Miracle-dz/UniFAR-FTUs huggingface dataset:
Probing high scale seesaw and PBH generated dark matter via gravitational waves with multiple tilts

We propose a scenario where a high scale seesaw origin of light neutrino mass and gravitational dark matter (DM) in MeV-TeV ballpark originating from primordial black hole (PBH) evaporation can be simultaneously probed by future observations of stochastic gravitational wave (GW) background with multiple tilts or spectral breaks. A high scale breaking of an Abelian gauge symmetry ensures the dynamical origin of the seesaw scale while also leading to the formation of cosmic strings responsible for generating stochastic GW background. The requirement of a correct DM relic in this ballpark necessitates the inclusion of a diluter as PBH typically leads to DM overproduction. This leads to a second early matter dominated epoch after PBH evaporation due to the long-lived diluter. These two early matter dominated epochs, crucially connected to the DM relic, lead to multiple spectral breaks in the otherwise scale-invariant GW spectrum formed by cosmic strings. We find interesting correlations between DM mass and turning point frequencies of GW spectrum which are within reach of several near future experiments like LISA, BBO, ET, CE, etc.
Tamperproof IoT with Blockchain

We investigate the tamper-resistant property of Blockchain and its effectiveness for IoT systems. In particular, we implemented an IoT testbed, and built a Blockchain into the testbed. A number of tamper-resistance experiments were conducted and analyzed to corroborate the process of block validation in Blockchain. Our analysis and experimental results demonstrate the tamper-resistant capability of Blockchain in securing trust in IoT systems. The demonstration video is provided at [1].
Ad Hoc Teamwork in the Presence of Adversaries

Advances in ad hoc teamwork have the potential to create agents that collaborate robustly in real-world applications. Agents deployed in the real world, however, are vulnerable to adversaries with the intent to subvert them. There has been little research in ad hoc teamwork that assumes the presence of adversaries. We explain the importance of extending ad hoc teamwork to include the presence of adversaries and clarify why this problem is difficult. We then propose some directions for new research opportunities in ad hoc teamwork that leads to more robust multi-agent cyber-physical infrastructure systems.
PEMODELAN TIME SERIES DENGAN MAXIMAL OVERLAP DISCRETE WAVELET TRANSFORM

Penggunaan dekomposisi wavelet untuk pemodelan statistika khususnya pada data time telah mengalami perkembangan yang pesat. Transformasi wavelet yang dipandang lebih sesuai untuk data time series adalah Maximal Overlap Discrete Wavelet Transform (MODWT) karena dalam setiap level dekomposisi terdapat koefisien wavelet dan skala sebanyak panjang data. Kelebihan ini mereduksi kelemahan pemfilteran dengan Discrete Wavelet Transform (DWT) yang tidak dapat dilakukan pada sebarang ukuran sampel. Penentuan level dekomposisi dan koefisien yang digunakan sebagai input model menggunakan dekomposisi multi skala. Pengembangan yang dilakukan pada tulisan ini adalah penyempurnaan pada teknik komputasi sehingga level dekomposisi dan banyaknya koefisien pada setiap level dapat terpilih secara otomatis berdasarkan nilai prediksi yang meminimalkan error. Kata Kunci: MODWT, time series, dekomposisi
RapiD_AI: A framework for Rapidly Deployable AI for novel disease & pandemic preparedness

COVID-19 is unlikely to be the last pandemic that we face. According to an analysis of a global dataset of historical pandemics from 1600 to the present, the risk of a COVID-like pandemic has been estimated as 2.63% annually or a 38% lifetime probability. This rate may double over the coming decades. While we may be unable to prevent future pandemics, we can reduce their impact by investing in preparedness. In this study, we propose RapiD_AI: a framework to guide the use of pretrained neural network models as a pandemic preparedness tool to enable healthcare system resilience and effective use of ML during future pandemics. The RapiD_AI framework allows us to build high-performing ML models using data collected in the first weeks of the pandemic and provides an approach to adapt the models to the local populations and healthcare needs. The motivation is to enable healthcare systems to overcome data limitations that prevent the development of effective ML in the context of novel diseases. We digitally recreated the first 20 weeks of the COVID-19 pandemic and experimentally demonstrated the RapiD_AI framework using domain adaptation and inductive transfer. We (i) pretrain two neural network models (Deep Neural Network and TabNet) on a large Electronic Health Records dataset representative of a general in-patient population in Oxford, UK, (ii) fine-tune using data from the first weeks of the pandemic, and (iii) simulate local deployment by testing the performance of the models on a held-out test dataset of COVID-19 patients. Our approach has demonstrated an average relative/absolute gain of 4.92/4.21% AUC compared to an XGBoost benchmark model trained on COVID-19 data only. Moreover, we show our ability to identify the most useful historical pretraining samples through clustering and to expand the task of deployed models through inductive transfer to meet the emerging needs of a healthcare system without access to large historical pretraining datasets.
Benzodiazepine Use for Pediatric Patients With Suspected Nonconvulsive Status Epilepticus With or Without Simplified Electroencephalogram

Objectives In the present study, we aimed to determine the changes in the administration rate of benzodiazepines for pediatric patients with suspected nonconvulsive status epilepticus (NCSE) before and after the introduction of simplified electroencephalography (sEEG) in the emergency department. Methods This retrospective cohort study included patients who were younger than 18 years and were admitted to the emergency department from August 1, 2009, to July 31, 2017, with altered level of consciousness and nonpurposeful movement of eyes or extremities after the cessation of convulsive status epilepticus. Patients with apparent persistent convulsions, those who were fully conscious on arrival, and those who were transferred from another hospital were excluded. The patients were categorized into pre and post groups based on the introduction of sEEG, and benzodiazepine administration was compared between the 2 groups. Results During the study period, 464 patients with status epilepticus visited our emergency department and 69 and 93 patients fulfilling the study criteria were categorized into the pre and post groups, respectively. There were no significant differences in patient background characteristics between the 2 groups. Simplified electroencephalography was recorded in 52 patients in the post group. Benzodiazepines were administered in 44 of 69 patients (63.8%) in the pre group and 44 of 93 (47.3%) in the post group, and the benzodiazepine administration rate was significantly decreased after the introduction of sEEG (P = 0.04). The hospitalization rate was significantly lower in the post group, but there were no significant differences in the rates of intensive care unit admission, reconvulsion after discharge, and final diagnoses between the 2 groups. Conclusions Simplified electroencephalography might aid in determining the need for anticonvulsant treatment for suspected NCSE in pediatric patients. Albeit not a definitive diagnostic tool, sEEG might be a reliable choice in the evaluation of pediatric patients with suspected NCSE.
Prospective and challenges of live bacterial therapeutics from a superhero Escherichia coli Nissle 1917

Abstract Escherichia coli Nissle 1917 (EcN), the active component of Mutaflor(R), is a notable probiotic from Gram-negative to treat Crohn’s disease and irritable bowel syndrome. Therefore, a comprehensive genomic database maximizes the systemic probiotic assessment to discover EcN’s role in human health. Recently, advanced synthetic and genetic tools have opened up a rich area to execute EcN as “living medicines” with controllable functions. Incorporating unique biomarkers allows the engineered EcN to switch genes on and off in response to environmental cues. Since EcN holds promise as a safe nature vehicle, more studies are desired to fully realize a wide range of probiotic potential for disease treatment. This review aims to deliver a historical origin of EcN, discuss the recent promising genetic toolbox in the rational design of probiotics, and pinpoint the clinical translation and evaluation of engineered EcN in vitro and in vivo. The summary of safety concerns, strategies of biotherapeutics development, and the challenges and prospects of engineered EcN is also concluded.
Hybrid fibroin/polyurethane small-diameter vascular grafts: from fabrication to in vivo preliminary assessment

To address the need of alternatives to autologous vessels for small-calibre vascular applications (e.g. cardiac surgery), a bio-hybrid semi-degradable material composed of silk fibroin (SF) and polyurethane (Silkothane®) was herein used to fabricate very small-calibre grafts (Øin = 1.5 mm) via electrospinning. Bio-hybrid grafts were in vitro characterized in terms of morphology and mechanical behaviour, and compared to similar grafts of pure SF. Similarly, two native vessels from a rodent model (abdominal aorta and vena cava) were harvested and characterized. Preliminary implants were performed on Lewis rats to confirm the suitability of Silkothane® grafts for small-calibre applications, specifically as aortic insertion and femoral shunt. The manufacturing process generated pliable grafts consisting of a randomized fibrous mesh and exhibiting similar geometrical features to rat aortas. Both Silkothane® and pure SF grafts showed radial compliances in the range from 1.37 ± 0.86 to 1.88 ± 1.01% 10−2 mmHg−1, lower than that of native vessels. The Silkothane® small-calibre devices were also implanted in rats demonstrating to be adequate for vascular applications; all the treated rats survived the surgery for three months after implantation, and 16 rats out of 17 (94%) still showed blood flow inside the graft at sacrifice. The obtained results lay the basis for a deeper investigation of the interaction between the Silkothane® graft and the implant site, which may deal with further analysis on the potentialities in terms of degradability and tissue formation, on longer time-points.
Interventions for reducing weight bias in healthcare providers: An interprofessional systematic review and meta‐analysis

Weight bias is prevalent in many healthcare disciplines and negatively impacts the quality of care for patients with obesity. This warrants interventions to reduce weight bias shown by providers to improve care for individuals with obesity. However, past reviews have identified only marginal success in improving the attitudes and beliefs of healthcare providers about individuals with obesity. This systematic review and meta‐analysis identifies and synthesizes recent peer‐reviewed intervention studies aimed at reducing weight bias in healthcare students and professionals. The databases Embase, Cumulative Index to Nursing and Allied Health Literature, PsychINFO, PubMed and Google Scholar were searched for peer‐reviewed studies published between 2016 and August 2021. Search terms included a combination of surrogate terms for the concepts of weight bias, intervention and healthcare students or professionals. The search yielded 1136 articles, and 14 articles met the inclusion criteria for the systematic review. Nineteen effect sizes from nine studies were included in the meta‐analysis. Overall, the interventions in the included studies result in a 0.38 SD reduction (Hedge's g) in obesity‐bias with 95% confidence intervals from −0.52 to −0.24, indicating a small to moderate effect size in the reduction of weight bias. Most studies included students and focused on evoking empathy or educating on the causality/controllability of obesity. Measurement tools, intervention type, limitations of the studies and recommendations for future research are discussed.
Person‐years of life lost and lost earnings from cigarette smoking‐attributable cancer deaths, United States, 2019

State‐specific information on lost earnings due to smoking‐attributable cancer deaths to inform and advocate for tobacco control policies is lacking. We estimated person‐years of life lost (PYLL) and lost earnings due to cigarette smoking‐attributable cancer deaths in the United States nationally and by state. Proportions and numbers of cigarette smoking‐attributable cancer deaths and associated PYLL among individuals aged 25 to 79 years in 2019 were calculated and combined with annual median earnings to estimate lost earnings attributable to cigarette smoking. In 2019, estimated total PYLL and lost earnings associated with cigarette smoking‐attributable cancer deaths in ages 25 to 79 years in the United States were 2 188 195 (95% CI, 2 148 707‐2 231 538) PYLL and $20.9 billion ($20.0 billion‐$21.7 billion), respectively. States with the highest overall age‐standardized PYLL and lost earning rates generally were in the South and Midwest. The estimated rate per 100 000 population ranged from 352 (339‐366) in Utah to 1337 (1310‐1367) in West Virginia for PYLL and from $4.3 million ($3.5 million‐$5.2 million) in Idaho to $14.8 million ($10.6 million‐$20.7 million) in Missouri for lost earnings. If age‐specific PYLL and lost earning rates in Utah had been achieved by all states, 58.2% (57.0%‐59.5%) of the estimated total PYLL (1 274 178; 1 242 218‐1 306 685 PYLL) and 50.5% (34.2%‐62.4%) of lost earnings ($10.5 billion; $7.1 billion‐$13.1 billion) in 2019 nationally would have been avoided. Lost earnings due to smoking‐attributable cancer deaths are substantial in the United States and are highest in states with weaker tobacco control policies.
Fast Lasso‐type safe screening for Fine‐Gray competing risks model with ultrahigh dimensional covariates

The Fine‐Gray proportional sub‐distribution hazards (PSH) model is among the most popular regression model for competing risks time‐to‐event data. This article develops a fast safe feature elimination method, named PSH‐SAFE, for fitting the penalized Fine‐Gray PSH model with a Lasso (or adaptive Lasso) penalty. Our PSH‐SAFE procedure is straightforward to implement, fast, and scales well to ultrahigh dimensional data. We also show that as a feature screening procedure, PSH‐SAFE is safe in a sense that the eliminated features are guaranteed to be inactive features in the original Lasso (or adaptive Lasso) estimator for the penalized PSH model. We evaluate the performance of the PSH‐SAFE procedure in terms of computational efficiency, screening efficiency and safety, run‐time, and prediction accuracy on multiple simulated datasets and a real bladder cancer data. Our empirical results show that the PSH‐SAFE procedure possesses desirable screening efficiency and safety properties and can offer substantially improved computational efficiency as well as similar or better prediction performance in comparison to their baseline competitors.
Nonparametric Divergence Estimation and its Applications to Machine Learning

Low-dimensional embedding, manifold learning, clustering, classification, and anomaly detection are among the most important problems in machine learning. Here we consider the setting where each instance of the inputs corresponds to a continuous probability distribution. These distributions are unknown to us, but we are given some i.i. d. samples from each of them. While most of the existing machine learning methods operate on points, i.e. finite-dimensional feature vectors, in our setting we study algorithms that operate on groups, i.e. sets of feature vectors. For this purpose, we propose new nonparametric, consistent estimators for a large family of divergences and describe how to apply them for machine learning problems. As important special cases, the estimators can be used to estimate Renyi, Tsallis, Kullback-Leibler, Hellinger, Bhattacharyya distance, L2 divergences, and mutual information. We present empirical results on synthetic data, real word images, and astronomical data sets.
Fully automated breast segmentation on spiral breast computed tomography images

Abstract Introduction The quantification of the amount of the glandular tissue and breast density is important to assess breast cancer risk. Novel photon‐counting breast computed tomography (CT) technology has the potential to quantify them. For accurate analysis, a dedicated method to segment the breast components—the adipose and glandular tissue, skin, pectoralis muscle, skinfold section, rib, and implant—is required. We propose a fully automated breast segmentation method for breast CT images. Methods The framework consists of four parts: (1) investigate, (2) segment the components excluding adipose and glandular tissue, (3) assess the breast density, and (4) iteratively segment the glandular tissue according to the estimated density. For the method, adapted seeded watershed and region growing algorithm were dedicatedly developed for the breast CT images and optimized on 68 breast images. The segmentation performance was qualitatively (five‐point Likert scale) and quantitatively (Dice similarity coefficient [DSC] and difference coefficient [DC]) demonstrated according to human reading by experienced radiologists. Results The performance evaluation on each component and overall segmentation for 17 breast CT images resulted in DSCs ranging 0.90–0.97 and in DCs 0.01–0.08. The readers rated 4.5–4.8 (5 highest score) with an excellent inter‐reader agreement. The breast density varied by 3.7%–7.1% when including mis‐segmented muscle or skin. Conclusion The automatic segmentation results coincided with the human expert's reading. The accurate segmentation is important to avoid the significant bias in breast density analysis. Our method enables accurate quantification of the breast density and amount of the glandular tissue that is directly related to breast cancer risk.
Prediction of nonalcoholic fatty liver disease using anthropometry and body fat measures by sex and race/ethnicity in the United States

The prevalence of nonalcoholic fatty liver disease (NAFLD) is increasing in the United States and is strongly linked to obesity in many, but not all, racial/ethnic groups. It is conceivable that the lack of correspondence is related to differential fat distribution. The study objective was to examine which fat distribution measures best predicted NAFLD by sex within racial/ethnic groups.
Understanding autobiographical memory content using computational text analysis

ABSTRACT Although research on autobiographical memory (AM) continues to grow, there remain few methods to analyze AM content. Past approaches are typically manual, and prohibitively time- and labour-intensive. These methodological limitations are concerning because content may provide insights into the nature and functions of AM. In particular, analyzing content in recurrent involuntary autobiographical memories (IAMs; those that spring to mind unintentionally and repetitively) could resolve controversies about whether these memories typically involve mundane or distressing events. Here, we present computational methods that can analyze content in thousands of participants’ AMs, without needing to hand-code each memory. A sample of 6,187 undergraduates completed surveys about recurrent IAMs, resulting in 3,624 text descriptions. Using frequency analyses, we identified common (e.g., “time”, “friend”) and distinctive words in recurrent IAMs (e.g., “argument” as distinctive to negative recurrent IAMs). Using structural topic modelling, we identified coherent topics (e.g., “Negative past relationships”, “Conversations”, “Experiences with family members”) within recurrent IAMs and found that topic use significantly differed depending on the valence of these memories. Computational methods allowed us to analyze large quantities of AM content with enhanced granularity and reproducibility. We present the means to enable future research on AM content at an unprecedented scope and scale.
SDF-1α/CXCR4 signaling promotes capillary tube formation of human retinal vascular endothelial cells by activating ERK1/2 and PI3K pathways in vitro

The purpose of this study is to address the effect and mechanism of stromal cell-derived factor-1 (SDF-1)α/chemokine (C-X-C motif) receptor 4 (CXCR4) signaling on capillary tube formation of human retinal vascular endothelial cells (HRECs). The expression of CXCR4 in HRECs was quantified by reverse transcription (RT-PCR) and western blotting. The effects of SDF-1α/CXCR4 signaling in capillary tube formation and migration of HRECs was examined using three-dimensional Matrigel assay and wound scratching assay respectively in vitro. Cell proliferation of HRECs was examined using cell counting kit (CCK)-8 assay in the presence of different concentrations of SDF-1α protein. The effect of SDF-1α/CXCR4 signaling in HREC expression of VEGF, basic fibroblast growth factor (bFGF), IL-8 and intercellular cell adhesion molecule (ICAM)-1 was examined using RT-PCR and western blotting. RT-PCR and western blot analysis revealed CXCR4 was expressed in HRECs. The number of intact capillary tubes formed by HRECs in the presence of SDF-1α was markedly more compared with a PBS treated control group. However, it was reduced with treatment with an CXCR4 antagonist. Wound scratching assay showed a significant increase in the number of migrated HRECs under SDF-1α stimulation and the number was reduced with treatment with an CXCR4 antagonist. RT-PCR and western blotting showed that SDF-1α significantly promoted VEGF, bFGF, IL-8 and ICAM-1 expression in HRECs. The proliferation of HRECs in the presence of SDF-1α was promoted in a dosage-dependent manner. SDF-1α/CXCR4 signaling can increase HREC capillary tube formation through promoting HREC migration, proliferation and expression of VEGF, bFGF, IL-8 and ICAM-1.
Adherence to and early adverse events of COVID-19 vaccine in a cohort of 600 Italian breastfeeding and pregnant physicians

ABSTRACT Pregnant and breastfeeding women (PBW) have been excluded from COVID-19 vaccine registry and the majority of post-marketing trials, despite the recognized increased risk of severe infections and complications. The lack of efficacy and safety data prevented the formulation of specific indications/guidelines for vaccination and could have also contributed to increased vaccine hesitancy (VH) in PBW. The aim of this cross-sectional study is to assess the rate and predictors of VH, and early adverse events (AEFI) following COVID-19 vaccine in PBW with a cross-sectional study. In January 2021, a purposely designed questionnaire was administered to 600 PBW part of a Facebook group of physicians, immunized with two doses of Comirnaty®. Thirty-eight (29%) pregnant women and 13 (2.8%) breastfeeders were hesitant. The only statistically significant negative predictor of COVID-19 VH appeared to be having had the flu shot in 2020/2021 (OR: 0.35; 95% CI: 0.13–0.97; p = .044). Approximately 27% of PBW reported hesitancy toward the 2020/2021 season influenza vaccine. Among the vaccinated subjects, 51.6% of pregnant and 75.2% of breastfeeding women reported at least one symptom after the first, and 82.4% and 81.6%, respectively, after the second dose. Nausea/vomiting, fatigue, headache and arthralgia/myalgia were the most common symptoms; dizziness, shivering, syncope and limb paresthesia were rarely reported. Among infants of breastfeeding mothers, six experienced fever, five rash and four moderate and self-limiting diarrhea. Preliminary data on mRNA COVID-19 vaccine in PBW and in their infants are reassuring since AEFI, although frequent, are typically mild and similar to those occurring in the general population, and in PBW after other vaccines. Larger studies with longer follow-up after vaccination are strongly recommended to develop recommendations in these patients.