Miracle-dz/UniFAR-FTUs huggingface dataset:
The first insight into transcriptome profile of herbaceous plant Nervilia fordii based on RNA-seq

Nervilia fordii is a famous and valuable herbaceous plant that has been used to cure pulmonary and respiratory diseases for hundred years. However, the wild resource of N. fordii has been virtually exhausted, and the available approaches such as artificial cultivation and tissue proliferation cannot produce enough plants to satisfy the clinic and market. Utilization of biological technique to regulate some physiological processes of the species is a considerable method to solve the scarcity of N. fordii. To date, little is known about functional genes from N. fordii, especially the genes involved in biosynthesis pathways of effective components. Herein, a transcriptomic study was performed using leaf and corm of N. fordii as material. A total of 102,258,558 high-quality reads were produced by Illumina RNA-seq platform and finally 142,220 unigenes were assembled with an average length of 518 bp. The distinct genes were searched against an Nr database and 37.9% (53,970) of them had at least one hit. We also found 38,640, 110,029, and 28,970 of the unigenes assigned in COG, GO and KEGG databases, respectively. We discovered almost every important gene participated in flavonoids and terpenoids biosynthesis pathways and illustrated their expression difference between leaf and corm of N. fordii. We presented the first transcriptome insight for N. fordii and provide a firm foundation for genetic manipulation of the endangered plant.
PREVENTION OF EAVESDROPPER ATTACKS USING DECORRELATION PATTERNS IN WIRELESS SENSOR NETWORKS

Traffic analysis has traditionally been a major threat to wireless tactical military communications. An adversary with the ability to obtain network measures such as packet counts at various links, correlations in sen ding and receiving times, etc. may deduce sensitive informat ion about existing communication patterns. In this paper, we propose Decorrelation techniques which prevent the activiti es performed by eavesdropper. In specific to, the glob al eavesdropper that threatens the sensitive attribute s like number of transmitted packets, inter-packet times, and traffic directionality, to infer event location, its occurrence time, and the sink location. The traffic analysis method is b een incorporated for finding the correlating patterns of the transmission systems. Based on the contextual infor mation of the eavesdropper, the similar patterns are correlat ed for efficient transmission systems. Experimental result s have shown the efficiency of the proposed algorithm. The proposed system has resolved the issue of high communication cost and delay overhead.
Learning to play Monopoly : A Reinforcement Learning approach

Reinforcement Learning is a rather popular machine learning paradigm which relies on an agent interacting with an environment and learning through trial and error to maximize the cummulative sum of rewards received by it. In this paper, we are proposing a novel representation of the famous board game Monopoly as a Markov Decision Process and a Reinforcement Learning agent capable of playing and learning winning strategies. The conclusions drawn from the experiments are particularly positive, since the proposed agent demonstrated intelligent behavior and high win rates against different types of agent-players.
GRASSLAND TENURE IN CHINA : AN ECONOMIC ANALYSIS

The literature on property rights in rural China has had an almost exclusive focus on cropland tenure. The primary purpose of this paper is to make a contribution towards extending the coverage of this literature to China’s extensive grasslands, which comprise some 40% of its territory. Two unique characteristics of grassland tenure are found: group tenure arrangements and ‘fuzzy’ boundaries. In conventional microeconomic analysis, both of these characteristics raise efficiency concerns. However, it is argued that these concerns are only partly justified. Furthermore, by using a new institutional economics perspective and giving due consideration to the nature of resource endowments and the economic environment, ‘hidden benefits’ of group tenure and fuzzy boundaries are revealed. These benefits include facilitating the realization of economies of size with respect to herding labour, equitable household access to resources, and insurance against economic risk. Whilst Chinese cropland tenure also facilitates the latter two, it does this through different tenure mechanisms and this reaffirms the role of natural resource endowments in shaping institutional arrangements. 1 DPhil(Development Studies) candidate, University of Sussex, Brighton, United Kingdom; Lecturer, Department of Applied and International Economics, Massey University, Palmerston North, New Zealand. Tel . +64-6-350-5799 ext.2659; fax +64-6-350-5660; e-mail T.J.Banks@massey.ac. nz
Are CNN Predictions based on Reasonable Evidence?

We propose Guided Zoom, an approach that utilizes spatial grounding to make more informed predictions. It does so by making sure the model has “the right reasons” for a prediction, being defined as reasons that are coherent with those used to make similar correct decisions at training time. The reason/evidence upon which a deep neural network makes a prediction is defined to be the spatial grounding, in the pixel space, for a specific class conditional probability in the model output. Guided Zoom questions how reasonable the evidence used to make a prediction is. We show that Guided Zoom results in the refinement of a model’s classification accuracy on two fine-grained classification datasets.
Highly Stable and Active Pt / Nb-TiO 2 Carbon-Free Electrocatalyst for Proton ExchangeMembrane Fuel Cells

The current materials used in proton exchange membrane fuel cells (PEMFCs) are not sufficiently durable for commercial deployment. One of the major challenges lies in the development of an inexpensive, efficient, and highly durable and active electrocatalyst. Here a new type of carbon-free Pt/Nb-TiO2 electrocatalyst has been reported. Mesoporous Nb-TiO2 hollow spheres were synthesized by the sol-gel method using polystyrene (PS) sphere templates. Pt nanoparticles (NPs) were then deposited onto mesoporous Nb-TiO2 hollow spheres via a simple wet-chemical route in aqueous solution, without the need for surfactants or potentiostats. The growth densities of Pt NPs on Nb-TiO2 supports could be easily modulated by simply adjusting the experimental parameters. Electrochemical studies of Pt/Nb-TiO2 show much enhanced activity and stability than commercial E-TEK Pt/C catalyst. PtNP/Nb-TiO2 is a promising new cathode catalyst for PEMFC applications.
A Moral Vocabulary

This study serves as an initial step in investigating the link between moral emotion labels (e.g., guilt and shame) and moral judgment/conscience development in preschool-aged children. Sixty 4-year-olds (31 male; average age= 57.6 months) completed measures of general vocabulary knowledge, basic emotion label knowledge, moral emotion label knowledge, and moral judgment. Multiple linear regression analyses showed that moral emotion label knowledge was a marginally significant predictor of overall conscience (β = .24, p = .06). More tellingly, knowledge of moral emotion label antecedents predicted overall conscience (β = .28, p = .03). General vocabulary knowledge did not predict performance on conscience measures (β = .96, p = .35), but knowledge of basic emotion labels did (β = .34, p = .01). Overall, the data support the study’s hypothesis that acquiring specific types of vocabulary knowledge (e.g., knowledge of emotion labels, both basic and moral) can be a critical factor in children’s conscience development.
Multi-Platform Genomic Data Fusion with Integrative Deep Learning

The abundance of next-generation sequencing (NGS) data has encouraged the adoption of machine learning methods to aid in the diagnosis and treatment of human disease. In particular, the last decade has shown the extensive use of predictive analytics in cancer research due to the prevalence of rich cellular descriptions of genetic and transcriptomic profiles of cancer cells. Despite the availability of wide-ranging forms of genomic data, few predictive models are designed to leverage multidimensional data sources. In this paper, we introduce a deep learning approach using neural network based information fusion to facilitate the integration of multi-platform genomic data, and the prediction of cancer cell sub-class. We propose the dGMU (deep gated multimodal unit), a series of multiplicative gates that can learn intermediate representations between multi-platform genomic data and improve cancer cell stratification. We also provide a framework for interpretable dimensionality reduction and assess several methods that visualize and explain the decisions of the underlying model. Experimental results on nine cancer types and four forms of NGS data (copy number variation, simple nucleotide variation, RNA expression, and miRNA expression) showed that the dGMU model improved the classification agreement of unimodal approaches and outperformed other fusion strategies in class accuracy. The results indicate that deep learning architectures based on multiplicative gates have the potential to expedite representation learning and knowledge integration in the study of cancer pathogenesis.
Designing communication add-ons for VR applications using VR-embedded Widgets

A software application rendered on a physical screen can naturally support social activities. Multiple users can watch the screen together, exchange ideas, or provide guidance. These interactions, however, are not easily supported in applications rendered in an enclosed Virtual Reality system. Information about how a user is using the application in VR such as the screen output and the controller interactions are not accessible to other users. Worse, users outside of VR also cannot easily interact with the user in VR. These barriers prevent fluid collaborative interactions around a VR application. This position paper discusses our ongoing effort in making VR applications more social using VR-embedded widget. These widgets are interactive interface elements that can be rendered on top of an existing VR application. They allow a VR application to interface with other applications without modifying the source code. We will motivate our initial effort in using VR-embedded widget in developing a tutorial system for VR painting, and illustrate several potential ideas and opportunities to apply VR-embedded widgets.
Deiodinase in Embryo Development

Neutrophils are essential effectors cells of the innate immune system that have recently been recognized as thyroid hormone (TH) target cells. Cellular TH bioavailability is regulated by the deiodinase enzymes, which can activate or inactivate TH. We have previously shown that the TH inactivating enzyme type 3 deiodinase (D3) is present in neutrophils. Furthermore, D3 knockout (D3KO) mice show impaired bacterial killing upon infection. We hypothesized that D3 plays a role in neutrophil function during infection by actively regulating local TH availability. We measured TH concentrations in cerebrospinal fluid (CSF) from patients with bacterial meningitis and controls. Bacterial meningitis resulted in marked changes in CSF TH levels, characterized by a strong increase of thyroxine and reverse-triiodothyronine concentrations. This altered TH profile was consistent with elevated D3 activity in infiltrating neutrophils at the site of infection. D3 knockdown in zebrafish embryos with pneumococcal meningitis resulted in increased mortality and reduced neutrophil infiltration during infection. Finally, stimulated neutrophils from female D3KO mice exhibited impaired NADPH-oxidase activity, an important component of the neutrophil bacterial killing machinery. These consistent findings across experimental models strongly support a critical role for reduced intracellular TH concentrations in neutrophil function during infection, for which the TH inactivating enzyme D3 appears essential [1]. The aim of this study was to examine the adverse effects of lead (Pb) exposure on Bufo gargarizans embryos. The 96 h-LC50 of Pb2+ for B. gargarizans embryos was determined to be 26.6 mg L-1 after an acute test. In the chronic test, B. gargarizans embryos at Gosner stage 3 were exposed to 10~2000 μg Pb2+ L-1 during embryogenesis. Total length, weight, developmental stage, and malformation were monitored. In addition, the transcript levels of type II and type III iodothyronine deiodinase (Dio2 and Dio3) and thyroid hormone receptors (TRα and TRβ) were determined to assess the thyroid-disrupting effects of Pb2+. Slightly increased growth and development of B. gargarizans embryos were observed at low concentrations of Pb2+ (10, 50, and 100 μg L-1), while retarded growth and development were found at high concentrations of Pb2+ (1000 and 2000 μg L-1). In addition, Pb2+ exposure induced morphological abnormalities, which were characterized by edema at
Author ' s personal copy

The Delaware Bay ecosystem has been the focus of extensive habitat restoration efforts to offset finfish losses due to mortality associated with power plant water intake. As a result, a 45 km2 or a 3% increase in total marsh area was achieved by 1996–1997 through the restoration efforts of the Public Service Enterprise Group (PSEG). To quantify the impact of restoration efforts on system productivity, an Ecopath with Ecosim model was constructed that represented all major components of the ecosystem. The model consisted of 47 functional groups including: 27 fish species, 5 invertebrate groups, 4 multi-species benthic groups, 6 multi-species fish groups, 3 plankton groups, 1 shorebird group and 1 marine mammal group. Biomass, abundance, catch, and demographic data were obtained from the literature or from individual stock assessments conducted for principal ecosystem components. A base Ecosim model was fitted to time series of key species in the Bay representing the period 1966–2003. To access the gains from marsh restoration, model simulations reflecting no restoration were conducted to estimate the productivity that would have been lost if restoration efforts had not occurred. The results indicated that restoration increased total ecosystem biomass by 47.7 t km−2 year−1. Simulations indicated increased biomasses across a wide range of species including important forage and commercially important species. The marsh restoration also significantly impacted ecosystem structure increasing the ratio of production-to-respiration, increasing system path length and decreasing the ratio of production-to-biomass. © 2010 Elsevier B.V. All rights reserved.
Incentive learning underlying cocaine relapse requires mGluR 5 receptors located on dopamine D 1 receptor expressing neurons

http://sro.sussex. ac.uk Novak, Martin, Halbout, Briac, O'Connor, Eoin, Parkitna, Jan Rodriguez, Su, Tian, Chai, Minqiang, Crombag, Hans, Bilbao, Ainhoa, Spanagel, Rainer, Stephens, David N, Schütz, Günther and Engblom, David (2010) Incentive learning underlying cocaine relapse requires mGluR5 receptors located on dopamine D1 receptor-expressing neurons. Journal of Neuroscience, 30 (36). pp. 11973-11982. ISSN 0270-6474
Lower-Extremity Muscle Strength , and Physical Function mong Older Mexican Americans oham

Objective: To examine the relation between pain on weight earing, lower-extremity muscle strength, and physical funcion among older Mexican Americans. Design: Cross-sectional study. Setting: Five Southwestern states: Texas, New Mexico, olorado, Arizona, and California. Participants: A population-based sample of 544 noninstiutionalized Mexican-American men and women age 71 years nd older. Interventions: Not applicable. Main Outcome Measures: Pain on weight bearing, lowerxtremity muscle strength, and physical function. Results: Of the 544 subjects, 244 (44.9%) reported pain on eight bearing. Mean muscle strength in men ranged from . 3kg for knee extension, 12.8kg for hip flexion, to 13.0kg for ip abduction. In women, mean strength ranged from 6.6kg for nee extension, 9.5kg for hip flexion, to 8.6kg for hip abducion. Mean of physical function score was 70.7 for men and 0.6 for women. Pain on weight bearing was negatively assoiated with summary lower-extremity muscle strength only in omen ( .05, P .001) after controlling for all covariates. ain on weight bearing was negatively associated with physical unction in both men ( 15.33, P .001) and women ( 11.03, .001), and lower-extremity muscle strength was positively ssociated with physical function in both men (37.77, P .001) nd women (73.50, P .001), after controlling for all ovariates. Conclusions: Among older Mexican Americans, the presnce of pain was associated with decreased muscle strength in omen and decreased physical function in both men and omen. High muscle strength was associated with high physcal function in both men and women.
To Ship or Not to ( Function ) Ship

Sampling is often used to reduce query latency for interactive big data analytics. The established parallel data processing paradigm relies on function shipping, where a coordinator dispatches queries to worker nodes and then collects the results. The commoditization of high-performance networking makes data shipping possible, where the coordinator directly reads data in the workers’ memory using RDMA while workers process other queries. In this work, we explore when to use function shipping or data shipping for interactive query processing with sampling. Whether function shipping or data shipping should be preferred depends on the amount of data transferred, the current CPU utilization and the sampling method. The results show that data shipping is up to 6.5× faster when performing clustered sampling with heavily-utilized workers.
An End-to-end Deep Convolutional Neural Network for a Multi-scale Image Matching and Localization Problem

Diverse imaging techniques are utilized in many scientific domains to acquire a rich description of the subject under study and to further discover a variety of its properties. Especially, in sample systems, by probing with optical, electron or x-ray beams, the captured images describe the sample in an extremely large range of length scales. This makes the correlation from one image to another very difficult in addition to the intrinsic appearance complexity of those scientific images. In this paper, we aim to tackle this multi-scale image matching and localization problem by proposing an end-to-end deep convolutional neural network. Our proposed network is designed to first generate different filters according to the two queried images originated from different length scales. Then, to compute the correlation map, we use these filters to predict the correspondence between the two images. For the training and evaluation, we collect a number of electron microscopy experiments to form a multi-scaled image patch dataset comprised of various material structures. We observe about 90% accuracy for multi-scale image matching and localization while a triplet-based network shows about 78% accuracy.
The Three-Way Decisions Method Based on Theory of Reliability with SV-Triangular Neutrosophic Numbers

Three-way decisions, as a decision-making mode which is consistent with human cognition, have been widely used in various fields. In this paper, we fuse the theory of reliability into the three-way decisions method, replace the conditional probability in the three-way decisions method with reliability, and then propose a novel three-way decisions method. We also describe the loss functions with single-valued triangular neutrosophic numbers (SVTNNs) and propose an operator to calculate the score function of triangular neutrosophic numbers. Then, the result of decision is attained according to the principle of minimizing loss. Finally, we apply this method to the overhaul of machines in a factory, which proves the practicability and effectiveness of the proposed methods.
Mitochondrial respiratory states and rates

As the knowledge base and importance of mitochondrial physiology to human health expands, the necessity for harmonizing the terminologyconcerning mitochondrial respiratory states and rates has become increasingly apparent. Thechemiosmotic theoryestablishes the mechanism of energy transformationandcoupling in oxidative phosphorylation. Theunifying concept of the protonmotive force providestheframeworkfordeveloping a consistent theoretical foundation ofmitochondrial physiology and bioenergetics.We followguidelines of the International Union of Pure and Applied Chemistry(IUPAC)onterminology inphysical chemistry, extended by considerationsofopen systems and thermodynamicsof irreversible processes.Theconcept-driven constructive terminology incorporates the meaning of each quantity and alignsconcepts and symbols withthe nomenclature of classicalbioenergetics. We endeavour to provide a balanced view ofmitochondrial respiratory control and a critical discussion on reporting data of mitochondrial respiration in terms of metabolic flows and fluxes.Uniform standards for evaluation of respiratory states and rates will ultimatelycontribute to reproducibility between laboratories and thussupport the development of databases of mitochondrial respiratory function in species, tissues, and cells.Clarity of concept and consistency of nomenclature facilitate effective transdisciplinary communication, education, and ultimately further discovery.
PREDIKSI FOREX MENGGUNAKAN MODEL NEURAL NETWORK

Prediction is one of the most important techniques in running forex business. Decision to predict is very important, because the predictions can help determine the value of forex at a certain time in the future so as to reduce the risk of loss. The purpose of this research is intended to predict business fores using neural network models with time series data per 1 minute to determine the value of the prediction accuracy so as to reduce the risk in running the forex business. The research method in this study include data collection methods and then proceed to the method of training, learning, using a neural network testing and evaluation methods. Once in the evaluation of the results of this study indicate that the application of Neural Network algorithm was able to predict with accuracy forex prediction +/0431 0096 so that these predictions can help reduce the risk of running a business in forex.