Miracle-dz/UniFAR-FTUs huggingface dataset:
Synergism Among the Four Tobacco Bushy Top Disease Casual Agents in Symptom Induction and Aphid Transmission

Tobacco bushy top disease (TBTD), caused by multiple pathogens including tobacco bushy top virus (TBTV), tobacco vein distorting virus (TVDV), TBTV satellite RNA (TBTVsatRNA), and TVDV-associated RNA (TVDVaRNA), is a destructive disease in tobacco fields. To date, how these causal agents are co-transmitted by aphid vectors in field and their roles in disease symptom induction remain largely unknown, due mainly to the lack of purified causal agents. In this study, we have constructed four full-length infectious clones, representing the Yunnan Kunming isolates of TVDV, TBTV, TBTVsatRNA, and TVDVaRNA (TVDV-YK, TBTV-YK, TBTVsatRNA-YK, and TVDVaRNA-YK), respectively. Co-inoculation of these four causal agents to tobacco K326 plants caused typical TBTD symptoms, including smaller leaves, necrosis, and plant stunting. In addition, inoculation of tobacco K326 plants with TBTV alone caused necrosis in systemic leaves by 7 dpi. Tobacco K326 and Nicotiana benthamiana plants infected by single virus or multiple viruses showed very different disease symptoms at various dpi. RT-PCR results indicated that co-infection of TVDVaRNA-YK could increase TVDV-YK or TBTV-YK accumulation in N. benthamiana plants, suggesting that TVDVaRNA-YK can facilitate TVDV-YK and TBTV-YK replication and/or movement in the infected plants. Aphid transmission assays showed that the successful transmission of TBTV-YK, TBTVsatRNA-YK, and TVDVaRNA-YK by Myzus persicae depended on the presence of TVDV-YK, while the presence of TBTVsatRNA-YK increased the aphid transmission efficiency of TBTV and TVDV. We consider that these four new infectious clones will allow us to further dissect the roles of these four causal agents in TBTD induction as well as aphid transmission.
Rotten Hazelnuts Prediction via Simulation Modeling—A Case Study on the Turkish Hazelnut Sector

The quality defects of hazelnut fruits comprise changes in morphology and taste, and their intensity mainly depends on seasonal environmental conditions. The strongest off-flavor of hazelnuts is known as rotten defect, whose candidate causal agents are a complex of fungal pathogens, with Diaporthe as the dominant genus. Timely indications on the expected incidence of rotten defect would be essential for buyers to identify areas where hazelnut quality will be superior, other than being useful for farmers to have the timely indications of the risk of pathogens infection. Here, we propose a rotten defect forecasting model, and we apply it in the seven main hazelnut producing municipalities in Turkey. We modulate plant susceptibility to fungal infection according to simulated hazelnut phenology, and we reproduce the key components of the Diaporthe spp. epidemiological cycle via a process-based simulation model. A model sensitivity analysis has been performed under contrasting weather conditions to select most relevant parameters for calibration, which relied on weekly phenological observations and the post-harvest analyses of rotten incidence in the period 2016–2019, conducted in 22 orchards. The rotten simulation model reproduced rotten incidence data in calibration and validation datasets with a mean absolute error below 1.8%. The dataset used for model validation (321 additional sampling locations) has been characterized by large variability of rotten incidence, in turn contributing to decrease the correlation between reference and simulated data (R2 = 0.4 and 0.21 in West and East Black Sea region, respectively). This denotes the key effect of other environmental and agronomic factors on rotten incidence, which are not yet taken into account by the predictive workflow and will be considered in further improvements. When applied in spatially distributed simulations, the model differentiated the rotten incidence across municipalities, and reproduced the interannual variability of rotten incidence. Our results confirmed that the rotten defect is strictly dependent on precipitation amount and timing, and that plant susceptibility is crucial to trigger fungal infections. Future steps will envisage the application of the rotten simulation model to other hazelnut producing regions, before being operationally used for in-season forecasting activities.
Trust and Engagement on Twitter During the Management of COVID-19 Pandemic: The Effect of Gender and Position

During the COVID-19 pandemic, health and political leaders have attempted to update citizens using Twitter. Here, we examined the difference between environments that social media has provided for male/female or health/political leaders to interact with people during the COVID-19 pandemic. The comparison was made based on the content of posts and public responses to those posts as well as user-level and post-level metrics. Our findings suggest that although health officers and female leaders generated more contents on Twitter, political leaders and male authorities were more active in building networks. Offensive language was used more frequently toward males than females and toward political leaders than health leaders. The public also used more appreciation keywords toward health leaders than politicians, while more judgmental and economy-related keywords were used toward politicians. Overall, depending on the gender and position of leaders, Twitter provided them with different environments to communicate and manage the pandemic.
Wood Nutrient-Water-Density Linkages Are Influenced by Both Species and Environment

Tropical trees store a large amount of nutrients in their woody tissues, thus triggering the question of what the functional association of these elements with other wood traits is. Given the osmotic activity of mineral elements such as potassium, sodium, and calcium, these elements should be strong candidates in mediating the water storing capacity in tropical trees. We investigated the role of wood nutrients in facilitating wood water storage in trees by using branch samples from 48 tropical tree species in South America and examined their associations with wood density (ρ). Wood density varied from 316 kg/m3 in Peru plots, where the soil nutrient status is relatively higher, to 908 kg/m3 in Brazil plots, where the nutrient availability is lower. Phosphorus content in wood varied significantly between plots with lowest values found in French Guiana (1.2 mol/m3) and plots with highest values found in Peru (43.6 mol/m3). Conversely, potassium in woody tissues showed a significant cross-species variation with Minquartia guianensis in Brazil showing the lowest values (8.8 mol/m3) and with Neea divaricata in Peru having the highest values (114 mol/m3). We found that lower wood density trees store more water in their woody tissues with cations, especially potassium, having a positive association with water storage. Specific relationships between wood cation concentrations and stem water storage potential nevertheless depend on both species’ identity and growing location. Tropical trees with increased water storage capacity show lower wood density and have an increased reliance on cations to regulate this reservoir. Our study highlights that cations play a more important role in tropical tree water relations than has previously been thought, with potassium being particularly important.
Development and Application of CRISPR-Cas Based Tools

Abundant CRISPR-Cas systems in nature provide us with unlimited valuable resources to develop a variety of versatile tools, which are powerful weapons in biological discovery and disease treatment. Here, we systematically review the development of CRISPR-Cas based tools from DNA nuclease to RNA nuclease, from nuclease dependent-tools to nucleic acid recognition dependent-tools. Also, considering the limitations and challenges of current CRISPR-Cas based tools, we discuss the potential directions for development of novel CRISPR toolkits in the future.
Genetic Modeling and Genomic Analyses of Yearling Temperament in American Angus Cattle and Its Relationship With Productive Efficiency and Resilience Traits

Cattle temperament has been considered by farmers as a key breeding goal due to its relevance for cattlemen’s safety, animal welfare, resilience, and longevity and its association with many economically important traits (e.g., production and meat quality). The definition of proper statistical models, accurate variance component estimates, and knowledge on the genetic background of the indicator trait evaluated are of great importance for accurately predicting the genetic merit of breeding animals. Therefore, 266,029 American Angus cattle with yearling temperament records (1–6 score) were used to evaluate statistical models and estimate variance components; investigate the association of sex and farm management with temperament; assess the weighted correlation of estimated breeding values for temperament and productive, reproductive efficiency and resilience traits; and perform a weighted single-step genome-wide association analysis using 69,559 animals genotyped for 54,609 single-nucleotide polymorphisms. Sex and extrinsic factors were significantly associated with temperament, including conception type, age of dam, birth season, and additional animal–human interactions. Similar results were observed among models including only the direct additive genetic effect and when adding other maternal effects. Estimated heritability of temperament was equal to 0.39 on the liability scale. Favorable genetic correlations were observed between temperament and other relevant traits, including growth, feed efficiency, meat quality, and reproductive traits. The highest approximated genetic correlations were observed between temperament and growth traits (weaning weight, 0.28; yearling weight, 0.28). Altogether, we identified 11 genomic regions, located across nine chromosomes including BTAX, explaining 3.33% of the total additive genetic variance. The candidate genes identified were enriched in pathways related to vision, which could be associated with reception of stimulus and/or cognitive abilities. This study encompasses large and diverse phenotypic, genomic, and pedigree datasets of US Angus cattle. Yearling temperament is a highly heritable and polygenic trait that can be improved through genetic selection. Direct selection for temperament is not expected to result in unfavorable responses on other relevant traits due to the favorable or low genetic correlations observed. In summary, this study contributes to a better understanding of the impact of maternal effects, extrinsic factors, and various genomic regions associated with yearling temperament in North American Angus cattle.
Impedance analysis of piezoelectric energy harvesting system using synchronized charge extraction interface circuit

The synchronous charge extraction (SCE) interface circuit is unique among the existing piezoelectric energy har- vesting (PEH) power conditioning circuits, for its output power is independent of the load. The previous studies about SCE have assumed lossless rectifier and ideal energy transfer through the inductor; the detailed energy flow picture in SCE was absent. This paper provides an impedance based analysis for the PEH system using SCE interface circuit. Through qualitative analysis on the energy cycle, the electrically induced dynamics of SCE is broken down into three components: the accompanied capacitance, dissipative resistance, and harvesting resis- tance, which correspond to an additional stiffness, a dissipative damper, and a regenerative damper, respectively, to the mechanical structure. Quantitative analysis on the harvested power is also carried out. Experiments on practical PEH system show good agreement with the theoretical results. The new insight provided in this study help better understand the dynamics and better evaluate the harvesting capability of the SCE circuit among those options of power conditioning towards practical PEH implementations.
Design and performance tests of a high volumetric figure of merit regenerative damper for vehicle suspension systems

Regenerative dampers for vehicle suspension systems that can harvest power at low frequencies efficiently are challenging to realise. To increase their energy harvesting efficiency, a type of high volumetric Figure of Merit magnetoelectric regenerative damper is proposed. To increase the magnetic linkage gradient of its coil in the moving direction and in its motion region, lumped parameter equivalent magnetic circuit model is adopted in its magnetic structural parameter optimization. Finite element analysis is then used to verify the analytical model and theoretical results. Finally, 3 prototypes with different parametric combinations are manufactured and fabricated to do experiments. The experiments indicate that the optimised megnetoelectric regenerative damper can harvest vibration power of 16.3 watts and provide an adjustable damping coefficient that can reach 1605Ns/m when the damper in a relative velocity of 0.2m/s. The corresponding volumetric Figure of Merit can reach 8.3%, which is higher than most of the works presented in recent literatures. This type of regenerative damper can be used in vehicle suspension systems and meet their requirements. It also offers the opportunity to be applied in semi-active vehicle suspensions.
New York City Drunk Driving After Uber

This study investigates the effect of the introduction of Uber in New York City in May 2011 on drunk-driving. A difference-in-differences estimation of this effect implies a 25-35% decrease in the alcohol-related collision rate for the affected New York City boroughs, or about 40 collisions per month. With differentiated treatment effects for each effected county, the difference-in- differences effect is higher for Manhattan, average for the Bronx and Brooklyn, and lower for Queens. A synthetic control analysis shows pronounced effects over time in the Bronx and Brooklyn, and a permutation test confirms the effect is not commonly reproducible using untreated counties.
A measure for objects clustering in principal component analysis biplot: A case study in inter-city buses maintenance cost data

This article presents the application of the principal component analysis (PCA) biplot for the needs of data mining. This article aims to simplify and objectify the methods for objects clustering in PCA biplot. The novelty of this paper is to get a measure that can be used to objectify the objects clustering in PCA biplot. Orthonormal eigenvectors, which are the coefficients of a principal component model representing an association between principal components and initial variables. The existence of the association is a valid ground to objects clustering based on principal axes value, thus if m principal axes used in the PCA, then the objects can be classified into 2m clusters. The inter-city buses are clustered based on maintenance costs data by using two principal axes PCA biplot. The buses are clustered into four groups. The first group is the buses with high maintenance costs, especially for lube, and brake canvass. The second group is the buses with high maintenance costs, especially for tire, and f.
Personalized Desire2Learn Recommender System based on Collaborative Filtering and Ontology

— In this century, attention has grown to recommendation systems (RS), especially in e-learning, to solve the problem of overloading information in e-learning systems. Elearning providers also play a major role in helping learners to find appropriate courses that fit their learning plan using Desire2Learn at Majmaah University. Although recommendation systems generally have a clear advantage in solving problems related to overloading information in various areas of e-business and making accurate recommendations, elearning recommendation systems still have problems with overloading information about the characteristics of the learning recipient Such as the appropriate education style, the level of skills provided and the student's level of education. In this paper, we suggest that a recommendation technique combining collaborative filtering and ontology be introduced to recommend courses for learning recipients through Desire2Learn. Ontology involves the integration of the characteristics of the learning recipient into the recommendation process as well as the classifications, while the liquidation process cooperates in the predictions and generates recommendations for e-learning. In addition, ontological knowledge is employed by the educational RS in the early stages if no assessments can be made to mitigate the cold start problem. The results of this study show that the proposed recommendation technique is distinguished and superior to the cooperative liquidation in terms of specialization and accuracy of the recommendation.
Activity Against SARS-CoV-2 in vitro: Rationale for a host-acting antiviral

a Clear Creek Bio, Cambridge, Massachusetts, USA 8 b NEIDL, Boston University, Boston, Massachusetts, USA 9 c Department of Experimental Therapeutics, MD Anderson Cancer Center, Houston, Texas, USA 10 d University of Louisville, Louisville, Kentucky, USA 11 e Center for Regenerative Medicine, Massachusetts General Hospital, Boston, MA. 12 f Harvard Stem Cell Institute, Cambridge, MA. 13 g Massachusetts General Hospital Cancer Center, Boston, MA. 14
An investigation into the development of the absorptive capacity of manufacturing SMEs

To sustain their competitive advantage in a highly competitive landscape, SMEs, in spite of their limited resources, need to effectively learn. The ability to access and successfully use knowledge is at the heart of absorptive capacity. The main purpose of this empirical study is to investigate the importance of awareness as an antecedent dimension of the concept of absorptive capacity of manufacturing SMEs from aerospace and advanced engineering industry in the UK. The study will also attempt to explore the impact of awareness on the other dimensions of absorptive capacity. Awareness is related to the importance, motivation and interest of learning and innovating. Our study, which focuses on internal processes adopts a mixed method approach and is based on the data collected from 43 SMEs. Our finding suggests that awareness as an antecedent dimension can play a vital role in helping SMEs to improve their knowledge-based resources through changes within their internal organisational and cognitive processes.
Learning Interpretable, Tree-Based Projection Mappings for Nonlinear Embeddings

Model interpretability is a topic of renewed interest given today’s widespread practical use of machine learning, and the need to trust or understand automated predictions. We consider the problem of optimally learning interpretable out-of-sample mappings for nonlinear embedding methods such as t-SNE. We argue for the use of sparse oblique decision trees because they strike a good tradeoff between accuracy and interpretability which can be controlled via a hyperparameter, thus allowing one to achieve a model with a desired explanatory complexity. The resulting optimization problem is difficult because decision trees are not differentiable. By using an equivalent formulation of the problem, we give an algorithm that can learn such a tree for any given nonlinear embedding objective. We illustrate experimentally how the resulting trees provide insights into the data beyond what a simple 2D visualization of the
IMPLEMENTATION & ANALYSIS OF QUERY KEYWORD SUGGESTION USING PROXIMITY

: The Keyword suggestion in web search helps users to access relevant data without having knowledge in accordance with precisely expressing their queries or desired information. If we consider location of the user then we can recommend the user or produce results based on users current location. The keyword suggestion is the technique helps to retrieve documents which relates to information provided by user and user’s location. Search engine retrieves the documents, which are semantically relevant to the original query using KD graph and it have as result documents that correspond to objects near to the user’s location. In this paper we have implementd and made analysis of location and proximity based keyword query suggestion framework using PHP and MySQL Database Along with use of XAMPP web Server. We have designed & also a weighted keyword-document graph, which grabs both the semantic relevance between keyword queries and the distance between the resulting documents and the query issuer’s location.
B ���� : Scaling up Distributed Learning with System Relaxations

Recent years have witnessed a growing list of systems for distributed data-parallel training. Existing systems largely � t into two paradigms, i.e., parameter server and MPI-style collective operations. On the algorithmic side, researchers have proposed a wide range of techniques to lower the communication via “system re-laxations”: quantization , decentralization , and communication delay . However, most, if not all, existing systems only rely on standard synchronous and asynchronous stochastic gradient (SG) based optimization, therefore, cannot take advantage of all possible optimizations that the machine learning community has been developing recently. Given this emerging gap between the current landscapes of systems and theory, we build B ���� , a MPI-style communication library, providing a collection of primitives, that is both � exible and modular to support state-of-the-art system relaxation techniques of distributed training. Powered by this design, B ���� has a great ability to implement and extend various state-of-the-art distributed learning algorithms. In a production cluster with up to 16 machines (128 GPUs), B ���� can outperform PyTorch-DDP, Horovod and BytePS in the end-to-end training time by a signi � cant margin (up to 2 ⇥ ) across a diverse range of tasks. Moreover, we conduct a rigorous tradeo � exploration showing that di � erent algorithms and system relaxations achieve the best performance over di � erent network conditions.
The mediating role of managerial accounting in non-profit organizations: a structural equation modelling approach

Structural equation modelling (SEM) characteristics seem to be adapted to answer various relevant research questions in management accounting research. Thus, this study aims to illustrate the use and advantages of SEM in management accounting research in the non-profit sector. The contributions of this study include revealing the relationship between the leadership style, managerial accounting, and the performance of non-profit organizations measured by their community engagement. Furthermore, the mediating role of managerial accounting in non-profit organizations is also investigated in this context. The empirical results prove the existence of direct relationships between analytical and proactive leadership style and managerial accounting on the one hand and between managerial accounting and successful community engagement of non-profit organizations (non-financial performance) on the other. The study also confirms that managerial accounting mediates the relationship between the analytical and proactive leadership style and successful community engagement of non-profit organizations. The findings provide a valuable insight to guide managers in non-profit organizations and improve their non-financial performance through managerial accounting.