Miracle-dz/UniFAR-FTUs huggingface dataset:
A preliminary study of liver fat quantification using reported longitudinal ultrasound speed of sound and attenuation parameters

The quantification of liver fat as a diagnostic assessment of steatosis remains an important priority for noninvasive imaging systems. We derive a framework in which the unknown fat volume percentage can be estimated from a pair of ultrasound measurements. The precise estimation of ultrasound speed of sound and attenuation within the liver are shown to be sufficient for estimating fat volume assuming a classical model of the properties of a composite elastic material. In this model, steatosis is represented as a random dispersion of spherical fat vacuoles with acoustic properties similar to those of edible oils. Using values of speed of sound and attenuation from the literature where normal and steatotic livers were studied near 3.5 MHz, we demonstrate agreement of the new estimation method with independent measures of fat. This framework holds the potential for translation to clinical scanners where the two ultrasound measurements can be made and utilized for improved quantitative assessment of steatosis.
Dynamical Bifurcation for a Class of Large-Scale Fractional Delayed Neural Networks With Complex Ring-Hub Structure and Hybrid Coupling

Real neural networks are characterized by large-scale and complex topology. However, the current dynamical analysis is limited to low-dimensional models with simplified topology. Therefore, there is still a huge gap between neural network theory and its application. This article proposes a class of large-scale neural networks with a ring-hub structure, where a hub node is connected to $n$ peripheral nodes and these peripheral nodes are linked by a ring. In particular, there exists a hybrid coupling mode in the network topology. The mathematical model of such systems is described by fractional-order delayed differential equations. The aim of this article is to investigate the local stability and Hopf bifurcation of this high-dimensional neural network. First, the Coates flow graph is employed to obtain the characteristic equation of the linearized high-dimensional neural network model, which is a transcendental equation including multiple exponential items. Then, the sufficient conditions ensuring the stability of equilibrium and the existence of Hopf bifurcation are achieved by taking time delay as a bifurcation parameter. Finally, some numerical examples are given to support the theoretical results. It is revealed that the increasing time delay can effectively induce the occurrence of periodic oscillation. Moreover, the fractional order, the self-feedback coefficient, and the number of neurons also have effects on the onset of Hopf bifurcation.
Insights into the Biosynthesis of Nanoparticles by the Genus Shewanella

The exploitation of microorganisms for the fabrication of nanoparticles (NPs) has garnered considerable research interest globally. The microbiological transformation of metals and metal salts into respective NPs can be achieved under environmentally benign conditions, offering a more sustainable alternative to chemical synthesis methods. ABSTRACT The exploitation of microorganisms for the fabrication of nanoparticles (NPs) has garnered considerable research interest globally. The microbiological transformation of metals and metal salts into respective NPs can be achieved under environmentally benign conditions, offering a more sustainable alternative to chemical synthesis methods. Species of the metal-reducing bacterial genus Shewanella are able to couple the oxidation of various electron donors, including lactate, pyruvate, and hydrogen, to the reduction of a wide range of metal species, resulting in biomineralization of a multitude of metal NPs. Single-metal-based NPs as well as composite materials with properties equivalent or even superior to physically and chemically produced NPs have been synthesized by a number of Shewanella species. A mechanistic understanding of electron transfer-mediated bioreduction of metals into respective NPs by Shewanella is crucial in maximizing NP yields and directing the synthesis to produce fine-tuned NPs with tailored properties. In addition, thorough investigations into the influence of process parameters controlling the biosynthesis is another focal point for optimizing the process of NP generation. Synthesis of metal-based NPs using Shewanella species offers a low-cost, eco-friendly alternative to current physiochemical methods. This article aims to shed light on the contribution of Shewanella as a model organism in the biosynthesis of a variety of NPs and critically reviews the current state of knowledge on factors controlling their synthesis, characterization, potential applications in different sectors, and future prospects.
Deep Reinforcement Learning for Equal Risk Pricing and Hedging under Dynamic Expectile Risk Measures

Recently equal risk pricing, a framework for fair derivative pricing, was extended to consider dynamic risk measures. However, all current implementations either employ a static risk measure that violates time consistency, or are based on traditional dynamic programming solution schemes that are impracticable in problems with a large number of underlying assets (due to the curse of dimensionality) or with incomplete asset dynamics information. In this paper, we extend for the first time a famous off-policy deterministic actor-critic deep reinforcement learning (ACRL) algorithm to the problem of solving a risk averse Markov decision process that models risk using a time consistent recursive expectile risk measure. This new ACRL algorithm allows us to identify high quality time consistent hedging policies (and equal risk prices) for options, such as basket options, that cannot be handled using traditional methods, or in context where only historical trajectories of the underlying assets are available. Our numerical experiments, which involve both a simple vanilla option and a more exotic basket option, confirm that the new ACRL algorithm can produce 1) in simple environments, nearly optimal hedging policies, and highly accurate prices, simultaneously for a range of maturities 2) in complex environments, good quality policies and prices using reasonable amount of computing resources; and 3) overall, hedging strategies that actually outperform the strategies produced using static risk measures when the risk is evaluated at later points of time.
Neural-IMLS: Learning Implicit Moving Least-Squares for Surface Reconstruction from Unoriented Point clouds

Surface reconstruction from noisy, non-uniformly, and unori-ented point clouds is a fascinating yet difﬁcult problem in computer vision and computer graphics. In this paper, we propose Neural-IMLS , a novel approach that learning noise-resistant signed distance function (SDF) for reconstruction. Instead of explicitly learning priors with the ground-truth signed distance values, our method learns the SDF from raw point clouds directly in a self-supervised fashion by minimizing the loss between the couple of SDFs, one obtained by the implicit moving least-square function (IMLS) and the other by our network. Finally, a watertight and smooth 2-manifold triangle mesh is yielded by running Marching Cubes. We conduct extensive experiments on various benchmarks to demonstrate the performance of Neural-IMLS, especially for point clouds with noise.
Recommend for a Reason: Unlocking the Power of Unsupervised Aspect-Sentiment Co-Extraction

Compliments and concerns in reviews are valuable for understanding users' shopping interests and their opinions with respect to specific aspects of certain items. Existing review-based recommenders favor large and complex language encoders that can only learn latent and uninterpretable text representations. They lack explicit user attention and item property modeling, which however could provide valuable information beyond the ability to recommend items. Therefore, we propose a tightly coupled two-stage approach, including an Aspect-Sentiment Pair Extractor (ASPE) and an Attention-Property-aware Rating Estimator (APRE). Unsupervised ASPE mines Aspect-Sentiment pairs (AS-pairs) and APRE predicts ratings using AS-pairs as concrete aspect-level evidence. Extensive experiments on seven real-world Amazon Review Datasets demonstrate that ASPE can effectively extract AS-pairs which enable APRE to deliver superior accuracy over the leading baselines.
Coordinated Effects in the American Airlines-U.S. Airways Merger

I study the pricing behavior of legacy airlines before and after the American Airlines-US Airways merger. Results are consistent with an increase in price coordination between legacy airlines after the merger. Descriptive evidence suggests the merger facilitated the observed increase in price coordination. U.S. Airways faced weak incentives to coordinate pricing decisions prior to the merger due to its unique route structure. The merger, by combining the networks of U.S. Airways and American Airlines, increased the sustainability of collusion between legacy airlines, allowing legacy airlines to successfully implement price increases.
Sporadic Creutzfeldt-Jakob Disease in a Very Young Person

Background and Objectives Sporadic Creutzfeldt-Jakob disease (sCJD) is the most common form of human prion disease and typically occurs in middle to late life. sCJD in early adulthood is extremely uncommon. The purpose of this report is to raise awareness of cases of sCJD in young patients that are not associated with a genetic mutation or acquired prion disease risk factors. Methods We describe the clinical presentation, diagnostic workup, and postmortem examination of a 22-year-old man with sCJD. Results The patient presented with a rapidly progressive neurocognitive disorder consisting of early and prominent psychiatric symptoms. CSF real-time quaking-induced conversion (RT-QuIC) was indeterminate, and brain MRI was suggestive of prion disease. Neuropathologic examination and the absence of a genetic mutation and acquired prion disease risk factors resulted in a final diagnosis of sCJD. Conclusion Although extremely rare, sCJD can occur in young people and should be considered in the setting of rapidly progressive neuropsychiatric conditions. Postmortem examination is required to diagnose the type of prion disease and remains important to surveil for known and potentially novel acquired prion diseases.
Effect of bioimpedance-defined overhydration parameters on mortality and cardiovascular events in patients undergoing dialysis: a systematic review and meta-analysis

Objective To evaluate the role of bioimpedance-defined overhydration (BI-OH) parameters in predicting the risk of mortality and cardiovascular (CV) events in patients undergoing dialysis. Methods We searched multiple electronic databases for studies investigating BI-OH indicators in the prediction of mortality and CV events through 23 May 2020. We assessed the effect of BI-OH indexes using unadjusted hazard ratios (HRs) and 95% confidence intervals (CIs). Sensitivity analysis was used for each outcome. Results We included 55 studies with 104,758 patients in the meta-analysis. Extracellular water/total body water (ECW/TBW) >0.4 (HR 5.912, 95% CI: 2.016–17.342), ECW/intracellular water (ICW) for every 0.01 increase (HR 1.041, 95% CI: 1.031–1.051), and OH/ECW >15% (HR 2.722, 95% CI: 2.005–3.439) increased the risk of mortality in patients receiving dialysis. ECW/TBW >0.4 (HR 2.679, 95% CI: 1.345–5.339) and ECW/ICW per increment of 10% (HR 1.032, 95% CI: 1.017–1.047) were associated with an increased risk of CV events in patients undergoing dialysis. A 1-degree increase in phase angle was a protective factor for both mortality (HR 0.676, 95% CI: 0.474–0.879) and CV events (HR 0.736, 95% CI: 0.589–0.920). Conclusions BI-OH parameters might be independent predictors for mortality and CV events in patients undergoing dialysis.
Prediction of Multidrug-Resistant Tuberculosis Using Machine Learning Algorithms in SWAT, Pakistan

In this paper, we have focused on machine learning (ML) feature selection (FS) algorithms for identifying and diagnosing multidrug-resistant (MDR) tuberculosis (TB). MDR-TB is a universal public health problem, and its early detection has been one of the burning issues. The present study has been conducted in the Malakand Division of Khyber Pakhtunkhwa, Pakistan, to further add to the knowledge on the disease and to deal with the issues of identification and early detection of MDR-TB by ML algorithms. These models also identify the most important factors causing MDR-TB infection whose study gives additional insights into the matter. ML algorithms such as random forest, k-nearest neighbors, support vector machine, logistic regression, leaset absolute shrinkage and selection operator (LASSO), artificial neural networks (ANNs), and decision trees are applied to analyse the case-control dataset. This study reveals that close contacts of MDR-TB patients, smoking, depression, previous TB history, improper treatment, and interruption in first-line TB treatment have a great impact on the status of MDR. Accordingly, weight loss, chest pain, hemoptysis, and fatigue are important symptoms. Based on accuracy, sensitivity, and specificity, SVM and RF are the suggested models to be used for patients' classifications.
Is self-determination good for your effectiveness? A study of factors which influence performance within self-determination theory

Despite the vast body of studies within self-determination theory, the impact of factors which influence performance in experimental paradigm is still underresearched. The aim of the two studies presented in this paper was to investigate the impact of basic psychological needs on performance with the simultaneous presence of external incentives. Study 1 tested whether the satisfaction of competence and relatedness during task performance (while external incentives were present) can impact individual’s performance. Study 2, on the other hand, investigated whether the basic psychological needs and provision of external incentives can impact an individual’s performance. Moreover, in both studies the mechanisms behind the need–performance relationship was checked. Our results showed that out of the three basic needs, competence had the strongest positive impact on performance, which was partially mediated by the subjective evaluation of the levels of difficulty and intrinsic motivation. The weak relationship between relatedness and task performance was fully mediated by the level of intrinsic motivation.
Computing the matrix fractional power with the double exponential formula

Two quadrature-based algorithms for computing the matrix fractional power $A^\alpha$ are presented in this paper. These algorithms are based on the double exponential (DE) formula, which is well-known for its effectiveness in computing improper integrals as well as in treating nearly arbitrary endpoint singularities. The DE formula transforms a given integral into another integral that is suited for the trapezoidal rule; in this process, the integral interval is transformed to the infinite interval. Therefore, it is necessary to truncate the infinite interval into an appropriate finite interval. In this paper, a truncation method, which is based on a truncation error analysis specialized to the computation of $A^\alpha$, is proposed. Then, two algorithms are presented -- one computes $A^\alpha$ with a fixed number of abscissas, and the other computes $A^\alpha$ adaptively. Subsequently, the convergence rate of the DE formula for Hermitian positive definite matrices is analyzed. The convergence rate analysis shows that the DE formula converges faster than the Gaussian quadrature when $A$ is ill-conditioned and $\alpha$ is a non-unit fraction. Numerical results show that our algorithms achieved the required accuracy and were faster than other algorithms in several situations.
miR-184 delays cell proliferation, migration and invasion in prostate cancer by directly suppressing DLX1

A number of previous studies have reported that dysregulated miR-184 expression is associated with the development of cancer. The aim of the present study was to investigate the role of miR-184 in prostate cancer (PC) and the mechanism underlying its effects. Data from human tumor tissue samples were collected from The CEancer Genome Atlas to determine the expression levels of miR-184 and DLX1. The miR-184 mimic and pcDNA3.1-DLX1 plasmid were utilized to induce overexpression of miR-184 and DLX1 in Du145 cells, respectively. Cell Counting Kit-8, wound healing and Transwell assays were performed to examine the effects of miR-184 on the aggressiveness of PC cells. Dual-luciferase reporter gene assay was used to investigate the association between miR-184 and DLX1, and reverse transcription-quantitative PCR and western blot analyses were utilized to determine the mRNA and protein levels. miR-184 expression was found to be downregulated whereas DLX1 was upregulated in PC tissues compared with normal prostate tissues. Cell propagation, migration and invasion were all inhibited by miR-184 upregulation in Du145 cells. Dual luciferase reporter assay confirmed the association between miR-184 and DLX1. The inhibitory effect of miR-184 mimic on cell behaviors was reversed by upregulation of DLX1. These findings suggest that miR-184 plays a beneficial role in suppressing the tumorigenesis of PC by directly targeting DLX1, and it may represent a potential therapeutic strategy for PC.
Xp11.2 Translocation Renal Cell Carcinoma: Clinical Characteristics and Potential Prognostic Predictors

Background Xp11.2 translocation renal cell carcinoma, a rare malignancy, has a higher prevalence in children than in adults. It is relatively indolent in children but manifests with an aggressive course in adults. Clinical characteristics and prognostic studies for adult patients are scarce due to its rarity. Methods This retrospective single-center study consecutively enrolled 24 newly diagnosed Xp11.2 translocation RCC adult patients. Clinical presentations were recorded, and baseline laboratory results and follow-up data were collected. Possible risk factors for progression-free survival and overall survival were first scanned with chi-square tests and t-tests to compare patients who suffered from progression or death with who did not. Multivariate Cox regression was further utilized to identify independent risk factors. Results Twenty-four adult patients (median age 32, range 16-73), with a male-to-female ratio of 1 : 1, was included from April 2010 to March 2020. After follow-up for 35.7 months (+/- months), seven patients died. With univariate analysis, higher C-reactive protein-to-albumin (CRP/Alb) ratio (p = 0.028), higher baseline fibrinogen (p = 0.006), and presence of distant metastasis (p = 0.007) were associated with progression of the disease; higher preoperative fibrinogen (p = 0.014) and distant metastasis (p = 0.020) were associated with death. With multivariate Cox regression, only baseline fibrinogen level (p = 0.001) was identified as an independent risk factor for progression-free survival; meanwhile, fibrinogen level (p = 0.048) and distant metastasis (p = 0.043) were identified as independent risk factors for survival. Conclusions Overall, relatively high CRP/Alb ratios, fibrinogen, and distant metastasis were associated with a poor prognosis of Xp11.2 tRCC adult patients; among them, only baseline fibrinogen levels independently predicted the progression of Xp11.2 tRCC; thus, it may help to identify patients with worse progression or death risk.
Levonorgestrel-Releasing Intrauterine System Use in Perimenopausal Women

The levonorgestrel-releasing intrauterine system (LNG-IUS) is a highly effective contraceptive method that has several noncontraceptive benefits. It has been used in various gynecological conditions, such as heavy menstrual bleeding, dysmenorrhea, and endometrial hyperplasia. During the perimenopausal period, hormonal fluctuations occur, and there is a high tendency for the development of several benign gynecologic diseases. Therefore, the use of LNG-IUS in perimenopausal women might be more beneficial than in women belonging to other age groups. Moreover, the insertion of LNG-IUS during the perimenopausal period could confer endometrial protection during estrogen replacement therapy. In this review, we discuss the use of LNG-IUS in perimenopausal women.
Impact of kidney function on the occurrence of new-onset atrial fibrillation in patients with ST-elevation myocardial infarction.

OBJECTIVE In this study, we aimed to examine the prognostic impact of decreased kidney function at admission on the occurrence of new-onset atrial fibrillation (AF) in patients with ST-elevation myocardial infarction (STEMI) treated with primary percutaneous coronary intervention (pPCI). METHODS The study enrolled 3,115 consecutive patients with STEMI. Kidney function was assessed by estimation of the glomerular filtration rate (eGFR) at admission. Patients with cardiogenic shock at admission, patients on hemodialysis, and patients with a medical history of previous AF (paroxysmal, persistent, or permanent) were excluded. The follow-up period was six years. RESULTS New-onset AF occurred in 215 (6.9%) patients, 75 (34.9%) patients presented with AF, and 140 (65.1%) patients developed AF after pPCI. The median time of AF occurrence in patients who did not present with AF was 4.5 (interquartile range 1-25) hours after pPCI. New-onset AF was associated with a higher short- and long-term mortality. In the multiple logistic regression analysis, all stages of reduced kidney function were independent predictors for the occurrence of new-onset AF, and negative prognostic impact increased with the deterioration of kidney function: eGFR <90 mL/min/m2, hazard ratio (HR) 1.96, 95% confidence interval (CI) 1.42-2.89, p=0.011; eGFR 60-89 mL/min/m2, HR 1.54, 95% CI 1.13-2.57, p=0.045; eGFR 45-59 mL/min/m2-, HR 2.09, 95% CI 1.24-2.85, p=0.023; eGFR 30-44 mL/min/m2-, HR 2.93, 95% CI 1.64-5.29, p<0.001; eGFR 15-29 mL/min/m2-, HR 5.51, 95% CI 2.67-11.39, p<0.001. CONCLUSION Decreased kidney function was significantly associated with the occurrence of new-onset AF, and its impact increased with the deterioration in kidney function, starting with an eGFR value of 90 mL/min/m2. New-onset AF was an independent predictor of long-term all-cause mortality in the analyzed patients.
Multivariate random forest prediction of poverty and malnutrition prevalence

Advances in remote sensing and machine learning enable increasingly accurate, inexpensive, and timely estimation of poverty and malnutrition indicators to guide development and humanitarian agencies’ programming. However, state of the art models often rely on proprietary data and/or deep or transfer learning methods whose underlying mechanics may be challenging to interpret. We demonstrate how interpretable random forest models can produce estimates of a set of (potentially correlated) malnutrition and poverty prevalence measures using free, open access, regularly updated, georeferenced data. We demonstrate two use cases: contemporaneous prediction, which might be used for poverty mapping, geographic targeting, or monitoring and evaluation tasks, and a sequential nowcasting task that can inform early warning systems. Applied to data from 11 low and lower-middle income countries, we find predictive accuracy broadly comparable for both tasks to prior studies that use proprietary data and/or deep or transfer learning methods.