Miracle-dz/UniFAR-FTUs huggingface dataset:
Barriers and Enablers to the Adoption of a Healthier Diet Using an App: Qualitative Interview Study With Patients With Type 2 Diabetes Mellitus

Background Adopting a healthy diet is one of the cornerstones of type 2 diabetes (T2D) management. Apps are increasingly used in diabetes self-management, but most studies to date have focused on assessing their impact in terms of weight loss or glycemic control, with limited evidence on the behavioral factors that influence app use to change dietary habits. Objective The main objectives of this study were to assess the enablers and barriers to adopting a healthier diet using the Gro Health app in 2 patient groups with T2D (patients with recently diagnosed and long-standing T2D) and to identify behavior change techniques (BCTs) to enhance enablers and overcome barriers. Methods Two semistructured qualitative interview studies were conducted; the first study took place between June and July 2021, with a sample of 8 patients with recently diagnosed (<12 mo) T2D, whereas the second study was conducted between May and June 2022 and included 15 patients with long-standing (>18 mo) T2D. In both studies, topic guides were informed by the Capability, Opportunity, Motivation, and Behavior model and the Theoretical Domains Framework. Transcripts were analyzed using a combined deductive framework and inductive thematic analysis approach. The Behavior Change Wheel framework was applied to identify appropriate BCTs that could be used in future iterations of apps for patients with diabetes. Themes were compared between the patient groups. Results This study identified similarities and differences between patient groups in terms of enablers and barriers to adopting a healthier diet using the app. The main enablers for recently diagnosed patients included the acquired knowledge about T2D diets and skills to implement these, whereas the main barriers were the difficulty in deciding which app features to use and limited cooking skills. By contrast, for patients with long-standing T2D, the main enablers included knowledge validation provided by the app, along with app elements to help self-regulate food intake; the main barriers were the limited interest paid to the content provided or limited skills engaging with apps in general. Both groups reported more enablers than barriers to performing the target behavior when using the app. Consequently, BCTs were selected to address key barriers in both groups, such as simplifying the information hierarchy in the app interface, including tutorials demonstrating how to use the app features, and redesigning the landing page of the app to guide users toward these tutorials. Conclusions Patients with recently diagnosed and long-standing T2D encountered similar enablers but slightly different barriers when using an app to adopting a healthier diet. Consequently, the development of app-based approaches to adopt a healthier diet should account for these similarities and differences within patient segments to reduce barriers to performing the target behavior.
Machine Learning Models for Predicting Early Neurological Deterioration and Risk Classification of Acute Ischemic Stroke

This study aimed to create machine learning models for predicting early neurological deterioration and risk classification in acute ischemic stroke (AIS) before intravenous thrombolysis (IVT). The study included 704 AIS patients categorized into END and non-END groups. The least absolute shrinkage and selection operator (LASSO) regression was employed to select the best predictors from clinical indicators, leading to the creation of Model 1. Univariate and multivariate logistic regression analyses identified independent predictive factors for END from inflammatory cell ratios. These factors were combined with clinical indicators, forming Model 2. Receiver operating characteristic (ROC) curves assessed the models’ predictive performance. Key variables for Model 1 included the NIHSS score, systolic blood pressure, and lymphocyte percentage. Neutrophil-to-Lymphocyte ratio, Platelet-to-Neutrophil ratio, and Platelet-to-Lymphocyte ratio independently predicted END. Model 1 exhibited moderate predictive ability (AUC 0.721 in training, AUC 0.635 in test). Model 2, which integrated clinical indicators and inflammatory cell ratios, demonstrated strong performance in both training (AUC 0.862) and test (AUC 0.816). Machine learning models, combining clinical indicators and inflammatory cell ratios before IVT, accurately predict END and associated risk in AIS.
The Effect of GOSAT Observations on Estimates of Net CO2 Flux in Semi-Arid Regions of the Southern Hemisphere

Greenhouse gases Observing SATellite (GOSAT) is the operational satellite dedicated to atmospheric CO2 observations. Assimilation of data provided by GOSAT is expected to yield reliable CO2 fluxes in semi-arid regions because of frequent observations owing to clear skies. Here we estimated net CO2 flux over semi-arid regions of the Southern Hemisphere using the GOSAT column averaged CO2 (XCO2) and surface CO2 measurements. Assimilation of GOSAT XCO2 indicated that semi-arid regions are integral components of recent terrestrial CO2 uptake, accounting for 44% globally. Compared with estimates assimilated from surface measurements, estimates by GOSAT XCO2 suggest a 50% reduction in the semi-arid CO2 uptake, amounting to 1.1 Pg C yr . Significant estimation differences occurred for South America and South Africa, where the GOSAT makes frequent measurements but where surface CO2 measurements are limited. In comparison, the two estimates varied less in Australia, where more surface measurements are available. These results suggest that GOSAT XCO2 is effective at regulating excess estimates of semi-arid CO2 uptake in regions that are less constrained by surface CO2 measurements. To promote understanding of climate change effects in semi-arid regions, it is important to continue monitoring trends in CO2 uptake with GOSAT. (Citation: Kondo, M., T. Saeki, H. Takagi, K. Ichii, and K. Ishijima, 2016: The effect of GOSAT observations on estimates of net CO2 flux in semi-arid regions of the Southern Hemisphere. SOLA, 12, 181−186, doi:10.2151/sola.2016-037.)
Prognostic nomogram and epidemiological analysis for lung atypical carcinoid: A SEER database and external validation study

Abstract Purpose Our study aims to delineate the epidemiological distribution of pulmonary carcinoids, including atypical carcinoid (AC) and typical carcinoid (TC), identify independent prognostic factors, develop an integrative nomogram and examine the effects of various surgical modalities on atypical carcinoid‐specific survival (ACSS). Methods Joinpoint regression model and age‐group distribution diagram were applied to determine the epidemiological trend of the pulmonary carcinoids. Univariate and least absolute shrinkage and selection operator (LASSO)‐based Cox regression models were used to identify independent factors, and a nomogram and web‐based predictor were developed to evaluate prognosis of AC patients individually. We performed Kaplan–Meier survival analyses to compare the scope of various surgical interventions, with and without G‐computation adjustment, utilising restricted mean survival time (RMST) to assess survival disparities. Results A total of 1132 patients were recruited from the Surveillance, Epidemiology, and End Results database (SEER) and a separate medical centre in China. The mean age of AC patients was 63.4 years and a smoking history was identified in 79.8% of AC patients. Joinpoint analysis shows rising annual rates of new AC and carcinoid cases among lung cancers. Both the proportion of pulmonary TC and AC within the total lung cancer population exhibits an L‐shaped trend across successive age groups. The nomogram predicted 1, 3 and 5 years of AC with excellent accuracy and discrimination. Kaplan–Meier survival analyses, conducted both pre‐ and post‐adjustment, demonstrated that sublobar resection's survival outcomes were not inferior to those of lobectomy in patients with stage I‐II and stage III disease. Conclusion This study is the first to reveal epidemiological trends in pulmonary carcinoids over the past decade and across various age cohorts. For patients with early‐stage AC, sublobar resection may be a viable surgical recommendation. The established nomogram and web‐based calculator demonstrated decent accuracy and practicality.
Overview of Levels of Organochlorine Pesticides in Surface Water and Food Items in Nigeria

Consumption of contaminated food is one of the main routes of human exposure to pesticides. Also, surface water plays an important role in long range transport of pesticides. Persistent organic pesticides are categories under Persistent Organic Pollutants (POPs) which are organic compounds that are resistant to environmental degradation and as a result of their persistence, bioaccumulate with potential significant impacts on human health and the environment. The persistent organochlorine pesticides (OCPs) have been listed by Stockholm Convention for elimination globally. Despite the ban, some OCPs are still used illegally in developing countries. Also, from other sources, OCPs find their way into water bodies. This review therefore investigates the current levels of OCPs in surface water and food items in Nigeria and environmental/human health implications. The methodology was mainly by review of available literatures. The results were discussed in the context of criteria that designate a substance as POP, as well as in the context of allowed limit levels. Levels reported from several studies in Nigeria exceeded the water-quality criteria for surface water and maximum residue limit for food. The illegal use may be the likely sources of these pollutants in the Nigerian environment. Aquatic organisms stand the risk of acute or chronic toxicity while human health is at risk of adverse effect through consumption of contaminated food or use of polluted surface water for domestic activities. There is need for enforcement of appropriate policy to safeguard the environment and human health. Keywords: organochlorine pesticides, persistent organic pollutants, surface water, food items
Satellite observations of energy‐banded ions during large geomagnetic storms: Event studies, statistics, and comparisons to source models

Energy‐banded ions from tens to ten thousands of eV are observed in the low‐latitude auroral and subauroral zones during every large (minimum Dst < −150 nT) geomagnetic storm encountered by the FAST satellite. The banded ions persist for many FAST orbits, lasting up to 12 h, in both the northern and southern hemispheres. The energy‐banded ions often have more than six distinct bands, and the O+, He+, and H+ bands are often observed at the same energies. The bands are extensive in latitude (~50–75° on the dayside, often extending to 45°) and magnetic local time, covering all magnetic local time over the data set of storms. The distributions are peaked in the perpendicular direction at the altitudes of the FAST satellite (~350–4175 km), although in some cases the precipitating component dominates for the lowest energy bands. At the same time, for some of the events studied in detail, long‐lasting intervals of field‐aligned energy dispersed ions from ~100 eV to 40 keV are seen in Los Alamos National Laboratory geosynchronous observations, primarily on the dayside and after magnetosheath encounters (i.e., highly compressed magnetosphere). We present both case and statistical studies of the banded ions. These bands are a new phenomenon associated with all large storms, which are distinctly different from other banded populations, and are not readily interpreted using previous models for particle sources, transport, and loss. The energy‐banded ions are an energetically important component of the inner magnetosphere during the most intense magnetic storms.
Do We Need New Electrocardiographic Criteria for Left Ventricular Hypertrophy? The Case of the Peguero–Lo Presti Criterion. A Narrative Review

Abstract The cardiovascular risk associated with left ventricular hypertrophy (LVH) in the community and, particularly, in the hypertensive fraction of the general population, represents the rationale for its timely and accurate identification in order to implement adequate preventive strategies. Although electrocardiography (ECG) is the first-line and most economical method of diagnosing LVH its accuracy is largely suboptimal. Over the last 70 years, dozens of different ECG criteria, mostly based on measurements of QRS voltages, have been proposed. In this long journey, a few years ago Peguero et al. developed a novel ECG voltage criterion, currently recognized as Peguero–Lo Presti (PLP) suggesting that it has greater sensitivity than traditional ECG-LVH criteria. Considering that in the last 5 years numerous studies have investigated the diagnostic value of this new index, this review aimed to summarize the data published so far on this topic focusing both on the accuracy in identifying the presence of LVH compared with imaging techniques such as echocardiography (ECHO) and magnetic resonance imaging (MRI) and the value in predicting hard outcomes. The evidence in favor of the greater diagnostic accuracy of the PLP criterion in detecting LVH, phenotyped by ECHO or MRI, and in the stratification of hard outcomes compared with traditional ECG criteria does not appear to be sufficiently proven. Given that the diagnosis of LVH by all ECG criteria (including the PLP) exclusively based on the QRS amplitude is largely imprecise, the development of new multiparametric ECG criteria based on artificial intelligence could represent a real improvement in the diagnostic capacity of the ECG.
Efficacy of different AV7909 dose regimens in a nonclinical model of pulmonary anthrax

ABSTRACT Pulmonary anthrax caused by exposure to inhaled Bacillus anthracis, the most lethal form of anthrax disease, is a continued military and public health concern for the United States. The vaccine AV7909, consisting of the licensed anthrax drug substance AVA adjuvanted with CpG7909, induces high levels of toxin neutralizing antibodies in healthy adults using fewer doses than AVA. This study compares the ability of one- or two-dose regimens of AV7909 to induce a protective immune response in guinea pigs challenged with a lethal dose of aerosolized B. anthracis spores 6 weeks after the last vaccine dose. The results indicated that AV7909 was less effective when delivered as a single dose compared to the two-dose regimen that resulted in dose-dependent protection against death. The toxin neutralizing assay (TNA) titer and anti-PA IgG responses were proportional to the protective efficacy, with a 50% TNA neutralizing factor (NF50) greater than 0.1 associated with survival in animals receiving two doses of vaccine. The strong protection at relatively low TNA NF50 titers in this guinea pig model supports the exploration of lower doses in clinical trials to determine if these protective levels of neutralizing antibodies can be achieved in humans; however, protection with a single dose may not be feasible.
A History of Endoscopic Lumbar Spine Surgery: What Have We Learnt?

The new development and finally the general acceptance of surgical techniques among the worldwide surgical community sometimes create fascinating stories. This is also true for the history of endoscopic lumbar spine surgery. In the last 100 years there was a “natural” evolution of surgical techniques with continuous improvement and “refinement” of lumbar decompression techniques towards less invasive operations with the final “endpoint” of microsurgery. However the application of percutaneous, image-guided, and endoscopic technologies has revolutionized minimally invasive surgery. This article describes the history of endoscopic lumbar spine surgery and its major milestones and protagonists which have helped to make endoscopic lumbar spine surgery “disruptive” minimally invasive surgical technology which has changed the world of lumbar decompression surgery. “The past is the mother of the future” Henri Cartier Bresson, French Photographer, 1908-2004
Clonal Hematopoiesis of Indeterminate Potential in Chronic Thromboembolic Pulmonary Hypertension: A Multicenter Study

BACKGROUND: The pathogenesis of chronic thromboembolic pulmonary hypertension (CTEPH) is multifactorial and growing evidence has indicated that hematological disorders are involved. Clonal hematopoiesis of indeterminate potential (CHIP) has recently been associated with an increased risk of both hematological malignancies and cardiovascular diseases. However, the prevalence and clinical relevance of CHIP in patients with CTEPH remains unclear. METHODS: Using stepwise calling on next-generation sequencing data from 499 patients with CTEPH referred to 3 centers between October 2006 and December 2021, CHIP mutations were identified. We associated CHIP with all-cause mortality in patients with CTEPH. To provide insights into potential mechanisms, the associations between CHIP and inflammatory markers were also determined. RESULTS: In total, 47 (9.4%) patients with CTEPH carried at least 1 CHIP mutation at a variant allele frequency of ≥2%. The most common mutations were in DNMT3A, TET2, RUNX1, and ASXL1. During follow-up (mean, 55 months), deaths occurred in 22 (46.8%) and 104 (23.0%) patients in the CHIP and non-CHIP groups, respectively (P<0.001, log-rank test). The association of CHIP with mortality remained robust in the fully adjusted model (hazard ratio, 2.190 [95% CI, 1.257–3.816]; P=0.006). Moreover, patients with CHIP mutations showed higher circulating interleukin-1β and interleukin-6 and lower interleukin-4 and IgG galactosylation levels. CONCLUSIONS: This is the first study to show that CHIP mutations occurred in 9.4% of patients with CTEPH are associated with a severe inflammatory state and confer a poorer prognosis in long-term follow-up.
Optimizing Excipient Properties to Prevent Aggregation in Biopharmaceutical Formulations

Excipients are included within protein biotherapeutic solution formulations to improve colloidal and conformational stability but are generally not designed for the specific purpose of preventing aggregation and improving cryoprotection in solution. In this work, we have explored the relationship between the structure and antiaggregation activity of excipients by utilizing coarse-grained molecular dynamics modeling of protein–excipient interaction. We have studied human serum albumin as a model protein, and we report the interaction of 41 excipients (polysorbates, fatty alcohol ethoxylates, fatty acid ethoxylates, phospholipids, glucosides, amino acids, and others) in terms of the reduction of solvent accessible surface area of aggregation-prone regions, proposed as a mechanism of aggregation prevention. Polyoxyethylene sorbitan had the greatest degree of interaction with aggregation-prone regions, decreasing the solvent accessible surface area of APRs by 20.7 nm2 (40.1%). Physicochemical descriptors generated by Mordred are employed to probe the structure–property relationship using partial least-squares regression. A leave-one-out cross-validated model had a root-mean-square error of prediction of 4.1 nm2 and a mean relative error of prediction of 0.077. Generally, longer molecules with a large number of alcohol-terminated PEG units tended to interact more, with qualitatively different protein interactions, wrapping around the protein. Shorter or less ethoxylated compounds tend to form hemimicellar clusters at the protein surface. We propose that an improved design would feature many short chains of 5 to 10 PEG units in many distinct branches and at least some hydrophobic content in the form of medium-length or greater aliphatic chains (i.e., six or more carbon atoms). The combination of molecular dynamics simulation and quantitative modeling is an important first step in an all-purpose protein-independent model for the computer-aided design of stabilizing excipients.
On the Cheeger inequality in Carnot-Carath\'eodory spaces

We generalize the Cheeger inequality, a lower bound on the first nontrivial eigenvalue of a Laplacian, to the case of geometric sub-Laplacians on rank-varying Carnot-Carath\'eodory spaces. The proof is geometric, and works for Dirichlet, Neumann and mixed boundary conditions. One of the main technical tools in the proof is a generalization of Courant's nodal domain theorem, which is proven from scratch for Neumann and mixed boundary conditions. Carnot groups and the Baouendi-Grushin cylinder are treated as examples.
First-Order Phase Transition of the Schwinger Model with a Quantum Computer

We explore the first-order phase transition in the lattice Schwinger model in the presence of a topological $\theta$-term by means of the variational quantum eigensolver (VQE). Using two different fermion discretizations, Wilson and staggered fermions, we develop parametric ansatz circuits suitable for both discretizations, and compare their performance by simulating classically an ideal VQE optimization in the absence of noise. The states obtained by the classical simulation are then prepared on the IBM's superconducting quantum hardware. Applying state-of-the art error-mitigation methods, we show that the electric field density and particle number, observables which reveal the phase structure of the model, can be reliably obtained from the quantum hardware. To investigate the minimum system sizes required for a continuum extrapolation, we study the continuum limit using matrix product states, and compare our results to continuum mass perturbation theory. We demonstrate that taking the additive mass renormalization into account is vital for enhancing the precision that can be obtained with smaller system sizes. Furthermore, for the observables we investigate we observe universality, and both fermion discretizations produce the same continuum limit.
D3Former: Jointly Learning Repeatable Dense Detectors and Feature-enhanced Descriptors via Saliency-guided Transformer

Establishing accurate and representative matches is a crucial step in addressing the point cloud registration problem. A commonly employed approach involves detecting keypoints with salient geometric features and subsequently mapping these keypoints from one frame of the point cloud to another. However, methods within this category are hampered by the repeatability of the sampled keypoints. In this paper, we introduce a saliency-guided trans\textbf{former}, referred to as \textit{D3Former}, which entails the joint learning of repeatable \textbf{D}ense \textbf{D}etectors and feature-enhanced \textbf{D}escriptors. The model comprises a Feature Enhancement Descriptor Learning (FEDL) module and a Repetitive Keypoints Detector Learning (RKDL) module. The FEDL module utilizes a region attention mechanism to enhance feature distinctiveness, while the RKDL module focuses on detecting repeatable keypoints to enhance matching capabilities. Extensive experimental results on challenging indoor and outdoor benchmarks demonstrate that our proposed method consistently outperforms state-of-the-art point cloud matching methods. Notably, tests on 3DLoMatch, even with a low overlap ratio, show that our method consistently outperforms recently published approaches such as RoReg and RoITr. For instance, with the number of extracted keypoints reduced to 250, the registration recall scores for RoReg, RoITr, and our method are 64.3\%, 73.6\%, and 76.5\%, respectively.