Miracle-dz/UniFAR-FTUs huggingface dataset:
The complete chloroplast genome of Epimedium sagittatum (Sieb. Et Zucc.) Maxim. (Berberidaceae), a traditional Chinese herb

Abstract Epimedium sagittatum is an important traditional medicinal plant in China. In this study, we assembled the complete chloroplast (cp) genome of E. sagittatum. The whole cp genome of E. sagittatum is 157,114 bp in length, comprising a pair of inverted repeat (IR) regions (25,775 bp) separated by a large single copy (LSC) region (88,507 bp) and a small single copy (SSC) region (17,057 bp). The E. sagittatum cp genome contains 133 genes, of which 82 protein-coding genes, 38 tRNA genes, 8 rRNA genes and 5 pseudogenes. Phylogenetic analysis shows that E. sagittatumis closely clustered with E. lishihchenii. This genome provides a wealth of information for distinguishing of Epimedium species.
Allele Based Inference on Evolution and Extinction; A Genetic Drift Approach

In other to present a series of stochastic models from population dynamics capable of describing rudimentary aspects of genetic evolution, we studied two-allele Wright–Fisher and the Moran models for evolution of the relative frequencies of two alleles at a diploid locus under random genetic drift in a population of fixed size “simplest form, selection, and random mutation”. Principal results were presented in qualitative terms, illustrated by Monte Carlo simulations from R and http://www.radford. edu/~rsheehy/Gen_flash/popgen. Moran and the Wright-Fisher Models exhibited the same fixation probabilities, only that the Moran model runs twice as fast as the Wright-Fisher Model. A clue that can help us to understand this result is provided by the variance in reproductive success in the two models. Genetic changes due to drift were neither directional nor predictable in any deterministic way. Nonetheless, genetic drift led to evolutionary change in the absence of mutation (P=0.5), natural selection or gene flow. In general, alleles drift to fixation is significantly faster in smaller populations. Probability of fixation of an allele A was approximately equivalent to the initial frequency of that allele. With the inclusion of selection in our model, probability of fixation of a favoured allele due to natural selection increased with increase in fitness advantage and population size. The time taken to reach fixation is much slower, in case of no selective advantage, than a fixation under mutation with selective advantage.
Evaluation of auxin tolerance in selected tomato germplasm under greenhouse and field conditions

Abstract Tomato is injured by low doses of 2,4-D, dicamba, quinclorac, and glyphosate. New crop varieties resistant to 2,4-D and dicamba are likely to increase use of these herbicides and may increase drift problems. There is a diverse germplasm of tomato available that includes wild relatives known to be tolerant to numerous biotic and abiotic stresses. A greenhouse and field study was conducted to investigate auxin tolerance in three wild tomato accessions (TOM199, TOM198, and TOM300) and compare them with two commercial tomato cultivars (‘Money Maker' and ‘Better Boy'). Auxin herbicides, which included 2,4-D, dicamba, and quinclorac, were applied at doses of 11, 3, and 39 g ae ha–1, respectively. Visible injury ratings of each accession for each herbicide treatment were recorded at 7, 14, 21, and 28 d after treatment (DAT) on a 0% to 100% scale. Results indicate that all three wild tomato accessions exhibited less than 15% injury compared with 100% injury for two commercial cultivars after application of dicamba. The three wild accessions (TOM199, TOM198, and TOM300) did not show any significant reduction in plant height compared with nontreated plants. At 28 DAT, plant heights of TOM199, TOM198, and TOM300 were 25, 25, and 28 cm when treated with dicamba and 31, 30, and 31 cm nontreated, respectively. Based on these results, the identified lines can serve as a genetic resource for developing herbicide-tolerant tomato, thus minimizing or eliminating the negative impacts of drift from nonlabeled herbicides tested in this project. Nomenclature: 2,4-D; dicamba; glyphosate; quinclorac; tomato, Solanum lycopersicum L.
Qualitative classification of the breast cancer genome and clustering of the cancer gene network

The purpose of cancer genome project is to classify the genetic variations that are related to clinical phenotypes. However, some studies showed that some specific cellular pathways are targeted by the cancer mutations genes. But a few of the pathway genes are mutated in each patient. In most approaches, only the existing pathways are considered and the topology of the pathways are ignored. Consequently, new attempts have been targeted on classifying significantly mutated subnetworks and combining them with cancer survival. We had proposed a novel bioinformatics pipeline to identify quantitative classification of the breast cancer genome to verify if the steps will be working or not on real dataset. We have generated a mutation matrix from the collected dataset and calculated pairwise gene similarity. After that, we have also done clustering of the identified cancer gene network, which may help cancer patients by suggesting optimal treatments. We hope our pipeline can also be used for other types of mutation data analysis.
East Asian origins of European holly oaks (Quercus section Ilex Loudon) via the Tibet‐Himalaya

Many subtropical organisms exhibit an East Asian‐Tethyan disjunction, a distribution split between East Asia and the Mediterranean. The underlying mechanisms and timing have remained unclear to date. The evolutionary history of Quercus section Ilex Loudon, a representative East Asian‐Tethyan disjunct lineage with a rich and widespread fossil record, was investigated to understand the key drivers of this disjunction.
Host–parasite interactions and climate change

This chapter offers a review of the papers published on the effect of climate change on bird–parasite interactions from 2010 to date. Climatic effects on phenology, prevalence and intensity, range expansion, virulence, anti-parasite defences, and coevolutionary interactions are reviewed. Most studies are centred on diseases that can also affect human populations or that are close phylogenetically to diseases of humans. However, diseases affecting birds are of great interest due to their importance in ecosystems. More empirical data are needed for a better understanding of how climate change affects bird–parasite relationships.
Secondary Motor Cortex Transforms Spatial Information into Planned Action during Navigation

Fluid navigation requires constant updating of planned movements to adapt to evolving obstacles and goals. A neural substrate for navigation demands spatial and environmental information and the ability to effect actions through efferents. Secondary motor cortex is a prime candidate for this role given its interconnectivity with association cortices that encode spatial relationships and its projection to primary motor cortex. Here we report that secondary motor cortex neurons robustly encode both planned and current left/right turning actions across multiple turn locations in a multi-route navigational task. Comparisons within a common statistical framework reveal that secondary motor cortex neurons differentiate contextual factors including environmental position, route, action sequence, orientation, and choice availability. Despite significant modulation by context, action planning and execution is the dominant output signal of secondary motor cortex neurons. These results identify secondary motor cortex as a structure integrating environmental context toward the updating of planned movements.
Image Registration Methods

Image registration, an important preprocess in image fusion, is the process of geometrically aligning two or more images. The image registration can be applied for fusion of medical images, satellite images or images obtained by different sensors at the same time or by same sensor at different times. Image registration involves selection of a control point, which can be done manually or automatically. Manual selection of control point, though simple, is time consuming and can be inaccurate. Automatic image registration does not involve human intervention and many algorithms are available in the literature. Though one algorithm cannot be applied to all applications, algorithms should not be very much application specific. In this paper we study different methods for image registration of different types of images.
Identification of selection and inhibition components in a Go/NoGo task from EEG spectra using a machine learning model

Prior Go/NoGo studies have localized specific regions and EEG spectra for which traditional approaches have distinguished between Go and NoGo conditions. A more detailed characterization of the spatial distribution and timing of the synchronization of frequency bands would contribute substantially to the clarification of neural mechanisms that underlie performance of the Go/NoGo task. The present study used a machine learning approach to learn the features that distinguish between ERSPs involved in selection and inhibition in a Go/NoGo task. A neural network classifier was used to predict task conditions for each subject to characterize ERSPs associated with Go versus NoGo trials. The final model accurately identified individual task conditions at an overall rate of 92%, estimated by 5-fold cross-validation. The detailed accounting of EEG time-frequency patterns localized to brain sources (i.e., thalamus, preSMA, orbitofrontal cortex, and superior parietal cortex) provides elaboration on previous findings from fMRI and EEG studies and more information about EEG power changes in multiple frequency bands (i.e., primarily theta power increase, alpha decreases, and beta increases and decreases) within these regions underlying the selection and inhibition processes engaged in the Go and NoGo trials. This extends previous findings, providing more information about neural mechanisms underlying selection and inhibition processes engaged in the Go and NoGo trials, respectively, and may offer insight into therapeutic uses of neuromodulation in neural dysfunction.
Effect of Using Thyme, Carrot and Hibiscus to Some Crackers Products on their Nutritional and the Rapeutical Value

The main objective of the present study was carried to evaluate the possibility of enhancing antioxidant activities by using 10% of thyme, carrot and hibiscus powder fortified to crackers as natural colored antioxidants to produce healthy crackers, chemical composition and rheological properties were carried out, determination of phenolic compounds and antioxidant activity% noticed that thyme powder had the highest level of total phenols (12366 mg/100g), followed by carrot powder which was (5087.4 mg/100gm) and hibiscus had (4262.9 mg/100g), while antioxidant activity were 96.7% for thyme, 89.59% for carrot and 86.17% for hibiscus. Also, this work aimed to study the anti-hyperlipidemic activities of crackers with 100% wheat flour and crackers fortified with natural additives (thyme, carrot and hibiscus powder) on rats. The experiment was performed in 36 male rats distributed into 6 equal groups. Group (1) was kept as a normal control (fed on a basal diet), while rats of the other five groups were fed on hypercholesterolemic diet for 8 weeks to induce hypercholesterolemia. Rats of group (2) were left as a positive control and those of groups (3, 4, 5 and 6) were given unfortified cracker 100% wheat flour (CWF), 10% thyme(Cth) , 10% carrot (CCa ) and 10% hibiscus (Chi). The results revealed that rats consumed crackers fortified with natural additives decreases adiposity index, body mass index, Serum Cholesterol, Triglycerides (TG), Low density lipoprotein cholesterol (LDL-C) and Very low density lipoprotein cholesterol (VLDL-C), while there was an increase in serum HDL-C. Also showed decreases in serum ALT, AST, AP, Uric acid, Urea and Creatinine levels. The best results showed in thyme crackers then carrot followed by hibiscus for the treatment of hypercholesterolemic.
DAVI: Deep learning-based tool for alignment and single nucleotide variant identification

Next-generation sequencing (NGS) technologies have provided affordable but errorful ways to generate raw genetic data. To extract variant information from billions of NGS reads is still a daunting task which involves various hand-crafted and parameterized statistical tools. Here we propose a deep neural networks (DNN) based alignment and single nucleotide variant (SNV) identifier tool known as DAVI: deep alignment and variant identification. DAVI consists of models for both global and local alignment and for variant calling. We have evaluated the performance of DAVI against existing state-of-the-art tool sets and found that its accuracy and performance is comparable to existing tools used for bench-marking. We further demonstrate that while existing tools are based on data generated from a specific sequencing technology, the models proposed in DAVI are generic and can be used across different NGS technologies as well as across different species. The use of DAVI will therefore help non-human sequencing projects to benefit from the wealth of human ground truth data. Moreover, this approach is a migration from expert-driven statistical models to generic, automated, self-learning models.
Towards the management of Ustilago kamerunensis H Sydow and Sydow, a smut pathogen of Napier grass (Pennisetum purpureum Schum.) in Kenya

Infection by headsmut, caused by Usti/ago kamerunensis, is the major cause of biomass reduction in Napier grass (Pennisetum purpureum). In Kenya, this grass is the primary source of fodder for zero grazed livestock, which are an important component of small-holder farm incomes in high potential areas of the country. Studies were conducted on the biology and ecology of the pathogen, with the objective of developing technologies for management of the disease. A survey in Kiambu District, central Kenya, of Napier grass plots was used to construct a model of smut incidence. Ten agro-ecological and environmental parameters were factored into the model but only altitude, using a quadratic function, was significant. Napier grass at altitudes between 1800 and 2000masl is particularly at risk from the disease. This was the first smut disease correlative model to make use of environmental factors. The presence of the fungus was demonstrated inside the host, and descriptions of ustilospore and sorus characteristics were made. Ustilospores in soil remained viable for less than eight weeks, and those air dried for less than 14 weeks. The infection court was identified as being restricted to germinating buds, which were the only sites on which ustilospores gave rise to appressoria. On other host surfaces, and on agars, sporidia and hyphae were formed. A disease severity scale was developed, related to biomass reduction in fresh weight of Napier grass stools, that can be used to assess losses in field situations. Using a spore dipping technique, resistance testing of locally available cultivars identified susceptible and resistant types. Evidence was also gained for the possibility of disease remission. These investigations lead to the production of management options for farmers, that are discussed in view of the increasing importance of the disease in Kenya.
Mining Patterns of Sequential Malicious APIS to Detect Malware

In the era of information technology and connected world, detecting malware has been a major security concern for individuals, companies and even for states. The New generation of malware samples upgraded with advanced protection mechanism such as packing, and obfuscation frustrate anti-virus solutions. API call analysis is used to identify suspicious malicious behavior thanks to its description capability of a software functionality. In this paper, we propose an effective and efficient malware detection method that uses sequential pattern mining algorithm to discover representative and discriminative API call patterns. Then, we apply three machine learning algorithms to classify malware samples. Based on the experimental results, the proposed method assures favorable results with 0.999 F-measure on a dataset including 8152 malware samples belonging to 16 families and 523 benign samples.
Hierarchical Random Boolean Network Reservoirs

Reservoir Computing (RC) is an emerging Machine Learning (ML) paradigm. RC systems contain randomly assembled computing devices and can be trained to solve complex temporal tasks. These systems are computationally cheaper to train than other ML paradigms such as recurrent neural networks, and they can also be trained to solve multiple tasks simultaneously [13]. Further, hierarchical RC systems with fixed topologies, were shown to outperform monolithic RC systems by up to 40% when solving temporal tasks [3, 24]. Although the performance of monolithic RC networks was shown to improve with increasing network size, building large monolithic networks may be challenging, for example because of signal attenuation . In this research, larger hierarchical RC systems were built using a network generation algorithm. The benefits of these systems are presented by evaluating their accuracy in solving three temporal problems: pattern detection, food foraging, and memory recall. This work also demonstrates the functionality of random Boolean networks being used as reservoirs. Networks with up to 5, 000 neurons were used to train 200 sequences from memory and to identify X or O patterns temporally. Also, a Genetic Algorithm (GA) was used to train different types of hierarchical RC networks, to find optimal solutions for food-foraging tasks. This research shows that about 80% of the possible different hierarchical configurations of RC systems can outperform monolithic RC systems by up to 60% while solving complex temporal tasks. These results suggest that hierarchical random Boolean network RC systems can be used to solve temporal tasks, instead of building large monolithic RC systems.
Offline Photovoltaic Maximum Power Point Tracking

As more renewable energy sources are connected to the electrical grid, it has become important that these sources participate in providing system support. It has become needful for grid-connected solar photovoltaics to participate in support functions like frequency support. However, photovoltaic systems need to implement a maximum power tracking algorithm to operate at maximum power and a method for de-loading photovoltaic systems is necessary for participation in frequency support. Some conventional maximum power tracking techniques are implemented in real time and will not adjust their output fast enough to provide system support while other may respond fast but are not very efficient in tracking the maximum power point of a photovoltaic system. This paper presents an offline method to estimate the maximum power voltage and current based on the characteristics of the photovoltaics module available in the datasheet and using the estimated values to operate the photovoltaics at maximum power. The performance of this technique is compared to the conventional technique. This paper also describes how the photovoltaic system can be de-loaded.
Fracture of the dimorphic fruits of Aethionema arabicum (Brassicaceae)

Fruits exhibit highly diversified morphology, and are arguably one of the most highly specialised organs to have evolved in higher plants. Fruits range in morphological, biomechanical, and textural properties, often as adaptations for their respective dispersal strategy. While most plant species possess monomorphic (of a single type) fruit and seeds, here we focus on Aethionema arabicum (L.) Andrz. ex DC. (Brassicaceae). Its production of two distinct fruit (dehiscent and indehiscent) and seed types on the same individual plant provides a unique model system with which to study structural and functional aspects of dimorphism. Using comparative analyses of fruit fracture biomechanics, fracture surface morphology, and internal fruit anatomy, we reveal that the dimorphic fruits of A. arabicum exhibit clear material, morpho-anatomical, and adaptive properties underlying their fracture behaviour. A separation layer along the valve–replum boundary is present in dehiscent fruit, whereas indehiscent fruit have numerous fibres with spiral thickening, linking their winged valves at the adaxial surface. Our study evaluates the biomechanics underlying fruit-opening mechanisms in a heteromorphic plant species. Elucidating dimorphic traits aids our understanding of adaptive biomechanical morphologies that function as a bet-hedging strategy in the context of seed and fruit dispersal within spatially and temporally stochastic environments.
Phytochemical screening antioxidant and antimicrobial activity of aeschynomene aspera linn root extract

Medicinal plant are the nature’s of gift to human being to make disease free healthy life.It play a vital role to preserve our health. In india different part have been used for curing various diseases from ancient life. In this present study focus on the analysis of priliminary phytochemical in ethanol extract of Asechynomene Aspera Linn root. Among this extract presence of therapeutically important and valuable secondary metabolites flavonoids, alkaloids, trephenoids, steriods and phenol. The antioxidant activity of root sample was evaluvated by using DPPH, ABTS and Nitric oxide reducing activity. The present study showed that good free radical scavenging properties and a clear correlation exist between the antioxidant activity.The ethanolic extract of AsechynomeneAspera Linn root sample was tested for its antibacterial activity against two human bacterial pathogens (E.coli and S.pneumoniae) and using three different concentration by disc diffusion method.The maximum inhibition of antibactrial activity was observed in E.coli. The study concluded that the ethanolic extract of Asechynomene Aspera Linn root has potential antioxidant and antimicrobial action which is responsible for the biological activities that is used for natural health.
Fire Scars Negatively Affect Hydraulic Conductivity in White Oak (Quercus alba)

Fire management is increasingly used to manage forest stand structure and dynamics. Relatively intense fires can injure the tree stem and induce fire scar formation, affecting subsequent tree growth and wood quality. Here, we consider the physiological effects of fire scarring in white oak. Potential hydraulic conductivity, estimated from the mean vessel area and vessel number, was determined for growth rings formed before, during, and after the year of injury. We measured vessel anatomy using the ROXAS image analysis tool on the cross-sections of 14 white oaks of various ages with fire scars originating in different years through the late 19th and early 20th century. We found that the mean vessel area and potential hydraulic conductivity were significantly reduced for the year of and the year immediately following fire injury. After this two-year period, mean vessel area returned to levels present in wood formed prior to the injury. Age when scarred, radius from the pith when scarred, scar height above ground, and percentage of circumference scarred did not explain the degree to which potential hydraulic conductivity was lost in the fire scar year compared to the year prior. Overall, the magnitude of reduction in potential hydraulic conductivity was small but significant. An earlier study on the same cross-sections verified no reductions in radial growth after fire injury. Thus, it is likely that the conductance of older rings is adequate to sustain conductance. Nonetheless, we recommend further investigation, in particular, the ability to predict how tree size, age, position along a slope, and other variables may influence the degree of wounding and possible losses of potential hydraulic conductivity after the fire. Information like this for white oak and other common tree species may help elucidate the physiological impacts fire injuries have on trees existing in forest stands with periodic fire.