Miracle-dz/UniFAR-FTUs huggingface dataset:
Design and fabrication of diffractive optical element applied to vertical-cavity surface emitting laser.doc

In this paper, a new application strategy of diffractive optical elements (DOEs) has been proposed. The research combined vertical-cavity surface emitting laser (VCSEL) with diffractive optical elements into a single opto-electric semiconductor device. In order to fabricate DOE-VCSEL, DOE surface profile can be integrated upon P-DBR layer of VCSEL structure by using semiconductor processing. We used scalar diffractive theory, Dammann grating and Fresnel lens concept to design DOE surface profile. From simulation, we determined the element size of 488 × 488 µm2. Experimental result shows the divergence angle of 8 degrees of DOE-VCSEL can be reduced. So, the design and fabrication of diffractive optical elements structure on VCSEL can achieve the effect of output beam concentration. Compared with a conventional fiber coupling system that uses a plano-convex lens, our design has the advantages of simplicity for fiber coupling systems.
Effectiveness of Education of Working Memory Strategies on Improvement of Reading Performance and Reduction of Depression in Children with Dyslexia

The present investigation was aimed to study the impact of education of working memory strategies on improvement of reading performance and reduction of depression in third grade elementary school students with dyslexia in Kouhdasht city. For this reason, after running the diagnostic test, 33 students were chosen among third grade elementary school dyslexic students with depression disorder through random cluster sampling method. Afterwards, they were placed in experimental and control groups randomly. The participants in the experimental group received training in strategies for working memory in 15 sessions. Eventually, the obtained data were analyzed using statistical covariance analysis. The results demonstrated that the reading performance of students in the experimental group significantly improved compared to the control group. Moreover, depression level significantly decreased compared to the control group. DOI: 10.5901/mjss.2015. v6n6s6p66
Economic evaluation of a participatory return-to-work intervention for temporary agency and unemployed workers sick-listed due to musculoskeletal disorders.

OBJECTIVES The aim of the study was to evaluate the cost-effectiveness, -utility, and -benefit of a newly developed participatory return-to-work (RTW) program for temporary agency and unemployed workers, sick-listed due to musculoskeletal disorders. METHODS An economic evaluation was conducted alongside a randomized controlled trial with a 12-month follow-up. Temporary agency and unemployed workers, sick-listed for 2â€"8 weeks due to musculoskeletal disorders, were randomized to the participatory RTW program (N=79) or usual care group (N=84). The new RTW program was aimed at making a consensus-based RTW action plan with the possibility of a temporary (therapeutic) workplace. Effect outcomes were sustainable RTW and quality-adjusted life years (QALY). Healthcare utilization was measured from the social insurerâ€™s perspective and societal perspective. RESULTS Total healthcare costs in the participatory RTW program group [10 189 (standard deviation [SD] 7055) euros] were statistically significantly higher compared to care-as-usual [7862 (SD 7394) euros]. The cost-effectiveness analyses showed that the new intervention was more effective but also more costly than usual care (ie, to gain RTW one day earlier in the participatory RTW program group approximately 80 euros needed to be invested). The net societal benefit of the participatory RTW program compared to care-as-usual was 2073 euros per worker. CONCLUSIONS The newly developed participatory RTW program was more effective but also more costly than usual care. The program enhanced work resumption and generated a net socioeconomic benefit. Hence, implementation of the participatory RTW program may have potential to achieve a sustainable contribution of vulnerable workers to the labor force.
Development of electrosprayed artesunate-loaded core–shell nanoparticles

Abstract Objective: Artesunate (ART) is proven to have potential anti-proliferative activities, but its instability and poor aqueous solubility limit its application as an anti-cancer drug. The present study was undertaken to develop coaxial electrospraying as a novel technique for fabricating nanoscale drug delivery systems of ART as the core–shell nanostructures. Methods: The core–shell nanoparticles (NPs) were fabricated with coaxial electrospraying and the formation mechanisms of NPs were examined. The physical solid state and drug–polymer interactions of NPs were characterized by X-ray powder diffraction (XRPD) and Fourier transform infrared (FTIR) spectroscopy. The effects of materials and electrospraying process on the particle size and surface morphology of NPs were investigated by scanning electron microscopy (SEM). The drug release from NPs was determined in vitro by a dialysis method. Results: The ART/poly(lactic-co-glycolic) acid (PLGA) chitosan (CS) NPs exhibited the mean particle size of 303 ± 93 nm and relatively high entrapment efficiency (80.5%). The release pattern showed an initial rapid release within two hours followed by very slow extended release. The release pattern approached the Korsmeyer–Peppas model. Conclusions: The present results suggest that the core–shell NPs containing PLGA and CS have a potential as carriers in the anticancer drug therapy of ART.
Real-Time Feature Matching using Adaptive and Spatially Distributed Classification Trees

This paper presents a method for real-time wide-baseline feature matching. The approach is based on the work of Lepetit and colleagues [9], where randomized decision trees are trained to establish correspondences between detected features in a training image, and those in input frames. Though extremely promising, their actual results can vary depending on the viewpoint and illumination conditions. We combine two approaches to alleviate its limitations. The first aims to update the trees at run-time, adapting them to the actual viewing conditions. The second consists in spatially distributing the trees, so that each of them models a certain viewing volume more precisely. The result is a more stable matching method that significantly extends detectable range and is much more robust to illumination changes, such as cast shadows or reflections.
Atmosphere and climate studies of Mars using the Mars Observer pressure modulator infrared radiometer

Studies of the climate and atmosphere of Mars are limited at present by a lack of meteorological data having systematic global coverage with good horizontal and vertical resolution. The Mars Observer spacecraft in a low, nearly circular, polar orbit will provide an excellent platform for acquiring the data needed to advance significantly our understanding of the Martian atmosphere and its remarkable variability. The Mars Observer pressure modulator infrared radiometer (PMIRR) is a nine-channel limb and nadir scanning atmospheric sounder which will observe the atmosphere of Mars globally from 0 to 80 km for a full Martian year. PMIRR employs narrow-band radiometric channels and two pressure modulation cells to measure atmospheric and surface emission in the thermal infrared; a visible channel (0.39–4.7 μm) is used to measure solar radiation reflected from the atmosphere and surface. Vertical profiles of atmospheric temperature, the infrared extinction of dust suspended in the atmosphere, atmospheric water vapor, and condensate hazes will be retrieved from infrared measurements having a vertical resolution of 5 km, which is half an atmospheric scale height. PMIRR infrared and visible measurements will be combined to determine the radiative balance of the polar regions, where a sizeable fraction of the global atmospheric mass annually condenses onto and sublimes from the surface. Derived meteorological fields, including diabatic heating and cooling and the vertical variation of horizontal winds, will be computed from the globally mapped fields retrieved from PMIRR data. Analyses of these observed and derived fields will address many key questions regarding the atmosphere and climate of Mars.
The target of rapamycin (TOR) proteins

Rapamycin potently inhibits downstream signaling from the target of rapamycin (TOR) proteins. These evolutionarily conserved protein kinases coordinate the balance between protein synthesis and protein degradation in response to nutrient quality and quantity. The TOR proteins regulate (i) the initiation and elongation phases of translation, (ii) ribosome biosynthesis, (iii) amino acid import, (iv) the transcription of numerous enzymes involved in multiple metabolic pathways, and (v) autophagy. Intriguingly, recent studies have also suggested that TOR signaling plays a critical role in brain development, learning, and memory formation.
UPB HES SO @ PlantCLEF 2017: Automatic Plant Image Identification using Transfer Learning via Convolutional Neural Networks

Recent advances in computer vision have made possible the use of neural networks in large scale image retrieval tasks. An example application is the automated plant classification. However, training a network from scratch takes a lot of computational effort and turns out to be very time consuming. In this paper, we investigate a transfer learning approach in the context of the 2017 PlantCLEF task, for automatic plant image classification. The proposed approach is based on the well-known AlexNet Convolutional Neural Network (CNN) model. The network was fine-tuned using the 2017 PlantCLEF Encyclopedia of Life (EOL) training data, which consists of approximately 260,000 plant images belonging to 10,000 species. The learning process was sped up in the upper layers leaving original features almost untouched. Our best proposed official run scored 0,361 in terms of the Mean Reciprocal Rank (MRR) when evaluated on the test dataset.
Notice of RetractionThe study and application of NMF-based visualization technology on Mineral Resources Prediction

Considering the practical problems existing in Mineral Resource Prediction, the paper will introduce NMF into this field. Comparing with the traditional methods, NMF proves to be feasible in solving the difficulties of large-scale data analysis during the process of data visualization and improving efficiency of data processing, which provides a new approach for Mineral Resource Prediction.
Crustal structure of central Tibet as derived from project INDEPTH wide-angle seismic data

Summary In the summer of 1998, project INDEPTH recorded a 400 km long NNW–SSE wide-angle seismic profile in central Tibet, from the Lhasa terrane across the Banggong-Nujiang suture (BNS) at about 89.5°E and into the Qiangtang terrane. Analysis of the P-wave data reveals that (1) the crustal thickness is 65 ± 5 km beneath the line; (2) there is no 20 km step in the Moho in the vicinity of the BNS, as has been suggested to exist along-strike to the east based on prior fan profiling; (3) a thick high-velocity lower crustal layer is evident along the length of the profile (20–35 km thick, 6.5–7.3 km s−1); and (4) in contrast to the southern Lhasa terrane, there is no obvious evidence of a mid-crustal low-velocity layer in the P-wave data, although the data do not negate the possibility of such a layer of modest proportions. Combining the results from the INDEPTH III wide-angle profile with other seismic results allows a cross-section of Moho depths to be constructed across Tibet. This cross-section shows that crustal thickness tends to decrease from south to north, with values of 70–80 km south of the middle of the Lhasa terrane, 60–70 km in the northern part of the Lhasa terrane and the Qiangtang terrane, and less than 60 km in the Qaidam basin. The overall northward thinning of the crust evident in the combined seismic observations, coupled with the essentially uniform surface elevation of the plateau south of the Qaidam basin, is supportive of the inference that northern Tibet until the Qaidam basin is underlain by somewhat thinner crust, which is isostatically supported by relatively low-density, hot upper mantle with respect to southern Tibet.
Joint Event Extraction via Recurrent Neural Networks

Event extraction is a particularly challenging problem in information extraction. The state-of-the-art models for this problem have either applied convolutional neural networks in a pipelined framework (Chen et al., 2015) or followed the joint architecture via structured prediction with rich local and global features (Li et al., 2013). The former is able to learn hidden feature representations automatically from data based on the continuous and generalized representations of words. The latter, on the other hand, is capable of mitigating the error propagation problem of the pipelined approach and exploiting the inter-dependencies between event triggers and argument roles via discrete structures. In this work, we propose to do event extraction in a joint framework with bidirectional recurrent neural networks, thereby beneﬁting from the advantages of the two models as well as addressing issues inherent in the existing approaches. We systematically investigate different memory features for the joint model and demonstrate that the proposed model achieves the state-of-the-art performance on the ACE 2005 dataset.
Metabolic Mapping of Proteinase Activity with Emphasis on In Situ Zymography of Gelatinases

Proteases are essential for protein catabolism, regulation of a wide range of biological processes, and in the pathogenesis of many diseases. Several techniques are available to localize activity of proteases in tissue sections or cell preparations. For localization of the activity of matrix metalloproteinases, in situ zymography was introduced some decades ago. The procedure is based on zymography using SDS polyacrylamide gels containing gelatin, casein, or fibrin as substrate. For in situ zymography, either a photographic emulsion containing gelatin or a fluorescence-labeled proteinaceous macromolecular substrate is brought into contact with a tissue section or cell preparation. After incubation, enzymatic activity is revealed as white spots in a dark background or as black spots in a fluorescent background. However, this approach does not allow precise localization of proteinase activity because of limited sensitivity. A major improvement in sensitivity was achieved with the introduction of dye-quenched (DQ-)gelatin, which is gelatin that is heavily labeled with FITC molecules so that its fluorescence is quenched. After cleavage of DQ-gelatin by gelatinolytic activity, fluorescent peptides are produced that are visible against a weakly fluorescent background. The incubation with DQ-gelatin can be combined with simultaneous immunohistochemical detection of a protein on the same section. To draw valid conclusions from the findings with in situ zymography, specific inhibitors need to be used and the technique has to be combined with immunohistochemistry and zymography. In that case, in situ zymography provides data that extend our understanding of the role of specific proteinases in various physiological and pathological conditions. (J Histochem Cytochem 52:711–722, 2004)
Identification of Igh-C-linked determinants on suppressor T cell hybrids and factors specific for L-glutamic acid60-L-alanine30-L- tyrosine10 (GAT)

Hyperimmunization of BALB/c mice with concanavalin A-stimulated blasts from the Ig allotype-congenic strain, C.B20, results in the production of antibodies reactive with T cells in an allotype-restricted manner. Spleen cells from these hyperimmune BALB/c mice were used to generate a panel of hybridomas that secrete monoclonal antibodies, reactive, in an allotype-restricted manner, exclusively with T cells subpopulations, and in particular, reactive with suppressor T cell hybridomas and their secreted soluble factors. Two functional classes of antibodies were identified: those that react with single polypeptide-chain suppressor T cell factors (TsF1) and the suppressor T cell hybridomas that produce such factors, and those that react with two polypeptide-chain suppressor T cell factors (TsF2) and their corresponding suppressor T cell hybridomas. These two classes of antibody were used to isolate molecules from the membranes of the respective suppressor T cell hybrids that are functionally and structurally related to the secreted suppressor T cell factors, suggesting a receptor function for these molecules.
C. elegans dystroglycan DGN-1 functions in epithelia and neurons, but not muscle, and independently of dystrophin

The C. elegans dystroglycan (DG) homolog DGN-1 is expressed in epithelia and neurons, and localizes to basement membrane (BM) surfaces. Unlike vertebrate DG, DGN-1 is not expressed in muscle or required for muscle function. dgn-1 null mutants are viable but sterile owing to severe disorganization of the somatic gonad epithelium, and show defects in vulval and excretory cell epithelia and in motoneuron axon guidance. The defects resemble those of epi-1 laminin αB mutants, suggesting that DGN-1 serves as a receptor for laminin. dgn-1(0)/+ animals are fertile but show gonad migration defects in addition to the defects seen in homozygotes, indicating that DGN-1 function is dosage sensitive. Phenotypic analyses show that DGN-1 and dystrophin-associated protein complex (DAPC) components have distinct and independent functions, in contrast to the situation in vertebrate muscle. The DAPC-independent functions of DGN-1 in epithelia and neurons suggest that vertebrate DG may also act independently of dystrophin/utrophin in non-muscle tissues.
Statistical Comparison of Machine Learning Techniques for Treatment Optimisation of Drug-Resistant HIV-1

Predicting the in-vivo effect of genotypic drug resistance of Human Immunodeficiency Virus type-1 (HIV-1) on response to antiretroviral therapies represents a major clinical issue. Different machine learning and feature selection methods are applied for the classification of treatment success, based on viral genotype, therapy and derived input features. The robustness of results is assessed through statistical validation. The procedures described are intended to be a general methodology in the challenging context of biology and medical science data mining.
Robust and Discriminative Concept Factorization for Image Representation

Concept Factorization (CF), as a variant of Nonnegative Matrix Factorization (NMF), has been widely used for learning compact representation for images because of its psychological and physiological interpretation of naturally occurring data. And graph regularization has been incorporated into the objective function of CF to exploit the intrinsic low-dimensional manifold structure, leading to better performance. But some shortcomings are shared by existing CF methods. 1) The squared loss used to measure the data reconstruction quality is sensitive to noise in image data. 2) The graph regularization may lead to trivial solution and scale transfer problems for CF such that the learned representation is meaningless. 3) Existing methods mostly ignore the discriminative information in image data. In this paper, we propose a novel method, called Robust and Discriminative Concept Factorization (RDCF) for image representation. Specifically, RDCF explicitly considers the influence of noise by imposing a sparse error matrix, and exploits the discriminative information by approximate orthogonal constraints which can also lead to nontrivial solution. We propose an iterative multiplicative updating rule for the optimization of RDCF and prove the convergence. Experiments on 5 benchmark image datasets show that RDCF can significantly out-perform several state-of-the-art related methods, which validates the effectiveness of RDCF.
Leukocyte Telomere Length Predicts Cancer Risk in Barrett's Esophagus

Purpose: Leukocyte telomere length has gained attention as a marker of oxidative damage and age-related diseases, including cancer. We hypothesize that leukocyte telomere length might be able to predict future risk of cancer and examined this in a cohort of patients with Barrett's esophagus, who are at increased risk of esophageal adenocarcinoma and thus were enrolled in a long-term cancer surveillance program. Patients and Methods: In this prospective study, telomere length was measured by quantitative PCR in baseline blood samples in a cohort of 300 patients with Barrett's esophagus followed for a mean of 5.8 years. Leukocyte telomere length hazard ratios (HR) for risk of esophageal adenocarcinoma were calculated using multivariate Cox models. Results: Shorter telomeres were associated with increased esophageal adenocarcinoma risk (age-adjusted HR between top and bottom quartiles of telomere length, 3.45; 95% confidence interval, 1.35-8.78; P = 0.009). This association was still significant when individually or simultaneously adjusted for age, gender, nonsteroidal anti-inflammatory drug (NSAID) use, cigarette smoking, and waist-to-hip ratio (HR, 4.18; 95% confidence interval, 1.60-10.94; P = 0.004). The relationship between telomere length and cancer risk was particularly strong among NSAID nonusers, ever smokers, and patients with low waist-to-hip ratio. Conclusion: Leukocyte telomere length predicts risk of esophageal adenocarcinoma in patients with Barrett's esophagus independently of smoking, obesity, and NSAID use. These results show the ability of leukocyte telomere length to predict the risk of future cancer and suggest that it might also have predictive value in other cancers arising in a setting of chronic inflammation. (Cancer Epidemiol Biomarkers Prev 2007;16(12):2649–55)
Classification of circulation types over Eastern mediterranean using a self-organizing map approach

In this study an Artificial Neural Network called Self-Organizing Map (SOM) is used in order to classify the synoptic circulation over Europe and especially Eastern Mediterranean. The classification of circulation types is an effective way of summarizing and describing the atmospheric circulation and it is useful in climatology because it provides a better understanding of the climatic variability over an area. Here, the SOM methodology is applied on winter daily geopotential height anomalies of the 500 hPa level, for the period 1971–2000. Twelve unique circulation patterns are identified. Eight of these types are characterized as cyclonic, representing 61% of the total days examined and four types are characterized as anticyclonic, representing 39% of the study period. The results of this classification are comparable to other objective classifications applied on the same study region and present a similar image. Therefore, the SOM methodology is found to be applicable and useful in the classification of circulation types.