nils-herrmann/clean_scirepeval huggingface dataset:
Nestin Is Essential for Zebrafish Brain and Eye Development through Control of Progenitor Cell Apoptosis

Nestin is expressed in neural progenitor cells (NPC) of developing brain. Despite its wide use as an NPC marker, the function of nestin in embryo development is unclear.



As nestin is conserved in zebrafish and its predicted sequence is clustered with the mammalian nestin orthologue, we used zebrafish as a model to investigate its role in embryogenesis. Injection of nestin morpholino (MO) into fertilized eggs induced time- and dose-dependent brain and eye developmental defects. Nestin morphants exhibited characteristic morphological changes including small head, small eyes and hydrocephalus. Histological examinations show reduced hind- and mid-brain size, dilated ventricle, poorly organized retina and underdeveloped lens. Injection of control nestin MO did not induce brain or eye changes. Nestin MO injection reduced expression of ascl1b (achaete-scute complex-like 1b), a marker of NPCs, without affecting its distribution. Nestin MO did not influence Elavl3/4 (Embryonic lethal, abnormal vision, Drosophila-like 3/4) (a neuronal marker), or otx2 (a midbrain neuronal marker), but severely perturbed cranial motor nerve development and axon distribution. To determine whether the developmental defects are due to excessive NPC apoptosis and/or reduced NPC proliferation, we analyzed apoptosis by TUNEL assay and acridine orange staining and proliferation by BrdU incorporation, pcna and mcm5 expressions. Excessive apoptosis was noted in hindbrain and midbrain cells. Apoptotic signals were colocalized with ascl1b. Proliferation markers were not significantly altered by nestin MO.



These results suggest that nestin is essential for zebrafish brain and eye development probably through control of progenitor cell apoptosis.
Bod-IDE: An Augmented Reality Sandbox for eFashion Garments

Electronic fashion (eFashion) garments use technology to augment the human body with wearable interaction. In developing ideas, eFashion designers need to prototype the role and behavior of the interactive garment in context; however, current wearable prototyping toolkits require semi-permanent construction with physical materials that cannot easily be altered. We present Bod-IDE, an augmented reality 'mirror' that allows eFashion designers to create virtual interactive garment prototypes. Designers can quickly build, refine, and test on-the-body interactions without the need to connect or program electronics. By envisioning interaction with the body in mind, eFashion designers can focus more on reimagining the relationship between bodies, clothing, and technology.
Clearance of amyloid-beta peptide across the blood-brain barrier: implication for therapies in Alzheimer's disease.

The main receptors for amyloid-beta peptide (Abeta) transport across the blood-brain barrier (BBB) from brain to blood and blood to brain are low-density lipoprotein receptor related protein-1 (LRP1) and receptor for advanced glycation end products (RAGE), respectively. In normal human plasma a soluble form of LRP1 (sLRP1) is a major endogenous brain Abeta 'sinker' that sequesters some 70 to 90 % of plasma Abeta peptides. In Alzheimer's disease (AD), the levels of sLRP1 and its capacity to bind Abeta are reduced which increases free Abeta fraction in plasma. This in turn may increase brain Abeta burden through decreased Abeta efflux and/or increased Abeta influx across the BBB. In Abeta immunotherapy, anti-Abeta antibody sequestration of plasma Abeta enhances the peripheral Abeta 'sink action'. However, in contrast to endogenous sLRP1 which does not penetrate the BBB, some anti-Abeta antibodies may slowly enter the brain which reduces the effectiveness of their sink action and may contribute to neuroinflammation and intracerebral hemorrhage. Anti-Abeta antibody/Abeta immune complexes are rapidly cleared from brain to blood via FcRn (neonatal Fc receptor) across the BBB. In a mouse model of AD, restoring plasma sLRP1 with recombinant LRP-IV cluster reduces brain Abeta burden and improves functional changes in cerebral blood flow (CBF) and behavioral responses, without causing neuroinflammation and/or hemorrhage. The C-terminal sequence of Abeta is required for its direct interaction with sLRP and LRP-IV cluster which is completely blocked by the receptor-associated protein (RAP) that does not directly bind Abeta. Therapies to increase LRP1 expression or reduce RAGE activity at the BBB and/or restore the peripheral Abeta 'sink' action, hold potential to reduce brain Abeta and inflammation, and improve CBF and functional recovery in AD models, and by extension in AD patients.
When Positive Sentiment Is Not So Positive : Textual Analytics and Bank Failures

We extend beyond healthiness assessment of banks using quantitative financial data by applying textual sentiment analysis. Looking at 10-K annual reports for a large sample of banks in the 2000-2014 period, 52 public bank holding companies that were associated with bank failures during the global financial crisis serve as a natural experiment. Utilizing negative and positive dictionaries proposed by Loughran and McDonald (2011), we find that both sentiments on average discriminate between failed and non-failed banks 80% of the time. However, we find that positive sentiment contains stronger predictive power than negative sentiment; out of ten failed banks, on average positive sentiment can identify seven true events, whereas negative sentiment identifies five failed banks at most. While one would link financial soundness with more positive sentiment, it appears that failed banks exhausted more positive sentiment than their non-failed peers, whether ex-ante in anticipation of good news or ex-post to conceal
Fast and High Quality Highlight Removal From a Single Image

Specular reflection exists widely in photography and causes the recorded color deviating from its true value, thus, fast and high quality highlight removal from a single nature image is of great importance. In spite of the progress in the past decades in highlight removal, achieving wide applicability to the large diversity of nature scenes is quite challenging. To handle this problem, we propose an analytic solution to highlight removal based on an L2 chromaticity definition and corresponding dichromatic model. Specifically, this paper derives a normalized dichromatic model for the pixels with identical diffuse color: a unit circle equation of projection coefficients in two subspaces that are orthogonal to and parallel with the illumination, respectively. In the former illumination orthogonal subspace, which is specular-free, we can conduct robust clustering with an explicit criterion to determine the cluster number adaptively. In the latter, illumination parallel subspace, a property called pure diffuse pixels distribution rule helps map each specular-influenced pixel to its diffuse component. In terms of efficiency, the proposed approach involves few complex calculation, and thus can remove highlight from high resolution images fast. Experiments show that this method is of superior performance in various challenging cases.
Reducing calorie sales from supermarkets – ‘silent’ reformulation of retailer-brand food products

Food product reformulation is seen as one among several tools to promote healthier eating. Reformulating the recipe for a processed food, e.g. reducing the fat, sugar or salt content of the foods, or increasing the content of whole-grains, can help the consumers to pursue a healthier life style. In this study, we evaluate the effects on calorie sales of a 'silent' reformulation strategy, where a retail chain's private-label brands are reformulated to a lower energy density without making specific claims on the product.



Using an ecological study design, we analyse 52 weeks' sales data - enriched with data on products' energy density - from a Danish retail chain. Sales of eight product categories were studied. Within each of these categories, specific products had been reformulated during the 52 weeks data period. Using econometric methods, we decompose the changes in calorie turnover and sales value into direct and indirect effects of product reformulation.



For all considered products, the direct effect of product reformulation was a reduction in the sale of calories from the respective product categories - between 0.5 and 8.2%. In several cases, the reformulation led to indirect substitution effects that were counterproductive with regard to reducing calorie turnover. However, except in two insignificant cases, these indirect substitution effects were dominated by the direct effect of the reformulation, leading to net reductions in calorie sales between -3.1 and 7.5%. For all considered product reformulations, the reformulation had either positive, zero or very moderate negative effects on the sales value of the product category to which the reformulated product belonged.



Based on these findings, 'silent' reformulation of retailer's private brands towards lower energy density seems to contribute to lowering the calorie intake in the population (although to a moderate extent) with moderate losses in retailer's sales revenues.
Lycopene supplementation attenuated xanthine oxidase and myeloperoxidase activities in skeletal muscle tissues of rats after exhaustive exercise.

Strenuous exercise is known to induce oxidative stress leading to the generation of free radicals. The purpose of the present study was to investigate the effects of lycopene, an antioxidant nutrient, at a relatively low dose (2.6 mg/kg per d) and a relatively high dose (7.8 mg/kg per d) on the antioxidant status of blood and skeletal muscle tissues in rats after exhaustive exercise. Rats were divided into six groups: sedentary control (C); sedentary control with low-dose lycopene (CLL); sedentary control with high-dose lycopene (CHL); exhaustive exercise (E); exhaustive exercise with low-dose lycopene (ELL); exhaustive exercise with high-dose lycopene (EHL). After 30 d, the rats in the three C groups were killed without exercise, but the rats in the three E groups were killed immediately after an exhaustive running test on a motorised treadmill. The results showed that xanthine oxidase (XO) activities of plasma and muscle, and muscular myeloperoxidase (MPO) activity in group E were significantly increased compared with group C. Compared with group E, the elevations of XO and MPO activities of muscle were significantly decreased in group EHL. The malondialdehyde concentrations of plasma and tissues in group E were significantly increased by 72 and 114 %, respectively, compared with those in group C. However, this phenomenon was prevented in rats of the ELL and EHL groups. There was no significant difference in the GSH concentrations of erythrocytes in each group; however, exhaustive exercise resulted in a significant decrease in the GSH content of muscle. In conclusion, these results suggested that lycopene protected muscle tissue from oxidative stress after exhaustive exercise.
Intragonadal somatic cells (ISCs) in the male oyster Crassostrea gigas: morphology and contribution in germinal epithelium structure.

The ultrastructure of somatic cells present in gonadal tubules in male oyster Crassostrea gigas was investigated. These cells, named Intragonadal Somatic Cells (ISCs) have a great role in the organization of the germinal epithelium in the gonad. Immunological detection of α-tubulin tyrosine illustrates their association in columns from the basis to the lumen of the tubule, stabilized by numerous adhesive junctions. This somatic intragonadal organization delimited some different groups of germ cells along the tubule walls. In early stages of gonad development, numerous phagolysosomes were observed in the cytoplasm of ISCs indicating that these cells have in this species an essential role in the removal of waste sperm in the tubules. Variations of lipids droplets content in the cytoplasm of ISCs were also noticed along the spermatogenesis course. ISCs also present some mitochondria with tubullo-lamellar cristae.
Advances in Medical Image Encryption

The content protection of digital medical images is getting more importance, especially with the advance of computerized systems and communication networks which allows providing high quality images, sending and receiving such data in a realtime manner. Medical concernslead healthcare organizations to encrypt every patient data, such as images before transferring the data over computer networks. Therefore, designing and developing qualified encryption algorithms is quite important for contemporary medicine.Medical image encryption algorithmstry to convert a digital image to another image data format which would bedefinitely hard to recognize. In this paper, we technically review image encryption methods for medical images. We do hope the present work can highlight the most recent contributions in the research area, and provide wellorganized information to the medical image community.
Context-aware recommendations from implicit data via scalable tensor factorization

Albeit the implicit feedback based recommendation problem— when only the user history is available but there are no ratings—is the most typical setting in real-world applications, it is much less researched than the explicit feedback case. State-of-the-art algorithms that are efficient on the explicit case cannot be automatically transformed to the implicit case if scalability should be maintained. There are few implicit feedback benchmark data sets, therefore new ideas are usually experimented on explicit benchmarks. In this paper, we propose a generic context-aware implicit feedback recommender algorithm, coined iTALS. iTALS applies a fast, ALS-based tensor factorization learning method that scales linearly with the number of non-zero elements in the tensor. We also present two approximate and faster variants of iTALS using coordinate descent and conjugate gradient methods at learning. The method also allows us to incorporate various contextual information into the model while maintaining its computational efficiency. We present two context-aware variants of iTALS incorporating seasonality and item purchase sequentiality into the model to distinguish user behavior at different time intervals, and product types with different repetitiveness. Experiments run on six data sets shows that iTALS clearly outperforms context-unaware models and context aware baselines, while it is on par with factorization machines (beats 7 times out of 12 cases) both in terms of recall and MAP.
Maximizing Cloud Providers' Revenues via Energy Aware Allocation Policies

Cloud providers, like Amazon, offer their data centers' computational and storage capacities for lease to paying customers. High electricity consumption, associated with running a data center, not only reflects on its carbon footprint, but also increases the costs of running the data center itself. This paper addresses the problem of maximizing the revenues of Cloud providers by trimming down their electricity costs. As a solution allocation policies which are based on the dynamic powering servers on and off are introduced and evaluated. The policies aim at satisfying the conflicting goals of maximizing the users' experience while minimizing the amount of consumed electricity. The results of numerical experiments and simulations are described, showing that the proposed scheme performs well under different traffic conditions.
GUISE: a uniform sampler for constructing frequency histogram of graphlets

Graphlet frequency distribution (GFD) has recently become popular for characterizing large networks. However, the computation of GFD for a network requires the exact count of embedded graphlets in that network, which is a computationally expensive task. As a result, it is practically infeasible to compute the GFD for even a moderately large network. In this paper, we propose Guise, which uses a Markov Chain Monte Carlo sampling method for constructing the approximate GFD of a large network. Our experiments on networks with millions of nodes show that Guise obtains the GFD with very low rate of error within few minutes, whereas the exhaustive counting-based approach takes several days.
Performance animation from low-dimensional control signals

This paper introduces an approach to performance animation that employs video cameras and a small set of retro-reflective markers to create a low-cost, easy-to-use system that might someday be practical for home use. The low-dimensional control signals from the user's performance are supplemented by a database of pre-recorded human motion. At run time, the system automatically learns a series of local models from a set of motion capture examples that are a close match to the marker locations captured by the cameras. These local models are then used to reconstruct the motion of the user as a full-body animation. We demonstrate the power of this approach with real-time control of six different behaviors using two video cameras and a small set of retro-reflective markers. We compare the resulting animation to animation from commercial motion capture equipment with a full set of markers.
Effect of Finger Pitch on the Driving Ability of a 40-nm MOSFET With Contact Etch Stop Layer Strain in Multifinger Gated Structure

Effects of the poly gate finger pitch on, hot-carrier-nduced reliability degradation, and radio frequency characteristics of the 40-nm n-channel metal-oxide-semiconductor field-effect transistors with contact-etch-stop-layer (CESL) strain and multifinger gate structures were systematically investigated by both experiment and technology computer-aided design simulation. The finger pitch influences both the transfer of CESL-induced stress into a channel and the shadow effect of a poly gate on a pocket implantation. The results showed that the effects of the poly gate finger pitch were more obvious for pitches less than 0.12 . Additionally, the change in stress on the channel was dominant for pitches larger than 0.12 , but for pitches less than 0.12 , the modulation of pocket implantation shadow effects became the main controlling factor.
Cross-Modal Retrieval in the Cooking Context: Learning Semantic Text-Image Embeddings

Designing powerful tools that support cooking activities has rapidly gained popularity due to the massive amounts of available data, as well as recent advances in machine learning that are capable of analyzing them. In this paper, we propose a cross-modal retrieval model aligning visual and textual data (like pictures of dishes and their recipes) in a shared representation space. We describe an effective learning scheme, capable of tackling large-scale problems, and validate it on the Recipe1M dataset containing nearly 1 million picture-recipe pairs. We show the effectiveness of our approach regarding previous state-of-the-art models and present qualitative results over computational cooking use cases.
A non-contact device for tracking gaze in a human computer interface

This paper presents a novel design for a non-contact eye detection and gaze tracking device. It uses two cameras to maintain real-time tracking of a person s eye in the presence of head motion. Image analysis techniques are used to obtain accurate locations of the pupil and corneal reflections. All the computations are performed in software and the device only requires simple, compact optics and electronics attached to the user s computer. Three methods of estimating the user s point of gaze on a computer monitor are evaluated. The camera motion system is capable of tracking the user s eye in real-time (9 fps) in the presence of natural head movements as fast as 100 /s horizontally and 77 /s vertically. Experiments using synthetic images have shown its ability to track the location of the eye in an image to within 0.758 pixels horizontally and 0.492 pixels vertically. The system has also been tested with users with different eye colors and shapes, different ambient lighting conditions and the use of eyeglasses. A gaze accuracy of 2.9 was observed.
Quantifying Uncertainty in Random Forests via Confidence Intervals and Hypothesis Tests

This work develops formal statistical inference procedures for predictions generated by supervised learning ensembles. Ensemble methods based on bootstrapping, such as bagging and random forests, have improved the predictive accuracy of individual trees, but fail to provide a framework in which distributional results can be easily determined. Instead of aggregating full bootstrap samples, we consider predicting by averaging over trees built on subsamples of the training set and demonstrate that the resulting estimator takes the form of a U-statistic. As such, predictions for individual feature vectors are asymptotically normal, allowing for confidence intervals to accompany predictions. In practice, a subset of subsamples is used for computational speed; here our estimators take the form of incomplete U-statistics and equivalent results are derived. We further demonstrate that this setup provides a framework for testing the significance of features. Moreover, the internal estimation method we develop allows us to estimate the variance parameters and perform these inference procedures at no additional computational cost. Simulations and illustrations on a real data set are provided.
Determinants of help-seeking behavior in depression: a cross-sectional study

Although evidence-based and effective treatments are available for people with depression, a substantial number does not seek or receive help. Therefore, it is important to gain a better understanding of the reasons why people do or do not seek help. This study examined what predisposing and need factors are associated with help-seeking among people with major depression.



A cross-sectional study was conducted in 102 subjects with major depression. Respondents were recruited from the general population in collaboration with three Municipal Health Services (GGD) across different regions in the Netherlands. Inclusion criteria were: being aged 18 years or older, a high score on a screening instrument for depression (K10 > 20), and a diagnosis of major depression established through the Composite International Diagnostic Interview (CIDI 2.1).



Of the total sample, 65 % (n = 66) had received help in the past six months. Results showed that respondents with a longer duration of symptoms and those with lower personal stigma were more likely to seek help. Other determinants were not significantly related to help-seeking.



Longer duration of symptoms was found to be an important determinant of help-seeking among people with depression. It is concerning that stigma was related to less help-seeking. Knowledge and understanding of depression should be promoted in society, hopefully leading to reduced stigma and increased help-seeking.