Miracle-dz/UniFAR-FTUs huggingface dataset:
Conditioned Stimulus Form Does Not Explain Failures to See Pavlovian‐Instrumental‐Transfer With Ethanol‐Paired Conditioned Stimuli

BACKGROUND Pavlovian-Instrumental-Transfer (PIT) examines the effects of associative learning upon instrumental responding. Previous studies examining PIT with ethanol (EtOH)-maintained responding showed increases in responding following presentation of an EtOH-paired conditioned stimulus (CS). Recently, we conducted 2 studies examining PIT with an EtOH-paired CS. One of these found increases in responding, while the other did not. This less robust demonstration of PIT may have resulted from the form of the CS used, as we used a 120-second light stimulus as a CS, while the previous studies used either a 120-second auditory stimulus or a 10-second light stimulus. This study examined whether using conditions similar to our earlier study, but with either a 120-second auditory or a 10-second light stimulus as a CS, resulted in more robust PIT. We also examined the reliability of our previous failure to observe PIT. METHODS Three experiments were conducted examining whether PIT was obtained using (i) a 120-second light stimulus, (ii) a 10-second light stimulus, or (iii) a 120-second auditory stimulus as CSs. RESULTS We found PIT was not obtained using (i) a 120-second light stimulus as a CS, (ii) a 10-second light stimulus as a CS, or (iii) a 120-second auditory stimulus as a CS. CONCLUSIONS These results suggest that CS form does not account for our earlier failure to see PIT. Rather, factors like rat strain or how EtOH drinking is induced may account for when PIT is or is not observed.
Learning deep and compact models for gesture recognition

We look at the problem of developing a compact and accurate model for gesture recognition from videos in a deep-learning framework. Towards this we propose a joint 3DCNN-LSTM model that is end-to-end trainable and is shown to be better suited to capture the dynamic information in actions. The solution achieves close to state-of-the-art accuracy on the ChaLearn dataset, with only half the model size. We also explore ways to derive a much more compact representation in a knowledge distillation framework followed by model compression. The final model is less than 1 MB in size, which is less than one hundredth of our initial model, with a drop of 7% in accuracy, and is suitable for real-time gesture recognition on mobile devices.
Two-dimensional discriminant multi-manifolds locality preserving projection for facial expression recognition

In this paper, we assume that samples of different expressions reside on different manifolds and propose a novel human emotion recognition framework named two-dimensional discriminant multi-manifolds locality preserving projection (2D-DMLPP). 2D-DMLPP focuses on salient regions which reflect the significant variation from facial expression images so that it can learn an expression-specific model from salient patches rather than that of subject-specific. Furthermore, conventional manifold learning methods ignore the variation among nearby samples from the same class, leading to serious overfitting. We construct three adjacency graphs to model the margin and information, including diversity and similarity of salient patches from the same expression, and then incorporate the information and margin into dimensionality reduction function. Several experiments show that the proposed method significantly improves the recognition performance of facial expression recognition.
An immunohistochemical finding in glioneuronal lesions associated with epilepsy: The appearance of nestin‐positive, CD34‐positive and tau‐accumulating cells

Several kinds of unusual cells have been pathologically identified in epileptic patients. CD34‐positive, nestin‐positive and tau‐positive cells are some of them. However, no reports have investigated the significance of these cells. We examined 14 cases of seizure‐associated glioneuronal lesions to investigate the incidences and distributions of these cells and the association between their incidence and clinical parameters. CD34‐positive and nestin‐positive cells were seen in 43% and 50% of cases, respectively. In the regions with structural anomalies, there were increased numbers of CD34‐positive cells and nestin‐positive cells, but they were identified as different cells. Both examinations showed many abnormal processes in oligodendroglial‐like cells with round nuclei. In contrast, few reactive astrocytes that demonstrated immunoreactivity for glial fibrillary acidic protein were found in this area. Tau accumulation was present in 37% of cases. There was no correspondence with the regions showing increasing numbers of nestin or CD34‐positive cells. There were no significant associations between epileptic clinical parameters and the incidences of the abovementioned immunopositive cells. CD34‐positive cells and nestin‐positive cells are found as frequently as balloon cells and are associated with abnormal reconstitution of the cortex. These findings support the assertion that increases in the numbers of these cells might contribute to promoting epilepsy. In addition, these immunopositive cells are valuable findings for the pathological identification of epileptogenic lesions.
Focal Adhesion Kinases in Adhesion Structures and Disease

Cell adhesion to the extracellular matrix (ECM) is essential for cell migration, proliferation, and embryonic development. Cells can contact the ECM through a wide range of matrix contact structures such as focal adhesions, podosomes, and invadopodia. Although they are different in structural design and basic function, they share common remodeling proteins such as integrins, talin, paxillin, and the tyrosine kinases FAK, Pyk2, and Src. In this paper, we compare and contrast the basic organization and role of focal adhesions, podosomes, and invadopodia in different cells. In addition, we discuss the role of the tyrosine kinases, FAK, Pyk2, and Src, which are critical for the function of the different adhesion structures. Finally, we discuss the essential role of these tyrosine kinases from the perspective of human diseases.
Apply neural network for improving production planning at Samarang petrol mine

Purpose – Artificial neural network (ANN) is considered a good solution for building non-linear relationship between input and output parameters, which is suitable for solving production back allocation, which is the most important step for production planning of petroleum mine. The purpose of this paper is to suggest a solution for solving production back allocation problem at Samarang petrol mine based on ANN approach. Design/methodology/approach – In this study, well operational parameters’ surveillance was conducted and ANN was used to build relationships between operation parameters and production rates. Experimental method is used for testing and evaluating the possibility of using ANN for supporting production planning at Samarang mine. Findings – Consequently, the proposed ANN solution can increase the accuracy of predicted values and could be used for supporting production planning at Samarang mine. Because ANN uses well test data for training and predicting (without adding new devices), it could.
Vanishing Point Extraction and Refinement for Robust Camera Calibration

This paper describes a flexible camera calibration method using refined vanishing points without prior information. Vanishing points are estimated from human-made features like parallel lines and repeated patterns. With the vanishing points extracted from the three mutually orthogonal directions, the interior and exterior orientation parameters can be further calculated using collinearity condition equations. A vanishing point refinement process is proposed to reduce the uncertainty caused by vanishing point localization errors. The fine-tuning algorithm is based on the divergence of grouped feature points projected onto the reference plane, minimizing the standard deviation of each of the grouped collinear points with an O(1) computational complexity. This paper also presents an automated vanishing point estimation approach based on the cascade Hough transform. The experiment results indicate that the vanishing point refinement process can significantly improve camera calibration parameters and the root mean square error (RMSE) of the constructed 3D model can be reduced by about 30%.
KRAS-mutation status dependent effect of zoledronic acid in human non-small cell cancer preclinical models

Background In non-small cell lung cancer (NSCLC) KRAS-mutant status is a negative prognostic and predictive factor. Nitrogen-containing bisphosphonates inhibit prenylation of small G-proteins (e.g. Ras, Rac, Rho) and thus may affect proliferation and migration. In our preclinical work, we investigated the effect of an aminobisphosphonate compound (zoledronic acid) on mutant and wild type KRAS-expressing human NSCLC cell lines. Results We confirmed that zoledronic acid was unable to inhibit the prenylation of mutant K-Ras unlike in the case of wild type K-Ras. In case of in vitro proliferation, the KRAS-mutant human NSCLC cell lines showed resistance to zoledronic acid wild-type KRAS-cells proved to be sensitive. Combinatory application of zoledronic acid enhanced the cytostatic effect of cisplatin. Zoledronic acid did not induce significant apoptosis. In xenograft model, zoledronic acid significantly reduced the weight of wild type KRAS-EGFR-expressing xenograft tumor by decreasing the proliferative capacity. Futhermore, zoledronic acid induced VEGF expression and improved in vivo tumor vascularization. Materials and methods Membrane association of K-Ras was examined by Western-blot. In vitro cell viability, apoptotic cell death and migration were measured in NSCLC lines with different molecular background. The in vivo effect of zoledronic acid was investigated in a SCID mouse subcutaneous xenograft model. Conclusions The in vitro and in vivo inhibitory effect of zoledronic acid was based on the blockade of cell cycle in wild type KRAS-expressing human NSCLC cells. The zoledronic acid induced vascularization supported in vivo cytostatic effect. Our preclinical investigation suggests that patients with wild type KRAS-expressing NSCLC could potentially benefit from aminobisphosphonate therapy.
A cis‐eQTL in OPRM1 is Associated with Subjective Response to Alcohol and Alcohol Use

BACKGROUND A functional polymorphism within the μ-opioid receptor (OPRM1) gene, rs1799971 (A118G), previously has been associated with measures of alcohol use and sensitivity to its effects, but findings have been inconclusive. A recent study suggested that a second nearby variant within OPRM1, rs3778150, is robustly associated with heroin dependence and fully explained a smaller observed association with rs1799971. Given evidence that the rs3778150-C allele is associated with decreased OPRM1 expression levels in the human brain, the current study sought to test the hypothesis that rs3778150 represents a causal variant within OPRM1 that increases risk for a variety of alcohol use phenotypes. METHODS Participants with genotype and phenotype data from a larger experimental study (N = 152) were assessed on measures of subjective response to alcohol and alcohol use. Measures included (i) the Self-Rating of the Effects of Alcohol and the Alcohol Sensitivity Questionnaire, (ii) the Biphasic Alcohol Effects Scale (BAES) and ratings of subjective intoxication, and (iii) average number of drinks per week in the past month. RESULTS Compared to rs3778150-T homozygous individuals, carriers of the rs3778150-C allele exhibited significantly lower retrospective self-report levels of alcohol sensitivity. Carriers of the rs3778150-C allele also exhibited lower levels of BAES alcohol-related stimulation during an alcohol challenge and reported higher levels of drinking in the last 30 days. With the exception of lower levels of BAES alcohol-related sedation, the rs1799971 variant did not show consistent significant association with any of the alcohol phenotypes in the presence of rs3778150. CONCLUSIONS Results suggest that rs3778150 may be causally related to alcohol use phenotypes, and could potentially account for previously observed associations of rs1799971 with substance use phenotypes. Future studies may investigate potential causal relations among genetic variants in OPRM1, subjective response to alcohol, and drinking phenotypes to further delineate the effects of rs3778150.
Photosensitive Alternative Splicing of the Circadian Clock Gene timeless Is Population Specific in a Cold-Adapted Fly, Drosophila montana

To function properly, organisms must adjust their physiology, behavior and metabolism in response to a suite of varying environmental conditions. One of the central regulators of these changes is organisms’ internal circadian clock, and recent evidence has suggested that the clock genes are also important in the regulation of seasonal adjustments. In particular, thermosensitive splicing of the core clock gene timeless in a cosmopolitan fly, Drosophila melanogaster, has implicated this gene to be involved in thermal adaptation. To further investigate this link we examined the splicing of timeless in a northern malt fly species, Drosophila montana, which can withstand much colder climatic conditions than its southern relative. We studied northern and southern populations from two different continents (North America and Europe) to find out whether and how the splicing of this gene varies in response to different temperatures and day lengths. Interestingly, we found that the expression of timeless splice variants was sensitive to differences in light conditions, and while the flies of all study populations showed a change in the usage of splice variants in constant light compared to LD 22:2, the direction of the shift varied between populations. Overall, our findings suggest that the splicing of timeless in northern Drosophila montana flies is photosensitive, rather than thermosensitive and highlights the value of studying multiple species and populations in order to gain perspective on the generality of gene function changes in different kinds of environmental conditions.
Evaluation of Cnn Architectures for Gait Recognition Based on Optical Flow Maps

This work targets people identification in video based on the way they walk (i.e. gait) by using deep learning architectures. We explore the use of convolutional neural networks (CNN) for learning high-level descriptors from low-level motion features (i.e. optical flow components). The low number of training samples for each subject and the use of a test set containing subjects different from the training ones makes the search of a good CNN architecture a challenging task. We carry out a thorough experimental evaluation deploying and analyzing four distinct CNN models with different depth but similar complexity. We show that even the simplest CNN models greatly improve the results using shallow classifiers. All our experiments have been carried out on the challenging TUM-GAID dataset, which contains people in different covariate scenarios (i.e. clothing, shoes, bags).
An assessment framework of SIAM/ARAI fuel efficiency using semi-supervised and similarity methods

In this paper, we are going to use the Pearson and Spearman methodologies for the real-time application of finding the fuel efficiency in 4 wheelers. The Pearson and Spearman methodologies are the correlation methodologies used mainly in the Software development process to identify the complexity between the various modules of the software. Identifying the complexity is important because if the complexity is higher then there is a higher chance of occurrence of the risk in the software. Hence we choose the Pearson and Spearman methods to analyze the fuel consumption of 4 wheelers between the City and Highway.
A Grammar and Style Checker Based on Internet Searches

In this paper we present an English grammar and style checker for non-native English speakers. The main characteristic of this checker is the use of an Internet search engine. As the number of web pages written in English is immense, the system hypothesizes that a piece of text not found on the Web is probably badly written. The system also hypothesizes that the Web will provide examples of how the content of the text segment can be expressed in a gramatical and idiomatic way. So, after the checker warns the user about the odd character of a text segment, the Internet engine searches for contexts that will be helpful for the user to decide whether he/she corrects the segment or not. By means of a search engine, the checker also suggests the writer to use expressions which are more frequent on theWeb other than the expression he/she actually wrote. Although the system is currently being developed for teachers of the Open University of Catalonia, the checker can also be useful for second-language learners, translators, and post-editors.
Large-Signal Static Compact Circuit Model of SiGe Heterojunction Bipolar Phototransistors: Effect of the Distributed Nature of Currents

This paper presents a physically based opto-electrical compact model of a SiGe/Si heterojunction phototransistor (HPT). This static large-signal model is intended to model the static electrical and optical static operation of the HPT. The measured HPT was developed by extending the structure of an existing SiGe heterojunction bipolar transistor (HBT) from the Telefunken GmbH process technology. Unusual behaviors are observed compared to the standard HBT behaviors when the optical access on the top of the HPT is created. The proposed compact model then includes a phenomenon also called “2-D electrical extension effect” that explains the 2-D (vertical and lateral) charge carriers flow within the SiGe HPT. The validity and accuracy of the proposed model in the static mode of operation are confirmed through <inline-formula> <tex-math notation="LaTeX">${I}_{C}$ </tex-math></inline-formula>–<inline-formula> <tex-math notation="LaTeX">${V}_{{\text{CE}}}$ </tex-math></inline-formula> and Gummel characteristics, both under dark and light conditions. The dc responsivity of the HPT is also extracted from the model and agrees quite well to measurement.
Ventilation/Perfusion distribution abnormalities in morbidly obese subjects before and after bariatric surgery.

BACKGROUND Obesity is a global and growing public health problem. Bariatric surgery (BS) is indicated in patients with morbid obesity. To our knowledge, the effects of morbid obesity and BS on ventilation/perfusion (V.a/Q.) ratio distributions using the multiple inert gas elimination technique have never before been explored. METHODS We compared respiratory and inert gas (V.a/Q. ratio distributions) pulmonary gas exchange, breathing both ambient air and 100% oxygen, in 19 morbidly obese women (BMI, 45 kg/m2), both before and 1 year after BS, and in eight normal-weight, never smoker, age-matched, healthy women. RESULTS Before BS, morbidly obese individuals had reduced arterial Po2 (76 ± 2 mm Hg) and an increased alveolar-arterial Po2 difference (27 ± 2 mm Hg) caused by small amounts of shunt (4.3% ± 1.1% of cardiac output), along with abnormally broadly unimodal blood flow dispersion (0.83 ± 0.06). During 100% oxygen breathing, shunt increased twofold in parallel with a reduction of blood flow to low V.a/Q. units, suggesting the development of reabsorption atelectasis without reversion of hypoxic pulmonary vasoconstriction. After BS, body weight was reduced significantly (BMI, 31 kg/m2), and pulmonary gas exchange abnormalities were decreased. CONCLUSIONS Morbid obesity is associated with mild to moderate shunt and V.a/Q. imbalance. These abnormalities are reduced after BS.
Cardiac Endothelial Cell Transcriptome

Objective— Endothelial cells (ECs) are a highly specialized cell type with marked diversity between different organs or vascular beds. Cardiac ECs are an important player in cardiac physiology and pathophysiology but are not sufficiently characterized yet. Thus, the aim of the present study was to analyze the cardiac EC transcriptome. Approach and Results— We applied fluorescence-assisted cell sorting to isolate pure ECs from adult mouse hearts. RNAseq revealed 1288 genes predominantly expressed in cardiac ECs versus heart tissue including several transcription factors. We found an overrepresentation of corresponding transcription factor binding motifs within the promotor region of EC-enriched genes, suggesting that they control the EC transcriptome. Cardiac ECs exhibit a distinct gene expression profile when compared with renal, cerebral, or pulmonary ECs. For example, we found the Meox2/Tcf15, Fabp4, and Cd36 signaling cascade higher expressed in cardiac ECs which is a key regulator of fatty acid uptake and involved in the development of atherosclerosis. Conclusions— The results from this study provide a comprehensive resource of gene expression and transcriptional control in cardiac ECs. The cardiac EC transcriptome exhibits distinct differences in gene expression compared with other cardiac cell types and ECs from other organs. We identified new candidate genes that have not been investigated in ECs yet as promising targets for future evaluation.
Subspace clustering via independent subspace analysis network

Previous work on image clustering focused on seeking a low-dimensional structure from the high-dimensional image data by a shallow linear model, such as sparse subspace clustering (SSC) or low-rank representation (LRR). The recent advance of deep learning shows its superiority via handling data with nonlinear structure, i.e., sparse auto-encoder and independent subspace analysis(ISA), etc. However, most of this type of methods may ignore lots of useful information embedded in the original data. To this end, we propose a novel unsuper-vised learning algorithm via ISA incorporating the subspace structure within data. Specifically, we adopt the ISA to learn local translation invariant feature from data and integrate a prior subspace information into the output of the network simultaneously. This method performs an impressive powerful ability to learn the nature of data. By evaluating on public databases, CMU-PIE and ORL, the experimental results show that the proposed approach achieves better clustering results compared with the state-of-the-art ones.
Active convolutional neural networks for cancerous tissue recognition

Deep neural networks typically require large amounts of annotated data to be trained effectively. However, in several scientific disciplines, including medical image analysis, generating such large annotated datasets requires specialized domain knowledge, and hence is usually very expensive. In this work, we present a novel application of active learning to data sample selection for training Convolutional Neural Networks (CNN) for Cancerous Tissue Recognition (CTR). Our main idea is to steer annotation efforts towards selecting the most informative samples for training the CNN. To quantify informativeness, we explore three choices based on discrete entropy, best-vs-second-best, and k-nearest neighbor agreement. Our results on three different types of cancer datasets consistently demonstrate that under limited annotated samples, our proposed training scheme converges faster than classical randomized stochastic gradient descent, while achieving the same (or sometimes superior) classification accuracy.