RESUMEN
Are members of marginalized communities silenced on social media when they share personal experiences of racism? Here, we investigate the role of algorithms, humans, and platform guidelines in suppressing disclosures of racial discrimination. In a field study of actual posts from a neighborhood-based social media platform, we find that when users talk about their experiences as targets of racism, their posts are disproportionately flagged for removal as toxic by five widely used moderation algorithms from major online platforms, including the most recent large language models. We show that human users disproportionately flag these disclosures for removal as well. Next, in a follow-up experiment, we demonstrate that merely witnessing such suppression negatively influences how Black Americans view the community and their place in it. Finally, to address these challenges to equity and inclusion in online spaces, we introduce a mitigation strategy: a guideline-reframing intervention that is effective at reducing silencing behavior across the political spectrum.
Asunto(s)
Racismo , Medios de Comunicación Sociales , Humanos , Negro o Afroamericano , AlgoritmosRESUMEN
With the advancement of cementitious material technologies, ultra-high performance concretes incorporating nano- and(or) micro-sized particle materials have been developed; however, their environmental risks are still poorly understood. This study investigates the ecotoxicological effects of ultra-high performance concrete (UC) leachate by comparing with that of the conventional concrete (CC) leachate. For this purpose, a dynamic leaching test and a battery test with algae, water flea, and zebrafish were performed using standardized protocols. The conductivity, concentration of inorganic elements (Al, K, Na, and Fe), and total organic concentration were lower in the UC leachate than in the CC leachate. The EC50 values of the CC and UC leachates were 44.9 % and >100 % in algae, and 8.0 % and 63.1 % in water flea, respectively. All zebrafish exposed to the CC and UC leachates survived. A comprehensive evaluation of the ecotoxicity of the CC and UC leachate based on the toxicity classification system (TCS) showed that their toxicity classification was "highly acute toxicity" and "acute toxicity", respectively. Based on the hazard quotient and principal component analysis, Al and(or) K could be significant factors determining the ecotoxicity of concrete leachate. Furthermore, the ecotoxicity of UC could not be attributed to the use of silica-based materials or multi-wall carbon nanotubes. This study is the first of its kind on the ecotoxicity of UC leachate in aquatic environments, and the results of this study can be used to develop environment-friendly UC.
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Organismos Acuáticos , Materiales de Construcción , Contaminantes Químicos del Agua , Pez Cebra , Animales , Contaminantes Químicos del Agua/toxicidad , Organismos Acuáticos/efectos de los fármacos , Ecotoxicología , Cladóceros/efectos de los fármacos , Pruebas de ToxicidadRESUMEN
Drug-induced diseases are the most important component of iatrogenic disease. It is the duty of doctors to provide a reasonable and safe dose of medication. Gunqile-7 is a Mongolian medicine with analgesic and anti-inflammatory effects. As a foreign substance in the body, even with reasonable medication, it may produce varying degrees of adverse reactions or toxic side effects. Since the cost of collecting Gunqile-7 for pharmacological animal trials is high and the data sample is small, this paper employs transfer learning and data augmentation methods to study the toxicity of Gunqile-7. More specifically, to reduce the necessary number of training samples, the data augmentation approach is employed to extend the data set. Then, the transfer learning method and one-dimensional convolutional neural network are utilized to train the network. In addition, we use the support vector machine-recursive feature elimination method for feature selection to reduce features that have adverse effects on model predictions. Furthermore, due to the important role of the pre-trained model of transfer learning, we select a quantitative toxicity prediction model as the pre-trained model, which is consistent with the purpose of this paper. Lastly, the experimental results demonstrate the efficiency of the proposed method. Our method can improve accuracy by up to 9 percentage points compared to the method without transfer learning on a small sample set.
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Aprendizaje Automático , Redes Neurales de la Computación , Máquina de Vectores de SoporteRESUMEN
Quantifying the hemolytic activity of peptides is a crucial step in the discovery of novel therapeutic peptides. Computational methods are attractive in this domain due to their ability to guide wet-lab experimental discovery or screening of peptides based on their hemolytic activity. However, existing methods are unable to accurately model various important aspects of this predictive problem such as the role of N/C-terminal modifications, D- and L- amino acids, etc. In this work, we have developed a novel neural network-based approach called HemoNet for predicting the hemolytic activity of peptides. The proposed method captures the contextual importance of different amino acids in a given peptide sequence using a specialized feature embedding in conjunction with SMILES-based fingerprint representation of N/C-terminal modifications. We have analyzed the predictive performance of the proposed method using stratified cross-validation in comparison with previous methods, non-redundant cross-validation as well as validation on external peptides and clinical antimicrobial peptides. Our analysis shows the proposed approach achieves significantly better predictive performance (AUC-ROC of 88%) in comparison to previous approaches (HemoPI and HemoPred with AUC-ROC of 73%). HemoNet can be a useful tool in the search for novel therapeutic peptides. The python implementation of the proposed method is available at the URL: https://github.com/adibayaseen/HemoNet.
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Péptidos Antimicrobianos , Aprendizaje Automático , Secuencia de Aminoácidos , Hemólisis , Humanos , PéptidosRESUMEN
Deep learning (DL) offers an unprecedented opportunity to revolutionize the landscape of toxicity prediction based on quantitative structure-activity relationship (QSAR) studies in the big data era. However, the structural description in the reported DL-QSAR models is still restricted to the two-dimensional level. Inspired by point clouds, a type of geometric data structure, a novel three-dimensional (3D) molecular surface point cloud with electrostatic potential (SepPC) was proposed to describe chemical structures. Each surface point of a chemical is assigned its 3D coordinate and molecular electrostatic potential. A novel DL architecture SepPCNET was then introduced to directly consume unordered SepPC data for toxicity classification. The SepPCNET model was trained on 1317 chemicals tested in a battery of 18 estrogen receptor-related assays of the ToxCast program. The obtained model recognized the active and inactive chemicals at accuracies of 82.8 and 88.9%, respectively, with a total accuracy of 88.3% on the internal test set and 92.5% on the external test set, which outperformed other up-to-date machine learning models and succeeded in recognizing the difference in the activity of isomers. Additional insights into the toxicity mechanism were also gained by visualizing critical points and extracting data-driven point features of active chemicals.
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Estrógenos , Relación Estructura-Actividad Cuantitativa , Estrógenos/toxicidad , Humanos , Electricidad EstáticaRESUMEN
As the number of man-made chemicals increases at an unprecedented pace, efforts of quickly screening and accurately evaluating their potential adverse biological effects have been hampered by prohibitively high costs of in vivo/vitro toxicity testing. While it is unrealistic and unnecessary to test every uncharacterized chemical, it remains a major challenge to develop alternative in silico tools with high reliability and precision in toxicity prediction. To address this urgent need, we have developed a novel mode-of-action-guided, molecular modeling-based, and machine learning-enabled modeling approach for in silico chemical toxicity prediction. Here we introduce the core element of this approach, Target-specific Toxicity Knowledgebase (TsTKb), which consists of two main components: Chemical Mode of Action (ChemMoA) database and a suite of prediction model libraries.