Miracle-dz/UniFAR-FTUs huggingface dataset:
Does hyperthermic intraperitoneal chemotherapy influence the rate of infective complications and change the pattern of infectious agents in patients undergoing cytoreductive surgery for peritoneal carcinomatosis?



Pattern of Infectious Agents in HIPEC

Aim: When CRS, in which complex surgical procedures are applied combined with HIPEC in which high dose cytotoxic agents are applied, many serious postoperative complications, especially infection-related complications, may develop in the patient group with peritoneal carcinomatosis whose general condition is impaired. In this study, it was aimed to investigate the effect of HIPEC application after SRC on infection rates and microbial agent spectrum alone. Materials and Methods: Between January 2012 and January 2020, one thousand six hundreds forty eight patients diagnosed with peritoneal carcinomatosis were scanned from hospital database and two hundred thirty-five eligible patients were included in the study. Demographic and clinical data, perioperative results and culture results were analyzed by grouping the patients as HIPEC and non-HIPEC. Results : It has been found that the incidence of surgical site infections was significantly higher in HIPEC group. (23.6%vs11.5%p=0.011). When the culture results were analyzed. The number of patients with E.coli (13.7% vs 5.7% p = 0.001), Klebsiella pneumoniae (9.9% vs 2.8% p = 0.001) and Pseudomonas aeruginosa (2.2% vs 0.9% p = 0.034) growth was significantly higher in the HIPEC group. Conclusions: It has been found that HIPEC application not only increases the infection rates but also expands the potential pathogen spectrum. Prediction of possible pathogens responsible for postoperative infections may affect the choice of prophylactic and ultimate treatment and so the risk of developing morbidity and mortality which may result from a possible septic progression, can be minimized with an effective antimicrobial therapy.
Research on the Information Tracing Model for Cross-border E-commerce Products based on Blockchain

Information asymmetry, information island, high regulatory cost, etc., emerge from the fast development of cross-border e-commerce in China, for which the blockchain, with its capabilities in information sharing, information traceability, smart contract, and so on, can play an effective role in product information tracing. Based on blockchain, this paper builds the product information tracing model using information chains of two different degrees of openness. The model includes the three major bodies of cross-border e-commerce—the supply chain alliance, the consumers and the regulatory departments, so as to produce information tracing, ensure information authenticity and integrity, and be applicable to all kinds of e-commerce platforms.
Early prediction of clinical response to checkpoint inhibitor therapy in human solid tumors through mathematical modeling

Checkpoint inhibitor therapy of cancer has led to markedly improved survival of a subset of patients in multiple solid malignant tumor types, yet the factors driving these clinical responses or lack thereof are not known. Here we present a translational mathematical model dependent on three key parameters for describing efficacy of checkpoint inhibitors in human cancer: tumor growth rate (), tumor immune infiltration ({Lambda}), and immunotherapy-mediated amplification of anti-tumor response (). The model was validated by fitting it to a compiled clinical tumor response dataset (n = 189 patients) obtained from published anti-PD-1 and anti-PD-L1 clinical trials, and then validated on an additional validation cohort (n = 64 patients) obtained from our in-house clinical trials. The derived parameters {Lambda} and were both significantly different between responding versus non-responding patients. Of note, our model appropriately classified response in 81.4% of patients by using only tumor volume measurements and within two months of treatment initiation in a retrospective analysis. The model reliably predicted clinical response to the PD-1/PD-L1 class of checkpoint inhibitors across multiple solid malignant tumor types. Comparison of model parameters to immunohistochemical measurement of PD-L1 and CD8+ T cells confirmed robust relationships between model parameters and their underlying biology, demonstrating reliable methods to inform model parameters directly from biopsy samples, which are conveniently obtainable as early as the start of treatment. Together, these results suggest that the model parameters may serve as early and robust biomarkers of the efficacy of checkpoint inhibitor therapy on an individualized per-patient basis.
To Hide Private Position Information in Localization for Internet of Underwater Things

Privacy-preserving localization for Internet of Underwater Things (IoUT) plays a fundamental role in the sensing, communication and control of ocean environments. However, the unique characteristics of underwater environment make it much more difficult to achieve such a task. In this article, we are concerned with a privacy-preserving localization issue for IoUT, subjected to asynchronous clock, stratification effect and forging attack in cyber channels. In order to eliminate the influence of asynchronous clock and hide the private position information, we develop a privacy-preserving asynchronous transmission protocol, where a received signal strength (RSS)-based detection strategy is given to detect the malicious anchor nodes. Based on this, a least squares estimator is designed to estimate the position information of target. Particularly, a ray compensation strategy is incorporated into the localization estimator, such that the localization bias from assuming the straight-line transmission can be avoided. It is worth mentioning that, the proposed localization solution in this article can not only hide the private position information, but also eliminate the influences of asynchronous clock, stratification effect and forging attack. Finally, simulation and experiment results are conducted to reveal that the proposed localization solution outperforms the other existing works in terms of localization accuracy and effectiveness.
A two-sided matching decision-making approach based on regret theory under intuitionistic fuzzy environment

This study proposes a two-sided matching decision-making (TSMDM) approach by combining the regret theory under the intuitionistic fuzzy environment. At first, according to the Hamming distance of intuitionistic fuzzy sets and regret theory, superior and inferior flows are defined to describe the comparative preference of subjects. Hereafter, the satisfaction degrees are obtained by integrating the superior and inferior flows of the subjects. The comprehensive satisfaction degrees are calculated by aggregating the satisfaction degrees, based on which, a multi-objective TSMDM model is built. Furthermore, the multi-objective TSMDM model is converted to a single-objective model, the optimal solution of the latter is derived. Finally, an illustrative example and several analyses are provided to verify the feasibility and the effectiveness of the proposed approach.
The Effect of University Context on Academics' Engagement with Industry

Drawing on the theory of planned behavior, this study investigates the impact of academics' perception of university context on their engagement with industry via the mediation of academics' intention to industrial collaborations. The third mission and supportive policy were put forward as two university influence factors. From analysis results of 564 questionnaire responses, we found that academics' perception of university context had significant effect on academic engagement. In addition, the relationship between university mission, supportive policy and academics' industrial engagement were partially mediated by academics' intention to industrial collaboration.
Development and Characterization of a Controlled Expression System for Osteogenic Genes

Current treatment methods for non-union bone defects present problems. The objective of this study was to genetically engineer primary and immortalized cell types to express osteogenic molecules BMP2, RUNX2, OSX, or VEGF in a doxycycline dose-dependent manner for tissue regeneration. Coding cDNA sequences for all four factors were sub-cloned into the pRTS-1 expression plasmid and transfected into HUCPVCs, RBMCs, ROS cells. Electroporation was the most effective method of transfection for all cells but stably transfected cells could only be established for RBMCs and ROS cells. Cells achieved maximum expression within 72hours of induction and returned to basal levels after 18 days. Enhanced osteogenic bioactivity was only observed upon activation of BMP-2. The tight regulation of the pRTS-1 system allowed for a controlled gene expression. Future transplantation experiments using these engineered RBMC and ROS cells in vivo will evaluate the usefulness of the dox-inducible gene expression system in bone defects.
Entrepreneurial Ecosystems for Tech Start-ups in India: Evolution, Structure and Role

Copyright © The Author(s). 2021 This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/ licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The 21st century has been a witness to unprecedented tectonic technology shifts in the world and the emergence of new global challenges impacting all countries across the world in different capacities. These developments have forced countries around the world to strategize their available resources to derive innovative solutions in response to these challenges, leading to long-term value creation and growth of their economies. The economies world over have focused on creating, nurturing, and promoting regional innovation systems (RIS) to address these challenges. Technology start-ups and entrepreneurial ecosystems that nurture them are an important and critical element of the responses formulated by the regional governments that have successfully led the innovation-based growth of their economies. [1] While tech start-ups introduce new knowledge-intensive products and services to the market and accelerate the rate of structural change in the economy, the entrepreneurial ecosystems provide a congenial support system to enable the growth of these start-ups by orchestrating the different elements such as customers, financiers, mentors, and incubators. [2]
Studies on Detarium microcarpium gum. II: Investigation as a prolonged release matrix for encapsulated chlorpheniramine maleate

Des granules a liberation prolongee de maleate de chlorpheniramine ont ete prepares en employant 10 a 40% de gomme de Detarium microcarpium comme matrice hydrophile. A titre de comparaison, des granules similaires ont ete prepares avec de la gomme adragante ou de la carboxymethylcellulose sodique. Un certain nombre de caracteristiques physiques de granules de granulomerie entre 0,5 et 1,00 mm ont ete etudiees. Les granules sont ensuite mis en gelules. En ce qui concerne la friabilite des granules, la gomme de detarium est plus performante que la gomme adragante, mais moins que la carboxymethylcellulose sodique
Design of Modern Distributed Systems based on Microservices Architecture

Distributed systems are very commonplace nowadays. They have seen an enormous growth in use during the past few years. The idea to design systems that are robust, scalable, reliable, secure and fault tolerance are some of the many reasons of this development and growth. Distributed systems provide a shift from traditional ways of building systems where the whole system is concentrated in a single and indivisible unit. The latest architectural changes are progressing toward what is known as microservices. The monolithic systems, which can be considered as ancestors of microservices, cannot fulfill the requirements of today’s big and complex applications. In this paper we decompose a monolithic application into microservices using three different architectural patterns and draw comparisons between the two architectural styles using detailed metrics that are generated from the Apache JMeter tool. The application is created via . NET framework, uses the MVC pattern and is fictive. The two comparable apps before testing with Apache JMeter, will be deployed in almost identical hosting environment in order to gain results that are valuable. Using the generated data, we deduce the advantages and disadvantages of the two architectural styles.
The Efficacy of Herbal Supplements and Nutraceuticals for Prevention of Migraine: Can They Help?

Migraine is a common neurological disorder associated with or without aura. Although the pathophysiology of migraine is not very well understood, pro-inflammatory cytokines and oxidative stress biomarkers are found to be increased in migraine. Multiple studies have been done to see if alternative medicine such as herbal supplements and nutraceuticals are effective in the prevention and treatment of migraine headaches. This review aimed to evaluate the effect of supplements like coenzyme Q10, riboflavin (vitamin B2), feverfew, and magnesium on the frequency, severity, and duration of migraine attacks. We performed a thorough literature search using mainly PubMed. We included studies published in the last 10 years, those conducted among adult human participants 18-65 years of age, and those published in the English language. Based on the articles selected for the final review, we concluded that herbal supplements and nutraceuticals help reduce the frequency of migraine headaches; however, mixed results were seen regarding the severity and duration of headaches. Moreover, there were no concerning side effects with these supplements. Therefore, physicians can suggest herbal supplements to patients who experience adverse effects from pharmaceutical drugs and desire a more natural treatment.
Comparative Analysis of Intelligent Driving and Safety Assistance Systems Using YOLO and SSD Model of Deep Learning

Bad road conditions are one of the main causes of road accidents around the world. These kinds of accidents prove to be fatal as many lives are lost in these accidents that are mainly caused by potholes or distress on surface of roads. This paper suggests a system that will not only help in reducing the chances of these accidents by making the driver aware of the upcoming distress/potholes on the road but also saving the location of these potholes which can be sent to respective authorities so that they can be repaired. The authors have used technologies like image processing, computer vision, deep learning, and internet of things (IoT) to make this happen. It uses a camera mounted in front near windshield that will capture the images which will be further be processed to get the location of the potholes and distress on road. These detected potholes can be projected on a heads-up display (HUD) placed near windshield which will notify the driver of the potholes.
Exploration of Ideological and Political Education in College Physics

Ideological and political education (IPE) of the curriculum is an important way to integrate IPE into curriculum teaching and realize all-round education. The integration of IPE elements into college physics teaching is the practice of the new IPE concept. This paper first analyzes the advantages and necessity of IPE in college physics, then analyzes the current situation of IPE in college physics at this stage, and finally discusses the implementation strategies of IPE in college physics teaching, and puts forward specific methods and ways to implement IPE.
On Linear Interpolation in the Latent Space of Deep Generative Models

The underlying geometrical structure of the latent space in deep generative models is in most cases not Euclidean, which may lead to biases when comparing interpolation capabilities of two models. Smoothness and plausibility of linear interpolations in latent space are associated with the quality of the underlying generative model. In this paper, we show that not all such interpolations are comparable as they can deviate arbitrarily from the shortest interpolation curve given by the geodesic. This deviation is revealed by computing curve lengths with the pull-back metric of the generative model, finding shorter curves than the straight line between endpoints, and measuring a non-zero relative length improvement on this straight line. This leads to a strategy to compare linear interpolations across two generative models. We also show the effect and importance of choosing an appropriate output space for computing shorter curves. For this computation we derive an extension of the pull-back metric.
Review of problem-based learning trends in 2010-2020: A meta-analysis study of the effect of problem-based learning in enhancing mathematical problem-solving skills of Indonesian students

The present study analyzes various inconsistent research findings on the effect of problem-based learning (PBL) in enhancing students’ mathematical problem-solving skills (MPSS) in Indonesia. A meta-analysis of 50 primary studies that conform to the inclusion criteria was conducted to summarize, estimate, and evaluate findings of the effect of PBL by analyzing the heterogeneity of the studies. Heterogeneity analysis was conducted by analyzing the effect of moderator variables, namely the characteristics of sample and publication. The analysis tool used the Comprehensive Meta-Analysis (CMA) application by selecting the Hedge’s equation to determine its effect size. The “One study removed” tool in the CMA application was used to analyze the sensitivity of effect size data, and publication bias was assessed by the funnel plot, fill and trim test, and Rosenthal’s fail-safe N test. The findings of the study showed that the overall implementation of PBL had a larger positive effect significantly compared to conventional learning on the students’ MPSS in Indonesia based on a random effect model. The characteristics of the education level and research area significantly affected the heterogeneity of the effect of PBL on students’ MPSS. Thus, these findings suggest that mathematics teachers and lecturers should choose PBL as one of the best solutions in implementing Mathematics learning in the classroom to enhance MPSS. Furthermore, they should pay attention and consider the education level of students and the area where students learn in implementing PBL in enhancing students’ MPSS.
DocOIE: A Document-level Context-Aware Dataset for OpenIE

Open Information Extraction (OpenIE) aims to extract structured relational tuples (subject, relation, object) from sentences and plays critical roles for many downstream NLP applications. Existing solutions perform extraction at sentence level, without referring to any additional contextual information. In reality, however, a sentence typically exists as part of a document rather than standalone; we often need to access relevant contextual information around the sentence before we can accurately interpret it. As there is no document-level context-aware OpenIE dataset available, we manually annotate 800 sentences from 80 documents in two domains (Healthcare and Transportation) to form a DocOIE dataset for evaluation. In addition, we propose DocIE, a novel document-level context-aware OpenIE model. Our experimental results based on DocIE demonstrate that incorporating document-level context is helpful in improving OpenIE performance. Both DocOIE dataset and DocIE model are released for public.
A non-Archimedean analogue of Campana's notion of specialness

Let $K$ be an algebraically closed, complete, non-Archimedean valued field of characteristic zero, and let $\mathscr{X}$ be a $K$-analytic space (in the sense of Huber). In this work, we pursue a non-Archimedean characterization of Campana's notion of specialness. We say $\mathscr{X}$ is $K$-analytically special if there exists a connected, finite type algebraic group $G/K$, a dense open subset $\mathscr{U}\subset G^{\text{an}}$ with $\text{codim}(G^{\text{an}}\setminus \mathscr{U}) \geq 2$, and an analytic morphism $\mathscr{U} \to \mathscr{X}$ which is Zariski dense. With this definition, we prove several results which illustrate that this definition correctly captures Campana's notion of specialness in the non-Archimedean setting. These results inspire us to make non-Archimedean counterparts to conjectures of Campana. As preparation for our proofs, we prove auxiliary results concerning the indeterminacy locus of a meromorphic mapping between $K$-analytic spaces, the notion of pseudo-$K$-analytically Brody hyperbolic, and extensions of meromorphic maps from smooth, irreducible $K$-analytic spaces to the analytification of a semi-abelian variety.
Effect of Real-Time Feedback Devices on Primary Care Patient Experience Scores: A Cluster-Randomized Trial

Patient experience is a critical measure for ambulatory primary care, although it is unclear how to best improve patient experience scores. This study aimed to determine whether use of a real-time feedback (RTF) device improved patient experience scores in a cluster-randomized trial. The primary outcomes were change from baseline in 9 Clinician and Group Consumer Assessment of Healthcare Providers and Systems (CG-CAHPS) question and domain scores most closely related to the RTF questions asked in a linear mixed effects model. There were no observed statistically significant intervention-related differences in CG-CAHPS scores in any of the 9 CG-CAHPS questions or domains (P = .12-.99). In intervention clinics, there were no statistically significant correlation between CG-CAHPS top box scores and RTF device scores (P = .23-.98). Clinics in an urban primary care network randomized to receive RTF devices did not significantly improve related CG-CAHPS question or domain scores nor were those scores correlated with RTF device scores. More research is needed to identify effective interventions to improve ambulatory primary care patient experience.
On the Distribution of the Information Density of Gaussian Random Vectors: Explicit Formulas and Tight Approximations

Based on the canonical correlation analysis, we derive series representations of the probability density function (PDF) and the cumulative distribution function (CDF) of the information density of arbitrary Gaussian random vectors as well as a general formula to calculate the central moments. Using the general results, we give closed-form expressions of the PDF and CDF and explicit formulas of the central moments for important special cases. Furthermore, we derive recurrence formulas and tight approximations of the general series representations, which allow efficient numerical calculations with an arbitrarily high accuracy as demonstrated with an implementation in Python publicly available on GitLab. Finally, we discuss the (in)validity of Gaussian approximations of the information density.
RAIL: A modular framework for Reinforcement-learning-based Adversarial Imitation Learning

While Adversarial Imitation Learning (AIL) algorithms have recently led to state-of-the-art results on various imitation learning benchmarks, it is unclear as to what impact various design decisions have on performance. To this end, we present here an organizing, modular framework called Reinforcement-learning-based Adversarial Imitation Learning (RAIL) that encompasses and generalizes a popular subclass of existing AIL approaches. Using the view espoused by RAIL, we create two new IfO (Imitation from Observation) algorithms, which we term SAIfO: SAC-based Adversarial Imitation from Observation and SILEM (Skeletal Feature Compensation for Imitation Learning with Embodiment Mismatch). We go into greater depth about SILEM in a separate technical report. In this paper, we focus on SAIfO, evaluating it on a suite of locomotion tasks from OpenAI Gym, and showing that it outperforms contemporaneous RAIL algorithms that perform IfO.