a work of philosophy and music – advances an ontology of the impossible (i.e., it thinks the being of what, properly speaking, can have no being) and considers its implications for music, articulating how ontological aporias – of the event, of thinking the absolute, and of sovereignty’s dismemberment – imply senses of music that are anterior to sound. John Cage’s Silent Prayer, a nonwork he never composed, compels a rerethinking of silence on the basis of its contradictory status of existence; Florian Hecker et al.’s Speculative Solution offers a basis for thinking absolute music anew to the precise extent that it is a discourse of meaninglessness; and Manfred Werder’s [yearn] pieces exhibit exemplarily that music’s sense depends on the possibility of its counterfeiting. Inso-much as these accounts produce musical senses that take the place of sound, they are also understood to be performances of these pieces. Here, then, thought is music’s organon and its instrument
Soil fungal community characteristics vary with bamboo varieties and soil compartments
Soil fungi play an important role in nutrient cycling, mycorrhizal symbiosis, antagonism against pathogens, and organic matter decomposition. However, our knowledge about the community characteristics of soil fungi in relation to bamboo varieties is still limited. Here, we compared the fungal communities in different soil compartments (rhizosphere vs. bulk soil) of moso bamboo (Phyllostachys edulis) and its four varieties using ITS high-throughput sequencing technology. The fungal α diversity (Shannon index) in bulk soil was significantly higher than that in rhizosphere soil, but it was not affected by bamboo variety or interactions between the soil compartment and bamboo variety. Soil compartment and bamboo variety together explained 31.74% of the variation in fungal community diversity. Soil compartment and bamboo variety were the key factors affecting the relative abundance of the major fungal taxa at the phylum and genus levels. Soil compartment mainly affected the relative abundance of the dominant fungal phylum, while bamboo variety primarily influenced the dominant fungal genus. Network analysis showed that the fungal network in rhizosphere soil was more complex, stable, and connected than that in bulk soil. A FUNGuild database analysis indicated that both soil compartment and bamboo variety affect fungal functions. Our findings provide new insights into the roles of both soil compartments and plant species (including variety) in shaping soil fungal communities
An Experimental Study on Sentiment Classification of Moroccan dialect texts in the web
With the rapid growth of the use of social media websites, obtaining the
users' feedback automatically became a crucial task to evaluate their
tendencies and behaviors online. Despite this great availability of
information, and the increasing number of Arabic users only few research has
managed to treat Arabic dialects. The purpose of this paper is to study the
opinion and emotion expressed in real Moroccan texts precisely in the YouTube
comments using some well-known and commonly used methods for sentiment
analysis. In this paper, we present our work of Moroccan dialect comments
classification using Machine Learning (ML) models and based on our collected
and manually annotated YouTube Moroccan dialect dataset. By employing many text
preprocessing and data representation techniques we aim to compare our
classification results utilizing the most commonly used supervised classifiers:
k-nearest neighbors (KNN), Support Vector Machine (SVM), Naive Bayes (NB), and
deep learning (DL) classifiers such as Convolutional Neural Network (CNN) and
Long Short-Term Memory (LTSM). Experiments were performed using both raw and
preprocessed data to show the importance of the preprocessing. In fact, the
experimental results prove that DL models have a better performance for
Moroccan Dialect than classical approaches and we achieved an accuracy of 90%.Comment: 13 pages, 5 tables, 2 figure
Activities of the "History and Criticism of Music" Department During the Years of Independence
This article provides information on the scientific-research works carried out in the musical-critical direction at the "History of Music" department. Among the measures implemented in the reform of the education system, the important tasks facing the department in bringing up well-rounded and mature young people and educating competitive personnel were highlighted
Human-Art: A Versatile Human-Centric Dataset Bridging Natural and Artificial Scenes
Humans have long been recorded in a variety of forms since antiquity. For
example, sculptures and paintings were the primary media for depicting human
beings before the invention of cameras. However, most current human-centric
computer vision tasks like human pose estimation and human image generation
focus exclusively on natural images in the real world. Artificial humans, such
as those in sculptures, paintings, and cartoons, are commonly neglected, making
existing models fail in these scenarios. As an abstraction of life, art
incorporates humans in both natural and artificial scenes. We take advantage of
it and introduce the Human-Art dataset to bridge related tasks in natural and
artificial scenarios. Specifically, Human-Art contains 50k high-quality images
with over 123k person instances from 5 natural and 15 artificial scenarios,
which are annotated with bounding boxes, keypoints, self-contact points, and
text information for humans represented in both 2D and 3D. It is, therefore,
comprehensive and versatile for various downstream tasks. We also provide a
rich set of baseline results and detailed analyses for related tasks, including
human detection, 2D and 3D human pose estimation, image generation, and motion
transfer. As a challenging dataset, we hope Human-Art can provide insights for
relevant research and open up new research questions.Comment: CVPR202
Neuroanatomical and gene expression features of the rabbit accessory olfactory system. Implications of pheromone communication in reproductive behaviour and animal physiology
Mainly driven by the vomeronasal system (VNS), pheromone
communication is involved in many species-specific fundamental innate socio-sexual behaviors such as mating and
fighting, which are essential for animal reproduction and survival. Rabbits are a unique model for studying
chemocommunication due to the discovery of the rabbit mammary pheromone, but paradoxically there has been a
lack of knowledge regarding its VNS pathway. In this work, we aim at filling this gap by approaching the system
from an integrative point of view, providing extensive anatomical and genomic data of the rabbit VNS, as well as
pheromone-mediated reproductive and behavioural studies. Our results build strong foundation for further
translational studies which aim at implementing the use of pheromones to improve animal production and welfare
Dataset and Baseline System for Multi-lingual Extraction and Normalization of Temporal and Numerical Expressions
Temporal and numerical expression understanding is of great importance in
many downstream Natural Language Processing (NLP) and Information Retrieval
(IR) tasks. However, much previous work covers only a few sub-types and focuses
only on entity extraction, which severely limits the usability of identified
mentions. In order for such entities to be useful in downstream scenarios,
coverage and granularity of sub-types are important; and, even more so,
providing resolution into concrete values that can be manipulated. Furthermore,
most previous work addresses only a handful of languages. Here we describe a
multi-lingual evaluation dataset - NTX - covering diverse temporal and
numerical expressions across 14 languages and covering extraction,
normalization, and resolution. Along with the dataset we provide a robust
rule-based system as a strong baseline for comparisons against other models to
be evaluated in this dataset. Data and code are available at
\url{https://aka.ms/NTX}.Comment: Technical Repor
Zero-Shot Rumor Detection with Propagation Structure via Prompt Learning
The spread of rumors along with breaking events seriously hinders the truth
in the era of social media. Previous studies reveal that due to the lack of
annotated resources, rumors presented in minority languages are hard to be
detected. Furthermore, the unforeseen breaking events not involved in
yesterday's news exacerbate the scarcity of data resources. In this work, we
propose a novel zero-shot framework based on prompt learning to detect rumors
falling in different domains or presented in different languages. More
specifically, we firstly represent rumor circulated on social media as diverse
propagation threads, then design a hierarchical prompt encoding mechanism to
learn language-agnostic contextual representations for both prompts and rumor
data. To further enhance domain adaptation, we model the domain-invariant
structural features from the propagation threads, to incorporate structural
position representations of influential community response. In addition, a new
virtual response augmentation method is used to improve model training.
Extensive experiments conducted on three real-world datasets demonstrate that
our proposed model achieves much better performance than state-of-the-art
methods and exhibits a superior capacity for detecting rumors at early stages.Comment: AAAI 202
A pilot investigation of differential hydroxymethylation levels in patient-derived neural stem cells implicates altered cortical development in bipolar disorder
IntroductionBipolar disorder (BD) is a chronic mental illness characterized by recurrent episodes of mania and depression and associated with social and cognitive disturbances. Environmental factors, such as maternal smoking and childhood trauma, are believed to modulate risk genotypes and contribute to the pathogenesis of BD, suggesting a key role in epigenetic regulation during neurodevelopment. 5-hydroxymethylcytosine (5hmC) is an epigenetic variant of particular interest, as it is highly expressed in the brain and is implicated in neurodevelopment, and psychiatric and neurological disorders.MethodsInduced pluripotent stem cells (iPSCs) were generated from the white blood cells of two adolescent patients with bipolar disorder and their same-sex age-matched unaffected siblings (n = 4). Further, iPSCs were differentiated into neuronal stem cells (NSCs) and characterized for purity using immuno-fluorescence. We used reduced representation hydroxymethylation profiling (RRHP) to perform genome-wide 5hmC profiling of iPSCs and NSCs, to model 5hmC changes during neuronal differentiation and assess their impact on BD risk. Functional annotation and enrichment testing of genes harboring differentiated 5hmC loci were performed with the online tool DAVID.ResultsApproximately 2 million sites were mapped and quantified, with the majority (68.8%) located in genic regions, with elevated 5hmC levels per site observed for 3’ UTRs, exons, and 2-kb shorelines of CpG islands. Paired t-tests of normalized 5hmC counts between iPSC and NSC cell lines revealed global hypo-hydroxymethylation in NSCs and enrichment of differentially hydroxymethylated sites within genes associated with plasma membrane (FDR = 9.1 × 10−12) and axon guidance (FDR = 2.1 × 10−6), among other neuronal processes. The most significant difference was observed for a transcription factor binding site for the KCNK9 gene (p = 8.8 × 10−6), encoding a potassium channel protein involved in neuronal activity and migration. Protein–protein-interaction (PPI) networking showed significant connectivity (p = 3.2 × 10−10) between proteins encoded by genes harboring highly differentiated 5hmC sites, with genes involved in axon guidance and ion transmembrane transport forming distinct sub-clusters. Comparison of NSCs of BD cases and unaffected siblings revealed additional patterns of differentiation in hydroxymethylation levels, including sites in genes with functions related to synapse formation and regulation, such as CUX2 (p = 2.4 × 10−5) and DOK-7 (p = 3.6 × 10−3), as well as an enrichment of genes involved in the extracellular matrix (FDR = 1.0 × 10−8).DiscussionTogether, these preliminary results lend evidence toward a potential role for 5hmC in both early neuronal differentiation and BD risk, with validation and more comprehensive characterization to be achieved through follow-up study
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