Potential Discrimination Research Articles

A Chiral Metal-Organic Framework Prepared on Large-Scale for Sensitive and Enantioselective Fluorescence Recognition.

MOF-based luminescent sensors have garnered considerable attention due to their potential in recognition and discrimination with high sensitivity, selectivity, and fast response in the last decades. Herein, this work describes the bulk preparation of a novel luminescent homochiral MOF, namely, [Cd(s-L)](NO3)2 (MOF-1), from an enantiopure pyridyl-functionalized ligand with rigid binaphthol skeleton under mild synthetic condition. Except for the features of porosity and crystallinity, the MOF-1 has also been characterized with water-stability, luminescence, and homochirality. Most important, the MOF-1 exhibits highly sensitive molecular recognition toward the4-nitrobenzoic acid (NBC) and moderate enantioselective detection of proline, arginine, and 1-phenylethanol.

Unsupervised deep learning techniques for automatic detection of plant diseases: reducing the need of manual labelling of plant images

Crop protection from diseases through applications of plant protection products is crucial to secure worldwide food production. Nevertheless, sustainable management of plant diseases is an open challenge with a major role in the economic and environmental impact of agricultural activities. A primary contribution is expected to come from precision crop protection approaches, with treatments tailored to spatial and time-specific needs of the crop, in contrast to the current practice of applying treatments uniformly to fields. In view of this, image-based automatic detection of early disease symptoms is considered a key enabling technology for high throughput scouting of the crop, in order to timely target the treatments on emerging infection spots. Thanks to the unprecedented performance in image-recognition problems, Deep Learning (DL) methods based on Convolutional Neural Networks (CNNs) have recently entered the domain of plant disease detection. This work develops two DL approaches for automatic recognition of powdery mildew disease on cucumber leaves, with a specific focus on exploring unsupervised techniques to overcome the need of large training set of manually labelled images. To this aim, autoencoder networks were implemented for unsupervised detection of disease symptoms through: i) clusterization of features in a compressed space; ii) anomaly detection. The two proposed approaches were applied to multispectral images acquired during in-vivo experiments, and the obtained results were assessed by quantitative indices. The clusterization approach showed only partially capability to provide accurate disease detection, even if it gathered some relevant information. Anomaly detection showed instead to possess a significant potential of discrimination which could be further exploited as a prior step to train more powerful supervised architectures with a very limited number of labelled samples.

Algorithmic discrimination in the credit domain: what do we know about it?

The widespread usage of machine learning systems and econometric methods in the credit domain has transformed the decision-making process for evaluating loan applications. Automated analysis of credit applications diminishes the subjectivity of the decision-making process. On the other hand, since machine learning is based on past decisions recorded in the financial institutions’ datasets, the process very often consolidates existing bias and prejudice against groups defined by race, sex, sexual orientation, and other attributes. Therefore, the interest in identifying, preventing, and mitigating algorithmic discrimination has grown exponentially in many areas, such as Computer Science, Economics, Law, and Social Science. We conducted a comprehensive systematic literature review to understand (1) the research settings, including the discrimination theory foundation, the legal framework, and the applicable fairness metric; (2) the addressed issues and solutions; and (3) the open challenges for potential future research. We explored five sources: ACM Digital Library, Google Scholar, IEEE Digital Library, Springer Link, and Scopus. Following inclusion and exclusion criteria, we selected 78 papers written in English and published between 2017 and 2022. According to the meta-analysis of this literature survey, algorithmic discrimination has been addressed mainly by looking at the CS, Law, and Economics perspectives. There has been great interest in this topic in the financial area, especially the discrimination in providing access to the mortgage market and differential treatment (different fees, number of parcels, and interest rates). Most attention has been devoted to the potential discrimination due to bias in the dataset. Researchers are still only dealing with direct discrimination, addressed by algorithmic fairness, while indirect discrimination (structural discrimination) has not received the same attention.

Untargeted metabolomics-based approach using UHPLC-HRMS to authenticate carrots (Daucus carota L.) based on geographical origin and production mode

Carrots produced in different agricultural regions with organic or conventional mode were analyzed by untargeted UHPLC-HRMS using reversed-phase and HILIC modes. Data were first treated separately, and further combined to possibly improve results. An in-house data processing workflow was applied to identify relevant features after peak detection. Based on these features, discrimination models were built using chemometrics. A tentative annotation of chemical markers was performed using online databases and UHPLC-HRMS/MS analyses. An independent set of samples was analyzed to assess the discrimination potential of these markers. Carrots produced in the New Aquitaine region could be successfully discriminated from carrots originating from the Normandy region by an OLPS-DA model. Arginine and 6-methoxymellein could be identified as potential markers with the C18-silica column. Additional markers (N-acetylputrescine, l-carnitine) could be identified thanks to the polar column. Discrimination based on production mode was more challenging: some trend was observed but model metrics remained unsatisfactory.

Towards the characterization of crop and weeds at leaf scale: A large comparison of shape, spatial and textural features

In the context of crop and weeds discrimination, different methods are used to detect and classify plants from an acquisition system. Various estimators and descriptors are commonly used to characterize plants within an image. However, the available studies are based on disparate criteria, plants, and acquisition materials which does not allow an accurate estimation of the potential of criteria combinations applied to a new study. Thus, the objective of this study is to: (1) experimentally evaluate the discrimination potential of each criterion at the leaf scale, using images taken in field condition; (2) optimize the parameters of these criteria; and (3) determine the best combination of criteria to use.A literature review is conducted to determine the set of criteria that could be used. A set of 3545 criteria is studied with an algorithm defined to select the best subsets of features (evaluated on a ground truth dataset). Finally, a classification of the vegetation cover is proposed, using the best performing subset. Results show the importance of selecting a smaller set of properties (at most 20 features among the 3545 available) and associating different feature types (for instance spatial with textural and morphological features).

A Fecal-Microbial-Extracellular-Vesicles-Based Metabolomics Machine Learning Framework and Biomarker Discovery for Predicting Colorectal Cancer Patients

Colorectal cancer (CRC) is one of the most common and lethal diseases among all types of cancer, and metabolites play a significant role in the development of this complex disease. This study aimed to identify potential biomarkers and targets in the diagnosis and treatment of CRC using high-throughput metabolomics. Metabolite data extracted from the feces of CRC patients and healthy volunteers were normalized with the median normalization and Pareto scale for multivariate analysis. Univariate ROC analysis, the t-test, and analysis of fold changes (FCs) were applied to identify biomarker candidate metabolites in CRC patients. Only metabolites that overlapped the two different statistical approaches (false-discovery-rate-corrected p-value < 0.05 and AUC > 0.70) were considered in the further analysis. Multivariate analysis was performed with biomarker candidate metabolites based on linear support vector machines (SVM), partial least squares discrimination analysis (PLS-DA), and random forests (RF). The model identified five biomarker candidate metabolites that were significantly and differently expressed (adjusted p-value < 0.05) in CRC patients compared to healthy controls. The metabolites were succinic acid, aminoisobutyric acid, butyric acid, isoleucine, and leucine. Aminoisobutyric acid was the metabolite with the highest discriminatory potential in CRC, with an AUC equal to 0.806 (95% CI = 0.700–0.897), and was down-regulated in CRC patients. The SVM model showed the most substantial discrimination capacity for the five metabolites selected in the CRC screening, with an AUC of 0.985 (95% CI: 0.94–1).

INTERACTIVE ARTIFICIAL INTELLIGENCE

Interactive AI is a field of artificial intelligence that allows computers to interact with humans in a natural and intuitive way. This technology uses machine learning algorithms to interpret and respond to voice, facial expressions, and other forms of input. Interactive AI has numerous applications in various fields, including natural language processing, computer vision, robotics, and virtual reality. It can be used to create more natural and intuitive user interfaces and to automate tedious tasks.However, the implementation of interactive AI also comes with challenges and opportunities. One of the primary challenges is the need for significant resources and expertise to develop and deploy this technology. In addition, there are concerns about privacy and security, as well as the potential for bias and discrimination in the algorithms. Despite these challenges, there are also significant opportunities associated with interactive AI. This technology has the potential to revolutionize the way we interact with computers, making them more user- friendly and efficient. It can also lead to the development of new products and services, and improve the overall user experience.To fully unlock the potential of interactive AI, further research is needed to address these challenges and ensure that the technology is developed in an ethical and responsible manner. This includes developing algorithms that are transparent and unbiased, ensuring that user data is protected and secure, and promoting diversity and inclusivity in the development process. Overall, interactive AI has the potential to transform numerous industries and enhance human-computer interactions, making it a field with great promise for the future International Scientific Journal of Engineering and Management ISSN: 2583-6129 Volume: 02 Issue: 04 | April – 2023 DOI: 10.55041/ISJEM00281 www.isjem.com An International Scholarly || Multidisciplinary || Open Access || Indexing in all major Database & Metadata © 2023, ISJEM (All Rights Reserved) | www.isjem.com | Page 2 KEYWORDS: Interactive AI, Artificial Intelligence, Machine Learning, Natural Language Processing, Computer Vision

Evaluating dried salted cod amino acid signature for nutritional quality assessment and discriminant analysis.

Thus, the aim of this study was to answer three scientific questions: (1) Are the protein content and amino acid profile of dried salted cod influenced by species (Gadus morhua and Gadus macrocephalus)? (2) Are the protein content and amino acid profile of dried salted cod influenced by the geographical area of capture (Iceland and Norway)? and (3) Does the amino acid profile have the potential to be used as a discriminator of species and geographical areas of capture? A total of 45 dried salted cods (2-3 kg of dry weight; n = 15 samples/origin) were used in this study. The Atlantic cod was fished in the Atlantic northeast (FAO 27 area) within the Exclusive Economic zones (EEZ) of Norway (n = 15) and Iceland (n = 15), while the Pacific cod was caught in the Pacific northeast (FAO 67 area) within the Alaska EEZ (n = 15). Total protein content was determined by the Kjeldahl method, in accordance with the AOAC procedures. The amino acid profile was analyzed by HPLC with fluorescence detection (at excitation and emission wavelengths of 338 and 425 nm, respectively). The Atlantic cod presented higher contents of total protein (33.90 versus 33.10 g/100 g of cod edible portion; p = 0.017) and total amino acid contents (32.52 versus 32.04 g/100 g of cod edible portion; p = 0.015) but displayed lower percentage of indispensable amino acids (32.16 versus 32.83 g/100 g of protein; p < 0.001) than Pacific cod. Among the Atlantic cod harvesting locations, the Norwegian cod displayed higher total amino acid contents (96.91 versus 96.81 g/100 g of protein; p = 0.012) and higher percentage of indispensable amino acids (35.38 versus 28.94 g/100 g of protein; p = 0.042) than the Icelandic counterpart. A correct classification of 100% was obtained for the Pacific and Icelandic cod varieties, but the classification accuracy in the Norwegian cod was of just 86.67%, since 2 samples out of 15 were incorrectly classified as Icelandic. The comparison of cod species showed that the Atlantic cod had a significantly lower EAAI than the Pacific cod (p < 0.001; 88.23 versus 88.61). On the other hand, the comparison of the two origins in the Atlantic cod, showed that Norwegian cod displayed a significantly higher EAAI than the Icelandic cod (99.15 versus 77.32). The assessment of the EAAI allows the classification of the protein's nutritional quality, allowing us to classify both cod species as a good protein source to human diet. However, within the Atlantic cod, the Norwegian cod's protein is classified as high quality, while the Icelandic cod attain the classification of useful quality. Regarding the amino acid profile discriminatory potential to classify cod samples. The results show that the AA profile has 100% accuracy in the separation of cod species, but was not globally efficient in the differentiation of the Norwegian from the Icelandic cod.

Osteometry of Foramen Magnum by Using Post-Mortem Computed Tomography (PMCT) for Discriminant Analysis of Sex and Population Affinity among Malaysian Population

Osteometry of the foramen magnum was assessed for discriminant function analysis of biological sex estimation and population affinity estimation based on post-mortem computed tomography (PMCT) images in this Malaysian population-based study. This retrospective cross-sectional study was performed using convenient sampling of PMCT skull images originating from 300 Malaysian adults. Linear dimensions of the foramen magnum transverse diameter (FMTD) and anterior-posterior diameter (FMAPD) were measured. Statistical analyses were performed via independent t-test, ANOVA, univariate and multivariate analyses concurrently with discriminant function analyses. There were significant differences in FMTD and FMAPD between males and females within respective population affinities. The foramen magnum dimensions of males were comparably larger than females. There were relatively significant differences in FMTD and generally sufficient significant differences in FMAPD among population affinities. The osteometry of foramen magnum dimensions of Chinese were generally larger than Malays and Indians. Discussion and Conclusion: Foramen magnum dimensions had a stronger discriminant function in biological sex estimation with 60% accuracy as compared to population affinity estimation with only 40.7% accuracy. FMTD and FMAPD are the potential sex discriminators among the Malaysian population particularly if other indicators are not available for evaluation.

Social media and hiring: a survey experiment on discrimination based on online social class cues

Abstract Discrimination based on social class is challenging to study, and therefore likely to be underappreciated due to its subtle nature. Social class is often difficult to gauge from traditional resumes, yet, the expansion of social networking platforms provides employers with an additional source of information. Given that many individuals have a social media presence today, employers can increasingly rely on additional information gleaned from such online platforms (e.g., Twitter, Instagram), which may alter hiring decisions. To study the role of social networking platforms vis-à-vis potential discrimination based on social class cues, I leverage an original online survey experiment in the United States. The aim of the investigation is (i) to measure the effect of cultural markers of social class expressed on social media profiles in a hypothetical hiring situation and (ii) to analyse potential channels that might explain class-based discrimination. I show that subjects favour the upper-class-signalling candidate over the lower-class-signalling candidate and that perceived competence and perceived warmth are two channels through which class-based discrimination may occur. The individual’s online presence might not be part of the formal application process, yet it seems to shape inferences about individuals’ employability, competence, and warmth.

Brainstem–cortex disconnection in amyotrophic lateral sclerosis: bulbar impairment, genotype associations, asymptomatic changes and biomarker opportunities

BackgroundBulbar dysfunction is a cardinal feature of ALS with important quality of life and management implications. The objective of this study is the longitudinal evaluation of a large panel imaging metrics pertaining to bulbar dysfunction, encompassing cortical measures, structural and functional cortico-medullary connectivity indices and brainstem metrics.MethodsA standardised, multimodal imaging protocol was implemented with clinical and genetic profiling to systematically appraise the biomarker potential of specific metrics. A total of 198 patients with ALS and 108 healthy controls were included.ResultsLongitudinal analyses revealed progressive structural and functional disconnection between the motor cortex and the brainstem over time. Cortical thickness reduction was an early feature on cross-sectional analyses with limited further progression on longitudinal follow-up. Receiver operating characteristic analyses of the panel of MR metrics confirmed the discriminatory potential of bulbar imaging measures between patients and controls and area-under-the-curve values increased significantly on longitudinal follow-up. C9orf72 carriers exhibited lower brainstem volumes, lower cortico-medullary structural connectivity and faster cortical thinning. Sporadic patients without bulbar symptoms, already exhibit significant brainstem and cortico-medullary connectivity alterations.DiscussionOur results indicate that ALS is associated with multi-level integrity change from cortex to brainstem. The demonstration of significant corticobulbar alterations in patients without bulbar symptoms confirms considerable presymptomatic disease burden in sporadic ALS. The systematic assessment of radiological measures in a single-centre academic study helps to appraise the diagnostic and monitoring utility of specific measures for future clinical and clinical trial applications.

Abstract 6162: High resolution detection of HER2 copy number variation in admixed breast cancer cell lines and HER2+ FFPE donor tissue using a digital PCR multigene reference panel

Abstract The measurement of HER2 (ERBB2) gene amplification guides both the prognosis and treatment of breast cancer, as amplification correlates with worse outcomes and the recommendation for anti-HER2 treatment. Digital PCR (dPCR) can serve as a useful tool for the direct quantification of copy number variants (CNV) in this gene, without the need for calibration standards as required for qPCR, or labor-intensive histological techniques such as FISH. Furthermore, increasing the number of available partitions in a digital PCR reaction can yield substantial gains in precision, thus offering the potential for ultra-high resolution discrimination of copy number changes. In this study, we evaluated the utility of a multiplex dPCR assay for the high-resolution detection of HER2 copy number variation in clinically relevant samples. The precision achievable by digital PCR correlates with the number of partitions, and the resolution of small CNV differences greatly relies on high precision. We utilized a dPCR setup with ~100,000 physical partitions per reaction, which allowed for a theoretical precision (relative confidence) of 1%, assuming optimal input and template. Five single-plex primer and probe sets, published by the National Institute of Standards and Technology, were assembled and optimized into a multiplex configuration. The final 5-plex consisted of four reference genes and HER2 as the target of interest. The accuracy and precision of the optimized five-plex assay was tested in nine breast cancer cell lines with established HER2 amplification ratios, ranging from 1.3 to 70 fold. The assay demonstrated the precise and accurate measurement of the HER2 amplification ratio across all nine cell lines over a wide dynamic range of sample input. Along with increasing the reaction partition number, the use of multiple reference genes also improved the precision of the HER2 amplification ratio measurement and avoided inaccurate copy number calls due to outlier references. The resolution of the assay was evaluated using admixed samples simulating low tumor fraction. A minimum of a 1.03 fold difference (3% difference from wild type) was detected with statistical significance. Assay concordance was evaluated against IHC-characterized HER2+ FFPE donor tissue with concordant FISH/CISH results. The assay demonstrated robust performance in clinically relevant sample types, supporting further investigation of dPCR as a complementary or even alternative diagnostic approach to current methods. Citation Format: Tyler Landrith, Samantha Smith, Jennifer Chan, Mari Christensen, Yu Chuan Tai, Megan Gonzales, William Yee, Nicolas Newton, Wouter Pattje, Patrick Bogard, Wei Yang. High resolution detection of HER2 copy number variation in admixed breast cancer cell lines and HER2+ FFPE donor tissue using a digital PCR multigene reference panel [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 6162.

Abstract 995: Evaluation of plasma cytokines and chemokines as potential biomarkers for colorectal cancer detection

Abstract Despite evidence suggesting that current Colorectal Cancer (CRC) screening strategies reduce mortality rates, screening compliance is suboptimal; for this, our study aimed to evaluate the discriminatory potential of plasma cytokine and chemokine levels for CRC detection in plasma samples in a Hispanic subpopulation. Using a retrospective case-control study design, we evaluated plasma samples in 20 CRC cases and 20 age-gender matched controls from the Puerto Rico Familial Colorectal Cancer Registry (PURIFICAR). BD CBA Human Th1/Th2/Th17 Cytokine and Human Chemokine kits were used to prepare plasma samples for flow cytometric analysis (Becton-Dickinson &amp; Co). Mann-Whitney-U test was used to detect differences between biomarkers and case-control groups. Measures of diagnostic accuracy were estimated for comparisons according to CRC disease stage. We compared multivariable models to determine the best CRC case-classifier. The median levels of six of the cytokines evaluated (IL-2, IL-4, IL-6, IL-10, TNF, and IL-17A) and one chemokine (CXCL10-/IP-10) significantly differed between cases and controls (P&amp;lt;0.05). ROC plots and logistic regression analysis showed that these seven biomarkers displayed good to excellent accuracy (AUC: 0.6-0.9). Multivariate analysis showed that the best-case classifying model included the seven statistically significant markers. Our findings suggest that discriminatory biomarker levels between CRC cases and controls observed show their great potential as targets in novel blood-based screening tests for CRC. The development of a non-invasive CRC screening test will provide an alternative to increasing adherence, early detection, survivorship, and a tool for a population-based prevention strategy approach. Citation Format: Elba V. Caraballo, Hilmaris Centeno-Girona, Yilda Macias, Madeline M. Mártir Ocasio, Marcia R. Cruz-Correa. Evaluation of plasma cytokines and chemokines as potential biomarkers for colorectal cancer detection [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 995.

Consumption of Alcohol Upon Admission to the Emergency Department in Patients With Trauma

AimFor 2016, around 32.5% of the population consumed alcoholic beverages. This is related to 1.6% of the disability-adjusted life years. Also, an association is described between trauma severity and a positive alcohol test on admission to the emergency room. In Colombia, the prevalence of alcohol consumption during the last year is 17.8–42.5%. However, the prevalence of alcohol consumption upon admission to emergency services is unknown. We aim to estimate the prevalence of alcohol consumption in cases admitted to an emergency room. MethodsThis was a cross-sectional study. Data were collected during four months in an emergency room located in a fourth-level hospital. Researchers interviewed participants to complete a questionnaire, additionally, a qualitative alcohol test was carried out with an alcohol-sensor FST, and the AUDIT-C scale was used to screen for alcohol abuse. ResultsIn 2524 patients, there was a prevalence of alcohol consumption on admission to the emergency room of 2.34%, and a median result of the quantitative test of 1.22mg/ml. We found that as ethanol levels increased, there was a greater potential for discrimination among the patients who identified themselves with potential risky consumption based on the scale score. ConclusionsAlcohol consumption is associated with high morbidity in patients admitted to the emergency room. This can increase the burden of disease and affect the quality of life. It is pertinent to recognize alcohol consumption as a possible focus to mitigate the impact of this condition on the well-being of patients and resources allocation.

Дискриминация с позиций семиотики культуры

The purpose of the article is a cultural–semiotic analysis of discrimination as a type of relationships and interactions between Ours and Others. The hypothesis of the study is that the transition to a post-cultural model of the functioning of the semiosphere will contribute to the weakening of the discriminatory potential of semiotic systems. To achieve this goal, the symbolic nature of the images of Ours and Others is considered, which are analysed in the context of their correlation with referents; discrimination as a set of worldview attitudes is associated with sign-generating and metalanguage activities in the field of regulating relations between Ours and Others as signs; outlines the contours of a culture-semiosphere with a reduced discriminatory charge. The novelty of the research lies in the application of a cultural-semiotic approach to the study of the phenomenon of discrimination. The author comes to the conclusion about the ambivalence of chaos as a referent in the context of the semiosis of discrimination: chaos can act both as a referent of discrimination signs and as a referent of anti-discrimination signs through the actualization of its threatening or creative potential. In this regard, a view is being formed on post-culture with its activation of personal life creation and individual semiospheres as a time and space favorable for weakening discriminatory attitudes, ideas and practices while maintaining alienation as a whole as a universal mechanism of cultural self-regulation.

From Bias to Fairness: A Review of Ethical Considerations and Mitigation Strategies in Artificial Intelligence

Abstract: Artificial intelligence (AI) has become increasingly popular in recent years and has been used in a range of industries to improve outcomes, streamline processes, and improve decision-making. But there are also moral questions raised by the employment of AI, particularly in light of potential bias and discrimination. In order to promote justice and reduce bias, this paper offers a thorough discussion of ethical issues and mitigation techniques in AI. The evolution of AI and its possible advantages and disadvantages are first covered in the paper. After that, it explores the different ethical issues surrounding AI, such as trust, accountability, fairness, and openness. The study emphasises the effects of bias and discrimination on AI systems as well as the possible outcomes of these problems. The study also discusses the various mitigation measures, such as algorithmic strategies, data pre-processing, and model validation, that have been suggested to mitigate bias and enhance justice in AI. In order to develop the subject of AI ethics, the study analyses the advantages and disadvantages of different frameworks and emphasises the necessity of continued interdisciplinary research and collaboration. The study's importance in advancing ethical concerns and fairness in AI is highlighted in the paper's conclusion. It offers information about the state of the field at the moment and points out potential directions for further study. Overall, the article is a useful tool for academics, professionals, and decision-makers who want to support ethical and responsible AI development and application.

Genetic pattern of the ST 61 cluster of the Cameroon family among Mycobacterium tuberculosis complex strains collected during the CANTAM I project in Cameroon

Tuberculosis (TB) remains one of the most outstanding diseases in global health concern. It is caused by Mycobacterium tuberculosis Complex (MTBC) strains. ST 61, a cluster of the LAM10_CAM family has been shown to be the most prevalent cluster implicated in TB transmission in Cameroon. The present study is aimed at revealing the most discriminative loci applied to this cluster and investigating the genetic pattern in the ST 61 cluster clinical samples. This was a 12 month cross- sectional study that included 45 randomly selected clinical specimens previously collected during the CANTAM 1 project. Following DNA extraction, amplification of 12-locus Mycobacterial Interspersed Repetitive Unit-Variable Tandem Repeat (MIRU-VNTR) analysis (MIRU 40, ETR A, QUB 26, Mtub 39, QUB 4156, MIRU26, MIRU 31, MIRU 16, MIRU 02, MIRU 04, MIRU 23, and MIRU 27) PCR assay and gel electrophoresis were performed. The amplicon sizes, the allele assignations, the allelic pattern and the technic discriminative potential were determined using MIRU-VNTR plus web application and a dendogram was generated. 45% genetic pattern, and 66,6% clustering rate were reported. Overall, ETR A, MIRU 4, MIRU 16, MIRU 23, MIRU 27 revealed as the most discriminating loci applied to the cluster ST 61. The identification of the most discriminative locus ETRA, MIRU 4, MIRU 16, MIRU 23, and MIRU 27 applied to a precise cluster is a safe economic and time gain option to enhance the difference between strains at the cluster level.

Legal issues pertaining to the collection of sociodemographic data in emergency departments.

Legal issues pertaining to the collection of sociodemographic data in emergency departments.

How Optimal Transport Can Tackle Gender Biases in Multi-Class Neural Network Classifiers for Job Recommendations

Automatic recommendation systems based on deep neural networks have become extremely popular during the last decade. Some of these systems can, however, be used in applications that are ranked as High Risk by the European Commission in the AI act—for instance, online job candidate recommendations. When used in the European Union, commercial AI systems in such applications will be required to have proper statistical properties with regard to the potential discrimination they could engender. This motivated our contribution. We present a novel optimal transport strategy to mitigate undesirable algorithmic biases in multi-class neural network classification. Our strategy is model agnostic and can be used on any multi-class classification neural network model. To anticipate the certification of recommendation systems using textual data, we used it on the Bios dataset, for which the learning task consists of predicting the occupation of female and male individuals, based on their LinkedIn biography. The results showed that our approach can reduce undesired algorithmic biases in this context to lower levels than a standard strategy.

Racial and Ethnic Differences in Emergency Department Wait Times for Patients with Substance Use Disorder

BackgroundSubstance use-related morbidity and mortality rates are at an all-time high in the United States, yet there remains significant stigma and discrimination in emergency medicine about patients with this condition. ObjectivesThe purpose of this study was to determine whether there are racial and ethnic differences in emergency department (ED) wait times among patients with substance use disorder. MethodsThe study uses pooled data from the National Hospital Ambulatory Medical Care Survey (NHAMCS) from 2016 to 2018. The dependent variable is length of time the patient with a diagnosis of substance use disorder waited in the ED before being admitted for care. The independent variable is patient race and ethnicity. Adjusted analyses were conducted using a generalized linear model. ResultsThere were a total of 3995 reported ED events among patients reporting a substance use disorder in the NHAMCS sample between 2016 and 2018. After adjusting for covariates, Black patients with substance use disorder were significantly more likely to wait longer in the ED (35% longer) than White patients with substance use disorder (p < 0.01). ConclusionsThe findings showed that Black patients with substance use disorder are waiting 35% longer, on average, than White patients with the same condition. This is concerning, given that emergency medicine is a critical frontline of care, and often the only source of care, for these patients. Furthermore, longer wait times can increase the likelihood of leaving the ED without being seen. Programs and policies should address potential stigma and discrimination among providers, and EDs should consider adding people with lived experiences to the staff to serve as peer recovery specialists and bridge the gap for care.