Miracle-dz/UniFAR-FTUs huggingface dataset:
Optical Coherence Tomography in the Management of Glaucoma and Macular Diseases

Optical coherence tomography (OCT) is a non contact, non invasive and repro-ducible imaging technique that produces thin slices of tissue section images. OCT identifies retinal nerve fiber damage before detection of visual field changes making it a handy and effective tool in early detection and monitoring in glaucoma. Retinal fiber layer thickness measurements provide vital knowledge of extent of neural damage. This enables the clinician to counsel the patient and take the best decision towards achieving glaucoma control. Early and quantifiable macular thickness measurements are obtained, allowing for detection of clinically significant diabetic macular edema. OCT allows monitoring of the impact of laser or other interventions. Changes in age-related macular degeneration are relatively easily determined and impact of treatment interventions monitored. In conclusion, OCT is a vital emerging tool in the evaluation and management glaucoma and macular diseases in all parts of the World, including low income countries of sub-Saharan Africa.
DNA Repair in Embryonic Stem Cells

DNA is the largest and most important molecule of a cell. Due to its chemical nature, DNA is particularly prone to numerous lesions. These lesions comprise different sugar and base modifications, deletion of bases as well as single (SSBs) and double DNA strand breaks (DSBs). All alterations in the DNA that a cell experiences sum up to more than 10.000 lesions per day (Lindahl, 1993). DNA damage can be brought about by several endogenous and exogenous factors including reactive oxygen species (ROS), ultraviolet light (UV), ionizing radiation (IR) or by DNA damaging chemicals. The different lesions are removed by several repair mechanisms that help the cell to preserve structure and sequence of the DNA. These repair pathways include mismatch repair (MMR), base excision repair (BER), nucleotide excision repair (NER), non-homologous end joining (NHEJ), microhomology-mediated end joining (MMEJ) and homologous recombination (HR). If DNA damage is too severe, cells can also initiate a cell death program that removes cells with damaged DNA from the population (reviewed in: Roos & Kaina, 2006). Stem cells comprise a group of self-renewing cells including embryonic and adult stem cells. Embryonic stem cells (ESCs) have the ability of indefinite self-renewal and rapid proliferation. They are pluripotent and can differentiate into cells of all three germ layers (ectoderm, mesoderm and endoderm) as well as of the germ cell lineage. Adult or tissue stem cells, in contrast, are multipotent and differentiate into only one or several specific cell lines (reviewed in: Barilleaux et al, 2006; Thomson & Marshall, 1998; Weissman et al, 2001). By proliferation and differentiation, adult stem cells replenish tissue cells that are lost during normal wear and tear or after injury and thus are key for tissue and organ regeneration as well as for the preservation of homeostasis in a living organism (Reya et al, 2001). ESCs can be isolated from different species, but the most investigated ones are human (hESCs) and murine (mESCs) ESCs. Both, hESCs and mESCs are derived from the inner cell mass of a blastocyst, an early stage of embryonic development (Martin, 1981; Thomson et al, 1998). Human ESCs are of particular interest because of their high potential for medical applications like tissue and organ regeneration (reviewed in: Donovan & Gearhart, 2001; Thomson & Odorico, 2000; Wobus, 2001). However, since hESCs are derived from human embryos, they also raise ethical, social and juristic problems (McLaren, 2000). A potential alternative to the use of hESCs is the employment of induced pluripotent stem (iPS) cells. By transfection of a combination of stem cell markers, iPS cells can be made from basically every differentiated cell (Kim et al, 2009; Okita et al, 2007; Takahashi et al, 2007; Takahashi & Yamanaka, 2006; Wernig et al, 2007; Yu et al, 2007). Thus, differentiated cells can be taken from a patient, modified and induced for pluripotency in vitro, and transplanted back into
An Empirical Investigation of Product Differentiation in the Retail Gasoline Industry

Abstract : This Trident project constructed and estimated a model of product differentiation in the retail gasoline industry. Retail gasoline stations product differentiate by choosing location and offering amenities such as pay at the pump, full service, car washes, service stations and food markets. Using daily price data from the Minneapolis retail gasoline market, the effect for spatial and quality differentiation was empirically investigated. Spatial differentiation was measured by creating three degrees of neighbors (competing stations) around each individual gasoline station. Each successive neighborhood had an area of increasing size around the station; this differentiated between closer directly competitive neighbors and indirectly competitive neighbors that were away from a station. Quality differentiation was measured by a binary variable when gasoline stations offer additional amenities. The estimation procedure accounted for spatial autocorrelation and market characteristics specific to the retail gasoline industry. The results indicated that spatial location of firms had the largest and most significant effect on firm price. The spatially lagged autoregressive coefficient was found to be positive and significant; indicating that gasoline firm prices move together and firms that are closer in neighborhood degree have a more significant effect on price. In addition, stations decreased price when the number of competitors in its closest neighborhood increased and when the number of independent stations (stations not connected with a major refiner) increased in the closest neighborhood. Price also increased when stations of the same brand overlapped into each other s neighborhood areas. Quality differentiation had less effect on price than spatial competition.
Reconstruction of phrenic neuron identity in embryonic stem cell-derived motor neurons

Air breathing is an essential motor function for vertebrates living on land. The rhythm that drives breathing is generated within the central nervous system and relayed via specialised subsets of spinal motor neurons to muscles that regulate lung volume. In mammals, a key respiratory muscle is the diaphragm, which is innervated by motor neurons in the phrenic nucleus. Remarkably, relatively little is known about how this crucial subtype of motor neuron is generated during embryogenesis. Here, we used direct differentiation of motor neurons from mouse embryonic stem cells as a tool to identify genes that direct phrenic neuron identity. We find that three determinants, Pou3f1, Hoxa5 and Notch, act in combination to promote a phrenic neuron molecular identity. We show that Notch signalling induces Pou3f1 in developing motor neurons in vitro and in vivo. This suggests that the phrenic neuron lineage is established through a local source of Notch ligand at mid-cervical levels. Furthermore, we find that the cadherins Pcdh10, which is regulated by Pou3f1 and Hoxa5, and Cdh10, which is controlled by Pou3f1, are both mediators of like-like clustering of motor neuron cell bodies. This specific Pcdh10/Cdh10 activity might provide the means by which phrenic neurons are assembled into a distinct nucleus. Our study provides a framework for understanding how phrenic neuron identity is conferred and will help to generate this rare and inaccessible yet vital neuronal subtype directly from pluripotent stem cells, thus facilitating subsequent functional investigations.
Towards real-time multilingual multimodal speech-to-speech translation

Speech-to-speech translation technology enables natural oral communication between different language speaking people. Many research projects have addressed speech-to-speech translation (S2ST) technology, such as ATR [1], VERBMOBIL [2], C-STAR [3], NESPOLE! [4], BABYLON [5], GALE [6], and EU-bridge [7]. The speechto-speech translation system is normally composed of automatic speech recognition (ASR), machine translation (MT), and speech synthesis (TTS). All of the modules are corpus-based and statistical model-based systems. In this talk, new challenges toward a real-time multimodal speechto-speech translation will be introduced.
Social Understanding in Young Israeli-Jewish, Israeli-Palestinian, Palestinian, and Jordanian Children: Moral Judgments and Stereotypes

Research Findings: An empirical investigation was conducted to test young Palestinian, Jordanian, Israeli-Palestinian, and Israeli-Jewish children's (N = 433; M = 5.7 years of age) cultural stereotypes and their evaluations of peer intergroup exclusion based upon a number of different factors, including being from a different country and speaking a different language. Children in this study lived in a geographical region that has a history of cultural and religious tension, violence, and extreme intergroup conflict. Our findings reveal that the negative consequences of living with intergroup tension are related to the use of stereotypes. At the same time, the results for moral judgments and evaluations about excluding peers provide positive results about the young children's inclusive views regarding peer interactions. Practice: These findings indicate that practitioners working with young children should focus on inclusion in peer contexts. Curricula, media, and social intervention programs must begin in early childhood before children begin to use stereotypes in peer situations, particularly when children from other cultural and ethnic backgrounds play together.
Tensor Decomposition for Topic Models : An Overview and Implementation

The goal of a topic model is to characterize observed data in terms of a much smaller set of unobserved topics. Topic models have proven especially popular for information retrieval. Latent Dirichlet Allocation (LDA) is the most popular generative model used for topic modeling. Learning the optimal parameters of the LDA model efficiently, however, is an open question. As [2] point out, the traditional techniques for learning latent variables have major disadvantages when it comes to topic modeling. Straightforward maximum likelihood estimation does not produce a closed-form solution for LDA, and its approximations are NP-hard. Approaches relying on Expectation-maximization (EM) have been the most popular way of learning LDA [4]. Unfortunately, such approaches suffer from a lack of guarantees about the quality of the locally optimal solutions they produce. They also exhibit slow convergence. Markov chain Monte Carlo (MCMC) approaches [5] are prone to failure (due to non-ergodicity, for example), and can also exhibit slow mixing. For these reasons, the tensor decomposition approach on high-order moments of the data seems like a promising option for recovering the topic vectors. [1] This approach has been applied successfuly to other latent variable models, such as Hidden Markov Models and Gaussian Mixture Models [2] [3].
Identification and modeling of a drug target for Clostridium perfringens SM101

In the present study, comparative genome analysis between Clostridium perfringens and the human genome was carried out to identify genes that are essential for the pathogen's survival, and non-homologous to the genes of human host, that can be used as potential drug targets. The study resulted in the identification of 426 such genes. The number of these potential drug targets thus identified is significantly lower than the genome's protein coding capacity (2558 protein coding genes). The 426 genes of C. perfringens were further analyzed for overall similarities with the essential genes of 14 different bacterial species present in Database of Essential Genes (DEG). Our results show that there are only 5 essential genes of C. perfringens that exhibit similarity with 12 species of the 14 different bacterial species present in DEG database. Of these, 1 gene was similar in 12 species and 4 genes were similar in 11 species. Thus, the study opens a new avenue for the development of potential drugs against the highly pathogenic bacterium. Further, by selecting these essential genes of C. perfringens, which are common and essential for other pathogenic microbial species, a broad spectrum anti-microbial drug can be developed. As a case study, we have built a homology model of one of the potential drug targets, ABC transporter-ATP binding protein, which can be employed for in silico docking studies by suitable inhibitors.
Finite State Machines from Feature Grammars

This paper describes the conversion of a set of feature grammar rules into a deterministic finite state machine that accepts the same language (or at least a well-defined related language). First the reasoning behind why this is an interesting thing to do within the Edinburgh speech recogniser project, is discussed. Then details about the compilation algorithm are given. Finally, there is some discussion of the advantages and disadvantages of this method of implementing feature based grammar formalisms.
Non-analogues in paleoecological reconstruction : Model behaviour and implications

Fossil pollen is a widespread proxy for past vegetation that is used for paleoclimatic reconstruction, but the limits of its utility are not well known. Newer methods for climate reconstruction (CR) using machine learning techniques may improve the abilities of CR techniques, but little is known about model accuracy under conditions of non-analogue vegetation known to have occurred in the past. Here we generate non-analogue pollen assemblages by excluding close neighbors from calibration datasets, testing the ability of five CR techniques using pollen, including two machine learning techniques, to accurately reconstruct climate under non-analogue conditions.
1D directional DCT-based stereo residual compression

A novel stereo image compression method based on disparity-compensated residual image structure analysis is presented in this paper. The residual image resulting from the subtraction of the target image by the predicted one is exploited in this scheme. Unlike natural images, for which 2D-orthogonal transforms such as 2D-DCT provides good energy compaction in the frequency domain, stereo residual images exhibit directional and sparse components making them difficult to compress with classical methods. We propose to exploit the directional properties of the residual image, to compress it more efficiently, by using 1D Directional Discrete Cosine Transform (1D-DDCT). This 1D transform is applied to non-overlapping blocks of the residual image, along four directions: i.e. horizontal, vertical, positive diagonal and negative diagonal. The experimental results show that the proposed method, on some test images, outperforms the considered state-of-the-art methods.
Bitline Leakage Current Compensation Circuit for High-Performance SRAM Design

The leakage current existing in the bitline of SRAM has attracted more and more concerns for the operation of highperformance SRAM design, especially with the decrease of the threshold voltage of the transistor for high-performance demand, the leakage current would increase exponentially. The increased leakage current may slowdown the performance of the read operation of SRAM because the existence of the leakage current in the bitline may postpone the time to resolve the sufficient differential bitline voltage for SA to sense correctly. In this paper, a new bitline leakage current compensation circuit has been proposed. Different from the traditional technique, the proposed bitline leakage current compensation circuit cancels the “predetermined” leakage current compensation process. Therefore, it dodges the dilemma where the performance of the SRAM may be degraded in some circumstances if the bit-line leakage current is compensated pre-determinedly. The simulation results show that by adopting the proposed compensation circuit, the time needed to develop 1/2 VDD can be reduced by almost 126.4% under the tt process corner.
Model-based optimization of MPDATA on Intel Xeon Phi through load imbalancing

Load balancing is a widely accepted technique for performance optimization of scientific applications on parallel architectures. Indeed, balanced applications do not waste processor cycles on waiting at points of synchronization and data exchange, maximizing this way the utilization of processors. In this paper, we challenge the universality of the load-balancing approach to optimization of the performance of parallel applications. First, we formulate conditions that should be satisfied by the performance profile of an application in order for the application to achieve its best performance via load balancing. Then we use a real-life scientific application, MPDATA, to demonstrate that its performance profile on a modern parallel architecture, Intel Xeon Phi, significantly deviates from these conditions. Based on this observation, we propose a method of performance optimization of scientific applications through load imbalancing. We also propose an algorithm that finds the optimal, possibly imbalanced, configuration of a data parallel application on a set of homogeneous processors. This algorithm uses functional performance models of the application to find the partitioning that minimizes its computation time but not necessarily balances the load of the processors. We show how to apply this algorithm to optimization of MPDATA on Intel Xeon Phi. Experimental results demonstrate that the performance of this carefully optimized load-balanced application can be further improved by 15\% using the proposed load-imbalancing optimization.
Thyroxine is the serum factor that regulates morphogenesis of columnar cartilage from isolated chondrocytes in chemically defined medium

Epiphyseal chondrocytes cultured in a medium containing 10% serum may be maintained as three dimensional aggregates and differentiate terminally into hypertrophic cells. There is an attendant expression of genes encoding type X collagen and high levels of alkaline phosphatase activity. Manipulation of the serum concentration to optimal levels of 0.1 or 0.01% in this chondrocyte pellet culture system results in formation of features of developing cartilage architecture which have been observed exclusively in growth cartilage in vivo. Cells are arranged in columns radiating out from the center of the tissue, and can be divided into distinct zones corresponding to the recognized stages of chondrocyte differentiation. Elimination of the optimal serum concentration in a chemically defined medium containing insulin eliminates the events of terminal differentiation of defined cartilage architecture. Chondrocytes continue to enlarge into hypertrophic cells and synthesize type X collagen mRNA and protein, but in the absence of the optimal serum concentration, alkaline phosphatase activity does not increase and the cells retain a random orientation. Addition of thyroxine to the chemically defined medium containing insulin and growth hormone results in dose-dependent increases in both type X collagen synthesis and alkaline phosphatase activity, and reproduces the optimal serum-induced morphogenesis of chondrocytes into a columnar pattern. These experiments demonstrate the critical role of thyroxine in cartilage morphogenesis.
Characterization of transferrin glycoforms in human serum by CE‐UV and CE‐ESI‐MS

Human transferrin (Tf) is a model glycoprotein for congenital disorders of glycosylation (CDG) diagnosis. In the last few years, new CE‐UV methods for intact Tf glycoforms analysis have been developed using nonvolatile BGEs and organic modifiers. However, the use of these BGEs does not allow the coupling of these procedures with electrospray MS (ESI‐MS). In this study, a new CE‐UV separation method of Tf glycoforms is developed, using a double‐layer stable coating and a volatile BGE based on ammonium acetate. The separation method is optimized using standard Tf and their potential is demonstrated applying the method to the analysis of sera Tf from healthy individuals and CDG patients. The CE‐UV separation method has been coupled to ESI‐MS detection. Main parameters such as sheath liquid composition are optimized in order to obtain a good sensitivity. The CE‐ESI‐MS method has also been used in serum samples obtaining the separation of the different proteins present in serum and partial separation of Tf glycoforms. Different mass spectra and deconvoluted molecular masses were obtained for each sialoform, allowing unequivocal glycoform identification.
The Effects of Environment on Morphological Evolution at 0 < z < 1.2 in the COSMOS Survey

We explore the evolution of the morphology-density relation using the COSMOS Advanced Camera for Surveys (ACS) and previous cluster studies. The Gini parameter measured in a Petrosian aperture is found to be an effective way of selecting early-type galaxies free from systematic effects with redshift. We find that galaxies are transformed from late- (spiral and irregular) to early-type (E+S0) galaxies more rapidly in dense than in sparse regions. At a given density, the early-type fraction grows constantly with cosmic time, but the growth rate increases with density as a power law of index 0.29 ± 0.02. However, at densities below 100 galaxies Mpc-2, no evolution is found at z > 0.4. In contrast, the star formation-density relation shows strong evolution at all densities and redshifts, suggesting that different physical mechanisms are responsible for the morphological and star formation transformation. We show that photometric redshifts can measure local galaxy environment, but that the present results are limited by photometric redshift error to densities above Σ = 3 galaxies Mpc-2.
Sequence Analysis and Expression of orf224 Gene Associated with Two Types of Cytoplasmic Male Sterility in Brassica napus L.

Polima and Shaan 2A are the two most widely used forms of cytoplasmic male sterility (CMS) in the utilization of heterosis of rapeseed (Brassica napus) in China. A previous study indicated that the mitochondrial gene, orf224, was the only gene with a differential expression pattern among the normal, sterile and fertility-restored lines in rapeseed. DNA sequences of orf224, including coding sequences from Shaan 2A and Polima CMS, were then amplified and analyzed. DNA sequence alignment indicated both the coding sequences were 675 bp in length and had 99.9 and 99% homology in nucleotides and amino acids, respectively, and shared certain similarity to homologues from other Brassica spp. and Arabidopsis thaliana. The probable promoter regions of orf224 were conserved between B. napus and A. thaliana, but the upstream regions of probable promoter regions were completely divergent from each other. Additionally, analysis of the primary and secondary structure of the proteins encoded by orf224 from the two lines predicted that the proteins contain a α-helix, extended strand, and random coil. After cloning a in vitro experiment showed that these two proteins could be expressed in Escherichia coli BL21.
Identifying Falls Risk Screenings Not Documented with Administrative Codes Using Natural Language Processing

Quality reporting that relies on coded administrative data alone may not completely and accurately depict providers' performance. To assess this concern with a test case, we developed and evaluated a natural language processing (NLP) approach to identify falls risk screenings documented in clinical notes of patients without coded falls risk screening data. Extracting information from 1,558 clinical notes (mainly progress notes) from 144 eligible patients, we generated a lexicon of 38 keywords relevant to falls risk screening, 26 terms for pre-negation, and 35 terms for post-negation. The NLP algorithm identified 62 (out of the 144) patients who falls risk screening documented only in clinical notes and not coded. Manual review confirmed 59 patients as true positives and 77 patients as true negatives. Our NLP approach scored 0.92 for precision, 0.95 for recall, and 0.93 for F-measure. These results support the concept of utilizing NLP to enhance healthcare quality reporting.