Miracle-dz/UniFAR-FTUs huggingface dataset:
Cholesterol in the cargo membrane amplifies tau inhibition of kinesin-1-based transport

Significance Kinesin-1 drives long-range vesicle transport along microtubules in eukaryotic cells. Tau is essential for microtubule assembly and stabilization but occludes kinesin-binding sites. In cells, kinesins are often attached to vesicle cargos via a fluid lipid membrane. Here we employ in vitro optical trapping to determine how a lipid cargo membrane affects kinesin-based transport in the presence of tau. We show that tau-inhibition of kinesin is tuned by cholesterol in the cargo membrane. Our results provide insight into how kinesin-based vesicle transport is maintained under tau-enriched conditions such as those found in neurons. Moreover, our results identify cargo membrane cholesterol as a potential therapeutic target to mitigate transport dysfunctions that are a common early hallmark of aging and neurodegeneration.
Proposal of Software Quality Confirmation Method Using Feature Model

The software product line(SPL) is a method for efficiently developing software of the same group of products in order to cope with various kinds of software products. In SPL development, the relationship between elements (equal features) that characterize each product is expressed by a feature model represented using a tree diagram. In this paper, we propose a method to confirm the quality of each product from the assurance case by converting the quality characteristic of the feature model to assurance case that can be explained using GSN notation.
The effects of cannabidiol on subjective states, cognition, and psychomotor function in healthy adults: A randomized clinical trial

Despite being added to numerous products, little is known about cannabidiol. Drowsiness is a self‐reported side effect, which could impact cognitive functioning. To determine whether cannabidiol impacts cognition and psychomotor function, a volunteer sample of healthy, college students were recruited for this randomized, parallel‐group, double‐blind, feasibility trial from April–November 2021. Participants completed a baseline survey, the Stanford Sleepiness Scale, Visual Analog Mood Scale, Digit Symbol Substitution Test, Trail Making Test, Psychomotor Vigilance Test, and Simple Reaction Time Tests. Participants were then randomized and allocated to receive 300 mg cannabidiol oil (N = 21) or placebo (N = 19). After 120 min, participants retook the tests. Performance between groups was compared using Analysis of Covariance and multi‐level Negative Binomial regression. Participants averaged 21 ± 3 years of age, and 52% were female. Self‐reported anxiety did not change posttreatment. Performances on the Stanford Sleepiness Scale, Visual Analog Mood Scale, and Psychomotor Vigilance Test increased for both groups. After accounting for baseline scores, attention lapse duration significantly increased for those receiving cannabidiol compared to placebo in the Psychomotor Vigilance Test (76 vs. 66 ms; p = 0.02). Auditory reaction time improved in the cannabidiol group versus placebo for one sound emitted during the Simple Reaction Time Test (241 vs. 245 ms; p = 0.02), but the number of early responses increased from 0.3 to 0.8 for those receiving cannabidiol. While performance on most tests was similar between those receiving cannabidiol and placebo, cannabidiol might affect certain aspects of vigilance. More research and larger trials are needed.
Improved left atrial appendage closure procedural efficiency using radiofrequency transseptal wire system

The radiofrequency (RF) needle has been shown to improve transseptal puncture efficiency and safety compared to mechanical needles. This study aimed to investigate the use of VersaCross RF transseptal wire system (Baylis Medical) to improve procedural efficiency of left atrial appendage closure (LAAC) compared to the standard RF needle‐based workflow.
Familial Thrombophilia Is Associated With Primary Multifocal Osteonecrosis: A Case-Control Study of 40 Patients.

To characterize pathoetiologic associations of heritable thrombophilia-hypofibrinolysis with idiopathic (primary) multifocal osteonecrosis (ON) (≥3 ON anatomic sites), we prospectively studied 28 women and 12 men with primary multifocal ON compared with 27 women and 24 men with primary nonmultifocal ON (<3 sites) and 110 healthy controls without ON. The 40 cases with primary multifocal ON differed from controls for 3 familial thrombophilias: Factor V Leiden heterozygosity (6 of 40 [15%] vs 2 of 109 [2%], P=.002), G20210A prothrombin gene heterozygosity (6 of 40 [15%] vs 3 of 110 [3%], P=.011), and high (>150%) Factor VIII (8 of 40 [20%] vs 7 of 103 [7%], P=.031). These case-control familial coagulation differences paralleled those in 51 concurrently evaluated cases with primary nonmulti-focal ON, 7 of 51 (14%) of whom had Factor V Leiden heterozygosity vs 2% of controls (P=.005) and 14 of 44 (32%) of whom had high Factor VIII vs 7 of 103 (7%) of controls (P=.0002). Recognition of familial thrombophilia as a common pathoetiology of primary multifocal ON provides an opportunity for early anticoagulation (before joint collapse), allowing both prophylaxis and therapy aimed at relieving pain and slowing or stopping progression of the disease to joint collapse. [Orthopedics. 202X;XX(X):xx-xx. ].
Distinctive Structure and Assembly of Phyllosphere Microbial Communities between Wild and Cultivated Rice

Plant microbiota are being considered a lever to increase the sustainability of food production under a changing climate. In particular, the microbiomes associated with ancestors of modern cultivars have the potential to support their domesticated cultivars. ABSTRACT Wild rice has been demonstrated to possess enriched genetic diversity and multiple valuable traits involved in disease/pest resistance and abiotic stress tolerance, which provides a potential resource for sustainable agriculture. However, unlike the plant compartments such as rhizosphere, the structure and assembly of phyllosphere microbial communities of wild rice remain largely unexplored. Through amplicon sequencing, this study compared the phyllosphere bacterial and fungal communities of wild rice and its neighboring cultivated rice. The core phyllosphere microbial taxa of both wild and cultivated rice are dominated with Pantoea, Methylobacterium, Nigrospora, and Papiliotrema, which are potentially beneficial to rice growth and health. Compared to the cultivated rice, Methylobacterium, Sphingomonas, Phaeosphaeria, and Khuskia were significantly enriched in the wild rice phyllosphere. The potentially nitrogen-fixing Methylobacterium is the dominated wild-enriched microbe; Sphingomonas is the hub taxon of wild rice networks. In addition, the microbiota of wild rice was more governed by deterministic assembly with a more complicated and stable community network than the cultivated rice. Our study provides a list of the beneficial microbes in the wild rice phyllosphere and reveals the microbial divergence between wild rice and cultivated rice in the original habitats, which highlights the potential selective role of wild rice in recruiting specific microbiomes for enhancing crop performance and promoting sustainable food production. IMPORTANCE Plant microbiota are being considered a lever to increase the sustainability of food production under a changing climate. In particular, the microbiomes associated with ancestors of modern cultivars have the potential to support their domesticated cultivars. However, few efforts have been devoted to studying the biodiversity and functions of microbial communities in the native habitats of ancestors of modern crop species. This study provides a list of the beneficial microbes in the wild rice phyllosphere and explores the microbial interaction patterns and the functional profiles of wild rice. This information could be useful for the future utilization of the plant microbiome to enhance crop performance and sustainability, especially in the framework of sustainable agroecosystems.
Basic fibroblast growth factor gel preparation induces angiogenesis during wound healing

Purpose: This study aimed to observe the effect of basic fibroblast growth factor (bFGF) gel preparation on wound repair in a full-thickness skin defect rat model and to further explore its mechanism. Methods: The full-thickness skin defect model of Wistar rats was created with circular wounds of 20 mm or 10 mm in diameter on both sides of the spine. The animals were divided into the normal, model, control gel, and bFGF gel groups (300 IU/cm2). The effects of the bFGF gel on wound healing were evaluated and compared. Optical coherence tomography (OCT)-based angiography (OCTA) was used to investigate the effects of bFGF on angiogenesis during wound healing. Western blotting, polymerase chain reaction (PCR), and enzyme-linked immunosorbent assay (ELISA) kits were used to detect the effect of the gel preparation on the levels of vascular endothelial growth factor (VEGF) and matrix metalloproteinases (MMP2 and MMP9) on the wound surface to explore the mechanism. Results: The bFGF gel significantly reduced wound area, promoted the formation of wound granulation tissue, and accelerated wound healing in the bFGF gel group on days 7 and 14, compared with the control gel group. OCTA results showed that bFGF significantly improved wound vascular density, diameter, and circumference. Western blot, PCR, and ELISA results showed that the gel preparation could promote the expression levels of MMP2, MMP9, and VEGF on the wound surface 7 and 14 days after injury. Conclusion: bFGF promotes angiogenesis in wound areas. Topical gel preparations of bFGF can be developed for use in wound repair.
Abiotic stress experiments need a reality check to improve translation to the field

In nearly every issue of this journal and other general plant biology journals, you will find articles on plant abiotic stress responses and signalling. These publications justify a focus on a particular stress acclimation mechanism by reviewing impacts of the associated stress on crop yield. Since the advent of genomics, the focus of abiotic stress response studies has shifted from whole-plant physiology to cellular and molecular mechanisms. Yet, little of this knowledge has been translated into improved crops that can better withstand abiotic constraints, lagging far behind the development of varieties resistant to pests and pathogens (Passioura, 2020).
What are the Better Gamification Tools for Elementary School Teachers?

Recent research demonstrates that the possibility of using gamification strategies to enhance the motivation and to foster the interaction and engagement in the society network for promote commercial market. However, this study intent to apply the gamification tools to the students, in which the teachers can use it as a classroom management tool and facilitated learning activity. Therefore, the purpose of this study is to compare the effectiveness of exiting gamification tools and mechanics for the elementary school teachers. The findings of the study reveal the most gamification tools have try to develop to become a massive multi-user online platform for connecting learning, however few evidence of supporting the live control of the classroom management provided from the comparison, thus leaving the gap for further study to explore. Valuable practical information from the teachers suggest that the success and the performance of a gamified system should be considered from several different aspects, and the gamification tools for improvement in the future are also reported. With the supports from the literature and empirical evidences, the study conclude that how gamification influencing students' classroom behavior changes also is worthy to be explored.
Cerebellar gamma‐aminobutyric acid: Investigation of group effects in neurodevelopmental disorders

Neurodevelopmental disorders (NDDs) including autism spectrum disorder (ASD), attention‐deficit hyperactivity disorder (ADHD) and obsessive–compulsive disorder (OCD) are thought to arise in part from the disruption in the excitatory/inhibitory balance of gamma‐aminobutyric acid (GABA) and glutamate in the brain. Recent evidence has shown the involvement of the cerebellum in cognition and affect regulation, and cerebellar atypical function or damage is reported frequently in NDDs. Magnetic resonance spectroscopy studies have reported decreases in GABA in cortical brain areas in the NDDs, however, GABA levels in the cerebellum have not been examined. To determine possible group effects, we used a MEGA‐PRESS acquisition to investigate GABA+ levels in a cerebellar voxel in 343 individuals (aged 2.5–22 years) with ASD, ADHD, OCD and controls. Using a mixed effects model, we found no significant differences between groups in GABA+ concentration. Our findings suggest that cerebellar GABA+ levels do not differentiate NDD groups.
Cross‐linguistic relations between voice quality and vowel quality

A survey of impressionistic and instrumental descriptions of diverse languages (including new data from the Nilotic languages Dinka and Acholi) reveals repeated cases of phonemic and subphonemic interaction between phonation type and aspects of oropharyngeal resonance. Generally, if there is a regular interaction between intra‐ and extralaryngeal factors in a language, vocal fold configurations with greater laryngeal laxness (as required for regular phonation in consonants, murmur in consonants and vowels, and pitch depression) are associated with lower F1 and, conversely, greater vocal fold tension (associated with voicelessness, creaky phonation, and higher pitch) occurs with higher F1. Explanations for this universal in biomechanical (Laver, 1980), neuromuscular (Laufer and Condax, 1981), and aerodynamic (Ohala, 1983) aspects of articulation are proposed. Cross‐linguistic evidence suggests that articulation from intra‐ and extralaryngeal domains (putatively separate for phonological purposes) may funct.
Quantifying Rare Events in Stochastic Reaction-Diffusion Dynamics Using Tensor Networks

The interplay between stochastic chemical reactions and diffusion can generate rich spatiotemporal patterns. While the timescale for individual reaction or diffusion events may be very fast, the timescales for organization can be much longer. That separation of timescales makes it particularly challenging to anticipate how the rapid microscopic dynamics gives rise to macroscopic rates in the non-equilibrium dynamics of many reacting and diffusing chemical species. Within the regime of stochastic fluctuations, the standard approach is to employ Monte Carlo sampling to simulate realizations of random trajectories. Here, we present an alternative numerically tractable approach to extract macroscopic rates from the full ensemble evolution of many-body reaction diffusion problems. The approach leverages the Doi-Peliti second-quantized representation of reaction-diffusion master equations along with compression and evolution algorithms from tensor networks. By focusing on a Schl\"{o}gl model with one-dimensional diffusion between $L$ otherwise well-mixed sites, we illustrate the potential of the tensor network approach to compute rates from many-body systems, here with approximately $3 \times 10^{15}$ microstates. Specifically, we compute the rate for switching between metastable macrostates, with the expense for computing those rates growing subexponentially in $L$. Because we directly work with ensemble evolutions, we crucially bypass many of the difficulties encountered by rare event sampling techniques$\unicode{x2013}$detailed balance and reaction coordinates are not needed.
How Data Scientists Review the Scholarly Literature

Keeping up with the research literature plays an important role in the workflow of scientists – allowing them to understand a field, formulate the problems they focus on, and develop the solutions that they contribute, which in turn shape the nature of the discipline. In this paper, we examine the literature review practices of data scientists. Data science represents a field seeing an exponential rise in papers, and increasingly drawing on and being applied in numerous diverse disciplines. Recent efforts have seen the development of several tools intended to help data scientists cope with a deluge of research and coordinated efforts to develop AI tools intended to uncover the research frontier. Despite these trends indicative of the information overload faced by data scientists, no prior work has examined the specific practices and challenges faced by these scientists in an interdisciplinary field with evolving scholarly norms. In this paper, we close this gap through a set of semi-structured interviews and think-aloud protocols of industry and academic data scientists (N = 20). Our results while corroborating other knowledge workers’ practices uncover several novel findings: individuals (1) are challenged in seeking and sensemaking of papers beyond their disciplinary bubbles, (2) struggle to understand papers in the face of missing details and mathematical content, (3) grapple with the deluge by leveraging the knowledge context in code, blogs, and talks, and (4) lean on their peers online and in-person. Furthermore, we outline future directions likely to help data scientists cope with the burgeoning research literature.
New LMS adaptive filter for GPR processing

The DFT method popular in GPR processing assumes that data is infinite outside a given interval. The selection of a finite time interval and of the orthogonal trigonometric basis over a given interval means that only those frequencies which coincide with the basis will project onto a single basis vector. The rest of the frequency set will give nonzero projections on the entire basis set [harris, 1978]. The finite data set is obtained by windowing an infinite data sequence. The assumption is that the unmeasured data is zero and this is not true. When the power of the signal is concentrated on a narrow BW this operation spreads that power into adjacent frequency regions. This phenomenon is called spectral leakage. Leakage affects power estimation, resolution, dynamic range, implementation and detectability of a sinusoidal component. Parametric methods can be used to describe the process that creates a signal. A priori knowledge is required to extrapolate the information from the input signal. This approach eliminates spectral leakage problems. Parametric methods create a model that use a number of parameters to describe the process that create the signal under observation. Adaptive filters are parametric and iterative, theses filters respond to the input by changing their model parameters. The number of samples used at the input is small, however the samples are stored in memory so that the parameters obtained from an estimate are statistically combined with those of the previous estimates. This gives an accurate reading over several iterations. To date the closed loop adaptive filters have been used more commonly in radar signal processing. The closed loop adaptive filters have a feedback factor. One of the main disadvantages of closed loop adaptive filters are firstly the need for continual optimization and secondly instability. The second little known group of adaptive filters are called open loop adaptive filters. These filters differ from open loop ADF in that they do not require feedback, the input signal is used to change the parameters of the filter. An adaptive algorithm used by an ALP in closed loop filters changes the FIR filter coefficients such that the transfer function of the adaptive filter become the inverse of that possessed by the input process. Once adapted the frequency response of the FIR filter will be unity at the frequency of the input signal and the ALP's output. The error will be at a minimal value. By comparison the frequency response of the FIR filter in an open loop configuration will be zero at the frequency of the input signal. Open loop filters are more stable than closed loops adaptive filters. A novel frequency domain open loop adaptive filter is also introduced. This filter offers more stability, does not need feed back and has increased convergence speed.
Qubit recycling and the path-counting problem

Recently, it was shown that the qudits used in circuits of a convolutional form (e.g., Matrix Product State sand Multi-scale Entanglement Renormalization Ansatz) can be reset unitarily \href{https://doi.org/10.1103/PhysRevA.103. 042613}{[Phys. Rev. A 103, 042613 (2021)]}, even without measurement. We analyze the fidelity of this protocol for a family of quantum circuits that interpolates between such circuits and local quantum circuits, averaged over Haar-random gates. We establish a connection between this problem and a counting of directed paths on a graph, which is determined by the shape of the quantum circuit. This connection leads to an exact expression for the fidelity of the protocol for the entire family that interpolates between convolutional circuit and random quantum circuit. For convolutional circuits of constant window size, the rate of convergence to unit fidelity is shown to be $\frac{q^2}{q^2+1}$, independent of the window size, where $q$ is the local qudit dimension. Since most applications of convolutional circuits use constant-sized windows, our result suggests that the unitary reset protocol will likely work well in such a regime. We also derive two extra results in the convolutional limit, which may be of an independent interest. First, we derive exact expressions for the correlations between reset qudits and show that it decays exponentially in the distance. Second, we derive an expression for the the fidelity in the presence of noise, expressed in terms of the quantities that define the property of the channel, such as the entanglement fidelity.
Trade-offs between cost and information in cellular prediction

Living cells can leverage correlations in environmental fluctuations to predict the future environment and mount a response ahead of time. To this end, cells need to encode the past signal into the output of the intracellular network from which the future input is predicted. Yet, storing information is costly while not all features of the past signal are equally informative on the future input signal. Here, we show, for two classes of input signals, that cellular networks can reach the fundamental bound on the predictive information as set by the information extracted from the past signal: pushpull networks can reach this information bound for Markovian signals, while networks that take a temporal derivative can reach the bound for predicting the future derivative of non-Markovian signals. However, the bits of past information that are most informative about the future signal are also prohibitively costly. As a result, the optimal system that maximizes the predictive information for a given resource cost is, in general, not at the information bound. Applying our theory to the chemotaxis network of Escherichia coli reveals that its adaptive kernel is optimal for predicting future concentration changes over a broad range of background concentrations, and that the system has been tailored to predicting these changes in shallow gradients.
Machine Learning Applied to Peruvian Vegetables Imports

The current research work is being developed as a training and evaluation object. the performance of a predictive model to apply it to the imports of vegetable products into Peru using artificial intelligence algorithms, specifying for this study the Machine Learning models: LSTM and PROPHET. The forecast is made with data from the monthly record of imports of vegetable products(in kilograms) from Peru, collected from the years 2021 to 2022. As part of applying the training methodology for automatic learning algorithms, the exploration and construction of an appropriate dataset according to the parameters of a Time Series. Subsequently, the model with better performance will be selected, evaluating the precision of the predicted values so that they account for sufficient reliability to consider it a useful resource in the forecast of imports in Peru.