Miracle-dz/UniFAR-FTUs huggingface dataset:
Physiological Responses of Cigar Tobacco Crop to Nitrogen Deficiency and Genome-Wide Characterization of the NtNPF Family Genes

Tobacco prefers nitrate as a nitrogen (N) source. However, little is known about the molecular components responsible for nitrate uptake and the physiological responses of cigar tobacco to N deficiency. In this study, a total of 117 nitrate transporter 1 (NRT1) and peptide transporter (PTR) family (NPF) genes were comprehensively identified and systematically characterized in the whole tobacco genome. The NtNPF members showed significant genetic diversity within and across subfamilies but showed conservation between subfamilies. The NtNPF genes are dispersed unevenly across the chromosomes. The phylogenetic analysis revealed that eight subfamilies of NtNPF genes are tightly grouped with their orthologues in Arabidopsis. The promoter regions of the NtNPF genes had extensive cis-regulatory elements. Twelve core NtNPF genes, which were strongly induced by N limitation, were identified based on the RNA-seq data. Furthermore, N deprivation severely impaired plant growth of two cigar tobaccos, and CX26 may be more sensitive to N deficiency than CX14. Moreover, 12 hub genes respond differently to N deficiency between the two cultivars, indicating the vital roles in regulating N uptake and transport in cigar tobacco. The findings here contribute towards a better knowledge of the NtNPF genes and lay the foundation for further functional analysis of cigar tobacco.
Stochastic nonlinear Schrödinger equations in the defocusing mass and energy critical cases

We study the stochastic nonlinear Schroedinger equations with linear multiplicative noise, particularly in the defocusing mass-critical and energy-critical cases. For general initial data, we prove the global existence and uniqueness of solutions in both cases. When the quadratic variation of noise is globally bounded, we also obtain the rescaled scattering behavior of solutions in the spaces $L^2$, $H^1$ as well as the pseudo-conformal space. Moreover, the Stroock-Varadhan type theorem for the topological support of solutions are also obtained in the Strichartz and local smoothing spaces.
Classification And Prediction Retinal Oct Images by CNN Algorithm

WHO says diabetic retinopathy is a leading cause of blindness. Complexity and quietness make early detection challenging. DR treatment is stage-dependent. Treatment slows disease progression. Ophthalmologists may initially just monitor DR. Diet, blood sugar, and exercise advice must be followed by DR patients. Slows disease progression. Injected medication can reduce DR damage. Advanced RD causes macula bleeding and swelling. Resulting macular edema. Photocoagulation stops retinal leakage. Lasers plug blood leaks by burning abnormal arteries. In the past decade, characterization has improved. We'll use MATLAB's CNN to detect diabetic retinopathy early. CNN is a well-known social model. Input, neurons, stored layers, and output make up CNN's master structure. Both solid and diabetic retinopathy fundus images are well-lit to reveal all hidden details. Mean, standard deviation, variance, skewness, and kurtosis. Bookkeeping is done after extraction. One secret layer, 16 information neurons, and 2 solid or not results make up a convolutional brain network. 70% is used for teaching, 15% for testing. Execution time varied on emphasis or age and location accuracy was 98.93 %. 98.24% exactness, 98.93% accuracy, 99.42% review, and 98.91 % AUC were reported for diabetic retinopathy identification and characterization.
Bacteriological Profile and their Antibiotic Susceptibility Pattern in Bloodstream Infections in a Tertiary Care Hospital in North India

Blood is a sterile, liquid connective tissue. When infected with microbes, grave consequences can occur, such as shock, multiple organ failure, disseminated intravascular coagulation (DIC), and death. The World Health Organization reported 49 million cases of sepsis and 11 million sepsis-related deaths in 2017, accounting for approximately 20% of deaths annually worldwide. Rapid identification of the causative organism and timely, appropriate treatment are required to reduce mortality due to bloodstream infections. This study was conducted to analyze the patterns of various bacteria causing bloodstream infections and their antibiotic susceptibility patterns. All blood samples received for diagnosing bloodstream infections at the Microbiology Department of Sri Guru Ram Das Institute of Medical Sciences and Research, Amritsar, were included in the study, the duration of which was 1 year, from January to December 2020. Blood samples of 5–10 ml from adult and 5 ml from pediatric patients, were collected under aseptic conditions, stored in BACTEC bottles, and processed in an automated BACTEC system before antimicrobial therapy. After 7 days of incubation, if no microbial growth was observed, the sample was reported as sterile for aerobic organisms. When growth was observed, broth from positive blood culture bottles was subcultured on blood and MacConkey agar for identification and antimicrobial susceptibility testing using Vitek 2 according to CLSI (Clinical Lab Standard Institute) guidelines and the manufacturer’s instructions. A total of 441 (14.5%) bacteria were isolated from 3007 blood samples from patients with suspected bacteremia. Contamination was observed at a rate of 2.5%. Gram-positive cocci (49%) were the predominant organisms recovered, followed by Gram-negative bacilli (34%). Gram-positive cocci were coagulase-negative Staphylococci (46%), Staphylococcus aureus (7%), and Enterococcus spp. (6%). Among the Gram-negative bacilli, E.coli (14%), Klebsiella spp. (13%), Acinetobacter baumannii (7%), Pseudomonas spp. (7%), Salmonella typhi (2%), Enterobacter spp. (1%), and Serratia spp. (1%) and single isolates of Aeromonas spp. , Morganella morgani, Pantoea spp. , Proteus mirabilis, and Providentia rettgeri were identified. Linezolid, teicoplanin, and vancomycin were the most effective drugs for treating Gram-positive bacteremia. Tigecycline, carbapenems, and aminoglycosides were the most effective treatments for Gram-negative bacteremia. The results stress the need for continued screening and surveillance in routine blood culture techniques to start empiric therapy for bloodstream infections.
Logarithmic sℓˆ(2)?> CFT models from Nichols algebras: I

We construct chiral algebras that centralize rank-2 Nichols algebras with at least one fermionic generator. This gives ‘logarithmic’ W-algebra extensions of a fractional-level sℓˆ(2)? > algebra. We discuss crucial aspects of the emerging general relation between Nichols algebras and logarithmic conformal field theory (CFT) models: (i) the extra input, beyond the Nichols algebra proper, needed to uniquely specify a conformal model; (ii) a relation between the CFT counterparts of Nichols algebras connected by Weyl groupoid maps; and (iii) the common double bosonization U(X) of such Nichols algebras. For an extended chiral algebra, candidates for its simple modules that are counterparts of the U(X) simple modules are proposed, as a first step toward a functorial relation between U(X) and W-algebra representation categories.
Border trees of complex networks

The comprehensive characterization of the structure of complex networks is essential to understand the dynamical processes which guide their evolution. The discovery of the scale-free distribution and the small-world properties of real networks were fundamental to stimulate more realistic models and to understand important dynamical processes related to network growth. However, the properties of the network borders (nodes with degree equal to 1), one of its most fragile parts, remained little investigated and understood. The border nodes may be involved in the evolution of structures such as geographical networks. Here we analyze the border trees of complex networks, which are defined as the subgraphs without cycles connected to the remainder of the network (containing cycles) and terminating into border nodes. In addition to describing an algorithm for identification of such tree subgraphs, we also consider how their topological properties can be quantified in terms of their depth and number of leaves. We investigate the properties of border trees for several theoretical models as well as real-world networks. Among the obtained results, we found that more than half of the nodes of some real-world networks belong to the border trees. A power-law with cut-off was observed for the distribution of the depth and number of leaves of the border trees. An analysis of the local role of the nodes in the border trees was also performed.
Artificial Intelligence-Assisted Interior Layout Design of CAD Painting

As of today, with the promotion and application of artificial intelligence, a large amount of manual labor the current one, and its application to interior layout design will inevitably promote interior layout. Design innovation and optimization can also ensure the quality of modern interior layout design and effectively improve efficiency. This paper utilizes adversarial learning to design a conditional generative adversarial network (CGAN)-based approach for indoor scene layout estimation to predict the spatial layout structure of a room. Firstly, aiming at the problem that the boundary line of the layout edge map is easily blurred by the interpolation enlargement, a strategy of increasing the depth of the convolution layer and the deconvolution layer is adopted, and a new encoder-decoder network (Encoder-Decoder) is proposed to construct a conditional generative confrontation. A generative network for the network that produces a layout edge map of the same size as the original image. Then, for the difficult convergence problem of generative adversarial network training, a multi-scale strategy is used to build a multi-scale supervised network of generative networks to accelerate the convergence. The experimental results and analysis of the LSUN and Hedau standard datasets show that, compared with other layout estimation methods, this method can understand the indoor scene layout from an overall perspective and more accurately predict the 3D spatial layout structure of the room.
Kronecker coefficients for one hook shape

We give a positive combinatorial formula for the Kronecker coefficient g_{lambda mu(d) nu} for any partitions lambda, nu of n and hook shape mu(d) := (n-d,1^d). Our main tool is Haiman's \emph{mixed insertion}. This is a generalization of Schensted insertion to \emph{colored words}, words in the alphabet of barred letters \bar{1},\bar{2},. and unbarred letters 1,2,. We define the set of \emph{colored Yamanouchi tableaux of content lambda and total color d} (CYT_{lambda, d}) to be the set of mixed insertion tableaux of colored words w with exactly d barred letters and such that w^{blft} is a Yamanouchi word of content lambda, where w^{blft} is the ordinary word formed from w by shuffling its barred letters to the left and then removing their bars. We prove that g_{lambda mu(d) nu} is equal to the number of CYT_{lambda, d} of shape nu with unbarred southwest corner.
Monic representations of finite higher-rank graphs

In this paper, we define the notion of monic representation for the $C^{\ast }$-algebras of finite higher-rank graphs with no sources, and we undertake a comprehensive study of them. Monic representations are the representations that, when restricted to the commutative $C^{\ast }$-algebra of the continuous functions on the infinite path space, admit a cyclic vector. We link monic representations to the $\unicode[STIX]{x1D6EC}$-semibranching representations previously studied by Farsi, Gillaspy, Kang and Packer (Separable representations, KMS states, and wavelets for higher-rank graphs. J. Math. Anal. Appl. 434 (2015), 241–270) and also provide a universal representation model for non-negative monic representations.
Analysis and Digital Predistortion of in-Band Cross Modulation in Concurrent Multi-Band Transmitters

In multiband transmitters where two or more radios share the same power amplifier (PA), interferences can usually be kept below an acceptable level with the help of multi-band digital predistortion (DPD) techniques. This article reports on novel frequency scenarios in dual-band transmitters where additional cross-modulation products not reported before, are found to overlap and interfer with both the lower and upper-band signals. These cross-modulation products arise in-band when the frequency separation between the bands is close to a sub-harmonic of the band frequencies. The leading nonlinear cross-modulation products are identified for various frequency plans and found to be of increasing order as the frequency interval between the two carriers shrinks. A PA model featuring the four leading cross-modulation products is proposed and found to exhibit an accuracy close to the measurement noise floor when applied to three different PAs. The additional in-band distortions generated by the PA under such operation cannot be filtered or compensated using conventional dual-band DPD. When using the proposed model architecture with the indirect-learning predistortion methodology for linearizing dual-band PAs, DPD is found experimentally to improve the ACPR and EVM by 25 dBc and 11 percentage points, respectively. These and the other experimental results reported indicate that the proposed linearization algorithm provides a suitable method with high efficacy for these special dual-band scenarios.
A∞-weights and compactness of conformal
metrics under Ln∕2 curvature bounds

We study sequences of conformal deformations of a smooth closed Riemannian manifold of dimension $n$, assuming uniform volume bounds and $L^{n/2}$ bounds on their scalar curvatures. Singularities may appear in the limit. Nevertheless, we show that under such bounds the underlying metric spaces are pre-compact in the Gromov-Hausdorff topology. Our study is based on the use of $A_\infty$-weights from harmonic analysis, and provides geometric controls on the limit spaces thus obtained. Our techniques also show that any conformal deformation of the Euclidean metric on $R^n$ with infinite volume and finite $L^{n/2}$ norm of the scalar curvature satisfies the Euclidean isoperimetric inequality.
An algorithm to improve the performance of convolutional neural networks for TSC tasks

The convolutional neural network (CNN) is an important network model for processing time series classification tasks. However, there can be a lot of noise in the data itself. The clarity of spatial feature expression of time‐series samples and the freeness and balance of data samples both have a big effect on how well CNNs can handle time series classification (TSC) tasks. This paper proposes a multi‐pose marking learning algorithm (MPML), which optimizes the spatial representation of data samples through multiple graphical representations of time series data and then solves the problems of freeness and heterogeneity of data samples through labeling and preferential learning of poorly trained sample data. The algorithm improves the model's robustness to noise by improving the spatial expression sentiment of the time series and the labeling learning of the feature data, thereby improving the convolutional neural network's performance to perform the TSC task. We show how well the algorithm works by testing it on 46 datasets from the UCR Time Series Classification Archive and comparing the results.
Smaller Universes for Uniform Sampling of 0,1-matrices with fixed row and column sums

This article considers the problem of sampling 0,1 matrices with fixed row and column sums uniformly at random. Such random matrices are commonly used in ecology, computational biology, medicine, social science and network science. We focus on Markov chain approaches: a close-to-random matrix is produced by applying a large number of small changes to a given matrix. Within theoretical research, much effort has gone into proving that specific Markov chains are rapidly mixing, i.e. their mixing time is polynomially bound. We introduce a simple preprocessing step to existing Markov chain algorithms and prove that this can only improve the mixing time. Theoretically, preprocessing corresponds to running a Markov chain from a better starting distribution, or equivalently to running a corresponding projected chain. We prove that the mixing time of the projected chain is smaller than or equal to the mixing time of the original chain. Our approach is useful to future empirical and theoretical research alike: practioners can implement the preprocessing step to reduce the run-time of their algorithms while theoretical research can benefit by studying the mixing time of Markov chains on (potentially much) smaller state spaces.
Generating series of Periodic Parallelogram polyominoes

The aim of this work is the study of the class of periodic parallelogram polyominoes, and two of its variantes. These objets are related to 321-avoiding affine permutations. We first provide a bijection with the set of triangles under Dyck paths. We then prove the ultimate periodicity of the generating series of our objects, and introduced a notion of primitive polyominoes, which we enumerate. We conclude by an asymptotic analysis.
Purely infinite simple Kumjian–Pask algebras

Abstract Given any finitely aligned higher-rank graph Λ and any unital commutative ring R, the Kumjian–Pask algebra KP R ⁢ ( Λ ) {\mathrm{KP}_{R}(\Lambda)} is known as the higher-rank generalization of Leavitt path algebras. After the characterization of simple Kumjian–Pask algebras by Clark and Pangalela among others, in this article we focus on the purely infinite simple ones. Briefly, we show that if KP R ⁢ ( Λ ) {\mathrm{KP}_{R}(\Lambda)} is simple and every vertex of Λ is reached from a generalized cycle with an entrance, then KP R ⁢ ( Λ ) {\mathrm{KP}_{R}(\Lambda)} is purely infinite. We also prove a dichotomy for simple Kumjian–Pask algebras: If each vertex of Λ is reached only from finitely many vertices and KP R ⁢ ( Λ ) {\mathrm{KP}_{R}(\Lambda)} is simple, then KP R ⁢ ( Λ ) {\mathrm{KP}_{R}(\Lambda)} is either purely infinite or locally matritial. This result covers all unital simple Kumjian–Pask algebras.
Mott Memristors and Neuronal Ion Channels: A Qualitative Analysis

Memristive devices have earned increased recognition over the past decade, owing to their application in memory and neuromorphic systems. Recently, memristive devices have found a propitious role in Spiking Neural Networks. This is partly attributed to an emerging family of memristive devices that are volatile. Volatile memristors, especially Mott memristors, have been used to implement Leaky Integrate and Fire neurons along with neuron circuits with richer dynamics, referred to as neuristors. The Mott memristor is often compared to voltage-gated ion channels in the Hodgkin-Huxley neuron model. However, voltage-gated ion channels in the Hodgkin-Huxley model do not exhibit voltage hysteresis in their quasi-static I-V characteristics, a critical property of Mott memristors. We discuss the association between Mott memristors based on joule heating and the Hodgkin-Huxley voltage-gated ion channels, and we prove that they are qualitatively dissimilar. Consequently, we propose a hypothetical potassium current-gated ion channel that exhibits voltage hysteresis. We simulate a Mott memristor Pearson-Anson oscillator and a current-gated ion channel oscillator. We use the nullcline plots and bifurcation diagrams of the circuits to demonstrate that both circuits are qualitatively similar. We further show that both circuits produce oscillations through a supercritical Andronov-Hopf bifurcation and have a similar oscillation-generation mechanism. We conclude that for the Mott memristor to share qualitative similarity to a neuronal ion channel, the channel must be current-gated rather than voltage-gated. Our aim is to supplement the understanding of how hardware neurons relate to their biological counterparts, which is a crucial first step towards implementing better brain-inspired systems.
MOGAD Involving Cranial Neuropathies: A Case Report and Review of Literature

Myelin-oligodendrocyte glycoprotein (MOG) antibody-associated disease (MOGAD) is an autoimmune-mediated demyelinating disease of the central nervous system (CNS). Patients with MOGAD may develop any combination of optic neuritis (ON), myelitis, brainstem syndrome and encephalitis. Reports of MOGAD with cranial nerve involvement are rare. Herein, we report a MOGAD patient with cranial neuropathies. In addition, we summarized the clinical features of the previously reported six MOG-IgG-positive cases with cranial nerve involvement and discussed the underlying mechanisms of MOGAD involving cranial nerves. Cranial neuropathy is an emerging phenotype in MOGAD, which has characteristics of both central and peripheral nervous system (PNS) involvement, with the trigeminal nerve being the most commonly affected nerve. MOG antibody testing in patients with cranial neuropathies is warranted, and immunotherapy is advocated when the risk of relapse is high. Although higher antibody titers and persistently positive serological test results are predictive of disease recurrence, the long-term outcomes of MOG-IgG-positive patients with cranial neuropathies remain largely unknown.
Effect Of Aromatic Oil Of Three Plants Of Labiatae Gene On Microorganism By Using In Preservation Of Minced Meat

The effect of oil extracts of three plants of Labiatae gene, Mentha longifolia, M. spicata and Ocimum basilicum, by the agar well diffusion method, were studied, the total count of the bacteria responsible for spoilage meat was kept at 4-6˚C for different periods (1, 72,192) hours. The results showed that the oils extract were inhibited all the treatment that were studied and more affected inhibition of the growth of Staphylococcus aureus than the growth of other microbes. The total count of the bacteria responsible for meat spoilage decrease in all treatments extracted oils for all different periods. Statistic analysis results showed high significant variations at the level P≤ 0.01 of all the treatment and control for all reservation periods.
Admissibility of Local Systems for some Classes of Line Arrangements

Let $\A$ be a line arrangement in the complex projective plane $\PP^2$. Denote by $M$ its complement and by $\M$ the set of points in $\A$ with multiplicity at least 3. A rank one local system $\mathcal{L}$ on $M$ is admissible if roughly speaking the dimension of the cohomology groups $H^m(M,\mathcal{L})$ can be computed directly from the cohomology algebra $H^{*}(M,\Bbb{C})$. In this work, we give a sufficient condition for the admissibility of all rank one local systems on $M$.
The international spillover behaviour of implied volatilities and forecasting ability of spillover indices

ABSTRACT In this study, we investigate the spillover behaviours of implied volatilities and test the forecasting power of spillover indices. By using a Diebold and Yilmaz’s (2012) framework, we capture a better overall picture of the developments of spillover effects and create a series of spillover indices by quantifying the spillover intensity for each rolling time window such that we are able to investigate the forecasting ability of spillover indices on market returns. There are several findings in this study including (1) the spillover indices of implied volatilities not only enable us to describe the spillover effects but also possess strong forecasting power on market returns, (2) implied volatilities which own emotional-related characteristic create stronger spillover mechanisms than spillover of market returns and (3) by observing the time-varying patterns of spillover indices of implied volatilities, we find that the peaks of spillover indices occur during well-known economic events such as the Lehman Brothers’ Bankruptcy and Greek debt restructuring.