Large Class Research Articles

Effects of SvAPETALA1-5 gene on floral organ development in Senecio vulgaris.

Asteraceae is a large class of eudicots with complex capitulum, and little is known regarding the molecular regulation mechanism of flower development. APETALA1(AP1) belongs to the MADS-box gene family and plays a key role in plant floral induction and floral organ development. In this study, the bioinformatics and tissue-specific expression of AP1 homologous gene SvAP1-5 in Senecio vulgaris were analyzed. Based on VIGS technology, SvAP1-5 gene silencing plants were created, and SvAP1-5 was overexpressed in Solanum nigrum. The results of bioinformatics analysis showed that SvAP1-5 gene had typical MADS-box and K-box structure, and contains FUL motif and paleoAP1 motif at the C-terminal. SvAP1-5 belongs to the euFUL branch of AP1 gene. qRT-PCR results showed that SvAP1-5 was expressed in bracts, petals and carpels, and was highly expressed in carpels. Compared with the control group, SvAP1-5 gene silencing resulted in irregular petal dehiscence, increased stigma division, and carpel dysplasia. The fruit development of SvAP1-5 overexpressing S.nigrum plants was abnormal, and the hyperplastic tissue similar to fruit appeared. In summary, SvAP1-5 gene may be involved in the development of petals and carpels and plays an important role during the development of S.vulgaris.

The Efficacy of a Novel Board Game to Teach Cable Properties to Introductory Neuroscience Undergraduate Students.

Engagement activities in large classrooms (>100 students) are difficult due to space constraints, number of participants, and overall noise. Additionally, electrophysiological concepts in foundational neuroscience courses can be confusing and lack excitement. Providing students an opportunity to further engage in the material they are learning and apply their knowledge promotes community in the classroom, a deeper understanding of the topic, and an overall increase in retention. Game-based learning has been used in education across all levels and disciplines to provide students with this opportunity. You're Getting on my Nerves is a board game created to offer students a fun way to learn and apply cable properties of action potential propagation. This game allows students to practice vocabulary terms, apply their knowledge of changes in the cell that impact the speed of an action potential, and develop comradery with their classmates. In this article, we have assessed the board game for its efficacy in teaching concepts of cable properties, its ability to promote engagement in a large classroom, its feasibility and timing with a large class, and its potential to elicit comparable formative assessment scores to students who learned these concepts through didactic lecture. Overall, the board game was feasible for a large class to complete within the class period. The results showed an increase in understanding and retention of the material in addition to preference over didactic lectures with students reporting higher engagement, interaction with their peers, and enjoyment in the activity.

Method for solving distributional problems of mathematical programming under conditions of fuzzy initial data

The object of this study is a large class of mathematical programming problems under conditions of uncertainty of initial data. The formulated object generates a subclass of problems of rational distribution of a limited resource under conditions of initial data described in terms of fuzzy mathematics. The conventional, standardly used method for solving such problems is based on optimization on average. To obtain such a solution, it is sufficient to replace all fuzzy initial data with their modal values in the analytical description of the mathematical model of the corresponding problem. To solve the resulting deterministic problem, one can use known methods of mathematical programming. However, the results of such a solution can be used in practice if the carriers of fuzzy parameters are specified compactly, that is, the intervals of possible values of fuzzy parameters of the problem are small. Otherwise, the implementation of this solution may lead to unpredictably large losses. Other alternative approaches are based on the use of insufficiently informative estimates of the best or worst possible values of fuzzy parameters of the problem. These circumstances make the statement of the problem and the objective of the study relevant: devising a method for solving the problem of rational distribution of a limited resource under conditions of fuzzy initial data. To solve the stated problem of rational distribution of a limited resource, a productive idea of constructing the proposed general optimization method under conditions of uncertainty of the initial data has been constructively implemented. In this case, the initial problem is reduced to a clear problem of optimizing a complex criterion constructed on the basis of the objective function of the initial problem and a set of membership functions of its fuzzy parameters. An example of solving the problem has been considered, leading to a solution that is better than that obtained on the basis of the modal values of the fuzzy parameters of the problem

Siegmund duality for physicists: a bridge between spatial and first-passage properties of continuous- and discrete-time stochastic processes

Abstract We consider a generic one-dimensional stochastic process x(t), or a random walk Xn , which describes the position of a particle evolving inside an interval [ a , b ] , with absorbing walls located at a and b. In continuous time, x(t) is driven by some equilibrium process θ ( t ) , while in discrete time, the jumps of Xn follow a stationary process that obeys a time-reversal property. An important observable to characterize its behavior is the exit probability E b ( x , t ) , which is the probability for the particle to be absorbed first at the wall b, before or at time t, given its initial position x. In this paper we show that the derivation of this quantity can be tackled by studying a dual process y(t) very similar to x(t) but with hard walls at a and b. More precisely, we show that the quantity E b ( x , t ) for the process x(t) is equal to the probability Φ ~ ( x , t | b ) of finding the dual process inside the interval [ a , x ] at time t, with y ( 0 ) = b . This is known as Siegmund duality in mathematics. Here we show that this duality applies to various processes that are of interest in physics, including models of active particles, diffusing diffusivity models, a large class of discrete- and continuous-time random walks, and even processes subjected to stochastic resetting. For all these cases, we provide an explicit construction of the dual process. We also give simple derivations of this identity both in the continuous and in the discrete time setting, as well as numerical tests for a large number of models of interest. Finally, we use simulations to show that the duality is also likely to hold for more complex processes, such as fractional Brownian motion.

Causality Bounds on Dissipative General-Relativistic Magnetohydrodynamics.

We derive necessary and sufficient conditions under which a large class of relativistic generalizations of Braginskii's magnetohydrodynamics with shear, bulk, and heat diffusion effects is causal and strongly hyperbolic in the fully nonlinear regime in curved spacetime. We find that causality severely constrains the size of nonideal effects and the onset of kinetic instabilities. Our results are crucial for assessing the regime of validity of fluid dynamical simulations of plasmas near supermassive black holes.

Is Autophagy Targeting a Valid Adjuvant Strategy in Conjunction with Tyrosine Kinase Inhibitors?

Tyrosine kinase inhibitors (TKIs) represent a relatively large class of small-molecule inhibitors that compete with ATP for the catalytic binding site of tyrosine kinase proteins. While TKIs have demonstrated effectiveness in the treatment of multiple malignancies, including chronic myelogenous leukemia, gastrointestinal tumors, non-small cell lung cancers, and HER2-overexpressing breast cancers, as is almost always the case with anti-neoplastic agents, the development of resistance often imposes a limit on drug efficacy. One common survival response utilized by tumor cells to ensure their survival in response to different stressors, including anti-neoplastic drugs, is that of autophagy. The autophagic machinery in response to TKIs in multiple tumor models has largely been shown to be cytoprotective in nature, although there are a number of cases where autophagy has demonstrated a cytotoxic function. In this review, we provide an overview of the literature examining the role that autophagy plays in response to TKIs in different preclinical tumor model systems in an effort to determine whether autophagy suppression or modulation could be an effective adjuvant strategy to increase efficiency and/or overcome resistance to TKIs.

Higher-Order Cellular Automata Generated Symmetry-Protected Topological Phases and Detection Through Multi Point Strange Correlators

In computer and system sciences, higher-order cellular automata (HOCA) are a type of cellular automata that evolve over multiple time steps and generate complex patterns, which have various applications, such as secret-sharing schemes, data compression, and image encryption. In this paper, we introduce HOCA to quantum many-body physics and construct a series of symmetry-protected topological (SPT) phases of matter, in which symmetries are supported on a great variety of subsystems embbeded in the SPT bulk. We call these phases HOCA-generated SPT (HGSPT) phases. Specifically, we show that HOCA can generate not only well-understood SPTs with symmetries supported on either regular (e.g., linelike subsystems in the two-dimensional cluster model) or fractal subsystems, but also a large class of unexplored SPTs with symmetries supported on more choices of subsystems. One example is that has either fractal and linelike subsystem symmetries simultaneously or two distinct types of fractal symmetries simultaneously. Another example is in which chaotic-looking symmetries are significantly different from and thus cannot reduce to fractal or regular subsystem symmetries. We also introduce a new notation system to characterize HGSPTs. We prove that all possible subsystem symmetries in a square lattice can be locally simulated by an HOCA-generated symmetry. As the usual two-point strange correlators are trivial in most HGSPTs, we find that the nontrivial SPT orders can be detected by what we call . We propose a universal procedure to design the spatial configuration of the multi point strange correlators for a given HGSPT phase. Specifically, we find deep connections between multi point strange correlators and the spurious topological entanglement entropy (STEE), both exhibiting long-range behavior in a short-range entangled state. Our HOCA approaches and multi point strange correlators pave the way for a unified paradigm to design, classify, and detect phases of matter with symmetries supported on a great variety of subsystems, and also provide potential useful perspective in surpassing the computational irreducibility of HOCA in a quantum mechanical way. Published by the American Physical Society 2024

STRATEGIES FOR INCLUSIVE EDUCATION AND INTERCULTURAL COMMUNICATION IN PRIMARY SCHOOL

This academic article delves into the realm of inclusive education and intercultural communication within the context of elementary schools. The study sheds light on the crucial role of culturally sensitive teaching and intercultural training, while also recognizing the challenges confronted by elementary educators, such as inadequate training, limited resources, and handling large class sizes. To enhance inclusive education and intercultural communication, this paper recommends targeted strategies, including fostering collaborative co-teaching partnerships between primary and resource teachers.

Machine learning in viscoelastic fluids via energy-based kernel embedding

The ability to measure differences in collected data is of fundamental importance for quantitative science and machine learning, motivating the establishment of metrics grounded in physical principles. In this study, we focus on the development of such metrics for viscoelastic fluid flows governed by a large class of linear and nonlinear stress models. To do this, we introduce energy-compatible families of kernel functions corresponding to a given viscoelastic stress model. Each kernel implicitly embeds flowfield snapshots into a Reproducing Kernel Hilbert Space (RKHS) in which distances and angles are computed and whose squared norm equals the total mechanical energy. Additionally, we present a solution to the preimage problem for these kernels, enabling accurate reconstruction of flowfields from their RKHS representations. Through numerical experiments on an unsteady viscoelastic lid-driven cavity flow, we demonstrate the utility of energy-compatible kernels for extracting energetically-dominant coherent structures in viscoelastic flows across a range of Reynolds and Weissenberg numbers. Specifically, the features extracted by Kernel Principal Component Analysis (KPCA) of flowfield snapshots using energy-compatible kernel functions yield reconstructions with superior accuracy in terms of mechanical energy compared to conventional methods such as ordinary Principal Component Analysis (PCA) with naïvely-defined state vectors or KPCA with ad-hoc choices of kernel functions. Our findings underscore the importance of principled choices of metrics in both scientific and machine learning investigations of complex fluid systems.

Extraction and Quantification of Phenolic Compounds in Maize.

Plants accumulate hundreds of thousands of specialized metabolites that participate in their interactions with the environment. Among these compounds, phenolics represent a large class, and they play important physiological roles, such as providing a first barrier against pathogens, cues to pollinators, and radiation protection. Maize is one of the most important crops worldwide for food, animal feed, and biofuels, and it has the potential to accumulate different phenolics in vegetative tissues as well as in seeds. Recent studies have identified a large number of phenolic compounds-with a diversity of chemical decorations-in different maize tissues, but these likely represent just a fraction of the metabolic diversity of maize. In this protocol, we describe a specific method for the extraction and quantification of maize phenolic compounds by ultra-high-pressure liquid chromatography-tandem multiple reaction monitoring mass spectrometry (UHPLC-MRM-MS/MS) analysis. We provide detailed instructions for the extraction of phenolics using acidic methanol, and for the quantification of 33 different compounds in maize stems, including flavonoids, phenolic acids, and lignin precursors.

Flavonoids and Derived Anthocyanin Pigments in Plants-Structure, Distribution, Function, and Methods for Quantification and Characterization.

Flavonoids represent a large class of phenolic specialized metabolites and play crucial roles in plant-environment interactions, including responses to biotic and abiotic factors. While the core flavonoid biosynthesis pathway is well known in several plant species, enzymes involved in modifying core flavonoid structures, furnishing them with distinct biological activities, continue to be identified. Anthocyanins, a specific type of flavonoid pigment, serve various functions, including attracting pollinators and seed-dispersing organisms when accumulated in flowers and seeds. Anthocyanins also accumulate in vegetative tissues of many plants, especially under unfavorable conditions. In this review, we present an overview of the diverse structures, various distributions, and multiple functions of flavonoids in plants.

From Germ Cells to Implantation: The Role of Extracellular Vesicles.

Extracellular vesicles represent a large heterogeneous class of near and long-distance intercellular communication mediators, released by both prokaryotic and eukaryotic cells. Specifically, the scientific community has shown growing interest in exosomes, which are nano-sized vesicles with an endosomal origin. Not so long ago, the physiological goal of exosome generation was largely unknown and required more investigation; at first, it was hypothesized that exosomes are able to remove excess, reject and unnecessary constituents from cells to preserve cellular homeostasis. However, thanks to recent studies, the central role of exosomes in regulating cellular communication has emerged. Exosomes act as vectors in cell-cell signaling by their cargo, proteins, lipids, and nucleic acids, and influence physiological and pathological processes. The findings on exosomes are widespread in a large spectrum of biomedical applications from diagnosis and prognosis to therapies. In this review, we describe exosome biogenesis and the current methods for their isolation and characterization, emphasizing the role of their cargo in female reproductive processes, from gametogenesis to implantation, and the potential involvement in human female disorders.

Management of Teaching Space and Time: Evidence from Tertiary Institutions in Ghana

Overcrowding in classrooms is a common issue in tertiary institutions in Ghana. Proper management of teaching space and time is critical for maximising the use of available resources and stressful-free learning environment for students in tertiary institutions. This article aims to provide empirical evidence and practical recommendations for improving the management of teaching space and time in Ghana's tertiary institutions. The study is grounded in utilization theory, and adopted a descriptive survey design. The population for this study consists of 26 general-purpose lecture rooms available at a university in Ghana. Census Sampling was applied to select all the 26 general-purpose lecture rooms. The instruments used for data collection was an observation checklist. Quantitative results from the observation checklist were analysed using teaching space utilization formulas. The study revealed that general-purpose lecture rooms were underutilized in terms of time but were efficiently utilized in terms of space. The study also found that the lecture rooms have high utilization rates on Mondays and low utilization on Fridays. Additionally, morning sessions revealed efficient utilization, while afternoon and evening sessions were underutilized. The study recommends implementation of centralized timetabling and computerization of space allocation. Additionally, the study recommends for provision of a few more large general purpose lecture rooms to accommodate very large class sizes, which can also be partitioned effectively to accommodate small class sizes for efficient utilization of the lecture rooms.

Topological recursion on transalgebraic spectral curves and Atlantes Hurwitz numbers

Given a spectral curve with exponential singularities (which we call a “transalgebraic spectral curve”), we extend the definition of topological recursion to include contributions from the exponential singularities in a way that is compatible with limits of sequences of spectral curves. This allows us to prove the topological recursion/quantum curve correspondence for a large class of transalgebraic spectral curves. As an application, we find that Atlantes Hurwitz numbers, which were previously thought to fall outside the scope of topological recursion, satisfy (our extended version of) topological recursion, and we construct the corresponding quantum curve directly from topological recursion.

Switched Observer Design for a Class of Non-Linear Systems

This paper is concerned with the switched observer design for a class of systems subject to locally Lipschitz non-linearities. By performing a suitable description of the estimation error dynamics into a linear parameter varying (LPV) system representation, sufficient conditions for the existence of a switching output injection gain are proposed such that the asymptotic stability of the estimation error is guaranteed. These conditions can be conveniently expressed by means of linear matrix inequalities (LMIs), which are easily computationally tractable. A numerical example is provided to show the favorable performance achieved by the proposed observer, which can be applied to a large class of non-linear systems.

From irregular to regular eutectic growth in the Al-Al3Ni system: In situ observations during directional solidification

We investigate the irregular eutectic growth dynamics of the Al-Al3Ni alloy, in which one of the solid phases (Al3Ni) grows faceted from the liquid. Leveraging in situ optical microscopy and synchrotron transmission x-ray microscopy, we address the question of the degree of coupling between Al and Al3Ni at the growth front and that of the shape of the microstructures left behind in the bulk solid during directional solidification. Real-time optical observations bring evidence for a morphological transition from a eutectic-grain dependent, irregular eutectic growth at low solidification velocity V (typically 1μms−1), to a weakly anisotropic, regular growth at higher V (reaching 10μms−1). Unprecedented x-ray nano-imaging of the solid–liquid interface, and 3D characterization of the growth patterns, were made possible by a new DS setup at Brookhaven National Laboratory’s NSLS-II. At low V, the leading tips of partly faceted Al3Ni crystals are observed to grow not far ahead of the Al growth front. Correlating in situ images and postmortem 3D tomographic reconstructions reveals that the presence of faceted and non-faceted regions of Al3Ni crystals in the solid is a direct consequence of coupling and decoupling during DS, respectively. Upon increasing V, the lead distance of Al3Ni vanishes, and the shape of Al3Ni ceases to be governed by faceted growth. These observations cast light onto the basic mechanisms (faceted growth, diffusive coupling, and the dynamics of trijunctions) governing a faceted to rod-like transition upon increasing V in the Al3Ni system, with broad implications to a large class of irregular eutectics.

Phage-mediated resolution of genetic conflict alters the evolutionary trajectory of Pseudomonas aeruginosa lysogens.

The opportunistic human pathogen Pseudomonas aeruginosa is naturally infected by a large class of temperate, transposable, Mu-like phages. We examined the genotypic and phenotypic diversity of P. aeruginosa PA14 lysogen populations as they resolve clustered regularly interspaced short palindromic repeat (CRISPR) autoimmunity, mediated by an imperfect CRISPR match to the Mu-like DMS3 prophage. After 12 days of evolution, we measured a decrease in spontaneous induction in both exponential and stationary phase growth. Co-existing variation in spontaneous induction rates in the exponential phase depended on the way the coexisting strains resolved genetic conflict. Multiple mutational modes to resolve genetic conflict between host and phage resulted in coexistence in evolved populations of single lysogens that maintained CRISPR immunity to other phages and polylysogens that lost immunity completely. This work highlights a new dimension of the role of lysogenic phages in the evolution of their hosts.IMPORTANCEThe chronic opportunistic multi-drug-resistant pathogen Pseudomonas aeruginosa is persistently infected by temperate phages. We assess the contribution of temperate phage infection to the evolution of the clinically relevant strain UCBPP-PA14. We found that a low level of clustered regularly interspaced short palindromic repeat (CRISPR)-mediated self-targeting resulted in polylysogeny evolution and large genome rearrangements in lysogens; we also found extensive diversification in CRISPR spacers and cas genes. These genomic modifications resulted in decreased spontaneous induction in both exponential and stationary phase growth, increasing lysogen fitness. This work shows the importance of considering latent phage infection in characterizing the evolution of bacterial populations.

Axiomatization of interventional probability distributions

Abstract Causal intervention is an essential tool in causal inference. It is axiomatized under the rules of do-calculus in the case of structure causal models. We provide simple axiomatizations for families of probability distributions to be different types of interventional distributions. Our axiomatizations neatly lead to a simple and clear theory of causality that has several advantages: it does not need to make use of any modelling assumptions such as those imposed by structural causal models; it only relies on interventions on single variables; it includes most cases with latent variables and causal cycles; and more importantly, it does not assume the existence of an underlying true causal graph as we do not take it as the primitive object; moreover, a causal graph is derived as a by-product of our theory. We show that, under our axiomatizations, the intervened distributions are Markovian to the defined intervened causal graphs, and an observed joint probability distribution is Markovian to the obtained causal graph; these results are consistent with the case of structural causal models, and as a result, the existing theory of causal inference applies. We also show that a large class of natural structural causal models satisfy the theory presented here. The aim of this paper is axiomatization of interventional families, which is subtly different from causal modelling.

Complexity of High-Dimensional Identity Testing with Coordinate Conditional Sampling

We study the identity testing problem for high-dimensional distributions. Given as input an explicit distribution \(\mu\) , an \(\varepsilon \gt 0\) , and access to sampling oracle(s) for a hidden distribution \(\pi\) , the goal in identity testing is to distinguish whether the two distributions \(\mu\) and \(\pi\) are identical or are at least \(\varepsilon\) -far apart. When there is only access to full samples from the hidden distribution \(\pi\) , it is known that exponentially many samples (in the dimension) may be needed for identity testing, and hence previous works have studied identity testing with additional access to various “conditional” sampling oracles. We consider a significantly weaker conditional sampling oracle, which we call the \(\mathsf{Coordinate\ Oracle}\) , and provide a computational and statistical characterization of the identity testing problem in this new model. We prove that if an analytic property known as approximate tensorization of entropy holds for an \(n\) -dimensional visible distribution \(\mu\) , then there is an efficient identity testing algorithm for any hidden distribution \(\pi\) using \(\widetilde{O}(n/\varepsilon)\) queries to the \(\mathsf{Coordinate\ Oracle}\) . Approximate tensorization of entropy is a pertinent condition as recent works have established it for a large class of high-dimensional distributions. We also prove a computational phase transition: for a well-studied class of \(n\) -dimensional distributions, specifically sparse antiferromagnetic Ising models over \(\{+1,-1\}^{n}\) , we show that in the regime where approximate tensorization of entropy fails, there is no efficient identity testing algorithm unless \(\mathsf{RP}=\mathsf{NP}\) . We complement our results with a matching \(\Omega(n/\varepsilon)\) statistical lower bound for the sample complexity of identity testing in the \(\mathsf{Coordinate\ Oracle}\) model.

Enriched Z-Contractions and Fixed-Point Results with Applications to IFS

In this manuscript, we initiate a large class of enriched (d,Z)-Z-contractions defined on Banach spaces and prove the existence and uniqueness of the fixed point of these contractions. We also provide an example to support our results and give an existence condition for the uniqueness of the solution to the integral equation. The results provided in the manuscript extend, generalize, and modify the existence results. Our research introduces novel fixed-point results under various contractive conditions. Furthermore, we discuss the iterated function system associated with enriched (d,Z)-Z-contractions in Banach spaces and define the enriched Z-Hutchinson operator. A result regarding the convergence of Krasnoselskii’s iteration method and the uniqueness of the attractor via enriched (d,Z)-Z-contractions is also established. Our discoveries not only confirm but also significantly build upon and broaden several established findings in the current body of literature.