The Promise of Explainable AI in Digital Health for Precision Medicine: A Systematic Review.

This review synthesizes the literature on explaining machine-learning models for digital health data in precision medicine. As healthcare increasingly tailors treatments to individual characteristics, the integration of artificial intelligence with digital health data becomes crucial. Leveraging a topic-modeling approach, this paper distills the key themes of 27 journal articles.

Secure key distribution exploiting error rate criticality for radio frequency links.

We propose a method by which two radio frequency (RF) communication terminals exchange encryption keys or other data securely. This method draws on the approach developed for quantum key distribution (QKD) for detecting eavesdroppers but our method does not use any quantum properties at all. Instead, by exploiting the effects an eavesdropper has on channel stability, we explore a line-of-sight link radio in which data transfer rates are so high as to approach the Shannon limit.

An interpretable machine learning model of cross-sectional U.S. county-level obesity prevalence using explainable artificial intelligence.

Background: There is considerable geographic heterogeneity in obesity prevalence across counties in the United States. Machine learning algorithms accurately predict geographic variation in obesity prevalence, but the models are often uninterpretable and viewed as a black-box.

Objective: The goal of this study is to extract knowledge from machine learning models for county-level variation in obesity prevalence.

Diversity and metabolic energy in bacteria.

Why are some groups of bacteria more diverse than others? We hypothesize that the metabolic energy available to a bacterial functional group (a biogeochemical group or 'guild') has a role in such a group's taxonomic diversity. We tested this hypothesis by looking at the metacommunity diversity of functional groups in multiple biomes. We observed a positive correlation between estimates of a functional group's diversity and their metabolic energy yield.

Discovering Themes in Deep Brain Stimulation Research Using Explainable Artificial Intelligence.

Deep brain stimulation is a treatment that controls symptoms by changing brain activity. The complexity of how to best treat brain dysfunction with deep brain stimulation has spawned research into artificial intelligence approaches. Machine learning is a subset of artificial intelligence that uses computers to learn patterns in data and has many healthcare applications, such as an aid in diagnosis, personalized medicine, and clinical decision support.

Digitally deployed, GP remote consultation video intervention that aims to reduce opioid prescribing in primary care: protocol for a mixed-methods evaluation.

Introduction: Opioid prescribing rates are disproportionately high in the North of England. In addition to patients' complex health needs, clinician prescribing behaviour is also a key driver. Although strategies have been initiated to reduce opioid prescribing nationally, the COVID-19 pandemic has interrupted service provision and created challenges for the system and health professionals to tackle this complex issue.

MGnify: the microbiome sequence data analysis resource in 2023.

The MGnify platform (https://www.ebi.ac.

Using Explainable Artificial Intelligence to Discover Interactions in an Ecological Model for Obesity.

Ecological theories suggest that environmental, social, and individual factors interact to cause obesity. Yet, many analytic techniques, such as multilevel modeling, require manual specification of interacting factors, making them inept in their ability to search for interactions. This paper shows evidence that an explainable artificial intelligence approach, commonly employed in genomics research, can address this problem.

Diagnostic performance of serum calprotectin in discriminating active from inactive ulcerative colitis in an outpatient setting.

There are limited and conflicting data on the value of serum calprotectin (sCp) in discriminating active from inactive disease activity in ulcerative colitis (UC). Faecal calprotectin (fCp), sCp, serum C-reactive protein (sCRP) and platelets were compared in patients with UC who had clinically active (n = 29) and clinically inactive (n = 42) disease. Serum calprotectin was measured with Bühlmann (BMN sCp) and Immunodiagnostik (IDK sCp) assays.

Experimental evaluation of a linear angular momentum multiplexed radio link for moving platforms.

Linear angular momentum multiplexing (LAMM) has recently been proposed for high spectral-efficiency communications between moving platforms, such as between trains and ground infrastructure. We present performance results obtained from a scale experimental system comprising a 2 × 2 antenna system operating at 2.35 GHz.

Looking for lipases and lipolytic organisms in low-temperature anaerobic reactors treating domestic wastewater.

Poor lipid degradation limits low-temperature anaerobic treatment of domestic wastewater even when psychrophiles are used. We combined metagenomics and metaproteomics to find lipolytic bacteria and their potential, and actual, cold-adapted extracellular lipases in anaerobic membrane bioreactors treating domestic wastewater at 4 and 15 °C. Of the 40 recovered putative lipolytic metagenome-assembled genomes (MAGs), only three (Chlorobium, Desulfobacter, and Mycolicibacterium) were common and abundant (relative abundance ≥ 1%) in all reactors.

Dual-axis cubic cavity for drift-compensated multi-wavelength laser stabilisation.

We describe a 'clock control unit' based on a dual-axis cubic cavity (DACC) for the frequency stabilisation of lasers involved in a strontium optical lattice clock. The DACC, which ultimately targets deployment in space applications, provides a short-term stable reference for all auxiliary lasers-i.e.

Conceptualising factors impacting nutrition services coverage of treatment for acute malnutrition in children: an application of the in Niger.

Objective: The is a conceptual model traditionally used to understand contributing factors of maternal mortality. It posits that most barriers to health services utilisation occur in relation to one of three delays: (1) Delay 1: delayed decision to seek care; (2) Delay 2: delayed arrival at health facility and (3) Delay 3: delayed provision of adequate care. We applied this model to understand why a community-based management of acute malnutrition (CMAM) services may have low coverage.

Valorization of pulp and paper industry wastewater using sludge enriched with nitrogen-fixing bacteria.

Nitrogen-fixing bacteria (NFB) can reduce nitrogen at ambient pressure and temperature. In this study, we treated effluent from a paper mill in sequencing batch reactors (SBRs) and monitored the abundance and activity of NFB with a view to producing a sludge that could work as a biofertilizer. Four reactors were inoculated with activated sludge enriched with NFB and fed with a high C/N waste (100:0.

Experimental evaluation of spectral efficiency from a circular array antenna producing a Laguerre-Gauss mode.

We present the four-dimensional volumetric electromagnetic field measurements (, , and frequency) of the complex radiated field produced by an 8-element circular antenna array. The array is designed to produce a Laguerre-Gauss (LG) mode = +1 over the frequency range of 9-10 GHz. We evaluate our findings in terms of far-field LG mode purity and spectral efficiency in terms of the quadrature amplitude modulation (QAM) modulation scheme that can be supported.

Linear angular momentum multiplexing-conceptualization and experimental evaluation with antenna arrays.

Linear angular momentum multiplexing is a new method for providing highly spectrally efficient short-range communication between a transmitter and receiver, where one may move at speed transverse to the propagation. Such applications include rail, vehicle and hyperloop transport systems communicating with fixed infrastructure on the ground. This paper describes how the scientific concept of linear angular momentum multiplexing evolves from orbital angular momentum multiplexing.

Cerebrovascular function in hypertension: Does high blood pressure make you old?

The majority of individuals over an age of 60 have hypertension. Elevated blood pressure and older age are associated with very similar changes in brain structure and function. We review the parallel brain changes associated with increasing age and blood pressure.

Differential Contribution of Pancreatic Fibroblast Subsets to the Pancreatic Cancer Stroma.

Background & Aims: Although the healthy pancreas consists mostly of epithelial cells, pancreatic cancer and the precursor lesions known as pancreatic intraepithelial neoplasia, are characterized by an extensive accumulation of fibroinflammatory stroma that includes a substantial and heterogeneous fibroblast population. The cellular origin of fibroblasts within the stroma has not been determined. Here, we show that the Gli1 and Hoxb6 markers label distinct fibroblast populations in the healthy mouse pancreas.

Frontostriatal Brain Activation Is Associated With the Longitudinal Progression of Cardiometabolic Risk.

Objective: Cardiometabolic risk refers to a set of interconnected factors of vascular and metabolic origin associated with both cardiovascular disease and various brain disorders. Although midlife cardiometabolic risk is associated with future brain dysfunction, emerging evidence suggests that alterations in autonomic and central nervous system function may precede increases in cardiometabolic risk.

Methods: The present study tested whether patterns of cerebral blood flow in brain areas associated with autonomic regulation were associated with increases in overall cardiometabolic risk.

Enrichment of Nitrogen-Fixing Bacteria in a Nitrogen-Deficient Wastewater Treatment System.

Anthropogenic nitrogen fixation is essential to sustain a global population of 7.7 billion. However, there has been a long-standing desire to find cheaper and more environmentally friendly alternatives to the Haber-Bosch process.

Active and adaptive case finding to estimate therapeutic program coverage for severe acute malnutrition: a capture-recapture study.

Background: Coverage is an important indicator to assess both the performance and effectiveness of public health programs. Recommended methods for coverage estimation for the treatment of severe acute malnutrition (SAM) can involve active and adaptive case finding (AACF), an informant-driven sampling procedure, for the identification of cases. However, as this procedure can yield a non-representative sample, exhaustive or near exhaustive case identification is needed for valid coverage estimation with AACF.

Decreased Cigarette Smoking but No Change in Use of Electronic Cigarettes Following a University-Wide Smoking Ban.

The prevalence of electronic cigarette use is increasing, particularly among youth. This recent trend is troubling given that electronic cigarette use is associated with future cigarette smoking. Here, we assessed the prevalence of cigarette smoking and use of electronic cigarettes among college students before and after implementation of a university-wide smoking ban on campus.

The prospective relationship between prehypertension, race, and whole-brain white matter microstructure.

Compared with whites, blacks develop hypertension earlier in life, progress from prehypertension to hypertension at an accelerated rate, and exhibit greater hypertension-mediated organ damage (e.g., kidney disease, stroke).

Estimating program coverage in the treatment of severe acute malnutrition: a comparative analysis of the validity and operational feasibility of two methods.

Background: Many health programs can assess coverage using standardized cluster survey methods, but estimating the coverage of nutrition programs presents a special challenge due to low disease prevalence. Used since 2012, the Semi-Quantitative Evaluation of Access and Coverage (SQUEAC) employs both qualitative and quantitative methods to identify key barriers to access and estimate coverage of therapeutic feeding programs. While the tool has been increasingly used in programs, the validity of certain methodological elements has been the subject of debate.