Miracle-dz/UniFAR-FTUs huggingface dataset:
Bad Healthy State Compress Temporal Extension Both in Past and Future Orientations

We examined how different health states and cancer-related depression influence time perception in temporal extension (TE) regarding the past and the future over individuals’ entire lives. We used the magnitude estimation method to investigate TE (past and future) and long duration (20 years), and directly measured patients with cancer and their subjective feelings about their lives. In Experiment 1, we investigated whether there were differences in perceptions of TE between patients with cancer (n = 144) compared to a healthy control group (n = 208). Results indicated that the patients with cancer group evaluated longer TE in healthy states (imagination condition) than in unhealthy states (objectivity condition), and the healthy control group evaluated longer TE in healthy states (objectivity condition) than in unhealthy states (imagination condition). Moreover, the patients with cancer group evaluated longer past-oriented TE than future-oriented TE, whereas the healthy control group evaluated longer future-oriented TE than past-oriented TE. Experiment 2 was conducted to further examine the unexplained findings of Experiment 1. Results indicated that because of the impact of depression on the cancer group, their embodied time system slowed down, and explicit behavior indicated an over-evaluation of time, resulting in group differences. In conclusion, TE could be affected by different health states. Moreover, healthy and unhealthy states may be more associated with future and past orientation, respectively. Lastly, individuals’ time perception can be influenced by depression.
Simultaneous collection of the portal and superior vena cava blood in conscious rats defined that intestinal epithelium is the major site of glucuronidation, but not sulfation and methylation, of quercetin

ABSTRACT Quercetin is a flavonoid with many physiological effects. Absorbed quercetin is rapidly conjugated in the intestinal epithelium and liver. Different positional isomers of quercetin conjugates have different physiological properties. However, the mechanisms of quercetin conjugation in the intestine are not fully clarified. We examined the regioselective quercetin conjugate formation in the intestine after oral administration of quercetin glycosides, by simultaneous sampling of blood from the portal vein and superior vena cava, and quantifying various positional isomers of quercetin glucuronides and sulfates in conscious rats. Concentrations of quercetin glucuronides were higher in blood from the portal vein than the superior vena cava, showing that glucuronidation mainly occurred in the intestine. Such differences were not observed for quercetin sulfates. Regioselectivity of the intestinal glucuronidation in quercetin hydroxyl groups were 7- >3′- >3- >4′-OH. Quercetin was mainly sulfated on 3′-OH at 30 min, but on 4′-OH at 240 min. Graphical Abstract Simultaneous collection of the portal and superior vena cava blood in conscious rats defined that intestinal epithelium is the major site of glucuronidation, but not sulfation and methylation, of quercetin.
The Role of Allograft Inflammatory Factor-1 in the Effects of Experimental Diabetes on B Cell Functions in the Heart

Diabetes mellitus (DM) often causes chronic inflammation, hypertrophy, apoptosis and fibrosis in the heart and subsequently leads to myocardial remodeling, deteriorated cardiac function and heart failure. However, the etiology of the cardiac disease is unknown. Therefore, we assessed the gene expression in the left ventricle of diabetic and non-diabetic mice using Affymetrix microarray analysis. Allograft inflammatory factor-1 (AIF-1), one of the top downregulated B cell inflammatory genes, is associated with B cell functions in inflammatory responses. Real-time reverse transcriptase-polymerase chain reaction confirmed the Affymetrix data. The expression of CD19 and AIF-1 were downregulated in diabetic hearts as compared to control hearts. Using in vitro migration assay, we showed for the first time that AIF-1 is responsible for B cell migration as B cells migrated to GFP-AIF-1-transfected H9C2 cells compared to empty vector-transfected cells. Interestingly, overexpression of AIF-1 in diabetic mice prevented streptozotocin-induced cardiac dysfunction, inflammation and promoted B cell homing into the heart. Our results suggest that AIF-1 downregulation inhibited B cell homing into diabetic hearts, thus promoting inflammation that leads to the development of diabetic cardiomyopathy, and that overexpression of AIF-1 could be a novel treatment for this condition.
A acessibilidade e a inclusão da pessoa com deficiência nas empresas de Cajazeiras - PB.

O presente trabalho tem como objetivo principal mostrar o valor da acessibilidade e da inclusao das pessoas com deficiencia nas empresas da cidade de Cajazeiras. Como tambem destacar a importância do direito a inclusao e a necessidade de se buscar formas para garantir a permanencia dos mesmos nas empresas, uma vez que muitos conseguem colocacao no mercado de trabalho por ser uma obrigacao das empresas com mais de 100 funcionarios. Mais que um direito ao acesso, e preciso garantir e assegurar a permanencia destes, com oportunidades e condicoes para se trabalhar. Outra finalidade foi identificar as leis constitucionais que garantem a inclusao de pessoas com deficiencias ao mercado de trabalho e analisar o cumprimento ou nao dessas leis nas empresas Cajazeirenses. Para a elaboracao desse trabalho usou-se como metodologia a pesquisa teorica e de campo, realizando um questionario com os gestores das empresas para saber quais as vantagens e desvantagens encontradas na inclusao dessas pessoas ao grupo de funcionarios, e tambem um questionario com os funcionarios deficientes para classificar as dificuldades encontradas pelos mesmos na busca de colocacao profissional.
LAMA5 promotes human umbilical vein endothelial cells migration, proliferation, and angiogenesis and is decreased in preeclampsia

Abstract Objective: Preeclampsia (PE) is currently thought to associated with oxidative stress and vascular endothelial dysfunction. LAMA5 is associated with the cell migration, proliferation, and vascular endothelial function. The aims of this study are to investigate the expression patterns of LAMA5 in normal and PE pregnancies, as well as evaluating the effects of LAMA5 on human umbilical vein endothelial cells (HUVECs) function. Methods: LAMA5 expression levels were examined by reverse-transcriptase polymerase chain reaction (RT-PCR) and further confirmed by western blot and immunofluorescence. Cell proliferation and apoptosis were measured by CCK-8 assay and flow cytometry respectively. Cell migration was assessed by transwell migration assay. Results: LAMA5 expression levels of vascular endothelial cells in PE placentas was significantly decreased than that in normal placentas. LAMA5 small-interfering RNA (siRNA) transfection and hypoxia/reoxygenation (H/R) treatments resulted in decreased proliferation, migration, and vascular formation ability of HUVECs but increased HUVECs apoptosis. Down-regulated LAMA5 could inhibit the protein expression of the PI3K downstream p-AKT and p-MTOR. Conclusions: Down-regulated LAMA5 is associated with PE placenta and restrained HUVECs proliferation, migration, and angiogenesis through PI3K-AKT-MTOR signaling pathways.
Automatic Classification Using DDC on the Swedish Union Catalogue

With more and more digital collections of various information resources becoming available, also increasing is the challenge of assigning subject index terms and classes from quality knowledge organization systems. While the ultimate purpose is to understand the value of automatically produced Dewey Decimal Classification (DDC) classes for Swedish digital collections, the paper aims to evaluate the performance of two machine learning algorithms for Swedish catalogue records from the Swedish union catalogue (LIBRIS). The algorithms are tested on the top three hierarchical levels of the DDC. Based on a data set of 143,838 records, evaluation shows that Support Vector Machine with linear kernel outperforms Multinomial Naive Bayes algorithm. Also, using keywords or combining titles and keywords gives better results than using only titles as input. The class imbalance where many DDC classes only have few records greatly affects classification performance: 81.37% accuracy on the training set is achieved when at least 1,000 records per class are available, and 66.13% when few records on which to train are available. Proposed future research involves an exploration of the intellectual effort put into creating the DDC to further improve the algorithm performance as commonly applied in string matching, and to test the best approach on new digital collections that do not have DDC assigned. (Less)
Standardized Automated Patient Movement Analysis Training System

A good doctor must do a deep understanding of the needs of patients. Doctors must listen into the patient illnesses overview, as well as to understand the cause of the patient based on the patient's background. In recent years, the major of Medicine have to import "standardized patients (SP)" courses. Out of the program, practical experience, and quickly determine the SP is urgently needed resources to treatment. In this research, the main use of the Kinect device to detect the SP in performing the action is consistent performances. Use of SP script This device records all joint action performances circumstances, further analysis of previous SP to participate in the screenplay. Each practice will be recorded this way to achieve the goal of each performance consistently. When the formal performance, not because of lack of practice, no guidance caused in performances on SP error or script misunderstanding. This system allows SP when performing a full practice, to avoid the error cause unable to in line with the theme of the performances.
RNAs as Candidate Diagnostic and Prognostic Markers of Prostate Cancer—From Cell Line Models to Liquid Biopsies

The treatment landscape of prostate cancer has evolved rapidly over the past five years. The explosion in treatment advances has been witnessed in parallel with significant progress in the field of molecular biomarkers. The advent of next-generation sequencing has enabled the molecular profiling of the genomic and transcriptomic architecture of prostate and other cancers. Coupled with this, is a renewed interest in the role of non-coding RNA (ncRNA) in prostate cancer biology. ncRNA consists of several different classes including small non-coding RNA (sncRNA), long non-coding RNA (lncRNA), and circular RNA (circRNA). These families are under active investigation, given their essential roles in cancer initiation, development and progression. This review focuses on the evidence for the role of RNAs in prostate cancer, and their use as diagnostic and prognostic markers, and targets for treatment in this disease.
Unpaired Image-to-Image Translation from Shared Deep Space

Unpaired image-to-image translation is a tricky task which aims at learning a mapping from one image collection to another image collection without any pair-labeled information. Recent works have proposed cycle-consistency assumption to deal with this task. However, the result is still unsatisfactory for geometric translation. To address this limitation, this paper proposes a novel method using shared deep space generative adversarial network (SDSGAN). Both two images are encoded into a shared deep space through a pre-trained VGG- 19 network, and then we use two decoders to convert them separately to corresponding image domains. In addition, we introduce skip-connection block and self-reconstruction loss to facilitate the mapping. Experimental results show that the proposed SDSGAN has both numerical and perceptual superiorities to existing methods.
Can Micro-Expression be Recognized Based on Single Apex Frame?

Micro-expressions are rapid and subtle facial movements such that they are difficult to detect and recognize. Most of recent works have attempted to recognize micro-expression by using the spatial and dynamic information from the video clip. Physiological studies have demonstrated that the apex frame can convey the most emotion expressed in facial expression. It may be reasonable to use apex frame for improving micro-expression recognition. However, it is wonder how much apex frames contribute to micro-expression recognition. In this paper, we primarily focus on resolving the contribution-level by using apex frame for micro-expression recognition. Firstly, we propose a new method to detect the apex frame in frequency domain, as it is found that apex frame has very correlated relationship with the amplitude change in frequency domain. Secondly, we propose to use deep convolutional neural network (DCNN) on apex frame to recognize micro-expression. Intensive experimental results on CASME II database shows that our method has achieved considerably improvement compared with the state-of-the-art methods in micro-expression recognition. These results also demonstrate that apex frame can express the major emotion in micro-expression.
EXS: Explainable Search Using Local Model Agnostic Interpretability

Retrieval models in information retrieval are used to rank documents for typically under-specified queries. Today machine learning is used to learn retrieval models from click logs and/or relevance judgments that maximizes an objective correlated with user satisfaction. As these models become increasingly powerful and sophisticated, they also become harder to understand. Consequently, it is hard for to identify artifacts in training, data specific biases and intents from a complex trained model like neural rankers even if trained purely on text features. EXS is a search system designed specifically to provide its users with insight into the following questions: "What is the intent of the query according to the ranker? '', "Why is this document ranked higher than another? '' and "Why is this document relevant to the query? ''. EXS uses a version of a popular posthoc explanation method for classifiers -- LIME, adapted specifically to answer these questions. We show how such a system can effectively help a user understand the results of neural rankers and highlight areas of improvement.
Cucurbitacin B suppresses proliferation of pancreatic cancer cells by ceRNA: Effect of miR‐146b‐5p and lncRNA‐AFAP1‐AS1

Cucurbitacin B (CuB) is a natural tetracyclic triterpene product that displays antitumor activity against a wide variety of cancers. In this study, we explored the antipancreatic cancer activity of CuB via the inhibition of expression of the cancer‐related long noncoding RNA, actin filament‐associated protein 1‐antisense RNA 1 (AFAP1‐AS1). CuB arrested pancreatic cancer (PC) cells in the G2/M cell cycle phase by suppressing the expression of AFAP1‐AS1. Insights into the mechanisms of competing endogenous RNAs (ceRNAs) gained from bioinformatics analysis and luciferase activity assays showed that the epidermal growth factor receptor (EGFR) and AFAP1‐AS1 directly compete for miR‐146b‐5p binding. CuB‐induced high miR‐146b‐5p expression and inhibited the expression of AFAP1‐AS1. In summary, reducing the expression of endogenous AFAP1‐AS1 effectively increased the available concentration of miR‐146b‐5p in PC, whereas miR‐146b‐5p overexpression prevented the expression of endogenous AFAP1‐AS1. In particular, we hypothesized that AFAP1‐AS1 might act as a ceRNA, effectively becoming a sponge for miR‐146b‐5p, thereby activating the expression of the EGFR. Thus, CuB suppresses the proliferation, in vitro and in vivo, of PC cells through the ceRNA effect of AFAP1‐AS1 on miR‐146b‐5p.
Discover the Effective Strategy for Face Recognition Model Compression by Improved Knowledge Distillation

For the sake of better accuracy, the face recognition model is becoming larger and larger, which makes them difficult to be deployed on embedded systems. This work proposes an effective model compression method using knowledge distillation, where a fast student model is trained under the guidance of a complex teacher model. Firstly, different loss combinations and network architectures are analyzed through comprehensive experiments to find the most effective approach. To augment the performance, the feature layer is further normalized to make the optimization objective consistent with cosine similarity metric. Moreover, a teacher weighting strategy is proposed to address the issue when teacher provides wrong guidance. Experimental results show that the student model built by our approach can surpass the teacher model while achieving 3× acceleration.
Modeling the information behaviour of software engineers using a work - task framework

Faced with the rapid proliferation of digital information resources within organizations, employees need targeted search systems in order to be effective in their jobs. The goal of this study is to develop a model of information behaviour within a work-task framework that can be used to inform the design of a workplace information search system. In a two-phase process, we conducted a series of studies using multiple methods to identify workplace characteristics and to understand how they influence the needs, search strategies, and information sources used by software engineers working as services consultants in a large high-tech company.
Environmental health aspects of coal ash phytoremediation by selected crops

The objective of this research was to determine the effects of growth media containing FA and FA mixed with soil on selected crop plants seedlings growth. We studied the influence of various FA concentrations (e.g., 0, 10, 20, 30, 40, 50, 60, 70, 80, 90, and 100% of FA in growth media by weight basis) in FA/soil composed media on the germination, growth, and heavy metals uptake of the following plants: barley, Sudan grass, ryegrass, rape, alfalfa, and canola. Plants were grown on Petri dishes (10 cm diameter, 3 replications) for 14–21 days, harvested, dried, and weighed. Experiments have been replicated three times. The concentrations of Al, B, Ba, Co, Cr, Cu, Mo, Sr, Ti, Tl, and V in growth media were determined, and the concentrations of the same elements in young plants were analyzed. Addition of 10, 20, and 30% of FA to the soil were acceptable for most plants, as compared to FA alone used as a growth media. Barley was the only plant of plants used in our research, which was able to sustain seedlings growth on media consisting on FA alone. Preliminary results of chemical analysis of FA and harvested young plants implicate that plants do not accumulate toxic amounts of heavy metals even being grown on media containing 100% FA. Our research results indicate that coal FA might be used as a plant growth media additive. However, additional studies should be undertaken to determine the effects of FA on plants grown till maturity.
Unsupervised Cross-lingual Transfer of Word Embedding Spaces

Cross-lingual transfer of word embeddings aims to establish the semantic mappings among words in different languages by learning the transformation functions over the corresponding word embedding spaces. Successfully solving this problem would benefit many downstream tasks such as to translate text classification models from resource-rich languages (e.g. English) to low-resource languages. Supervised methods for this problem rely on the availability of cross-lingual supervision, either using parallel corpora or bilingual lexicons as the labeled data for training, which may not be available for many low resource languages. This paper proposes an unsupervised learning approach that does not require any cross-lingual labeled data. Given two monolingual word embedding spaces for any language pair, our algorithm optimizes the transformation functions in both directions simultaneously based on distributional matching as well as minimizing the back-translation losses. We use a neural network implementation to calculate the Sinkhorn distance, a well-defined distributional similarity measure, and optimize our objective through back-propagation. Our evaluation on benchmark datasets for bilingual lexicon induction and cross-lingual word similarity prediction shows stronger or competitive performance of the proposed method compared to other state-of-the-art supervised and unsupervised baseline methods over many language pairs.
A Multi-Exposure Image Fusion Based on the Adaptive Weights Reflecting the Relative Pixel Intensity and Global Gradient

This paper presents a new multi-exposure fusion algorithm. The conventional approach is to define a weight map for each of the multi-exposure images, and then obtain the fusion image as their weighted sum. Most of existing methods focused on finding weight functions that assign larger weights to the pixels in better-exposed regions. While the conventional methods apply the same function to each of the multi-exposure images, we propose a function that considers all the multi-exposure images simultaneously to reflect the relative intensity between the images and global gradients. Specifically, we define two kinds of weight functions for this. The first is to measure the importance of a pixel value relative to the overall brightness and neighboring exposure images. The second is to reflect the importance of a pixel value when it is in a range with relatively large global gradient compared to other exposures. The proposed method needs modest computational complexity owing to the simple weight functions, and yet it achieves visually pleasing results and gets high scores according to an image quality measure.
Improving Adversarial Discriminative Domain Adaptation

Adversarial discriminative domain adaptation (ADDA) is an efficient framework for unsupervised domain adaptation, where the source and target domains are assumed to have the same classes, but no labels are available for the target domain. While ADDA has already achieved better training efficiency and competitive accuracy in comparison to other adversarial based methods, we investigate whether we can improve performance by incorporating task knowledge into the adversarial loss functions. We achieve this by extending the discriminator output over the source classes and leverage on the distribution over the source encoder posteriors, which is fixed during adversarial training, in order to align a shared encoder distribution to the source domain. The shared encoder can receive a proportion of examples from both the source and target datasets, in order to smooth the learned distribution and improve its convergence properties during adversarial training. We additionally consider how the extended discriminator can be regularized in order to further improve performance, by treating the discriminator as a denoising autoencoder and corrupting its input. Our final design employs maximum mean discrepancy and reconstruction-based loss functions for adversarial training. We validate our framework on standard datasets like MNIST, USPS, SVHN, MNIST-M and Office-31. Our results on all datasets show that our proposal is both simple and efficient, as it competes or outperforms the state-of-the-art in unsupervised domain adaptation, whilst offering lower complexity than other recent adversarial methods such as DIFA and CoGAN.