Miracle-dz/UniFAR-FTUs huggingface dataset:
Cognitive reappraisal of food craving and emotions: a coordinate-based meta-analysis of fMRI studies

Abstract Growing evidence supports the effectiveness of cognitive reappraisal in down-regulating food desire. Still, the neural bases of food craving down-regulation via reappraisal, as well as their degree of overlap vs specificity compared with emotion down-regulation, remain unclear. We addressed this gap through activation likelihood estimation meta-analyses of neuroimaging studies on the neural bases of (i) food craving down-regulation and (ii) emotion down-regulation, alongside conjunction and subtraction analyses among the resulting maps. Exploratory meta-analyses on activations related to food viewing compared with active regulation and up-regulation of food craving have also been performed. Food and emotion down-regulation via reappraisal consistently engaged overlapping activations in dorsolateral and ventrolateral prefrontal, posterior parietal, pre-supplementary motor and lateral posterior temporal cortices, mainly in the left hemisphere. Its distinctive association with the right anterior/posterior insula and left inferior frontal gyrus suggests that food craving down-regulation entails a more extensive integration of interoceptive information about bodily states and greater inhibitory control over the appetitive urge towards food compared with emotion down-regulation. This evidence is suggestive of unique interoceptive and motivational components elicited by food craving reappraisal, associated with distinctive patterns of fronto-insular activity. These results might inform theoretical models of food craving regulation and prompt novel therapeutic interventions for obesity and eating disorders.
URANIUM VANADIUM MINERALIZATION IN MISSISSIPPIAN AGED PALEOKARST, NORTHERN BIGHORN BASIN, MONTANA AND WYOMING, INDICATES A HYDROTHERMAL PERMIAN PHOSPHORIA FORMATION SOURCE OF METALS INCLUDING REE AND TL

(A) The 18O and  13C compositions distinguish Mississippian age host (B) Variable 87Sr/86Sr composition indicates the precipitation of minerals did not rock from the most depleted values of this study that plot in the oval. all form simultaneously from the same fluid. The LMD samples reflect a Mm The depleted  13C of may reflect carbon derived from contamination Sr source while the PMD reflect Pennsylvanian-Permian hosted fluids likely with with hydrocarbons supporting mixing with basin derived fluids. Phosphoria Formation sourced hydrocarbons mixing with the Madison host rock.
The mitotic checkpoint kinase BUB1 is a direct and actionable target of MYB in adenoid cystic carcinoma.

Adenoid cystic carcinoma (ACC) is a head and neck cancer that frequently originates in salivary glands, but can also strike other exocrine glands such as the breast. A key molecular alteration found in the majority of ACC cases is MYB gene rearrangements, leading to activation of the oncogenic transcription factor MYB. In this study, we used immortalised breast epithelial cells and an inducible MYB transgene as a model of ACC. Molecular profiling confirmed that MYB-driven gene expression causes a transition into an ACC-like state. Using this new cell model, we identified BUB1 as a targetable kinase directly controlled by MYB, whose pharmacological inhibition caused MYB-dependent synthetic lethality, growth arrest and apoptosis of patient-derived cells and organoids.
A New Multi-Atlas Based Deep Learning Segmentation Framework With Differentiable Atlas Feature Warping

Deep learning based multi-atlas segmentation (DL-MA) has achieved the state-of-the-art performance in many medical image segmentation tasks, e.g., brain parcellation. In DL-MA methods, atlas-target correspondence is the key for accurate segmentation. In most existing DL-MA methods, such correspondence is usually established using traditional or deep learning based registration methods at image level with no further feature level adaption. This could cause possible atlas-target feature inconsistency. As a result, the information from atlases often has limited positive and even counteractive impact on the final segmentation results. To tackle this issue, in this paper, we propose a new DL-MA framework, where a novel differentiable atlas feature warping module with a new smooth regularization term is presented to establish feature level atlas-target correspondence. Comparing with the existing DL-MA methods, in our framework, atlas features containing anatomical prior knowledge are more relevant to the target image feature, leading the final segmentation results to a high accuracy level. We evaluate our framework in the context of brain parcellation using two public MR brain image datasets: LPBA40 and NIREP-NA0. The experimental results demonstrate that our framework outperforms both traditional multi-atlas segmentation (MAS) and state-of-the-art DL-MA methods with statistical significance. Further ablation studies confirm the effectiveness of the proposed differentiable atlas feature warping module.
Characterization of Myocardial Injury With High-Sensitivity Troponin

Abstract Background: High-sensitivity troponin I, cardiac form (hs-cTnI) accelerates the assessment of acute coronary syndrome. Little has been documented about its performance, how it relates to different types of myocardial injury, and its impact on morbidity and mortality. This study sought to expand understanding of hs-cTnI by characterizing types of myocardial injury, the impact of comorbidities, and 30-day outcomes. Methods: The study retrospectively evaluated 1,975 patients with hs-cTnI levels obtained in the emergency department or inpatient setting from June to September 2020. Troponin was considered elevated if it was higher than the 99th percentile for either sex. Charts were reviewed to determine the presence of myocardial injury. Troponin elevation was adjusted for demographics, comorbidities, and kidney dysfunction. Thirty-day mortality and readmission rates were calculated. Results: Of 1,975 patients, 468 (24%) had elevated hs-cTnI, and 330 (17%) had at least 1 type of myocardial injury, type 2 myocardial infarction being the most frequent. Sensitivity and specificity using the 99th percentile as a cutoff were 99% and 92%, respectively. The average maximum hs-cTnI level was significantly higher for type 1 myocardial infarction (P < .001). Being male, Black, non-Hispanic, and a hospital inpatient were all associated with higher initial and peak hs-cTnI levels (P < .001). Elevated hs-cTnI level, age, heart disease, kidney dysfunction, and inpatient status were predictive of 30-day mortality on multivariate analysis. Conclusion: Elevated hs-cTnI levels in emergency department and inpatient settings occurs most commonly because of type 2 myocardial infarction. Maximum hs-cTnI level is associated with the patient's particular type of myocardial injury, certain demographics, and cardiovascular comorbidities, and it may be a predictor of 30-day outcomes.
Drug–drug interaction prediction: databases, web servers and computational models

Abstract In clinical treatment, two or more drugs (i.e. drug combination) are simultaneously or successively used for therapy with the purpose of primarily enhancing the therapeutic efficacy or reducing drug side effects. However, inappropriate drug combination may not only fail to improve efficacy, but even lead to adverse reactions. Therefore, according to the basic principle of improving the efficacy and/or reducing adverse reactions, we should study drug–drug interactions (DDIs) comprehensively and thoroughly so as to reasonably use drug combination. In this review, we first introduced the basic conception and classification of DDIs. Further, some important publicly available databases and web servers about experimentally verified or predicted DDIs were briefly described. As an effective auxiliary tool, computational models for predicting DDIs can not only save the cost of biological experiments, but also provide relevant guidance for combination therapy to some extent. Therefore, we summarized three types of prediction models (including traditional machine learning-based models, deep learning-based models and score function-based models) proposed during recent years and discussed the advantages as well as limitations of them. Besides, we pointed out the problems that need to be solved in the future research of DDIs prediction and provided corresponding suggestions.
Optimization of lung ventilation and perfusion in anesthetized horses using a ventilation mode with flow-limited expiration.

OBJECTIVE To investigate the mechanisms underlying the improved arterial oxygenation described with flow-limited expiration (FLEX) ventilation in anesthetized horses. ANIMALS 5 healthy adult research horses. METHODS Horses underwent volume-controlled ventilation for 60 minutes (VCV1), followed by 60 minutes of FLEX, and 30 minutes of VCV (VCV2). Main outcomes included the arterial partial pressure of oxygen-to-Fio2 (PF) ratio and electrical impedance tomography (EIT)-derived functional indices at the end of each phase. The EIT data were used to create regional maps of relative lung ventilation and perfusion as well as regional maps of ventilation/perfusion (V/Q) ratios. Ventilation indices derived from EIT included the fraction of expired volume in 1 second (FEV1; %) and the time it took for the EIT signal to drop to 50% of the peak signal at end-inspiration (TClose50; seconds). Data were analyzed with 2-way ANOVA for repeated measures. P < . 05 was considered significant. RESULTS The PF ratio increased significantly with FLEX compared to both VCV1 and VCV2 (P < .01). There were no differences in the relative distribution of ventilation nor perfusion between ventilation strategies. However, when ventilation and perfusion were superimposed and V/Q ratio maps were constructed, FLEX had a homogenizing effect toward values of 1.0. The FEV1 was shorter (P < .01) and the TClose50 was longer (P < .001) in all regions during FLEX compared to both VCV1 and VCV2. CLINICAL RELEVANCE Our findings suggest that FLEX ventilation in anesthetized horses enhances regional V/Q matching, likely by prolonging expiratory aeration and reducing airway closure.
River detection using LBP and morphology in infrared image

Detecting landscapes such as rivers, mountains or forests in the complex scenes is a challenging problem in the field of infrared image, which has been applied widely in military and civilian. This paper aims to detect the river from infrared images, presenting an approach that combines the local binary pattern (LBP) and morphology to extract the river. The LBP descriptor is highly discriminative, computationally efficient and stable for monotonous gray level images. In this paper, we propose an improved LBP descriptor, which adopts a radius of 1.5 pixels and uses every neighbor pixel of center to acquire more information. Firstly, we preprocess the data and extract the feature by the improved LBP descriptor. Then a combination of the threshold processing, filtering and morphological operator is used to emphasize the feature result. Finally, through connected component analysis, the maximum connected component is focused to detect the river in the infrared image. The performance of algorithm is tested on a set of images. Areas of the extracted river and time cost are measured as well.
Impact of preoperative chemotherapy on cutaneous wound healing in lung cancer patients: A meta‐analysis

Abstract As part of their treatment, lung cancer patients frequently endure thoracic oncological surgery, with preoperative chemotherapeutic interventions being the common approach. However, the potential impact of these chemotherapeutic regimens on cutaneous wound healing outcomes following surgery remains the topic of considerable clinical interest. This meta‐analysis sought to evaluate comprehensively the effect of preoperative chemotherapeutic regimens on cutaneous wound healing in lung cancer patients following thoracic oncological surgery. Extensive literature searches were conducted using the leading databases PubMed, Embase, Cochrane Library and Scopus. Eight studies out of 1342 identified satisfied the inclusion criteria. Consideration was given to both randomized controlled trials (RCTs) and observational studies. Data pertaining to study characteristics, patient demographics, chemotherapeutic regimens and wound healing outcomes were extracted with great attention to detail. The examination of these varied studies provided insights into the fluctuations in rates of recovery following treatment, incidences of wound infections and frequencies of surgical complications. The research studies provided odds ratios for recovery that varied significantly in magnitude from 0.95 to 0.38, with regard to the probability of wound infection. Furthermore, a range of odds ratios for complications were disclosed, with certain odds ratios displaying narrow confidence intervals. The complexity of the effect of preoperative chemotherapy on wound closure subsequent to thoracic oncologic surgery is highlighted by our findings. The results underscore the need for individualized treatment strategies for lung cancer patients undergoing surgical procedures that strike a balance between patient safety and optimal clinical outcomes.
Insulin initiation in patients with type 2 diabetes is often delayed, but access to a diabetes nurse may help—insights from Norwegian general practice

Abstract Objective: We opted to study how support staff operational capacity and diabetes competences may impact the timeliness of basal insulin-initiation in general practice patients with type 2 diabetes (T2D). Design/Setting/Outcomes: This was an observational and retrospective study on Norwegian primary care patients with T2D included from the ROSA4-dataset. Exposures were (1) support staff size, (2) staff size relative to number of GPs, (3) clinic access to a diabetes nurse and (4) share of staff with diabetes course (1 and 2 both relate to staff operational capacity, whereas 3 and 4 are both indicatory of staff diabetes competences). Outcomes were ‘timely basal insulin-initiation’ (primary) and ‘attainment of HbA1c<7%’ after insulin start-up (secondary). Associations were analyzed using multiple linear regression, and directed acyclic graphs guided statistical adjustments. Subjects: Insulin naïve patients with ‘timely’ (N = 294), ‘postponed’ (N = 219) or ‘no need of’ (N = 3,781) basal insulin-initiation, respectively. Results: HbA1c [median (IQR)] increased to 8.8% (IQR, 8.0, 10.2) prior to basal insulin-initiation, which reduced HbA1c to 7.3 (6.8–8.1) % by which only 35% of the subjects reached HbA1c <7%. Adjusted risk of ‘timely basal insulin-initiation’ was more than twofold higher if access to a diabetes nurse (OR = 2.40, [95%CI, 1.68, 3.43]), but related only vaguely to staff size (OR = 1.01, [95%CI, 1.00, 1.03]). No other staff factors related significantly to neither the primary nor the secondary outcome. Conclusion: In Norwegian general practice, insulin initiation in people with T2D may be affected by therapeutic inertia but access to a diabetes nurse may help facilitating more timely insulin start-up.
Arteriosclerosis, Thrombosis, and Vascular Biology

BACKGROUND: Thrombosis is a major complication after cardiac surgery in children with congenital heart disease. The mechanisms underlying thrombosis development remain poorly understood. We aimed to identify novel circulating metabolites before cardiac surgery that are associated with thrombosis after surgery in children with congenital heart disease. METHODS: In this prospective cohort study, all blood samples were drawn right before surgical incision and after the induction of anesthesia, and plasma was separated immediately under 4 °C. Untargeted metabolomic data were measured by Metabolon in plasma from children (age range, 0 days–18 years) with congenital heart disease undergoing cardiac surgery. The primary outcome was thrombosis within 30 days of surgery or before discharge. Associations of individual metabolites with thrombosis were assessed with logistic regression with false discovery rate correction for multiple comparison and adjustment for clinical characteristics; elastic net regression was used to select a prediction model. RESULTS: Out of 1115 metabolites measured in samples from 203 children, 776 met the quality control criteria. In total, 25 children (12.3%) developed thrombosis. Among the 776 metabolites, 175 were significantly associated with thrombosis (false discovery rate Q <0.05). The top 3 metabolites showing the strongest associations with thrombosis were eicosapentaenoate, stearidonate, and andro steroid monosulfate C 19 H 28 O 6 S (false discovery rate, 0.01 for all). Pathway analysis showed that the pathways of nicotinate and nicotinamide metabolism and glycerophospholipid metabolism were enriched (false discovery rate, 0.003 for both) and had significant impact on the development of thrombosis. In elastic net regression analysis, the area under the receiver operating-characteristic curve of a prediction model for thrombosis was 0.969 in the training sample (70% of the total sample) and 0.833 in the testing sample (the remaining 30%). CONCLUSIONS: We have identified promising novel metabolites and metabolic pathways associated with thrombosis. Future studies are warranted to confirm these findings and examine the mechanistic pathways to thrombosis. GRAPHIC ABSTRACT: A graphic abstract is available for this article.
Impact of different surgical and non-surgical interventions on health-related quality of life after thoracolumbar burst fractures without neurological deficit: protocol for a comprehensive systematic review with network meta-analysis

Introduction There is no international consensus on how to treat thoracolumbar burst fractures (TLBFs) without neurological deficits. The planned systematic review with network meta-analyses (NMA) aims to compare the effects on treatment outcomes, focusing on midterm health-related quality of life (HRQoL). Methods and analysis We will conduct a comprehensive and systematic literature search, identifying studies comparing two or more treatment modalities. We will search MEDLINE, EMBASE, Google Scholar, Scopus and Web of Science from January 2000 until July 2023 for publications. We will include (randomised and non-randomised) controlled clinical trials assessing surgical and non-surgical treatment methods for adults with TLBF. Screening of references, data extraction and risk of bias (RoB) assessment will be done independently by two reviewers. We will extract relevant studies, participants and intervention characteristics. The RoB will be assessed using the revised Cochrane RoB V.2. 0 tool for randomised trials and the Newcastle-Ottawa Scale for controlled trials. The OR for dichotomous data and standardised mean differences for continuous data will be presented with their respective 95% CIs. We will conduct a random-effects NMA to assess the treatments and determine the superiority of the therapeutic approaches. Our primary outcomes will be midterm (6 months to 2 years after injury) overall HRQoL and pain. Secondary outcomes will include radiological or clinical findings. We will present network graphs, forest plots and relative rankings on plotted rankograms corresponding to the treatment rank probabilities. The ranking results will be represented by the area under the cumulative ranking curve. Analyses will be performed in Stata V.16. 1 and R. The quality of the evidence will be evaluated according to the Grading of Recommendations, Assessment, Development and Evaluations framework. Ethics and dissemination Ethical approval is not required. The research will be published in a peer-reviewed journal.
Searching Dark Photons using displaced vertices at Belle II -- with backgrounds

Dark photons in the MeV to GeV range with kinetic mixing of the order of $\lesssim 10^{-4}-10^{-3}$ can be produced in significant numbers at low energy colliders such as Belle II. Their decay length can be macroscopic raising the hope for a fairly clean search via displaced vertices as proposed in Ref. [1]. However, even this is not background free. Here, we calculate and discuss problematic backgrounds from displaced photon conversion and discuss their potential impact on the sensitivity. In addition we also briefly consider the dangers of prompt backgrounds.
FiFAR: A Fraud Detection Dataset for Learning to Defer

Public dataset limitations have significantly hindered the development and benchmarking of learning to defer (L2D) algorithms, which aim to optimally combine human and AI capabilities in hybrid decision-making systems. In such systems, human availability and domain-specific concerns introduce difficulties, while obtaining human predictions for training and evaluation is costly. Financial fraud detection is a high-stakes setting where algorithms and human experts often work in tandem; however, there are no publicly available datasets for L2D concerning this important application of human-AI teaming. To fill this gap in L2D research, we introduce the Financial Fraud Alert Review Dataset (FiFAR), a synthetic bank account fraud detection dataset, containing the predictions of a team of 50 highly complex and varied synthetic fraud analysts, with varied bias and feature dependence. We also provide a realistic definition of human work capacity constraints, an aspect of L2D systems that is often overlooked, allowing for extensive testing of assignment systems under real-world conditions. We use our dataset to develop a capacity-aware L2D method and rejection learning approach under realistic data availability conditions, and benchmark these baselines under an array of 300 distinct testing scenarios. We believe that this dataset will serve as a pivotal instrument in facilitating a systematic, rigorous, reproducible, and transparent evaluation and comparison of L2D methods, thereby fostering the development of more synergistic human-AI collaboration in decision-making systems. The public dataset and detailed synthetic expert information are available at: https://github.com/feedzai/fifar-dataset
Particle Gibbs for Likelihood-Free Inference of State Space Models with Application to Stochastic Volatility

State space models (SSMs) are widely used to describe dynamic systems. However, when the likelihood of the observations is intractable, parameter inference for SSMs cannot be easily carried out using standard Markov chain Monte Carlo or sequential Monte Carlo methods. In this paper, we propose a particle Gibbs sampler as a general strategy to handle SSMs with intractable likelihoods in the approximate Bayesian computation (ABC) setting. The proposed sampler incorporates a conditional auxiliary particle filter, which can help mitigate the weight degeneracy often encountered in ABC. To illustrate the methodology, we focus on a classic stochastic volatility model (SVM) used in finance and econometrics for analyzing and interpreting volatility. Simulation studies demonstrate the accuracy of our sampler for SVM parameter inference, compared to existing particle Gibbs samplers based on the conditional bootstrap filter. As a real data application, we apply the proposed sampler for fitting an SVM to S&P 500 Index time-series data during the 2008 financial crisis.