Miracle-dz/UniFAR-FTUs huggingface dataset:
HEIP1 regulates crossover formation during meiosis in rice

Significance Crossovers (COs) ensure the accurate segregation of homologous chromosomes during meiosis. Failure to create the right number of crossovers may lead to unequal distribution of genetic materials to daughter cells and sterility. CO proteins, which specially localize to crossover sites during meiosis, play critical roles in CO formation. Here, we identify a CO protein in rice, named HEI10 Interaction Protein 1 (HEIP1), which is conserved in higher plants. Our results reveal its possible molecular mechanism for CO control in meiosis. During meiosis, the number of double-strand breaks (DSBs) far exceeds the final number of crossovers (COs). Therefore, to identify proteins involved in determining which of these DSBs repaired into COs is critical in understanding the mechanism of CO control. Across species, HEI10-related proteins play important roles in CO formation. Here, through screening for HEI10-interacting proteins via a yeast two-hybrid system, we identify a CO protein HEI10 Interaction Protein 1 (HEIP1) in rice. HEIP1 colocalizes with HEI10 in a dynamic fashion along the meiotic chromosomes and specially localizes onto crossover sites from late pachytene to diplotene. Between these two proteins, HEI10 is required for the loading of HEIP1, but not vice versa. Moreover, mutations of the HEIP1 gene cause the severe reduction of chiasma frequency, whereas early homologous recombination processes are not disturbed and synapsis proceeds normally. HEIP1 interacts directly with ZIP4 and MSH5. In addition, the loading of HEIP1 depends on ZIP4, but not on MER3, MSH4, or MSH5. Together, our results suggest that HEIP1 may be a member of the ZMM group and acts as a key element regulating CO formation.
Micro Imaging Displays the Sucrose Landscape within and along Its Allocation Pathways1

A procedure for the quantitative mapping of sucrose in plant tissue with spatial resolution sufficient to measure sucrose in individual phloem bundles and across transport boundaries. Sucrose (Suc) is the major transport sugar in plants and plays a primary role as an energy source and signal in adaptive and stress responses. An ability to quantify Suc over time and space would serve to advance our understanding of these important processes. Current technologies used for Suc mapping are unable to quantitatively visualize its distribution within tissues. Here, we present an infrared-based microspectroscopic method that allows for the quantitative visualization of Suc at a microscopic level of resolution (∼12 µm). This method can successfully model the sugar concentration in individual vascular bundles and within a complex organ such as the stem, leaf, or seed. The sensitivity of the assay ranges from 20 to 1,000 mm. We applied this method to the cereal crop barley (Hordeum vulgare) and the model plant Arabidopsis (Arabidopsis thaliana) to highlight the potential of the procedure for resolving the spatial distribution of metabolites. We also discuss the relevance of the method for studies on carbon allocation and storage in the context of crop improvement.
The Protective Roles of Dietary Selenium Yeast and Tea Polyphenols on Growth Performance and Ammonia Tolerance of Juvenile Wuchang Bream (Megalobrama amblycephala)

In order to investigate protective roles of dietary selenium yeast (SY) and tea polyphenols (TPs) on growth of juvenile Wuchang bream (Megalobrama amblycephala) and its resistance under ammonia stress, juvenile Wuchang bream were randomly assigned into four groups: a control group fed basal diets and three treatment groups fed basal diets supplemented with 0.50 mg/kg SY, 50 mg/kg TPs and a combination of 0.50 mg/kg SY and 50 mg/kg TPs, respectively. After 60 days of feeding, growth parameters of Wuchang bream were measured along with serum hormones and the transcription of growth axis-related genes. Then fish were exposed to ammonia stress of 22.5 mg/L total ammonia nitrogen. Hepatic oxidative damage parameters, antioxidant responses and ultrastructure were evaluated before ammonia exposure (0 h) and at 3, 6, 12, 24, and 48 h after ammonia exposure. Results show that before ammonia exposure, the growth parameters, serum GH and IGF-1 levels as well as the growth axis-related gene expression (gh, ghr2 and igf-1) for the SY and combination groups were higher than those determined for the fish on the control diet. In contrast, the administration of TP alone didn’t have significant effects on the growth parameters and growth-related hormones. After ammonia exposure, compared with the control, remarkable increases in the activity and mRNA expression of hepatic antioxidant enzymes (glutathione peroxidase and catalase superoxide dismutase) in three treatment groups were observed along with decreases of hepatic malondialdehyde and protein carbonylation levels, indicating that the single and combined supplementation of SY and TPs could enhance antioxidant capacity to alleviate oxidative stress and damage by ammonia. Consistent with this finding, alterations of the liver ultrastructure in three treatment groups were less severe and faster recovery than in the control group after ammonia exposure. In conclusion, a basal diet supplemented with the combination of 0.50 mg/kg SY and 50 mg/kg TPs could has very beneficial effects on the whole aspects of the growth and ammonia resistance in Wuchang bream juveniles.
Integrating Global Attention for Pairwise Text Comparison

Attention guides computation to focus on important parts of the input data. For pairwise input, existing attention approaches tend to bias towards trivial repetitions (e.g. punctuations and stop words) between two texts, and thus failed to contribute reasonable guidance to model predictions. As a remedy, we suggest taking into account the corpus-level information via global-aware attention. In this paper, we propose an attention mechanism that makes use of intratext, inter-text and global contextual information. We undertake an ablation study on paraphrase identification, and demonstrate that the proposed attention mechanism can obviate the downsides of trivial repetitions and provide interpretable word weightings.
Motor Reproduction of Time Interval Depends on Internal Temporal Cues in the Brain: Sensorimotor Imagery in Rhythm

How the human brain perceives time intervals is a fascinating topic that has been explored in many fields of study. This study examined how time intervals are replicated in three conditions: with no internalized cue (PT), with an internalized cue without a beat (AS), and with an internalized cue with a beat (RS). In PT, participants accurately reproduced the time intervals up to approximately 3 s. Over 3 s, however, the reproduction errors became increasingly negative. In RS, longer presentations of over 5.6 s and 13 beats induced accurate time intervals in reproductions. This suggests longer exposure to beat presentation leads to stable internalization and efficiency in the sensorimotor processing of perception and reproduction. In AS, up to approximately 3 s, the results were similar to those of RS whereas over 3 s, the results shifted and became similar to those of PT. The time intervals between the first two stimuli indicate that the strategies of time-interval reproduction in AS may shift from RS to PT. Neural basis underlying the reproduction of time intervals without a beat may depend on length of time interval between adjacent stimuli in sequences.
Polyline Simplification has Cubic Complexity

In the classic polyline simplification problem we want to replace a given polygonal curve $P$, consisting of $n$ vertices, by a subsequence $P'$ of $k$ vertices from $P$ such that the polygonal curves $P$ and $P'$ are as close as possible. Closeness is usually measured using the Hausdorff or Fr\'echet distance. These distance measures can be applied "globally", i.e., to the whole curves $P$ and $P'$, or "locally", i.e., to each simplified subcurve and the line segment that it was replaced with separately (and then taking the maximum). This gives rise to four problem variants: Global-Hausdorff (known to be NP-hard), Local-Hausdorff (in time $O(n^3)$), Global-Fr\'echet (in time $O(k n^5)$), and Local-Fr\'echet (in time $O(n^3)$). Our contribution is as follows. - Cubic time for all variants: For Global-Fr\'echet we design an algorithm running in time $O(n^3)$. This shows that all three problems (Local-Hausdorff, Local-Fr\'echet, and Global-Fr\'echet) can be solved in cubic time. All these algorithms work over a general metric space such as $(\mathbb{R}^d,L_p)$, but the hidden constant depends on $p$ and (linearly) on $d$. - Cubic conditional lower bound: We provide evidence that in high dimensions cubic time is essentially optimal for all three problems (Local-Hausdorff, Local-Fr\'echet, and Global-Fr\'echet). Specifically, improving the cubic time to $O(n^{3-\epsilon} \textrm{poly}(d))$ for polyline simplification over $(\mathbb{R}^d,L_p)$ for $p = 1$ would violate plausible conjectures. We obtain similar results for all $p \in [1,\infty), p \ne 2$. In total, in high dimensions and over general $L_p$-norms we resolve the complexity of polyline simplification with respect to Local-Hausdorff, Local-Fr\'echet, and Global-Fr\'echet, by providing new algorithms and conditional lower bounds.
Introducing WESAD, a Multimodal Dataset for Wearable Stress and Affect Detection

Affect recognition aims to detect a person's affective state based on observables, with the goal to e.g. improve human-computer interaction. Long-term stress is known to have severe implications on wellbeing, which call for continuous and automated stress monitoring systems. However, the affective computing community lacks commonly used standard datasets for wearable stress detection which a) provide multimodal high-quality data, and b) include multiple affective states. Therefore, we introduce WESAD, a new publicly available dataset for wearable stress and affect detection. This multimodal dataset features physiological and motion data, recorded from both a wrist- and a chest-worn device, of 15 subjects during a lab study. The following sensor modalities are included: blood volume pulse, electrocardiogram, electrodermal activity, electromyogram, respiration, body temperature, and three-axis acceleration. Moreover, the dataset bridges the gap between previous lab studies on stress and emotions, by containing three different affective states (neutral, stress, amusement). In addition, self-reports of the subjects, which were obtained using several established questionnaires, are contained in the dataset. Furthermore, a benchmark is created on the dataset, using well-known features and standard machine learning methods. Considering the three-class classification problem ( baseline vs. stress vs. amusement ), we achieved classification accuracies of up to 80%,. In the binary case ( stress vs. non-stress ), accuracies of up to 93%, were reached. Finally, we provide a detailed analysis and comparison of the two device locations ( chest vs. wrist ) as well as the different sensor modalities.
Estimation of Missing Values in Incomplete Industrial Process Data Sets Using ECM Algorithm

Estimation of missing values is an essential step in data pre-processing to increase the data quality for further data mining approaches. The significance of estimation of missing values in industrial data sets is that different operational situations cannot be describe properly while data sets includes missing values. In this paper, Expectation Conditional Maximization is used to find an approximated model over the data based on Gaussian distribution. Then, in the Expectation step, Sweep operation is used to obtain the regression model of missing values on observable values and estimate the missing values based on observable data. In order to evaluate the results a process data set for a real industrial production system is considered. The missing values are simulated by randomly removing the data from variables. Finally, the accuracy of the proposed method in estimation of missing values is discussed as well as the effect of imputation of missing values on further data analysis.
Decentralized Distributed Deep Learning in Heterogeneous WAN Environments

Motivation: Large volumes of customer-related data and logs are increasingly generated in a geo-distributed manner, e.g., by users and edge devices distributed geographically. There is a growing interest to analyze this data and provide insightful user experiences through cloud-based deep learning (DL) products such as Amazon Sagemaker, Rekognition
An Efficient Secure Generalized Comparison Protocol

In this paper, we present a protocol for generalized secure comparison problem. Yao's millionaire problem is for comparing the wealth of two parties by preserving their privacy. If $n$ parties want to compare their wealth by using Yao's protocol, it should be run $n(n-1)$ 2 times; if their wealth interval is as large as N, the complexity of Yao's protocol for $n$ parties is in order of N. $\mathbf{n}^{2}, \mathbf{i}. \mathbf{e}$. $\mathbf{O}\ (\mathbf{N}.\mathbf{n}^{2})$. In this paper, we present a generalized secure comparison protocol, which need not to be run by every two parties separately. We use an easy knapsack problem for distinguishing parties' indices. Our protocol computation and communication complexities are bounded by 0 (n).
An Efficient system to stumble on and Mitigate DDoS attack in cloud Environment

Cloud computing is an assured progression inside the future of facts generation. It's far a sub-domain of network security. These days, many huge or small organizations are switching to cloud which will shop and arrange their facts. As a result, protection of cloud networks is the want of the hour. DDoS is a killer software for cloud computing environments on net today. It is a distributed denial of carrier. we will beat the ddos attacks if we have the enough assets. ddos attacks can be countered by means of dynamic allocation of the assets. In this paper the attack is detected as early as possible and prevention methods is done and also mitigation method is also implemented thus attack can be avoided before it may occur.
Twitter-Based Influenza Detection After Flu Peak via Tweets With Indirect Information: Text Mining Study

Background The recent rise in popularity and scale of social networking services (SNSs) has resulted in an increasing need for SNS-based information extraction systems. A popular application of SNS data is health surveillance for predicting an outbreak of epidemics by detecting diseases from text messages posted on SNS platforms. Such applications share the following logic: they incorporate SNS users as social sensors. These social sensor–based approaches also share a common problem: SNS-based surveillance are much more reliable if sufficient numbers of users are active, and small or inactive populations produce inconsistent results. Objective This study proposes a novel approach to estimate the trend of patient numbers using indirect information covering both urban areas and rural areas within the posts. Methods We presented a TRAP model by embedding both direct information and indirect information. A collection of tweets spanning 3 years (7 million influenza-related tweets in Japanese) was used to evaluate the model. Both direct information and indirect information that mention other places were used. As indirect information is less reliable (too noisy or too old) than direct information, the indirect information data were not used directly and were considered as inhibiting direct information. For example, when indirect information appeared often, it was considered as signifying that everyone already had a known disease, leading to a small amount of direct information. Results The estimation performance of our approach was evaluated using the correlation coefficient between the number of influenza cases as the gold standard values and the estimated values by the proposed models. The results revealed that the baseline model (BASELINE+NLP) shows . 36 and that the proposed model (TRAP+NLP) improved the accuracy (.70, +.34 points). Conclusions The proposed approach by which the indirect information inhibits direct information exhibited improved estimation performance not only in rural cities but also in urban cities, which demonstrated the effectiveness of the proposed method consisting of a TRAP model and natural language processing (NLP) classification.
Enumerating Trillion Subgraphs On Distributed Systems

How can we find patterns from an enormous graph with billions of vertices and edges? The subgraph enumeration, which is to find patterns from a graph, is an important task for graph data analysis with many applications, including analyzing the social network evolution, measuring the significance of motifs in biological networks, observing the dynamics of Internet, and so on. Especially, the triangle enumeration, a special case of the subgraph enumeration, where the pattern is a triangle, has many applications such as identifying suspicious users in social networks, detecting web spams, and finding communities. However, recent networks are so large that most of the previous algorithms fail to process them. Recently, several MapReduce algorithms have been proposed to address such large networks; however, they suffer from the massive shuffled data resulting in a very long processing time. In this article, we propose scalable methods for enumerating trillion subgraphs on distributed systems. We first propose PTE (Pre-partitioned Triangle Enumeration), a new distributed algorithm for enumerating triangles in enormous graphs by resolving the structural inefficiency of the previous MapReduce algorithms. PTE enumerates trillions of triangles in a billion scale graph by decreasing three factors: the amount of shuffled data, total work, and network read. We also propose PSE (Pre-partitioned Subgraph Enumeration), a generalized version of PTE for enumerating subgraphs that match an arbitrary query graph. Experimental results show that PTE provides 79 times faster performance than recent distributed algorithms on real-world graphs, and succeeds in enumerating more than 3 trillion triangles on the ClueWeb12 graph with 6.3 billion vertices and 72 billion edges. Furthermore, PSE successfully enumerates 265 trillion clique subgraphs with 4 vertices from a subdomain hyperlink network, showing 47 times faster performance than the state of the art distributed subgraph enumeration algorithm.
A Method for Plagiarism Detection over Academic Citation Networks

: Whereas in the academic publication, citation has been used for a long time to borrow ideas from another document and show the credit to the authors of that document, plagiarism, which does not indicate the appropriate credit for a borrowed idea, has of late become problematic. Because plagiarism detection has been formulated as ﬁnding partial near-duplicate in response to a document for a suspected case of plagiarism, in this paper we propose a method to improve the similarity computation between text fragments. Our contribution is to formulate three document similarities based on citation and content analysis, and to combine them in our method. We also show the effectiveness of our method experimentally and discuss its advantages and limitation.
Constructing comprehensive summaries of large event sequences

Event sequences capture system and user activity over time. Prior research on sequence mining has mostly focused on discovering local patterns. Though interesting, these patterns reveal local associations and fail to give a comprehensive summary of the entire event sequence. Moreover, the number of patterns discovered can be large. In this paper, we take an alternative approach and build short summaries that describe the entire sequence, while revealing local associations among events. We formally define the summarization problem as an optimization problem that balances between shortness of the summary and accuracy of the data description. We show that this problem can be solved optimally in polynomial time by using a combination of two dynamic-programming algorithms. We also explore more efficient greedy alternatives and demonstrate that they work well on large datasets. Experiments on both synthetic and real datasets illustrate that our algorithms are efficient and produce high-quality results, and reveal interesting local structures in the data.
Association between AXL promoter methylation and lung function growth during adolescence

ABSTRACT AXL is one of the TAM (TYRO3, AXL and MERTK) receptor tyrosine kinases and may be involved in airway inflammation. Little is known about how epigenetic changes in AXL may affect lung development during adolescence. We investigated the association between AXL DNA methylation at birth and lung function growth from 10 to 18 years of age in 923 subjects from the Children’s Health Study (CHS). DNA methylation from newborn bloodspots was measured at multiple CpG loci across the regulatory regions of AXL using Pyrosequencing. Linear spline mixed-effects models were fitted to assess the association between DNA methylation and 8-year lung function growth. Findings were evaluated for replication in a separate population of 237 CHS subjects using methylation data from the Illumina HumanMethylation450 (HM450) array when possible. A 5% higher average methylation level of the AXL promoter region at birth was associated with a 48.4 ml decrease in mean FEV1 growth from 10 to 18 years of age in the primary study population (95% CI: −100.2, 3.4), and a 53.9 ml decrease in mean FEV1 growth from 11 to 15 years of age in the replication population (95% CI: −104.3, −3.5). One CpG locus in the promoter region, cg10564498, was significantly associated with decreased growth in FEV1, FVC and MMEF from 10 to 18 years of age and the negative associations were observed in a similar age range in the replication population. These findings suggest a potential association between AXL promoter methylation at birth and lower lung function growth during adolescence.
Optimal Allocation of Distributed Generations and Shunt Capacitors Using Salp Swarm Algorithm

This paper presents a novel technique for solving the simultaneous optimal distributed generators (DGs) and shunt capacitors (SCs) placement problem in order to minimize active power losses. A meta-heuristic Salp Swarm Algorithm (SSA) inspired from the swarming behavior of salps in oceans is implemented to determine optimal solution to this optimization problem. The proposed method is applied to 33 and 69- bus test systems. The obtained results have been compared with other existing methods, so that the comparisons confirm the performance as well as the effectiveness of the proposed method to determine the global optimum solution.
Optical Micro/Nanofiber-Based Localized Surface Plasmon Resonance Biosensors: Fiber Diameter Dependence

Integration of functional nanomaterials with optical micro/nanofibers (OMNFs) can bring about novel optical properties and provide a versatile platform for various sensing applications. OMNFs as the key element, however, have seldom been investigated. Here, we focus on the optimization of fiber diameter by taking micro/nanofiber-based localized surface plasmon resonance sensors as a model. We systematically study the dependence of fiber diameter on the sensing performance of such sensors. Both theoretical and experimental results show that, by reducing fiber diameter, the refractive index sensitivity can be significantly increased. Then, we demonstrate the biosensing capability of the optimized sensor for streptavidin detection and achieve a detection limit of 1 pg/mL. Furthermore, the proposed theoretical model is applicable to other nanomaterials and OMNF-based sensing schemes for performance optimization.