Miracle-dz/UniFAR-FTUs huggingface dataset:
Turnkey algorithmic approach for the evaluation of gastroesophageal reflux disease after bariatric surgery

Abstract Bariatric surgeries are often complicated by de-novo gastroesophageal reflux disease (GERD) or worsening of pre-existing GERD. The growing rates of obesity and bariatric surgeries worldwide are paralleled by an increase in the number of patients requiring post-surgical GERD evaluation. However, there is currently no standardized approach for the assessment of GERD in these patients. In this review, we delineate the relationship between GERD and the most common bariatric surgeries: sleeve gastrectomy (SG) and Roux-en-Y gastric bypass (RYGB), with a focus on pathophysiology, objective assessment, and underlying anatomical and motility disturbances. We suggest a stepwise algorithm to help diagnose GERD after SG and RYGB, determine the underlying cause, and guide the management and treatment.
Pulmonary hypertension secondary to interstitial fibrosis with pulmonary venous lesions masquerading pulmonary veno‐occlusive disease

We present a 41‐year‐old man with idiopathic interstitial pneumonia and pulmonary hypertension (PH) in the setting of a non‐autoimmune background whose clinical presentation masqueraded pulmonary veno‐occlusive disease (PVOD). Because of no histological evidence of venous occlusion in his previous lung biopsy, phosphodiesterase type‐5 inhibitor was given, resulting in sudden onset of pulmonary edema. At autopsy, there were histological features of interstitial fibrosis with occlusion of the lobular septal veins and venules. Clinical presentations of PH due to interstitial fibrosis with pulmonary venous lesions may simulate those of PVOD and careful diagnostic and therapeutic approaches are required.
A Hybrid Genetic Algorithm for Globally Optimal Solution

A hybrid genetic algorithm is presented to overcome the disadvantages of the classical genetic algorithm. The basic idea of HGA is that initial population is obtained by some methods, then the fitness function is modified step by step. This algorithm can raise the scouting velocity and precision. The simulation sample results show that the effect of this algorithm is perfect.
High Speed Reverse Converter for the Five Moduli Set {2 n , 2 n -1, 2 n +1, 2 n -3, 2 n-1 -1}

The new moduli set {2 n , 2 n -1, 2 n +1, 2 n -3, 2 n-1 -1} is profitable to construct high performance residue number system (RNS) due to well-formed moduli set and high dynamic range. Conversion from residues to binary is a bottleneck in RNS. With growth of number of moduli, this problem has been more critical due to complex multiplicative inverses. In this paper a high speed design of reverse converter for the moduli set {2 n , 2 n -1, 2 n +1, 2 n -3, 2 n-1 -1} is presented. This design is derived by using
Ultracold Atoms in Optical Cavities

This thesis summarizes the research work we did in the past years, which focuses on the theoretical study of quantum effects of ordered atomic structures interacting with the quantum electric field of an optical resonator. Within this work we investigated the quantum properties of the light at the cavity output, emitted by this kind of system, and the quantum state of the atoms confined by the light fields of the cavity mode. Following one possible partition, we have organized the thesis in two parts. The first part of the thesis is devoted to the quantum properties of the light at the output of optical cavities, in which atoms are confined. After a brief introduction on the basics of atom-photon interactions, we show theoretically, that two atomic dipoles in a resonator can behave as a quantum non-linear medium, whose response can be controlled through the interactomic distance of the atoms inside the resonator. We identify the parameter regime, for which the system operates as a parametric amplifier, based on the atoms' collective dipole cascade emission. Moreover we also determine the corresponding field squeezing spectrum at the cavity output. These dynamics could be observed as a result of self-organization processes of laser-cooled atoms in resonators. The second part of the thesis studies the quantum ground state of an ultracold atomic gas trapped inside an optical resonator by the mechanical effects of atom-photon interactions. Due to the strong coupling regime, the ultracold atomic gas feels a quantum potential, where the fluctuations at the single photon level may be relevant in determining the trapping conditions. This potential depends on the atomic distribution, which at the same time determines the intracavity field amplitude. Here, after a brief introduction on ultracold atoms in optical lattices, we study the low temperature physics of an ultracold atomic gas in the quantum potential formed inside a pumped cavity, by mapping the dynamics to an effective Bose-Hubbard model. We predict the existence of non-compressible states of the atomic gas, and determine a phase diagram as a function of the cavity parameters. Novel effects are encountered, such as the existence of overlapping stability regions corresponding to competing insulator-like states when the laser pumps the cavity, to provide one example out of many. At the end of the thesis a concluding chapter presents some general outlooks to this thesis work.
Noninvasive imaging evaluation of peritoneal recurrence and chemotherapy benefit in gastric cancer after gastrectomy: a multicenter study

Background: Peritoneal recurrence (PR) is the predominant pattern of relapse after curative-intent surgery in gastric cancer (GC) and indicates a dismal prognosis. Accurate prediction of PR is crucial for patient management and treatment. The authors aimed to develop a noninvasive imaging biomarker from computed tomography (CT) for PR evaluation, and investigate its associations with prognosis and chemotherapy benefit. Methods: In this multicenter study including five independent cohorts of 2005 GC patients, the authors extracted 584 quantitative features from the intratumoral and peritumoral regions on contrast-enhanced CT images. The artificial intelligence algorithms were used to select significant PR-related features, and then integrated into a radiomic imaging signature. And improvements of diagnostic accuracy for PR by clinicians with the signature assistance were quantified. Using Shapley values, the authors determined the most relevant features and provided explanations to prediction. The authors further evaluated its predictive performance in prognosis and chemotherapy response. Results: The developed radiomics signature had a consistently high accuracy in predicting PR in the training cohort (area under the curve: 0.732) and internal and Sun Yat-sen University Cancer Center validation cohorts (0.721 and 0.728). The radiomics signature was the most important feature in Shapley interpretation. The diagnostic accuracy of PR with the radiomics signature assistance was improved by 10.13–18.86% for clinicians (P<0.001). Furthermore, it was also applicable in the survival prediction. In multivariable analysis, the radiomics signature remained an independent predictor for PR and prognosis (P<0.001 for all). Importantly, patients with predicting high risk of PR from radiomics signature could gain survival benefit from adjuvant chemotherapy. By contrast, chemotherapy had no impact on survival for patients with a predicted low risk of PR. Conclusion: The noninvasive and explainable model developed from preoperative CT images could accurately predict PR and chemotherapy benefit in patients with GC, which will allow the optimization of individual decision-making.
Diagnostic value of radiological staging and surveillance for T1 colorectal carcinomas: A multicenter cohort study

Abstract Background The role of radiological staging and surveillance imaging is under debate for T1 colorectal cancer (CRC) as the risk of distant metastases is low and imaging may lead to the detection of incidental findings. Objective The aim of this study was to evaluate the yield of radiological staging and surveillance imaging for T1 CRC. Methods In this retrospective multicenter cohort study, all patients of 10 Dutch hospitals with histologically proven T1 CRC who underwent radiological staging in the period 2000–2014 were included. Clinical characteristics, pathological, endoscopic, surgical and imaging reports at baseline and during follow‐up were recorded and analyzed. Patients were classified as high‐risk T1 CRC if at least one of the histological risk factors (lymphovascular invasion, poor tumor differentiation, deep submucosal invasion or positive resection margins) was present and as low‐risk when all risk factors were absent. Results Of the 628 included patients, 3 (0.5%) had synchronous distant metastases, 13 (2.1%) malignant incidental findings and 129 (20.5%) benign incidental findings at baseline staging. Radiological surveillance was performed among 336 (53.5%) patients. The 5‐year cumulative incidence of distant recurrence, malignant and benign incidental findings were 2.4% (95% confidence interval (CI): 1.1%–5.4%), 2.5% (95% CI: 0.6%–10.4%) and 18.3% (95% CI: 13.4%–24.7%), respectively. No distant metastatic events occurred among low‐risk T1 CRC patients. Conclusion The risk of synchronous distant metastases and distant recurrence in T1 CRC is low, while there is a substantial risk of detecting incidental findings. Radiological staging seems unnecessary prior to local excision of suspected T1 CRC and after local excision of low‐risk T1 CRC. Radiological surveillance should not be performed in patients with low‐risk T1 CRC.
Significant discordance between point of care BNP values obtained by the i-STAT and the Beckman DXI 800, causing confusion in patient management when both methods are used interchangeably by clinicians

Abstract B-type natriuretic peptide (BNP) is used as a biomarker of heart failure. In our hospital point of care (POCT) BNP test is performed in EDTA whole blood using i-STAT (Abbott Laboratories, Abbott Park, IL, USA) and in the clinical laboratory using EDTA plasma and the DXI 800 analyzer (Beckman, Brea, CA, USA). We compared BNP values in 88 patients obtained by using the i-STAT followed by using the DXI 800. The time difference between the two analyses varied from 32 min to less than 12 h. In addition, 11 specimens were simultaneously analyzed for BNP using both the i-STAT and the DXI 800 analyzer. Plotting BNP concentrations obtained by the DXI 800 in the x-axis (reference method) and the i-STAT values in the y-axis, we observed the following regression equation; y = 1.4758 x + 23.452 (n = 88, r = 0.96), indicating significant positive bias with the i-STAT. In addition, we also observed significant differences between BNP values obtained by the i-STAT and the DXI 800 in 11 specimens analyzed simultaneously. Therefore, clinicians should not use BNP concentrations obtained by the i-STAT interchangeably with BNP concentrations obtained by the DXI 800 analyzer for patient management.
Symmetric Key Cryptosystem Using 2d Reversible Cellular Automata

Traditional 2-dimentional reversible cellular automata (2-D RCA) is fit for cryptography for its rules being an affine function, i.e. . two reversible CA's rules can be applied in encryption process while another counterpart rules can be applied in decryption process In this paper, a 2- D RCA is proposed aimed to be applied in cryptography.
Parkinson’s Disease Drug Therapies in the Clinical Trial Pipeline: 2023 Update

Background: Since 2020, annual reports on the clinical development of new drug-based therapies for the neurodegenerative condition of Parkinson’s disease (PD) have been generated. These reviews have followed the progress of both “symptomatic treatments” (ST – improves/reduces symptoms of the condition) and “disease modifying treatments” (DMT - attempts to delay/slow progression by addressing the underlying biology of PD). Additional efforts have been made to further categorize these experimental treatments based on their mechanisms of action and class of drug. Methods: A dataset of clinical trials for drug therapies in PD was obtained using trial data downloaded from the ClinicalTrials.gov online registry. A breakdown analysis of all the studies that were active as of January 31st, 2023, was conducted. Results: There was a total of 139 clinical trials registered on the ClinicalTrials.gov website as active (with 35 trials newly registered since our last report). Of these trials, 76 (55%) were considered ST and 63 (45%) were designated DMT. Similar to previous years, approximately a third of the studies were in Phase 1 (n = 47; 34%), half (n = 72, 52%) were in Phase 2 and there were 20 (14%) studies in Phase 3. Novel therapies again represented the most dominant group of experimental treatments in this year’s report with 58 (42%) trials testing new agents. Repurposed drugs are present in a third (n = 49, 35%) of trials, with reformulations and new claims representing 19% and 4% of studies, respectively. Conclusions: Our fourth annual review of active clinical trials evaluating ST and DMT therapeutics for PD demonstrates that the drug development pipeline is dynamic and evolving. The slow progress and lack of agents transitioning from Phase 2 to Phase 3 is concerning, but collective efforts by various stakeholders are being made to accelerate the clinical trial process, with the aim of bringing new therapies to the PD community sooner.
The role of artificial intelligence in hypertensive disorders of pregnancy: towards personalized healthcare

ABSTRACT Introduction Guidelines advise ongoing follow-up of patients after hypertensive disorders of pregnancy (HDP) to assess cardiovascular risk and manage future patient-specific pregnancy conditions. However, there are limited tools available to monitor patients, with those available tending to be simple risk assessments that lack personalization. A promising approach could be the emerging artificial intelligence (AI)-based techniques, developed from big patient datasets to provide personalized recommendations for preventive advice. Areas covered In this narrative review, we discuss the impact of integrating AI and big data analysis for personalized cardiovascular care, focusing on the management of HDP. Expert opinion The pathophysiological response of women to pregnancy varies, and deeper insight into each response can be gained through a deeper analysis of the medical history of pregnant women based on clinical records and imaging data. Further research is required to be able to implement AI for clinical cases using multi-modality and multi-organ assessment, and this could expand both knowledge on pregnancy-related disorders and personalized treatment planning.
Trends in metabolic indicators and microvascular complications in Chinese adults with newly diagnosed type 2 diabetes: A retrospective, single-centre study of twenty-years

Background The purpose of this study is to assess metabolic indicators and trends in microvascular complications among Chinese adults with newly diagnosed type 2 diabetes during 2000–2020. Methods 3,907 patients were included and divided into three groups according to a time period of 7 years. This study analyzed trends in proportions of patients reached therapeutic targets of blood glucose, pressure and lipids, and trends in albuminuria, retinopathy, and peripheral neuropathy. Results In the past 20 years, the age of adults with newly diagnosed type 2 diabetes tended to be younger, and the proportion of female patients increased. There seemed no improvements in blood glucose and pressure. The rate of awareness and treatment on target of hypertension was less than 50%. There was a significant decrease in the prevalence of retinopathy, but no changes in nephropathy or peripheral neuropathy. Complications were more common for patients who were smoker, male, or with hypertension and obesity. Conclusions Over the past two decades, there have been encouraging reductions in retinopathy in Chinese adults with newly diagnosed diabetes, but no significant change in albuminuria and peripheral neuropathy. It may be related to the low awareness of diabetes and insufficient controlled blood glucose, pressure and lipids on target.
Effects of soil moisture on tunneling, survivorship, and food consumption of the Formosan and eastern subterranean termites (Blattodea: Rhinotermitidae)

Soil moisture is a critical environmental factor for the survival and behavior of subterranean termites (family Rhinotermitidae). The invasive Formosan subterranean termite, Coptotermes formosanus Shiraki, and the native eastern subterranean termite, Reticulitermes flavipes (Kollar), co-occur in the southeastern United States, while R. flavipes is distributed in a wider geoclimatic range. Previous studies showed that subterranean termites preferred higher soil moisture levels for tunneling and feeding; however, the impacts of constant moisture remained to be characterized to understand their moisture tolerance. In this study, we hypothesized that different soil moisture regimes can alter termite foraging and survival, and that the effects differ between the two species. The tunneling activity, survivorship, and food consumption of termites were documented for 28 days with different sand moisture conditions ranging from no moisture to full saturation (0%, 1%, 5%, 15%, 25%, and 30%). We found that there were no significant differences in the responses between C. formosanus and R. flavipes. In both species, termites did not survive or tunnel with 0% moisture. Termites performed tunneling with only 1% sand moisture, although they did not survive for 28 days. A minimal of 5% sand moisture was required for survival, and there were no significant differences in survivorship, tunneling activity, or food consumption among moisture contents of 5–30%. The results suggest that subterranean termites are resilient to moisture extremes. Colonies can tolerate low moisture conditions in their foraging environment for extended times, which may allow them to tunnel and find new moisture sources for colony survival.
A survey of Igusa’s local zeta function

The origin and development of the Igusa local zeta function, by Igusa and others, is pre- sented. In particular, we discuss the various conjectures Igusa made and the notable results that have so far been obtained. We also explain how topological and motivic zeta functions arose from the Igusa local zeta function and present the current status of the analogous conjectures. Igusa’s conjecture on exponential sums that are related to his zeta function is described
HUMAN ACTION RECOGNITION BASED ON DISCRIMINATIVE SPARSE REPRESENTATION ON MULTI-MANIFOLDS

Human action recognition is an important problem in computer vision. One of the methods that are recently used is sparse coding. Conventional sparse coding algorithms learn dictionaries and codes in an unsupervised manner and neglect class information that this problem, we use a discriminative sparse code based on multi divide labeled data samples into multi reduce dimension of manifolds. W neighbors for each data sample in each manifold. The intra class variance should be minimized while the inter class variance should be maximized, in the result we could calculate laplacian matrix and optim discriminative sparse error for classification. We run this method on KTH and UCF sport datasets. Results show that we obtain a better result dataset.
Machine Learning for Mortality Prediction in Patients With Heart Failure With Mildly Reduced Ejection Fraction

Background Machine‐learning‐based prediction models (MLBPMs) have shown satisfactory performance in predicting clinical outcomes in patients with heart failure with reduced and preserved ejection fraction. However, their usefulness has yet to be fully elucidated in patients with heart failure with mildly reduced ejection fraction. This pilot study aims to evaluate the prediction performance of MLBPMs in a heart failure with mildly reduced ejection fraction cohort with long‐term follow‐up data. Methods and Results A total of 424 patients with heart failure with mildly reduced ejection fraction were enrolled in our study. The primary outcome was all‐cause mortality. Two feature selection strategies were introduced for MLBPM development. The “All‐in” (67 features) strategy was based on feature correlation, multicollinearity, and clinical significance. The other strategy was the CoxBoost algorithm with 10‐fold cross‐validation (17 features), which was based on the selection result of the “All‐in” strategy. Six MLBPMs with 5‐fold cross‐validation based on the “All‐in” and the CoxBoost algorithm with 10‐fold cross‐validation strategy were developed by the eXtreme Gradient Boosting, random forest, and support vector machine algorithms. The logistic regression model with 14 benchmark predictors was used as a reference model. During a median follow‐up of 1008 (750, 1937) days, 121 patients met the primary outcome. Overall, MLBPMs outperformed the logistic model. The “All‐in” eXtreme Gradient Boosting model had the best performance, with an accuracy of 85.4% and a precision of 70.3%. The area under the receiver‐operating characteristic curve was 0.916 (95% CI, 0.887–0.945). The Brier score was 0.12. Conclusions The MLBPMs could significantly improve outcome prediction in patients with heart failure with mildly reduced ejection fraction, which would further optimize the management of these patients.
Understanding Sleep Regulation in Normal and Pathological Conditions, and Why It Matters

Sleep occupies a peculiar place in our lives and in science, being both eminently familiar and profoundly enigmatic. Historically, philosophers, scientists and artists questioned the meaning and purpose of sleep. If Shakespeare’s verses from MacBeth depicting “Sleep that soothes away all our worries” and “relieves the weary laborer and heals hurt minds” perfectly epitomize the alleviating benefits of sleep, it is only during the last two decades that the growing understanding of the sophisticated sleep regulatory mechanisms allows us to glimpse putative biological functions of sleep. Sleep control brings into play various brain-wide processes occurring at the molecular, cellular, circuit, and system levels, some of them overlapping with a number of disease-signaling pathways. Pathogenic processes, including mood disorders (e.g., major depression) and neurodegenerative illnesses such Huntington’s or Alzheimer’s diseases, can therefore affect sleep-modulating networks which disrupt the sleep-wake architecture, whereas sleep disturbances may also trigger various brain disorders. In this review, we describe the mechanisms underlying sleep regulation and the main hypotheses drawn about its functions. Comprehending sleep physiological orchestration and functions could ultimately help deliver better treatments for people living with neurodegenerative diseases.
Accurate Computations of Eigenvalues of Extremely Ill-conditioned Matrices and Differential Operators

This paper is concerned with computations of a few smaller eigenvalues (in absolute value) of a large extremely ill-conditioned matrix. It is shown that smaller eigenvalues can be accurately computed for a diagonally dominant matrix or a product of diagonally dominant matrices by combining a standard iterative method with the accurate inversion algorithms that have been developed for such matrices. Applications to the finite difference discretization of differential operators are discussed. In particular, a new discretization is derived for the 1-dimensional biharmonic operator that can be written as a product of diagonally dominant matrices. Numerical examples are presented to demonstrate the accuracy achieved by the new algorithms.