Type I IFNs play a pivotal role in immune response modulation, yet dysregulation is implicated in various disorders. Therefore, it is crucial to develop tools that facilitate the understanding of their mechanism of action and enable the development of more effective anti-IFN therapeutic strategies. In this study, we isolated, cloned, and characterized anti-IFN-α and anti-IFN-β Abs from PBMCs of individuals treated with IFN-α or IFN-β, harboring confirmed neutralizing Abs. Clones AH07856 and AH07857 were identified as neutralizing anti-IFN-α-specific with inhibition against IFN-α2a, -α2b, and -αK subtypes. Clones AH07859 and AH07866 were identified as neutralizing anti-IFN-β1a-specific signaling and able to block lipopolysaccharide or S100 calcium-binding protein A14-induced IFN-β signaling effects. Cloned Abs bind rhesus but not murine IFNs. The specificity of inhibition between IFN-α and IFN-β suggests potential for diverse research and clinical applications.

Non-reciprocal (NR) effective interactions violating Newton's third law occur in many biological systems, but can also be engineered in synthetic, colloidal systems. Recent research has shown that such NR interactions can have tremendous effects on the overall collective behavior and pattern formation, but can also influence aggregation processes on the particle scale. Here, we focus on the impact of non-reciprocity on the self-assembly of a colloidal system (originally passive) with anisotropic interactions whose character is tunable by external fields. In the absence of non-reciprocity, that is, under equilibrium conditions, the colloids form square-like and hexagonal aggregates with extremely long lifetimes yet no large-scale phase separation [Kogler et al., Soft Matter 11, 7356 (2015)], indicating kinetic trapping. Here, we study, based on Brownian dynamics simulations in 2D, an NR version of this model consisting of two species with reciprocal isotropic, but NR anisotropic interactions. We find that NR induces an effective propulsion of particle pairs and small aggregates ("active colloidal molecules") forming at the initial stages of self-assembly, an indication of the NR-induced non-equilibrium. The shape and stability of these initial clusters strongly depend on the degree of anisotropy. At longer times, we find, for weak NR interactions, large (even system-spanning) clusters where single particles can escape and enter at the boundaries, in stark contrast to the small rigid aggregates appearing at the same time in the passive case. In this sense, weak NR shortcuts the aggregation. Increasing the degree of NR (and thus, propulsion), we even observe large-scale phase separation if the interactions are weakly anisotropic. In contrast, systems with strong NR and anisotropy remain essentially disordered. Overall, the NR interactions are shown to destabilize the rigid aggregates interrupting self-assembly and phase separation in the passive case, thereby helping the system to overcome kinetic barriers.

ABSTRACT Principles of diversity and difference are and have historically been, essential to social work education. However, preparing students with the knowledge, awareness, and skills of anti-racist and anti-oppressive practices has been less central in social work education until recently. In June 2022, the Council on Social Work Education issued new Educational Policy and Accreditation Standards (EPAS) guidelines. Most notable was the addition of EPAS 2.0, requiring social work programs to integrate anti-racism, diversity, equity, and inclusion (ADEI) approaches throughout social work programs’ context and learning environment. Responding to EPAS 2022 and our program’s commitment to social and racial justice, this article presents our school’s process for critically assessing and revising how our explicit curriculum integrates critical ADEI perspectives. We highlight the findings from a rapid scoping review of ADEI content in the social work literature. Additionally, we discuss the revisions and recommendations used in our school to ensure the intentional integration of ADEI critical perspectives in our curriculum and the future efforts our school is making to support ADEI perspectives.

Promoters are important cis-regulatory elements for the regulation of gene expression, and their accurate predictions are crucial for elucidating the biological functions and potential mechanisms of genes. Many previous prokaryotic promoter prediction methods are encouraging in terms of the prediction performance, but most of them focus on the recognition of promoters in only one or a few bacterial species. Moreover, due to ignoring the promoter sequence motifs, the interpretability of predictions with existing methods is limited. In this work, we present a generalized method Prompt (Promoters in multiple prokaryotes) to predict promoters in 16 prokaryotes and improve the interpretability of prediction results. Prompt integrates three methods including RSK (Regression based on Selected k-mer), CL (Contrastive Learning) and MLP (Multilayer Perception), and employs a voting strategy to divide the datasets into high-confidence and low-confidence categories. Results on the promoter prediction tasks in 16 prokaryotes show that the accuracy (Accuracy, Matthews correlation coefficient) of Prompt is greater than 80% in highly credible datasets of 16 prokaryotes, and is greater than 90% in 12 prokaryotes, and Prompt performs the best compared with other existing methods. Moreover, by identifying promoter sequence motifs, Prompt can improve the interpretability of the predictions. Prompt is freely available at https://github.com/duqimeng/PromptPrompt , and will contribute to the research of promoters in prokaryote.

Laboratory testing, crucial for medical diagnosis, has 3 phases: preanalytical, analytical, and postanalytical. This study set out to demonstrate whether automating tube labeling through artificial intelligence (AI) support enhances efficiency, reduces errors, and improves outpatient phlebotomy services. The NESLI tube-labeling robot (Labenko Informatics), which uses AI models for tube selection and handling, was used for the experiments. The study evaluated the NESLI robot's operational performance, including labelling time, technical problems, tube handling success, and critical stock alerts. The robot's label readability was also tested on various laboratory devices. This research will contribute to the field's understanding of the potential impact of automated tube-labeling systems on laboratory processes in the preanalytical phase. NESLI demonstrated high performance in labeling processes, achieving a success rate of 99.2% in labeling parameters and a success rate of 100% in other areas. For nonlabeling parameters, the average labeling time per tube was measured at 8.96 seconds, with a 100% success rate in tube handling and critical stock warnings. Technical issues were promptly resolved, affirming the NESLI robot's effectiveness and reliability in automating the tube-labeling processes. Robotic systems using AI, such as NESLI, have the potential to increase process efficiency and reduce errors in the preanalytical phase of laboratory testing. Integration of such systems into comprehensive information systems is crucial for optimizing phlebotomy services and ensuring timely and accurate diagnostics.

Surface-enhanced Raman scattering (SERS) offers a promising, cost-effective alternative for the rapid, sensitive, and quantitative analysis of potential biomarkers in exhaled gases, which is crucial for early disease diagnosis. However, a major challenge in SERS is the effective detection of gaseous analytes, primarily due to difficulties in enriching and capturing them within the substrate's "hotspot" regions. This study introduces an advanced gas sensor combining mesoporous gold (MesoAu) and metal-organic frameworks (MOFs), exhibiting high sensitivity and rapid detection capabilities. The MesoAu provides abundant active sites and interconnected mesopores, facilitating the diffusion of analytes for detection. A ZIF-8 shell enveloping MesoAu further enriches target molecules, significantly enhancing sensitivity. A proof-of-concept experiment demonstrated a detection limit of 0.32 ppb for gaseous benzaldehyde, indicating promising prospects for the rapid diagnosis of early stage lung cancer. This research also pioneers a novel approach for constructing hierarchical plasmonic nanostructures with immense potential in gas sensing.

ABSTRACT This article analyses the technologies of participatory authoritarianism (Owen 2020) and how they construct citizenship through discourses and spaces. It further analyses how marginalised citizens generate a sense of citizen agency through recognising their personal and collective agency and constructing alternative narratives. This article theorises marginalised citizenship by drawing on existing theory, and on data generated through participatory research with groups of marginalised citizens in Nicaragua. The research illuminated how ideas, experiences and expressions of citizenship are shaped by governmental discourses and invited spaces of participation, and by citizens’ own subjectivities and histories. The research employed mixed methods including document analysis, key informant interviews, and participatory methods which incorporated sequenced storytelling and collective analysis. This approach identified factors which are generative of citizen subjectivity and agency within participatory authoritarianism.

This paper is dedicated to the quantum chemical package Jaguar, which is commercial software developed and distributed by Schrödinger, Inc. We discuss Jaguar's scientific features that are relevant to chemical research as well as describe those aspects of the program that are pertinent to the user interface, the organization of the computer code, and its maintenance and testing. Among the scientific topics that feature prominently in this paper are the quantum chemical methods grounded in the pseudospectral approach. A number of multistep workflows dependent on Jaguar are covered: prediction of protonation equilibria in aqueous solutions (particularly calculations of tautomeric stability and pKa), reactivity predictions based on automated transition state search, assembly of Boltzmann-averaged spectra such as vibrational and electronic circular dichroism, as well as nuclear magnetic resonance. Discussed also are quantum chemical calculations that are oriented toward materials science applications, in particular, prediction of properties of optoelectronic materials and organic semiconductors, and molecular catalyst design. The topic of treatment of conformations inevitably comes up in real world research projects and is considered as part of all the workflows mentioned above. In addition, we examine the role of machine learning methods in quantum chemical calculations performed by Jaguar, from auxiliary functions that return the approximate calculation runtime in a user interface, to prediction of actual molecular properties. The current work is second in a series of reviews of Jaguar, the first having been published more than ten years ago. Thus, this paper serves as a rare milestone on the path that is being traversed by Jaguar's development in more than thirty years of its existence.

Addressing social determinants of health in nursing care is important for improving health outcomes and reducing health inequities. Using standardized nursing terminologies to capture this information generates sharable data that can be used to achieve these goals and create new knowledge. The purpose of this integrative review was to examine use of standardized nursing terminologies for collecting social determinants of health data in nursing research and practice. The CINAHL, MEDLINE, and Web of Science databases were searched using the terms "social determinants of health" [and] "nursing" [and] "standardized terminology" or names for each of the 12 American Nurses Association-approved terminologies. Limiters included peer-reviewed and English language. After removal of duplicates, 120 articles were found and screened for relevance and quality using a three-step process. This yielded a final sample of seven articles. Article data were extracted and analyzed for themes. In all articles, retrospective, observational, or secondary analysis research designs were used to analyze previously collected data from large, deidentified datasets or research studies. The Omaha System was the only standardized nursing terminology represented in the sample. All operational definitions of social determinants of health included behavioral items. In most studies, a social determinants of health index score was calculated, and data were analyzed using descriptive statistics and visualization methods. Results reported across the articles were diverse; some themes were identified. This review revealed published literature on this topic is limited. More quality improvement and multisite studies that examine the use of standardized nursing terminologies by nurses to collect and use social determinants of health data are needed.

Stem cells, with their ability to self-renew and differentiate into various cell types, are a unique and valuable resource for medical research and toxicological studies. The liver is the most crucial metabolic organ in the human body, and serves as the primary site for the accumulation of environmental pollutants. Enrichment with environmental pollutants can disrupt the early developmental processes of the liver and have a significant impact on liver function. The liver comprises a complex array of cell types, and different environmental pollutants have varying effects on these cells. Currently, there is a lack of well-established research models that can effectively demonstrate the mechanisms by which environmental pollutants affect human liver development. The emergence of liver cells and organoids derived from stem cells offers a promising tool for investigating the impact of environmental pollutants on human health. Therefore, this study systematically reviewed the developmental processes of different types of liver cells and provided an overview of studies on the developmental toxicity of various environmental pollutants using stem cell models.