Miracle-dz/UniFAR-FTUs huggingface dataset:
Synthetic genetic circuits enable reprogramming of plant roots

The shape of a plant’s root system influences its ability to reach essential nutrients in the soil and to acquire water during drought. Progress in engineering plant roots to optimize water and nutrient acquisition has been limited by our capacity to design and build genetic programs that alter root growth in a predictable manner. Here, we construct synthetic genetic circuits to control gene expression with high spatial precision across root tissues. These circuits produce specific patterns of gene expression by combining the activity of multiple native promoters using logical operations. We then use the circuits to predictably alter root structure. This work demonstrates the ability of synthetic genetic circuits to control gene expression across tissues and offers an exciting means to reprogram plant growth.
Design of Membrane Active Peptides Considering Multi-Objective Optimization for Biomedical Application

A multitude of membrane active peptides exists that divides into subclasses, such as cell penetrating peptides (CPPs) capable to enter eukaryotic cells or antimicrobial peptides (AMPs) able to interact with prokaryotic cell envelops. Peptide membrane interactions arise from unique sequence motifs of the peptides that account for particular physicochemical properties. Membrane active peptides are mainly cationic, often primary or secondary amphipathic, and they interact with membranes depending on the composition of the bilayer lipids. Sequences of these peptides consist of short 5–30 amino acid sections derived from natural proteins or synthetic sources. Membrane active peptides can be designed using computational methods or can be identified in screenings of combinatorial libraries. This review focuses on strategies that were successfully applied to the design and optimization of membrane active peptides with respect to the fact that diverse features of successful peptide candidates are prerequisites for biomedical application. Not only membrane activity but also degradation stability in biological environments, propensity to induce resistances, and advantageous toxicological properties are crucial parameters that have to be considered in attempts to design useful membrane active peptides. Reliable assay systems to access the different biological characteristics of numerous membrane active peptides are essential tools for multi-objective peptide optimization.
MODELLING DYNAMICS OF THE TALC WITH SYSTEM DYNAMICS METHODOLOGY AND DPSIR FRAMEWORK

Purpose – touristic destinations develop over time, which is why, in order to get a comprehensive picture of their development, it is necessary to observe it's dynamics. Methodology – in this paper system dynamics methodology and of DPSIR framework will use. In order to model reasoning behind the TALC behaviour, presented research in this paper leans on TALC logistic curve. Findings – deeper analysis of the causes and/or consequences elements of destination (sub)system (supply and demand) will indicate way of affect touristic area life cycle dynamics. Contribution – better understanding of the background structure of TALC pattern behaviour may help destination managers/planners to bring appropriate policies to move destination’s sustainability towards higher level of organisation.
Capturing indirect genetic effects on phenotypic variability: Competition meets canalization

Phenotypic variability of a genotype is relevant both in natural and domestic populations. In the past two decades, variability has been studied as a heritable quantitative genetic trait in its own right, often referred to as inherited variability or environmental canalization. So far, studies on inherited variability have only considered genetic effects of the focal individual, that is, direct genetic effects on inherited variability. Observations from aquaculture populations and some plants, however, suggest that an additional source of genetic variation in inherited variability may be generated through competition. Social interactions, such as competition, are often a source of Indirect Genetic Effects (IGE). An IGE is a heritable effect of an individual on the trait value of another individual. IGEs may substantially affect heritable variation underlying the trait, and the direction and magnitude of response to selection. To understand the contribution of IGEs to evolution of environmental canalization in natural populations, and to exploit such inherited variability in animal and plant breeding, we need statistical models to capture this effect. To our knowledge, it is unknown to what extent the current statistical models commonly used for IGE and inherited variability capture the effect of competition on inherited variability. Here, we investigate the potential of current statistical models for inherited variability and trait values, to capture the direct and indirect genetic effects of competition on variability. Our results show that a direct model of inherited variability almost entirely captures the genetic sensitivity of individuals to competition, whereas an indirect model of inherited variability captures the cooperative genetic effects of individuals on their partners. Models for trait levels, however, capture only a small part of the genetic effects of competition. The estimation of direct and indirect genetic effects of competition, therefore, is possible with models for inherited variability but may require a two‐step analysis.
The Nurse Plant Acacia spirorbis Enriches Ectomycorrhizal Community Composition of a Target Species: Tristaniopsis calobuxus

We investigated the suitability of Acacia spirorbis Labill. , a tropical ectomycorrhizal (ECM) tree, as a nurse plant to improve the growth of Tristaniopsis calobuxus Brongn. & Gris seedlings for the restoration of nickel mines in New Caledonia. Rehabilitation of nickel mines in New Caledonia is a major concern. In such harsh soil conditions, ectomycorrhizal (ECM) symbiosis is important for tree growth, survival, and resistance. To improve ecological restoration in New Caledonia, new technical itineraries have undergone experimentation using ECM as a plant nurse, allowing ECM saplings to rapidly acquire a wide range of ECM fungi. We transplanted ECM seedlings of Tristaniopsis calobuxus from the nursery to bare ferralitic soils harbouring some scattered 12-year-old Acacia spirorbis to be used as ECM nurse plants. Using molecular characterisation of ITS rDNA, we characterised ECM fungal communities of A. spirorbis and of T. calobuxus saplings at transplanting time and 13 months later. We observed changes in the composition of fungal communities of T. calobuxus with an increase in diversity, notably the appearance of operational taxonomic units (OTUs) affiliated with /russulaceae, /boletus and /pisolithus-scleroderma and a decrease in ubiquitous nursery orders such as /sebacinales and /helotiales. We also observed a higher number of shared OTUs between T. calobuxus and A. spirorbis. The vicinity of A. spirorbis enabled diversification and adaptation of the T. calobuxus ECM fungal community. These results led us to recommend A. spirorbis as a good nurse tree candidate in the framework of ecological restoration of mine sites.
Multi-Modality Cardiovascular Imaging Assessment in Fabry Disease

Fabry disease (FD) is a rare X-linked lysosomal storage disorder manifesting as progressive multi-organ accumulation of sphingolipids due to deficiency in the enzyme α-Galactosidase A. Sphingolipid accumulation can take place in all cardiac cell types which manifests as left ventricular hypertrophy, microvascular ischaemia, conduction abnormalities, arrhythmia, heart failure, and valvular disease. The use of advanced cardiovascular imaging techniques have enabled clinicians to stage and prognosticate the disease and guide therapy. Transthoracic echocardiography (TTE) and cardiac magnetic resonance imaging (CMR) in conjunction are the hallmark imaging modalities to allow for this assessment. Traditionally, the assessment of cardiac involvement in FD was based on the assessment of maximal wall thickness (MWT) and the development of left ventricular hypertrophy (LVH). It is now understood that sphingolipid accumulation takes place before the development of LVH. Advances in techniques within TTE and CMR, particularly that of strain assessment and T1/T2 mapping, have meant that Fabry cardiomyopathy (FCM) can be diagnosed earlier in the disease process. This potentially provides a window for initiation of enzyme replacement therapy (ERT) at a stage where it is likely to have the most beneficial effect in reducing the high mortality associated with FCM. This review outlines the advances in multimodality imaging in staging and prognosticating FCM, as well as the applications of cardiac imaging in assessing symptoms and complications of FCM.
Totally Excited about Moving Mobility and Exercise (TEAM Me): A Successful Multidisciplinary Exercise Quality Improvement Initiative for Pediatric and Young Adult Oncology and Stem Cell Transplant Inpatients

Background: Pediatric, adolescent and young adult (PAYA) patients are less active than their healthy counterparts, particularly during inpatient stays. Methods: We conducted a quality improvement initiative to increase activity levels in patients admitted to our pediatric oncology and cellular therapy unit using a Plan-Do-Study-Act (PDSA) model. An interdisciplinary team was assembled to develop an incentive-based inpatient exercise and activity program titled Totally Excited About Moving Mobility and Exercise (TEAM Me). As part of the program, patients were encouraged by their care team to remain active during their inpatient stay. As an additional incentive, patients earned stickers to display on TEAM Me door boards along with tickets that could be exchanged for prizes. Activity was assessed by documentation of physical therapy participation, tests of physical function, and surveys of staff perceptions of patient activity levels, motivations, and barriers. Results: Compared to baseline, patient refusals to participate in physical therapy decreased significantly (24% vs. 2%) (p < 0.02), and staff perceptions of patient motivation to stay active increased from 40% to 70% in the post implementation period. There were no changes in physical function tests. Conclusions: An incentive-based exercise program for young oncology inpatients greatly improved patient activity levels, participation in physical therapy and influenced professional caregivers’ beliefs.
AUGMENTED REALITY IN TECHNOLOGY-ENHANCED LEARNING: SYSTEMATIC REVIEW 2011-2021

: With the raise of COVID-19 pandemic in 2020, the traditional teaching-learning process became inefficient. Technology-Enhanced Learning (TEL) research has increasingly focused on emergent technologies such as Augmented Reality (AR). It became one of the technologies that has received great attention and interest in the last decade. In this paper, we conducted a systematic review that describes the current state of using AR as a learning tool. Taking into consideration the needs of all students including those with a disability, in different levels of education. It is done through the analysis of the following factors: AR in learning system, AR in levels of education and categories of educational AR applications. A total of 103 studies between 2011 and 2021 were analyzed through searching in four interdisciplinary databases: Springer, IEEE Xplore, ResearchGate, and Google Scholar. This analysis helped to see in which direction AR systems for education are heading and how it will be designed to fit the students’ needs and improve their learning. Further research and development will make AR a more promising learning tool.
An Improved Supervised Classification Algorithm in Healthcare Diagnostics for Predicting Opioid Habit Disorder

Opioid Habit Disorder (OHD), which has become a mass health epidemic, is defined as the psychological or physical dependency on opioids. This study demonstrates how supervised machine learning procedures help us investigate and examine massive data to discover the hidden patterns in any disease to deliver adapted dealing and predict the disease in any patient. This work presents a generalized model for forecasting a disease in the healthcare sector. The proposed model was investigated and tested using a reduced feature-set of the Opioid Habit Disorder (OHD) dataset collected from the National Survey on Drug Use and Health (NSDUH) using an improved Iterative Dichotomiser 3 (pro-IDT) algorithm. The proposed healthcare model is also compared with further machine learning algorithms such as ID3, Random Forest, and Bayesian Classifier in Python programming. The performance of the proposed work and other machine-learning algorithms has estimated accuracy, precision, misclassification rate, recall, specificity, and F1 score.
Stingless bee resources during reforestation of the Atlantic domain of Rio De Janeiro city, Brazil

Abstract Extensive farms were developed from the first settlements in the city of Rio de Janeiro. The scarcity of water year after year currently necessitates the reforestation of degraded areas to protect the remaining forestlands and water resources, mainly on hillsides. Native species of plants and stingless bees were introduced, with the main intention of pollination intensification and seed production to grow new shrubs and trees. These reforested areas produced honey and bee pot-pollen. The present study elucidated the pollination dynamics in three areas under reforestation (Serra da Posse area, Morro da Formiga area, and Serra dos Pretos Forros area) considering three native bee species (Nannotrigona testaceicornes, Tetragonisca angustula and Melipona quadrifasciata anthidioides) and pollen analysis of bee products. For sample processing, neither the acetolysis methodology, nor staining was applied. Paired honey and pollen samples were considered, and in addition anemophilous and exotic pollen grains, as well as structured elements. It is suggested to intensify the planting of native trees and shrubs, such as Arecaceae, Mimosaceae, Myrtaceae, Alchornea, Anadenanthera, Piptadenia and Schizolobium, thus allowing the establishment of a diversity of parasitic Loranthaceae species appreciated by native bees. Food competition among bees was low. The vegetation in newly forested areas did not completely meet the food needs of the bees. The cultivation of a great diversity of native plants and bee species in degraded areas and under reforestation certainly yields a good result more quickly.
Support Vector Machine-Regression을 이용한 주기신호의 이상탐지

This paper presents a non-linear control chart based on support vector machine regression (SVM-R) to improve the accuracy of fault detection of cyclic signals. The proposed algorithm consists of the following two steps. First, the center line of the control chart is constructed by using SVM-R. Second, we calculate control limits by variances that are estimated by perpendicular and normal line of the center line. For performance evaluation, we apply proposed algorithm to the industrial data of the chemical vapor deposition process which is one of the semiconductor processes. The proposed method has better fault detection performance than other existing method
RumorDecay: Rumor Dissemination Interruption for Target Recipients in Social Networks

Rumors (i.e., untrue emergence saying of COVID-19 in an area) that rapidly disseminate on the ubiquitous social media easily cause public panics and irrational behaviors (e.g., taking unnecessary medicine) of many very sensitive individuals referred to as <italic>target recipients</italic>. Thus, rumor controlling or blocking for these target recipients is very critical, which differs from the traditional way of protecting all individuals and remains an open and challenging problem so far. In this work, on the basis that rumors are disseminated from the given sources to target recipients via multiple paths which may be significantly interrupted by deleting a few key links referred to as <italic>protectors</italic>, we first mathematically define a general target information disseminating (TID) model and do theoretical proofs. Second, based on the TID model, we introduce a random walk algorithm to sample the paths of rumor dissemination for recipients. Third, aiming at deleting a budget-limited set of protectors efficiently in a large number of selected paths to reduce or weaken the negative rumor influences on the target recipients, we propose a heuristical strategy-based rumor influence decay mechanism referred to as <italic>RumorDecay</italic> (i.e., RumorDecay <inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> hop nearest neighbor method and RumorDecay <inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> hop random walk method in this work) which can locate the optimal protectors quickly and efficiently. Finally, we conduct extensive experiments on many real social networks and the results show that the RumorDecay strategy can significantly weaken the rumor dissemination ability with less time cost.
Accelerated exacerbation of global extreme heatwaves under warming scenarios

It is generally believed that global warming drives an increase in heatwaves, but these changes vary regionally. Projected trends of heatwaves and comparisons between observed and projected heatwave trends are poorly understood. We selected multiple characteristics of global heatwave events, including indicators on heat‐related health impacts under historical and future scenarios from the NASA Earth Exchange/Global Daily Downscaled Projections (NEX‐GDDP) dataset. We quantified the trends in the frequency, intensity, duration and peak temperature of heatwave events and identified heatwave hotspots that respond dramatically to radiative forcing. Future simulations suggest a four‐fold increase in the duration of heatwaves by 2050s, spatially concentrated in central Africa, northern South America and Southeast Asia, and the maximum duration of single heatwave event will be up to 44 days under a high emission scenario. Accelerated increasing trends are also detected in intensity, total duration and temperature of heatwaves with up to 2‐fold, 8‐fold and 9‐fold larger than the trends of the baseline period under the high emission scenario. Considering socioeconomic exposure to extreme heatwaves, we identified some hotspot areas in western Europe, eastern North America and northern China that will face greater potential risks in the coming future and therefore need to urgently strengthen their adaptation capacity.
Development of Readout Electronics System of Detector for the External Target Experiment in CSR

Neutron detection wall and time of flight wall at external target experiment in HIRFL-CSR measure flight time of neutron and charged particles.The detectors require readout electronics systems that have high resolution time measurement.Readout electronics board with 8 channels adopts time measurement with leading end discriminator,charge measurement with time over threshold technique and PXI crate.RMS resolution of time measurement after electronics test is better than 25ps.
IMAGE RETRIEVAL USING BLENDING OF EXTENDED FEATURE COMPONENTS

: Receiving the most relevant images from image databases is a challenging and critical issue in many applications. Texture is a substantial feature of an image which depicts the spatial behavior of gray-levels in any given neighborhood. Color features uses a variety of color systems and are meaningful to differentiate image segments. Presently, many of the favorable methods for image content description use local descriptors as their starting point with several conducts. The content in an image may appear in some feature descriptor's components more accurately than other components. This paper presents an innovative idea for local image retrieval using a new methodology for feature extraction welding named Blend of Extended Features’ Components (BoEFC). The paper shows that an image's content may be described individually by the feature descriptor's components or collectively through the Extended Feature Components (EFC). Retrieval options are attempted using a selection method of Feature Components then the relevant results are collected and ordered according to newly adapted feature similarity measures. The experiments were performed using a general-purpose image database which itself represent a challenge and the INRIA Holiday image database. The experiments was performed by varying the EFCs to compute recall, precision and draw the Precision-Recall (PR) curves which showed increased recall and precision with some components. In addition, calculating mAP and mAR showed increased performance due to the BoEFC blending process.
Research and Application of PCA in Process Fault Diagnosis Based on Wavelet Theory

By analyzing of PCA and Wavelet,fault diagnosis results are not only including the real faults but also including the disturbances caused by the noise.In order to eliminate the influence of noise,wavelet transform is used for preprocessing industrial data before using the PCA method to fault diagnosis.The results to apply these two integrated methods for distillation unit in practical process industry are satisfied.
Fake-News Network Model: A Conceptual Framework for Strategic Communication to Deal with Fake News

ABSTRACT This article analyzes the entire life span of a corporate fake-news report as a case study, proposing a conceptual framework for strategic fake-news communication. Using the confirmation-bias theoretical model, this qualitative textual analysis examines the most widely circulated tweets of a fake-news item about Nike, 603 replies to the tweets, users’ biographical profiles (e.g., political affiliations), the role of opinion leader(s), and relevant prior contexts. The findings provide in-depth insight into how people believe fake news and how their conversations about fake news (re)shape the victim brand’s social realities. Overall, the findings of this study illustrate a “Fake-News Network Model” that explains the underlying mechanisms of how a fake-news item functions together with other aspects (e.g., context, perception, opinion leaders, and cognitive processes), prompting certain people to believe particular fake-news reports and, discuss the victim brand (e.g., Nike) based on that perceived truth. The article discusses the implications of this network model for both fake-news researchers and strategic communication professionals.