Miracle-dz/UniFAR-FTUs huggingface dataset:
Mental Training for Better Achievement: Effects of Verbal Suggestions and Evaluation (of Effectiveness) on Cognitive Performance

Objective: There is only some literature regarding the influence of verbal suggestions on cognitive performance in healthy volunteers. For example, the performance in a knowledge test was enhanced when participants were told that they had subliminally received the correct answer. However, enhancing cognitive performance only via verbal suggestions without prior conditioning phases has not yet been examined. The goal of our study was therefore to investigate the effects of a mental training based on verbal suggestions compared to a control training on cognitive performance in a student population using a balanced-placebo-design. Methods: In total, 103 participants were randomly assigned either to listening to a 20 min audio-taped mental training or to a 20 min philosophy lecture (control training) via headphones. Participants were individually tested before and after the training concerning their cognitive performance. Information about the type of training were varied in both intervention conditions (“You are part of our experimental condition and you will receive an effective mental training” or “You are part of our control group and you will receive the control condition”). At the end of the assessment, participants were asked what kind of training they believed they had received and how effective they would rate the received training. Results: Overall, the cognitive performance improved in all participants, F (1, 99) = 490.01, p < 0.001. Contrary to our hypotheses, we found no interaction of the type of training and type of instruction on the cognitive performance. Participants who rated the received training as being effective at the end of the experiment (regardless if it was the mental or the control training), have before experienced a greater improvement in their cognitive performance [F (2,100) = 7.26, p = 0.001] and showed higher scores in the ability to absorb [F(2, 99) = 3.75, p = 0.027]. Conclusion: The subjects' own experiences in the task might have influenced the rating of the training rather than the actual training or the information they receive regarding the type of training. This finding underlines the relevance of enhancing the subjective beliefs and self-efficacy in situations where cognitive attention processes are important and of individually tailoring mental trainings.
Identification of Candidate Biomarkers in Malignant Ascites from Patients with Hepatocellular Carcinoma by iTRAQ-Based Quantitative Proteomic Analysis

Almost all the patients with hepatocellular carcinoma (HCC) at advanced stage experience pathological changes of chronic liver cirrhosis, which generally leads to moderate ascites. Recognition of novel biomarkers in malignant ascites could be favorable for establishing a diagnosis for the HCC patients with ascites, and even predicting prognosis, such as risk of distant metastasis. To distinguish the proteomic profiles of malignant ascites in HCC patients from those with nonmalignant liver cirrhosis, an iTRAQ pipeline was built up to analyze the differentially distributed proteins in the malignant ascites from HCC patients (n=10) and benign ascites from hepatic decompensation (HD) controls (n=9). In total, 112 differentially distributed proteins were identified, of which 69 proteins were upregulated and 43 proteins were downregulated (ratio <0.667 or >1.3, respectively) in the malignant ascites. Moreover, 19 upregulated proteins (including keratin 1 protein and rheumatoid factor RF-IP20, ratio>1.5) and 8 downregulated proteins (including carbonic anhydrase 1, ratio<0.667) were identified from malignant ascites samples. Functional categories analyses indicated that membrane proteins, ion regulation, and amino acid metabolism are implicated in the formation of HCC malignant ascites. Pathways mapping revealed that glycolysis/gluconeogenesis and complement/coagulation cascades are the mostly affected cell life activities in HCC malignant ascites, suggesting the key factors in these pathways such as Enolase-1 and fibrinogen are potential ascitic fluid based biomarkers for diagnosis and prognosis for HCC.
Spin Caloritronic Measurements: A Round Robin Comparison of the Longitudinal Spin Seebeck Effect

The rising field of spin caloritronics focuses on the interactions between spin and heat currents in a magnetic material. The observation of the spin Seebeck effect opened the route to this branch of research making possible the design of thermoelectric devices made of insulating magnetic materials. A round robin test performed by five partners on a single device highlighted the reproducibility problems related to the measurements of the spin Seebeck coefficient, the quantity that describes the strength of the spin Seebeck effect. This work stimulated the search for more reproducible measurement methods.
RNA Interference to Control Asian Corn Borer Using dsRNA from a Novel Glutathione-S-Transferase Gene of Ostrinia furnacalis (Lepidoptera: Crambidae)

Abstract Glutathione-S-transferases (GST) comprise a multifunctional protein superfamily, which plays important roles as detoxifiers and antioxidants in insects. The GST in Asian corn borer has not been previously characterized. In this study, we cloned, characterized, and expressed the complete GST genes from the midgut of Asian corn borer. Furthermore, we designed htL4440-OfGST vector to exploit this gene for RNA interference (RNAi) strategy to control this pest. A complete GST cDNA sequence in Asian corn borer was obtained by reverse transcription polymerase chain reaction (RT-PCR) and rapid amplification of cDNA ends technology. The gene was 887bp in length and contained a 705bp open reading frame and 5′ UTR and 3′ UTR lengths of 89 and 93bp, respectively. The putative sequence encoded a putative 234 amino acid residue peptide and had a predicted molecular weight of ~26kDa. The GST protein of Asian corn borer is hydrophilic and may have a 30 amino acid signal peptide with a cleavage site between L30 and K31. A recombination vector pET28a-OfGST was constructed for purification and antibody preparation. Western blotting analysis showed that this protein reached the maximum expression level around 24 h in Asian corn borer larvae fed the plant toxin 2,4-dihydroxy-7-methoxy-1,4-benzoxazin-3-one. A second vector, htL4440-OfGST, was constructed to generate the dsRNA of the GST gene. A larval feeding bioassay showed that the expressed dsRNA significantly reduced the detoxification ability of Asian corn borer larvae and increased mortality rate up to 54%. Our data indicated that GST plays very important roles in detoxifying in Asian corn borer and can be used as an RNAi method to control this pest in the field.
Multiple Kernel Boosting Based Two-level RGBD Image Co-Segmentation

RGBD image co-segmentation aims at automatically discovering and segmenting common objects from a set of relevant RGBD images. In this paper, we propose a novel RGBD image co-segmentation method which takes advantage of sample selection as well as multiple kernel boosting (MKB) classifiers. First, the initial co-segmentation results are generated based on RGBD co-saliency maps. Then the sample selection is performed on the basis of pre-segmented regions according to their mutual similarity. The most similar regions with high co-saliency values are further picked out from different images in the image set. Subsequently, on the region level, all positive and negative samples selected above are used to train the MKB classifier for the whole image set. Meanwhile, on the pixel level, the samples selected in each image are exploited to learn a pixel-level MKB classifier. Finally, the co-segmentation results are generated by fusing the results of region-level co-segmentation results and pixel-level segmentation results as well as initial co-segmentation results. Experimental results on a public RGBD image co-segmentation dataset demonstrate that the proposed co-segmentation method outperforms the state-of-the-art co-segmentation methods.
Suprachiasmatic function in a circadian period mutant: Duper alters light‐induced activation of vasoactive intestinal peptide cells and PERIOD1 immunostaining

Mammalian circadian rhythms are entrained by photic stimuli that are relayed by retinal projections to the core of the suprachiasmatic nucleus (SCN). Neuronal activation, as demonstrated by expression of the immediate early gene c‐fos, leads to transcription of the core clock gene per1. The duper mutation in hamsters shortens circadian period and amplifies light‐induced phase shifts. We performed two experiments to compare the number of c‐FOS immunoreactive (ir) and PER1‐ir cells, and the intensity of staining, in the SCN of wild‐type (WT) and duper hamsters at various intervals after presentation of a 15‐min light pulse in the early subjective night. Light‐induced c‐FOS‐ir within 1 hr in the dorsocaudal SCN of duper, but not WT hamsters. In cells that express vasoactive intestinal peptide (VIP), which plays a critical role in synchronization of SCN cellular oscillators, light‐induced c‐FOS‐ir was greater in duper than WT hamsters. After the light pulse, PER1‐ir cells were found in more medial portions of the SCN than FOS‐ir, and appeared with a longer latency and over a longer time course, in VIP cells of duper than wild‐type hamsters. Our results indicate that the duper allele alters SCN function in ways that may contribute to changes in free running period and phase resetting.
A 3D Visual Comfort Metric Based on Binocular Asymmetry Factor

How to evaluate 3D visual discomfort is a challenging problem in stereoscopic image quality assessment. This is due to an existing gap between human binocular visual perception and current stereoscopic image representation techniques. As an indicator of binocular images' relationship for stereoscopic image, binocular asymmetry has been found that it is one of the most important discomfort-induced factors. Based on the factor, this paper proposes an objective stereoscopic visual comfort assessment (SVCA) model for stereoscopic images. Specifically, binocular asymmetry is interpreted as the two views' image texture features which are represented by the histograms of oriented gradient (HOG) feature and local binary pattern (LBP) feature. The HOG/LBP are integrated into an overall visual comfort score by support vector regression (SVR). Two stereoscopic image databases are chosen to evaluate the applicability of the proposed metric. The experimental results show that the proposed SVCA metric can efficiently predict visual discomfort for stereoscopic image.
Unsupervised Identification of Disease Marker Candidates in Retinal OCT Imaging Data

The identification and quantification of markers in medical images is critical for diagnosis, prognosis, and disease management. Supervised machine learning enables the detection and exploitation of findings that are known a priori after annotation of training examples by experts. However, supervision does not scale well, due to the amount of necessary training examples, and the limitation of the marker vocabulary to known entities. In this proof-of-concept study, we propose unsupervised identification of anomalies as candidates for markers in retinal optical coherence tomography (OCT) imaging data without a constraint to a priori definitions. We identify and categorize marker candidates occurring frequently in the data and demonstrate that these markers show a predictive value in the task of detecting disease. A careful qualitative analysis of the identified data driven markers reveals how their quantifiable occurrence aligns with our current understanding of disease course, in early- and late age-related macular degeneration (AMD) patients. A multi-scale deep denoising autoencoder is trained on healthy images, and a one-class support vector machine identifies anomalies in new data. Clustering in the anomalies identifies stable categories. Using these markers to classify healthy-, early AMD- and late AMD cases yields an accuracy of 81.40%. In a second binary classification experiment on a publicly available data set (healthy versus intermediate AMD), the model achieves an area under the ROC curve of 0.944.
Growth in Hyper-Concentrated Sweet Whey Triggers Multi Stress Tolerance and Spray Drying Survival in Lactobacillus casei BL23: From the Molecular Basis to New Perspectives for Sustainable Probiotic Production

Lactobacillus casei BL23 has a recognized probiotic potential, which includes immune modulation, protection toward induced colitis, toward induced colon cancer and toward dissemination of pathogens. In L. casei, as well as in other probiotics, both probiotic and technological abilities are highly dependent (1) on the substrate used to grow bacteria and (2) on the process used to dry and store this biomass. Production and storage of probiotics, at a reasonable financial and environmental cost, becomes a crucial challenge. Food-grade media must be used, and minimal process is preferred. In this context, we have developed a “2-in-1” medium used both to grow and to dry L. casei BL23, considered a fragile probiotic strain. This medium consists in hyper-concentrated sweet whey (HCSW). L. casei BL23 grows in HCSW up to 30% dry matter, which is 6 times-concentrated sweet whey. Compared to isotonic sweet whey (5% dry matter), these growth conditions enhanced tolerance of L. casei BL23 toward heat, acid and bile salts stress. HCSW also triggered intracellular accumulation of polyphosphate, of glycogen and of trehalose. A gel-free global proteomic differential analysis further evidenced overexpression of proteins involved in pathways known to participate in stress adaptation, including environmental signal transduction, oxidative and metal defense, DNA repair, protein turnover and repair, carbohydrate, phosphate and amino acid metabolism, and in osmoadaptation. Accordingly, HCSW cultures of L. casei BL23 exhibited enhanced survival upon spray drying, a process known to drastically affect bacterial viability. This work opens new perspectives for sustainable production of dried probiotic lactobacilli, using food industry by-products and lowering energy costs.
Dose–response association of dietary sodium intake with all-cause and cardiovascular mortality: a systematic review and meta-analysis of prospective studies

Abstract Objective High Na intake has been associated with different health problems. However, serious controversies exist over studies investigating associations of Na intake with mortality from all-causes and CVD. The present systematic review and meta-analysis was done to investigate, for the first time, the dose–response association of dietary Na intake with all-cause and CVD mortality among prospective studies. Design Relevant papers published up to August 2017 were searched in MEDLINE, EMBASE and Google Scholar databases. Prospective cohort studies on the association of dietary Na intake with all-cause or/and CVD mortality were included. Linear and non-linear dose–response associations between Na intake and CVD and all-cause mortality were examined. Results Overall, twenty publications met inclusion criteria. A significant non-linear association (P<0·001) was found between Na intake and CVD mortality risk among studies assessing urinary Na excretion, with a relatively steep slope at Na intakes above 2400mg/d. However, the association was not significant in studies using dietary Na intake (P=0·61). Additionally, the non-linear association of Na intake with all-cause mortality was also non-significant. No linear association (effect size; 95 % CI; I 2) was seen between 100mg/d increment in Na intake and CVD mortality (1·01; 0·97, 1·05; 98·4 %) or all-cause mortality (1·01; 1·00, 1·02; 89·2 %). Following subgroup analyses, the association between Na intake and CVD mortality was observed only among studies conducted in the USA (0·99; 0·99, 1·00; 20·0 %). Conclusions The study showed a direct association between urinary Na excretion and CVD mortality which was more considerable at intakes above 2400mg/d. In contrast, no significant association was found between Na intake and all-cause mortality. Further long-term prospective studies on different populations are required to confirm these findings.
Childhood Abuse and Adulthood IPV, Depression, and High-Risk Drinking in Latinas

Abstract Little is known about relationships between child maltreatment and adulthood intimate partner violence (IPV), depression, and risky drinking in Latinas. 548 Latinas in a sexual health randomized control trial (RCT) self-reported childhood physical, sexual, and emotional abuse, IPV, depression, and risky drinking. Childhood abuse was related to adulthood IPV, OR = 1.27, depression, OR = 2.02, and high-risk drinking, OR = 2.16. Childhood emotional abuse was linked to depression, OR = 2.19; childhood physical abuse to risky drinking, OR = 2.62; and childhood sexual abuse to depression, OR = 2.78 and risky drinking, OR = 2.38. Results may inform prevention/intervention efforts for mental health nurses.
Segmentation d'images de façades de bâtiments acquises d'un point de vue terrestre

L'analyse de facades (detection, comprehension et reconstruction) a partir d'images acquises depuis la rue est aujourd'hui un theme de recherche tres actif en photogrammetrie et en vision par ordinateur de part ses nombreuses applications industrielles. Cette these montre des avancees realisees dans le domaine de la segmentation generique de grands volumes de ce type d'images, contenant une ou plusieurs zones de facades (entieres ou tronquees). Ce type de donnees se caracterise par une complexite architecturale tres riche ainsi que par des problemes lies a l'eclairage et au point de vue d'acquisition. La genericite des traitements est un enjeu important. La contrainte principale est de n'introduire que le minimum d'a priori possible. Nous basons nos approches sur les proprietes d'alignements et de repetitivite des structures principales de la facade. Nous proposons un partitionnement hierarchique des contours de l'image ainsi qu'une detection de grilles de structures repetitives par processus ponctuels marques. Sur les resultats, la facade est separee de ses voisines et de son environnement (rue, ciel). D'autre part, certains elements comme les fenetres, les balcons ou le fond de mur, sans etre reconnus, sont extraits de maniere coherente. Le parametrage s'effectue en une seule passe et s'applique a tous les styles d'architecture rencontres. La problematique se situe en amont de nombreuses thematiques comme la separation de facades, l'accroissement du niveau de detail de modeles urbains 3D generes a partir de photos aeriennes ou satellitaires, la compression ou encore l'indexation a partir de primitives geometriques (regroupement de structures et espacements entre elles
Student Engagement Predictions in an e-Learning System and Their Impact on Student Course Assessment Scores

Several challenges are associated with e-learning systems, the most significant of which is the lack of student motivation in various course activities and for various course materials. In this study, we used machine learning (ML) algorithms to identify low-engagement students in a social science course at the Open University (OU) to assess the effect of engagement on student performance. The input variables of the study included highest education level, final results, score on the assessment, and the number of clicks on virtual learning environment (VLE) activities, which included dataplus, forumng, glossary, oucollaborate, oucontent, resources, subpages, homepage, and URL during the first course assessment. The output variable was the student level of engagement in the various activities. To predict low-engagement students, we applied several ML algorithms to the dataset. Using these algorithms, trained models were first obtained; then, the accuracy and kappa values of the models were compared. The results demonstrated that the J48, decision tree, JRIP, and gradient-boosted classifiers exhibited better performance in terms of the accuracy, kappa value, and recall compared to the other tested models. Based on these findings, we developed a dashboard to facilitate instructor at the OU. These models can easily be incorporated into VLE systems to help instructors evaluate student engagement during VLE courses with regard to different activities and materials and to provide additional interventions for students in advance of their final exam. Furthermore, this study examined the relationship between student engagement and the course assessment score.
A Deep-Learning Based Multi-Modality Sensor Calibration Method for USV

The automatic obstacle avoidance and other tasks of the unmanned surface vehicle rely on the fusion of multi-modality onboard sensors. The accurate calibration method is the foundation of sensor fusion. This paper proposes an online calibration method based on the deep learning for visual sensor and depth sensor. Through an end-to-end network, we combine feature extraction, feature matching and global optimization process of sensor calibration. After initial training, the network can continuously calibrate multi-modality sensors. It solves the USV calibration challenges under complex operating environment. In the simulation environment and realistic environment, we conducted a fast online calibration of the camera, LIDAR and depth camera, which showed the effectiveness of the algorithm.
A New Measurement Method for Supraharmonics in 2–150 kHz

The 32 equal-width segments supraharmonics measurement method, recommended in the newly revised standard IEC 61000-4-30, has some disadvantages, such as the large frequency resolution, and the large effect of spectral leakage. To overcome this, a new measurement method for supraharmonics in 2–150 kHz based on compressive sensing is proposed. Simulation results and measured data analysis indicate that the proposed approach can overcome DFT algorithm's limitation, and the frequency resolution can be improved by an order-of-magnitude without extending the signal's observation time. The amplitude of supraharmonics can also be calculated accurately.
Information-Based Boundary Equilibrium Generative Adversarial Networks with Interpretable Representation Learning

This paper describes a new image generation algorithm based on generative adversarial network. With an information-theoretic extension to the autoencoder-based discriminator, this new algorithm is able to learn interpretable representations from the input images. Our model not only adversarially minimizes the Wasserstein distance-based losses of the discriminator and generator but also maximizes the mutual information between small subset of the latent variables and the observation. We also train our model with proportional control theory to keep the equilibrium between the discriminator and the generator balanced, and as a result, our generative adversarial network can mitigate the convergence problem. Through the experiments on real images, we validate our proposed method, which can manipulate the generated images as desired by controlling the latent codes of input variables. In addition, the visual qualities of produced images are effectively maintained, and the model can stably converge to the equilibrium. However, our model has a difficulty in learning disentangling factors because our model does not regularize the independence between the interpretable factors. Therefore, in the future, we will develop a generative model that can learn disentangling factors.