ahmedabdelwahed/Medical_papers_title_and_abstract_NLP_dataset huggingface dataset:
Anthranilic Acid Accumulation in Saccharomyces cerevisiae Induced by Expression of a Nonribosomal Peptide Synthetase Gene from Paecilomyces cinnamomeus BCC 9616

Heterologous expression of nrps33, a nonribosomal peptide synthetase gene, from Paecilomyces cinnamomeus BCC 9616 in Saccharomyces cerevisiae unexpectedly resulted in the accumulation of anthranilic acid, an intermediate in tryptophan biosynthesis. Based on transcriptomic and real-time quantitative polymerase chain reaction (RT-qPCR) results, expression of nrps33 affected the transcription of tryptophan biosynthesis genes especially TRP1 which is also the selectable auxotrophic marker for the expression vector used in this work. The product of nrps33 could inhibit the activity of Trp4 involved in the conversion of anthranilate to N-(5'-phosphoribosyl)anthranilate and therefore caused the accumulation of anthranilic acid. This accumulation could in turn result in down-regulation of downstream tryptophan biosynthesis genes. Anthranilic acid is typically produced by chemical synthesis and has been used as a substrate for synthesising bioactive compounds including commercial drugs; our results could provide a new biological platform for production of this compound.
Surv-CRM-12: A Bayesian phase I/II survival CRM for right-censored toxicity endpoints with competing disease progression

The growing interest in new classes of anti-cancer agents, such as molecularly-targeted therapies and immunotherapies with modes of action different from those of cytotoxic chemotherapies, has changed the dose-finding paradigm. In this setting, the observation of late-onset toxicity endpoints may be precluded by treatment and trial discontinuation due to disease progression, defining a competing event to toxicity. Trial designs where dose-finding is modeled in the framework of a survival competing risks model appear particularly well-suited. We aim to provide a phase I/II dose-finding design that allows dose-limiting toxicity (DLT) outcomes to be delayed or unobserved due to competing progression within the possibly long observation window. The proposed design named the Survival-continual reassessment method-12, uses survival models for right-censored DLT and progression endpoints. In this competing risks framework, cause-specific hazards for DLT and progression-free of DLT were considered, with model parameters estimated using Bayesian inference. It aims to identify the optimal dose (OD), by minimizing the cumulative incidence of disease progression, given an acceptable toxicity threshold. In a simulation study, design operating characteristics were evaluated and compared to the TITE-BOIN-ET design and a nonparametric benchmark approach. The performance of the proposed method was consistent with the complexity of scenarios as assessed by the nonparametric benchmark. We found that the proposed design presents satisfying operating characteristics in selecting the OD and safety.
The complete chloroplast genome of Schrenkiella parvula (Brassicaceae)

Schrenkiella parvula is an Arabidopsis-related model species used here for studying plant stress tolerance. In this study, the complete chloroplast genome sequence of S. parvula has been reported for the first time. The total length of the chloroplast genome was 153 979 bp, which had a typical quadripartite structure. The annotated plastid genome includes 87 protein-coding genes, 39 tRNA genes and 8 ribosomal RNA genes. The evolutionary relationships revealed by our phylogenetic analysis indicated that S. parvula is closer to the Brassiceae species when compared with Eutrema salsugineum.
In Situ Activation of Superhydrophobic Surfaces with Triple Icephobicity at Low Temperatures

Superhydrophobic surfaces have been widely studied due to their potential applications in aerospace fields. However, superhydrophobic surfaces with excellent water-repellent, anti-icing, and icephobic performances at low temperatures have rarely been reported. Herein, superhydrophobic surfaces with heating capability were prepared by etching square micropillar arrays on the surface of multiwalled carbon nanotube (MWCNT)/poly(dimethylsiloxane) (PDMS) films. The fabricated superhydrophobic surface has triple icephobicity, which can be activated even at low temperatures. The triple icephobicity is triggered by an applied voltage to achieve excellent water-repellent and icephobic capabilities, even at -40 °C. Additionally, theoretical calculations reveal that a droplet on a superhydrophobic surface loses heat at a rate of 8.91 × 10-5 J/s, which is 2 orders of magnitude slower than a flat surface (2.15 × 10-3 J/s). Also, at -40 °C, the mechanical interlocking force formed between the superhydrophobic surface and ice can be released by the heating property of the superhydrophobic surface. This low-energy, multifunctional superhydrophobic surface opens up new possibilities for bionic smart multifunctional materials in icephobic applications.
Quality-of-Experience-Aware Incentive Mechanism for Workers in Mobile Device Cloud

Mobile device cloud (MDC) is a collaborative cloud computing platform over which neighboring smart devices form an alliance of shared resources to mitigate resource-scarcity of an individual user device for running compute-intensive applications. A major challenge of such a platform is maximizing user quality-of-experience (QoE) at minimum cost while providing attractive incentives to workers' mobile devices. In state-of-the-art works, either a voluntary task execution or merely resource-cost driven mechanism has been applied to minimize the task execution time while overlooking payment of any additional incentive to the worker devices for their quality services. In this paper, we develop a computational framework for MDC where the afore-mentioned challenging problem is formulated as a multi-objective linear programming (MOLP) optimization function that exploits reverse-auction bidding policy. Due to the NP-hardness of MOLP, we offer two greedy worker selection algorithms for maximizing user QoE or minimizing execution cost. In both algorithms, the amount of incentive awarded to a worker is determined following the QoE offered to a user. Theoretical proofs of desirable properties of the proposed incentive mechanisms are presented. Simulation results illustrate the effectiveness of our incentive algorithms compared to the state-of-the-art approaches.
Neural Architecture Search for LF-MMI Trained Time Delay Neural Networks

State-of-the-art automatic speech recognition (ASR) system development is data and computation intensive. The optimal design of deep neural networks (DNNs) for these systems often require expert knowledge and empirical evaluation. In this paper, a range of neural architecture search (NAS) techniques are used to automatically learn two types of hyper-parameters of factored time delay neural networks (TDNN-Fs): i) the left and right splicing context offsets; and ii) the dimensionality of the bottleneck linear projection at each hidden layer. These techniques include the differentiable neural architecture search (DARTS) method integrating architecture learning with lattice-free MMI training; Gumbel-Softmax and pipelined DARTS methods reducing the confusion over candidate architectures and improving the generalization of architecture selection; and Penalized DARTS incorporating resource constraints to balance the trade-off between performance and system complexity. Parameter sharing among TDNN-F architectures allows an efficient search over up to 7(28) different systems. Statistically significant word error rate (WER) reductions of up to 1.2% absolute and relative model size reduction of 31% were obtained over a state-of-the-art 300-hour Switchboard corpus trained baseline LF-MMI TDNN-F system featuring speed perturbation, i-Vector and learning hidden unit contribution (LHUC) based speaker adaptation as well as RNNLM restoring. Performance contrasts on the same task against recent end-to-end systems reported in the literature suggest the best NAS auto-configured system achieves state-of-the-art WERs of 9.9% and 11.1% on the NIST HubS' 00 and Rt03 s test sets respectively with up to 96% model size reduction. Further analysis using Bayesian learning shows that the proposed NAS approaches can effectively minimize the structural redundancy in the TDNN-F systems and reduce their model parameter uncertainty. Consistent performance improvements were also obtained on a UASpeech dysarthric speech recognition task.
Living in Contaminated Areas-Consideration of Different Perspectives

Following large-scale nuclear power plant accidents such as those that occurred at Chernobyl (Ukraine) in 1986 and Fukushima Daiichi (Japan) in 2011, large populations are living in areas containing residual amounts of radioactivity. As a key session of the ConRad conference, experts were invited from different disciplines to provide state-of-the-art information on the topic of "living in contaminated areas." These experts provided their different perspectives on a range of topics including radiation protection principles and dose criteria, environmental measurements and dose estimation, maintaining decent living and working conditions, evidence of health risks, and social impact and risk communication. A short summary of these different perspectives is provided in this paper.
HTLV-1-associated adult T cell leukemia is highly susceptible to Navitoclax due to enhanced Bax expression

Over-expression of Bcl-2, Bcl-xL and Bcl-w is frequently associated with cancer resistance to chemotherapy. Navitoclax (ABT-263), an orally bio-available small-molecule mimetic of the Bcl-2 homology domain 3, specifically inhibits Bcl-2, Bcl-xL and Bcl-w. Despite promising results obtained from the clinical trials, the use of Navitoclax in patients is dose-limited due to induction of death of platelets via inhibition of Bcl-xL and subsequent thrombocytopenia. This side effect limits the use of Navitoclax in low doses and to very sensitive tumors. In this study, we show that HTLV-1-associated adult T-cell leukemia/lymphoma (ATL) cells, which over-express Bcl-2, Bcl-xL and Bcl-w, show a 10- to 20-fold higher sensitivity (EC50 = ∼ 25-50 nM) to Navitoclax compared to non-HTLV-1-associated leukemic cells (EC50 = ∼ 1 μM). Investigation of the molecular mechanisms revealed that the HTLV-1 oncogenic protein Tax up-regulates expression of the pro-apoptotic protein Bax which enhances the therapeutic efficacy of Navitoclax. In addition, we show that agents that inhibit the transcription elongation or translation initiation such as Wogonin and Roc-A can further decrease the effective dose of Navitoclax. Our study suggests that HTLV-1 ATL may be a good candidate disease for low dose Navitoclax therapy and probably with less risk of thrombocytopenia.
STI-BT: A Scalable Transactional Index

Distributed Key-Value (DKV) stores have been intensively used to manage online transaction processing on large data-sets. DKV stores provide simplistic primitives to access data based on the primary key of the stored objects. To help programmers to efficiently retrieve data, some DKV stores provide distributed indexes. Besides that, and also to simplify programming such applications, several proposals have provided strong consistency abstractions via distributed transactions. In this paper we present STI-BT, a highly scalable, transactional index for Distributed Key-Value stores. STI-BT is organized as a distributed B(+)Tree and adopts an innovative design that allows to achieve high efficiency in large-scale, elastic DKV stores. As such, it provides both the desirable properties identified above, and does so in a far more efficient and scalable way than the few existing state of the art proposals that also enable programmers to have strongly consistent distributed transactional indexes. We have implemented STI-BTon top of an open-source DKV store and deployed it on a public cloud infrastructure. Our extensive study demonstrates scalability in a cluster of 100 machines, and speed ups with respect to state of the art up to 5.4x.
Haladaptatus halobius sp. nov. and Haladaptatus salinisoli sp. nov., two extremely halophilic archaea isolated from Gobi saline soil

Two extremely halophilic archaeal strains, PSR5T and PSR8T, were isolated from a saline soil sample collected from the Tarim Basin, Xinjiang, PR China. Both strains had two copies of the 16S rRNA genes rrn1 and rrn2, showing 2.6 and 3.9% divergence, respectively. The rrn1 gene of PSR5T showed 98.4 and 95.3% similarity to the rrn1 and rrn2 genes of strain PSR8T; the rrn2 gene of PSR5T displayed 97.4 and 96.7% similarity to those of strain PSR8T, respectively. Phylogenetic analyses based on the 16S rRNA and rpoB' genes revealed that strains PSR5T and PSR8T formed a single cluster, and then tightly clustered with the current four Haladaptatus species (93.5-97.1% similarities for the 16S rRNA gene and 89.3-90.9% similarities for the rpoB' gene, respectively). Several phenotypic characteristics differentiate strains PSR5T and PSR8T from current Haladaptatus members. The polar lipids of the two strains are phosphatidic acid, phosphatidylglycerol, phosphatidylglycerol phosphate methyl ester phosphatidylglycerol sulphate and three glycolipids. One of the glycolipids is sulphated mannosyl glucosyl diether, and the remaining two glycolipids are unidentified. The average nucleotide identity, in silico DNA-DNA hybridization, amino acid identity and percentage of conserved proteins values between the two strains were 88.5, 39.1, 89.3 and 72.8 %, respectively, much lower than the threshold values proposed as a species boundary. These values among the two strains and Haladaptatus members were 77.9-79.2, 22.0-23.5, 75.1-78.2 and 56.8-69.9 %, respectively, much lower than the recommended threshold values for species delimitation. These results suggested that strains PSR5T and PSR8T represent two novel species of Haladaptatus. Based on phenotypic, chemotaxonomic, genomic and phylogenetic properties, strains PSR5T (=CGMCC 1.16851T=JCM 34141T) and PSR8T (=CGMCC 1.17025T=JCM 34142T) represent two novel species of the genus Haladaptatus, for which the names Haladaptatus halobius sp. nov. and Haladaptatus salinisoli sp. nov. are proposed.
Studying the genotoxic effects of high intensity terahertz radiation on fibroblasts and CNS tumor cells

The data is obtained on the effect of high-intensity pulses of terahertz (THz) radiation with a broad spectrum (0.2-3 THz) on cell cultures. We have evaluated the threshold exposure parameters of THz radiation causing genotoxic effects in fibroblasts. Phosphorylation of histone H2AX at Ser 139 (γH2AX) was chosen as a marker for genotoxicity and a quantitative estimation of γH2AX foci number in fibroblasts was performed after cell irradiation with THz pulses for 30 min. No genotoxic effects of THz radiation were observed in fibroblasts unless peak intensity and electric field strength exceeded 21 GW cm-2 and 2.8 MV cm-1 , respectively. In tumor cell lines (neuroblastoma (SK-N-BE (2)) and glioblastoma (U87)), exposure to THz pulses with peak intensity of 21 GW cm-2 for 30 min caused no morphological changes as well as no statistically significant increase in histone phosphorylation foci number.
Visualization of the microscopic flow profile of state-of-the-art absorption heat pump working pairs under operational conditions

The research and development of novel working pairs and highly efficient heat exchangers for absorption heat pump applications require a detailed analysis of the microscopic flow profile at the absorber heat exchanger during the absorption process. A recently developed Single Aperture Defocussing Micro Particle Tracking method, based on a conventional Particle Image Velocimetry system, demonstrates the possibility of simultaneous measurements of velocity profiles and film thicknesses in falling film absorbers at steady-state flows. We show first results of velocity profile measurements of the state-of-the-art working pair LiBr/water, with and without a 2-Ethyl-1-Hexanol additive, during operation in a lab-scale absorption heat pump. Based on the results of the measurements, the advantages and disadvantages of the developed measurement technique and their potential application in the characterization of novel working pairs is discussed. (C) 2014 Elsevier Ltd. All rights reserved.
IEC 62697-2012: State of the Art Methods for Quantification of DBDS and Other Corrosive Sulfur Compounds in Unused and Used Insulating Liquids

This article describes state of the art methods developed by IEC TC-10 WG-37 for quantitative determination of a highly corrosive sulfur compound dibenzodisulfide (DBDS) and other corrosive sulfur compounds in unused and used insulating liquids. The methods permit an objective approach for assessing corrosiveness of insulating liquids rather than subjective assessment based on color perception that is prevalent in the current standard test methods for corrosive or potentially corrosive sulfur in insulating liquids. In addition, quantification of total corrosive sulfur TCS) in insulating liquids permits an objective ranking of sulfur compounds according to their corrosivity towards copper.
Motion Segmentation in the Presence of Outlying, Incomplete, or Corrupted Trajectories

In this paper, we study the problem of segmenting tracked feature point trajectories of multiple moving objects in an image sequence. Using the affine camera model, this problem can be cast as the problem of segmenting samples drawn from multiple linear subspaces. In practice, due to limitations of the tracker, occlusions, and the presence of nonrigid objects in the scene, the obtained motion trajectories may contain grossly mistracked features, missing entries, or corrupted entries. In this paper, we develop a robust subspace separation scheme that deals with these practical issues in a unified mathematical framework. Our methods draw strong connections between lossy compression, rank minimization, and sparse representation. We test our methods extensively on the Hopkins155 motion segmentation database and other motion sequences with outliers and missing data. We compare the performance of our methods to state-of-the-art motion segmentation methods based on expectation-maximization and spectral clustering. For data without outliers or missing information, the results of our methods are on par with the state-of-the-art results and, in many cases, exceed them. In addition, our methods give surprisingly good performance in the presence of the three types of pathological trajectories mentioned above. All code and results are publicly available at http://perception.csl.uiuc.edu/coding/motion/.
Deep feature extraction for document forgery detection with convolutional autoencoders

Context: Document forgery is a significant problem for ages due to paper-based documents' pervasive use. Classical destructive approaches for this problem, such as chromatography and electrophoresis, cannot be implemented as they flaw the document under analysis. Hyperspectral imaging - non-destructive approach that assists in finding the unique features of an image under investigation through their unique spectral signatures. It captures multiple narrow-band images at the electromagnetic spectrum, which is difficult through conventional imaging. Deep learning approaches for hyperspectral images have attained state-of-the-art results for solving many complex and challenging problems. Supervised classification of hyperspectral images is a tedious task since obtaining image labels and labeling the training data is a time-consuming and expensive process. In this paper, an unsupervised approach for classification of hyperspectral document images is proposed. Objective: To propose an unsupervised deep learning approach for ink mismatch detection in hyperspectral document images using spectral features. Approach: CAE-LR approach is proposed that uses Convolutional Autoencoder (CAE) for feature extraction and utilizing them for ink mismatch detection through Logistic Regression (LR). Results: We evaluated the performance of CAE-LR on UWA writing ink hyperspectral images dataset for blue and black inks. Artificially similar color inks of different types (2 similar to 5) were mixed in varying proportions to detect ink mismatch. Additionally, results are compared with three machine learning algorithms with variants of each, CNN, and five state-of-art methods used by the researchers. Experimental results illustrated that the CAE-LR outperforms all the above - mentioned approaches by achieving the state of art results, which depicts the efficacy of unsupervised deep learning approach for ink mismatch detection in hyperspectral document images.
FANCD2 promotes the malignant behavior of endometrial cancer cells and its prognostic value

Defective DNA damage repair is a key mechanism affecting tumor susceptibility, treatment response, and survival outcome of endometrial cancer (EC). Fanconi anemia complementation group D2 (FANCD2) is the core component of the Fanconi anemia repair pathway. To explore the function of FANCD2 in EC, we examined the expression of FANCD2 in human specimens and databases, and discussed the possible mechanism of carcinogenesis by in vitro assays. Immunohistochemistry results showed overexpression of FANCD2 was detected in EC tissues compared to normal and atypical hyperplasia endometrium. Higher FANCD2 expression was correlated with deeper myometrial invasion (MI) and proficient mismatch repair status. The Cancer Genome Atlas (TCGA) database analysis showed FANCD2 was upregulated in EC compared with normal tissue. The high expression of FANCD2 was associated with poor overall survival in EC. Knockdown of FANCD2 expression in EC cell lines inhibited malignant proliferation and migration ability. We demonstrated that decreased FANCD2 expression results in increased DNA damage and decreased S-phase cells, leading to a decrease in proliferative capacity in EC cells. Down-regulated FANCD2 confers sensitivity of EC cells to interstrand crosslinking agents. This study provides evidence for the malignant progression and prognostic value of FANCD2 in EC.
New polyacetylenes from Bidens procera

A phytochemical investigation of Bidens procera L.C.Xu ex X.W.Zheng afforded two novel polyacetylenes, tridecane-2E-monoene-4,6,8-triyntylen-1,13-diol-12-O-β-glucoside (1) and tetradecane-2E,8E-diene-4,6-diyne-1,14-diol-13-O-β-glucoside (2), together with ten known compounds (3 - 12). Their chemical structures were elucidated by NMR and MS spectrums as well as the comparison of the published data. Furthermore, the chemotaxonomy of the yielded compounds was also discussed.
Intracellular infection by symbiotic bacteria requires the mitotic kinase AURORA1

The subcellular events occurring in cells of legume plants as they form transcellular symbiotic-infection structures have been compared with those occurring in premitotic cells. Here, we demonstrate that Aurora kinase 1 (AUR1), a highly conserved mitotic regulator, is required for intracellular infection by rhizobia in Medicago truncatula. AUR1 interacts with microtubule-associated proteins of the TPXL and MAP65 families, which, respectively, activate and are phosphorylated by AUR1, and localizes with them within preinfection structures. MYB3R1, a rhizobia-induced mitotic transcription factor, directly regulates AUR1 through two closely spaced, mitosis-specific activator cis elements. Our data are consistent with a model in which the MYB3R1-AUR1 regulatory module serves to properly orient preinfection structures to direct the transcellular deposition of cell wall material for the growing infection thread, analogous to its role in cell plate formation. Our findings indicate that the eukaryotically conserved MYB3R1-TPXL-AUR1-MAP65 mitotic module was conscripted to support endosymbiotic infection in legumes.