Miracle-dz/UniFAR-FTUs huggingface dataset:
Estrogen Receptor- (cid:1) Is Critical to Granulosa Cell Differentiation and the Ovulatory Response to Gonadotropins

The process of granulosa cell differentiation that occurs in preovulatory follicles is dependent on FSH but requires aug-mentationbyestradiol.Todeterminewhichestrogenreceptor(ER)formmediatestheeffectsofestradiolduringgonado-tropin-inducedfolliclegrowth,differentiation,andrupture,wecharacterizedtheresponseofER (cid:2) - and ER (cid:1) -null mice to gonadotropin-induced ovulation. Immature mice were treated with an ovulatory regimen of exogenous gonadotro- pins and tissues were collected at distinct time points for morphological, biochemical, gene expression, and immuno- histochemical analyses. Granulosa cells of ER (cid:1) knockout (ERKO) preovulatory follicles exhibited an attenuated re- sponse to FSH-induced differentiation, as evident by reduced aromatase activity and estradiol synthesis, and insufficient expression of LH receptor. As a result, (cid:1) ERKO ovaries were unable to fully respond to an ovulatory bolus of gonadotropin, leading to a reduced rate of follicle rupture; insufficient in- duction of prostaglandin-synthase 2 and progesterone receptor; an aberrant increase in aromatase activity and plasma estradiol; and incomplete expansion of the cumulus-oocyte complex. Parallel characterization of (cid:2) ERKO females indi- cated a minimal role for ER (cid:2) in granulosa cell differentiation, ovulation, and the concomitant changes in gene expression, although some abnormalities were revealed. These studies demonstrate that ER (cid:1) -mediated estradiol actions are vital to FSH-induced granulosa Because the oocyte yield data were skewed, the Kruskal-Wallis ANOVA was used followed by the Mann-Whitney U test to determine where significant differences existed. Plasma steroid levels and gene expression data sets were first tested for homoscedas-ticity of variance using the Levene’s test and, if failed, were log trans- formed before further statistical analysis. All data sets were then eval-uatedusingaone-wayANOVAfollowedbytheTukey-Kramerhonestly significant difference post hoc test when applicable.
Coreference resolution using expressive logic models

Coreference resolution is regarded as a crucial step for acquiring linkages among pieces of information extracted. Traditionally, coreference resolution models make use of independent attribute-value features over pairs of noun phrases. However, dependency and deeper relations between features can more adequately describe the properties of coreference relations between noun phrases. In this paper, we propose a framework of coreference resolution based on first-order logic and probabilistic graphical model, the Markov Logic Network. The proposed framework enables the use of background knowledge and captures more complex coreference linkage properties through rich expression of conditions. Moreover, the proposed conditions can capture the structural pattern within a noun phrase as well as contextual information between noun phrases. Our experiments show improvement with the use of the expressive logic models and the use of pattern-based conditions.
High resolution Magnetic Current Imaging for die level short localization

Magnetic Field Imaging (MFI) technology is capable of localize shorts using Magnetic Current Imaging (MCI) technique with a very high spatial resolution [1]. In this paper we demonstrate that a Giant Magneto Resistance (GMR) sensor positioned in close proximity to the front side of a die sample enables MFI to achieve sub micron resolution.
Can Non-volatile Memory Benefit MapReduce Applications on HPC Clusters?

Modern High-Performance Computing (HPC) clusters are equipped with advanced technological resources that need to be properly utilized to achieve supreme performance for end applications. One such example, Non-Volatile Memory (NVM), provides the opportunity for fast scalable performance through its DRAM-like performance characteristics. On the other hand, distributed processing engines, such as MapReduce, are continuously being enhanced with features enabling high-performance technologies. In this paper, we present a novel MapReduce framework with NVRAM-assisted map output spill approach. We have designed our framework on top of the existing RDMA-enhanced Hadoop MapReduce to ensure both map and reduce phase performance enhancements to be present for end applications. Our proposed approach significantly enhances map phase performance proven by a wide variety of MapReduce benchmarks and workloads from Intel HiBench [9] and PUMA [18] suites. Our performance evaluation illustrates that NVRAM-based spill approach can improve map execution performance by 2.73x which contributes to the overall execution improvement of 55% for Sort. Our design also guarantees significant performance benefits for other workloads: 54% for TeraSort, 21% for PageRank, 58% for SelfJoin, etc. To the best of our knowledge, this is the first approach towards leveraging NVRAM in MapReduce execution frameworks for applications on HPC clusters.
Localization of eIF4A-III in the nucleolus and splicing speckles is an indicator of plant stress

The mechanisms of long-term adaptation to low oxygen environment are quite well studied, but little is known about the sensing of oxygen shortage, the signal transduction and the short-term effects of hypoxia in plant cells. We have found that an RNA helicase eIF4A-III, a putative component of the Exon Junction Complex, rapidly changes its pattern of localisation in the plant nucleus under hypoxic conditions. In normal cell growth conditions GFP- eIF4A-III was mainly nucleoplasmic, but in hypoxia stress conditions it moved to the nucleolus and splicing speckles. This transition occurred within 15-20 min in Arabidopsis culture cells and seedling root cells, but took more than 2 h in tobacco BY-2 culture cells. Inhibition of respiration, transcription or phosphorylation in cells and ethanol treatment had similar effects to hypoxia. The most likely consequence is that a certain mRNA population will remain bound to the eIF4A-III and other mRNA processing proteins, rather than being transported from the nucleus to the cytoplasm, and thus its translation will be suspended.
Personality differences in monozygotic twins discordant for cannabis use.

AIMS To explore the association between cannabis and personality scores when genetic background and shared environment are controlled for. DESIGN The co-twin control design. This design provides a powerful method for controlling for the effects of potentially confounding familial factors that may act to predispose subjects both to cannabis use and a particular personality profile. PARTICIPANTS 118 monozygotic twin pairs discordant for cannabis use. MEASUREMENTS Data on personality and cannabis use were obtained through a questionnaire survey. The Dutch Sensation Seeking List was used to assess various aspects of sensation seeking. The Amsterdamse Biografische Vragenlijst assessed extraversion, neuroticism, somatic complaints and test attitude. FINDINGS The affected twins scored higher on all scales than their unaffected co-twins, especially on experience seeking (P = 0.004), total sensation seeking score (P = 0.004) and neuroticism (P = 0.039). Differences were also observed when items on drug use were removed from the experience seeking scale (P = 0.037) and total sensation seeking score (P = 0.009) although these differences were no longer significant after Bonferoni correction (P < 0.005). CONCLUSIONS Cannabis use is associated with a higher score on personality scales. This result was obtained in a sample of monozygotic twins discordant for cannabis use. Thus, at least part of the association between cannabis use and experience seeking cannot be attributed to underlying genetic or shared environmental factors that influence both personality and cannabis use and must be explained by unique environmental influences.
Intelligent and nature inspired optimization methods in medicine: the Pap smear cell classification problem

Abstract: The classification problem consists of using some known objects, usually described by a large vector of features, to induce a model that classifies others into known classes. Feature selection is widely used as the first stage of the classification task to reduce the dimension of the problem, decrease noise and improve speed by the elimination of irrelevant or redundant features. The present paper deals with the optimization of nearest neighbour classifiers via intelligent and nature inspired algorithms for a very significant medical problem, the Pap smear cell classification problem. The algorithms used include tabu search, genetic algorithms, particle swarm optimization and ant colony optimization. The proposed complete algorithmic scheme is tested on two sets of data. The first consists of 917 images of Pap smear cells and the second set consists of 500 images, classified carefully by expert cyto‐technicians and doctors. Each cell is described by 20 numerical features, and the cells fall into seven classes representing a variety of normal and abnormal cases. Nevertheless, from the medical diagnosis viewpoint, a minimum requirement corresponds to the general two‐class problem of correct separation between normal and abnormal cells.
A planar QRS-T angle >90 degrees is associated with multivessel coronary artery disease in patients undergoing coronary angiography.

BACKGROUND The aim of the study was to investigate the severity of coronary artery disease (CAD) in patients who had a planar QRS-T angle >90 degrees versus <or=90 degrees. MATERIAL/METHODS Coronary angiography was performed in 1,229 consecutive patients. Obstructive CAD was diagnosed if there was >50% obstruction of >or=1 major coronary artery. All QRS-T angle measurements were made from a 12-lead electrocardiogram by 2 authors who agreed on the measurement and who were blinded to the coronary angiographic findings. A QRS-T angle >90 degrees was considered abnormal. RESULTS Obstructive CAD of 2 or 3 vessels was present in 309 of 495 patients (62%) with a planar QRS-T angle >90 degrees and in 250 of 734 patients (34%) with a planar QRS-T angle <or=90 degrees . (p<0.0001). Stepwise logistic regression analyses showed that significant independent risk factors for 2- or 3-vessel CAD were age (odds ratio =1.05), male gender (odds ratio =1.8), black race (odds ratio =0.34), unstable angina (odds ratio =0.16), positive stress test (odds ratio =3.0), hypertension (odds ratio =3.0), dyslipidemia (odds ratio =2.9), QRS-T angle (odds ratio =7.2), left bundle branch block (odds ratio =2.9), right bundle branch block (odds ratio =0.17), smoking (odds ratio =9.7), and body mass index >or=30 kg/m2 (odds ratio =1.5). CONCLUSIONS The prevalence of 2- or 3-vessel obstructive CAD was significantly higher in patients with a planar QRS-T angle >90 degrees than in patients with a planar QRS-T angle <or=90 degrees (p<0.0001).
Does Adaptive Random Testing Deliver a Higher Confidence than Random Testing?

Random testing (RT) is a fundamental software testing technique. Motivated by the rationale that neighbouring test cases tend to cause similar execution behaviours, adaptive random testing (ART) was proposed as an enhancement of RT, which enforces random test cases evenly spread over the input domain. ART has always been compared with RT from the perspective of the failure-detection capability. Previous studies have shown that ART can use fewer test cases to detect the first software failure than RT. In this paper, we aim to compare ART and RT from the perspective of program-based coverage. Our experimental results show that given the same number of test cases, ART normally has a higher percentage of coverage than RT. In conclusion, ART outperforms RT not only in terms of the failure-detection capability, but also in terms of the thoroughness of program-based coverage. Therefore, ART delivers a higher confidence of the software under test than RT even when no failure has been revealed.
Loss of ER retention and sequestration of the wild-type ELOVL4 by Stargardt disease dominant negative mutants.

PURPOSE Mutations in ELOVL4, a member of the fatty acid elongase (ELO) family, are responsible for autosomal dominant Stargardt-like macular degeneration. The specific role of ELOVL4 in photoreceptors and the degenerative events induced by dominant ELOVL4 mutations are not well understood. As a first step to identifying possible mechanisms contributing to cellular dysfunction, we transfected HEK293 and COS cells with fluorescent-labeled wild-type and mutant ELOVL4 constructs. Effects of mutant ELOVL4 on interaction with wild-type protein were examined in this in vitro model. METHODS Wild-type and mutant ELOVL4 proteins including ELOVL4 truncation (270X, a truncated ELOVL4 protein at amino acid position 270) and ELOVL4 5 bp deletion (5bp-del) and ELOVL4 (5A, substituting the ER retention signal, KAKGD, with a five alanine amino acid tract) were expressed as EGFP or DsRed fusion proteins. Cellular localization of these proteins was examined by fluorescence microscopy. ELOVL4 protein aggregates were measured by co-immunoprecipitation and by sucrose gradient centrifugation followed by immunodetection with western blots. To study cellular status of cells expressing mutant ELOVL4 proteins, transfected cells were examined for upregulation of Bip and CHOP, markers for the unfolded protein response (UPR) by western blotting. RESULTS ELOVL4 mutants were not retained within the ER but were rather mislocalized and formed aggregates. Importantly, when cotransfected with wild-type ELOVL4, the mutants bound to and sequestered the wild-type protein into the aggregates. Expression of ELOVL4 mutants also induced UPR as evidenced by Bip and CHOP expression. CONCLUSIONS Using this in vitro cell system, we have identified alterations in wild-type ELOVL4 protein localization, aggregate formation, and the induction of cellular stress by the ELOVL4 mutants. We propose that "inactivation" of the wild-type ELOVL4 protein through sequestration to a non-ER compartment by ELOVL4 mutants may play a role in cellular dysfunction.
Early growth conditions, phenotypic development and environmental change

Phenotypic development is the result of a complex interplay involving the organism's own genetic make-up and the environment it experiences during development. The latter encompasses not just the current environment, but also indirect, and sometimes lagged, components that result from environmental effects on its parents that are transmitted to their developing offspring in various ways and at various stages. These environmental effects can simply constrain development, for example, where poor maternal condition gives rise to poorly provisioned, low-quality offspring. However, it is also possible that environmental circumstances during development shape the offspring phenotype in such a way as to better prepare it for the environmental conditions it is most likely to encounter during its life. Studying the extent to which direct and indirect developmental responses to environmental effects are adaptive requires clear elucidation of hypotheses and careful experimental manipulations. In this paper, I outline how the different paradigms applied in this field relate to each other, the main predictions that they produce and the kinds of experimental data needed to distinguish among competing hypotheses. I focus on birds in particular, but the theories discussed are not taxon specific. Environmental influences on phenotypic development are likely to be mediated, in part at least, by endocrine systems. I examine evidence from mechanistic and functional avian studies and highlight the general areas where we lack key information.
Reconstructing cetacean brain evolution using computed tomography.

Until recently, there have been relatively few studies of brain mass and morphology in fossil cetaceans (dolphins, whales, and porpoises) because of difficulty accessing the matrix that fills the endocranial cavity of fossil cetacean skulls. As a result, our knowledge about cetacean brain evolution has been quite limited. By applying the noninvasive technique of computed tomography (CT) to visualize, measure, and reconstruct the endocranial morphology of fossil cetacean skulls, we can gain vastly more information at an unprecedented rate about cetacean brain evolution. Here, we discuss our method and demonstrate it with several examples from our fossil cetacean database. This approach will provide new insights into the little-known evolutionary history of cetacean brain evolution.
A comparative study of multi-scale image super-resolution techniques

Super-resolution imaging is a technique that can be used to construct high-resolution imagery from low-resolution images. Low resolution images are often of the same scene, but contain aliasing and different subpixel shifts, which when combined can increase high frequency components while removing blurring. Superresolution reconstruction techniques include methods such as the Interpolation Approach (IA) [1] or the Frequency Domain Approach (FDA) [2]. The IA and FDA methods make use of aliasing in low-resolution images as well as the shifting property of the Fourier transform. Problems arise with both approaches, such as use of blurred images for creating non-optimal reconstructions. In this paper, we propose a comparative analysis if IA methods using reference and no-reference evaluation metrics. The IA methods used are Sparse Reconstruction and Example-based Learning Reconstruction. We evaluate the performance of each algorithms with Root Mean Square Error (RMSE), Peak- Signal-Noise Ratio (PSNR), Mutual Information (MI), Tsallis Entropy (TE), Non-linear Correlation (NLC), Multi-Scale Decomposition (MSD), Spatial Frequency (SF), Structural Similarity Index (SSIM), and Piella’s Metric. Initial results are show that Sparse Reconstruction performs better when evaluating with the proposed metrics, which indicate that this non-linear based interpolation techniques have good predictive capabilities.
Deletion of the Mitochondrial Superoxide Dismutase sod-2 Extends Lifespan in Caenorhabditis elegans

The oxidative stress theory of aging postulates that aging results from the accumulation of molecular damage caused by reactive oxygen species (ROS) generated during normal metabolism. Superoxide dismutases (SODs) counteract this process by detoxifying superoxide. It has previously been shown that elimination of either cytoplasmic or mitochondrial SOD in yeast, flies, and mice results in decreased lifespan. In this experiment, we examine the effect of eliminating each of the five individual sod genes present in Caenorhabditis elegans. In contrast to what is observed in other model organisms, none of the sod deletion mutants shows decreased lifespan compared to wild-type worms, despite a clear increase in sensitivity to paraquat- and juglone-induced oxidative stress. In fact, even mutants lacking combinations of two or three sod genes survive at least as long as wild-type worms. Examination of gene expression in these mutants reveals mild compensatory up-regulation of other sod genes. Interestingly, we find that sod-2 mutants are long-lived despite a significant increase in oxidatively damaged proteins. Testing the effect of sod-2 deletion on known pathways of lifespan extension reveals a clear interaction with genes that affect mitochondrial function: sod-2 deletion markedly increases lifespan in clk-1 worms while clearly decreasing the lifespan of isp-1 worms. Combined with the mitochondrial localization of SOD-2 and the fact that sod-2 mutant worms exhibit phenotypes that are characteristic of long-lived mitochondrial mutants—including slow development, low brood size, and slow defecation—this suggests that deletion of sod-2 extends lifespan through a similar mechanism. This conclusion is supported by our demonstration of decreased oxygen consumption in sod-2 mutant worms. Overall, we show that increased oxidative stress caused by deletion of sod genes does not result in decreased lifespan in C. elegans and that deletion of sod-2 extends worm lifespan by altering mitochondrial function.
Magnesium Silicide: a novel, Silicon-based material for printable thermoelectric devices

The aim of the thesis is the synthesis of eco-friendly, silicon-based thermoelectric (TE) materials for printable devices. In particular way, the individuated, fabricated, and printed material is magnesium silicide (Mg2Si). It is based on magnesium and silicon, two abundant and low-cost elements. Magnesium silicide has been recognized by several authors such as a novel TE material with a combination of interesting characteristics and performances. Magnesium silicide has been fabricated starting by powder constituents, combining mechanical action by ball milling, followed by a thermal treatment. The obtained Mg2Si powder has been 3d printed by a paste-extrusion method. Analytical techniques such as x-ray diffraction (XRD), spectroscopy (Raman and infrared), scanning electron microscopy (SEM) and thermo-analytical techniques have been used to characterize the fabricated powder and the printed devices.
Computational and statistical approaches to optical spectroscopy

Compact and smart optical sensors have had a major impact on people's lives over the last decade. Although the spatial information provided by optical imaging systems has already had a major impact, there is untapped potential in the spectroscopic domain. By transforming molecular information into wavelength-domain data, optical spectroscopy techniques have become some of the most popular scientific tools for examining the composition and nature of materials and chemicals in a non-destructive and non-intrusive manner. However, unlike imaging, spectroscopic techniques have not achieved the same level of penetration due to multiple challenges. These challenges have ranged from a lack of sensitive, miniaturized, and low-cost systems, to the general reliance on domain-specific expertise for interpreting complex spectral signals. In this thesis, we aim to address some of these challenges by combining modern computational and statistical techniques with physical domain knowledge. In particular, we focus on three aspects where computational or statistical knowledge have either enabled realization of a new instrument-with a compact form factor yet still maintaining a competitive performance-or deepened statistical insights of analyte detection and quantification in highly mixed or heterogeneous environments. In the first part, we utilize the non-paraxial Talbot effect to build compact and high-performance spectrometers and wavemeters that use computational processing for spectral information retrieval without the need for a full-spectrum calibration process. In the second part, we develop an analyte quantification algorithm for Raman spectroscopy based on spectral shaping modeling. It uses a hierarchical Bayesian inference model and reversible-jump Markov chain Monte Carlo (RJMCMC) computation with a minimum training sample size requirement. In the last part, we numerically investigate the spectral characteristics and signal requirements for universal and predictive non-invasive glucose estimation with Raman spectroscopy, using an in vivo skin Raman spectroscopy dataset. These results provide valuable advancements and insights in bringing forth smart compact optical spectroscopic solutions to real-world applications. Thesis Supervisor: Rajeev J. Ram Title: Professor of Electrical Engineering and Computer Science