Miracle-dz/UniFAR-FTUs huggingface dataset:
Multiphasic Metabolic Changes in the Heart of Rats Fed a Sucrose-Rich Diet

Summary Long term feeding a sucrose-rich diet (SRD) to normal Wistar rats led to multiphasic changes in the activity of the pyruvate dehydrogenase complex (PDH), characterized by a significant decrease in PDHa (active form) in the short term on SRD (3 weeks) when compared to control rats fed the standard chow (STD). Although PDHa returned spontaneously to control values in the medium term (6-8 weeks) on SRD, an even more pronounced decrease was recorded when rats were kept long term on the SRD (15 weeks). Low PDHa levels recorded in the short and long term were accompanied by a two fold increase in heart acetyl-CoA concentration and the acetyl-CoA/CoASH ratio. Tissue long-chain acyl-CoA and triacylglycerol levels were also significantly higher in SRD fed rats. Spontaneous nor malization of all the above metabolic parameters was ob served during the medium term on SRD. Glucose-6-phosphate levels remained within control values during the short and medium term, in contrast to a two fold increase recorded in the long term on SRD. Glycogen concentrations were found moderately elevated only in the long term. Ci trate concentrations were slightly increased in the short and greatly in the long term, and the fructose-2,6-bis-phosphate/citrate ratio was found significantly decreased only during the long term on SRD. After 3 weeks on SRD, the protal vein Insulin/Glucagon (I/G) molar ratio was three times higher in SRD than STD rats, as opposed to an un changed I/G ratio found in the long term. Our findings suggest that the heart muscle of rats fed chronically a SRD had an impaired glycolytic flux and glucose oxidation as a result of increased fatty acids availability probably second ary to profound and changing shifts in the hormonal and metabolic milieu at different time periods of high sucrose intake.
The Accuracy of Sampling Coal

In sampling a week's output of 300 wagons of coal in order to estimate the mean ash content, how many visits should be paid to the colliery each week? How many wagons should be sampled on each visit? How many ‘increments’ should be taken from each wagon? What errors are introduced by the techniques of ‘reduction’ and analysis? This article describes some experiments made to provide information on questions such as these, and discusses the application of the results.
A Multiscale Contrast Enhancement for Mammogram Using Dynamic Unsharp Masking in Laplacian Pyramid

Mammography is the first option for screening breast cancer. However, some lesions may be missed due to superimposition of breast parenchymal patterns/tissues. Current enhancement methods can highlight some specific tissues, but the edges are weakened or extra noise is added. For improving the solution, this paper proposed a multiscale contrast enhancement for mammogram using dynamic unsharp masking (UM) in Laplacian pyramid. Laplacian pyramid is utilized to preserve the fine structure at each scale. Dynamic UM is presented to adaptively enhance details and suppress noise simultaneously. The proposed method mainly consists of five steps: 1) down-sample images; 2) calculate the weight of dynamic UM; 3) utilize dynamic UM enhancement for each scale image; 4) subtract the enhancement results for each scale image to acquire Laplacian pyramid images; and 5) restore the final enhanced image. To evaluate the proposed method qualitatively and quantitatively, one phantom and three clinical mammography cases were evaluated. Results showed the proposed method can provide much clearer details of the mammary gland. Quantitatively, information entropy and peak signal-to-noise ratio are increased by 0.96 and 3.89 at most compared with the state-of-the-art method. The proposed method has demonstrated that different types of regions are enhanced with the help of a regional adaptive evolution, which has great potential for adaptively enhancing the details and relatively suppressing the noise.
Necessary Conditions for Optimal Control of Diffusions with Hard Terminal State Restrictions

Necessary conditions are proved for certain problems of optimal control of diffusions where hard end constraints occur. The main results apply to several dimensional problems, where some of the state equations involve Brownian motions, but not the equations corresponding to states being hard restricted at the terminal time. The necessary conditions are stated in terms of weak variations. Two versions of necessary conditions are given, one version involving solutions of variational equations, the other one involving first order adjoint equations.
Analogues of Leavitt path algebras for higher-rank graphs

Directed graphs and their higher-rank analogues provide an intuitive framework to study a class of C∗-algebras which we call graph algebras. The theory of graph algebras has been developed by a number of researchers and also influenced other branches of mathematics: Leavitt path algebras and Cohn path algebras, to name just two. Leavitt path algebras for directed graphs were developed independently by two groups of mathematicians using different approaches. One group, which consists of Ara, Goodearl and Pardo, was motivated to give an algebraic framework of graph algebras. Meanwhile, the motivation of the other group, which consists of Abrams and Aranda Pino, is to generalise Leavitt’s algebras, in which the name Leavitt comes from. Later, Abrams and now with Mesyan introduced the notion of Cohn path algebras for directed graphs. Interestingly, both Leavitt path algebras and Cohn path algebras for directed graphs can be viewed as algebraic analogues of C∗-algebras of directed graphs. In 2013, Aranda Pino, J. Clark, an Huef and Raeburn introduced a higherrank version of Leavitt path algebras which we call Kumjian-Pask algebras. At their first appearance, Kumjian-Pask algebras were only defined for rowfinite higher-rank graphs with no sources. Clark, Flynn and an Huef later extended the coverage by also considering locally convex row-finite higherrank graphs. On the other hand, Cohn path algebras for higher rank graphs still remained a mystery. This thesis has two main goals. The first aim is to introduce Kumjian-Pask algebras for a class of higher-rank graphs called finitely-aligned higher-rank graphs. This type of higher-rank graph covers both row-finite higher-rank
A Novel Genome Optimization Tool for Chromosome-Level Assembly across Diverse Sequencing Techniques

This paper introduces a novel genome assembly optimization tool named LOCLA, which stands for “Local Optimization for Chromosome-Level Assembly”. It identifies reads and contigs aligned locally with high quality on gap flanks or scaffold boundaries of draft assemblies for gap filling and scaffold connection. LOCLA applies to both de novo and reference-based assemblies. It can also utilize reads produced by diverse sequencing techniques, e.g., 10x Genomics (10xG) Linked-Reads, and PacBio HiFi reads. We validated LOCLA on three human samples and one non-model organism. For the first two human samples, LLD0021C and CHM13, we generated de novo draft assemblies from 10xG Linked-Reads. On LLD0021C, LOCLA improves the draft assembly by adding 23.3 million bases using only 10xG Linked-Reads. These additional bases cover 28,746 protein-coding regions, particularly in pericentromeric and telomeric regions. On the CHM13 sample, we took 10xG Linked-Reads and PacBio HiFi reads as input. As a result, LOCLA added 46.2 million bases to the draft assembly. The increased content enables us to identify genes linked to complex diseases (e.g., ARHGAP11A) and critical biological pathways. We created two reference-guided draft assemblies on the third human sample, HG002, using contigs assembled from PacBio HiFi reads. LOCLA enhances the two draft assemblies by recovering 27.9 million bases (22.26%) and 35.7 million bases (30.93%) of the sequences discarded by the reference-guided assembly tool. The results indicate the robustness of LOCLA’s contig detection algorithm on gap flanks. Furthermore, we show that 95% of the sequences filled in by LOCLA have over 80% accuracy compared with the HG002 reference genome published by the Human Pan-genome Reference Consortium. On the non-model organism, LOCLA enhanced the genome assembly of Bruguiera sexangula (JAHLGP000000000) by decreasing 41.4% of its gaps and raising the Benchmarking Universal Single-Copy Orthologs (BUSCO) analysis score to 98.10%. LOCLA can optimize de novo and reference-guided assemblies using varied sequencing reads. The final assemblies produced by LOCLA have improved in both quantity and quality. The increased gene content may provide a valuable resource in advancing personalized medicine.
Western diet consumption impairs memory function via dysregulated hippocampus acetylcholine signaling

Western diet (WD) consumption during development yields long-lasting memory impairments, yet the underlying neurobiological mechanisms remain elusive. Here we developed an early life WD rodent model to evaluate whether dysregulated hippocampus (HPC) acetylcholine (ACh) signaling, a pathology associated with memory impairment in human dementia, is causally-related to WD-induced cognitive impairment. Rats received a cafeteria-style WD (access to various high-fat/high-sugar foods; CAF) or healthy chow (CTL) during the juvenile and adolescent periods (postnatal days 26-56). Behavioral, metabolic, and microbiome assessments were performed both before and after a 30-day healthy diet intervention beginning at early adulthood. Results revealed CAF-induced HPC-dependent contextual episodic memory impairments that persisted despite healthy diet intervention, whereas CAF was not associated with long-term changes in body weight, body composition, glucose tolerance, anxiety-like behavior, or gut microbiome. HPC immunoblot analyses after the healthy diet intervention identified reduced levels of vesicular ACh transporter in CAF vs. CTL rats, indicative of chronically reduced HPC ACh tone. To determine whether these changes were functionally related to memory impairments, we evaluated temporal HPC ACh binding via ACh-sensing fluorescent reporter in vivo fiber photometry during memory testing, as well as whether the memory impairments could be rescued pharmacologically. Results revealed dynamic HPC ACh binding during object-contextual novelty recognition was highly predictive of memory performance and was disrupted in CAF vs. CTL rats. Further, HPC alpha-7 nicotinic receptor agonist infusion during consolidation rescued memory deficits in CAF rats. Overall, these findings identify dysregulated HPC ACh signaling as a mechanism underlying early life WD-associated memory impairments.
Coronin-1 controls directional cell rearrangement in Drosophila wing epithelium

Directional cell rearrangement is a critical process underlying correct tissue deformation during morphogenesis. Although the involvement of F-actin regulation in cell rearrangement has been established, the role and regulation of actin binding proteins (ABPs) in this process are not well understood. In this study, we investigated the function of Coronin-1, a WD-repeat actin-binding protein, in controlling directional cell rearrangement in the Drosophila pupal wing. Transgenic flies expressing Coronin-1-EGFP were generated using CRISPR-Cas9. We observed that Coronin-1 localizes at the reconnecting junction during cell rearrangement, which is dependent on actin interacting protein 1 (AIP1), an actin disassembler and a known regulator of wing cell rearrangement. Loss of Coronin-1 function reduces cell rearrangement directionality and hexagonal cell fraction. These results suggest that Coronin-1 promotes directional cell rearrangement via its interaction with AIP1, highlighting the role of ABPs in the complex process of morphogenesis.
Post-training sleep modulates motor adaptation and task-related beta oscillations

Motor adaptation reflects the ability of the brain’s sensorimotor system to flexibly deal with environmental changes to generate effective motor behaviour. Whether sleep contributes to the consolidation of motor adaptation remains controversial. In this study, we investigated the impact of sleep on motor adaptation and its neurophysiological correlates in a novel motor adaptation task that leverages a highly automatized motor skill, i.e., typing. We hypothesized that sleep-associated memory consolidation would benefit motor adaptation and induce modulations in task-related beta band (13-30Hz) activity during adaptation. Healthy young male experts in typing on the regular computer keyboard were trained to type on a vertically mirrored keyboard while brain activity was recorded using electroencephalography (EEG). Typing performance was assessed either after a full night of sleep with polysomnography or a similar period of daytime wakefulness. Results showed improved motor adaptation performance after nocturnal sleep but not after daytime wakefulness, and decreased beta power (a) during mirrored typing as compared to regular typing, and (b) in the post-sleep vs. the pre-sleep mirrored typing sessions. Furthermore, the slope of the EEG signal, a measure of aperiodic brain activity, decreased during mirrored as compared to regular typing. Changes in the EEG spectral slope from pre- to post-sleep mirrored typing sessions were correlated with changes in task performance. Finally, increased fast sleep spindle density (13-15Hz) during the night following motor adaptation training was predictive of successful motor adaptation. These findings suggest that post-training sleep modulates neural activity mechanisms supporting adaptive motor functions.
Orientation truncated centre learning for deep face recognition

Recently, centre loss that aiming to assist Softmax loss with the objectives of both inter-class dispension and intra-class compactness simultaneously, has achieved remarkable performance on convolutional neural network-based face recognition. However, its advantages highly rely on the centre feature assumption, which influences the capacity of the final obtained face features. Inspired by the centre loss approach, a novel Orientation Truncated Centre Learning is proposed, which takes advantage of an orientation truncated centre function to make the centre feature learning have more suitable orientation for deep face recognition. Three metrics are proposed to evaluate how discriminative are the distributions of the learned features for MNIST visualisation. Experimental results on several challenging benchmarks, including fine-grained labelled faces in the wild (FGLFW), labelled faces in the wild (LFW), YouTube faces (YTF), and benchmark of large-scale unconstrained face recognition (BLUFR), show that the proposed approach can easily generate more favourable results than several state-of-the-art competitors.
Glucose Intolerance and Islet Damage in Mice Immunized with Homologous Endocrine Pancreas - A Preliminary Communication

Summary Mice developed degenerative changes of B-cells with loss of insulin granules, extravascularly situated mononuclear cell infiltration of the islets of Langerhans, and glucose intole rance eight and fifteen days after immunization with homo logous islets in complete Freund's adjuvant. Murine insulin and adjuvants in comparable preparations did not cause si milar changes. The condition was transient, but has the following features in common with juvenile diabetes mellitus in man: glucose intolerance, mononuclear cell infiltration of the islets and organ specific, anti-pancreatic cellular hyper sensitivity. 1t may therefore serve as a useful model in dia betes research.
Fractal Analysis on Morphology of Laser Irradiated Vanadium Surfaces Under Different Ambient

Abstract Pulsed laser irradiated vanadium surface morphology under different ambient has been prepared and characterized using fractal dimension analysis method on scanning electron microscopy (SEM) images. In presence of different ambient, self-periodic and self-similar surface patterns (e.g. dots, islands, and pins) were grown and appeared in different shapes. The fractal dimension (FD) of this developed vanadium nanostructure was calculated by fractal box count method (FBM). The calculated fractal dimension (FD, Df) shows dependence on the different type on ambient and the number of laser shots.
Map-based interaction with trajectory data

With the increasing popularity of location based services and mobile tracking technologies, the collection of large amounts of spatio-temporal data became an increasingly common, easier, and more reliable task. In turn, this has emphasized the possibility of analysing georeferenced information, particularly associated with human trajectory data, to identify and understand movement patterns and activities, ultimately, supporting decision making in various contexts. In order to properly analyse and understand the spatio-temporal and the thematic properties associated with these data, adequate visualization techniques are needed. Due to the spatial properties of trajectories, map-based techniques, such as 2D static maps or 3D space-time cubes (STCs) are considered as essential tools for their visualization. However, despite the increasing number of visualization systems, the study regarding their usability, alongside the role of the human user, sometimes with a limited background in data visualization and analysis, are often neglected. In addition to the somewhat disperse, and sometimes even contradictory, results in the literature, these factors, ultimately, emphasize the lack of knowledge to support the choice of particular visualizations, and their design, in different types of tasks. This dissertation addresses these issues through three main sets of contributions, focusing on inexperienced users, in terms of data visualization and analysis: i) the characterization of the dis/advantages of existing map-based techniques (2D static maps and STCs), depending on the types of visual analysis tasks and the focus of the analysis; ii) the improvement of existing visualization techniques, either through the inclusion of additional spatial cues within the STC, or combining both types of techniques in various ways; and iii) the identification of design guidelines for trajectory data visualization, describing various considerations/criteria for the selection of different map-based visualization techniques and their possible interactive features.
Statistical Approaches to Two-Dimensional Turbulence

of “ Statistical Approaches to Two-Dimensional Turbulence ” by Wanming Qi, Ph.D., Brown University, May 2015 Coherent structures such as large-scale jets and vortices are ubiquitous in natural quasi-two-dimensional (2D) turbulence such as atmospheres. I study statistical approaches to 2D turbulence with the goal of predicting coherent structures from the equations of motion. The approach contrasts with the traditional approach of direct numerical simulation (DNS). First I consider equilibrium statistical mechanics of Euler flows. Coherent structures that form in numerical simulation on the torus and the sphere are compared against the truncated Miller-Robert-Sommeria equilibria where only some of the conservation laws are imposed. The description that conserves energy and enstrophy produces a complete energy condensation to the largest scales, and in the absence of rotation correctly predicts the number and polarity of coherent vortices. Perturbative imposition of the quartic Casimir constraint improves agreement with simulation by sharpening the cores and transferring some energy to smaller-scale modes. Effect of rotation is beyond the conservation of energy and enstrophy. Numerical simulation also shows nonergodicity, limiting the usefulness of equilibrium statistical mechanics. Next I investigate two nonequilibrium statistical mechanical approaches. One is direct statistical simulation implemented by systematic expansion in equaltime cumulants. The approach is illustrated on the sphere by a barotropic model of jet formation. Closures of cumulant evolution equations at second order (CE2) or third order (CE3), or an intermediate closure (CE2.5) may be used. For Euler flows, CE2 and CE2.5 both conserve angular momentum, energy and enstrophy; CE3 further conserves the cubic Casimir. Comparison with DNS shows that CE2 reproduces qualitative features of the zonal jets, and CE2.5 and CE3 further improve quantitative agreement with DNS. In the other approach, Wegner’s flow equation is integrated forward numerically to a fixed point to yield the equal-time steady-state statistics that is encoded in the Hopf functional. It is applied to a system with two degrees of freedom, the Lorenz attractor, and turbulence on the sphere, with some success. The statistical approaches provide new insights into coherent structures and may also bring new tools to lead to a better understanding of astrophysical and geophysical flows.
Channel Selective Activity Recognition with WiFi: A Deep Learning Approach Exploring Wideband Information

WiFi-based human activity recognition explores the correlations between body movement and the reflected WiFi signals to classify different activities. State-of-the-art solutions mostly work on a single WiFi channel and hence are quite sensitive to the quality of a particular channel. Co-channel interference in an indoor environment can seriously undermine the recognition accuracy. In this paper, we for the first time explore wideband WiFi information with advanced deep learning toward more accurate and robust activity recognition. We present a practical Channel Selective Activity Recognition system (CSAR) with Commercial Off-The-Shelf (COTS) WiFi devices. The key innovation is to actively select available WiFi channels with good quality and seamlessly hop among adjacent channels to form an extended channel. The wider bandwidth with more subcarriers offers stable information with a higher resolution for feature extraction. Conventional classification tools, e.g., hidden Markov model and k-nearest neighbors, however, are not only sensitive to feature distortion but also not smart enough to explore the time-scale correlations from the extracted spectrogram. We accordingly explore advanced deep learning tools for this application context. We demonstrate an integration of channel selection and long short term memory network (LSTM), which seamlessly combine the richer time and frequency features for activity recognition. We have implemented a CSAR prototype using Intel 5300 WiFi cards. Our real-world experiments show that CSAR achieves a stable recognition accuracy around 95 percent even in crowded wireless environments (compared to 80 percent with state-of-the-art solutions that highly depend on the quality of the working channel). We have also examined the impact of environments and persons, and the results reaffirm its robustness.
Efficient willow tree method for European-style and American-style moving average barrier options pricing

Moving average options are widely traded in financial markets, but exiting methods for pricing this type of option are too slow. This paper proposes two efficient willow tree methods for pricing European-style and American-style moving average barrier options (MABOs). We first solve the finite-dimensional partial differential equation model for discretely monitored MABOs by willow tree methods, and then compute the value of continuously monitored MABOs by Richardson’s two-point extrapolation. Our new willow tree method employs the interpolation error minimization technique to reduce complexity. The corresponding convergence rate and error bounds are also analyzed. It shows that our proposed methods can provide the same accuracy as the binomial tree approach and Monte Carlo simulation, but require much less computing time. The numerical experiments support our claims.