Miracle-dz/UniFAR-FTUs huggingface dataset:
Comparative analysis of Cf-4 and Cf-19 in tomato (Solanum lycopersicum) – A bioinformatics study

Tomatoes (Solanum lycopersicum L.), a model fruit crop, are largely affected by leaf mould disease, caused by an Ascomycete (Cladosporium fulvum) leading to significant economic loss across the globe. Though many R genes corresponding to this disease, viz. Cf-2, Cf-4, Cf-5, Cf-6, Cf-9 and Cf-19 are reported but such studies are confined to genomic level rather than proteomic level. Since host parasite interaction occurs at protein level thus in the present study, attempt has been made to carry out the studies of these R genes at proteomic level. Protein sequences of Cf-4 and Cf-19 genes were used for sequence analysis by Conserved Domain Database followed by construction of their three-dimensional models using Modeller. The obtained models were further validated and finalized protein models of both the genes were compared with the help of DALI Server. The present study revealed that there was 89% sequence identity between these proteins whereas they were confined to 78% only at structural level. Model generated from the present study can be used in interaction studies between Cf-4 and Cf-19 and their corresponding avirulence (Avr) protein. Such study would lead to better understanding of genetic basis of susceptibility of the tomato plants to the invading pathogen. This can also help in establishing them as putative candidate gene, which are desirable in development of disease resistant tomato varieties, combating pathogen attack in endeavour of tomato improvement program.
Numerical Solution for the Composition of a Thermosphere in the Presence of a Steady Subsolar to-Antisolar Circulation with Application to Venus

Abstract We consider the thermosphere of a nonrotating planet with a large-scale circulation from dayside to nightside driven by differential solar heating. Return flow is assumed to occur only below the region considered. The motion is moixotonically upward on the dayside and downward on the nightside. A numerical model is developed and a method of solution derived for the distribution of N components in the presence of sources and sinks due to photodissociation. These components, any of which way be major species, are transported vertically by molecular diffusion and carried horizontally and vertically by the large-scale circulation. The model is integrated for parameters appropriate to the Venusian upper atmosphere, assuming only CO2 is carried upward on the dayside through the bottom boundary at 0.1 mb. This boundary condition requires that photodissociation fragments carried out at the bottom on the nightside essentially recombine into CO2 before they are carried back upward into the dayside integrat.
Local governments as key agents in climate change adaptation: challenges and opportunities for institutional capacity-building in Mexico

ABSTRACT Institutional capacity is one of the dimensions of adaptive capacity that determines the level and pace of climate change adaptation at the local level. Local governments are key actors in climate change adaptation because they have the responsibility to translate top-down risk information to vulnerable populations and can scale bottom-up initiatives of communities in adaptation planning. There is a need for integrated approaches for adaptive capacity building that incorporate specific capacities to tackle multiple climate risks and generic capacities to address basic needs for human development. In this analysis, we assess the institutional capacity profiles of local governments in Mexico through a cluster analysis to understand how different aspects of generic and specific capacities, which operate through bottom-up or top-down approaches, coincide in local governments. Our results show that local governments in Mexico can be grouped by type and level of institutional capacity, as follows: those that (1) engage in intergovernmental coordination; (2) focus on safety-first; (3) exhibit high capacities in transparency and citizen participation; and (4) are in a poverty trap. These groups reflect challenges for local governments in Mexico, including the need to resolve short-term crises, the lack of climate change awareness, and a low capacity to access and mobilize economic resources for adaptation. Conversely, horizontal and vertical coordination, transparency, and citizen participation are found to be key elements able to strengthen institutional capacities for adaptation. In the context of multiple climate risks and underdeveloped enabling conditions for adaptation, there is a need for strategic investment in capacities that are contextually relevant and that can reduce the adaptation gap for climate action in Mexico. Further analysis will be needed to evaluate how the mobilization of the institutional capacities by local governments in each group influences climate change adaptation outcomes, particularly differentiated for urban and rural contexts, as well as for metropolitan scales. Key policy insights - Local governments in Mexico are faced with the challenge of resolving urgent and short-term issues, making it difficult to face climate change adaptation. - Intergovernmental coordination is a key attribute that local governments can develop to address and overcome deficiencies in institutional capacities for climate change adaptation - Administrative capacity, transparency, accountability, and public participation are critical elements for local governments and will assist them to access and mobilize economic resources for climate change adaptation.
Cell division and migration in a ‘genotype’ for neural networks

Much research has recently been dedicated to applying genetic algorithms to populations of neural networks. However. While in real organisms the inherited genotype maps in complex ways into the resulting phenotype, in most of this research the development process that creates the individual phenotype is ignored. In this paper we present a model of neural development which includes cell division and cell migration in addition to axonal growth and branching. This reflects, in a very simplified way, what happens in the ontogeny of real organisms. The development process of our artificial organisms shows successive phases of functional differentiation and specialization. In addition, we find that mutations that affect different phases of development have very different evolutionary consequences. A single change in the early stages of cell division and migration can have huge effects on the phenotype, while changes in later stages usually have a less dramatic impact. Sometimes, changes that affect the first de.
Deep Learning Approaches for Traffic Prediction Forecasting in Multi-Level Cloud Architecture for Autonomous Vehicle Services

Autonomous vehicle (AV) is one of the most new use cases and a technology for fifth-generation (5G) and next-generation mobile networks in numerous applications. , the use of AVs has exponentially worldwide due to the rapidly growing awareness and use of artificial intelligence (AI) methods in various fields. Predicting data flows is essential for AVs to improve data transmission and decrease delays through more efficient use of appropriate capabilities, monitoring, management, and control of the traffic system. This paper proposes a deep learning approach (DL) with the bidirectional long-short-term memory model (BI-LSTM) for predicting the traffic rates of AVs with multi-cloud services. In terms of prediction accuracy, a comparison is conducted between the BI-LSTM and the unidirectional LSTM based on the number of batch sizes used. The prediction accuracy is computed using the root mean square error (RMSE), mean absolute percentage error (MAPE), coefficient of determination (R2), and processing time. The results show that the prediction accuracy with BI-LSTM outperforms the LSTM model. Besides, the prediction accuracy using 8 batch sizes outperforms the competitors and offers outstanding performance.
Strongly bonded water monomers on the ice Ih basal plane: Density-functional calculations

The adsorption of water monomers on noncrystallographic sites of the ice Ih basal plane is calculated from first principles. The results presented here do not support previous theoretical findings that state molecular adsorption energies in excess of the bulk cohesive energy. Still, due to an enhanced strength of the surface hydrogen bonds compared to the bulk, the admolecules are found to lower the ice surface energy. For the surface structures investigated here, the monomer adsorption energies are found to be rather insensitive with respect to the detailed microscopic structure of the underlying substrate. The latter, however, determines the adsorption-induced changes of the surface dipole layer.
A statistically derived two-equation model of turbulent shear flows in a rotating system

Based on the statistical theoretical results obtained in the previous work [J. Phys. Soc. Jpn. 55 (1986) 1904], a two-equation turbulence model in a rotating system is constructed. This model can explain experimentally observed asymmetric profiles of the mean streamwise velocity in a channel rotating about the spanwise direction.
Multivariate elliptically contoured and ө-generalized normal models

Tn this article the main results of the Multivariate elliptically contoured linear models are considered. Multivariate elliptically contoured linear models arise in many areas of application. See the reference given in this paper for some of these applications. Note that applications of Multivariate elliptically contoured linear models are not exhausted by the applications in physics, multivariate statistical analysis, and the theory of nonordered structures. Now, theory of random matrices is also used in the theory of stability of solutions of stochastic systems, in linear stochastic programming, in molecular chemistry and in the theory of experiment disign. This paper is designed for statisticians, mathematicians, physicists, scientists, and engineers of different specialties, who use random matrices and theoretical-probability methods in their work. 1. MAXIMUM LIKELIHOOD ESTIMATES OF THE PARAMETERS OF A MULTIVARIATE NORMAL DISTRIBUTION In this section we consider the mean and . covariance matrix formed from a sample from a multivariate normal distribution. The normal density of a random vector is where α in the mean vector and R is the covariance matrix. We will assume throughout sections 1-3 that R is a positive definite matrix. Let fi,. ,xn be independent observations of a normally, ATm(a,A), distributed random vector. Then the sample mean vector is α = η" ]ζ£=1 χ*, and the sample empirical covariance matrix is R = (n — l)"" Σ£=1(χ* "~ ^)(* ~ ^)· The vector α and the matrix R are stochastically independent. For n> m the maximum likelihood estimates of α and A are α and (n — l)n~R. 2. THE WISHART DENSITY wn(a,R) If a;i,. ,£n+i are independent observations from a normally, Nm(a,R) ,distributed random vector where n > m, then the Wishart density function of the sample covariance matrix n+l n+1 = n-£(ii)(£<-S)> where 2=(n + 1Γ £>* t=l Jb=l
The Impact of a Late Diagnosis: A Case of Charcot-Marie-Tooth Type 1

Charcot-Marie-Tooth (CMT) is a hereditary motor and sensory neuropathy. The disease consists of a spectrum of inherited disorders caused by pathogenic variants in genes, which lead to multiple different clinical phenotypes. It is one of the most common inherited neuromuscular disorders. This disease most commonly presents with symptoms of distal weakness and muscular atrophy, which then lead to foot drop and pés cavus. In this article, we describe the case of a patient who developed muscle atrophy and distal weakness over the course of his 52 years of life, leading to gait impairment and foot deformities. Subsequent investigation led to the acknowledgment of chronic axonal sensorimotor polyneuropathy and genetic identification of the disease's genotype, CMT type 1.
Accepting social support in single-parent families in Belgium

ABSTRACT Being one of the quintessential resources to bounce back from adversities, the complexities of social support in the context of transitions to single parenthood are seldom fully grasped. While many studies are limited by (quantitative) data on type and intensity of support received, in-depth qualitative data facilitates an understanding of the hurdles in the support exchange. In this study, we describe six hurdles single parents have to overcome and four perspectives single parents have of these hurdles. These results are based on a grounded theory study using 244 semistructured interviews with Flemish lone parents.
Pathogenic Drug Resistant Fungi: A Review of Mitigation Strategies

Fungal pathogens cause significant human morbidity and mortality globally, where there is a propensity to infect vulnerable people such as the immunocompromised ones. There is increasing evidence of resistance to antifungal drugs, which has significant implications for cutaneous, invasive and bloodstream infections. The World Health Organization (WHO) published a priority list of fungal pathogens in October 2022, thus, highlighting that a crisis point has been reached where there is a pressing need to address the solutions. This review provides a timely insight into the challenges and implications on the topic of antifungal drug resistance along with discussing the effectiveness of established disease mitigation modalities and approaches. There is also a need to elucidate the cellular and molecular mechanisms of fungal resistance to inform effective solutions. The established fungal decontamination approaches are effective for medical device processing and sterilization, but the presence of pathogenic fungi in recalcitrant biofilms can lead to challenges, particularly during cleaning. Future design ideas for implantable and reusable medical devices should consider antifungal materials and appropriates for disinfection, and where it is relevant, sterilization. Preventing the growth of mycotoxin-producing fungi on foods through the use of appropriate end-to-end processes is advisable, as mycotoxins are recalcitrant and challenging to eliminate once they have formed.
Structure of the Gravitational Field at Spatial Infinity

A formalism is introduced for analyzing the structure of the gravitational field in the asymptotic limit at spatial infinity. Consider a three‐dimensional surface S in the space‐time such that the initial data on S is asymptotically flat in an appropriate sense. Using a conformal completion of S by a single point A ``at spatial infinity,'' the asymptotic behavior of fields on S can be described in terms of local behavior at A. In particular, the asymptotic behavior of the initial data on S defines four scalars which depend on directions at A. Since there is no natural choice of a surface S in a space‐time, the dependence of these scalars on S is essential. The asymptotic symmetry group at spatial infinity, whose elements represent transformations from S to other asymptotically flat surfaces, is introduced. It is found that this group, which emerges initially as an infinite‐dimensional generalization of the Poincare group, can be reduced to the Lorentz group. A set of evolution equations is obtained: These.
Pseudo-Wind Perception Induced by Cross-Modal Reproduction of Thermal, Vibrotactile, Visual, and Auditory Stimuli

Numerous wind displays have been developed to provide wind sensation in virtual reality applications. However, in previous studies, wind displays required the production of physical wind using a wind source. To release cold or hot wind, previous wind displays required large equipment for making the air temperature changes. In this study, we propose to induce pseudo-wind perception, perceiving wind even in the absence of physical wind, using visuo-audio-haptic cross-modal effects. We provide audio-visual stimuli with a head-mounted display and headphones, and vibrotactile and thermal stimuli to the skin using belt-type devices, instead of physical wind. We found that vibrotactile and thermal stimuli based on the measurement of skin displacement and temperature exposed to the wind are suitable for the tactile sensation of wind. We also found that thermal and audio-visual stimuli can induce wind sensation of cold wind, and that vibro-thermal stimuli in addition to audio-visual stimuli are effective for the wind sensation and realness of the experience of hot wind. In future, inducing variable types of wind sensations will be pursued as a research direction regarding pseudo-wind perception.
Influence of Metformin Dose and Treatment Adherence on Glycemic Control, Adiposity, and Cardiovascular Risk Markers in Iraqi Patients with T2DM

Background: Numerous factors, including metformin doses and treatment adherence, may contribute to significant variations in glycemic control and adiposity markers of type 2 diabetes (T2DM) patients. Objectives : This study aims to determine the influence of metformin dose and treatment adherence on glycemic control and adiposity markers in Iraqi patients with T2DM. Methods: Between October 2021 and March 2022, a case-series study at the Diabetes and Endocrinology Center – Baghdad included 153 T2DM patients with a disease duration of more than one year. Clinical and physical examinations were conducted before enrolment. We measured anthropometric variables to calculate the body mass index (BMI), waist-to-hip ratio (WHR), visceral adiposity index (VAI), and other surrogate indicators. We measured glycated hemoglobin (HbA1c), leptin, C-reactive protein (CRP), total cholesterol, HDL-c, and triglycerides in the serum. Results: Increasing metformin doses did not improve the studied parameters. Adherence to treatment significantly influences fasting glycemia, HbA1c level, and the markers of adiposity. Meanwhile, increasing metformin doses is not associated with changes in insulin resistance and cardiovascular disease risk markers. Conclusion: Beyond metformin dose up-titration, treatment adherence affects glycemic control, visceral adiposity, and CVD risk surrogates. Metformin dose up-titration was not linked to insulin resistance and body fat contents.
Interaction in a Chemostat: Introduction of a Competitor Can Promote Greater Diversity

It is well known that models of exploitative competition in a chemostat with constant input, by n populations of microorganisms for a single, essential, nonreproduc- ing, growth-limiting resource predict competitive exclusion. That is, they predict that at most one population avoids ex- tinction. In this paper we consider two scenarios based in a chemostat in which the introduction of a population that ex- ploits common resource(s), actually promotes greater diversity. In the ﬁrst example, feeding on more than one trophic level is allowed. In the second example, two perfectly substitutable resources are involved. In both examples, the extinc- tion of a population is averted by the introduction of a population that interacts only by consuming common resource(s).
The Flipped Break-Even: Re-Balancing Demand- and Supply-Side Financing of Health Centers in Cambodia

Supply-side healthcare financing still dominates healthcare financing in many countries where the government provides line-item budgets for health facilities irrespective of the quantity or quality of services rendered. There is a risk that this approach will reduce the efficiency of services and the value of money for patients. This paper analyzes the situation of public health centers in Cambodia to determine the relevance of supply- and demand-side financing as well as lump sum and performance-based financing. Based on a sample of the provinces of Kampong Thom and Kampot in the year 2019, we determined the income and expenditure of each facility and computed the unit cost with comprehensive step-down costing. Furthermore, the National Quality Enhancement Monitoring Tool (NQEMT) provided us with a quality score for each facility. Finally, we calculated the efficiency as the quotient of quality and cost per service unit as well as correlations between the variables. The results show that the largest share of income was received from supply-side financing, i.e., the government supports the health centers with line-item budgets irrespective of the number of patients and the quality of care. This paper demonstrates that the efficiency of public health centers increases if the relevance of performance-based financing increases. Thus, the authors recommend increasing performance-based financing in Cambodia to improve value-based healthcare. There are several alternatives available to re-balance demand- and supply-side financing, and all of them must be thoroughly analyzed before they are implemented.
A new loop algebra and a corresponding integrable hierarchy, as well as its integrable coupling

A type of new interesting loop algebra GM (M=1,2,…) with a simple commutation operation just like that in the loop algebra A1 is constructed. With the help of the loop algebra GM, a new multicomponent integrable system, M-AKNS-KN hierarchy, is worked out. As reduction cases, the M-AKNS hierarchy and M-KN hierarchy are engendered, respectively. In addition, the system 1-AKNS-KN, which is a reduced case of the M-AKNS-KN hierarchy above, is a unified expressing integrable model of the AKNS hierarchy and the KN hierarchy. Obviously, the M-AKNS-KN hierarchy is again a united expressing integrable model of the multicomponent AKNS hierarchy (M-AKNS) and the multicomponent KN hierarchy(M-KN). This article provides a simple method for obtaining multicomponent integrable hierarchies of soliton equations. Finally, we work out an integrable coupling of the M-AKNS-KN hierarchy.
Visible light emission from quantized planar Ge structures

Visible photoluminescence has been observed near 1.9 eV at 300 K from quantized planar Ge structures. This is the first observation of luminescence in Ge and is similar to the recently reported luminescence from porous Si. The quantum structures are prepared from bulk Ge substrates, and both n‐ and p‐type Ge produce luminescence at room temperature. These structures are fabricated by plasma‐assisted etching using a CF4/O2 gas mixture.
Adaptive neighbourhood recovery method for machine learning based 3D point cloud classification

ABSTRACT 3D scene analysis and classification by automatically assigning semantic labels to 3D points has become a major concern in remote sensing, photogrammetry and computer vision. Due to the high redundancy of point clouds and unbalanced sampling density, it is difficult to recover appropriately sized neighbourhoods to extract local features from 3D point clouds, which will decrease the classification performance. Existing point cloud neighborhood recovery methods usually use fixed-size neighbourhoods, where the shape structure of the point cloud and the distribution of the input points are usually disregarded. To avoid the above shortcomings, an adaptive 3D point cloud neighbourhood recovery method is proposed, which fully considers the correlation between point clouds and the surface variation of local neighbourhoods in the neighbourhood recovery process, and recovers dynamically sized optimal neighbourhoods for point clouds. The performance of our proposed method is evaluated on the Oakland dataset and the Vaihingen dataset with various modern classifiers. Experiments demonstrate that our proposed method achieves a superior classification result.