Miracle-dz/UniFAR-FTUs huggingface dataset:
Biochemical Responses to Salt Stress and Biostimulant Action in Tomato Plants Grown in Two Different Soil Types

Soil salinity is one of the major causes of losses in agricultural production, which is accentuated by global warming. A sustainable strategy to mitigate the effects of the increasing soil salinisation is the use of biostimulants. In this study, the effect of a plant-based biostimulant was tested on tomato plants exposed to different salinity levels in loamy and sandy soils. Salinity in the soils reached a maximum value of 14.35 dS m−1. The treatments included a control (tap water) and a dose of 0.4 mL L−1 of the commercial biostimulant BALOX®, containing polyphenols and glycine betaine. After 60 days of treatment, several growth and biochemical parameters were evaluated in the plants, and different responses were found depending on the type of soil texture. Salt stress inhibited plant growth, mainly affecting the roots of plants not treated with the biostimulant, in both soil texture types. It also caused a reduction of total chlorophyll and carotenoid levels by 44% and 38%, respectively, under severe salinity conditions. High salinity induced a significant increase in ionic, osmotic and oxidative stress in plants, as indicated by the accumulation of toxic Na+ and Cl− ions, higher proline and MDA levels, and increased antioxidant enzyme activities. However, the application of BALOX® stimulated plant growth and root system development in all experimental conditions. The areas of roots and leaves and the stem length and diameter showed higher mean values in biostimulant-treated plants on both soil types, with more pronounced differences with the controls in sandy soils. The total fresh weight showed maximum increases of 54% and 93% on loamy and sandy soils, respectively. In addition, BALOX® significantly improved the active transport of nutrients such as K+ and Ca2+, and the concentration of photosynthetic pigments by up to 64% over control values; it also allowed greater protection against salt stress as shown by a significant reduction of proline levels, by up to 36%, and MDA by 51% under strongly saline conditions. The application of BALOX® generally decreased the level of stress in the tomato plants, promoting plant growth and enhancing their biochemical responses, even on strongly salinised soils.
On the impossibility of using analogue machines to calculate non-computable functions

A number of examples have been given of physical systems (both classical and quantum mechanical) which when provided with a (continuously variable) computable input will give a non-computable output. It has been suggested that these systems might allow one to design analogue machines which would calculate the values of some number-theoretic non-computable function. Analysis of the examples show that the suggestion is wrong. In Section 4 I claim that given a reasonable definition of analogue machine it will always be wrong. The claim is to be read not so much as a dogmatic assertion, but rather as a challenge. In Sections 1 and 2 I discuss analogue machines, and lay down some conditions which I believe they must satisfy. In Section I discuss the particular forms which a paradigm undecidable problem (or non-computable function) may take. In Sections 5 and 6 I justify any claim for two particular examples lying within the range of classical physics, and in Section 7 I justify it for two (closely connected) examples from quantum mechanics, and discuss, very briefly, other possible quantum mechanical situations. Section 8 contains various remarks and comments. In Section 9 I consider the suggestion made by Penrose that a (future) theory of quantum gravity may predict non-locally-determined, and perhaps non-computable patterns of growth for microsopic structures. My conclusion is that such a theory will have to have non-computability built into it.
An Evaluation of Hybrid Deep Learning Models for Classifying Multiple Lower Limb Actions

Brain-computer Interfaces (BCIs) interpret electroencephalography (EEG) signals and translate them into control commands for operating external devices. The motor imagery (MI) paradigm is popular in this context. Recent research has demonstrated that deep learning models, such as convolutional neural network (CNN) and long short-term memory (LSTM), are successful in a wide range of classification applications. This is because CNN has the property of spatial invariance, and LSTM can capture temporal associations among features. A combination of CNN and LSTM could enhance the classification performance of EEG signals due to the complementation of their strengths. Such a combination has been applied to MI classification based on EEG. However, most studies focused on either the upper limbs or treated both lower limbs as a single class, with only limited research performed on separate lower limbs. We, therefore, explored hybrid models (different combinations of CNN and LSTM) and evaluated them in the case of individual lower limbs. In addition, we classified multiple actions: MI, real movements and movement observations using four typical hybrid models and aimed to identify which model was the most suitable. The comparison results demonstrated that no model was significantly better than the others in terms of classification accuracy, but all of them were better than the chance level. Our study informs the possibility of the use of multiple actions in BCI systems and provides useful information for further research into the classification of separate lower limb actions.
The Formal Language Theory Column

A new representation of languages for multitape ﬁnite automata is considered based on a special binary coding of elements in a free partially commutative semigroup. The mentioned coding was used previously for solution of several problems in theory of automata. An overview of these results, as well as, related works are presented. Some new results based on this representation, which are currently under review
Turkish Journal of Electrical

: 3D UNet has achieved high brain tumor segmentation performance but requires high computation, large memory, abundant training data, and has limited interpretability. As an alternative, the paper explores using 2D triplanar (2.5D) processing, which allows images to be examined individually along axial, sagittal, and coronal planes or together. The individual plane captures spatial relationships, and combined planes capture contextual (depth) information. The paper proposes and analyzes an ensemble of uniplanar and triplanar UNets combined with channel and spatial attention for brain tumor segmentation. It investigates the significance of each plane and analyzes the impact of uniplanar and triplanar ensembles with attention to segmentation. We tested the performance of these variants on the BraTS2020 training and validation datasets. The best dice similarity coeﬀicients for enhancing tumor, whole tumor, and tumor core over the training set are 0.712, 0.897, and 0.837, while they are 0.699, 0.875, and 0.782, over the validation set, respectively (obtained through BraTS model evaluation platform). The scores are at par with the leading 2D and 3D BraTS models. Therefore, the proposed approach with fewer parameters (almost 3 × less) demonstrates comparable performance to that of a 3D model, making it suitable for brain
MIT Open Access Articles Optimal relay location and power allocation for low SNR broadcast relay channels

—We consider the broadcast relay channel (BRC), where a single source transmits to multiple destinations with the help of a relay, in the limit of a large bandwidth. We address the problem of optimal relay positioning and power allocations at source and relay, to maximize the multicast rate from source to all destinations. To solve such a network planning problem, we develop a three-faceted approach based on an underlying information theoretic model, computational geometric aspects, and network optimization tools. Firstly, assuming superposition coding and frequency division between the source and the relay, the information theoretic framework yields a hypergraph model of the wideband BRC, which captures the dependency of achievable rate-tuples on the network topology. As the relay position varies, so does the set of hyperarcs constituting the hypergraph, rendering the combinatorial nature of optimization problem. We show that the convex hull C of all nodes in the 2-D plane can be divided into disjoint regions corresponding to distinct hyperarcs sets. These sets are obtained by superim-posing all k-th order Voronoi tessellation of C. We propose an easy and efﬁcient algorithm to compute all hyperarc sets, and prove they are polynomially bounded. Then, we circumvent the combinatorial nature of the problem by introducing continuous switch functions, that allows adapting the network hypergraph in a continuous manner. Using this switched hypergraph approach, we model the original problem as a continuous yet non-convex network optimization program. Ultimately, availing on the techniques of geometric programming and p -norm surrogate approximation, we derive a good convex approximation. We provide a detailed characterization of the problem for collinearly located destinations, and then give a generalization for arbitrarily located destinations. Finally, we show strong gains for the optimal relay positioning compared to seemingly interesting positions
Phosphorus Rather than Nitrogen Addition Changed Soil Cyanobacterial Community in a Tropical Secondary Forest of South China

Soil cyanobacteria in tropical forests is understudied despite its important role in soil biochemical process and plant growth. Under a nitrogen (N) deposition background in tropical forests, it is important to learn how soil cyanobacterial communities respond to N deposition and whether phosphorus (P) mediated this response. A fully two-factor (N and P additions) factorial design with four blocks (replicates), each including a 12 × 12 m plot per treatment (Control, +N, +P, and +NP) were established in a tropical secondary forest in 2009. In July of 2022, soil cyanobacteria at 0–10 cm and 10–20 cm depths in the experimental site were collected and analyzed using a metagenomic method. The impact of N and P additions on soil cyanobacteria remained consistent across the different soil depths, even though there was a significant contrast between the two layers. The effect of N addition on soil cyanobacteria did not significantly interact with P addition. N addition increased soil N availability and decreased soil pH but did not significantly affect the soil cyanobacterial community. In contrast, P addition increased soil P availability and soil pH, but decreased soil N availability and substantially changed the soil cyanobacterial community. P addition significantly decreased the abundance of soil cyanobacteria, especially abundant ones. P addition also increased cyanobacterial species richness and Shannon’s diversity, which might be explained by the decline in dominant species and the emergence of new species as nestedness and indicator species analyses suggest. We concluded that (1) soil cyanobacteria in tropical forests exhibits a greater sensitivity to elevated P availability compared to N; (2) an increase in soil P supply may mitigate the advantage held by dominant species, thus facilitating the growth of other species and leading to alterations in the soil cyanobacterial community. This study improves our understanding on how soil cyanobacterial communities in tropical forest responds to N and P addition.
Evolution of Avian Pathology in Europe during the past 50 Years

With the development of the modern poultry industry, several poultry diseases spread due to vertical transmission and caused high mortality. Mycoplasma gallisepticum (Mg), a trigger of E. Coli, caused substantial losses on broiler farms. Pullorum and Leucosis virus types A in layers and type J in broilers had to be eradicated from breeding stock to stop vertical transmission. New diseases spread during the last decades: In 1969, Biggs identified the cause of the tumor form of Marek’s Disease and, together with Witter, developed vaccines based on attenuated Marek herpes virus (Biggs) and turkey herpes virus FC 126 (Witter). The CVI 988 (Rispens) virus has been used since 40 years and is still effective. V. Bülow and Vielitz identified the chicken anemia virus in 1983 as the cause of anemia in broiler flocks. EDS virus and other types of adeno virus were identified as causes of mortality and depressed production and egg quality. Vaccines to control Salmonella infections with S. typhimurium and S. enteritidis were developed and are widely used to reduce the risk of food poisoning. The re-introduction of free range management and the ban on effective drugs to control diseases like Histomoniasis and necrotic ente-ritis brings us back to disease problems of the 1960s.
Intake of dark green vegetables may beneﬁt speciﬁc cognitive domains in US men and women aged 60 years or older

BACKGROUND: The prevalence and relationship between dark green vegetables (DGV) and speciﬁc cognitive domains in the aging US population are not well-established for men and women. OBJECTIVE: To explore the associations between DGV, its bioactive nutrients, and cognitive function, including its speciﬁc domains, and whether they differ by sex METHODS: Thestudyanalyzedacross-sectionalsampleof2,
Cyberbullying Detection using Machine Learning and Deep Learning

—With the human passion for gaining knowledge, learning new things and knowing the news that surrounds the world, social networks were invented to serve the human need, which resulted in the rapid spread and use among people, but social networks have a dark and bright side. The dark side is that strangers or anonymous people harass some users with obscene words that the user feels wrong about, which leads to psychological harm to him, and here we try to discover how to discover electronic bullying to block this alarming phenomenon. In this context, the utility of Natural Language Processing (NLP) is employed in the present investigation to detect electronic bullying and address this alarming phenomenon. The machine learning (ML) method is moderated based on specific features or criteria for detecting cyberbullying on social media. The collected characteristics were analyzed using the K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Naive Bayes (NB), Decision Trees (DT), and Random Forest (RF) methods. Naturally, there are test results that use or operate on the proposed framework in a multi-category setting and are encouraged by kappa, classifier accuracy, and f-measure standards. These apparent outcomes show that the suggested model is a valuable method for predicting the behavior of cyberbullying, its strength, and its impact on social networks via the Internet. In the end, we evaluated the results of the proposed and basic features with machine learning techniques, which shows us the importance and effectiveness of the proposed features for detecting cyberbullying. We evaluated the models, and we got the accuracy of the KNN (0,90), SVM (0,92), and Deep learning (0,96)
Investigating the determinants of students drop out at Injibara University: The way to retain students

The purpose of this study is to determine the reasons for students' dropout at Injibara University using tuition fees, geographic location, job, quality of education, and war as independent variables and students' dropout as a dependent variable. Methodologically, the researchers used a five-Point Likert-scale questionnaire to collect primary data from the entire sample of 366 students. The data was analysed with the Logit model. After the analysis, it was concluded that Tuition fees, geographic location, job, assessment techniques, lack of personalized attention for students, and war were shown to be the most significant predictors of weekend students dropping out of university. Finally, the researchers recommends that opening continuing and distance education offices (in addition to the main campus) in selected three areas where students can attend their class in their locality; and exam committees in each department ought to be functionalized
Reducing the Risk of Birth Defects Associated with Maternal Influenza: Insights from a Hungarian Case—Control Study

Influenza viruses can cause several complications during pregnancy. Therefore, we aimed to investigate the effects of influenza on the development of congenital abnormalities (CAs) by analyzing the database of the Hungarian Case–Control Surveillance of Congenital Abnormalities (HCCSCA). In our multicenter, case–control, population-based study, we processed clinician-reported outcomes and diagnoses collected in the HCCSCA. The case group included newborns with different non-chromosomal birth defects, while the controls were newborns without CAs. Maternal influenza, as a risk factor for CAs, was analyzed by using a logistic regression model and odds ratios with 95% confidence intervals (CIs). Our results showed that maternal influenza in the first trimester was associated with increased odds of developing non-chromosomal CAs (OR: 1.41, CI: 1.28–1.55). There were increased odds of neural tube defects (OR: 2.22, CI: 1.78–2.76), orofacial clefts (OR: 2.28, CI: 1.87–2.78), and congenital heart defects (OR: 1.28, CI: 1.10–1.49) after influenza infection. In all cases, we found a protective effect of folic acid supplementation in the first trimester. In summary, the odds of non-chromosomal birth defects are higher after maternal influenza in the first trimester, and folic acid or pregnancy vitamin supplementation and antipyretic therapy may reduce the effect of maternal influenza during the first trimester.
Identification of QTLs for Cooking and Eating Quality of Rice Grain

The BIL (backcross inbred line) population derived from the cross between Koshihikari (good eating and cooking quality, japonica) and Kasalath (poor quality, indica) was used to analyze the QTLs for amylose content (AC), gelatinization temperature (GT), gel consistency (GC) and protein content (PC). Eight main-effect QTLs including 2 for AC, 3 for GT, 2 for GC and 1 for PC were identified. Moreover, 27 epistatic QTL pairs including 7 for AC, 5 for GT, 4 for GC and 11 for PC were also detected while for AC and GT, one main-effect QTL with a major gene was detected, respectively. Therefore, the main-effect QTL might be more responsible for the current variation than the epistatic QTL. The result indicated that the main-effect QTL is the primary genetic basis for those traits. However, for PC, the epistatic QTL explained a much greater portion of the total variation than main-effect QTL, suggesting that epistatic loci are the primary genetic basis for such trait. In the experiment, chromosome segment substitution lines (CSSLs) were used to confirm reliabilities of the main effect QTLs detected in BIL population. Of the 8 main-effect QTLs for 4 traits in BIL analysis, 6 were confirmed and 2 remained unconfirmed by CSSLs analysis. Rice is one of the major crops served as the staple food of more than 50% of the world's population. Recently, more attention has been paid to rice quality, especially the cooking and eating quality than achieving the higher yield. The amylose content (AC), gelatinization temperature (GT) and gel consistency (GC) are the three major rice characteristics directly related to cooking and eating quality, responsible for the physical and chemical characteristics of the starch in the endosperm (1-4) . The GT is a physical characteristic responsible for cooking time and the capacity of absorbing water as cooking. The GC is responsible for softness in rice. The AC, the most important factor to rice quality, is negatively related to eating and cooking quality, including viscosity, transparency and milling quality. The protein content in rice is not only a good source of protein in food, but also an important factor influences the cooking and eating quality. Thus, the improvement of rice quality is a major goal in rice breeding programs. However, complex inheritance traits for rice quality led to the failure of breeding efforts.
Immune Mediators Profiles in the Aqueous Humor of Patients with Simple Diabetic Retinopathy

Various immune mediators identified to date are associated with the development of advanced forms of diabetic retinopathy (DR), such as proliferative DR and diabetic macular edema, although the exact pathophysiological mechanisms of early stages of DR such as simple DR remain unclear. We determined the immune mediator profile in the aqueous humor of eyes with simple DR. Fifteen eyes of fifteen patients with simple DR were studied. Twenty-two eyes of twenty-two patients with cataracts and no DR served as controls. Undiluted aqueous humor samples were collected, and a cytometric bead array was used to determine the aqueous humor concentrations of 32 immune mediators comprising 13 interleukins (IL), interferon-γ, interferon-γ-inducible protein-10 (IP-10), monocyte chemoattractant protein-1, macrophage inflammatory protein (MIP)-1α, MIP-1β, regulated on activation, normal T cell expressed and secreted (RANTES), monokine induced by interferon-γ, basic fibroblast growth factor (bFGF), Fas ligand, granzyme A, granzyme B, interferon-inducible T-cell alpha chemoattractant (ITAC), fractalkine, granulocyte macrophage colony-stimulating factor, granulocyte colony-stimulating factor (G-CSF), vascular endothelial growth factor (VEGF), angiogenin, tumor necrosis factor-α, and CD40 ligand. Among the 32 immune mediators, 10 immune mediators, including bFGF, CD40 ligand, fractalkine, G-CSF, IL-6, IL-8, MIP-α, MIP-1β, and VEGF, showed significantly higher aqueous humor concentrations and the Fas ligand had significantly lower concentration (p < 0.05) in eyes with simple DR compared with control eyes. Of these 10 cytokines with significant concentration alteration, protein–protein interaction analysis revealed that 8 established an intricate interaction network. Various immune mediators may contribute to the pathogenesis of simple DR. Attention should be given to the concentrations of immune mediators in ocular fluids even in simple DR. Large-scale studies are warranted to assess whether altered aqueous humor concentrations of these 10 immune mediators are associated with an increased risk of progression to advanced stages of DR.
High rates of detection of Clade 2.3.4.4 HPAI H5 viruses in wild birds in the Pacific Northwest during the winter of 2014/2015

Abstract In 2014, Clade 2.3. 4.4 H5N8 highly pathogenic avian influenza (HPAI) viruses spread across the Republic of Korea and ultimately were reported in China, Japan, Russia and Europe. Mortality associated with a reassortant HPAI H5N2 virus was detected in poultry farms in Western Canada at the end of November. The same strain (with identical genetic structure) was then detected in free-living wild birds that had died prior to December 8 of unrelated causes in Whatcom County, Washington, USA in an area contiguous with the index Canadian location. A gyrfalcon (Falco rusticolus) that had hunted and fed on an American wigeon (Anas americana) on December 6 in the same area and died two days later, tested positive for the Eurasian origin HPAI H5N8. Subsequently, an Active Surveillance Program using hunter-harvest waterfowl in Washington and Oregon detected ten HPAI H5 viruses, of three different subtypes (four H5N2, three H5N8 and three H5N1) with 4 segments in common (HA, PB2, NP and MA). In addition, a mor.
Beyond the Brain: How Body and Environment Shape Animal and Human Minds

Acknowledgments ix Chapter 1: Removing Ourselves from the Picture 1 Chapter 2: The Anthropomorphic Animal 20 Chapter 3: Small Brains, Smart Behavior 39 Chapter 4: The Implausible Nature of Portia 57 Chapter 5: When Do You Need a Big Brain? 71 Chapter 6: The Ecology of Psychology 94 Chapter 7: Metaphorical Mind Fields 112 Chapter 8: There Is No Such Thing as a Naked Brain 135 Chapter 9: World in Action 152 Chapter 10: Babies and Bodies 175 Chapter 11: Wider than the Sky 197 Epilogue 223 Notes 225 References 251 Index 269