Draft genome of Pseudomonas sp. XK-1, a marine bacterium capable of degrading lignin and Mn(II) oxidation.

We report the draft genome sequence of marine bacteria, Pseudomonas sp. XK-1. Strain XK-1 could facilitate Mn(II) oxidation with lignin as the sole carbon source. The genome length of XK-1 is 4,751,776 bp, with a G + C content of 62.61%. Genome analyses reveal the carbon and manganese cycling driven by bacteria.

Algorithmic discrimination: examining its types and regulatory measures with emphasis on US legal practices.

Introduction: Algorithmic decision-making systems are widely used in various sectors, including criminal justice, employment, and education. While these systems are celebrated for their potential to enhance efficiency and objectivity, they also pose risks of perpetuating and amplifying societal biases and discrimination. This paper aims to provide an indepth analysis of the types of algorithmic discrimination, exploring both the challenges and potential solutions. Methods: The methodology includes a systematic literature review, analysis of legal documents, and comparative case studies across different geographic regions and sectors. This multifaceted approach allows for a thorough exploration of the complexity of algorithmic bias and its regulation. Results: We identify five primary types of algorithmic bias: bias by algorithmic agents, discrimination based on feature selection, proxy discrimination, disparate impact, and targeted advertising. The analysis of the U.S. legal and regulatory framework reveals a landscape of principled regulations, preventive controls, consequential liability, self-regulation, and heteronomy regulation. A comparative perspective is also provided by examining the status of algorithmic fairness in the EU, Canada, Australia, and Asia. Conclusion: Real-world impacts are demonstrated through case studies focusing on criminal risk assessments and hiring algorithms, illustrating the tangible effects of algorithmic discrimination. The paper concludes with recommendations for interdisciplinary research, proactive policy development, public awareness, and ongoing monitoring to promote fairness and accountability in algorithmic decision-making. As the use of AI and automated systems expands globally, this work highlights the importance of developing comprehensive, adaptive approaches to combat algorithmic discrimination and ensure the socially responsible deployment of these powerful technologies.

Unlabeled data selection for active learning in image classification.

Active Learning has emerged as a viable solution for addressing the challenge of labeling extensive amounts of data in data-intensive applications such as computer vision and neural machine translation. The main objective of Active Learning is to automatically identify a subset of unlabeled data samples for annotation. This identification process is based on an acquisition function that assesses the value of each sample for model training. In the context of computer vision, image classification is a crucial task that typically requires a substantial training dataset. This research paper introduces innovative selection methods within the Active Learning framework, aiming to identify informative images from unlabeled datasets while minimizing the number of required training data. The proposed methods, namely Similari-ty-based Selection, Prediction Probability-based Selection, and Competence-based Active Learning, have been extensively evaluated through experiments conducted on popular datasets like Cifar10 and Cifar100. The experimental results demonstrate that the proposed methods outperform random selection and conventional selection techniques. The superior performance of the novel selection methods underscores their effectiveness in enhancing the Active Learning process for image classification tasks.

DMF mineralization and substrate specificity mechanism of Aminobacter ciceronei DMFA1.

N,N-dimethylformamide (DMF) is widely used in various industries, but its direct release into water poses high risks to human beings. Although a lot of DMF-degrading bacteria has been isolated, limited studies focus on the degradation preference among DMF and its analogues. In this study, an efficient DMF mineralization bacterium designated Aminobacter ciceronei DMFA1 was isolated from marine sediment. When exposed to a 0.2% DMF (∼1900 mg/L), strain DMFA1 exhibited a degradation efficiency of 100% within 4 days. The observed growth using formamide as the sole carbon source implied the possible DMF degradation pathway of strain DMFA1. Meanwhile,the strain DMFA1 possesses a broad-spectrum substrate degradation, which could effectively degraded 0.2% N,N-dimethylacetamide (DMAC) and N-methylformamide (NMF). Genomic analysis further confirmed the supposed pathway through annotating the genes encoding N, N-dimethylformamidase (DMFase), formamidase, and formate dehydrogenase. The existence of sole DMFase indicating its substrate specificity controlled the preference of DMAc of strain DMFA1. By integrating multiple sequence alignment, homology modeling and molecular docking, the preference of the DMFase in strain DMFA1 towards DMAc are related to: 1) Mutations in key active site residues; 2) the absence of small subunit; and 3) no energy barrier for substrates entering the active site.

Digital intervention in improving the outcomes of mental health among LGBTQ+ youth: a systematic review.

LGBTQ+ youth experience mental health disparities and higher rates of mental disorders due to barriers to accessing care, including insufficient services and the anticipated stigma of revealing their identities. This systematic review incorporated 15 empirical studies on digital interventions' impact on LGBTQ+ youth mental health, examining their potential to address these inequities. This study innovatively categorized existing digital interventions into four streams: Structured Formal (telehealth, online programs), Structured Informal (serious games), Unstructured Formal (mobile applications), and Unstructured Informal (social media). We found that S&F and U&F effectively reduced symptoms. U&F showed potential but required enhancement, while U&I fostered resilience but posed risks. Further integration of emerging technologies like virtual reality may strengthen these interventions. This review identifies the characteristics of effective digital health interventions and evaluates the overall potential of digital technologies in improving LGBTQ+ youth mental health, uniquely contributing insights on digital solutions advancing LGBTQ+ youth mental healthcare.