Miracle-dz/UniFAR-FTUs huggingface dataset:
Technical innovations to optimize continence recovery after robotic assisted radical prostatectomy.

BACKGROUND Urinary incontinence is one of the most significant causes of concern among patients who get surgical treatment for prostate cancer, even after the introduction of the robot. The aim of this study is to summarize current knowledge of the factors influencing urinary continence (UC) and the technical innovations to optimize UC recovery after robotic radical prostatectomy (RARP). METHODS A non-systematic review was conducted from January 2000 to October 2018 to identify original and review articles in English describing the anatomy of the prostate and pelvis. An emphasis was addressed to article describing technical innovations to optimize UC after RARP. RESULTS Improved knowledge of the normal structure in the pelvis should lead to a greater understanding of the pathophysiology of urinary incontinence, and further development of intraoperative techniques to improve the outcomes of UC. The literature has shown certain technique modification to meliorate UC as potential benefit to reduce the risk of urinary incontinence after RARP. These techniques might be divided in 3 categories to improve an early return to UC: 1) preservation, 2) reconstruction and 3) reinforcement of the anatomic structures in the pelvis, which will make new supporting system after RARP. CONCLUSIONS In the present review, the authors summarize factors influencing incontinence after RARP and outline a common denominator for all the surgical techniques described in the literature for UC recovery.
Micro-computed tomography for discriminating between different forming techniques in ancient pottery: new segmentation method and pore distribution recognition

Micro-computed tomography is a valuable tool for studying ancient ceramics technology. Analysing pottery 3-D images is a challenging issue, the data being extremely noisy and heterogeneous. Quantitative criteria are introduced for the characterisation of a previously unrecognised pottery building method, the Spiralled Patchwork Technology (SPT). An analytical protocol has been implemented which applies to 3-D reconstructions of ceramic sherds and integrates automatic segmentation of porous systems and shape recognition using Hough transform. It enables discriminating between SPT and other techniques for vessels manufacture, and opens up many perspectives for independent characterisation of ancient technical gestures in ceramic technology.
Recurrent Stroke With Rivaroxaban Compared With Aspirin According to Predictors of Atrial Fibrillation: Secondary Analysis of the NAVIGATE ESUS Randomized Clinical Trial.

Importance The NAVIGATE ESUS randomized clinical trial found that 15 mg of rivaroxaban per day does not reduce stroke compared with aspirin in patients with embolic stroke of undetermined source (ESUS); however, it substantially reduces stroke risk in patients with atrial fibrillation (AF). Objective To analyze whether rivaroxaban is associated with a reduction of recurrent stroke among patients with ESUS who have an increased risk of AF. Design, Setting, and Participants Participants were stratified by predictors of AF, including left atrial diameter, frequency of premature atrial contractions, and HAVOC score, a validated scheme using clinical features. Treatment interactions with these predictors were assessed. Participants were enrolled between December 2014 and September 2017, and analysis began March 2018. Intervention Rivaroxaban treatment vs aspirin. Main Outcomes and Measures Risk of ischemic stroke. Results Among 7112 patients with a mean (SD) age of 67 (9.8) years, the mean (SD) HAVOC score was 2.6 (1.8), the mean (SD) left atrial diameter was 3.8 (1.4) cm (n = 4022), and the median (interquartile range) daily frequency of premature atrial contractions was 48 (13-222). Detection of AF during follow-up increased for each tertile of HAVOC score: 2.3% (score, 0-2), 3.0% (score, 3), and 5.8% (score, >3); however, neither tertiles of the HAVOC score nor premature atrial contractions frequency impacted the association of rivaroxaban with recurrent ischemic stroke (P for interaction = .67 and .96, respectively). Atrial fibrillation annual incidence increased for each tertile of left atrial diameter (2.0%, 3.6%, and 5.2%) and for each tertile of premature atrial contractions frequency (1.3%, 2.9%, and 7.0%). Among the predefined subgroup of patients with a left atrial diameter of more than 4.6 cm (9% of overall population), the risk of ischemic stroke was lower among the rivaroxaban group (1.7% per year) compared with the aspirin group (6.5% per year) (hazard ratio, 0.26; 95% CI, 0.07-0.94; P for interaction = .02). Conclusions and Relevance The HAVOC score, left atrial diameter, and premature atrial contraction frequency predicted subsequent clinical AF. Rivaroxaban was associated with a reduced risk of recurrent stroke among patients with ESUS and moderate or severe left atrial enlargement; however, this needs to be independently confirmed before influencing clinical practice.
Programming event processors with thingflow

We present ThingFlow, a software architecture to write event-processing pipelines for Internet-of-things (IoT) applications. Such applications typically involve sensing, stateful transformations of event streams, state estimation, and control/actuation, learning, and data analytics. They are currently programmed using a zoo of languages, systems, and APIs with ad hoc data passing protocols. ThingFlow provides three abstractions: sensors which can sample changing physical values, streams of (potentially real-valued) data, and stateful filters, which transform input streams to output streams, and can be composed. ThingFlow programs consist of compositions of filters scheduled and run by an explicit, asynchronous, scheduler. ThingFlow is implemented as a Python API whose core is compact enough to run on very limited microcontrollers such as the ESP8266, while providing support for sensors, message streams over the network, machine learning, and cloud backends. We show the expressiveness, versatility, and simplicity of ThingFlow on a number of examples from the IoT domain, incorporating sensing, filtering, actuation, data analysis, and learning.
WHAT ARE THE IMPACTS OF COVID-19 ON SMALL BUSINESSES IN THE U.S.? EARLY EVIDENCE BASED ON THE LARGEST 50 MSAS

ABSTRACT The novel coronavirus (covid-19) has caused an unprecedented impact on both the health and economic well-being of the United States. Small businesses have been particularly affected amid the outbreak of covid-19. Based on the first nine weeks of U.S. Small Business Pulse Survey data, this study examines the impacts of covid-19 on small businesses across the 50 most populous metropolitan areas in the United States with an individual growth model. The results demonstrate significant disparities in impacts across regions and over time. Although the death incidents directly impacted business operations, the social, economic, demographic vulnerability, and public policies were additionally critical to our understanding of these patterns. The findings directly contribute to scholarship on regional resilience under pandemic disruption from the perspective of small businesses. More specifically, our work sheds light on the relationship between regional socioeconomic vulnerability and small-business resilience. The results provide rich implications for practices and public policymaking.
Achieving Sendai Framework in Africa: Progress and challenges toward Target E

Abstract. Disaster risk reduction (DRR) strategies are critical for formalizing effective disaster risk reduction. The overall objective of this study was to assess the progress the Africa Union member states are making in developing DRR strategies as required by Target E of the Sendai Framework. The study used both qualitative and quantitative approaches where in-depth desk review of DRR strategies and online questionnaires were administered to 53 respondents. The findings show about 68% of the AU member states have national strategies and 35% have sub-national strategies but generally, there is sluggish progress in updating DRR strategies in accordance with Sendai Framework Target E. Weak technical and institutional capacities, inadequate funds, limited decentralization of DRR and poor governance are key challenges hampering their implementation. Governments in Africa and partners should pay immediate attention to accelerate the development of risk-informed strategies to achieve the aspirations of the Sendai Framework.
A Pilot Study on Hepatobiliary Scintigraphy to Monitor Regional Liver Function in 90Y Radioembolization

Radioembolization is increasingly used as a bridge to resection (i.e., radiation lobectomy). It combines ipsilateral tumor control with the induction of contralateral hypertrophy to facilitate lobar resection. The aim of this pilot study was to investigate the complementary value of hepatobiliary scintigraphy (HBS) before and after radioembolization in the assessment of the future remnant liver. Methods: Consecutive patients with liver tumors who underwent HBS before and after 90Y radioembolization were included. Regional (treated/nontreated) and whole liver function and volume were determined on HBS and CT. Changes in regional liver function and volume were correlated with the functional liver absorbed doses, determined on 90Y PET/CT. In addition, the correlation between liver volume and function change was evaluated. Results: Thirteen patients (10 hepatocellular carcinoma, 3 metastatic colorectal carcinoma) were included. Liver function of the treated part declined after radioembolization (HBS-pre, 4.0%/min/m2; HBS-post, 1.9%/min/m2; P = 0.001), whereas the function of the nontreated part increased (HBS-pre, 1.4%/min/m2; HBS-post, 2.8%/min/m2; P = 0.009). Likewise, treated volume decreased (pretreatment, 1,118.7 cm3; posttreatment, 870.7 cm3; P = 0.003), whereas the nontreated volume increased (pretreatment, 412.7 cm3; posttreatment, 577.6 cm3; P = 0.005). Bland–Altman analysis revealed a large bias (29%) between volume decrease and function decrease in the treated part and wide limits of agreement (−7.7%–65.6%). The bias between volume and function change was smaller (±6.0%) in the nontreated part of the liver, but limits of agreement were still wide (−117.9%–106.7%). Conclusion: Radioembolization induces regional changes in liver function that are accurately detected by HBS. Limits of agreement between function and volume changes were wide, showing large individual differences. This finding indicates that HBS may have a complementary role in the management of patients for radiation lobectomy.
Adapting Physical Therapy Management of Patients With COVID-19 in the Acute Care Setting: A Clinical Perspective

Purpose: Using emerging evidence and information pertaining to the SARS-CoV-2 virus, a team of physical therapists (PTs) and occupational therapists (OTs) at the Duke University Hospital (DUH) created a screening algorithm to guide PT/OT evaluation readiness and treatment decisions. This article aims to discuss factors that were considered in the initial development of therapy guidelines and, in so doing, emphasizes the need for ongoing analysis of such screening algorithms and treatment guidelines as evidence continues to emerge. Summary of Key Points: The interdisciplinary team, leveraging their collective experience combined with the updated literature, changed the initial medical management for patients diagnosed with coronavirus disease 2019 (COVID-19). As a result, the screening algorithm was adapted to reflect evolving evidence-based practices. This facilitated earlier and more skillful PT intervention as well as continued PT involvement in the treatment process throughout the course of the patients' stay in the hospital. Conclusions: To guide clinical decisions regarding medical stability of patients and appropriate timing for PT intervention, the therapists at DUH considered factors including defining and identifying stages of disease severity, assessment of laboratory values, and monitoring oxygen stability. Beyond medical complexity, mobility was limited by several additional isolation barriers which presented challenges for PTs, nursing staff, and the patient.
Did Going North Give Us Migraine? An Evolutionary Approach on Understanding Latitudinal Differences in Migraine Epidemiology

This commentary discusses a recent publication by evolutionary biologists with strong implications for migraine experts. The Authors showed that a gene polymorphism associated with migraine gave our ancestors an evolutionary advantage when colonizing northern, and thus colder, territories. They then highlight that the prevalence of migraine may differ among countries because of climatic adaptation. These results may prove useful in planning both epidemiological and physiological studies in the field of migraine.
Fast-Exponential Sliding Mode Control of Robotic Manipulator With Super-Twisting Method

This brief addresses the trajectory tracking problem for robotic manipulators without angular velocity measurements in the presence of parameter perturbation and torque disturbance. A novel fast terminal sliding mode surface is developed to ensure global strong robustness and fast error convergence. Then, a high-order sliding mode observer (HOSMO) is constructed to estimate unknown joints’ angular velocities and the system lumped disturbance in a finite time. Chattering avoidance can be realized via the continuous control strategy based on super-twisting method. The finite-time arrival of sliding mode surface and fast-exponential convergence of the tracking error are strictly proved based on the Lyapunov stability theory. Finally, an example of a two-link robotic manipulator is provided. From the comparison with other control strategies, the advantages of proposed method are fully demonstrated.
The Role of Nanoliposomal Irinotecan Plus Fluorouracil and Folinic Acid as Second-Line Treatment Option in Patients With Metastatic Pancreatic Ductal Adenocarcinoma

BackgroundAccording to the NAPOLI-1 trial, nanoliposomal irinotecan (nal-IRI) plus 5-fluorouracil/leucovorin (5-FU/LV) showed improved overall survival compared to fluorouracil alone for patients with metastatic pancreatic cancer who previously treated gemcitabine-based therapy. In that trial, Asian patients had frequent dose modification due to hematological toxicity. There has been limited information on the clinical benefit and toxicity of this regimen in a real-world setting. Herein, we assessed real-world experience of nal-IRI plus 5-FU/LV in patients with advanced pancreatic cancer after gemcitabine failure.MethodsWe conducted a single institution retrospective analysis of response, survival and safety in patients who had been treated with nal-IRI with 5-FU/LV. Patients with metastatic pancreatic ductal adenocarcinoma previously treated with gemcitabine-based therapy received nal-IRI (80mg/m2) with 5-FU/LV every 2 weeks. ResultsFifty-one patients received nal-IRI plus 5-FU/LV between January 2015 and December 2020. The median age was 67 years, and males were 58.8%. A total of 40 (78.4%) and 11 (21.6%) patients had received one and two lines of prior chemotherapy before enrollment, respectively. Median progression-free survival was 2.8 months (95% confidence interval [CI] 1.8-3.7) and median overall survival was 7.0 months (95% CI 6.0-7.9). Chemotherapy doses were reduced or delayed in 33 (64.7%) patients during the first 6 weeks and median relative dose intensity was 0.87. Thirty-six (70.6%) patients experienced any grade 3 or 4 adverse events. Most common grade 3 or 4 adverse event was neutropenia (58.8%) and most non-hematologic adverse events were under grade 2. Since the start of first-line chemotherapy, median overall survival was 16.3 months (95% CI 14.1-18.4). ConclusionsNal-IRI plus 5-FU/LV seems to be effective, with manageable toxicities, after gemcitabine-based treatment in patients with metastatic pancreatic ductal adenocarcinoma. Trial registration Retrospectively registered
Multiphase Level-Set Loss for Semi-Supervised and Unsupervised Segmentation with Deep Learning

Recent state-of-the-art image segmentation algorithms are mostly based on deep neural network, thanks to its high performance and fast computation time. However, these methods are usually trained in a supervised manner, which requires large number of high quality ground-truth segmentation masks. On the other hand, classical image segmentation approaches such as level-set methods are still useful to help generation of segmentation masks without labels, but these algorithms are usually computationally expensive and often have limitation in semantic segmentation. In this paper, we propose a novel multiphase level-set loss function for deep learning-based semantic image segmentation without or with small labeled data. This loss function is based on the observation that the softmax layer of deep neural networks has striking similarity to the characteristic function in the classical multiphase level-set algorithms. We show that the multiphase level-set loss function enables semi-supervised or even unsupervised semantic segmentation. In addition, our loss function can be also used as a regularized function to enhance supervised semantic segmentation algorithms. Experimental results on multiple datasets demonstrate the effectiveness of the proposed method.
To Design and Implement a Recommender System based on Brainwave: Applying Empirical Model Decomposition (EMD) and Neural Networks

Recommender systems collect and analyze users’ preferences to help users overcome information overload and make their decisions. In this research, we develop an online book recommender system based on users’ brainwave information. We collect users’ brainwave data by utilizing electroencephalography (EEG) device and apply empirical mode decomposition (EMD) to decompose the brainwave signals into intrinsic mode functions (IMFs). We propose a back-propagation neural networks (BPNN) model to portrait the user’s brainwave preference correlations based on IMFs of brainwave signals, thereby designing and developing the book recommender system. The experimental results show that the recommender system combined with the brainwave analysis can improve accuracy significantly. This research has highlighted a future direction for research and development on humancomputer interaction (HCI) design and recommender system.
Loosely Coupled Payload Transport System with Robot Replacement

In this work, we present an algorithm for robot replacement to increase the operational time of a multi-robot payload transport system. Our system comprises a group of nonholonomic wheeled mobile robots traversing on a known trajectory. We design a multi-robot system with loosely coupled robots that ensures the system lasts much longer than the battery life of an individual robot. A system level optimization is presented, to decide on the operational state (charging or discharging) of each robot in the system. The charging state implies that the robot is not in a formation and is kept on charge whereas the discharging state implies that the robot is a part of the formation. Robot battery recharge hubs are present along the trajectory. Robots in the formation can be replaced at these hub locations with charged robots using a replacement mechanism. We showcase the efficacy of the proposed scheduling framework through simulations and experiments with real robots.
CLEARumor at SemEval-2019 Task 7: ConvoLving ELMo Against Rumors

This paper describes our submission to SemEval-2019 Task 7: RumourEval: Determining Rumor Veracity and Support for Rumors. We participated in both subtasks. The goal of subtask A is to classify the type of interaction between a rumorous social media post and a reply post as support, query, deny, or comment. The goal of subtask B is to predict the veracity of a given rumor. For subtask A, we implement a CNN-based neural architecture using ELMo embeddings of post text combined with auxiliary features and achieve a F1-score of 44.6%. For subtask B, we employ a MLP neural network leveraging our estimates for subtask A and achieve a F1-score of 30.1% (second place in the competition). We provide results and analysis of our system performance and present ablation experiments.
Revisiting the Technology Challenges and Proposing Enhancements in Ambient Assisted Living for the Elderly

Several social and technical trends support the elderly’s desire to live independently in their preferred environment, despite their increasing medical needs, and enhance their quality of life at home. Ambientassisted living (AAL) has the capabilities to support the elderly and to decrease their dependency on formal or informal caregivers. We provide a review of the technological challenges that were identified as inhibiting factors in the past decade and then present recent technological advances, e.g., cloud computing, machine learning, artificial intelligence, the Internet of Things. We also fill the gap in the current literature in regard to specific AAL solutions and propose fourthgeneration AAL technology design. We find that most informal caregivers are family members who are medically untrained and that the use of advanced analytical processes on AAL-generated data could significantly increase symptom identification. We also present the implications and remaining challenges along with recommendations for future research.
On the Baum--Connes conjecture with coefficients for linear algebraic groups

We prove the Baum--Connes conjecture with arbitrary coefficients for some classes of groups: (1) Linear algebraic groups over a non-archimedean local field. (2) Linear algebraic groups over the adeles of a global field k, provided that at every archimedean place of k the associated Lie group is amenable. (3) All closed subgroups of the above groups. This includes linear algebraic groups over global fields - with the same condition as in (2). Update: proof of main results incomplete, maybe not repairable.
Commercialized Content on Social Media Platforms: Exploring the Drivers of the Viewership of Paid Q&A

Paying to view others’ answers is an emerging business model happening on Weibo, a Chinese version of Twitter. Yet, little is known about what drives people to pay to view others’ answers. Based on signaling theory and related literature, we develop a model to predict the viewership of paid-for answers. Using unique panel data of 417 question-to-answers, we find that answer providers’ Weibo level, the number of comments that the paid-for answer receives, as well as the question price positively affect the viewership of the paid-for answer. Our findings contribute to the literature and enlighten content providers and platform organization on how to facilitate individual users to commercialize content for profits.