Miracle-dz/UniFAR-FTUs huggingface dataset:
Localization of Phosphorylated ERK/MAP Kinases to Mitochondria and Autophagosomes in Lewy Body Diseases

We previously found that sustained ERK activation contributes to toxicity elicited by the parkinsonian neurotoxin 6‐hydroxydopamine. In addition, substantia nigra neurons from patients with incidental Lewy body disease, Parkinson's disease (PD), and diffuse Lewy body dementia (DLB) display abnormal phospho‐ERK accumulations in the form of discrete cytoplasmic granules. In this study, we investigated the subcellular localization of phospho‐ERK immunoreactive granules using double label confocal microscopy and immunoelectron microscopy. A small percentage of phospho‐ERK granules colocalized with the early endosome marker Rab5, but not with cathepsin D, 20S proteasome β‐subunit, or cytochrome P450 reductase. Phospho‐ERK immunoreactivity was often associated with mitochondrial proteins (MnSOD, 60 kDa and 110 kDa mitochondrial antigens), and some vesicular‐appearing phospho‐ERK granules appeared to envelop enlarged mitochondria by confocal laser scanning microscopy. Ultrastructural immuno‐gold studies revealed phospho‐ERK labeling in mitochondria and in association with bundles of ∼10 nm fibrils. Heavily labeled mitochondria were observed within autophagosomes. As mitochondrial pathology may play a pivotal role in Parkinson's and other related neurodegenerative diseases, these studies suggest a potential interaction between dysfunctional mitochondria, autophagy, and ERK signaling pathways.
Group sparse regularization based iterative incremental image deblurring

In the imaging process of the remote sensing, there was degradation phenomenon in the acquired images. This paper, we present a group sparse regularization based iterative incremental for remote image deblurring estimating a single latent sharp image given either a single or multiple blurry and/or noisy observations. Considering the sparseness and nonlocal similarity properties of image, a group sparse representation based incremental iterative method is established for blurry image restoration. The proposed sparse representation enforces the intrinsic local sparsity and nonlocal self-similarity of images simultaneously in a unified framework. Comprehensive experiments demonstrate that the framework integrating the sparseness property of images significantly improves the deblurring performance.
PSEUDOSPECTRA COMPUTATION OF LARGE MATRICES

Transfer functions have been shown to provide monotonic approximations to the resolvent 2-norm of A, R(z) = (A − zI), when associated with a sequence of nested spaces. This paper addresses the open question of the effectiveness of the transfer function scheme for the computation of the pseudospectrum of large matrices. It is shown that the scheme can be combined with certain Krylov type linear solvers, such as restarted fom, for the efficient solution of shifted linear systems of the form (A − zkI) b, for a large number of shifts zk. Extensive numerical experiments illustrate the performance of the methods developed in this paper. Tools for the effective combination of the transfer function framework with path following methods are developed. A hybrid method is proposed that combines transfer functions with iterative solvers and path following and is shown to be a powerful and cost effective scheme for computing pseudospectra of very large matrices.
Cuts, Trees and ` 1 -Embeddings of Graphs (cid:3)

Motivated by many recent algorithmic applications, this paper aims to promote a systematic study of the relationship between the topology of a graph and the metric distortion incurred when the graph is embedded into `1 space. The main results are: 1. Explicit constant-distortion embeddings of all series-parallel graphs, and all graphs with bounded Euler number. These are the (cid:12)rst natural families known to have constant distortion (strictly greater than 1). Using the above embeddings, algorithms are obtained which approximate the sparsest cut in such graphs to within a constant factor. 2. A constant-distortion embedding of outerplanar graphs into the restricted class of `1-metrics known as \dominating tree metrics". A lower bound of (cid:10)(logn) on the distortion for embeddings of series-parallel graphs into (distributions over) dominating tree metrics is also presented. This shows, surprisingly, that such metrics approximate distances very poorly even for families of graphs with low treewidth, and excludes the possibility of using them to explore the (cid:12)ner structure of `1-embeddability.
The predictive power of structural MRI in Autism diagnosis

Diagnosis of Autism Spectrum Disorder (ASD) using structural magnetic resonance imaging (sMRI) of the brain has been a topic of significant research interest. Previous studies using small datasets with well-matched Typically Developing Controls (TDC) report high classification accuracies (80-96%) but studies using the large heterogeneous ABIDE dataset report accuracies less than 60%. In this study we investigate the predictive power of sMRI in ASD using 373 ASD and 361 TDC male subjects from the ABIDE. Brain morphometric features were derived using FreeSurfer and classification was performed using three different techniques: Random Forest (RF), Support Vector Machine (SVM) and Gradient Boosting Machine (GBM). Although high classification accuracies were possible in individual sites (with a maximum of 97% in Caltech), the highest classification accuracy across all sites was only 60% (sensitivity = 57%, specificity = 64%). However, the accuracy across all sites improved to 67% when IQ and age information were added to morphometric features. Across all three classifiers, volume and surface area had more discriminative power. In general, important features for classification were present in the frontal and temporal regions and these regions have been implicated in ASD. This study also explores the effect of demographics and behavioral measures on the predictive power of sMRI. Results show that classification accuracy increases with autism severity and that ASD detection with sMRI is easier before the age of 10 years.
Code, Cache and Deliver on the Move: A Novel Caching Paradigm in Hyper-Dense Small-Cell Networks

Caching popular content files at small-cell base stations (SBSs) has emerged as a promising technique to meet the overwhelming growth in mobile data demand. Despite the plethora of work in this field, a specific aspect has been overlooked. It is assumed that all users remain stationary during data transfer and therefore a complete copy of the requested file can always be downloaded by the associated SBSs. In this work, we revisit the caching problem in realistic environments where moving users intermittently connect to multiple SBSs encountered at different times. Due to connection duration limits, users may download only parts of the requested files. Requests for files that failed to be delivered on time by the SBSs are redirected to the coexisting macro-cell. We introduce an optimization framework that models user movements via random walks on a Markov chain aimed at minimizing the load of the macro-cell. As the main contribution, we put forward a distributed caching paradigm that leverages user mobility predictions and innovative information-mixing methods based on the principle of network coding. Systematic experiments based on measured traces of human mobility patterns demonstrate that our approach can offload <inline-formula> <tex-math notation="LaTeX">$65 \text{percent}$</tex-math><alternatives> <inline-graphic xlink:href="poularakis-ieq1-2575837.gif"/></alternatives></inline-formula> more macro-cell traffic than existing caching schemes in realistic settings.
The Fast Downward Planning System

Fast Downward is a classical planning system based on heuristic search. It can deal with general deterministic planning problems encoded in the propositional fragment of PDDL2.2, including advanced features like ADL conditions and effects and derived predicates (axioms). Like other well-known planners such as HSP and FF, Fast Downward is a progression planner, searching the space of world states of a planning task in the forward direction. However, unlike other PDDL planning systems, Fast Downward does not use the propositional PDDL representation of a planning task directly. Instead, the input is first translated into an alternative representation called multivalued planning tasks, which makes many of the implicit constraints of a propositional planning task explicit. Exploiting this alternative representation, Fast Downward uses hierarchical decompositions of planning tasks for computing its heuristic function, called the causal graph heuristic, which is very different from traditional HSP-like heuristics based on ignoring negative interactions of operators. In this article, we give a full account of Fast Downward's approach to solving multivalued planning tasks. We extend our earlier discussion of the causal graph heuristic to tasks involving axioms and conditional effects and present some novel techniques for search control that are used within Fast Downward's best-first search algorithm: preferred operators transfer the idea of helpful actions from local search to global best-first search, deferred evaluation of heuristic functions mitigates the negative effect of large branching factors on search performance, and multiheuristic best-first search combines several heuristic evaluation functions within a single search algorithm in an orthogonal way. We also describe efficient data structures for fast state expansion (successor generators and axiom evaluators) and present a new non-heuristic search algorithm called focused iterative-broadening search, which utilizes the information encoded in causal graphs in a novel way. Fast Downward has proven remarkably successful: It won the "classical" (i. e., propositional, non-optimising) track of the 4th International Planning Competition at ICAPS 2004, following in the footsteps of planners such as FF and LPG. Our experiments show that it also performs very well on the benchmarks of the earlier planning competitions and provide some insights about the usefulness of the new search enhancements.
Mycobacterium bovis Bacillus Calmette-Guérin Vaccination Mobilizes Innate Myeloid-Derived Suppressor Cells Restraining In Vivo T Cell Priming via IL-1R–Dependent Nitric Oxide Production

Early immune response to the largely used Mycobacterium bovis bacillus Calmette-Guérin (BCG) intradermal vaccine remains ill defined. Three days after BCG inoculation into the mouse ear, in addition to neutrophils infiltrating skin, we observed CD11b+Ly-6CintLy-6G− myeloid cells. Neutrophil depletion markedly enhanced their recruitment. These cells differed from inflammatory monocytes and required MyD88-dependent BCG-specific signals to invade skin, whereas neutrophil influx was MyD88 independent. Upon BCG phagocytosis, CD11b+Ly-6CintLy-6G− cells produced NO, which required the IL-1 receptor. Despite NO production, they were unable to kill BCG or the nonpathogenic Mycobacterium smegmatis. However, they markedly impaired T cell priming in the draining lymph node. Their elimination by all-trans retinoid acid treatment increased the number of IFN-γ–producing CD4 T cells. Thus, BCG vaccination recruits innate myeloid-derived suppressor cells, akin to mouse tumor-infiltrating cells. These propathogenic cells dampen the early T cell response and might facilitate BCG persistence.
Dopamine D2 Receptor as a Cellular Component Controlling Nocturnal Hyperactivities in Drosophila melanogaster

Dysfunctional regulation of brain dopamine (DA) functions has been found in patients with drug addiction and various neurological disorders that frequently accompany disturbance in sleep behavior. In this study, the roles of the dopaminergic nervous system on the regulation of daily locomotor activity rhythm were investigated in Drosophila melanogaster. Reduced synaptic DA release by expressing tetanus toxin gradually attenuated peak activity levels by altering activity patterns, particularly under constant darkness. Besides, flies with a mutant dopamine transporter fumin (fmn), in which the synaptic DA levels were elevated, displayed increased activities in both daytime and nighttime, but did more so at nighttime, suggesting that DA function is involved in regulation of fruit fly's nocturnal locomotor activities. Furthermore, flies treated with bromocriptine, an agonist of Drosophila dopamine D2 receptor (dD2R), exhibited nocturnal locomotor hyperactivity in a dose-dependent manner and this effect was inhibited in dD2R knockdown flies. When mutant flies null for period (per), timeless (tim), dClock (dClk), or cycle (cyc) were treated with bromocriptine, only cycle-null flies (cyc01) did not show induced nocturnal hyperactivities, suggesting that cyc might play a role in bromocriptine-induced nocturnal hyperactivities. Elevation of experimental temperature also increased nocturnal activities at the expense of daytime activities. The heat-induced increase in nocturnal activities gradually returned to basal levels at continuously elevated temperature. Inhibition of DA synthesis did not suppress heat-induced early development of nocturnal hyperactivity but prevented gradual decrement of initially elevated nocturnal activities, suggesting that DA impinges on certain adaptive roles in response to changes in environmental temperature. These results overall suggest that controlling dopaminergic transmission is important for daily locomotor behavior and bromocriptine-induced nocturnal hyperactivity which is mediated through dD2R receptor and CYC functions. In parallel to these results, excessive activation of dopaminergic neurotransmission, the primary cause of schizophrenia, is associated with abnormally elevated nocturnal locomotor activities through D2-type receptor in Drosophila. The results suggest that fruit flies are an excellent model system to provide some answers to previously unexplainable observations regarding the compromised dopaminergic nervous system and the related therapeutic agents. (Author correspondence: kmkim@jnu.ac.kr)
Towards Glyph-based visualizations for big data clustering

Data Analysts have to deal with an ever-growing amount of data resources. One way to make sense of this data is to extract features and use clustering algorithms to group items according to a similarity measure. Algorithm developers are challenged when evaluating the performance of the algorithm since it is hard to identify features that influence the clustering. Moreover, many algorithms can be trained using a semi-supervised approach, where human users provide ground truth samples by manually grouping single items. Hence, visualization techniques are needed that help data analysts achieve their goal in evaluating Big data clustering algorithms. In this context, Multidimensional Scaling (MDS) has become a prominent visualization tool. In this paper, we propose a combination with glyphs that can provide a detailed view of specific features involved in MDS. In consequence, human users can understand, adjust, and ultimately improve clustering algorithms. We present a thorough glyph design, which is founded in a comprehensive survey of related work and report the results of a controlled experiments, where participants solved data analysis tasks with both glyphs and a traditional textual display of data values.
Energy Efficient Free Cooling System for Data Centers

A data center is a facility used to keep computer related equipments. It is estimated that heat production rate of the data center is doubled in every two years and hence the inevitability of the cooling system gets increased. In due course power consumption of a data center is augmented and more cost is spent on the power usage of the cooling system rather than the equipment purchase. As a result power savings for the cooling system is strongly desired. In this paper we conferred two primary free cooling systems namely air economizer and water economizer. A free cooling economizer system uses the outside air which is forced to the data center when outside climate is suitable to meet the ASHRE's cooling requirements. We have also conducted a survey and simulation based estimation using TRACE[TM] Chiller Plant Analyzer Tool. In this study, the energy consumption in a data center using conventional cooling system is compared with Air Economizer and Water Economizer for three different Zones namely Chicago, Atlanta and Phoenix in view of the fact that the outside air is relatively cool most of the year. From the projected result it is observed that both economizers reduce energy and cost when compared with conventional system and the usage of Economizer permits the chiller to shut down or reduce chiller energy load under suitable weather conditions. The results show that Water economizers are shown to consistently outperform air economizer which provides significant improvement in cooling system efficiency and cost at data center. The performance ratio of the conventional, air economizer and the water economizers are 50%, 76% and 79% respectively that shows economizers provide more savings relative to the conventional system.
A renewable heat plant Modelica library for dynamic optimization with Optimica

Almost half of the energy consumed globally is under the form of heat, produced mainly through fossil fuels. Switching to using renewable energy instead is a real challenge. Combining renewable thermal energy with thermal storage is a complex system to operate. To harness the full potential of thermal plants, advanced control strategies need to be implemented. Dynamic real-time optimization (DRTO) seems promising to fine tune controller setpoints of plants. The goal of our study is to ultimately enable DRTO by using Optimica because of its ease of use and Modelica’s modularity. This paper presents a Modelica library developed to first perform offline dynamic optimization with Optimica, and would ultimately be used in a DRTO strategy. The library enables to model a renewable thermal plant composed of solar thermals, heat pumps and thermal storages. The model of each subcomponent has been validated. Initial dynamic optimizations of plant operation give promising results.
Lifting Condensation Level Climatology over South America

Este estudo é conduzido por duas equações empíricas utilizando as reanálises do ERA5 para determinar a climatologia do Nível de Condensação por Levantamento (NCL) da América do Sul (AS) de 1991 a 2021. Antes de calcular a climatologia NCL, o desempenho das equações empíricas é verificado através de um estudo de caso realizado entre 30 e 31 de dezembro de 2021. Embora os resultados das equações comparados aos dados de referência apresentem alguns desvios, eles não invalidam a aplicação das equações em um estudo climatológico. Os principais achados da climatologia revelaram que o NCL normalmente atinge maior altitude em 1800 Z. A estação durante a qual o NCL atinge sua altitude máxima varia dependendo das características climáticas da região analisada. Por exemplo, a maior altura do NCL foi observada durante o verão na Patagônia (4.000 m), a primavera no Nordeste do Brasil (3.000 m), o inverno no centro da África do Sul (2.000 m) e na Amazônia (1.500 m). Além disso, em cinco cidades estudadas em todo o Brasil, o NCL apresentou uma tendência positiva em sua altitude, que foi acompanhada por uma tendência negativa na precipitação.
Research and practice reciprocity in software process simulation

The young field of software process simulation was born out of research that stimulated the imagination of many researchers who created a vision for addressing “the software problem. ” Though the vision has yet to be realized widely, the need for the field is growing and benefits are being seen slowly. Opportunity for continued growth of this field lies in the reciprocity between research and industrial practice. An agenda for advancing software process simulation through this reciprocity is offered.
Spike wave location and density disturb sleep slow waves in patients with CSWS (continuous spike waves during sleep)

In CSWS (continuous spike waves during sleep) activation of spike waves during slow wave sleep has been causally linked to neuropsychological deficits, but the pathophysiologic mechanisms are still unknown. In healthy subjects, the overnight decrease of the slope of slow waves in NREM (non–rapid eye movement) sleep has been linked to brain recovery to regain optimal cognitive performance. Here, we investigated whether the electrophysiologic hallmark of CSWS, the spike waves during sleep, is related to an alteration in the overnight decrease of the slope, and if this alteration is linked to location and density of spike waves.
Discrete-time methods for the analysis of event history data: with applications of the beta-geometric model.

A discrete-time survival model allowing for both observed and unobserved time-varying heterogeneity in risk was recently proposed by Weinberg and Gladen. The basic beta- geometric model and its extensions which take into account explanatory variables and a subpopulation with zero risk have great potential for the analysis of fertility behavior. To study the practical application of the beta-geometric model to real data we apply the model to the analysis of the waiting time to conception and the length of the first birth interval using Portuguese data. In particular we describe problems encountered in fitting a beta-geometric model to the analysis of birth intervals in a population where modern efficient contraception is used. Despite some open questions about model specification for time-dependent covariates the model clearly has real practical potential. (excerpt)
University of Oklahoma Graduate College Automatic Recognition of Supercells Thunderstorms in Numerical Weather Data Automatic Recognition of Supercells Thunderstorms in Numerical Weather Data a Thesis Approved for the School of Computer Science

Acknowledgments I would like to thank Professor Amy McGovern, my supervisor at the University of Oklahoma as well as the chair of my master's thesis committee, for accepting me as her student, and for helping me all along during my time in the United States. I would like to thank all my current and former committee members, namely Dr. Rodger Brown, for his helpful counseling and insights concerning meteorological subjects, Dr Dean Hougen, Dr. Valliappa Lakshmanan and Dr. Adam Clark for helping me and orienting me when I started this research project. I also thank Brittany Benson Dahl for her patience and for providing all the simulation data that I have used.
Fast neural learning and control of discrete-time nonlinear systems

The problem of learning control for a general class of discrete-time nonlinear systems is addressed in this paper using multilayered neural networks (MNNs) with feedforward connections. A suitable extension of the concept of input-output linearization of discrete-time nonlinear systems is used to develop the control schemes for both output tracking and model reference control purposes. The ability of MNNs to model arbitrary nonlinear functions is incorporated to approximate the unknown nonlinear input-output relationship and its inverse using a new weight learning algorithm. In order to overcome the difficulties associated with simultaneous online identification and control in neural networks based adaptive control systems, the new learning control architectures are developed for both adaptive tracking and adaptive model reference control systems with online identification and control ability. The potentials of the proposed methods are demonstrated by simulation examples. >