Miracle-dz/UniFAR-FTUs huggingface dataset:
Effect of Phenotypic Screening of Extracts and Fractions of Erythrophleum ivorense Leaf and Stem Bark on Immature and Adult Stages of Schistosoma mansoni

Schistosomiasis is a disease caused by a flatworm parasite that infects people in tropical and subtropical regions of Sub-Saharan Africa, South America, China, and Southeast Asia. The reliance on just one drug for current treatment emphasizes the need for new chemotherapeutic strategies. The aim of this study was to determine the phenotypic effects of extracts and fractions of leaf and stem bark of Erythrophleum ivorense (family Euphorbiaceae), a tree that grows in tropical parts of Africa, on two developmental stages of Schistosoma mansoni, namely, postinfective larvae (schistosomula or somules) and adults. Methanol leaf and stem bark extracts of E. ivorense were successively fractionated with acetone, petroleum ether, ethyl acetate, and methanol. These fractions were then incubated with somules at 0.3125 to 100 μg/mL and with adults at 1.25 μg/mL. The acetone fractions of both the methanol leaf and bark of E. ivorense were most active against the somules whereas the petroleum ether fractions showed least activity. For adult parasites, the acetone fraction of methanol bark extract also elicited phenotypic changes. The data arising provide the first step in the discovery of new treatments for an endemic infectious disease using locally sourced African medicinal plants.
A Formal Ontology-Based Classification of Lexemes and its Applications

The paper describes the enrichment of OntoSenseNet - a verb-centric lexical resource for Indian Languages. A major contribution of this work is preservation of an authentic Telugu dictionary by developing a computational version of the same. It is important because native speakers can better annotate the sense-types when both the word and its meaning are in Telugu. Hence efforts are made to develop the aforementioned Telugu dictionary and annotations are done manually. The manually annotated gold standard corpus consists 8483 verbs, 253 adverbs and 1673 adjectives. Annotations are done by native speakers according to defined annotation guidelines. In this paper, we provide an overview of the annotation procedure and present the validation of the developed resource through inter-annotator agreement. Additional words from Telugu WordNet are added to our resource and are crowd-sourced for annotation. The statistics are compared with the sense-annotated lexicon, our resource for more insights.
Graphene loaded frequency reconfigurable metal antenna

In this work, we present the impact of graphene layer in conventional copper antenna from microwave to terahertz (THz) by numerical simulations. The use of graphene sheet below the metal radiator as parasitic layer in metal antenna can make easier to tune the resonance frequency using electric field effect via DC bias voltage. The graphene has a variable conductivity, which can be increased with the increases of chemical potential by the application of external DC voltage. However, graphene has moderate conductivity with poor tunability in the microwave frequency range and excellent conductivity with good tunability in the THz frequency range. Due to poor tunability conductivity effect of graphene in microwave frequencies, there is no more frequency reconfiguration of microwave copper antenna with variation of graphene chemical potential. THz copper antenna enables frequency reconfiguration with variation of graphene chemical potential, because of good tunability conductivity effect of graphene in THz frequencies.
Low-Profile Wideband Slot-Loaded Patch Antenna With Multiresonant Modes

A low-profile and wideband patch antenna is proposed for base station applications. In order to realize a wide band, two types of slots are adopted and three resonant modes are constructed within a band of interest. The mode analysis is conducted to clarify the wideband performance. The designed antenna has an impedance bandwidth of 52.7% (from 1.62 to 2.78 GHz) with a low-profile structure of <inline-formula><tex-math notation="LaTeX"> ${\text{0.7}}\lambda _{0}\,\times \,{\text{0.47}}\lambda _{0}\,\times \,{\text{0.084}}\lambda _{0}$</tex-math> </inline-formula>, where <inline-formula><tex-math notation="LaTeX">$\lambda _{0}$</tex-math></inline-formula> is the free-space wavelength at the center frequency. The measurement results of a prototype agree well with the simulations. The measured average gain is 6 dBi, and the radiation patterns remain stable in the required band with low cross polarization, low backlobes, and nearly identical E- and H-plane patterns. The proposed multiresonant antenna can cover six bands of wireless communication, such as DCS, PCS, UMTS, WLAN, LTE2300, and LTE2600, exhibiting great potentials in applications of multiband wireless communication systems.
QTL Mapping and Validation of Adult Plant Resistance to Stripe Rust in Chinese Wheat Landrace Humai 15

Stripe rust caused by Puccinia striiformis f. sp. tritici (Pst) is a devastating foliar disease that affects common wheat and barley throughout the world. The reasonable deployment of adult plant resistance (APR) wheat varieties is one of the best methods for controlling this disease. Wheat landraces are valuable resources for identifying the genes/QTLs responsible for disease resistance. Humai 15 is a Chinese spring wheat landrace and it has exhibited adequate levels of APR to the prevalent Pst races in field environments for many years. In this study, a population of 177 recombinant inbred lines (RILs) was derived from Humai 15 × Mingxian 169. After screening based on a 90K chip array using 45 RILs and Kompetitive Allelic Specific PCR marker genotyping for the population of RILs, a major effect QTL in Humai 15 was located on the centromere of chromosome 2B, where it accounted for up to 47.2% of the phenotypic variation. Two other minor QTL genes from Humai 15 were located on chromosome arms 3BS and 4BL. The Yr18 gene was identified on chromosome arm 7DS in Mingxian 169.
The Relationship between Hospital Ethical Climate , Job Satisfaction , and Intent to Turnover

s increasingly difficult, they so this study was conducted to examine the nurses self-administered online questionnaires of 150 convenient nurses e and nurse job satisfaction and In conclusion mate for nursing practice might lead to more job satisfaction words: Environment, Ethical climate, Hospital Climate, Intent to turnover, Nurses nursing to improve the health care environment and provide the context for ethical decision making (Schluter Job satisfaction among health care p important issue particularly because they affect turnover rates, staff retention and eventually the quality of patient care (Atencio et al., 2003; Kalliath Recently, there has been a high turnover of t This continuous replacement of nursing staffs unconstructively affects not only the nursing care standards but also the quality of the patient care. Job dissatisfaction was the most common cause for the nurses to give up their job, which led to shortage of the nurses (Marriner, 1980). Job satisfaction is explained as “the feelings a worker has about his or her job experiences related to previous ex periences, current expectations, or available alternatives” face challenging nover has rarely been ’ perception
Open Logo Detection Challenge

Existing logo detection benchmarks consider artificial deployment scenarios by assuming that large training data with fine-grained bounding box annotations for each class are available for model training. Such assumptions are often invalid in realistic logo detection scenarios where new logo classes come progressively and require to be detected with little or none budget for exhaustively labelling fine-grained training data for every new class. Existing benchmarks are thus unable to evaluate the true performance of a logo detection method in realistic and open deployments. In this work, we introduce a more realistic and challenging logo detection setting, called Open Logo Detection. Specifically, this new setting assumes fine-grained labelling only on a small proportion of logo classes whilst the remaining classes have no labelled training data to simulate the open deployment. We further create an open logo detection benchmark, called OpenLogo,to promote the investigation of this new challenge. OpenLogo contains 27,083 images from 352 logo classes, built by aggregating/refining 7 existing datasets and establishing an open logo detection evaluation protocol. To address this challenge, we propose a Context Adversarial Learning (CAL) approach to synthesising training data with coherent logo instance appearance against diverse background context for enabling more effective optimisation of contemporary deep learning detection models. Experiments show the performance advantage of CAL over existing state-of-the-art alternative methods on the more realistic and challenging OpenLogo benchmark.
Early Detection of Brain Tumor in MRI Images-A Comparative Study

Glioma is a common and malign-ant tumor, which may lead to short life span in their highest grade. Therefore to improve the quality of life of oncological patients is the early diagnosis of brain tumor, which is a major stage of treatment. MRI (Magnetic Resonance Imaging) is widely used medical imaging technique used to assess tumors, but large amount of data produced by MRI may vary greatly. Thus manual detection will be challenging. There exist many automated diagnostic systems which plays a major role in detection of brain tumors in MRI images. In this paper a comparative study of classification techniques namely Decision tree, SVM (Support Vector Machine), kNN (k Nearest Neighbor), CNN (Convolutional Neural Networks) and DBN has been done. The system may mainly include three steps namely preprocessing, classification and post processing. In the above survey the performance of classifiers in diagnosing the brain tumor is compared using the parameters speed, accuracy and complexity.
Higher-dimension Tensor Completion via Low-rank Tensor Ring Decomposition

The problem of incomplete data is common in signal processing and machine learning. Tensor completion algorithms aim to recover the incomplete data from its partially observed entries. In this paper, taking advantages of high compressibility and flexibility of recently proposed tensor ring (TR) decomposition, we propose a new tensor completion approach named tensor ring weighted optimization (TR-WOPT). It finds the latent factors of the incomplete tensor by gradient descent algorithm, then the latent factors are employed to predict the missing entries of the tensor. We conduct various tensor completion experiments on synthetic data and real-world data. The simulation results show that TR-WOPT performs well in various high-dimension tensors. Furthermore, image completion results show that our proposed algorithm outperforms the state-of-the-art algorithms in many situations. Especially when the missing rate of the test images is high (e.g., over 0.9), the performance of our TR-WOPT is significantly better than the compared algorithms.
Fast automatic segmentation of cells and nucleuses in large-scale liquid-based monolayer smear images

In automated cervical cancer screening, the segmentation to find the contours of cells and nucleuses is one of the most important challenges. Existing methods are computationally expensive and may not be effective for both normal and cancerous cases. In this paper, we propose a novel approach for the rapid segmentation of cells and nucleuses in partial image of large-scale liquid-based monolayer smears. The image is first enhanced by contrast limited adaptive histogram equalization (CLAHE) and a bilateral filter. Otsu's double thresholding is used to initialize cells contours. Afterwards, we propose a robust low-intensity segmentation (RLIS) approach to initializes the contours of nucleuses. In addition, we propose two optimizers: adaptive non-iterative active contours (ANAC) and active contour with discretization (ACWD). One of the optimizers is selected to optimize the initial contours of the cells and nucleuses. Experimental results show that our method is able to achieve above 85% in accuracy, sensibility and specificity for both normal and cancerous cases with time complexity of O(n). Implemented in Python (ver. 3.5) on a 3.4GHz clock rate CPU, our method takes less than 1 second to process an image of 2048×2048.
A Tensor-Train Deep Computation Model for Industry Informatics Big Data Feature Learning

The deep computation model has been proved to be effective for big data hierarchical feature and representation learning in the tensor space. However, it requires expensively computational resources including high-performance computing units and large memory to train a deep computation model with a large number of parameters, limiting its effectiveness and efficiency for industry informatics big data feature learning. In this paper, a tensor-train deep computation model is presented for industry informatics big data feature learning. Specially, the tensor-train network is used to compress the parameters significantly by converting the dense weight tensors into the tensor-train format. Furthermore, a learning algorithm is implemented based on gradient descent and back-propagation to train the parameters of the presented tensor-train deep computation model. Extensive experiments are carried on STL-10, CUAVE, and SNAE2 to evaluate the presented model in terms of the approximation error, classification accuracy drop, parameters reduction, and speedup. Results demonstrate that the presented model can improve the training efficiency and save the memory space greatly for the deep computation model with small accuracy drops, proving its potential for industry informatics big data feature learning.
Hemicellulose-derived chemicals: one-step production of furfuryl alcohol from xylose.

One-pot production of furfuryl alcohol via xylose dehydration followed by furfural hydrogenation was investigated over a dual catalyst system composed of Pt/SiO2 and sulfated ZrO2 as metal and acid catalysts, respectively. All samples were characterized by XRD, XRF, N2 physisorption, TG-MS and FTIR regarding their most fundamental properties for the studied process. A systematic study is reported on the effects of the reaction temperature, the composition of the binary solvent and the molar ratio between acid and metal sites in the catalyst system. The results revealed the feasibility of the one-step process for furfuryl alcohol synthesis and showed that the occurrence of both acid and metal sites is compulsory in order to promote the dehydration of xylose to furfural and its further hydrogenation to furfuryl alcohol. Selectivity towards furfuryl alcohol was found to be strongly dependent on the solvent, which can inhibit its polymerization to some extent.
Long-term effect of bariatric surgery on respiratory function in severe uncomplicated obesity

Background: Obesity can have profound adverse effects on the respiratory system. The long-term effects of weight loss attained by bariatric surgery on pulmonary function tests (PFT) are not well known. The aim of the study was to find out whether weight reduction induced by laparoscopic adjustable gastric banding is associated with a sustained improvement of the pulmonary functional parameters in the severely obese. Materials & methods: A consecutive series of 32 obese patients who had laparoscopic adjustable gastric banding were studied. BMI, walking distance, dyspnea and PFT were evaluated at baseline and after 12, 24 and 30 months after surgery. Results: 32 patients (24 females, age mean 36 ± 11 years) were evaluated. Mean BMI (kg/m2) decreased from 43 ± 6 before to 35 ± 5 12 months postoperatively. The BMI remained decreased 24 and 30 months after surgery. In obese PFT increased 12 months postoperatively and it remained increased 24 and 30 months after bariatric surgery. Similarly, the distance walked.
Visualizing spreading phenomena on complex networks

Graph drawings are useful tools for exploring the structure and dynamics of data that can be represented by pair-wise relationships among a set of objects. Typical real-world social, biological or technological networks exhibit high complexity resulting from a large number and broad heterogeneity of objects and relationships. Thus, mapping these networks into a low-dimensional space to visualize the dynamics of network-driven processes is a challenging task. Often we want to analyze how a single node is influenced by or is influencing its local network as the source of a spreading process. Here I present a network layout algorithm for graphs with millions of nodes that visualizes spreading phenomena from the perspective of a single node. The algorithm consists of three stages to allow for an interactive graph exploration: First, a global solution for the network layout is found in spherical space that minimizes distance errors between all nodes. Second, a focal node is interactively selected, and distances to this node are further optimized. Third, node coordinates are mapped to a circular representation and drawn with additional features to represent the network-driven phenomenon. The effectiveness and scalability of this method are shown for a large collaboration network of scientists, where we are interested in the citation dynamics around a focal author.
Molecular characterization of isopentenyltransferase (BoAIPT1) from Bambusa oldhamii expressed in Escherichia coli

Cytokinins (CKs) are a group of phytohormones involved in stages of plant development, such as cell division and various other growth processes. The key enzyme in de novo biosynthesis of naturally occurring cytokinins is adenylate isopentenyltransferase (AIPT). This study amplified a PCR product, containing partial sequence encoding putative AIPT, using primers based on conserved protein sequences. Genomic DNA containing a full-length of AIPT was obtained by screening a genomic DNA library. This study used 5'-RACE and 3'-RACE, using gene specific primers, to clone and sequence cDNA encoding BoAIPT1. The open reading frame of BoAIPT1 was 1035 bp in size, encoding 344-amino acids, with a molecular mass of 37.7 kDa. No introns were found by comparing AIPT from bamboo with AIPT sequences from other species. BoAIPT1 contains a putative ATP/GTP binding motif at the N-terminal as observed in other known AIPT. Recombinant BoAIPT1 expressed in Escherichia coli catalyzed the isopentenyl transfer reaction from dimethylallyldiphosphate (DMAPP) to the N^6 adenylate amino group. Real-time PCR analyses revealed BoAIPT1 expression increased simultaneously with green bamboo shoot growth, with the top green shoot section having the highest expression level.
Denoising Auto-Encoder with Recurrent Skip Connections and Residual Regression for Music Source Separation

Convolutional neural networks with skip connections have shown good performance in music source separation. In this work, we propose a denoising Auto-encoder with Recurrent skip Connections (ARC). We use 1D convolution along the temporal axis of the time-frequency feature map in all layers of the fully-convolutional network. The use of 1D convolution makes it possible to apply recurrent layers to the intermediate outputs of the convolution layers. In addition, we also propose an enhancement network and a residual regression method to further improve the separation result. The recurrent skip connections, the enhancement module, and the residual regression all improve the separation quality. The ARC model with residual regression achieves 5.74 siganl-to-distoration ratio (SDR) in vocals with MUSDB (used in SiSEC 2018). We also evaluate the ARC model alone on the older dataset DSD100 (used in SiSEC 2016) and it achieves 5.91 SDR in vocals.
High Quality de Novo Transcriptome Assembly of Croton tiglium

Croton tiglium is one of more than 1,200 different species in the large genus Croton, belonging to the family Euphorbiaceae (Kalwij, 2012; The Plant List, 2014). C. tiglium can be found in subtropical and tropical regions on both hemispheres (Salatino et al., 2007). This plant was first mentioned in the medical literature over 2,200 years ago in China. The medical relevance is probably due to a huge variety of different secondary metabolites (Pope, 1824). Traditionally utilized as a purgative to treat gastrointestinal and intestinal disorders, as an abortifacient and counterirritant, the commercially available seed oil of C. tiglium is nowadays applied in homeopathy and acupuncture (Glaser et al., 1988). The pharmacologic mechanism of the laxative properties of ethanol extracts of C. tiglium has been studied on rat intestinal epithelium (Tsai et al., 2004). C. tiglium produces various phorbol esters, including substances that were reported to be tumor-promoting (Van Duuren et al., 1963), antileukemic and antimycobacterial (Goel et al., 2007; Salatino et al., 2007), and even candidates for the treatment of HIV (El-Mekkawy et al., 2000). Beside the tumor-promoting factors, some cytotoxic phorbol esters were isolated from plant extracts and evaluated in cell culture assays (Zhang et al., 2013). In contrast to the co-carcinogenic substances, C. tiglium was shown to produce a ribonucleoside analog of guanosine with antitumor activity (Kim et al., 1994). In this work, we present a comprehensive de novo transcriptome assembly of C. tiglium based on a normalized library to cover a huge variety of transcripts. In addition, tissue-specific transcript libraries were generated to enable differential gene expression analysis between tissues. This will facilitate the identification of candidate genes involved in growth, development, and metabolism of this plant species.
Contemporary Guidance for Stated Preference Studies

This article proposes contemporary best-practice recommendations for stated preference (SP) studies used to inform decision making, grounded in the accumulated body of peer-reviewed literature. These recommendations consider the use of SP methods to estimate both use and non-use (passive-use) values, and cover the broad SP domain, including contingent valuation and discrete choice experiments. We focus on applications to public goods in the context of the environment and human health but also consider ways in which the proposed recommendations might apply to other common areas of application. The recommendations recognize that SP results may be used and reused (benefit transfers) by governmental agencies and nongovernmental organizations, and that all such applications must be considered. The intended result is a set of guidelines for SP studies that is more comprehensive than that of the original National Oceanic and Atmospheric Administration (NOAA) Blue Ribbon Panel on contingent valuation, is more germane to contemporary applications, and reflects the two decades of research since that time. We also distinguish between practices for which accumulated research is sufficient to support recommendations and those for which greater uncertainty remains. The goal of this article is to raise the quality of SP studies used to support decision making and promote research that will further enhance the practice of these studies worldwide.