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Introduction: In the medical and veterinary fields, understanding the significance of physiological signals for assessing patient state, diagnosis, and treatment outcomes is paramount. There are, in the domain of machine learning (ML), very many methods capable of performing automatic feature selection. We here explore how such methods can be applied to select features from electroencephalogram (EEG) signals to allow the prediction of depth of anesthesia (DoA) in pigs receiving propofol.Methods: We evaluated numerous ML methods and observed that these algorithms can be classified into groups based on similarities in selected feature sets explainable by the mathematical bases behind those approaches. We limit our discussion to the group of methods that have at their core the computation of variances, such as Pearson’s and Spearman’s correlations, principal component analysis (PCA), and ReliefF algorithms.Results: Our analysis has shown that from an extensive list of time and frequency domain EEG features, the best predictors of DoA were spectral power (SP), and its density ratio applied specifically to high-frequency intervals (beta and gamma ranges), as well as burst suppression ratio, spectral edge frequency and entropy applied to the whole spectrum of frequencies.Discussion: We have also observed that data resolution plays an essential role not only in feature importance but may impact prediction stability. Therefore, when selecting the SP features, one might prioritize SP features over spectral bands larger than 1 Hz, especially for frequencies above 14 Hz.

Abstract Erythrocyte aggregation kinetics is accelerated in diseases with a strong inflammation component. This study aimed to evaluate whether, in an emergency setting, a new point-of-care test measuring erythrocyte aggregation kinetics (EAK) can identify patients with underlying inflammation. Patients visiting an emergency department and needing a blood exam were successively included. EAK was measured at the point-of-care in 20 s directly on the blood samples collected in regular tubes without any manipulation. The primary measure was EAK’s half-life during the first 5 s (EAK5s). Each patient’s inflammation status was assessed blind to the EAK test results. Receiver Operating Characteristic (ROC) curves for inflammation status were built. 268 patients had their EAK5s measured, and a clear inflammation status was determined for 214 patients (65 had inflammation). Mean EAK5s were 2.18 s and 1.75 s for no inflammation and inflammation groups respectively (p

Growth in the complexity of advanced systems is mirrored by a growth in the number of engineering requirements and related upstream and downstream tasks. These requirements are typically expressed in natural language and require human expertise to manage. Natural language processing (NLP) technology has long been seen as promising to increase requirements engineering (RE) productivity but has yet to demonstrate substantive benefits. The recent addition of large language models (LLMs) to the NLP toolbox is now generating renewed enthusiasm in the hope that it will overcome past shortcomings. This article scrutinizes this claim by reviewing the application of LLMs for engineering requirements tasks. We survey the success of applying LLMs and the scale to which they have been used. We also identify groups of challenges shared across different engineering requirement tasks. These challenges show how this technology has been applied to RE tasks that need reassessment. We finalize by drawing a parallel to other engineering fields with similar challenges and how they have been overcome in the past – and suggest these as future directions to be investigated.

The present study aimed to examine the stereoselective pharmacokinetics of racemic ketamine in dogs at low doses. The secondary aims were to identify associated behavioural effects and propose a ketamine infusion rate. The study was conducted on nine intact male beagles, with each dog undergoing two treatments (BOL and INF). For treatment BOL, an intravenous bolus of 1 mg/kg was administered over 2 min. The treatment INF involved an initial bolus of 0.5 mg/kg given over 1 min, followed by an infusion at 0.01 mg/kg/min for 1 h. Blood samples were collected for pharmacokinetic analysis. The median R/S enantiomer ratio of ketamine remained close to 1 throughout the study. Levels of S-norketamine were significantly higher than those of R-norketamine across all time points. Based on the collected data, the infusion rate predicted to achieve a steady-state racemic ketamine plasma concentration of 150 ng/mL was 0.028 mg/kg/min. Higher scores for behavioural effects were observed within the first five minutes following bolus administration. The most common behaviours observed were disorientation, head movements and staring eyes. Furthermore, employing ROC curve analysis, a racemic ketamine plasma concentration of 102 ng/mL was defined as the cut-off value, correlating with the occurrence of undesirable behavioural patterns.

Abstract Erythrocyte aggregation (EA) is a physiological process by which erythrocytes reversibly stick together within the blood vessels. EA plays a major role in blood viscosity in vivo, thereby impacting blood flow to organs. EA is no doubt greatly important in both physiological and pathophysiological conditions, but the studies its importance calls for are complicated by the lack of a reliable and easy way to measure it. We have developed a new point-of-care technique which can very specifically measure EA initial kinetics (EAK) in 20 s directly on blood samples routinely collected in tubes commonly used in clinical settings. We present the results of the validation studies of this EAK test: A mono-exponential curve explains 99% of EAK variance. EAK is normally distributed in healthy individuals, with an interindividual 15% coefficient of variation and is stable for least one hour after blood collection. Intraindividual coefficient of variation is 2.6%. EA can now be easily measured in any clinical setting.

Abstract Background The COVID-19 pandemic has affected the health and well-being of people worldwide, yet few studies have qualitatively examined its cumulative effects on ciswomen living with HIV (WLWH). We aimed to explore how the pandemic has impacted WLWH, including challenges related to HIV care, employment, finances, and childcare. We also investigated how HIV status and different psychosocial stressors affected their mental health. Methods We performed 25 semi-structured qualitative interviews with WLWH regarding the ways in which COVID-19 impacted their social determinants of health and physical well-being during the pandemic. 19 WLWH who received care at the University of Chicago Medicine (UCM) and 6 women who received care at Howard Brown Health, a federally qualified health center (FQHC) in Chicago, were interviewed remotely from June 2020 to April 2021. All interviews were audio recorded and transcribed. Interviews were thematically analyzed for commonalities regarding HIV-specific and general experiences of WLWH during the pandemic. Results The majority of participants reported COVID-19 impacted their HIV care, such as appointment cancellations and difficulties adhering to antiretroviral therapy. In addition to HIV care obstacles, almost all participants described perceived heightened vulnerability to or fear of COVID-19. The pandemic also affected the socioeconomic well-being of participants, with reported financial strains and employment disruptions. Some mothers took on additional childcare responsibilities, such as homeschooling. Increased mental health concerns and negative psychological effects from the social isolation associated with the pandemic were also experienced by most participants. Conclusions We gained invaluable insight into how WLWH were challenged by and adapted to the COVID-19 pandemic, including its destabilizing effects on their HIV care and mental health. Women described how they undertook additional childcare responsibilities during the pandemic and how their HIV status compounded their concerns (e.g., perceived heightened vulnerability to COVID-19). Strategies to better support WLWH in maintaining their overall health throughout the pandemic include childcare assistance, access to affordable mental health services, support groups, and education from HIV care providers. These findings have significant implications for examining future health crises through the perspective of potential gender inequalities.

With the growth of residential rooftop PV adoption in recent decades, the problem of effective layout design has become increasingly important in recent years. Although a number of automated methods have been introduced, these tend to rely on simplifying assumptions and heuristics to improve computational tractability. We demonstrate a fully automated layout design pipeline that attempts to solve a more general formulation with greater geometric flexibility that accounts for shading losses. Our approach generates rooftop areas from satellite imagery and uses MINLP optimization to select panel positions, azimuth angles and tilt angles on an individual basis rather than imposing any predefined layouts. Our results demonstrate that shading plays a critical role in automated rooftop PV optimization and significantly changes the resulting layouts. Additionally, they suggest that, although several common heuristics are often effective, they may not be universally suitable due to complications resulting from geometric restrictions and shading losses. Finally, we evaluate a few specific heuristics from the literature and propose a potential new rule of thumb that may help improve rooftop solar energy potential when shading effects are considered.

Global solar photovoltaic capacity has consistently doubled every 18 months over the last two decades, going from 0.3 GW in 2000 to 643 GW in 2019, and is forecast to reach 4240 GW by 2040. However, these numbers are uncertain, and virtually all reporting on deployments lacks a unified source of either information or validation. In this paper, we propose, optimize, and validate a deep learning framework to detect and map solar farms using a state-of-the-art semantic segmentation convolutional neural network applied to satellite imagery. As a final step in the pipeline, we propose a model to estimate the energy generation capacity of the detected solar energy facilities. Objectively, the deep learning model achieved highly competitive performance indicators, including a mean accuracy of 96.87%, and a Jaccard Index (intersection over union of classified pixels) score of 95.5%. Subjectively, it was found to detect spaces between panels producing a segmentation output at a sub-farm level that was better than human labeling. Finally, the detected areas and predicted generation capacities were validated against publicly available data to within an average error of 4.5% Deep learning applied specifically for the detection and mapping of solar farms is an active area of research, and this deep learning capacity evaluation pipeline is one of the first of its kind. We also share an original dataset of overhead solar farm satellite imagery comprising 23,000 images (256 × 256 pixels each), and the corresponding labels upon which the machine learning model was trained.

While clinical evidence remains limited, an extensive amount of research suggests a beneficial role of n-3 polyunsaturated fatty acid supplementation in cancer treatment. One potential benefit is an improvement of protein homeostasis, but how protein metabolism depends on proinflammatory cytokines in this context remains unclear. Here, using the natural abundance of the stable isotopes of nitrogen as a marker of changes in protein metabolism during a randomized, double-blind, controlled clinical trial, we show that protein homeostasis is affected way faster than proinflammatory cytokines in metastatic breast cancer patients supplemented with n-3 polyunsaturated fatty acids. We provide some evidence that this response is unrelated to major changes in whole-body substrate oxidation. In addition, we demonstrate that more fatty acids were impacted by metabolic regulations than by differences in their intake levels during the supplementation. This study documents that the percentage of patients that complied with the supplementation decreased with time, making compliance assessment crucial for the kinetic analysis of the metabolic and inflammatory responses. Our results highlight the time-dependent nature of metabolic and inflammatory changes during long-chain n-3 fatty acid supplementation.

Abstract Backround The number of cancer survivors is growing steadily and increasingly, clinical trials are being designed to include long-term follow-up to assess not only survival, but also late effects and health-related quality of life (HRQOL). Therefore it is is essential to develop patient-reported outcome measures (PROMs) that capture the full range of issues relevant to disease-free cancer survivors. The objectives of this project are: 1) to develop a European Organisation for Research and Treatment of Cancer (EORTC) questionnaire that captures the full range of physical, mental and social HRQOL issues relevant to disease-free cancer survivors; and 2) to determine at which minimal time since completion of treatment the questionnaire should be used. Methods We reviewed 134 publications on cancer survivorship and interviewed 117 disease-free cancer survivors with 11 different types of cancer across 14 countries in Europe to generate an exhaustive, provisional list of HRQOL issues relevant to cancer survivors. The resulting issue list, the EORTC core questionnaire (QLQ-C30), and site-specific questionnaire modules were completed by a second group of 458 survivors. Results We identified 116 generic survivorship issues. These issues covered body image, cognitive functioning, health behaviors, negative and positive outlook, health distress, mental health, fatigue, sleep problems, physical functioning, pain, several physical symptoms, social functioning, and sexual problems. Patients rated most of the acute symptoms of cancer and its treatment (e.g. nausea) as no longer relevant approximately one year after completion of treatment. Conclusions Compared to existing cancer survivorship questionnaires, our findings underscore the relevance of assessing issues related to chronic physical side effects of treatment such as neuropathy and joint pain. We will further develop a core survivorship questionnaire and three site-specific modules for disease-free adult cancer survivors who are at least one year post-treatment.

Little AI is a pedagogical game aimed at presenting the founding concepts of constructivist learning and developmental Artificial Intelligence. It primarily targets students in computer science and cognitive science but it can also interest the general public curious about these topics. It requires no particular scientific background; even children can find it entertaining. Professors can use it as a pedagogical resource in class or in online courses. The player presses buttons to control a simulated “baby robot”. The player cannot see the robot and its environment, and initially ignores the effects of the commands. The only information received by the player is feedback from the player’s commands. The player must learn, at the same time, the functioning of the robot’s body and the structure of the environment from patterns in the stream of commands and feedback. We argue that this situation is analogous to how infants engage in early-stage developmental learning (e.g., Piaget (1937), [1]). Keywords: Pedagogical game, Artificial intelligence, Developmental learning, Constructivist learning

Food choices affect the isotopic composition of the body with each food item leaving its distinct isotopic imprint. The common view is that the natural abundance of the stable isotopes of nitrogen (expressed as δ15N) is higher in animals than in plants that constitute our contemporary diets. Higher δ15N is thus increasingly viewed as a biomarker for meat and fish intake. Here we show that organic compared to conventional farming increases plant δ15N to an extent that can appreciably impact the performance of δ15N as a biomarker. The error that can arise when organic plants are consumed was modelled for the entire range of proportions of plant versus animal protein intake, and accounting for various intakes of organic and conventionally grown crops. This mass balance model allows the interpretation of differences in δ15N in light of organic food consumption. Our approach shows that the relationship between δ15N and meat and fish intake is highly contextual and susceptible to variation at the population, community or group level. We recommend that fertilization practices and organic plant consumption must not be overlooked when using δ15N as a biomarker for meat and fish intake or to assess compliance to nutritional interventions.

Aims: Assess the impact of radiation doses to neural stem cell (NSC) niches in patients with IDH-wild-type glioblastoma.Materials and Methods: Fifty patients were included in the study. NSC niches [SubVentricular Zone (SVZ) and Sub Granular Zone (SGZ)] were contoured by fusing CT scans and pre-therapy MRI, Tumor location defined ipsilateral and contralateral SVZ and SGZ. Prognostic significance of clinical, biological and dosimetric parameters were examined. We generated a Recursive Partitioning Analysis (RPA) model with independent prognostic classes.Results: Median follow-up: 23.8 months. Event free and overall survival (OS): 10 and 19.1 months. Incomplete surgery, PTV (planning target volume), ipsilateral SVZ or NSC niche mean dose > 57.4 Gy, contralateral NSC niche mean dose > 35 Gy and bilateral NSC niche mean dose > 44 Gy were significantly correlated with reduced OS. Only EGFR amplification was an independent prognostic factor (p = 0.019) for OS. RPA generated independent risk groups: 1 (low risk): [ipsilateral NSC mean dose (INMD) < 58.01 Gy and methylated MGMT promoter], 2: (INMD < 58.01 Gy and unmethylated MGMT promoter and contralateral SVZ mean dose < 18.6 Gy; p = 0.43), 3: (INMD < 58.01 Gy and unmethylated MGMT promoter and contralateral SVZ mean dose > 18.6 Gy; p = 0.002) and 4: (very high risk) (INMD > 58.01 Gy; p < 0.001).Conclusion: High radiation doses to ipsilateral NSC and contralateral SVZ could have a negative impact on overall survival in IDH-wild-type glioblastoma population.

A growing body of evidence supports a role for tissue-to-diet 15N and 13C discrimination factors (Δ15N and Δ13C), as biomarkers of metabolic adaptations to nutritional stress, but the underlying mechanisms remain poorly understood. In obese rats fed ad libitum or subjected to gradual caloric restriction (CR), under a maintained protein intake, we measured Δ15N and Δ13C levels in tissue proteins and their constitutive amino acids (AA) and the expression of enzymes involved in the AA metabolism. CR was found to lower protein mass in the intestine, liver, heart and, to a lesser extent, some skeletal muscles. This was accompanied by Δ15N increases in urine and the protein of the liver and plasma, but Δ15N decreases in the proteins of the heart and the skeletal muscles, alongside Δ13C decreases in all tissue proteins. In Lys, Δ15N levels rose in the plasma, intestine, and some muscles, but fell in the heart, while in Ala, and to a lesser extent Glx and Asx, Δ13C levels fell in all these tissues. In the liver, CR was associated with an increase in the expression of genes involved in AA oxidation. During CR, the parallel rises of Δ15N in urine, liver, and plasma proteins reflected an increased AA catabolism occurring at the level of the liver metabolic branch point, while Δ15N decreases in cardiac and skeletal muscle proteins indicated increased protein and AA catabolism in these tissues. Thus, an increased protein and AA catabolism results in opposite Δ15N effects in splanchnic and muscular tissues. In addition, the Δ13C decrease in all tissue proteins, reflects a reduction in carbohydrate (CHO) oxidation and routing towards non-indispensable AA, to achieve fuel economy.

Purpose – – This research was performed with the aim of determining if the emptying of latrines in a flood‐prone urban slum area would be a sustainable and profitable business for private‐sector service providers. Design/methodology/approach – – Monte‐Carlo analysis was used to evaluate the economic sustainability of a proposed public‐private waste transportation service. A GIS‐assisted route analysis was also performed, with participation by private and public sector stakeholders. Findings – – The analysis also showed that if a low‐cost subscription service is implemented in the area, the commercial service providers will operate at a loss in all cases, unless changes are made in the truck fuel efficiency, the operating hours of discharge sites, and the transportation network. Research limitations/implications – – The research was based on service provider operations costs extrapolated from previous studies and updated through informal interviews. A thorough and transparent review of cost accounting procedures is necessary to validate the results. Practical implications – – This study identified challenges and potential solutions which must be addressed by practitioners in order to ensure success of a subscription‐based service. Originality/value – – This study adds to the existing literature by demonstrating the applicability of an analytic modeling technique based on Monte‐Carlo simulation and provides an example of how academic research can be tightly coupled with practitioner needs in order to have a direct impact on operational humanitarian projects.

Among reactions with strong impact on classical novae model predictions, 30P(p,γ)31S is one of the few remained that are worthy to be measured accurately, because of their rate uncertainty, as like as 18F(p,α)15O and 25Al(pγ)26Si. To reduce the nuclear uncertainties associated to this reaction, we performed an experiment at ALTO facility of Orsay using the 31P(3He,t)31S reaction to populate 31S excited states of astrophysical interest and detect in coincidence the protons coming from the decay of the populated states in order to extract the proton branching ratios. After a presentation of the astrophysical context of this work, the current situation of the 30P(p,γ)31S reaction rate will be discussed. Then the experiment set-up of this work and the analysis of the single events will be presented.

Within the SEASTAR project at RIKEN-RIBF, 66Cr and 70,72Fe have been produced via protonknockout reactions, and their first excited 2+ and 4+ states have been discovered. The combination of the liquid-hydrogen target and TPC system MINOS has been used in combination with the DALI2 detector array for the first time. A 345 MeV/u 238U beam with a mean intensity of about 12 pnA impinged on a Be target. Fission fragments were separated and identified using the BigRIPS spectrograph, and reaction products were analyzed using the ZeroDegree spectrograph. A plateau of excitation energies, with a small change in the systematic trends past N = 44, reveals an extension of the N = 40 region of collectivity toward N = 50. Hence, the isotopes of interest are located within the N = 40 island of inversion. An interpretation of the observed trends is offered through large scale shell model calculations.