Miracle-dz/UniFAR-FTUs huggingface dataset:
The end of male dominance?: an analysis of the Brazilian labor market using the APC approach

Historically, it is well known in the labor market literature that men tend to earn more than women. However, some recent trends might render this an obsolete perspective. The 21th century might the beginning of the female era of labor market dominance in Brazil. Based on the APC approach, we used different PNADs and Mincerian wage equation models to address this point. The results clearly indicated that gaps in the labor market are shrinking between sexes in all analyzed perspectives. The general results showed a slight tendency of end of male dominance, however not impressive. Excluding the control for educational level, the results showed a greater tendency of convergence between the sexes. For cohorts, the results were much more strikingly: without controlling for education, as women have higher levels than men, women with age under 20 in 2012 had a tendency of earning similar or superior wages than men. Therefore, male dominance might have already ended for young cohorts.
A Combined Representation Learning Approach for Better Job and Skill Recommendation

Job recommendation is an important task for the modern recruitment industry. An excellent job recommender system not only enables to recommend a higher paying job which is maximally aligned with the skill-set of the current job, but also suggests to acquire few additional skills which are required to assume the new position. In this work, we created three types of information net- works from the historical job data: (i) job transition network, (ii) job-skill network, and (iii) skill co-occurrence network. We provide a representation learning model which can utilize the information from all three networks to jointly learn the representation of the jobs and skills in the shared k-dimensional latent space. In our experiments, we show that by jointly learning the representation for the jobs and skills, our model provides better recommendation for both jobs and skills. Additionally, we also show some case studies which validate our claims.
Incidence, Epidemiology, and Outcomes of Pediatric Tracheostomy in the United States from 2000 to 2012

Objectives To investigate national and regional variations in pediatric tracheostomy rates, epidemiology, and outcomes from 2000 to 2012. Study Design Retrospective cohort analysis. Setting Previous research with the 1997 edition of the Kids’ Inpatient Database (KID), a national database of pediatric hospital discharge data, demonstrated that rates and outcomes of pediatric tracheostomy vary among US geographic regions. The KID has since been released an additional 5 times, increasing in size with successive editions. Subjects and Methods Patients ≤18 years old with procedure codes for permanent or temporary tracheostomy from 2000 to 2012 were included. Primary outcome was a weighted population-based rate of tracheostomy stratified by year. Secondary analysis included epidemiologic characteristics and outcomes stratified by year and geographic region. Results A weighted total of 24,354 cases was analyzed. Population-based tracheostomy rates decreased from 6.8 ± 0.2 (mean ± SD) tracheostomies per 100,000 child-years in 2000 to 6.0 ± 0.2 in 2012. Minorities increased from 53.3% in 2000 to 56.4% in 2012. Patients experienced increased procedures, diagnoses, length of stay, and hospital charges with time. From 2000 to 2012, rates and outcomes varied by US geographic region. Mortality during hospitalization (8%) did not vary by year, patient age, region, or sex. Conclusions Pediatric tracheostomy is associated with variation in incidence, epidemiology, and hospitalization outcomes in the United States from 2000 to 2012. While rates of pediatric tracheostomy decreased, patients became increasingly medically complicated and ethnically diverse with outcomes varying according to geographic region.
Recommending Adaptive Changes for Framework Evolution

In the course of a framework’s evolution, changes ranging from a simple refactoring to a complete rearchitecture can break client programs. Finding suitable replacements for framework elements that were accessed by a client program and deleted as part of the framework’s evolution can be a challenging task. We present a recommendation system, SemDiff, that suggests adaptations to client programs by analyzing how a framework was adapted to its own changes. In a study of the evolution of one open source framework and three client programs, our approach recommended relevant adaptive changes with a high level of precision. In a second study of the evolution of two frameworks, we found that related change detection approaches were better at discovering systematic changes and that SemDiff was complementary to these approaches by detecting non-trivial changes such as when a functionality is imported from an external library.
Sensory pudendal nerve stimulation increases bladder capacity through sympathetic mechanisms in cyclophosphamide‐induced cystitis rats

Interstitial cystitis and bladder pain syndrome is a prevalent health concern with inadequate treatments. Neuromodulation has emerged as a therapeutic option to treat patients refractory to standard care. The objective of this study was to determine the efficacy and mechanism(s) of sensory pudendal nerve stimulation on bladder function in cystitis rats.
Incompatibilities in Mismatch Repair Genes MLH1-PMS1 Contribute to a Wide Range of Mutation Rates in Human Isolates of Baker’s Yeast

Baker’s yeast bearing incompatible MLH1 and PMS1 mismatch repair alleles are mutators that can adapt more rapidly to stress—but do so at the cost of long-term fitness. Laboratory baker’s yeast strains bearing an incompatible combination of MLH1 and PMS1 mismatch repair alleles are mutators that can adapt more rapidly to stress, but do so at the cost of long-term fitness. We identified 18 baker’s yeast isolates from 1011 surveyed that contain the incompatible MLH1-PMS1 genotype in a heterozygous state. Surprisingly, the incompatible combination from two human clinical heterozygous diploid isolates, YJS5845 and YJS5885, contain the exact MLH1 (S288c-derived) and PMS1 (SK1-derived) open reading frames originally shown to confer incompatibility. While these isolates were nonmutators, their meiotic spore clone progeny displayed mutation rates in a DNA slippage assay that varied over a 340-fold range. This range was 30-fold higher than observed between compatible and incompatible combinations of laboratory strains. Genotyping analysis indicated that MLH1-PMS1 incompatibility was the major driver of mutation rate in the isolates. The variation in the mutation rate of incompatible spore clones could be due to background suppressors and enhancers, as well as aneuploidy seen in the spore clones. Our data are consistent with the observed variance in mutation rate contributing to adaptation to stress conditions (e.g., in a human host) through the acquisition of beneficial mutations, with high mutation rates leading to long-term fitness costs that are buffered by mating or eliminated through natural selection.
Learning Local Descriptors with Adversarial Enhancer from Volumetric Geometry Patches

Local matching problems (e.g. key point matching, geometry registration) are significant but challenging tasks in computer vision field. In this paper, we propose to learn a robust local 3D descriptor from volumetric point patches to tackle the local matching tasks. Intuitively, given two inputs, it would be easy for a network to map the inputs to a space with similar characteristics (e.g. similar outputs for similar inputs, far different outputs for far different inputs), but the difficult case for a network would be to map the inputs into a space with opposite characteristics (e.g. far different outputs for very similar inputs but very similar outputs for far different inputs). Inspired by this intuition, in our proposed method, we design a siamese-network-based local descriptor generator to learn a local descriptor with small distances between match pairs and large distances between non-match pairs. Specifically, an adversarial enhancer is introduced to map the outputs of the local descriptor generator into an opposite space that match pairs have the maximum differences and non-match pairs have the minimum differences. The local descriptor generator and the adversarial enhancer are trained in an adversarial manner. By competing with the adversarial enhancer, the local descriptor generator learns to generate a much stronger descriptor for given volumetric point patches. The experiments conducted on real-world scan datasets, including 7-scenes and SUN3D, and the synthetic scan augmented ICL-NUIM dataset show that our method can achieve superior performance over other state-of-the-art approaches on both keypoint matching and geometry registration, such as fragment alignment and scene reconstruction.
METIC: Multi-Instance Entity Typing from Corpus

This paper addresses the problem ofmulti-instance entity typing from corpus. Current approaches mainly rely on the structured features (\textitattributes, attribute-value pairs andtags ) of the entities. However, their effectiveness is largely dependent on the completeness of structured features, which unfortunately is not guaranteed in KBs. In this paper, we therefore propose to use the text corpus of an entity to infer its types, and propose a multi-instance method to tackle this problem. We take each mention of an entity in KBs as an instance of the entity, and learn the types of these entities from multiple instances. Specifically, we first use an end-to-end neural network model to type each instance of an entity, and then use an integer linear programming (ILP) method to aggregate the predicted type results from multiple instances. Experimental results show the effectiveness of our method.
Collaborative Multi-objective Ranking

This paper proposes to jointly resolve row-wise and column-wise ranking problems when an explicit rating matrix is given. The row-wise ranking problem, also known as personalized ranking, aims to build user-specific models such that the correct order of items (in terms of user preference) is most accurately predicted and then items on the top of ranked list will be recommended to a specific user, while column-wise ranking aims to build item-specific models focusing on targeting users who are most interested in the specific item (for example, for distributing coupons to customers). In recommender systems, ranking-based collaborative filtering (known as collaborative ranking (CR)) algorithms are designed to solve the aforementioned ranking problems. The key part of CR algorithms is to learn effective user and item latent factors which are combined to decide user preference scores over items. In this paper, we demonstrate that by individually solving row-wise or column-wise ranking problems using typical CR algorithms is only able to learn one set of effective (user or item) latent factors. Therefore, we propose to jointly solve row-wise and column-wise ranking problems through a parameter sharing framework which optimizes three objectives together: to accurately predict rating scores, to satisfy the user-specific order constraints on all the rated items, and to satisfy the item-specific order constraints. Our extensive experimental results on popular datasets confirm significant performance gains of our proposed method over state-of-the-art CR approaches in both of row-wise and column-wise ranking tasks.
Nanoparticle-Mediated Combination Therapy: Two-in-One Approach for Cancer

Cancer represents a group of heterogeneous diseases characterized by uncontrolled growth and spread of abnormal cells, ultimately leading to death. Nanomedicine plays a significant role in the development of nanodrugs, nanodevices, drug delivery systems and nanocarriers. Some of the major issues in the treatment of cancer are multidrug resistance (MDR), narrow therapeutic window and undesired side effects of available anticancer drugs and the limitations of anticancer drugs. Several nanosystems being utilized for detection, diagnosis and treatment such as theranostic carriers, liposomes, carbon nanotubes, quantum dots, polymeric micelles, dendrimers and metallic nanoparticles. However, nonbiodegradable nanoparticles causes high tissue accumulation and leads to toxicity. MDR is considered a major impediment to cancer treatment due to metastatic tumors that develop resistance to chemotherapy. MDR contributes to the failure of chemotherapies in various cancers, including breast, ovarian, lung, gastrointestinal and hematological malignancies. Moreover, the therapeutic efficiency of anticancer drugs or nanoparticles (NPs) used alone is less than that of the combination of NPs and anticancer drugs. Combination therapy has long been adopted as the standard first-line treatment of several malignancies to improve the clinical outcome. Combination therapy with anticancer drugs has been shown to generally induce synergistic drug actions and deter the onset of drug resistance. Therefore, this review is designed to report and analyze the recent progress made to address combination therapy using NPs and anticancer drugs. We first provide a comprehensive overview of the angiogenesis and of the different types of NPs currently used in treatments of cancer; those emphasized in this review are liposomes, polymeric NPs, polymeric micelles (PMs), dendrimers, carbon NPs, nanodiamond (ND), fullerenes, carbon nanotubes (CNTs), graphene oxide (GO), GO nanocomposites and metallic NPs used for combination therapy with various anticancer agents. Nanotechnology has provided the convenient tools for combination therapy. However, for clinical translation, we need continued improvements in the field of nanotechnology.
Block periodization of strength and endurance training is superior to traditional periodization in ice hockey players

Team sports like ice hockey require high levels of performance in numerous physical characteristics such as strength, power, and endurance. As such, training is associated with a potential interference effect. The present study randomized well‐trained ice hockey players into a block periodization group (BP; n = 8), focusing on the development of either strength and power or endurance on a weekly, undulating basis, and a traditional group (TRAD; n = 8), performing a mixed training model, with simultaneous focus of strength, power, and endurance training every week. During the 6‐week intervention, the two groups performed equal volumes and intensities of both strength, power, and endurance training. BP led to larger improvements than TRAD in knee extension peak torque at 180° s−1 (6.6 ± 8.7 vs −4.2% ± 6.3%, respectively; P < 0.05) and maximal oxygen uptake (5.1 ± 3.3 vs 1.1% ± 3.5%, respectively; P < 0.05). There was also a trend toward larger improvements in BP than TRAD in peak torque in knee extension at 60° s−1 (2.1 ± 2.5 vs −0.1% ± 2.5%, respectively; P < 0.1, effect size = 0.83) and mean power output during a 30‐s cycling sprint (4.1 ± 2.5 vs −0.3% ± 5.9%, respectively; P < 0.1, effect size = 0.89). Overall, BP exhibited a moderate to large effect size for all these variables compared to TRAD. The present study suggests that block periodization of strength and endurance training induces superior adaptations in both strength and endurance capacities in well‐trained ice hockey players compared to traditional mixed organization, despite similar training volume and intensity.
Robustness and Discrimination Oriented Hashing Combining Texture and Invariant Vector Distance

Image hashing is a novel technology of multimedia processing with wide applications. Robustness and discrimination are two of the most important objectives of image hashing. Different from existing hashing methods without a good balance with respect to robustness and discrimination, which largely restrict the application in image retrieval and copy detection, i.e., seriously reducing the retrieval accuracy of similar images, we propose a new hashing method which can preserve two kinds of complementary features (global feature via texture and local feature via DCT coefficients) to achieve a good balance between robustness and discrimination. Specifically, the statistical characteristics in gray-level co-occurrence matrix (GLCM) are extracted to well reveal the texture changes of an image, which is of great benefit to improve the perceptual robustness. Then, the normalized image is divided into image blocks, and the dominant DCT coefficients in the first row/column are selected to form a feature matrix. The Euclidean distance between vectors of the feature matrix is invariant to commonly-used digital operations, which helps make hash more compact. Various experiments show that our approach achieves a better balance between robustness and discrimination than the state-of-the-art algorithms.
On Real-time Detecting Passenger Flow Anomalies

In large and medium-sized cities, detecting unusual changes of crowds of people on the streets is needed for public security, transportation management, emergency control, and terrorism prevention. As public transportation has the capability to bring a large number of people to an area in a short amount of time, real-time discovery of anomalies in passenger numbers is an effective way to detect crowd anomalies. In this paper, we devise an approach called Kochab. Kochab adopts a generative model and combines the prior knowledge about passenger flows. Hence, it can detect anomalies in the numbers of incoming and outgoing passengers within a certain time and spatial area, including anomalous events along with their durations and severities. Through well-designed inference algorithms, Kochab requires only a moderate amount of historical data to be sample data. As such, Kochab shows good performance in real time and makes prompt responses to user' s interactive analysis requests. In particular, based on the recognized anomalous events, we capture event patterns which give us hints to link to activities or status in cities. In addition, for the convenience of method evaluation and comparison, we create an open Stream Anomaly Benchmark on the basis of large-scale real-world data. This benchmark will prove useful for other researchers too. Using this benchmark, we compare Kochab with four other methods. The experimental results show that Kochab is sensitive to population flow anomalies and has superior accuracy in detecting anomalies in terms of precision, recall and the F1 score.
Genetic diversity in two Plasmodium vivax protein ligands for reticulocyte invasion

The interaction between Plasmodium vivax Duffy binding protein (PvDBP) and Duffy antigen receptor for chemokines (DARC) has been described as critical for the invasion of human reticulocytes, although increasing reports of P. vivax infections in Duffy-negative individuals questions its unique role. To investigate the genetic diversity of the two main protein ligands for reticulocyte invasion, PvDBP and P. vivax Erythrocyte Binding Protein (PvEBP), we analyzed 458 isolates collected in Cambodia and Madagascar. First, we observed a high proportion of isolates with multiple copies PvEBP from Madagascar (56%) where Duffy negative and positive individuals coexist compared to Cambodia (19%) where Duffy-negative population is virtually absent. Whether the gene amplification observed is responsible for alternate invasion pathways remains to be tested. Second, we found that the PvEBP gene was less diverse than PvDBP gene (12 vs. 33 alleles) but provided evidence for an excess of nonsynonymous mutations with the complete absence of synonymous mutations. This finding reveals that PvEBP is under strong diversifying selection, and confirms the importance of this protein ligand in the invasion process of the human reticulocytes and as a target of acquired immunity. These observations highlight how genomic changes in parasite ligands improve the fitness of P. vivax isolates in the face of immune pressure and receptor polymorphisms.
Automatic Segmentation of Left Myocardium in CMR Based on Fully Convolutional Networks

Myocardial segmentation plays an important role for quantitative evaluation of heart diseases and cardiac image processing and analysis. However, myocardial segmentation has always been a challenging task because gray scale intensities of the myocardium and tissues around the heart are very close and that significant differences exist in myocardial structure between different slices or slices at different times. Traditional segmentation algorithms are difficult to obtain accurate and robust segmentation results and are usually semi-automatic which require manual operations and extra workload. Therefore, the development of a fully automatic myocardial segmentation algorithm is an appealing research goal. In this paper, we propose an automatic myocardial segmentation algorithm based on fully convolutional neural networks. By building an end-to-end model, the segmentation speed has been improved without affecting the segmentation accuracy. Performance comparisons between the proposed HeartNet and state-of-art methods demonstrated the effectiveness of our algorithm, which achieved an average DSC of 90.48% by segmenting 144.9 frames per second.
Should we target TNF receptors in the intestinal epithelium with glucocorticoids during systemic inflammation?

ABSTRACT Introduction: Reverting Systemic inflammatory response syndromes (SIRS), particularly sepsis, is a huge challenge of contemporary medicine. Inhibition of the cytokine tumor necrosis factor alpha (TNFα), originally considered as a mediator in sepsis, has led to frustrating results. Equally so, glucocorticoids (GCs), renowned for their role in numerous inflammatory diseases, remain controversial in sepsis. Areas covered: We discuss how, in SIRS, the intestinal epithelium is a critical TNF-responsive target. Inhibition of TNF receptor 1 (TNFR1), rather than TNF, may be a more targeted and safe therapeutic approach. In intestinal epithelial cells (IECs), a strong interplay between GCs and TNF exists. Addressing GCs in these cells is crucial in SIRS and sepsis and would avoid dose-limiting off-target effects, for example on immune cells and phagocytes. Expert opinion: The targeting of TNFR1 specifically at the level of IECs, potentially combined with IEC-specific stimulation of GR, could lead to a more safe and targeted treatment for SIRS and sepsis.
Construction of Efficient V-Gram Dictionary for Sequential Data Analysis

This paper presents a new method for constructing an optimal feature set from sequential data. It creates a dictionary of n-grams of variable length (we call them v-grams), based on the minimum description length principle. The proposed method is a dictionary coder and works simultaneously as both a compression algorithm and as unsupervised feature extraction. The length of constructed v-grams is not limited by any bound and exceeds 100 characters in provided experiments. Constructed v-grams can be used for any sequential data analysis and allows transfer bag-of-word techniques to non-text data types. The method demonstrates a high compression rate on various real-life datasets. Extracted features generate a practical basis for text classification, that shows competitive results on standard text classification collections without using the text structure. Combining extracted character v-grams with the words from the original text we achieved substantially better classification quality than on words or v-grams alone.