Miracle-dz/UniFAR-FTUs huggingface dataset:
Studies on Peroxidation in Rats Ingesting Copper Sulphate and Effect of Subsequent Treatment with Ocimum sanctum.

This study was aimed at determining the basic biochemical changes induced by the toxicity of copper sulphate (CuSO4) and analyzing the detoxifying effects of Ocimum sanctum, a medicinal plant commonly used in India. CuSO4 dissolved in saline was injected into rats for 30 days. Determinations of serum or tissue level of copper, lipid peroxides, iron, ferritin and antiperoxidative enzymes like SOD, CAT, GSH, GST, GPx, and ceruloplasmin were then carried out. CuSO4 toxicity manifested itself as significantly increased production of lipid peroxides from lipid products by a free-radical mechanism involving hydroxyl free radicals. The anti-oxidant enzyme levels were also notably increased in CuSO4 toxicity, but not to the extent of substantially reducing the hydroxyl free radicals produced by CuSO4. Ocimum sanctum administration restored the various parameters to near their normal values.
Growth response functions improved by accounting for nonclimatic site effects

Growth response functions (GRFs) that relate the growth of a population to the climate of the sites in which it is tested are gaining attention for their ability to predict impacts of climate change on tree growth. However, nonclimatic site to site variation introduces error into GRFs. Using data from a large lodgepole pine (Pinus contorta Dougl. ex Loud.) provenance test in British Columbia and the Yukon, Canada, a technique is presented that accounts for the effect of nonclimatic variation in GRFs. The mean height of the “local” provenances at each test site was used to predict “site height” from site climate variables in multiple regression. Residuals from the site height equation provided an index of the nonclimatic effect for each site and were included as a covariate in quadratic GRFs that related provenance height at each test site to mean annual temperature at each test site. Inclusion of the nonclimatic index in the model resulted in a moderate or large displacement of GRFs for 25% of the provena.
Taphonomic Signatures of a New Upper Triassic Phytosaur (Diapsida, Archosauria) Bonebed from India: Aggregation of a Juvenile-Dominated Paleocommunity

ABSTRACT A monotaxic bonebed containing numerous phytosaur specimens is reported from the Upper Triassic Tiki Formation of India. The comprehensive minimum number of individuals is estimated to be 21, which includes multiple juveniles and subadults, and only a few adults. Such intraspecific aggregation of juvenile-dominated phytosaurs is explained here by parental care and juveniles living in cohorts, as are evident from extant phylogenetic bracketing of either lepidosaurs or crocodiles and birds. The cause of this mass death is probably disease related, after which the carcasses bloated, floated, and disarticulated at the site of death, resulting in an autochthonous assemblage. The carcasses remained submerged under shallow, slow-moving or standing water for a prolonged time and later were subaerially exposed after the recession of water. Subsequently, flooding events resulted in their burial under the overbank fines. Comparison of biostratinomic variables with those of a rhynchosaur-dominated bonebed recovered previously from the same stratigraphic horizon showed distinct differences in their causes of death and fossilization pathways. The latter may be attributed to their habitat differences because the phytosaurs lived in the low-lying areas or ponds and the rhynchosaurs resided on relatively higher and drier areas of the Tiki floodplain.
Genetic variation in Candidatus Phytoplasma australiense

This study examined whether genes that are less conserved than the 16S rRNA gene can distinguish Candidatus Phytoplasma australiense strains that are identical based on their 16S rRNA genes, with a view to providing insight into their origins and distribution, and any patterns of association with particular plant hosts. Sequence analysis of the tuf gene and rp operon showed that Ca. P. australiense strains could be differentiated into four subgroups, named 16SrXII-B (tuf-Australia I; rp-A), 16SrXII-B (tuf-New Zealand I; rp-B), 16SrXII-B (tuf-New Zealand II) and 16SrXII-B (rp-C). Strawberry lethal yellows 1, strawberry green petal, Australian grapevine yellows, pumpkin yellow leaf curl and cottonbush witches' broom phytoplasmas were designated members of the 16SrXII-B (tuf-Australia I; rp-A) subgroup. The strawberry lethal yellows 2 and cottonbush reduced yellow leaves phytoplasmas were assigned to the 16SrXII (tuf-New Zealand II; rp-B) subgroup. No relationship was observed between these phytoplasma subgroups and collection date, location or host plant. However, the study revealed evolutionary divergence in the 16SrXII group.
Coating Iron Oxide Nanoparticles with Chitosan for Targeted Delivery of Nanocurcumin

BackgroundCurcumin is an active non-toxic component of turmeric and is a substance with strong anti-inflammatory and antioxidant properties. Iron, as a micronutrient, plays an important role in physiological processes. Chitosan is a natural polymer derived from chitin and is known as a biocompatible product due to its high biocompatibility, non-toxicity, and biodegradation to harmless products.ObjectivesThis study aimed to design and synthesize a modified magnetic nanoparticle to be used as a targeted drug delivery system.MethodsNanocurcumin loading was performed on the surface of chitosan-modified iron magnetic nanoparticles in two stages. In the first stage, chitosan was loaded on the surface of iron magnetic nanoparticles as a coating polymer. In the next step, the final reaction took place by the loading of nanocurcumin on the surface of magnetic iron nanoparticles modified by chitosan.ResultsThe results of transmission electron microscopy and scanning electron microscopy demonstrated the structure, morphology, physicochemical properties, and presence of nanocurcumin layers on chitosan in nanoparticles with a diameter of 20 nm. In the Fourier transform infrared spectroscopy, the iron-oxygen peak indicated the magnetic nanoparticles of iron, and the oxygen-hydrogen peak showed nanocurcumin layers on chitosan. Moreover, X-ray spectroscopy confirmed the presence of these elements in the final composition by showing iron, carbon, oxygen, and nitrogen peaks.ConclusionsThe results of this study showed that nanocurcumin was successfully loaded on the surface of magnetic iron nanoparticles modified by chitosan.
Consumo e digestibilidades dos nutrientes em bovinos recebendo dietas contendo silagens de milho e sorgo, com e sem inoculante microbiano

Four crossbred Holstein x Zebu steers fitted with ruminal and abomasal cannulas were randomly assigned to a 4 x 4 Latin square to evaluate the effects of feeding corn or sorghum silages with and without microbial inoculant on intake, ruminal metabolism, total and partial digestibility of nutrients, and digesta passage rate. Diets contained, dry matter (DM) basis, a forage to concentrate ratio of 63:37. No significant differences were observed on the intakes of DM, crude protein (CP), and total digestible nutrients, which averaged 6.61, 0.75 and 3.96 kg/day across diets, respectively. Total and ruminal apparent digestibilities of DM, CP and neutral detergente fiber (NDF) also were not affected by diets averaging 61.17 and 61.91, 61.43 and 13.21, and 52.13 and 92.96%, respectively. However, a dietary effect was observed for the intestinal digestibility of NDF; steers fed corn silage had greater intestinal digestibility of NDF (9.08%) than those fed sorghum silage (4.77%). Ruminal pH was not affected by diet and time of collection, with an overall mean of 5.58. Conversely, concentration of ruminal ammonia was affected by time of collection with estimated maximum values of 9.04, 10.82, 8.84, and 9.47 mg/100 mL for diets containing corn and sorghum silages with and without microbial inoculant, respectively. Estimated digesta passage rates of diets containing corn and sorghum silages with and without microbial inoculant were of 4.29, 5.05, 4.44, and 5.12 %/h, respectively.
The economic consequences of the coronavirus pandemic: which groups will suffer more in terms of loss of employment and income?

The paper estimates the scale of the possible impact of the coronavirus epidemic on the Russian labor market and household incomes. We consider vulnerable employment, which includes workers with increased risks of job loss, wage cuts and other negative events that reduce labor incomes. The analysis shows that every second Russian worker can be classified as vulnerable employee. The highest risks for reducing labor incomes as a result of the epidemic are faced by young people, workers with a low level of education, as well as residents of regional centers. About a third of vulnerable employees experience complex vulnerability, i.e. meet several criteria of vulnerability. A decrease in labor incomes could also affect the wealth of about 40% of Russian households. The worst situation is for younger families, whose incomes are much more dependent on the labor market. Most of the households in which vulnerable workers live do not receive social benefits, and therefore it would be rather difficult for them to count on prompt assistance from the social protection system. Currently Russia is actively developing a set of measures to support employment. In particular, government decided to provide support for the most affected by COVID sectors, the size of unemployment benefits has been increased for citizens who have lost their jobs due to the epidemic. However, it is obvious that this list should be extended and account for various forms of deterioration of employment.
Deep Attention Learning Mechanisms for Social Media Sentiment Image Revelation

: Sentiment analysis systems can handle social media images by interpreting the embedded emotional responses in those images. This represents an interesting and challenging problem that tries to figure out the high-level content of large-scale visual data based on algorithms devised from computer vision. This paper presents a system to analyze social media images and visualize the implied emotions from each image as (Happy, Sad, and Neutral). The objective of this work is to introduce a system model with features extraction basis utilizing some adequate technique of machine learning. The applied methodology is pivoted on implementing the required system through several steps of processing. This involves social media image displaying and video frames grabbing, image features extraction, then embedded emotions patterns classification and recognition utilizing a proper convolutional neural network (CNN). Flickr and Twitter datasets were utilized while the pertinent algorithm was developed using “Matlab20 1 7b” platform. This can help social media users visualizing their interests besides forming a better scope of visualization. It will further assist companies in envisaging the mood of users/costumers towards their stock prices in order to set competitive prices for both sides. We design a Deep Attention Network Mechanisms (DANM) to achieve a higher level of social media sentiment image analysis and classify them as (Highly positive mood and highly negative mood). The DANM produces features maps basis utilizing the adequate focusing technique of machine learning based on a proper convolutional neural network (CNN). The proposed CNN training system has proven better results with respect to accuracy and efficiency in comparison with some other similar works. When experimentations on both real and synthetic datasets were conducted, the system showed a percentile improvement of about 14.2%. This system is applicable to a broad horizon of applications such as studying the emotional response of humans on visual stimuli, visual sentiment analysis algorithms and modeling, building machine learning-based robust visual sentiment classifier, as well as in most online websites that involve visual data mining for business intelligence, e-commerce, stock market prediction, political vote forecasts, and video gaming.
Energy Management in Microgrids Containing Electric Vehicles and Renewable Energy Sources Considering Demand Response

Microgrid and smart electrical grids are among the new concepts in power systems that support new technologies within themselves. Electric cars are some advanced technologies that their optimized use can increase grid efficiency. The modern electric cars sometimes, through the necessary infrastructure and proper management, can serve as an energy source to supply grid loads. This study was conducted to investigate the energy management for production and storage resources. For this purpose, we considered the market price of energy, the prices quoted by distributed generation sources, and electric vehicles in the grid and responsive loads. The load response programs used include the time of use and direct load control. The problem has a linear mixed-integer planning structure that was simulated using the GAMS software. The results show that with this planning, the proposed load response programs have a positive impact on cost reduction. Keyword: Microgrid, energy management, electric vehicles, renewable resources, demand response program.
Can We Accurately Characterize Wildlife Resource Use When Telemetry Data Are Imprecise?

Abstract Telemetry data have been widely used to quantify wildlife habitat relationships despite the fact that these data are inherently imprecise. All telemetry data have positional error, and failure to account for that error can lead to incorrect predictions of wildlife resource use. Several techniques have been used to account for positional error in wildlife studies. These techniques have been described in the literature, but their ability to accurately characterize wildlife resource use has never been tested. We evaluated the performance of techniques commonly used for incorporating telemetry error into studies of wildlife resource use. Our evaluation was based on imprecise telemetry data (mean telemetry error = 174 m, SD = 130 m) typical of field-based studies. We tested 5 techniques in 10 virtual environments and in one real-world environment for categorical (i.e., habitat types) and continuous (i.e., distances or elevations) rasters. Technique accuracy varied by patch size for the categorical rasters, with higher accuracy as patch size increased. At the smallest patch size (1 ha), the technique that ignores error performed best on categorical data (0.31 and 0.30 accuracy for virtual and real data, respectively); however, as patch size increased the bivariate-weighted technique performed better (0.56 accuracy at patch sizes >31 ha) and achieved complete accuracy (i.e., 1.00 accuracy) at smaller patch sizes (472 ha and 1,522 ha for virtual and real data, respectively) than any other technique. We quantified the accuracy of the continuous covariates using the mean absolute difference (MAD) in covariate value between true and estimated locations. We found that average MAD varied between 104 m (ignore telemetry error) and 140 m (rescale the covariate data) for our continuous covariate surfaces across virtual and real data sets. Techniques that rescale continuous covariate data or use a zonal mean on values within a telemetry error polygon were significantly less accurate than other techniques. Although the technique that ignored telemetry error performed best on categorical rasters with smaller average patch sizes (i.e., ≤31 ha) and on continuous rasters in our study, accuracy was so low that the utility of using point-based approaches for quantifying resource use is questionable when telemetry data are imprecise, particularly for small-patch habitat relationships.
Characterization of endophytic bacteria associated with some medicinal plants

A total of 89 endophytic bacterial cultures were isolated by two techniques from seven medicinal plants; Coriandrum sativum, Anethum graveolens, Pelargonium graveolens, Ocimum basilicum, Rosmarinus officinalis, Saliva officinalis and Origanum majorana. To address the biological activity of these isolates as endophytic bacteria, preliminary screening showed 44, 36, 28, 22, 82 isolates out of total one gave positive results for cellulase, pectinase, amylase activities, indole acetic acid (IAA) and gibberellic acid (GA3) production, respectively. The secondary screening twenty endophytic bacterial isolates were selected and examined for their antagonistic effect against four pathogenic fungi, antioxidants activity, total phenols, indole acetic acid, ammonia (NH3), siderophores production and nitrogenase activity. According to the last screening; six isolates were chosen because of their multi-plant growth promoting (PGP) traits. They recorded a wide range of total flavonoids concentration in endophytic bacterial supernatant. The values of total flavonoids varied from 1.43 to 31.14 ppm. While the alkaloids production was detected by all tested isolates except isolate S14. Isolate B3 gave the maximum alkaloids concentration in its supernatant being 0.34 ppm. Four isolates are able to solubilize phosphate, low variations between tested isolates in phosphate solubilization ranged from 5.65 ppm by isolate RO14 to 3.85 ppm by isolate C8. All isolates showed negative result for hydrogen cyanide (HCN) production. Furthermore, the colonization of the six most efficient isolates inside the tissue of two medicinal plant Ocimum basilicum and Coriandrum sativum were performed by two techniques; 2,3,5-triphenyl tetrazolium chloride (TTC) stain and Transmission electron microscope (TEM). Based on some morphological and biochemical characteristics of the most efficient isolates (RO10, RO14, S14, D6, C8 and B3); the six isolates were similar to three genera (Enterobacter sp., Aeromonas sp. and Bacillus sp.). Hypersensitivity test proved that all the six endophytic bacterial isolates are nonpathogenic bacteria. Where, these isolates gave negative symptoms of hypersensitivity reaction (HR) test on the pepper plants (Capsicum annuum) used as indicator plant.
A computational super-resolution technique based on coded aperture imaging

We report a new computational super-resolution (SR) imaging technique, termed as coded aperture super-resolution imaging (CASR), which is to modulate the point spread function (PSF) of the imaging system by rotating the aperture pattern. The pattern is designed in an anisotropic manner so that the PSF spreads across multiple pixels and contains clues about high-frequency structure. A fundamental difference between our approach and conventional multi-image superresolution is that CASR accounts for the diffraction effect explicitly with no need for relative motion between the scene and the detector. With CASR, we design and construct two sets of programmable aperture photoelectric imaging systems in the visible spectrum. The achievable equivalent Nyquist sampling frequency of the detectors is increased to 3.57×. Furthermore, it can be flexibly applied to long-distance HR detection due to its advantages of fast response, no mechanical movement, and anti-airflow disturbance.
The historical biogeography of sturgeons (Osteichthyes: Acipenseridae): a synthesis of phylogenetics, palaeontology and palaeogeography

Abstract. The historical biogeography of sturgeons is explored using information from palaeogeography, palaeontology and phylogenetic interrelationships. The integration of information from these diverse sources indicates that sturgeons reached a wide Laurasian distribution in the Cretaceous and Tertiary by freshwater and coastal dispersal routes across land connections and along newly forming continental margins. The fossil record also suggests a considerable degree of morphological stasis and also supports an estuarine habit, and perhaps diadromy, as an old and conserved life history trait. While a ‘centre of origin’ for sturgeons remains elusive, phylogenetic relationships indicate that diversification appears to have been associated with fragmentation of biota, and of landmasses and basins, by late Tertiary geological and climatic phenomena, such as orogeny and unequal glaciation over North America, the desiccation of central Asia and alteration of its drainages, and the formation of discrete Ponto‐Caspian basins by the fragmentation of the Paratethys. Amphi‐oceanic distributions of certain species (Acipenser medirostris Ayres) and sister taxa (e.g. A. oxyrhynchus Mitchill and A. sturio L.) are explained by coastal dispersal and subsequent vicariance by geological (sea‐floor spreading and development of new continental margins) and climatic (Pliocene cooling) changes during the Tertiary. An hypothesis is developed for the relationships of the North American sturgeons and their potential relationships with the Siberian sturgeon A. baeri. Late Tertiary climatic and geological phenomena are hypothesized as mediators of vicariance and subsequent diversification of these acipenserids. It appears that although acipenserids are a geologically old group, the historical biogeography of surviving lineages is best explained by more recent geological and climatic changes.
An In Vitro Inhibition Test That Predicts Toxicity of Bacterial Pathogen Combinations in the Colorado Potato Beetle

Many insect bacterial pathogens are not toxic enough for field control. Combinations of bacteria may increase toxicity. Bacteria toxic to Colorado potato beetle , Photorhabdus luminescens, Chromobacterium violaceum and Serratia marcescens , were tested in pair-wise combinations in an in vitro double streak test to determine bacterial compatibility. Only C. violaceum and S. marcescens grew to confluency. Their combined toxicity in vivo was additive. Other bacterial combinations had clear zones between bacterial streaks indicating inhibition. In the insect, the combined toxicity was less than the most toxic bacteria of the pair. For these strains, this in vitro test predicted compatibility in the insect.
The need for pure enzymes that degrade cell wall polysaccharides and how to get them

Pure, well-characterized, polysaccharide-degrading enzymes are highly desirable for the study of plant cell walls, but until recently have been tedious to prepare. We have expressed polysaccharide-degrading enzymes from fungi in Pichia pastoris, from which they can be purified very easily, and have partially characterized their modes of action.
Deep instance segmentation and 6D object pose estimation in cluttered scenes for robotic autonomous grasping

This paper aims to design a deep neural network for object instance segmentation and six-dimensional (6D) pose estimation in cluttered scenes and apply the proposed method in real-world robotic autonomous grasping of household objects. ,A novel deep learning method is proposed for instance segmentation and 6D pose estimation in cluttered scenes. An iterative pose refinement network is integrated with the main network to obtain more robust final pose estimation results for robotic applications. To train the network, a technique is presented to generate abundant annotated synthetic data consisting of RGB-D images and object masks in a fast manner without any hand-labeling. For robotic grasping, the offline grasp planning based on eigengrasp planner is performed and combined with the online object pose estimation. ,The experiments on the standard pose benchmarking data sets showed that the method achieves better pose estimation and time efficiency performance than state-of-art methods with depth-based ICP refinement. The proposed method is also evaluated on a seven DOFs Kinova Jaco robot with an Intel Realsense RGB-D camera, the grasping results illustrated that the method is accurate and robust enough for real-world robotic applications. ,A novel 6D pose estimation network based on the instance segmentation framework is proposed and a neural work-based iterative pose refinement module is integrated into the method. The proposed method exhibits satisfactory pose estimation and time efficiency for the robotic grasping.