Miracle-dz/UniFAR-FTUs huggingface dataset:
Gamma radiosensitivity study on wheat (Triticum turgidum ssp. durum)

Abstract Fifty wheat seed variety (var.) CIRNO C2008 were gamma irradiated with 100, 200, 300, 400, 500, or 600 Gy by using a Cobalt-60 source at 907.39 Gy/h, and the nonirradiated seeds were considered the control (M0). A photoperiod of 13 h of darkness (14°C) and 11 h of light (2 h at 18°C, 7 h at 25°C, and 2 h at 18°C) was used for 30 days, in a growth chamber (BJPX-A450; BIOBASE) for quantifying the morphometric traits of the irradiated vs nonirradiated seeds. The germination percentage (from 88.89% in M0 to 77.78% in 600 Gy) did not show significant differences among treatments; while the survival percentage (from 100% in M0 to 6.66% in 600 Gy) and plant height (from 19.21 cm in M0 to 1.16 cm in 600 Gy) showed negative significant (p < 0.05) difference among nonirradiated seeds (M0) and the rest of the treatments. Finally, based on the survival percentage, the calculated lethal dose was 290.6 Gy, which will allow obtaining promising wheat mutants. Gamma irradiation is a promising alternative to obtain a greater wheat variability for advanced mutant lines.
SC-JNMF: single-cell clustering integrating multiple quantification methods based on joint non-negative matrix factorization

Unsupervised cell clustering is important in discovering cell diversity and subpopulations. Single-cell clustering using gene expression profiles is known to show different results depending on the method of expression quantification; nevertheless, most single-cell clustering methods do not consider the method. In this article, we propose a robust and highly accurate clustering method using joint non-negative matrix factorization (joint NMF) based on multiple gene expression profiles quantified using different methods. Matrix factorization is an excellent method for dimension reduction and feature extraction of data. In particular, NMF approximates the data matrix as the product of two matrices in which all factors are non-negative. Our joint NMF can extract common factors among multiple gene expression profiles by applying each NMF to them under the constraint that one of the factorized matrices is shared among the multiple NMFs. The joint NMF determines more robust and accurate cell clustering results by leveraging multiple quantification methods compared to the conventional clustering methods, which uses only a single quantification method. In conclusion, our study showed that our clustering method using multiple gene expression profiles is more accurate than other popular methods.
PsychRNN: An Accessible and Flexible Python Package for Training Recurrent Neural Network Models on Cognitive Tasks

Visual Abstract Example workflow for using PsychRNN. First, the task of interest is defined, and a recurrent neural network (RNN) model is trained to perform the task, optionally with neurobiologically informed constraints on the network. After the network is trained, the researchers can investigate network properties including the synaptic connectivity patterns and the dynamics of neural population activity during task execution, and other studies, e.g., those on perturbations, can be explored. The dotted line shows the possible repetition of this cycle with one network, which allows investigation of training effects of task shaping, or curriculum learning, for closed-loop training of the network on a progression of tasks. Task-trained artificial recurrent neural networks (RNNs) provide a computational modeling framework of increasing interest and application in computational, systems, and cognitive neuroscience. RNNs can be trained, using deep-learning methods, to perform cognitive tasks used in animal and human experiments and can be studied to investigate potential neural representations and circuit mechanisms underlying cognitive computations and behavior. Widespread application of these approaches within neuroscience has been limited by technical barriers in use of deep-learning software packages to train network models. Here, we introduce PsychRNN, an accessible, flexible, and extensible Python package for training RNNs on cognitive tasks. Our package is designed for accessibility, for researchers to define tasks and train RNN models using only Python and NumPy, without requiring knowledge of deep-learning software. The training backend is based on TensorFlow and is readily extensible for researchers with TensorFlow knowledge to develop projects with additional customization. PsychRNN implements a number of specialized features to support applications in systems and cognitive neuroscience. Users can impose neurobiologically relevant constraints on synaptic connectivity patterns. Furthermore, specification of cognitive tasks has a modular structure, which facilitates parametric variation of task demands to examine their impact on model solutions. PsychRNN also enables task shaping during training, or curriculum learning, in which tasks are adjusted in closed-loop based on performance. Shaping is ubiquitous in training of animals in cognitive tasks, and PsychRNN allows investigation of how shaping trajectories impact learning and model solutions. Overall, the PsychRNN framework facilitates application of trained RNNs in neuroscience research.
Validation of Functional Polymorphisms Affecting Maize Plant Height by Unoccupied Aerial Systems (UAS) allows Novel Temporal Phenotypes

Plant height (PHT) in maize (Zea mays L.) has been scrutinized genetically and phenotypically due to relationship with other agronomically valuable traits (e.g. yield). Heritable variation of PHT is determined by many discovered quantitative trait loci (QTLs); however, phenotypic effects of such loci often lack validation across environments and genetic backgrounds, especially in the hybrid state grown by farmers rather than the inbred state preferred by geneticists. A previous genome wide association study using a hybrid diversity panel identified two novel quantitative trait variants (QTVs) controlling both PHT and grain yield. Here, heterogeneous inbred families demonstrated that these two loci, characterized by two single nucleotide polymorphisms (SNPs), cause phenotypic variation in inbred lines, but that size of these effects were variable across four different genetic backgrounds, ranging from 1 to 10 cm. Weekly unoccupied aerial system flights demonstrated both SNPs had larger effects, varying from 10 to 25 cm, in early growth while SNPs effects decreased towards the end of the season. These results show that allelic effect sizes of economically valuable loci are both dynamic in temporal growth and dynamic across genetic backgrounds resulting in informative phenotypic variability overlooked following traditional phenotyping methods. Public genotyping data shows recent favorably selection in elite temperate germplasm with little change across tropical backgrounds. As these loci remain rare in tropical germplasm, with effects most visible early in growth, they are useful for breeding and selection to expand the genetic basis of maize.
Soil microbial diversity impacts plant microbiomes more than herbivory

Interactions between plants and microbiomes play a key role in ecosystem functioning, and are of broad interest due to their influence on nutrient cycling and plant protection. However, we do not yet have a complete understanding of how plant microbiomes are assembled. Here, for the first time, we show interactions between plant-associated microbial communities that drive their diversity and community composition. We manipulated soil microbial diversity, plant species, and herbivory, and found that soil microbial diversity influenced the herbivore-associated microbiome composition, but also plant species and herbivory influenced the soil microbiome composition. We used a novel approach, quantifying the relative strength of these effects, and demonstrated that the initial soil microbiome diversity explained the most variation in plant- and herbivore-associated microbial communities. Our findings strongly suggest that soil microbial community diversity is a driver of the composition of multiple associated microbiomes (plant and insect), and this has implications for the importance of management of soil microbiomes in multiple systems.
Epistasis on the stability landscape of de novo TIM barrels explored by a modular design approach

The design of stable proteins with custom-made functions is a major goal in biochemistry with practical relevance for our environment and society. High conformational stability lowers protein sensitivity to mutations and changes in the environment; thus, understanding and manipulating protein stability will expand the applications of de novo proteins. Since the (β/α)8-barrel or TIM-barrel fold is one of the most common functional scaffolds, in this work we designed a collection of stable de novo TIM barrels (NovoTIMs), using a computational fixed-backbone and modular approach based on improved hydrophobic packing of sTIM11, the first validated de novo TIM barrel. NovoTIMs navigate a region of the stability landscape previously uncharted by natural TIM barrels, with variations spanning 60 degrees in melting temperature and 25 kcal per mol in conformational stability throughout the designs. Significant non-additive or epistatic effects were observed when stabilizing mutations from different regions of the barrel were combined. The molecular basis of epistasis in NovoTIMs appears to be related to the extension of the hydrophobic cores. This study is an important step towards the fine-tuned modulation of protein stability by design. Significance Statement De novo protein design expands our knowledge about protein structure and stability. The TIM barrel is a highly relevant fold used in nature to host a rich variety of catalytic functions. Here, we follow a modular approach to design and characterize a collection of de novo TIM barrels and subjected them to a thorough folding analysis. Non-additive effects modulate the increase in stability when different regions of the barrel are mutated, showing a wide variety of thermodynamic properties that allow them to navigate an unexplored region of the stability landscape found in natural TIM barrels. The design of stable proteins increases the applications of de novo proteins and provides more information on the molecular determinants that modulate structure and stability.
Positive Selection Inhibits Plasmid Coexistence in Bacterial Genomes

Bacterial genomes often contain multiple coexisting plasmids that provide important functions like antibiotic resistance. Using lab experiments, we show that such plasmid coexistence within a genome is stable only in environments where the function they encode is useless but is unstable if the function is useful and beneficial for bacterial fitness. ABSTRACT Plasmids play an important role in bacterial evolution by transferring niche-adaptive functional genes between lineages, thus driving genomic diversification. Bacterial genomes commonly contain multiple, coexisting plasmid replicons, which could fuel adaptation by increasing the range of gene functions available to selection and allowing their recombination. However, plasmid coexistence is difficult to explain because the acquisition of plasmids typically incurs high fitness costs for the host cell. Here, we show that plasmid coexistence was stably maintained without positive selection for plasmid-borne gene functions and was associated with compensatory evolution to reduce fitness costs. In contrast, with positive selection, plasmid coexistence was unstable despite compensatory evolution. Positive selection discriminated between differential fitness benefits of functionally redundant plasmid replicons, retaining only the more beneficial plasmid. These data suggest that while the efficiency of negative selection against plasmid fitness costs declines over time due to compensatory evolution, positive selection to maximize plasmid-derived fitness benefits remains efficient. Our findings help to explain the forces structuring bacterial genomes: coexistence of multiple plasmids in a genome is likely to require either rare positive selection in nature or nonredundancy of accessory gene functions among the coexisting plasmids. IMPORTANCE Bacterial genomes often contain multiple coexisting plasmids that provide important functions like antibiotic resistance. Using lab experiments, we show that such plasmid coexistence within a genome is stable only in environments where the function they encode is useless but is unstable if the function is useful and beneficial for bacterial fitness. Where competing plasmids perform the same useful function, only the most beneficial plasmid is kept by the cell, a process that is similar to competitive exclusion in ecological communities. This process helps explain how bacterial genomes are structured: bacterial genomes expand in size by acquiring multiple plasmids when selection is relaxed but subsequently contract during periods of strong selection for the useful plasmid-encoded function.
Long-Term Ovariectomy Effect on Plasma Glucose, Lipids, Antioxidant Vitamins, and Lipid Peroxides

This study was performed to determine plasma lipid changes and antioxidant status in relation to cholesterol content and fatty acid distribution in the aorta of rats after long-term ovariectomy (OV). After 12 months, OV induced in rat plasma a significant increase in glucose, total cholesterol, and triglycerides compared with the corresponding values for the control rats (C) of the same age (p<0.0005, p<0.005, p<0.000S, respectively). These changes were associated with a significant increase in lipid peroxide level (p<0.05) and a significant decrease in vitamin E in the plasma from group OV compared with the group C level (p<0.0001). Conversely, OV after 7 and 12 months induced a significant increase in vitamin C (p<0.001, p<0.0001, respectively). Concerning plasma fatty acid distribution, after 12 months, OV caused a significant increase in gamma and dihomo gamma linolenic acid (p<0.0005, p<0.05, respectively). These changes were associated with a significant increase in aortic cholesterol in group OV (p<0.0005), and a significant decrease in the percentage of arachidonic acid (p<0.005) and of the total amount of fatty acids of the n-3 series (p<0.0001) and plasmalogen fatty acids (C16:0, p<0.005; C 18:0, p<0.05) compared with the corresponding values for group C. These results show that OV induced, independently of any effects of aging, plasma-specific lipid alterations with dysregulation of oxidative status, which could be crucial in the development of atherothrombotic disease.
CLASSIFICATION AND MONITORING PLAINS COTTONWOOD ECOLOGICAL TYPE IN THE NORTHERN GREAT PLAINS

A multivariate statistical model was developed for the plains cottonwood (Populus deltoides) ecological type to classify seral stages and to monitor succession based on three key variables. Four ecological seral stages representing early to late succession were identified quantitatively with a classification accuracy of 95%. All seral stages were significantly different (P < 0.001). Information required to predict seral stages within 0.2 acre or 0.08 ha macroplots by the model includes DBH of plains cottonwood trees >1 inch, stem number <1 inch and tree number. These are the only measurements required for seral stage classification and monitoring. Resource managers will be able to use this model to evaluate management actions by monitoring changes within and among seral stages. This model is simple to use, reliable, repeatable, accurate, and cost effective to meet resource management objectives and monitoring plans.
Application of machine vision systems in aquaculture with emphasis on fish: state-of-the-art and key issues

Demands of aquatic products are increasing dramatically during past decades. Also quality assurance has gradually received more attention by both producers and consumers. Thus, fish producers are exploring all possible approaches for improving the productivity and profitability. Monitoring of fish state and behaviour during cultivation may help to improve profitability for producers and also reduce the threat of severe loss because of disease and stress incidents. It is necessary to evaluate and measure quality of fish products in accurate, fast and objective way for meeting the different demands of the fish-processing industry after harvesting. Traditional methods are usually time-consuming, expensive, laborious and invasive. Using rapid, inexpensive and noninvasive methods is therefore important and desirable. Optical sensors and machine vision system provide the possibility of developing faster, cheaper and noninvasive methods for in situ and after harvesting monitoring of quality in aquaculture. This review describes the most recent technologies and the suitability of different optical sensors for the fish farming management and also assessment, measurement and prediction of fish products quality. Two major areas of optical sensors applications in aquaculture are discussed in this review: (i) preharvesting and during cultivation; and (ii) post-harvesting. Finally, accuracy and uncertainty of optical sensors applications in aquaculture are discussed. This review showed that MVSs and optical sensors have found real-world application based on tremendous possibility offered by digital camera development and increasing the speed of computer-based processing; however, still new algorithms, methods and re-engineered sensors need to be developed to meet real-world requirements.
Poverty Prediction Using Satellite Imagery And Machine Learning

: Poverty is a major problem in developing countries as it becomes an obstacle to development. Identifying poor areas is a big task for the government to make policy. Earlier household survey method takes more time, effort and cost. To overcome this limitation, the method of predicting poverty using satellite images is covered in the paper. Composite data of nighttime satellite images of Rwanda country of Africa is taken from DMSP-OLS’s satellite series under EOG of NOAA is trained with a Regression model. 5 night light intensity features used to train the Regression model with a label of DHS data of Rwanda which includes latitude, longitude and respective wealth index. The result shows there is a good correlation with night light intensity and wealth index with R 2 of 0.752. In the second experiment daytime satellite images taken from Google static maps, extracted 4096 CNN features and trained with the CNN model using transfer learning. Which also gives a good result with 82% testing accuracy. Thus both the experiment reveals that satellite data and machine learning can be a useful technique for estimating poverty.
Role of stearoyl-CoA desaturase-1 in maintaining muscle activity : study of a lesion model for understanding the meatbolic alterations characteristics of amyotrophic lateral sclerosis

ALS patients and mouse model manifest metabolic dysfunctions that coincide with the modified levels of various lipid species. We postulated that metabolic dysfunctions in muscles function as a leading preliminary change in ALS. We have noted that the expression of stearoyl-CoA desaturase 1 (SCD1), a key enzyme that synthesises monounsaturated fatty acids (MUFAs) from saturated fatty acids (SFAs), is diminished even at pre-symptomatic stage in the muscles of an ALS mouse model. In these mice, alterations in circulating and hepatic fatty acid composition, resulting from SCD1 modification, arise at a critical stage of disease onset. Of note, inhibition of SCD1 enzymatic activity by a specific pharmacological agent mimics the metabolicphenotype of the ALS mouse model. Our study also elucidates that the lack of SCD1 plays a vital role in neuromuscular function. It modulates energy supply, and maintains muscle activity by increasing oxidative metabolism and the expression of genes involved in neuromuscular junction development and function. In addition, ablation of SCD1 gene stimulates functional recovery of muscles after a nerve lesion. Pharmacological SCD1 inhibition also provides a protection to muscle function. We conclude that alteration in SCD1 expression and related altered fatty acid profile may protect muscles against pathology. Therefore, SCD1 inhibitors can be developed as a therapeutic intervention.
Status and Developments in the Deposition of Sputtered Nb/Cu QWRs by using a New Magnetron Configuration Source

Nowadays in order to increase the beam energy of HIEISOLDE facility at CERN, QWR (Quarter Wave Resonator) cavities are required. For this reason, a parallel R&D is necessary both at CERN and LNL, in the quest for higher performances. The operating frequency at 4.5 K is 101.28 MHz and the required performance for the high beta cavity is 6 MV/m accelerating field for 10 W maximum power dissipation. The intrinsic Quality factor must be at least 5e8 [1]. The DC biased diode sputtering technique allowed the achievement of Q-values of the order of 1e9 at accelerating fields of 7MV/m, but it is relatively tricky and requires long deposition times (around 36 hours or more). For this reason the challenge was to find an alternative method to deposit thin films in shorter times. An innovative system for magnetron sputtering HIE-ISOLDE cavities was set up at INFN-LNL, getting very good results [2,3]. The setup achieved high sputtering rates (above 10 Å/s), and very good thickness homogeneity (2±1 μm). The superconducting properties were comparable with those of niobium bulk (Tc around 9,27K) with a Residual Resistivity Ratio from 20 to 61. The construction of a cryostat to test the RF performance of QWR cavities as well as the method of measurement will be reported in this paper.
The enigmatic evolutionary relationships of Palaeocene mammals and their relevance for the Tertiary radiation of placental mammals

Understanding the general pattern of how a clade evolves over time is a central aim of palaeontology and evolutionary biology. The observation that the tree of life is asymmetric in species distribution necessitates that rates of evolution, speciation, and extinction vary through time and across phylogeny. The way this variation is distributed can help to inform on historic events, selection pressures, and relationships. Often, at the origination of a clade, it is supposed that there is an ‘early burst’ of diversification, before rates of speciation and morphological evolution slow down as the clade ages. One example of a supposed ‘early burst’ is that of placental mammals, but the internal relationships of the earliest members of this group have prevented further study of macroevolutionary parameters. In this thesis, by building the largest cladistic data matrix to date, I test the relationships of mammals from the earliest Cenozoic, and from the resulting phylogenies, test the hypotheses that the end-Cretaceous mass extinction resulted in an adaptive radiation of placental mammals. I show that Phenacodontidae are most parsimoniously ancestral to Perissodactyla, that a division between Boreoeutheria and Atlantogenata is better supported than one between Xenarthra and Epitheria or Afrotheria and Exafroplacentalia at the root of Placentalia, and that all “condylarths” can be placed, with varying degrees of confidence, as stem members of laurasiatherian orders. I show that there was an increase in rate of morphological evolution immediately after the end-Cretaceous mass extinction, that Placentalia is extremely likely to have originated less than 70 million years ago, and that the rise of Placentalia was associated with an increase in morphospace occupation, and, with a lag, mean pairwise dissimilarity of taxa. These conclusions support the contention that the end-Cretaceous mass extinction was not just an important time in Earth’s ecological history, but crucial to the diversification of mammals to the level observed today.