Miracle-dz/UniFAR-FTUs huggingface dataset:
Trifluridine/tipiracil overcomes the resistance of human gastric 5-fluorouracil-refractory cells with high thymidylate synthase expression

Trifluridine/tipiracil (FTD/TPI or TFTD, also known as TAS-102) is a combination of the antineoplastic thymidine analog, FTD, and thymidine phosphorylase inhibitor, TPI (molar ratio 1:0.5). FTD/TPI was approved in Japan, the United States, and the European Union for the treatment of unresectable advanced or recurrent colorectal cancer. We evaluated the in vitro and in vivo efficacy and mechanisms of action of FTD and FTD/TPI against 5-fluorouracil (5-FU)-resistant MKN45/5FU, MKN74/5FU, and KATOIII/5FU human gastric cancer cells overexpressing thymidylate synthase (TS) and their respective parent cell lines. MKN45/5FU and KATOIII/5FU cells were not cross-resistant to FTD, whereas MKN45/5FU cells were 3.7-fold more resistant than the parental cells in vitro. FTD was also incorporated into genomic DNA in a concentration-dependent manner in 5-FU-resistant and parental cells. Additionally, deoxyuridine monophosphate levels in MKN45/5FU cells after 24-h FTD treatment were 3.0-fold higher than those in parental cells, and FTD treatment for 72 h induced G2/M arrest in MKN45/5FU cells, unlike the S phase arrest in MKN45 cells. Thus, TS-overexpressing MKN45/5FU cells, but not MKN74/5FU and KATOIII/5FU cells, showed partial cross-resistance to FTD. However, FTD/TPI (administered orally twice a day) exhibited antitumor activity to the same extent in MKN45 and MKN45/5FU xenograft mouse models, overcoming in vitro cross-resistance to FTD. DNA incorporation rather than TS inhibition seems to be the main action of FTD under these in vivo conditions. Thus, FTD/TPI is a promising chemotherapeutic agent against gastric cancers recurring following 5-FU therapy.
Estimating Credibility of News Authors from their WIKI Validated Predictions

In this paper, we consider a set of articles or reports by journalists or others, wherein they predict or promise something about future. The problem we approach is determining the credibility of the authors based on the predictions coming out to be true. The two specific problems we address are extracting the predictions from the articles and annotating with various prediction attributes. And then we determine the truth of these predictions, using Wikipedia as a credible source to extract relevant facts which can ascertain the validity of the predictions. We proposed and built an end to end system for automated predictions validation(APV) by extracting future speculations and predictions from news articles and social media. We considered 28 news articles and extracted 97 predictions from these articles and the range of credibility scores(F-scores) for these articles are (0.57-0.71).
Efficient approach for complex video description into english text

Human activity and role recognition play an important part in complex event understanding. This paper present a system to automatically generate natural language descriptions from complex videos. The system consists of mainly two parts training and testing. The first part is training section in which the complex videos are trained by storing its features, subject, verb, and objects, tense (past tense, future tense or present tense), and actual description of video into the database. In testing part, the testing video is taken as an input and after applying all video processes and classified using SVM classifier, the grammatically correct description is generated using NLP (Natural Language) processing. The video frames are processed through Gaussian and Canny edge detection. Further features of every frame of video is detected by using SIFT algorithm.
A comprehensive analysis for fair probability marking based traceback approach in WSNs

Currently, the analysis result of fair probability marking approach can only be got in the single-source linear and single-source tree network, which cannot meet the requirement of applications. This paper fills in this gap by providing an accurate marking probability analysis model for linear network, tree network, and planar network. Compare with the marking packets mechanism with equal marking probability, the analysis result of this paper shows that FPM mechanisms cannot only reduce convergence time up to 20%-60% for medium-sized plane sensor networks, but also reduces the amount of data, so the network lifetime is 1.2 times to 3.37 times than previous methods. The mark probability i¾? of each node is given and its performance is comprehensively analyzed for FPM approach. The results have important guiding significance for designing traceback scheme. Copyright © 2016 John Wiley & Sons, Ltd.
Current Strategies to Manage a Thin Endometrium

One of the key factors for successful embryo implantation is the presence of a receptive endometrium. The achievement of a receptive endometrium involves proliferation and thickening of the endometrium, synchronization of the micro architectural changes of the endometrium with the stage of embryo development and the presence of a proper cytokine environment [1]. Despite its complexity, the process of achieving endometrial receptivity can be attained in most women by a successive exposure of the endometrium to estrogen and progesterone and is likely similar in spontaneous pregnancy as well as with fertility treatment [2].
Dopamine, Effort-Based Choice, and Behavioral Economics: Basic and Translational Research

Operant behavior is not only regulated by factors related to the quality or quantity of reinforcement, but also by the work requirements inherent in performing instrumental actions. Moreover, organisms often make effort-related decisions involving economic choices such as cost/benefit analyses. Effort-based decision making is studied using behavioral procedures that offer choices between high-effort options leading to relatively preferred reinforcers vs. low effort/low reward choices. Several neural systems, including the mesolimbic dopamine (DA) system and other brain circuits, are involved in regulating effort-related aspects of motivation. Considerable evidence indicates that mesolimbic DA transmission exerts a bi-directional control over exertion of effort on instrumental behavior tasks. Interference with DA transmission produces a low-effort bias in animals tested on effort-based choice tasks, while increasing DA transmission with drugs such as DA transport blockers tends to enhance selection of high-effort options. The results from these pharmacology studies are corroborated by the findings from recent articles using optogenetic, chemogenetic and physiological techniques. In addition to providing important information about the neural regulation of motivated behavior, effort-based choice tasks are useful for developing animal models of some of the motivational symptoms that are seen in people with various psychiatric and neurological disorders (e.g., depression, schizophrenia, Parkinson’s disease). Studies of effort-based decision making may ultimately contribute to the development of novel drug treatments for motivational dysfunction.
A Semi-Supervised Two-Stage Approach to Learning from Noisy Labels

The recent success of deep neural networks is powered in part by large-scale well-labeled training data. However, it is a daunting task to laboriously annotate an ImageNet-like dateset. On the contrary, it is fairly convenient, fast, and cheap to collect training images from the Web along with their noisy labels. This signifies the need of alternative approaches to training deep neural networks using such noisy labels. Existing methods tackling this problem either try to identify and correct the wrong labels or reweigh the data terms in the loss function according to the inferred noisy rates. Both strategies inevitably incur errors for some of the data points. In this paper, we contend that it is actually better to ignore the labels of some of the data points than to keep them if the labels are incorrect, especially when the noisy rate is high. After all, the wrong labels could mislead a neural network to a bad local optimum. We suggest a two-stage framework for the learning from noisy labels. In the first stage, we identify a small portion of images from the noisy training set of which the labels are correct with a high probability. The noisy labels of the other images are ignored. In the second stage, we train a deep neural network in a semi-supervised manner. This framework effectively takes advantage of the whole training set and yet only a portion of its labels that are most likely correct. Experiments on three datasets verify the effectiveness of our approach especially when the noisy rate is high.
Professional Gender Gaps Across US Cities

Gender imbalances in work environments have been a long-standing concern. Identifying the existence of such imbalances is key to designing policies to help overcome them. In this work, we study gender trends in employment across various dimensions in the United States. This is done by analyzing anonymous, aggregate statistics that were extracted from LinkedIn's advertising platform. The data contain the number of male and female LinkedIn users with respect to (i) location, (ii) age, (iii) industry and (iv) certain skills. We studied which of these categories correlate the most with high relative male or female presence on LinkedIn. In addition to examining the summary statistics of the LinkedIn data, we model the gender balance as a function of the different employee features using linear regression. Our results suggest that the gender gap, as measured using LinkedIn data, varies across all feature types, but the differences are most profound among industries and skills. A high correlation between gender ratios of people in our LinkedIn data set, and data provided by the US Bureau of Labor Statistics, serves as external validation for our results.
Hierarchical Distributed Balancing Control for Large-Scale Reconfigurable AC Battery Packs

This paper presents a hierarchical balancing algorithm for use in large-scale reconfigurable ac battery packs. Using a decentralized battery management system, the pack is organized in control layers with each layer controller responsible for a small number of slave controllers, thereby significantly reducing communication and centralized processing requirements. The hierarchical balancing algorithm uses all available voltage steps in the pack ensuring a high-quality sinusoidal output voltage while balancing the objects of different layers. Balancing priority can be swapped dynamically between layers according to the maximum state of charge (SoC) variation between the objects of each layer while also ensuring that the output voltage reference is always met even when a cell failure occurs, as long as there are an adequate number of cells available in the pack. An experimental demonstration of the proposed balancing algorithm is presented using an ac battery pack comprising 144 20-Ah lithium–titanate cells. Cell balancing to within 2 mV of the cell open-circuit voltage is achieved between cells with 20% initial SoC variation and 5% capacity difference.
Detecting Promotion Campaigns in Community Question Answering

With Community Question Answering (CQA) evolving into a quite popular method for information seeking and providing, it also becomes a target for spammers to disseminate promotion campaigns. Although there are a number of quality estimation efforts on the CQA platform, most of these works focus on identifying and reducing low-quality answers, which are mostly generated by impatient or inexperienced answerers. However, a large number of promotion answers appear to provide high-quality information to cheat CQA users in future interactions. Therefore, most existing quality estimation works in CQA may fail to detect these specially designed answers or question-answer pairs. In contrast to these works, we focus on the promotion channels of spammers, which include (shortened) URLs, telephone numbers and social media accounts. Spammers rely on these channels to connect to users to achieve promotion goals so they are irreplaceable for spamming activities. We propose a propagation algorithm to diffuse promotion intents on an "answerer-channel" bipartite graph and detect possible spamming activities. A supervised learning framework is also proposed to identify whether a QA pair is spam based on propagated promotion intents. Experimental results based on more than 6 million entries from a popular Chinese CQA portal show that our approach outperforms a number of existing quality estimation methods for detecting promotion campaigns on both the answer level and QA pair level.
Risk of Hospitalization Due to Medication Nonadherence Identified Through EMRs of Patients With Psychosis.

OBJECTIVE This study examined whether outpatients with a psychotic disorder who are at risk of hospitalization can be identified by using data from electronic medical records (EMRs). METHODS Data from EMRs of outpatients enrolled in two clinics for treatment of psychotic disorders were abstracted. Monthly data were collected for 75 patients over two years. The study examined the association of medication nonadherence, substance use, participation in psychiatric rehabilitation, and long-acting injectable antipsychotic use in any given month with the risk of hospitalization in the subsequent month by using generalized estimating equations. RESULTS The only variable found to increase the relative risk of future hospitalization was recorded medication nonadherence (adjusted relative risk=7.19, p<.001). CONCLUSIONS Results suggest that recording medication nonadherence in EMRs is feasible and that these data may be used to identify patients at high risk of future hospitalization, who may require more intensive intervention.
Polyether Based Thermoplastic Polyurethane Melt Blown Nonwovens

A series of melt blown samples were produced from three hardness grades of ether based thermoplastic polyurethane elastomers (TPU). The fabrics were tested to investigate their structure-property relationship in a melt blown process. Solution viscosities of the web were only 20–26% of there original values indicating a large loss in polymer molecular weight during melt blowing. Fiber diameter distributions measured on melt blown samples were found comparable to those made with more conventional polymers. The fiber orientation distribution functions (ODF) suggest slight fiber orientation in machine direction. Tensile and elongation properties depended on die-to-collector distance (DCD), polymer hardness and fiber ODF. A strong relationship between the tensile strength and die-to-collector distance was identified and attributed to reduced interfiber adhesion in the web with increasing DCD. The reduction in adhesion was attributed to greater extents of solidification before reaching the forming belt for longer DCDs. This paper is the first in a series relating the influence of the melt blowing process parameters on the polymer properties and the nonwoven fabric properties for block thermoplastic elastomers.
Evaluation of ultrasonic irrigation systems for debris and smear removal in root canals : a scanning electron microscope study

ii Preface . iv Table of Contents . v List of Tables . viii List of Figures . ix List of Abbreviations . x Acknowledgements . xi Dedication . xiii Chapter 1: Introduction . 1 1.1 Outcome of Endodontic Treatment . 3 1.2 A Brief History of Irrigation . 5 1.3 Endodontic Treatment . 7 1.3. 1 Mechanical Preparation of the Root Canal . 7 1.3. 2 The Importance of Chemomechanical Preparation . 10 1.3. 3 Treatment Considerations . 13 1.3. 4 Desired Properties of Irrigants . 15 1.3. 5 Sodium Hypochlorite . 16 1.3. 5.1 Tissue Dissolving Effects . 17 1.3. 5.2 Antibacterial Effect . 19 1.3. 5.3 Limitations of Sodium Hypochlorite . 20
PAVE: Write-print Creation with MapReduce

Abstract : Cyber-crime is becoming alarmingly common through the use of anonymous e-mails. Author attribution helps digital forensics investigators filter through a large set of possible authors and focus traditional investigative techniques on the most probable culprits. A recent promising technique is to construct a write-print for each known author and compare it to the write-print extracted from the anonymous message(s). A write-print is a unique digital fingerprint created by mining frequent patterns from a particular authors writing style. Parallel computing enables us to leverage multiple cores in the creation of author write-prints. We introduce Parallel Author Verification of E-mail (PAVE), a MapReduce algorithm for generating author write-prints in parallel. Our algorithm is able to achieve up to 90 accuracy when tested on a subset of the Enron dataset. We believe the community will find the PAVE system useful to expedite author identification in time sensitive situations.
Moisture Contents of Thick Layer Base Material Spraying Bio-slope Engineering

Effective moisture of base material mixture is one of the important indices evaluating thick layer base material spraying bio-slope engineering. Based on spraying experiments on simulated rock slope, different organic matter and absorbent agent contents' effect on base material mixture's moisture constants are studied. This has some significance for the selection of base material mixture composition and proportion.
Forecasting of Chaotic Cloud Absorption Time Series for Meteorological and Plume Dispersion Modeling

A nonlinear forecasting method based on the reconstruction of a chaotic strange attractor from about 1.5 years of cloud absorption data obtained from half-hourly Meteosat infrared images was used to predict the behavior of the time series 24 h in advance. The forecast values are then used by a meteorological model for daily prediction of plume transport from the As Pontes 1400-MW power plant in northwestern Spain. Results from the meteorological model, using the cloud absorption predictions, are compared with measurements obtained from meteorological towers and a Remtech PA-3 sodar. The effects of cloud absorption on SO2 ground-level concentration forecasts are analyzed for two different days.
Parent- and Teacher-Child Relationships and Engagement at School Entry: Mediating, Interactive, and Transactional Associations Across Contexts

Early school engagement patterns set the stage for short- and long-term academic behaviors and progress, and low engagement at school entry can give rise to dysfunctional school behavior and underachievement in later years. Relationships with parents and teachers provide a foundation upon which children develop the skills and behaviors that are critical for engagement in early elementary school. However, the cross-contextual and transactional processes by which these relationships are associated with engagement in early elementary school remain unclear. This investigation therefore considers how children’s relationships with parents prior to school entry are indirectly and interactively associated with 1st grade engagement through teacher–child relationships. It also considers how early cognitive, self-regulatory, and behavioral skills and competencies elicit these relational responses. This investigation drew upon multimethod data from triangulated sources (parents, teachers, and direct observation) available within a large longitudinal study, the National Institute of Child Health and Human Development Study of Early Child Care and Youth Development. Results indicate that neither 54-month or 1st grade parent–child relationships were directly associated with children’s engagement in early elementary school. However, 1st grade teacher conflict was problematic for children’s developing engagement. In turn, teacher conflict partially mediated linkages between maternal sensitivity and conflict prior to school entry and engagement in 1st grade. Parental closeness and sensitivity also buffered children against negative associations between teacher conflict and engagement. Finally, children’s early skills and competencies at 36 months elicited relational processes that were indirectly associated with engagement. Conceptual, theoretical, and methodological challenges are also discussed.
Experimental maps of DNA structure at nucleotide resolution distinguish intrinsic from protein-induced DNA deformations

Abstract Recognition of DNA by proteins depends on DNA sequence and structure. Often unanswered is whether the structure of naked DNA persists in a protein–DNA complex, or whether protein binding changes DNA shape. While X-ray structures of protein–DNA complexes are numerous, the structure of naked cognate DNA is seldom available experimentally. We present here an experimental and computational analysis pipeline that uses hydroxyl radical cleavage to map, at single-nucleotide resolution, DNA minor groove width, a recognition feature widely exploited by proteins. For 11 protein–DNA complexes, we compared experimental maps of naked DNA minor groove width with minor groove width measured from X-ray co-crystal structures. Seven sites had similar minor groove widths as naked DNA and when bound to protein. For four sites, part of the DNA in the complex had the same structure as naked DNA, and part changed structure upon protein binding. We compared the experimental map with minor groove patterns of DNA predicted by two computational approaches, DNAshape and ORChID2, and found good but not perfect concordance with both. This experimental approach will be useful in mapping structures of DNA sequences for which high-resolution structural data are unavailable. This approach allows probing of protein family-dependent readout mechanisms.
Study on nano-TiO_2 modified anti-corrosion coating of oil pipeline

Nano-TiO2 modified anti-corrosion coatings were prepared and their properties were investigated.The results show that the conventional performance,friction resistance and corrosion resistance of the anti-corrosion coating modified with 3% nano-TiO2 are much higher than that of the none modified anti-corrosion coating.This indicates that the nano-TiO2 exert a critical influence on improving the properties of the anti-corrosion coating used in oil pipeline.
Thermodynamics and Kinetics of Bulk Metallic Glass

Bulk metallic glasses (BMGs) are multicomponent alloys with typically three to five components with large atomic size mismatch and a composition close to a deep eutectic. Packing in BMG liquids is very dense, with a low content of free volume resulting in viscosities that are several orders of magnitude higher than in pure metal melts. The dense packing accomplished by structural and chemical atomic ordering also brings the BMG-forming liquid energetically and entropically closer to its corresponding crystalline state. These factors lead to slow crystallization kinetics and consequentially to high glass-forming ability. This article highlights the thermodynamic and kinetic properties of BMGs and their contributions to extraordinarily high glass-forming ability. Some possible links with mechanical properties are also suggested.