Miracle-dz/UniFAR-FTUs huggingface dataset:
Process and effect evaluation of the app-based parenting program Samen Happie! on infant zBMI: A randomized controlled trial

Background Although energy balance-related parenting practices are regarded critical components in the prevention of childhood obesity, most programs targeting parenting practices with respect to a wide range of energy balance-related behaviors were not aimed at high-risk families with a lower socioeconomic position (SEP). Objective The Samen Happie! app-based program aimed to stimulate healthy child weight development especially among families with a lower SEP, by encouraging healthy energy balance-related parenting practices. Methods A two-armed randomized controlled trial examined the process and effectiveness of the Samen Happie! program on child zBMI outcomes at 6- and 12-months follow-up. In total, 357 Dutch parents with infants aged 5–15 months old at baseline participated. Parents in the app condition (n = 179) received access to the Samen Happie! app and were compared to a waitlist-control condition (n = 178). Changes in zBMI were examined through linear mixed-effects models based on intention-to-treat and exploratory per-protocol principles. Results Process data showed low levels of sustained app use and moderate app acceptability. A general increase in child zBMI was observed in both conditions after 6 and 12 months. Intention-to-treat analyses using multiple imputations showed several statistically significant differences between conditions and high-risk subgroups. Specifically, at 6-months follow-up, zBMI increase was least pronounced in the app condition among children of parents with lower educational level. These findings were supported by exploratory per-protocol analyses including only frequent app users. In addition, per-protocol analyses showed benefits of app use at 6-months follow-up for children of parents with higher BMI. However, these effects were reversed at 12-months follow-up in both intention-to-treat and per-protocol analyses, where children of parents in the app condition in general increased the most in zBMI. Conclusions This study suggests that the Samen Happie! program might prevent zBMI increases after 6 months among children of parents with lower educational level, and children of parents with higher BMI who more frequently use the app. However, the app did not prevent increases in zBMI after 12 months. Future research should investigate strategies to increase sustained app use and engagement in mHealth parenting programs for childhood obesity as well as options to combine app-based programs with additional support strategies aimed at high-risk families. Trial registration Netherlands trial register (ID: NTR6938), https://trialsearch.who. int/Trial2.aspx? TrialID=NTR6938.
Recent development in the preservation effect of lactic acid bacteria and essential oils on chicken and seafood products

Chicken and seafood are highly perishable owing to the higher moisture and unsaturated fatty acids content which make them more prone to oxidation and microbial growth. In order to preserve the nutritional quality and extend the shelf-life of such products, consumers now prefer chemical-free alternatives, such as lactic acid bacteria (LAB) and essential oils (EOs), which exert a bio-preservative effect as antimicrobial and antioxidant compounds. This review will provide in-depth information about the properties and main mechanisms of oxidation and microbial spoilage in chicken and seafood. Furthermore, the basic chemistry and mode of action of LAB and EOs will be discussed to shed light on their successful application in chicken and seafood products. Metabolites of LAB and EOs, either alone or in combination, inhibit or retard lipid oxidation and microbial growth by virtue of their principal constituents and bioactive compounds including phenolic compounds and organic acids (lactic acid, propionic acid, and acetic acid) and others. Therefore, the application of LAB and EOs is widely recognized to extend the shelf-life of chicken and seafood products naturally without altering their functional and physicochemical properties. However, the incorporation of any of these agents requires the optimization steps necessary to avoid undesirable sensory changes. In addition, toxicity risks associated with EOs also demand the regularization of an optimum dose for their inclusion in the products.
An updated meta-analysis of device related thrombus following left atrial appendage closure in patients with atrial fibrillation

Aims Device related thrombus (DRT) is a known complication of left atrial appendage closure (LAAC). However, the relation between DRT and elevated risk of ischemic events remains controversial. This study is sought to reassessed the incidence of DRT following LAAC and the relation between DRT and elevated risk of ischemic stroke and systemic embolism (SE) with latest clinical trials included. Methods The PubMed, Embase, and Cochrane Library databases were systematically searched from their inception until April 2022 for studies that reported the incidence of DRT and compared the incidence of both stroke and SE between DRT patients and non-DRT patients. Results In 59 eligible studies, the incidence of DRT was 366/12,845 (2.8%, ranging from 0 to 11%, I2 = 64%). The incidence of DRT was not statistically different between single-seal device (SS) and dual-seal device (DS) in subgroup analysis [171/6,190 (2.8%) vs. 78/3,023 (3.6%); p = 0.93]. The pooled incidence of stroke (26 studies, 7,827 patients) in patients with and without DRT was 11.5% in DRT patients and 2.9% among non-DRT patients (OR: 5.08; 95% CI = 3.47–7.44). In the sensitivity analysis, DRT was associated with higher rate of stroke (12.1 vs. 3.2%; OR: 4.14; 95% CI = 2.69–6.38) and SE (16.0 vs. 3.8%; OR: 4.48; 95% CI = 3.04–6.62). Conclusion The incidence of DRT was low and similar between SS and DS devices. DRT was associated with increased rates of ischemic events. The occurrence rate of ischemic events associated DRT was comparable between two occlusion mechanism devices. Systematic review registration [https://www.crd. york.ac. uk/], identifier [CRD42022326179].
Time-varying dimension analysis of EEG using adaptive principal component analysis and model selection

Presents a new approach to the analysis of nonstationary possibly nonlinear time series. It is based on an adaptive autocorrelation eigenspectrum computation known as APEX together with a model selection rule. New concepts of stochastic instantaneous embedding dimension and time averaged instantaneous embedding dimension are introduced. The motivation for this new approach is the analysis of newborn electroencephalogram for which nonstationarity is a crucial property. Experimental data are analyzed using the proposed scheme.
Arterial site selection for measurement of mean arterial pressure in septic shock patients on high-dose norepinephrine

Background The guidelines of the Surviving Sepsis Campaign suggest using invasive blood pressure (IBP) measurement in septic shock patients, without specifying for a preferred arterial site for accuracy in relation to the severity of septic shock. The objective of this study was to determine the mean arterial pressure (MAP) gradient between the femoral and radial artery sites in septic shock patients. Method This prospective study was carried out at a 20-bed ICU in a university hospital. Simultaneous MAP measurements at femoral and radial arterial sites were obtained in septic shock patients receiving norepinephrine (≥0.1 μg/kg/min), with a pre-planned subgroup analysis for those receiving a high dose of norepinephrine (≥0.3 μg/kg/min). Results The median norepinephrine dose across all 80 patients studied, including 59 patients on a high dose, was 0.4 (0.28–0.7) μg/kg/min. Overall, simultaneous measurement of MAP (mmHg) at the femoral and radial arterial sites produced mean (95% CI) MAP values of 81 (79–83) and 78 (76–80), respectively, with a mean difference of 3.3 (2.67–3.93), p < 0.001. In Bland–Altman analysis of MAP measurements, the detected effect sizes were 1.14 and 1.04 for the overall and high-dose cohorts, respectively, which indicates a significant difference between the measurements taken at each of the two arterial sites. The Pearson correlation coefficient indicated a weak but statistically significant correlation between MAP gradient and norepinephrine dose among patients receiving a high dose of norepinephrine (r = 0.289; p = 0.026; 95% CI 0.036–0.508). Conclusion In septic shock patients, MAP readings were higher at the femoral site than at the radial site, particularly in those receiving a high dose of norepinephrine. Clinical trial registration [ClinicalTrials.gov], identifier [NCT03475667].
The association between problematic smartphone use and the severity of temporomandibular disorders: A cross-sectional study

Objective To evaluate the prevalence of different types of temporomandibular disorders (TMD) symptoms in young adults and determine their associations with problematic smartphone use (PSU). Methods The data of the study were collected from local university students through an online questionnaire survey. Demographic information, Fonseca Anamnestic Index (FAI), Smartphone Addiction Scale-Short Version (SAS-SV), and Patient Health Questionnaire-4 (PHQ-4) responses were gathered electronically and analyzed using multiple logistic regression analysis. Results There were 163 male and 307 female respondents were participated in this study. The prevalence of PSU and TMD were 83.6% and 66.4%, respectively. There was a moderate statistical correlation between PSU and TMD among young adults (r = 0.31, p < 0.01). The logistic regression model revealed that the risk of TMD was 1.77 times higher in people with PSU than in those without PSU (OR = 1.77; 95% CI 1.04–3.06). PSU is a risk factor for pain-related TMD (OR = 1.81; 95% CI 1.08–3.04) but not intra-articular TMD. Conclusion Subjects showed high prevalence of both TMD and PSU. People with PSU experienced more severe and frequent pain-related rather than intra-articular TMD symptoms than those without PSU. By reducing the problematic smartphone use, the risk factor of TMD might be avoided.
Case report: Gemcitabine intravesical hyperthermic infusion combined with tislelizumab in muscle invasive bladder urothelium carcinoma

Background Muscle invasive bladder urothelium carcinoma is a common urinary tract tumor. With the deepening of research, more and more treatment methods are applied in clinical practice, extending the life of patients. Among them, the clinical application of chemotherapeutic intravesical hyperthermia and tumor immunotherapy provides new ideas for our treatment. Case report An 81-year-old female patient was diagnosed with stage T2N0M0 bladder cancer in our hospital. Because the patient and her family were keen to preserve her bladder, they declined surgery and opted for combined chemotherapy. After informed consent from the patient and her family, she received cisplatin combined with gemcitabine intravesical hyperthermic infusion. But the side effects of cisplatin made her intolerable to chemotherapy. With their informed consent we changed her to intravenous tislelizumab in combination with gemcitabine intravesical hyperthermic infusion to continue her treatment. During the subsequent follow-up visits, we found a surprising effect of the treatment. Conclusion Gemcitabine intravesical hyperthermia therapy combined with intravenous tislelizumab in the treatment of muscle invasive bladder urothelium carcinoma may provide a new possible therapeutic strategy of some patients who are inoperable or refuse surgery.
Religion and Spirituality Among LGBTQ Youth

Religion and spirituality play important roles in the lives of many Americans. Overall, 68% of young adults ages 18 29 in the United States report that they believe in God, and 30% report that they believe God hears people’s prayers and can intervene (Jones, 2022 . Numerous studies have documented the ways that religion and spirituality are associated with positive mental health and healthy development among adolescents, including prosocial behavior and be er physical health (Hardy et al., 2019; Canda et al., 2019; Koenig, 2018; Smith & Denton, 2005; Yonker et al., 2012; Nadal et al., 2018 . Despite this, religion and spirituality can be fraught topics among LGBTQ communities due to historical anti-LGBTQ sentiments among many religious groups. Religious dogma has been used to dehumanize LGBTQ people, and LGBTQ people have often been excluded from religious spaces, ceremonies, and communities (Sherkat, 2019; Taylor & Cuthbert, 2019 . While some American religious communities are shifting toward a irming and celebrating their LGBTQ members (Barton & Currier, 2019; Rodriguez & Etengo , 2016 , significant strides remain to ensure LGBTQ inclusion in many religious communities and spaces. Perhaps due to this historical exclusion and dehumanization, religion and spirituality have mixed associations with mental health among LGBTQ youth and adults. While, LGBQ young adults who report that religion is important to them also report higher odds of recent suicidal ideation (Lytle et al., 2018 , LGB adults who report that they left their religion due to conflict with their sexual identity report higher odds of a empting suicide, compared to their peers who had no conflict between their faith and sexuality (Gibbs et al., 2015 , suggesting the mental health impact is related to how they are treated in these spaces. Historical conflicts between religious communities and LGBTQ people have created a common misperception that LGBTQ people cannot be religious or spiritual. Using data from The Trevor Project’s 2022 National Survey on LGBTQ Youth Mental Health, this brief sheds light on the importance of religion and spirituality in the lives of LGBTQ youth.
bacterial consortium : 8 Fermentation characteristics and protozoal population in vitro 9 Running title : Interaction between Entodinium caudatum and an amino acid-fermenting bacteria 10 11

Authors' contributions Please specify the authors’ role using this form. Conceptualization: Park T, Yu Z. Data curation: Park T. Formal analysis: Park T. Methodology: Park T, Yu Z. Software: Park T. Validation: Park T, Yu Z. Investigation: Park T, Yu Z. Writing original draft: Park T. Writing review & editing: Park T, Yu Z. Ethics approval and consent to participate This article does not require IRB/IACUC approval because no human and animal participants are involved.
Estimating Spillovers from Publicly Funded R&D: Evidence from the US Department of Energy †

We quantify the magnitude of R&D spillovers created by grants to small firms from the US Department of Energy. Our empirical strategy leverages variation due to state-specific matching policies , and we develop a new approach to measuring both geographic and technological spillovers that does not rely on an observable paper trail. Our estimates suggest that for every patent produced by grant recipients , three more are produced by others who benefit from spillovers. Sixty percent of these spillovers occur within the United States , and many of them occur in technological areas substantially different from those targeted by the grants. ( JEL H81, L25, O33, O34, Q40 )
Interpretability Approaches for a Breast Lesion Detection Model

Medical imaging holds an important role due to its ability to non-invasively visualize and analyze the internal structures of the human body. The rise in medical imaging data is putting an increased pressure on physicians/radiologists to efficiently perform clinical imaging tasks, which has in turn driven the need for development of diagnosis tools in healthcare. Therefore, over the last decade, there have been significant breakthroughs in the field of artificial intelligence for healthcare. Of these breakthroughs, the most important would be the development and deployment of deep neural networks (DNNs). These DNNs have a complex structure and consist of several computation layers. Their complex structure is what owes their ability to resolve challenging imaging tasks. However, several issues have been raised considering the black box nature of deep learning algorithms, which is why regardless of their impressive performance, DNNs have not achieved significant deployment in medical practice. Specifically in healthcare, deep learning algorithms cannot be used for patient care unless the reasoning behind their outputs is explained due to the high stakes involved. Considering this, model interpretability is very important. It helps identify hidden information from the medical imaging data that may otherwise be invisible to the human eye. Model interpretability also adds to the trust of healthcare providers and patients involved. Beyond interpretability, analyzing model performance on targeted features could also allow model developers to debug their models. In this thesis, we perform an analysis of a breast lesion detection model using different interpretability approaches. More specifically, we perform a preliminary analysis using the existing data and metadata. Then, we analyze hidden features for medical imaging data—that is, radiomic features—to further investigate model performance on certain input features. Finally, we determine the impact of the input data’s location from the mammogram and suggest some future methods to improve our existing approaches.
Segmentation of data when analyzing the state of telecommunication systems

The identification of abnormal situations in information and telecommunication systems is considered, based on analyze statistical information of network traffic packages. The method of identifying an anomalous situation based on segmentation of data sample is proposed. The method is aimed at using classifying algorithms that have the best quality indicators on individual data segments. The proposed method will be useful for monitoring information security systems. The method registers of factors that affect the change in the properties of targeted variables. Impact detection allows you to generate data samples, depending on current and expected situations. On the example of the NSL-KDD dataset, there was a division of many data into subset, taking into account the influence of the factors on the range of values. The processing of factors is shown using the change point detection function in the time series. With its use, a division of data sample by the final number of non-intersecting measurable subsets has been made. The results of Accuracy, Precision, F-Measure, Recall for various classifiers are shown. The proposed method allows to increase the quality indicators of classification in continuously changing operating conditions of telecommunication systems.
Current concepts in evaluation and management of preoperative anaemia in patients undergoing thoracic surgery

Purpose of review The purpose of this review is to evaluate the current recommendations for management of perioperative anaemia in patients undergoing thoracic surgery, present the impact of anaemia on outcomes in this patient population and suggest an algorithm for evaluating and treating anaemia preoperatively. Recent findings Anaemia is a common medical issue noted preoperatively in patients presenting for thoracic surgery and carries significant morbidity. Preoperative anaemia increases the likelihood of receiving a perioperative transfusion, which represents a significant risk factor for morbidity and reduced disease-free survival in lung cancer patients presenting for thoracic surgery. It is also associated with longer hospital lengths of stay and increased risk for reoperation following lung transplantation. An algorithm-based approach to management of anaemia is beneficial and treatment with iron has been shown to reduce transfusions. Summary Patients undergoing thoracic surgery have a high incidence of preoperative anaemia that increases the risk of transfusion and postoperative morbidity. Preoperative evaluation and tailored treatment based on the underlying cause of anaemia reduces the incidence of anaemia prior to surgery and decreases transfusion rates.
Surgical left atrial appendage occlusion with concomitant cardiac surgical procedures: exacerbating heart failure or preventing stroke

Purpose of review The left atrial appendage (LAA) is a common source of thromboembolic stroke in patients with atrial fibrillation. Current guidelines recommend consideration of surgical LAA occlusion concomitant with other cardiac surgical procedures based mostly on observational data and a few small trials. Recently published results of several large retrospective studies and one prospective trial are reviewed herein. Recent findings Large retrospective studies using quality and administrative databases show mixed results with regard to efficacy of surgical LAA occlusion in preventing stroke, although most showed stroke reduction in patients with a history of atrial fibrillation (AF). Safety concerns have been raised based on nonrandomized data suggesting increased complications. A recent large, multicenter international randomized study with 3-year follow-up demonstrated significant reduction in stroke following LAA occlusion with no differences in death or heart failure exacerbations. Summary Most patients with AF undergoing another cardiac surgical procedure should be considered for concomitant LAA occlusion as part of a heart team discussion. The choice of surgical closure technique is critical. There is insufficient data to recommend LAA occlusion as an alternative to anticoagulation.
Machine Learning models for the Cognitive Stress Detection Using Heart Rate Variability Signals

Cognitive domains play a critical role in daily functioning. The prediction of cognitive stress state is important to better monitor work performance. This study aims to explore machine learning models to detect cognitive load or state using heart rate variability (HRV) signals. HRV data were recorded from thirty subjects during rest, two cognitive tasks (d2 Attention and Featuring Switcher task), and recovery. Seven HRV indexes from both time and frequency domains, extracted from raw R-R intervals, were used to identify whether subjects performed cognitive tasks or not. Five classifier models: linear support vector machine (LSVM), kernel SVM radial basis function, k-nearest neighbor (KNN), and random forest (RF), were trained and evaluated using a leave-one-out cross-validation approach. The accuracies and F1-score range from 0.54 to 0.62, with LSVM, showing the best. These acceptable performances indicate the machine learning approach could be used to further distinguish between rest and cognitive state. With the ubiquity of non-invasive and low-cost wearable devices, this finding offers insight to be incorporated into personal work performance monitoring in the digital age.
The effect of an outlier on L-estimators of location in symmetric distributions

The effect is studied of an outlier which has the same symmetric distribution as the other observations except for a change in location and a possible increase in scale. We show that the median is the most bias-resistant estimator, in the class of L-statistics with symmetric nonnegative coefficients that add up to one, for a class of distributions which includes the normal, double-exponential and logistic distributions.
Phonon spectrum of single-walled boron nitride nanotubes

Based on a force constant model, we investigated the phonon spectrum and then specific heat of single-walled boron nitride nanotubes. The results show that the frequencies of Raman and infrared active modes decrease with increasing diameter in the low frequency, which is consistent with the results calculated by density functional theory. The fitting formulae for diameter and chirality dependence of specific heat at 300K are given.