Miracle-dz/UniFAR-FTUs huggingface dataset:
RNA-guided silencing pathways in Coffea spp.: genome-transcriptome-wide analyses

Brazil is the major producer and exporter of coffee beans, which are the second most traded commodity worldwide. Coffee is a biannual crop whose phenology is strongly influenced by environmental factors, such as photoperiod, temperature and rainfall distribution. The coffee tree presents sequential flower development and several blossoming events. These events lead to unequal maturation of fruits and, therefore, impair the quality of the beverage and increase the production costs. Within the panorama of climate changes, studies aiming to clarify the regulation of developmental mechanisms for crop breeding are extremely required. microRNAs (miRNAs) are a class of non-coding small RNAs (sRNAs), 21 nt long on average, that regulate mRNA expression in plants inhibiting their translation with or without cleavage of the target. The aim of this dissertation was to provide a wide analysis of coffee miRNAs and to unravel the RNA-guided silencing pathways. To do that, the Coffea canephora genome was surveyed for the identification and characterization of the miRNAs and the components of RNA-silencing pathways. Furthermore, small-RNAseq libraries were constructed from floral buds in different development stages, aiming to identify conserved and genus-specific miRNAs related to floral development in coffee. These two studies represent a pioneering work that offers a significant step towards a better understanding of the transcriptional and post-transcriptional regulation of floral development in this major crop. The identification and characterization of the components in the RNA-guided silencing pathways in this important crop provide not only knowledge of the plant biology, but also basis for further enhancement through biotechnological tools to address its constraints.
Psychobiotic Effects of Multi-Strain Probiotics Originated from Thai Fermented Foods in a Rat Model

Abstract This work aimed to investigate the psychobiotic effects of six bacterial strains on the mind and behavior of male Wistar rats. The probiotic (PRO) group (n=7) were rats pre-treated with antibiotics for 7 days followed by 14-day probiotic administration, antibiotics (ANT) group (n=7) were rats treated with antibiotics for 21 days without probiotics. The control (CON) group (n=7) were rats that received sham treatment for 21 days. The six bacterial strains with probiotic properties were mostly isolated from Thai fermented foods; Pedicoccus pentosaceus WS11, Lactobacillus plantarum SK321, L. fermentum SK324, L. brevis TRBC 3003, Bifidobacterium adolescentis TBRC 7154 and Lactococcus lactis subsp. lactis TBRC 375. The probiotics were freeze-dried into powder (6×109 CFU/5 g) and administered to the PRO group via oral gavage. Behavioral tests were performed. The PRO group displayed significantly reduced anxiety level and increased locomotor function using a marble burying test and open field test, respectively and significantly improved short-term memory performance using a novel object recognition test. Antibiotics significantly reduced microbial counts in rat feces in the ANT group by 100 fold compared to the PRO group. Probiotics significantly enhanced antioxidant enzymatic and non-enzymatic defenses in rat brains as assessed using catalase activity and ferric reducing antioxidant power assay, respectively. Probiotics also showed neuroprotective effects with less pyknotic cells and lower frequency of vacuolization in cerebral cortex. This multi-strain probiotic formulation from Thai fermented foods may offer a potential to develop psychobiotic-rich functional foods to modulate human mind and behaviors.
Academic Literacy of Students for Scientific Paper Competition in National Level

--Students’ academic literacy is closely related to scholarly which is supported by creative and critical characters. Without such characters, the quality of student scientific paper will not comply with the writing guidelines. As a result, Program Kreativitas Mahasiswa (PKM)/Student Creativity Program are not qualified or failed in the competition. This failure is even more painful when it is caused by administrative errors. In terms of content substance, PKM which passes to be funded has scientific literacy that is fostered through multi-literacy learning obtained during lectures. Because, academic literacy is a vehicle for students to improve their ability to learn. The processes avoid the forms of plagiarism.
A Coastal Resilience Analysis of a Heterogeneous Landscape

This paper develops a fine-scaled analysis in order to determine the cost and benefit of flood protection using hardened coastal structures within a large coastal segment. The probability distribution of surges and the relative rate of sea level rise are estimated from local tidal data and combined with detailed GIS data of all buildings to compute flood damage. Examining a heterogeneous suburban coastline of 110 km length (Branford, Connecticut), the paper defines a complete set of small segments along the coast between high elevation points. For each segment, the study determines whether the benefit of seawalls exceeds the cost and the optimal height for each wall. The analysis compares a uniform wall across the entire town, a uniform wall across only the low lying parts of the coastline, and a unique wall in each micro segment that maximizes net benefits. The uniform wall across the entire town fails a benefit cost analysis. By simply restricting the wall to the 30% of the coastline that is low lying, the flood benefits begin to exceed the cost of the walls. By carefully identifying just the low lying segments where the benefit exceeds the cost, the overall benefit to cost ratio can be increased to 3 to 1. The optimal flood protection program builds walls along only 10% of the coastline. These optimal micro segments are dispersed throughout the entire town including inland along a coastal river. The optimal elevation of the top of the walls is 2.3 m which is well below the 1/100 year storm height of 3.2 m. The benefit versus cost does not justify protection against rare but locally catastrophic storms such as hurricanes. Sea level rise increases the benefits of protection but plays only a small role in current protection decisions.
Target Detection Through Tree-Structured Encoding for Hyperspectral Images

Target detection aims to locate targets of interest within a specific scene. The traditional model-driven detectors based on signal processing have proved to be very effective. However, the detection performance of such traditional methods relies heavily on the model assumption, which is limited by the discrepancy with real hyperspectral images (HSIs) data. In this article, a target detection method through tree-structured encoding (TD-TSE) for HSIs is proposed. Instead of modeling the target and the background to extract valid features, we construct a binary tree based on the features of the data itself and segment the HSI to improve the separability of the target and the background. For the purpose of highlighting the target and suppressing the background, a novel measurement of separation, distance on tree, is calculated via binary encoding based on the constructed tree structure, and the detection output can be obtained according to such distance. To further reduce the generalization error resulting from random subsampling, the statistical average of the distances on multiple independent trees is estimated to improve the robustness of TD-TSE. The proposed method is not constrained by any model assumptions, which is fundamentally different from the most widely used hyperspectral target detectors in the field of signal processing. Moreover, the construction of binary trees without any labeled samples and the linear complexity of the proposed method make it highly practical for the hyperspectral data in real scenes. Extensive experiments on three benchmark HSI data sets demonstrate the effectiveness of the proposed TD-TSE for hyperspectral target detection.
RoIP Compression Method in Burst Noise Upstream Environment for CATV Network

A hybrid fiber coaxial (HFC) network uses both optical fibers and coaxial cables for cable TV networks. Coaxial cables are mainly used for access networks. Recently, fiber to the home (FTTH) is being widely deployed. For cable TV companies to utilize readily deployed FTTH, legacy coax network-based customer premise equipment (CPE) such as cable modems and set top boxes may have to be replaced with fiber network based CPE. In order to reduce the expenditure incurred in this process, radio over Internet Protocol (RoIP) technology has been proposed. In the RoIP system, RF signal for coax generated by legacy CPE toward the headend is first sampled, and digitized, and then encapsulated in IP packets, which are then sent to the headend using FTTH access technologies such as passive optical network (PON). In the process of digitizing RF signal, a huge amount of data much more than the base band data is generated. So, a data compression method is required to lower the upstream bit rate maintaining the signal quality. Most compression methods are significantly degraded due to the interference caused by burst noise in the upstream link. Therefore, this paper proposes a new compression method which combines linear predictive coding (LPC), block scaling and non-linear quantization for CATV upstream links. The proposed method is demonstrated to greatly reduce the impact of burst noise while maintaining the compression performance.
Watch Out for Extrinsic Bugs! A Case Study of Their Impact in Just-In-Time Bug Prediction Models on the OpenStack Project

Intrinsic bugs are bugs for which a bug-introducing change can be identified in the version control system of a software. In contrast, extrinsic bugs are caused by external changes to a software, such as errors in external APIs; thereby they do not have an explicit bug-introducing change in the version control system. Although most previous research literature has assumed that all bugs are of intrinsic nature, in a previous study, we show that not all bugs are intrinsic. This paper shows an example of how considering extrinsic bugs can affect software engineering research. Specifically, we study the impact of extrinsic bugs in Just-In-Time bug prediction by partially replicating a recent study by McIntosh and Kamei on JIT models. These models are trained using properties of earlier bug-introducing changes. Since extrinsic bugs do not have bug-introducing changes in the version control system, we manually curate McIntosh and Kamei’s dataset to distinguish between intrinsic and extrinsic bugs. Then, we address their original research questions, this time removing extrinsic bugs, to study whether bug-introducing changes are a moving target in Just-In-Time bug prediction. Finally, we study whether characteristics of intrinsic and extrinsic bugs are different. Our results show that intrinsic and extrinsic bugs are of different nature. When removing extrinsic bugs the performance is different up to 16 percent Area Under the Curve points. This indicates that our JIT models obtain a more accurate representation of the real world. We conclude that extrinsic bugs negatively impact Just-In-Time models. Furthermore, we offer evidence that extrinsic bugs should be further investigated, as they can significantly impact how software engineers understand bugs.
Siamese Neural Network-Based Supervised Slow Feature Extraction for Soft Sensor Application

Slow feature analysis (SFA) is an unsupervised learning method that extracts the latent variables from a time series dataset based on the temporal slowness aspect. Neural networks, owing to their ability to model complex nonlinearities, can be used to extract slow features (SFs) from a dataset obtained from a complicated process. Siamese neural networks can be used for this purpose. Siamese neural networks have a provision of handling two samples at a time and this aspect helps in extracting SFs. For supervised learning applications, the extracted SFs should help predict the outputs. In this article, we present two approaches that extract SFs using Siamese neural networks. The output relevance aspect is brought into feature extraction as a regularization term in the objective function of the Siamese neural network. Such regularization improves the performance of the neural network model. The proposed approaches are implemented on three datasets to demonstrate their effectiveness.
Examining Teacher Agency Among Teacher Educators: An Action Research In Vietnam

Teacher agency is an important topic in educational research, but its theoretical observations have not filtered down into practical Vietnamese teaching contexts. The action research project described here showcases a training-based intervention for teacher educators in various disciplines at eight universities in Vietnam. The intervention aimed to develop teaching skills and increase knowledge for teacher educators by helping them explore their students’ learning needs and facilitate their professional development. This study occurred within the context of changing Vietnamese political, cultural, economic, and social ways of being. Eight Vietnamese teacher educators of different cultural backgrounds, and working across a variety of subjects were invited to participate in the project. Two stages of research sought to help educators in higher education reflect on their exploration and perception of their teacher agency. The first stage involved training sessions that offered educators necessary knowledge and skills to exercise agency effectively. In the second stage, educators’ promotion of their agency in classrooms was analytically observed. Data collected included observation notes, reflective journals, and recordings from semi-structured interviews. Analysis of this data suggested that educators became more knowledgeable, intercultural, and inspirational agents in their classrooms after the educational intervention. Educators exhibited that the more positive attitudes, the more active participation. Conclusions discussed the benefits of fostering teacher agency and how this can be facilitated through professional development.
Markov Models for Anomaly Detection in Wireless Body Area Networks for Secure Health Monitoring

The use of Wireless Body Area Networks (WBANs) in healthcare for pervasive monitoring enhances the lives of patients and allows them to fulfill their daily life activities while being monitored. Various non-invasive sensors are placed on the skin to monitor several physiological attributes, and the measured data are transmitted wirelessly to a centralized processing unit to detect changes in the health of the monitored patient. However, the transferred data are vulnerable to various sources of interference, sensor faults, measurement faults, injection and alteration by malicious attackers, etc. In this article, we propose a change point detection model based on a Markov chain for centralized anomaly detection in WBANs. The model is derived from the Root Mean Square Error (RMSE) between the forecasted and measured values for whole attributes. The RMSE transforms the monitored attributes into a univariate times series which is divided into overlapping sliding window. The joint probability of the sequence of RMSE values in each sliding window is calculated to decide whether a change has occurred or not. When an effective change is detected over $k$ consecutive windows, the number of deviated attributes is used to distinguish faulty measurements from a health emergency. We apply our proposed approach on real physiological data from the Physionet database and compare it with existing approaches. Our experimental results prove the effectiveness of our proposed approach, as it achieves high detection accuracy with a low false alarm rate (5.2%).
An Anchor-Free Method Based on Feature Balancing and Refinement Network for Multiscale Ship Detection in SAR Images

Recently, deep-learning methods have been successfully applied to the ship detection in the synthetic aperture radar (SAR) images. It is still a great challenge to detect multiscale SAR ships due to the broad diversity of the scales and the strong interference of the inshore background. Most prevalent approaches are based on the anchor mechanism that uses the predefined anchors to search the possible regions containing objects. However, the anchor settings have a great impact on their detection performance as well as the generalization ability. Furthermore, considering the sparsity of the ships, most anchors are redundant and will lead to the computation increase. In this article, a novel detection method named feature balancing and refinement network (FBR-Net) is proposed. First, our method eliminates the effect of anchors by adopting a general anchor-free strategy that directly learns the encoded bounding boxes. Second, we leverage the proposed attention-guided balanced pyramid to balance semantically the multiple features across different levels. It can help the detector learn more information about the small-scale ships in complex scenes. Third, considering the SAR imaging mechanism, the interference near the ship boundary with the similar scattering power probably affects the localization accuracy because of feature misalignment. To tackle the localization issue, a feature-refinement module is proposed to refine the object features and guide the semantic enhancement. Finally, extensive experiments are conducted to show the effectiveness of our FBR-Net compared with the general anchor-free baseline. The detection results on the SAR ship detection dataset (SSDD) and AIR-SARShip-1.0 dataset illustrate that our method achieves the state-of-the-art performance.
Genetic Diversity, Population Structure and Demographic History of Brandt and Long Eared Hedgehogs in Iran

Ehsan Kashani1, Hamid Reza Rezaei2*, Morteza Naderi3 and Nematolah Khorasani4 1Department of Environmental Sciences, Faculty of Natural Resources and Environment, Science and Research Branch, Islamic Azad University P.O. Box 14515775, Tehran, Iran 2Department of Environment, Faculty of Fisheries and Environmental Science, Gorgan University of Agricultural Sciences and Natural Resources, Iran 3Department of Environmental Science, Faculty of Agriculture and Natural Resources, Arak University, Arak, Iran Article Information Received 03 August 2019 Revised 25 December 2019 Accepted 14 February 2020 Available online 06 July 2020
Predicting Human Mobility With Semantic Motivation via Multi-Task Attentional Recurrent Networks

Human mobility prediction is of great importance for a wide spectrum of location-based applications. However, predicting mobility is not trivial because of four challenges: 1) the complex sequential transition regularities exhibited with time-dependent and high-order nature; 2) the multi-level periodicity of human mobility; 3) the heterogeneity and sparsity of the collected trajectory data; and 4) the complicated semantic motivation behind the mobility. In this paper, we propose DeepMove, an attentional recurrent network for mobility prediction from lengthy and sparse trajectories. In DeepMove, we first design a multi-modal embedding recurrent neural network to capture the complicated sequential transitions by jointly embedding the multiple factors that govern human mobility. Then, we propose a historical attention model with two mechanisms to capture the multi-level periodicity in a principle way, which effectively utilizes the periodicity nature to augment the recurrent neural network for mobility prediction. Furthermore, we design a context adaptor to capture the semantic effects of Point-Of-Interest (POI)-based activity and temporal factor (e.g., dwell time). Finally, we use the multi-task framework to encourage the model to learn comprehensive motivations with mobility by introducing the task of the next activity type prediction and the next check-in time prediction. We perform experiments on four representative real-life mobility datasets, and extensive evaluation results demonstrate that our model outperforms the state-of-the-art models by more than 10 percent. Moreover, compared with the state-of-the-art neural network models, DeepMove provides intuitive explanations into the prediction and sheds light on interpretable mobility prediction.
Energy and Spectral Efficiency Tradeoff via Rate Splitting and Common Beamforming Coordination in Multicell Networks

Rate splitting (RS) has the potential to significantly enhance both energy efficiency (EE) and spectral efficiency (SE) of wireless networks. In this paper, we propose joint design of the beamforming and rate allocation to maximize both EE and SE of a downlink multicell multiple-input single-output (MISO) system with rate splitting and common beamforming coordination (RS-CBC). This design problem is formulated as a non-convex quadratically-constrained multi-objective optimization problem (MOOP). By investigating the quasi-concavity relationship between EE and SE, the formulated MOOP is transformed into a single-objective optimization problem (SOOP) to offer a tradeoff between EE and SE by maximizing EE in any achievable SE region. A series of transformations are then applied to make the SOOP tractable, after which an efficient iterative algorithm based on successive convex approximation (SCA) is proposed to solve the problem. Simulation results demonstrate the effectiveness of the proposed algorithm and unveil interesting tradeoffs between EE and SE under different parameter settings.
Scale Formation on HSLA Steel during Continuous Casting Part II: The Effect of Surface Conditions

The present research addresses the effect of surface condition on oxide scale formation at high temperatures such as those experienced during secondary cooling in Continuous Casting. Tests were carried out in clean, as-cast and surfaces covered with casting powder to replicate the oxidation/re-oxidation after the mould. Specimens oxidized at 1000, 1100 and 1200 °C under dry air and water-vapour conditions revealed that the oxide scale formation is strongly influenced by temperature, environmental and surface conditions. The oxide scale thickness increases with temperature alterations in the surface (e.g., as-cast and covered with powder) where oxides and carbonates from the casting powder accelerate oxidation kinetics leading to thick and unstable scales. A high amount of carbon is present on surfaces covered with casting powder where it diffuses through the oxide scale forming CO and CO2 which lead to stress accumulation that makes scales prone to defects such as pores, voids and micro-cracks. Ultimately, if wüstite remains attached to the steel surface or inside oscillation marks, it may disturb heat transfer during secondary cooling which has deep industrial implications for crack formation and overall casting yield. Therefore, accurate insights on scale type and growth mechanisms could lead to accurate control of its formation during casting.