16,897 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
Pollution-induced community tolerance in freshwater biofilms – from molecular mechanisms to loss of community functions
Exposure to herbicides poses a threat to aquatic biofilms by affecting their community structure, physiology and function. These changes render biofilms to become more tolerant, but on the downside community tolerance has ecologic costs. A concept that addresses induced community tolerance to a pollutant (PICT) was introduced by Blanck and Wängberg (1988). The basic principle of the concept is that microbial communities undergo pollution-induced succession when exposed to a pollutant over a long period of time, which changes communities structurally and functionally and enhancing tolerance to the pollutant exposure. However, the mechanisms of tolerance and the ecologic consequences were hardly studied up to date. This thesis addresses the structural and functional changes in biofilm communities and applies modern molecular methods to unravel molecular tolerance mechanisms.
Two different freshwater biofilm communities were cultivated for a period of five weeks, with one of the communities being contaminated with 4 ÎĽg L-1 diuron. Subsequently, the communities were characterized for structural and functional differences, especially focusing on their crucial role of photosynthesis. The community structure of the autotrophs was assessed using HPLC-based pigment analysis and their functional alterations were investigated using Imaging-PAM fluorometry to study photosynthesis and community oxygen profiling to determine net primary production. Then, the molecular fingerprints of the communities were measured with meta-transcriptomics (RNA-Seq) and GC-based community metabolomics approaches and analyzed with respect to changes in their molecular functions. The communities were acute exposed to diuron for one hour in a dose-response design, to reveal a potential PICT and uncover related adaptation to diuron exposure. The combination of apical and molecular methods in a dose-response design enabled the linkage of functional effects of diuron exposure and underlying molecular mechanisms based on a sensitivity analysis.
Chronic exposure to diuron impaired freshwater biofilms in their biomass accrual. The contaminated communities particularly lost autotrophic biomass, reflected by the decrease in specific chlorophyll a content. This loss was associated with a change in the molecular fingerprint of the communities, which substantiates structural and physiological changes. The decline in autotrophic biomass could be due to a primary loss of sensitive autotrophic organisms caused by the selection of better adapted species in the course of chronic exposure. Related to this hypothesis, an increase in diuron tolerance has been detected in the contaminated communities and molecular mechanisms facilitating tolerance have been found. It was shown that genes of the photosystem, reductive-pentose phosphate cycle and arginine metabolism were differentially expressed among the communities and that an increased amount of potential antioxidant degradation products was found in the contaminated communities. This led to the hypothesis that contaminated communities may have adapted to oxidative stress, making them less sensitive to diuron exposure. Moreover, the photosynthetic light harvesting complex was altered and the photoprotective xanthophyll cycle was increased in the contaminated communities. Despite these adaptation strategies, the loss of autotrophic biomass has been shown to impair primary production. This impairment persisted even under repeated short-term exposure, so that the tolerance mechanisms cannot safeguard primary production as a key function in aquatic systems.:1. The effect of chemicals on organisms and their functions .............................. 1
1.1 Welcome to the anthropocene .......................................................................... 1
1.2 From cellular stress responses to ecosystem resilience ................................... 3
1.2.1 The individual pursuit for homeostasis ....................................................... 3
1.2.2 Stability from diversity ................................................................................. 5
1.3 Community ecotoxicology - a step forward in monitoring the effects of chemical
pollution? ................................................................................................................. 6
1.4 Functional ecotoxicological assessment of microbial communities ................... 9
1.5 Molecular tools – the key to a mechanistic understanding of stressor effects
from a functional perspective in microbial communities? ...................................... 12
2. Aims and Hypothesis ......................................................................................... 14
2.1 Research question .......................................................................................... 14
2.2 Hypothesis and outline .................................................................................... 15
2.3 Experimental approach & concept .................................................................. 16
2.3.1 Aquatic freshwater biofilms as model community ..................................... 16
2.3.2 Diuron as model herbicide ........................................................................ 17
2.3.3 Experimental design ................................................................................. 18
3. Structural and physiological changes in microbial communities after chronic
exposure - PICT and altered functional capacity ................................................. 21
3.1 Introduction ..................................................................................................... 21
3.2 Methods .......................................................................................................... 23
3.2.1 Biofilm cultivation ...................................................................................... 23
3.2.2 Dry weight and autotrophic index ............................................................. 23
3.2.4 Pigment analysis of periphyton ................................................................. 23
3.2.4.1 In-vivo pigment analysis for community characterization ....................... 24
3.2.4.2 In-vivo pigment analysis based on Imaging-PAM fluorometry ............... 24
3.2.4.3 In-vivo pigment fluorescence for tolerance detection ............................. 26
3.2.4.4 Ex-vivo pigment analysis by high-pressure liquid-chromatography ....... 27
3.2.5 Community oxygen metabolism measurements ....................................... 28
3.3 Results and discussion ................................................................................... 29
3.3.1 Comparison of the structural community parameters ............................... 29
3.3.2 Photosynthetic activity and primary production of the communities after
selection phase ................................................................................................. 33
3.3.3 Acquisition of photosynthetic tolerance .................................................... 34
3.3.4 Primary production at exposure conditions ............................................... 36
3.3.5 Tolerance detection in primary production ................................................ 37
3.4 Summary and Conclusion ........................................................................... 40
4. Community gene expression analysis by meta-transcriptomics ................... 41
4.1 Introduction to meta-transcriptomics ............................................................... 41
4.2. Methods ......................................................................................................... 43
4.2.1 Sampling and RNA extraction................................................................... 43
4.2.2 RNA sequencing analysis ......................................................................... 44
4.2.3 Data assembly and processing................................................................. 45
4.2.4 Prioritization of contigs and annotation ..................................................... 47
4.2.5 Sensitivity analysis of biological processes .............................................. 48
4.3 Results and discussion ................................................................................... 48
4.3.1 Characterization of the meta-transcriptomic fingerprints .......................... 49
4.3.2 Insights into community stress response mechanisms using trend analysis
(DRomic’s) ......................................................................................................... 51
4.3.3 Response pattern in the isoform PS genes .............................................. 63
4.5 Summary and conclusion ................................................................................ 65
5. Community metabolome analysis ..................................................................... 66
5.1 Introduction to community metabolomics ........................................................ 66
5.2 Methods .......................................................................................................... 68
5.2.1 Sampling, metabolite extraction and derivatisation................................... 68
5.2.2 GC-TOF-MS analysis ............................................................................... 69
5.2.3 Data processing and statistical analysis ................................................... 69
5.3 Results and discussion ................................................................................... 70
5.3.1 Characterization of the metabolic fingerprints .......................................... 70
5.3.2 Difference in the metabolic fingerprints .................................................... 71
5.3.3 Differential metabolic responses of the communities to short-term exposure
of diuron ............................................................................................................ 73
5.4 Summary and conclusion ................................................................................ 78
6. Synthesis ............................................................................................................. 79
6.1 Approaches and challenges for linking molecular data to functional
measurements ...................................................................................................... 79
6.2 Methods .......................................................................................................... 83
6.2.1 Summary on the data ............................................................................... 83
6.2.2 Aggregation of molecular data to index values (TELI and MELI) .............. 83
6.2.3 Functional annotation of contigs and metabolites using KEGG ................ 83
6.3 Results and discussion ................................................................................... 85
6.3.1 Results of aggregation techniques ........................................................... 85
6.3.2 Sensitivity analysis of the different molecular approaches and endpoints 86
6.3.3 Mechanistic view of the molecular stress responses based on KEGG
functions ............................................................................................................ 89
6.4 Consolidation of the results – holistic interpretation and discussion ............... 93
6.4.1 Adaptation to chronic diuron exposure - from molecular changes to
community effects.............................................................................................. 93
6.4.2 Assessment of the ecological costs of Pollution-induced community
tolerance based on primary production ............................................................. 94
6.5 Outlook ............................................................................................................ 9
Countermeasures for the majority attack in blockchain distributed systems
La tecnologĂa Blockchain es considerada como uno de los paradigmas informáticos más importantes posterior al Internet; en funciĂłn a sus caracterĂsticas Ăşnicas que la hacen ideal para registrar, verificar y administrar informaciĂłn de diferentes transacciones. A pesar de esto, Blockchain se enfrenta a diferentes problemas de seguridad, siendo el ataque del 51% o ataque mayoritario uno de los más importantes. Este consiste en que uno o más mineros tomen el control de al menos el 51% del Hash extraĂdo o del cĂłmputo en una red; de modo que un minero puede manipular y modificar arbitrariamente la informaciĂłn registrada en esta tecnologĂa. Este trabajo se enfocĂł en diseñar e implementar estrategias de detecciĂłn y mitigaciĂłn de ataques mayoritarios (51% de ataque) en un sistema distribuido Blockchain, a partir de la caracterizaciĂłn del comportamiento de los mineros. Para lograr esto, se analizĂł y evaluĂł el Hash Rate / Share de los mineros de Bitcoin y Crypto Ethereum, seguido del diseño e implementaciĂłn de un protocolo de consenso para controlar el poder de cĂłmputo de los mineros. Posteriormente, se realizĂł la exploraciĂłn y evaluaciĂłn de modelos de Machine Learning para detectar software malicioso de tipo Cryptojacking.DoctoradoDoctor en IngenierĂa de Sistemas y ComputaciĂł
Transcriptomic analysis identified SLC40A1 as a key iron metabolism-related gene in airway macrophages in childhood allergic asthma
Introduction: Asthma is the most common chronic condition in children, with allergic asthma being the most common phenotype, accounting for approximately 80% of cases. Growing evidence suggests that disruption of iron homeostasis and iron regulatory molecules may be associated with childhood allergic asthma. However, the underlying molecular mechanism remains unclear.Methods: Three childhood asthma gene expression datasets were analyzed to detect aberrant expression profiles of iron metabolism-related genes in the airways of children with allergic asthma. Common iron metabolism-related differentially expressed genes (DEGs) across the three datasets were identified and were subjected to functional enrichment analysis. Possible correlations between key iron metabolism-related DEGs and type 2 airway inflammatory genes were investigated. Single-cell transcriptome analysis further identified major airway cell subpopulations driving key gene expression. Key iron metabolism-related gene SLC40A1 was validated in bronchoalveolar lavage (BAL) cells from childhood asthmatics with control individuals by quantitative reverse transcription-polymerase chain reaction (qRT-PCR) and immunofluorescence. The intracellular iron content in BAL cells was assessed by Perls iron staining and the iron levels in BAL supernatant was measured by iron assay to assess airway iron metabolism status in childhood asthmatics.Results: Five common iron metabolism-related DEGs were identified, which were functionally related to iron homeostasis. Among these genes, downregulated SLC40A1 was strongly correlated with type 2 airway inflammatory markers and the gene signature of SLC40A1 could potentially be used to determine type 2-high and type 2-low subsets in childhood allergic asthmatics. Further single-cell transcriptomic analysis identified airway macrophages driving SLC40A1 expression. Immunofluorescence staining revealed colocalization of FPN (encoded by SLC40A1) and macrophage marker CD68. Down-regulation of SLC40A1 (FPN) was validated by qRT-PCR and immunofluorescence analysis. Results further indicated reduced iron levels in the BAL fluid, but increased iron accumulation in BAL cells in childhood allergic asthma patients. Furthermore, decreased expression of SLC40A1 was closely correlated with reduced iron levels in the airways of children with allergic asthma.Discussion: Overall, these findings reveal the potential role of the iron metabolism-related gene SLC40A1 in the pathogenesis of childhood allergic asthma
On the Robustness of ChatGPT: An Adversarial and Out-of-distribution Perspective
ChatGPT is a recent chatbot service released by OpenAI and is receiving
increasing attention over the past few months. While evaluations of various
aspects of ChatGPT have been done, its robustness, i.e., the performance to
unexpected inputs, is still unclear to the public. Robustness is of particular
concern in responsible AI, especially for safety-critical applications. In this
paper, we conduct a thorough evaluation of the robustness of ChatGPT from the
adversarial and out-of-distribution (OOD) perspective. To do so, we employ the
AdvGLUE and ANLI benchmarks to assess adversarial robustness and the Flipkart
review and DDXPlus medical diagnosis datasets for OOD evaluation. We select
several popular foundation models as baselines. Results show that ChatGPT shows
consistent advantages on most adversarial and OOD classification and
translation tasks. However, the absolute performance is far from perfection,
which suggests that adversarial and OOD robustness remains a significant threat
to foundation models. Moreover, ChatGPT shows astounding performance in
understanding dialogue-related texts and we find that it tends to provide
informal suggestions for medical tasks instead of definitive answers. Finally,
we present in-depth discussions of possible research directions.Comment: Technical report; code is at:
https://github.com/microsoft/robustlear
One Adapter for All Programming Languages? Adapter Tuning for Code Search and Summarization
As pre-trained models automate many code intelligence tasks, a widely used
paradigm is to fine-tune a model on the task dataset for each programming
language. A recent study reported that multilingual fine-tuning benefits a
range of tasks and models. However, we find that multilingual fine-tuning leads
to performance degradation on recent models UniXcoder and CodeT5.
To alleviate the potentially catastrophic forgetting issue in multilingual
models, we fix all pre-trained model parameters, insert the parameter-efficient
structure adapter, and fine-tune it. Updating only 0.6\% of the overall
parameters compared to full-model fine-tuning for each programming language,
adapter tuning yields consistent improvements on code search and summarization
tasks, achieving state-of-the-art results. In addition, we experimentally show
its effectiveness in cross-lingual and low-resource scenarios. Multilingual
fine-tuning with 200 samples per programming language approaches the results
fine-tuned with the entire dataset on code summarization. Our experiments on
three probing tasks show that adapter tuning significantly outperforms
full-model fine-tuning and effectively overcomes catastrophic forgetting.Comment: Accepted to the 45th International Conference on Software Engineering
(ICSE 2023
Learning Spiking Neural Systems with the Event-Driven Forward-Forward Process
We develop a novel credit assignment algorithm for information processing
with spiking neurons without requiring feedback synapses. Specifically, we
propose an event-driven generalization of the forward-forward and the
predictive forward-forward learning processes for a spiking neural system that
iteratively processes sensory input over a stimulus window. As a result, the
recurrent circuit computes the membrane potential of each neuron in each layer
as a function of local bottom-up, top-down, and lateral signals, facilitating a
dynamic, layer-wise parallel form of neural computation. Unlike spiking neural
coding, which relies on feedback synapses to adjust neural electrical activity,
our model operates purely online and forward in time, offering a promising way
to learn distributed representations of sensory data patterns with temporal
spike signals. Notably, our experimental results on several pattern datasets
demonstrate that the even-driven forward-forward (ED-FF) framework works well
for training a dynamic recurrent spiking system capable of both classification
and reconstruction
latent Dirichlet allocation method-based nowcasting approach for prediction of silver price
Silver is a metal that offers significant value to both investors and companies. The purpose of this study is to make an estimation of the price of silver. While making this estimation, it is planned to include the frequency of searches on Google Trends for the words that affect the silver price. Thus, it is aimed to obtain a more accurate estimate. First, using the Latent Dirichlet Allocation method, the keywords to be analyzed in Google Trends were collected from various articles on the Internet. Mining data from Google Trends combined with the information obtained by LDA is the new approach this study took, to predict the price of silver. No study has been found in the literature that has adopted this approach to estimate the price of silver. The estimation was carried out with Random Forest Regression, Gaussian Process Regression, Support Vector Machine, Regression Trees and Artificial Neural Networks methods. In addition, ARIMA, which is one of the traditional methods that is widely used in time series analysis, was also used to benchmark the accuracy of the methodology. The best MSE ratio was obtained as 0,000227131 ± 0.0000235205 by the Regression Trees method. This score indicates that it would be a valid technique to estimate the price of "Silver" by using Google Trends data using the LDA method
Sustainable intensification of small-scale aquaculture production in Myanmar through diversification and better management practices
Small-scale aquaculture systems can contribute significantly to food and nutritional security, poverty alleviation, and rural development, especially in developing countries. However, the intensification of aquaculture systems often has negative environmental outcomes. The adoption of diversification practices (e.g. polyculture, pond-dike cropping (PDC)) and better management practices (BMPs) has been identified as a possible approach to intensify sustainably small-scale aquaculture production. This study assesses the sustainability outcomes of the adoption of diversification practices and BMPs in small-scale production models. We focus on Myanmar, a developing country characterized by a rapidly expanding small-scale aquaculture sector. We analyze 624 household surveys with small-scale aquaculture producers in central and northern Myanmar. We estimate the effects of diversification practices and BMPs on different sustainability outcomes, namely economic outcomes (i.e. aquaculture yield and benefit-cost ratio), environmental outcomes (i.e. nitrogen and phosphorus use efficiency), and food security outcomes (i.e. fish self-consumption and household dietary diversity) through linear mixed-effects models. Our results reveal that diversified production models (whether integrating or not integrating BMPs) could have significant positive effects on economic and food security outcomes, as well as phosphorus use efficiency, compared to 'unimproved monoculture'. However, such production models do not seem to have any major effect on nitrogen use efficiency. The adoption of BMPs on diversified production models seems to have little (if any) added effect on any of the studied sustainability outcomes, which suggests the need to improve existing BMPs or even develop new BMPs fit for Myanmar's context. These findings have implications about the possible contribution of diversification practices and BMPs for enabling sustainable intensification in small-scale aquaculture settings in Myanmar, and other rural developing contexts
Développement d’un système intelligent de reconnaissance automatisée pour la caractérisation des états de surface de la chaussée en temps réel par une approche multicapteurs
Le rôle d’un service dédié à l’analyse de la météo routière est d’émettre des prévisions et des avertissements aux usagers quant à l’état de la chaussée, permettant ainsi d’anticiper les conditions de circulations dangereuses, notamment en période hivernale. Il est donc important de définir l’état de chaussée en tout temps. L’objectif de ce projet est donc de développer un système de détection multicapteurs automatisée pour la caractérisation en temps réel des états de surface de la chaussée (neige, glace, humide, sec). Ce mémoire se focalise donc sur le développement d’une méthode de fusion de données images et sons par apprentissage profond basée sur la théorie de Dempster-Shafer. Les mesures directes pour l’acquisition des données qui ont servi à l’entrainement du modèle de fusion ont été effectuées à l’aide de deux capteurs à faible coût disponibles dans le commerce. Le premier capteur est une caméra pour enregistrer des vidéos de la surface de la route. Le second capteur est un microphone pour enregistrer le bruit de l’interaction pneu-chaussée qui caractérise chaque état de surface. La finalité de ce système est de pouvoir fonctionner sur un nano-ordinateur pour l’acquisition, le traitement et la diffusion de l’information en temps réel afin d’avertir les services d’entretien routier ainsi que les usagers de la route. De façon précise, le système se présente comme suit :1) une architecture d’apprentissage profond classifiant chaque état de surface à partir des images issues de la vidéo sous forme de probabilités ; 2) une architecture d’apprentissage profond classifiant chaque état de surface à partir du son sous forme de probabilités ; 3) les probabilités issues de chaque architecture ont été ensuite introduites dans le modèle de fusion pour obtenir la décision finale. Afin que le système soit léger et moins coûteux, il a été développé à partir d’architectures alliant légèreté et précision à savoir Squeeznet pour les images et M5 pour le son. Lors de la validation, le système a démontré une bonne performance pour la détection des états surface avec notamment 87,9 % pour la glace noire et 97 % pour la neige fondante
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