Miracle-dz/UniFAR-FTUs huggingface dataset:
Activated Notch Causes Deafness by Promoting a Supporting Cell Phenotype in Developing Auditory Hair Cells

Purpose To determine whether activated Notch can promote a supporting cell fate during sensory cell differentiation in the inner ear. Methods An activated form of the Notch1 receptor (NICD) was expressed in early differentiating hair cells using a Gfi1-Cre mouse allele. To determine the effects of activated Notch on developing hair cells, Gfi1-NICD animals and their littermate controls were assessed at 5 weeks for hearing by measuring auditory brainstem responses (ABRs) and distortion product otoacoustic emissions (DPOAEs). The differentiation of NICD-expressing hair cells was assessed at postnatal day (P) 6, 11 and 20, using histological and molecular markers for hair cells, as well as supporting cells/progenitor cells. We also examined whether the effects of Notch were mediated by SOX2, a gene expressed in supporting cells and a likely downstream target of Notch, by crossing an inducible form of SOX2 to the Gfi1-Cre. Results Activation of Notch1 in developing auditory hair cells causes profound deafness. The NICD-expressing hair cells switch off a number of hair cell markers and lose their characteristic morphology. Instead, NICD-expressing hair cells adopt a morphology resembling supporting cells and upregulate a number of supporting cell markers. These effects do not appear to be mediated by SOX2, because although expression of SOX2 caused some hearing impairment, the SOX2-expressing hair cells did not downregulate hair cell markers nor exhibit a supporting cell-like phenotype. Conclusions Our data show that Notch signaling inhibits hair cell differentiation and promotes a supporting cell-like phenotype, and that these effects are unlikely to be mediated by SOX2.
Improving the I / O Throughput for Data-Intensive Scientific Applications with Efficient Compression Mechanisms

Today’s science is generating significantly larger volume of data than before. Data compression can potentially improve application performance. However, in many scientific applications and especially in large scale parallel scientific applications, each process often just accesses parts of the data. This can result in some data that are decompressed by a process but not used. General compression libraries (e.g. LZO [1], bzip2 [2] and zlib [3]) do not consider chunk size in the context of parallel and distributed file systems. Indexing is a widely used technique for online scientific encoding and query e.g. [4–6], even though it would not yield a high compression ratio for large chunks. Some mechanisms [7–9] were proposed by providing user level libraries, indicating the application developers need to modify the application and/or the high-level library. We propose two techniques to leverage compression to improve the performance of large scale parallel applications. First, we enable decompression from the middle of the chunk and stop decompression after we extract the data that we need, which eliminates the overhead of decompressing the data that is not needed by the process. Second, we build compression into the parallel file system which allows caching and prefeteching to be seamlessly integrated and allows applications to transparently leverage compression. Caching decompressed data allows the data to be accessed in later points.
Improving Read Performance with BP-DAGs for Storage-Efficient File Backup

The continued growth of data and high-continuity of application have raised a critical and mounting demand on storage-efficient and high-performance data protection. New technologies, especially the D2D (Disk-to-Disk) de- duplication storage are therefore getting wide attention both in academic and industry in the recent years. Existing de- duplication systems mainly rely on duplicate locality inside the backup workload to achieve high throughput but suffer from read performance degrading under conditions of poor duplicate locality. This paper presents the design and perform- ance evaluation of a D2D-based de-duplication file backup system, which employs caching techniques to improve write throughput while encoding files as graphs called BP-DAGs (Bi-pointer-based Directed Acyclic Graphs). BP-DAGs not only satisfy the 'unique' chunk storing policy of de-duplication, but also help improve file read performance in case of poor duplicate locality workloads. Evaluation results show that the system can achieve comparable read performance than non de-duplication backup systems such as Bacula under representative workloads, and the metadata storage overhead for BP-DAGs are reasonably low.
Origin and shaping of the laterality organ in zebrafish

Handedness of the vertebrate body plan critically depends on transient embryonic structures/organs that generate cilia-dependent leftward fluid flow within constrained extracellular environments. Although the function of ciliated organs in laterality determination has been extensively studied, how they are formed during embryogenesis is still poorly understood. Here we show that Kupffer's vesicle (KV), the zebrafish organ of laterality, arises from a surface epithelium previously thought to adopt exclusively extra-embryonic fates. Live multi-photon confocal imaging reveals that surface epithelial cells undergo Nodal/TGFβ signalling-dependent ingression at the dorsal germ ring margin prior to gastrulation, to give rise to dorsal forerunner cells (DFCs), the precursors of KV. DFCs then migrate attached to the overlying surface epithelium and rearrange into rosette-like epithelial structures at the end of gastrulation. During early somitogenesis, these epithelial rosettes coalesce into a single rosette that differentiates into the KV with a ciliated lumen at its apical centre. Our results provide novel insights into the morphogenetic transformations that shape the laterality organ in zebrafish and suggest a conserved progenitor role of the surface epithelium during laterality organ formation in vertebrates.
Chronic Treatment with Novel GPR40 Agonists Improve Whole-Body Glucose Metabolism Based on the Glucose-Dependent Insulin Secretion

GPR40 is a free fatty acid receptor that has been shown to regulate glucose-dependent insulin secretion. This study aimed to discover novel GPR40 agonists and investigate the whole-body effect on glucose metabolism of GPR40 activation using these novel GPR40 agonists. To identify novel GPR40-specific agonists, we conducted high-throughput chemical compound screening and evaluated glucose-dependent insulin secretion. To investigate the whole-body effect on glucose metabolism of GPR40 activation, we conducted repeat administration of the novel GPR40 agonists to diabetic model ob/ob mice and evaluated metabolic parameters. To characterize the effect of the novel GPR40 agonists more deeply, we conducted an insulin tolerance test and a euglycemic-hyperinsulinemic clamp test. As a result, we discovered the novel GPR40-specific agonists, including AS2034178 [bis{2-[(4-{[4′-(2-hydroxyethoxy)-2′-methyl[1,1′-biphenyl]-3-yl]methoxy}phenyl)methyl]-3,5-dioxo-1,2,4-oxadiazolidin-4-ide} tetrahydrate], and found that its exhibited glucose-dependent insulin secretion enhancement both in vitro and in vivo. In addition, the compounds also decreased plasma glucose and HbA1c levels after repeat administration to ob/ob mice, with favorable oral absorption and pharmacokinetics. Repeat administration of AS2034178 enhanced insulin sensitivity in an insulin tolerance test and a euglycemic-hyperinsulinemic clamp test. These results indicate that improvement of glucose-dependent insulin secretion leads the improvement of whole-body glucose metabolism chronically. In conclusion, AS2034178 and other GPR40 agonists may become useful therapeutics in the treatment of type 2 diabetes mellitus.
The Role of Typicality in Object Classification: Improving The Generalization Capacity of Convolutional Neural Networks

Deep artificial neural networks have made remarkable progress in different tasks in the field of computer vision. However, the empirical analysis of these models and investigation of their failure cases has received attention recently. In this work, we show that deep learning models cannot generalize to atypical images that are substantially different from training images. This is in contrast to the superior generalization ability of the visual system in the human brain. We focus on Convolutional Neural Networks (CNN) as the state-of-the-art models in object recognition and classification; investigate this problem in more detail, and hypothesize that training CNN models suffer from unstructured loss minimization. We propose computational models to improve the generalization capacity of CNNs by considering how typical a training image looks like. By conducting an extensive set of experiments we show that involving a typicality measure can improve the classification results on a new set of images by a large margin. More importantly, this significant improvement is achieved without fine-tuning the CNN model on the target image set.
Selecting Autoencoder Features for Layout Analysis of Historical Documents

Automatic layout analysis of historical documents has to cope with a large number of different scripts, writing supports, and digitalization qualities. Under these conditions, the design of robust features for machine learning is a highly challenging task. We use convolutional autoencoders to learn features from the images. In order to increase the classification accuracy and to reduce the feature dimension, in this paper we propose a novel feature selection method. The method cascades adapted versions of two conventional methods. Compared to three conventional methods and our previous work, the proposed method achieves a higher classification accuracy in most cases, while maintaining low feature dimension. In addition, we find that a significant number of autoencoder features are redundant or irrelevant for the classification, and we give our explanations. To the best of our knowledge, this paper is one of the first investigations in the field of image processing on the detection of redundancy and irrelevance of autoencoder features using feature selection.
Explaining Differing Democratic Norm Commitment: Rethinking the Religion-Psychological Security-Democratic Norm Support Connection

Abstract The psychological security index is an important predictor of democratic norm support. However, insufficient attention has been paid to the separate but independent contributions of dogmatism, self-esteem, and trust, the three components typically combined to create that index. Thus, we do not know the unique influence of these components on democratic norm commitment. We also do not know the exact nature of the influence of the multiple dimensions of religion (belief, belonging, behavior) on the separate psychological security components. Using structural equation modeling and two national surveys, we examine the religion-psychological security-democratic norm support relationship. Our findings do not fully support a negative association between religion and democratic values. In fact, no matter the influence of religious belief, belonging, or behavior on psychological security, the mediating link of psychological security to democratic norm support is what is most important. Indeed, any differences that manifest as a consequence of the various dimensions of religion disappear in the religion-psychological security-democratic norm support linkage. As such, anti-democratic values are a product of one's psychological attributes and are almost entirely unrelated to one's religion. Further, our findings challenge the theoretical underpinnings of the psychological security index, as we find little evidence that the separate components used to construct the composite measure, particularly trust, are an appropriate proxy for authoritarianism.
Contributions of proteomics to understanding phagosome maturation

In metazoans macrophage cells use phagocytosis, the process of engulfing large particles, to control the spread of pathogens in the body, to clear dead or dying cells, and to aid in tissue remodelling, while the same process is also used by unicellular eukaryotes to ingest food. Phagocytosing cells essentially swallow the particles, trapping them in vacuoles called phagosomes that go through a series of maturation steps, culminating in the destruction of the internalized cargo. Because of their central role in innate immunity and their relatively simple structure (one membrane bilayer surrounding a single particle), phagosomes have been a popular subject for organelle proteomics studies. Qualitative proteomic technologies are now very sensitive so hundreds of different proteins have been identified in phagosomes from several species, revealing new properties of these intriguing compartments. More recently, quantitative proteomic approaches have also been applied, shedding new light on the dynamics and composition of maturing phagosomes. In this review we summarize the studies that have applied proteomic technologies to phagosomes and how they have changed our understanding of phagosome biology.
X-ray sources in the starburst spiral galaxy M 83 - Nuclear region and discrete source population

Chandra has resolved the starburst nuclear region of the face-on grand-design spiral M 83. Eighty-one point sources are detected (above 3.5-σ) in the ACIS S3 image, and 15 of them are within the inner 16" region of the galaxy. A point source with L x 3 x 10 3 8 erg s - 1 in the 0.3-8.0 keV band is found to coincide with the infra-red nuclear photometric peak, one of the two dynamical nuclei of the galaxy. No point-like sources are resolved (at a 2.5-a level) at the centre of symmetry of the outer optical isophote ellipses, suspected to be another dynamical nucleus. About 50% of the total emission in the nuclear region is unresolved; of this, about 70% can be attributed to hot thermal plasma, and the rest is probably due to unresolved point sources (e.g., faint X-ray binaries). The azimuthally-averaged radial distribution of the unresolved emission has a King-like profile, with no central cusp. Strong emission lines are seen in the spectrum of the optically thin plasma component. The high abundances of C, Ne, Mg, Si and S with respect to Fe suggest that the interstellar medium in the nucleus is enriched and heated by type-II supernova explosions and winds from massive stars. The cumulative luminosity distribution of the discrete X-ray sources is neither a single nor a broken power law. Separating the sources in the nuclear region (within a distance of 60" from the X-ray centre) from the rest reveals that the two groups have different luminosity distributions. The log N(>S) - log S curve of the sources in the inner region (nucleus and stellar bar) is a single power law, which we interpret as due to continuous, ongoing star formation. Outside the central region, there is a smaller fraction of sources brighter than the Eddington limit for an accreting neutron star.
Belief propagation for joint sparse recovery

Compressed sensing (CS) demonstrates that sparse signals can be recovered from underdetermined linear measurements. We focus on the joint sparse recovery problem where multiple signals share the same common sparse support sets, and they are measured through the same sensing matrix. Leveraging a recent information theoretic characterization of single signal CS, we formulate the optimal minimum mean square error (MMSE) estimation problem, and derive a belief propagation algorithm, its relaxed version, for the joint sparse recovery problem and an approximate message passing algorithm. In addition, using density evolution, we provide a sufficient condition for exact recovery.
Automated Cancer Stage Classification from Free-text Histology Reports

Objectives: This article describes a system to automatically classify the stage of a lung cancer patient based on text analysis of their histology reports. Methods: The system uses machine learning techniques to train a statistical classifier, specifically a support vector machine, for each TNM stage category based on word occurrences in a corpus of histology reports for staged patients. New reports can then be classified according to the most likely stage, facilitating the collection and analysis of population staging data. While the system could in principle be applied to stage different cancer types, the current work focuses on staging lung cancer due to data availability. Results: The article presents initial experiments quantifying system performance on a corpus of reports from more than 1000 lung cancer patients. Results give average sensitivity of 0.72 and specificity of 0.87 for pathologic staging based on histology report text.
Segmentation of Anatomical Structures in Brain MR Images Using Atlases in FSL - A Quantitative Approach

Segmentation of brain structures from MR images is crucial in understanding the disease progress, diagnosis, and treatment monitoring. Atlases, showing the expected locations of the structures, are commonly used to start and guide the segmentation process. In many cases, the quality of the atlas may have a significant effect in the final result. In the literature, commonly used atlases may be obtained from one subject’s data, only from the healthy, or depict only certain structures that limit their accuracy. Anatomical variations, pathologies, imaging artifacts all could aggravate the problems related to application of atlases. In this paper, we propose to use multiple atlases that are sufficiently different from each other as much as possible to handle such problems. To this effect, we have built a library of atlases and computed their similarity values to each other. Our study showed that the existing atlases have varying levels of similarity for different structures.
Perceptual effects of scene context and viewpoint for virtual pedestrian crowds

In this article, we evaluate the effects of position, orientation, and camera viewpoint on the plausibility of pedestrian formations. In a set of three perceptual studies, we investigated how humans perceive characteristics of virtual crowds in static scenes reconstructed from annotated still images, where the orientations and positions of the individuals have been modified. We found that by applying rules based on the contextual information of the scene, we improved the perceived realism of the crowd formations when compared to random formations. We also examined the effect of camera viewpoint on the plausibility of virtual pedestrian scenes, and we found that an eye-level viewpoint is more effective for disguising random behaviors, while a canonical viewpoint results in these behaviors being perceived as less realistic than an isometric or top-down viewpoint. Results from these studies can help in the creation of virtual crowds, such as computer graphics pedestrian models or architectural scenes, and identify situations when users' perception is less accurate.
Neurodegenerative Shielding by Curcumin and Its Derivatives on Brain Lesions Induced by 6-OHDA Model of Parkinson's Disease in Albino Wistar Rats

Study was undertaken to evaluate the neurodegenerative defending potential of curcumin (CUR), demethoxycurcumin (DMC), and bisdemethoxycurcumin (BDMC) on 6-hydroxydopamine-(6-OHDA) induced Parkinsonism model in rats. Curcuminoids were administered (60 mg/kg, body weight, per oral) for three weeks followed by unilateral injection of 6-OHDA on 22nd day (10 μg/2 μL) into the right striatum leading to extensive loss of dopaminergic cells. The behavioral observations, biochemical markers, quantification of dopamine (DA), DOPAC, and HVA followed by dopamine (D2) receptor binding assay and tyrosine hydroxylase (TH, using immunohistochemistry) were evaluated using HPLC after three weeks of lesion. Pretreated animals showed significant protection against neuronal degeneration compared to lesion animals by normalizing the deranged levels of biomarkers and showed the potency in the order CUR > DMC > BDMC. The same order of effectiveness was observed in D2 receptors binding assay and TH immunohistochemistry study. We conclude that curcuminoids appear to shield progressive neuronal degeneration from increased oxidative attack in 6-OHDA-lesioned rats through its free radical scavenging mechanism, and DA, DOPAC, and HVA enhancing capabilities in the sequence of efficacy CUR > DMC > BDMC. Further, curcuminoids may have potential utility in treatment of many more oxidative stress-induced neurodegenerative disorders.
Unsupervised Parse Selection for HPSG

Parser disambiguation with precision grammars generally takes place via statistical ranking of the parse yield of the grammar using a supervised parse selection model. In the standard process, the parse selection model is trained over a hand-disambiguated treebank, meaning that without a significant investment of effort to produce the treebank, parse selection is not possible. Furthermore, as treebanking is generally streamlined with parse selection models, creating the initial treebank without a model requires more resources than subsequent treebanks. In this work, we show that, by taking advantage of the constrained nature of these HPSG grammars, we can learn a discriminative parse selection model from raw text in a purely unsupervised fashion. This allows us to bootstrap the treebanking process and provide better parsers faster, and with less resources.
In Vivo Measurement of D 2 Receptor Density and Affinity for 18 F-( 3N-Methyl ) Benperidol in the Rat Striatum with a PET System for Small Laboratory Animals

A recent investigation showed that intracerebral radioactivity concentrations can reliably be quantified in vivo with a smallanimal PET device. The purpose of the current study was to investigate the binding characteristics of the D2 receptor radioligand 18F-(3-N-methyl)benperidol (18FMB) in rat striatum by determining receptor density (Bmax) and affinity (Kd) in vivo. For validation, Kd and Bmax additionally were determined in vitro using storage phosphor autoradiography. Methods: Striatal radioactivity was measured with PET in 8 Sprague–Dawley rats after injection of 18FMB in increasing specific activities. Free radioligand concentrations were estimated from cortical radioactivity concentrations and were subtracted from striatal radioactivity concentrations to obtain specific binding. In vitro saturation experiments were performed on 7 further rats according to the isotopic dilution method. Specific binding was determined by both subtraction of 18FMB binding in the presence of raclopride and subtraction of cortical radioactivity concentrations from total radioligand binding. Saturation binding curves were obtained by plotting specifically bound radioligand concentrations against free radioligand concentrations and were evaluated with regression analysis. Results: PET yielded a Kd of 6.2 nmol/L and a Bmax of 16 fmol/mg for the striatal D2 receptor. In vitro, Kd and Bmax amounted to 4.4 nmol/L and 84.1 fmol/mg (subtraction of 18FMB binding in the presence of raclopride), respectively, and 7.9 nmol/L and 70.1 fmol/mg (subtraction of cortical radioactivity concentrations), respectively. Conclusion: Kd values measured with PET and autoradiography agreed and corresponded to inhibition constants obtained in previous in vitro studies. Bmax values lay within the same order of magnitude. The results of in vitro saturation binding analyses also agreed, irrespective of the mode of determination of free radioligand concentrations. Thus, Bmax and Kd may be determined with PET in analogy to the evaluation of in vitro binding data by regression analysis of bound-versus-free ligand concentrations. Our results show that small-animal tomographs are valuable tools for the in vivo characterization of receptor radioligands as an alternative to autoradiography.