Miracle-dz/UniFAR-FTUs huggingface dataset:
LHNHLDA: A Novel Approach Based on LHN-2 Algorithm for Predicting Associations Between LncRNAs and Diseases

With rapid development of high-throughput technique in the field of life science, LncRNAs are found to be inextricably linked to many diseases that seriously endanger human health. However, traditional experiment-based methods for inferring unknown diseases-LncRNA associations are time-consuming and laborious, therefore, it has become an effective way to adopt computational models to predict potential LncRNA-disease associations in recent years. In this article, a novel prediction model called LHNHLDA has been proposed. In LHNHLDA, based on known LncRNA-disease associations downloaded from benchmark databases, a heterogeneous LncRNA-disease network is built first by integrating LncRNA-LncRNA similarities with disease-disease similarities. And then, through adopting the LHN-2 algorithm, the path-based similarities between different nodes in the newly constructed heterogeneous network are obtained, which can be utilized to infer potential associations between LncRNAs and diseases. Finally, in order to evaluate the performance of LHNHLDA, intensive experiments have been done, and experimental results show that LHNHLDA can achieve reliable AUCs of 0.8155, 0.8281 and 0.8569 under the frameworks of 2-Fold CV, 5-Fold CV and LOO-CV respectively. Furthermore, case studies on lung cancer and leukemia illustrate that there are 10 and 7 potential LncRNAs out of the top 10 related LncRNAs of leukemia and lung cancer predicted by LHNHLDA having been confirmed to be linked to these two kinds of diseases by latest studies separately. Hence, due to the satisfactory prediction performance achieved by LHNHLDA, it is obvious that LHNHLDA may be a useful tool for future researches in the field of bioinformatics.
Progress Toward an Attract-and-Kill Device for Asian Citrus Psyllid (Hemiptera: Liviidae) Using Volatile Signatures of Citrus Infected With Huanglongbing as the Attractant

Abstract Asian citrus psyllid, Diaphorina citri (Kuwayama), preferentially orient toward citrus hosts infected with the phytopathogenic bacterium, Candidatus liberibacter asiaticus (CLas) the agent of citrus greening (Huanglongbing, HLB), compared to uninfected counterparts. We investigated whether this preference for the odors of infected plants could be useful for the development of an attract-and-kill (AK) device for D. citri. Twenty-nine blends of volatile organic compounds derived from the odor of citrus infected with CLas were tested in laboratory olfactometer tests, and two blends were also assessed under field conditions. A seven component blend of tricosane: geranial: methyl salicylate: geranyl acetone: linalool: phenylacetaldehyde: (E)-β-ocimene in a 0.40: 0.06: 0.08: 0.29: 0.08: 0.06: 0.03 ratio released from a proprietary slow-release matrix attracted twice more D. citri to yellow sticky traps compared with blank control traps. The attractive blend was subsequently co-formulated with spinosad insecticide into a slow-release matrix to create a prototype AK formulation against D. citri. This formulation effectively reduced the population density of D. citri up to 84% as measured with tap counts when deployed at a density of eight 2.5 g dollops per tree as compared with untreated controls in small plot field trials conducted in citrus orchards. Psyllid populations were not statistically affected at a deployment rate of four dollops per tree. Our results indicate that an AK formulation incorporating spinosad and a volatile blend signature of citrus greening into a slow-release matrix may be useful to suppress D. citri populations.
Third-order NLOS Reflection Sensing in Ultraviolet Spectrum

In this work, we preliminarily investigate the non-line of sight (NLOS) sensing based on the third-order reflection. Ultraviolet (UV) spectrum is adopted to achieve photon-level sensing sensitivity and negligible ambient light intensity. We numerically evaluate the sensing results after two mirror reflections and one diffuse reflection, and show the sensing feasibility based on the third-order reflection. Moreover, we demonstrate the sensing experiment results using an UV light-emitting diode (LED) with power 600µW. The sensing performance can be further improved via adopting higher-power UV LED, more sensitive sensors, and more advanced image reconstruction approaches.
A Quick Crossover Design of a Three-Section Branch Line Structure

This paper presents a quick method by using design chart to make a compact crossover design for planar circuit. The crossover is derived from the conventional three-section branch line structure. The proposed crossover planar circuit feature is implemented with moderate line impedance, compact layout and good performance. For verification, a single band crossover circuit works at 2.45GHz is fabricated and tested. Both simulated and measured results are found to be well matching with each other.
Sex differences in diaphragmatic fatigue: Effects of hypoxia during inspiratory loading

Under normoxic conditions, both healthy female and male diaphragms fatigue at a similar degree when matched for absolute diaphragmatic work during inspiratory loading. We investigated whether similarities in diaphragm fatigability persist under acute hypoxic conditions. We found that, in acute hypoxia, fatigue of the diaphragm is greater in women compared to men, whereas the magnitude of fatigue in normoxia did not differ between sexes. When matched for maximal diaphragm strength, women and men had a similar pressor response to work‐matched inspiratory loading, independent of oxygen availability.
ADetect: Hybrid Analysis Feature Extraction for Android malware Detection

Today, mobile devices are very popular in everyday life of our sociality for communication or many perspectives. They become an interesting point for malicious attackers. Malicious software which can destroy mobile devices or steal sensitive information are growing in every form of people’s live. A number of researches have been proposed to detect malicious software in recent year. However, they still suffer with many intelligent malicious software that a traditional methodology is not sufficient to detect the key features of intelligent malware. To address this problem, this paper proposes a feature extraction method from Android malware applications using hybrid analysis method to improve Machine Learning based detection framework. ADetect can achieve 80% detection accuracy, which are tested by using Random Forest, K-nearest Neighbor and Naïve Bayes (NB) classifiers.
LOW-COMPLEXITY ACOUSTIC SCENE CLASSIFICATION WITH SMALL CONVOLUTIONAL NEURAL NETWORKS AND CURRICULUM LEARNING Technical

The report presents the results of submission to Task 1B of Detection and Classiﬁcation of Acoustic Scenes and Events Challenge (DCASE) 2020. Main issue in this task was size limitation of 500 KB for each of submitted models. Such limitations are important when model ought to be implemented on device with low memory size. For this task four different models based on convolutional neural networks were developed, varying from data preprocessing methods, data architectures etc. Crucial techniques used in complexity management were curriculum learning and the use of depth-wise and separable convolutions, along with ensembling models trained on 3 and 10 classes for performance preservation. Best models improved baseline by 10% increase in accuracy and by 60% decrease in log-loss.
Runs of homozygosity and analysis of inbreeding depression.

Pedigree information was traditionally used to assess inbreeding. Availability of high-density marker panels provides an alternative to assess inbreeding, particularly in the presence of incomplete and error-prone pedigrees. Assessment of autozygosity across chromosomal segments using runs of homozygosity (ROH) has emerged as a valuable tool to estimate inbreeding due to its general flexibility and ability to quantify chromosomal contribution to genome-wide inbreeding. Unfortunately, identification of ROH segments is sensitive to the parameters used during the search process. These parameters are heuristically set, leading to significant variation in the results. The minimum length required to identify a ROH segment has major effects on the estimation of inbreeding and inbreeding depression, yet it is arbitrarily set. To overcome this limitation, a search algorithm to approximate mutation enrichment was developed to determine the minimum length of ROH segments. It consists in finding genome segments with significant effect differences in trait means between animals with high and low burdens of autozygous intervals with a specific length. The minimum length could be determined heuristically as the smallest interval at which a significant signal is detected. The proposed method was tested in an inbred Hereford cattle population genotyped for 30,220 SNPs. Phenotypes recorded for six traits were used for the approximation of mutation loads. The estimated minimum length was around 1 Mb for yearling weight (YW) and average daily gain (ADG), and 4 Mb for birth weight (BW) and weaning weight (WW). These trait-specific thresholds estimated using the proposed method could be attributed to a trait-dependent effect of homozygosity. The detection of significant inbreeding effects was well aligned with the estimated thresholds, especially for YW and ADG. Although highly deleterious alleles are expected to be more frequent in recent inbreeding (long ROH), short ROH segments (< 5 Mb) could contain a large number of less deleterious mutations with substantial joint effects on some traits (YW and ADG). Our results highlight the importance of accurate estimation of the ROH-based inbreeding and the necessity to consider a trait-specific minimum length threshold for the identification of ROH segments in inbreeding depression analyses. These thresholds could be determined using the proposed method provided the availability of phenotypic information.
A New Nonuniform Random Deployment Method to Minimize Cost for Large-Scale Wireless Sensor Networks

Network deployment is one of the key research issues of Wireless Sensor Networks(WSNs), and it has an essential impact on the performance of the network. Due to the geographical environment limitation, random deployment has become a critical deployment method. There are many random deployment methods, and they focus on different network performance (network coverage, connectivity, energy efficiency, lifetime, and so on). This paper proposes a nonuniform random deployment method to minimize the network cost for homogeneous WSNs. Firstly, we define the problem to be solved and give an optimization model. A new cost concept is defined that combines several measurements, such as covered area, lifetime, and cost. Secondly, based on the theoretical analysis of energy consumption, node density, and network cost, a new deployment method and corresponding routing strategy are proposed. To ensure energy efficiency and minimize cost, a multi-sink and corona structure is adopted. Finally, simulation experiments verify the effectiveness of the method by comparing it with the other four random deployment methods. Compared with the other four methods at the two densities, the cost is reduced by at least 40% (density is 0.04) and 24.1% (density is 0.025), respectively.
Performance Analysis of FSO Systems over Imperfect Málaga Atmospheric Turbulence Channels with Pointing Errors

In this study, we investigate the performance of FSO communication systems under more realistic channel model considering atmospheric turbulence, pointing errors and channel estimation errors together. For this aim, we first derived the composite probability density function (PDF) of imperfect Málaga turbulence channel with pointing errors. Then using this PDF, we obtained bit-error-rate (BER) and ergodic channel capacity (ECC) expressions in closed forms. Additionally, we present the BER and ECC metrics of imperfect Gamma-Gamma and K turbulence channels with pointing errors as special cases of Málaga channel. We further verified our analytic results through Monte-Carlo simulations.
WiderFrame: An Automatic Customization Framework for Building CNN Accelerators on FPGAs: Work-in-Progress

Hardware acceleration based on FPGA has been an important means to improve the computational efficiency of CNNs. However, due to the increasing complexity of the modern CNNs and the diversity of neural computing engines, it is challenging to make full use of FPGAs' customizability for efficient and fast accelerator designs. This paper proposes Wider-Frame, an automatic customization framework for building CNN accelerators on FPGA. Towards fully exploiting the customiz-ability of FPGA for specific computing scenarios, WiderFrame integrates a systematical design space exploration methodology considered with different parallel and data reuse manners among various neural computing engines, a parameterized configurable code template with a set of macro instruction mechanism, for automatically generating the underlying hardware units and the control flow. Evaluation results show that WiderFrame can well support more CNN types, and can improve the performance and the energy efficiency up to 1.25 x and 1.68 x compared with state-of-the-art frameworks.
A Machine Learning-Based Strategy For Predicting The Fault Recovery Duration Class In Electric Power Transmission System

Energy security program which becomes the part of energy management must ensure the high reliability of the electric power transmission system so that the customer can be served very well. However, there are several problems that can hinder reliability achievement such as the long duration of fault recovery. On the other side, the prediction of fault recovery duration becomes a very challenging task. Because there are still few machine learning-based solution offer this paper proposes a machine learning-based strategy by using Naive-Bayes Classifier (NBC) and Support Vector Machine (SVM) in predicting the fault recovery duration class. The dataset contains 3398 rows of non-temporary-fault type records, six input features (Substation, Asset Type, Fault Catego r y, Outage Start Time, Outage Day, and Outage Month) and single target feature (Fault Recovery Duration). According to the performance test result, those two methods reach around 97-99% of accuracy, average sensitivity, and average specificity. In addition, one of the advantages obtained in field of fault recovery prediction is increasing the accuracy of likelihood level calculation of the long fault recovery time risk.
Silicon 2D Magnetic-field Multisensor

A silicon 2D multisensor measuring simultaneously and independently the in-plane components of the magnetic field is experimentally tested. The devise contains one central ohmic contact and symmetrically to it - two perpendicular to each other pair of ohmic contacts. The sensitivities of the two sensor channels in accordance with symmetry are equal, reaching $\mathrm{S}=25\ \mathrm{V}/\mathrm{AT}$ at $T=300\ \kappa$ and $\mathrm{S}=119\ \mathrm{V}/\mathrm{AT}$ at $T=77\ \mathrm{K}$. The channel cross-sensitivity does not exceed 5% as the internal noise is of the $1/f$ type.
A Dynamic, Split-Luciferase-Based Mini-G Protein Sensor to Functionally Characterize Ligands at All Four Histamine Receptor Subtypes

In drug discovery, assays with proximal readout are of great importance to study target-specific effects of potential drug candidates. In the field of G protein-coupled receptors (GPCRs), the determination of GPCR-G protein interactions and G protein activation by means of radiolabeled GTP analogs ([35S]GTPγS, [γ-32P]GTP) has widely been used for this purpose. Since we were repeatedly faced with insufficient quality of radiolabeled nucleotides, there was a requirement to implement a novel proximal functional assay for the routine characterization of putative histamine receptor ligands. We applied the split-NanoLuc to the four histamine receptor subtypes (H1R, H2R, H3R, H4R) and recently engineered minimal G (mini-G) proteins. Using this method, the functional response upon receptor activation was monitored in real-time and the four mini-G sensors were evaluated by investigating selected standard (inverse) agonists and antagonists. All potencies and efficacies of the studied ligands were in concordance with literature data. Further, we demonstrated a significant positive correlation of the signal amplitude and the mini-G protein expression level in the case of the H2R, but not for the H1R or the H3R. The pEC50 values of histamine obtained under different mini-G expression levels were consistent. Moreover, we obtained excellent dynamic ranges (Z’ factor) and the signal spans were improved for all receptor subtypes in comparison to the previously performed [35S]GTPγS binding assay.
Segment-Level Sentiment Classification for Online Comments of Legal Cases

Sentiment analysis (SA) is to predict the sentiment polarity towards a given document. Our work focuses on sentiment analysis towards comments of legal cases, which reflects people’s concern about public safety and the impartiality of judgment result. The existing methods treat this problem as a coarse document-level classification and ignore topics information, which cannot obtain an accurate sentiment polarity towards a sentence. With topics information attended, we present a novel segment-level model, to boost the performance. Firstly, we divide a sentence into several segments delimited by some important conjunctions. We find that opinion in a segment is usually explicit. Secondly, we apply two-step attention to obtain accurate representation. To enhance the interactions between comments and topics, we apply attention mechanisms twice, once between topics and context in the segment level, and the other between segments. Thirdly, we concatenate the segment and context representation to receive final representation. It unifies two types of information into a classifier. Experimental results on real-world datasets demonstrate that our model outperforms existing methods.
WGVD: an integrated web-database for wheat genome variation and selective signatures

Abstract Bread wheat is one of the most important crops worldwide. With the release of the complete wheat reference genome and the development of next-generation sequencing technology, a mass of genomic data from bread wheat and its progenitors has been yield and has provided genomic resources for wheat genetics research. To conveniently and effectively access and use these data, we established Wheat Genome Variation Database, an integrated web-database including genomic variations from whole-genome resequencing and exome-capture data for bread wheat and its progenitors, as well as selective signatures during the process of wheat domestication and improvement. In this version, WGVD contains 7 346 814 single nucleotide polymorphisms (SNPs) and 1 044 400 indels focusing on genic regions and upstream or downstream regions. We provide allele frequency distribution patterns of these variations for 5 ploidy wheat groups or 17 worldwide bread wheat groups, the annotation of the variant types and the genotypes of all individuals for 2 versions of bread wheat reference genome (IWGSC RefSeq v1.0 and IWGSC RefSeq v2.0). Selective footprints for Aegilops tauschii, wild emmer, domesticated emmer, bread wheat landrace and bread wheat variety are evaluated with two statistical tests (FST and Pi) based on SNPs from whole-genome resequencing data. In addition, we provide the Genome Browser to visualize the genomic variations, the selective footprints, the genotype patterns and the read coverage depth, and the alignment tool Blast to search the homologous regions between sequences. All of these features of WGVD will promote wheat functional studies and wheat breeding. Database URL http://animal.nwsuaf. edu.cn/code/index.php/Wheat
Stochastic Preferences Model

Determining the preferences of individuals is important for many scientific and practical problems, especially when modeling information control and information warfare. Previously, a model was developed that allows taking into consideration the ideological and political preferences of citizens of the Russian Federation in the three-dimensional basis derzhavnik (great power nationalist)–liberal–socialist model, as well as several methods and technologies that determine the preferences of users of the online social network VKontakte. In this paper, we propose approaches to identifying individual preferences in a stochastic preference model based on an opinion poll, which allows these preferences to be compared with the results of the analysis of an online social network.
Overexpression of a Cytosolic 6-Phosphogluconate Dehydrogenase Gene Enhances the Resistance of Rice to Nilaparvata lugens

The pentose phosphate pathway (PPP) plays an important role in plant growth and development, and plant responses to biotic and abiotic stresses. Yet, whether the PPP regulates plant defenses against herbivorous insects remains unclear. In this study, we cloned a rice cytosolic 6-phosphogluconate dehydrogenase gene, Os6PGDH1, which encodes the key enzyme catalyzing the third step in the reaction involving the oxidative phase of the PPP, and explored its role in rice defenses induced by brown planthopper (BPH) Nilaparvata lugens. Levels of Os6PGDH1 transcripts were detected in all five examined tissues, with the highest in outer leaf sheaths and lowest in inner leaf sheaths. Os6PGDH1 expression was strongly induced by mechanical wounding, infestation of gravid BPH females, and jasmonic acid (JA) treatment. Overexpressing Os6PGDH1 (oe6PGDH) decreased the height of rice plants and the mass of the aboveground part of plants, but slightly increased the length of plant roots. In addition, the overexpression of Os6PGDH1 enhanced levels of BPH-induced JA, jasmonoyl-isoleucine (JA-Ile), and H2O2, but decreased BPH-induced levels of ethylene. Bioassays revealed that gravid BPH females preferred to feed and lay eggs on wild-type (WT) plants over oe6PGDH plants; moreover, the hatching rate of BPH eggs raised on oe6PGDH plants and the fecundity of BPH females fed on these were significantly lower than the eggs and the females raised and fed on WT plants. Taken together, these results indicate that Os6PGDH1 plays a pivotal role not only in rice growth but also in the resistance of rice to BPH by modulating JA, ethylene, and H2O2 pathways.
How to Predict Metastasis in Luminal Breast Cancer? Current Solutions and Future Prospects

Breast cancer metastasis is the main cause of breast cancer mortality. Luminal breast cancer represents the majority of breast cancer cases and, despite relatively good prognosis, its heterogeneity creates problems with a proper stratification of patients and correct identification of the group with a high risk of metastatic relapse. Current prognostic tools are based on the analysis of the primary tumor and, despite their undisputed power of prediction, they might be insufficient to foresee the relapse in an accurate and precise manner, especially if the relapse occurs after a long period of dormancy, which is very common in luminal breast cancer. New approaches tend to rely on body fluid analyses, which have the advantage of being non-invasive and versatile and may be repeated and used for monitoring the disease in the long run. In this review we describe the current, newly-developed, and only-just-discovered methods which are or may become useful in the assessment of the probability of the relapse.