Miracle-dz/UniFAR-FTUs huggingface dataset:
State estimation of Active Distribution Networks: Comparison between WLS and iterated kalman-filter algorithm integrating PMUs

One of the challenging tasks related to the realtime control of Active Distribution Networks (ADNs) is represented by the development of fast (i.e. sub-second) state estimation (SE) processes. As known, the problem of SE of power networks links the measurements performed in the network with a set of non-linear equations representing the links between the network node voltage phasors (i.e. the system states) and measured quantities. The calculation of these voltages is accomplished by the solution of a minimization problem by using, for instance, Weighted Least Squares (WLS) or Kalman filter (KF) methods. The availability of phasor measurement units (PMUs), characterized by high accuracy and able to directly measure node voltage phasors, allows, in principle, a simplification of the SE problem. Within this framework, the paper has two aims. The first is to propose a procedure based on the use of the Iterated KF (IKF) aiming at making achievable, in a straightforward manner, the SE of ADNs integrating PMU measurements. The second goal is to present a sensitivity analysis of the performances of WLS vs IKF methods as a function of the measurements and process covariance matrices.
Optimal auctions with positive network externalities

We consider the problem of designing auctions in social networks for goods that exhibit single-parameter submodular network externalities in which a bidder's value for an outcome is a fixed private type times a known submodular function of the allocation of his friends. Externalities pose many issues that are hard to address with traditional techniques; our work shows how to resolve these issues in a specific setting of particular interest. We operate in a Bayesian environment and so assume private values are drawn according to known distributions. We prove that the optimal auction is APX-hard. Thus we instead design auctions whose revenue approximates that of the optimal auction. Our main result considers step-function externalities in which a bidder's value for an outcome is either zero, or equal to his private type if at least one friend has the good. For these settings, we provide a e/e+1-approximation. We also give a $0.25$-approximation auction for general single-parameter submodular network externalities, and discuss optimizing over a class of simple pricing strategies.
Learning Representations for Nonspeech Audio Events Through Their Similarities to Speech Patterns

The human auditory system is very well matched to both human speech and environmental sounds. Therefore, the question arises whether human speech material may provide useful information for training systems for analyzing nonspeech audio signals, e.g., in a classification task. In order to answer this question, we consider speech patterns as basic acoustic concepts, which embody and represent the target nonspeech signal. To find out how similar the nonspeech signal is to speech, we classify it with a classifier trained on the speech patterns and use the classification posteriors to represent the closeness to the speech bases. The speech similarities are finally employed as a descriptor to represent the target signal. We further show that a better descriptor can be obtained by learning to organize the speech categories hierarchically with a tree structure. Furthermore, these descriptors are generic. That is, once the speech classifier has been learned, it can be employed as a feature extractor for different datasets without retraining. Lastly, we propose an algorithm to select a sufficient subset, which provides an approximate representation capability of the entire set of available speech patterns. We conduct experiments for the application of audio event analysis. Phone triplets from the TIMIT dataset were used as speech patterns to learn the descriptors for audio events of three different datasets with different complexity, including UPC-TALP, Freiburg-106, and NAR. The experimental results on the event classification task show that a good performance can be easily obtained even if a simple linear classifier is used. Furthermore, fusion of the learned descriptors as an additional source leads to state-of-the-art performance on all the three target datasets.
3D-QSAR study on heterocyclic topoisomerase II inhibitors using CoMSIA

Selective topoisomerase II (Topo II) inhibitors have interested to a great extent for the design of new antitumoral compounds in recent years. Comparative molecular similarity indices analysis (CoMSIA) was performed on a series of previously synthesized benzoxazole, benzimidazole, and oxazolo(4,5-b)pyridine derivatives as eukaryotic Topo II inhibitors. A training set of 16 heterocyclic compounds was used to establish the CoMSIA model. They were constructed and geometrically optimized using SYBYL v7.0. The predictive ability of the model was assessed using a test set of 7 compounds. The best model has demonstrated a good fit having r2 value of 0.968 and cross-validated coefficient q2 value as 0.562 including steric and hydrophobic fields. The hydrophobic interactions showed a dominant role for increasing Topo II inhibitor activity and hydrophilic substituent was found more important than hydrophobic one on the 5 or 6 position of benzazole moiety. The model obtained from the present study can be useful for the modification and/or evaluation of the development of new Topo II inhibitors as potential antitumor compounds. †Presented at CMTPI 2005: Computational Methods in Toxicology and Pharmacology Integrating Internet resources (Shanghai, China, October 29–November 1 2005).
Paraoxonase Polymorphisms, Haplotypes, and Enzyme Activity in Latino Mothers and Newborns

Recent studies have demonstrated widespread pesticide exposures in pregnant women and in children. Plasma paraoxonase 1 (PON1) plays an important role in detoxification of various organophosphates. The goals of this study were to examine in the Center for Health Assessment of Mothers and Children of Salinas (CHAMACOS) birth cohort of Latina mothers and their newborns living in the Salinas Valley, California, the frequencies of five PON1 polymorphisms in the coding region (192QR and 55LM) and the promoter region (−162AG, −909CG, and −108CT) and to determine their associations with PON1 plasma levels [phenylacetate arylesterase (AREase)] and enzyme activities of paraoxonase (POase) and chlorpyrifos oxonase (CPOase). Additionally, we report results of PON1 linkage analysis and estimate the predictive value of haplotypes for PON1 plasma levels. We found that PON1−909, PON1−108, and PON1192 had an equal frequency (0.5) of both alleles, whereas PON1−162 and PON155 had lower variant allele frequencies (0.2). Nearly complete linkage disequilibrium was observed among coding and promoter polymorphisms (p < 0.001), except PON1192 and PON1−162 (p > 0.4). Children’s PON1 plasma levels (AREase ranged from 4.3 to 110.7 U/mL) were 4-fold lower than their mothers’ (19.8 to 281.4 U/mL). POase and CPOase activities were approximately 3-fold lower in newborns than in mothers. The genetic contribution to PON1 enzyme variability was higher in newborns (R2 = 25.1% by genotype and 26.3% by haplotype) than in mothers (R2 = 8.1 and 8.8%, respectively). However, haplotypes and genotypes were comparable in predicting PON1 plasma levels in mothers and newborns. Most of the newborn children and some pregnant women in this Latino cohort may have elevated susceptibility to organophosphate toxicity because of their PON1192 genotype and low PON1 plasma levels.
Enhancement of spatial resolution using metamaterial sensor in NonDestructive evaluation

The current stage of non-destructive evaluation techniques imposes the development of new electromagnetic methods that are based on high spatial resolution and increased sensitivity. Printed circuit boards, integrated circuit boards, composite materials with polymeric matrix containing conductive fibers, as well as some types of biosensors are devices of interest in using such evaluation methods. In order to achieve high performance, the work frequencies must be either radiofrequencies or microwaves. At these frequencies, at the dielectric/conductor interface, plasmon polaritons can appear, propagating between conductive regions as evanescent waves. Detection of these waves, containing required information, can be done using sensors with metamaterial lenses. We propose in this paper the enhancement of the spatial resolution using electromagnetic methods, which can be accomplished in this case using evanescent waves that appear in the current study in slits of materials such as the spaces between carbon fibers in Carbon Fibers Reinforced Plastics or in materials of interest in the nondestructive evaluation field with industrial applications, where microscopic cracks are present. We propose herein a unique design of the metamaterials for use in nondestructive evaluation based on Conical Swiss Rolls configurations, which assure the robust concentration/focusing of the incident electromagnetic waves (practically impossible to be focused using classical materials), as well as the robust manipulation of evanescent waves. Applying this testing method, spatial resolution of approximately λ/2000 can be achieved. This testing method can be successfully applied in a variety of applications of paramount importance such as defect/damage detection in materials used in a variety of industrial applications, such as automotive and aviation technologies.
Capacitive coupling noise in high-speed VLSI circuits

Rapid technology scaling along with the continuous increase in the operation frequency cause the crosstalk noise to become a major source of performance degradation in high-speed integrated circuits. This paper presents an efficient metric to estimate the capacitive crosstalk in nanometer high-speed very large scale integration circuits. In particular, we provide closed-form expressions for the peak amplitude, the pulsewidth, and the time-domain waveform of the crosstalk noise. Experimental results show that the maximum error of our noise predictions is less than 13%, while the average error is only 5.82%.
Pregnancy and Smoothelin-like Protein 1 (SMTNL1) Deletion Promote the Switching of Skeletal Muscle to a Glycolytic Phenotype in Human and Mice*

Background: Pregnancy promotes physiological adaptations throughout the body mediated by the female sex hormones. Results: Pregnancy promotes switching of skeletal muscle to a glycolytic phenotype through the smoothelin-like protein 1 transcriptional cofactor. Conclusion: Deletion of SMTNL1 is able to mimic the effect of pregnancy in mice. Significance: Novel mechanism to explain insulin resistance during pregnancy. Pregnancy promotes physiological adaptations throughout the body, mediated by the female sex hormones progesterone and estrogen. Changes in the metabolic properties of skeletal muscle enable the female body to cope with the physiological challenges of pregnancy and may also be linked to the development of insulin resistance. We conducted global microarray, proteomic, and metabolic analyses to study the role of the progesterone receptor and its transcriptional regulator, smoothelin-like protein 1 (SMTNL1) in the adaptation of skeletal muscle to pregnancy. We demonstrate that pregnancy promotes fiber-type changes from an oxidative to glycolytic isoform in skeletal muscle. This phenomenon is regulated through an interaction between SMTNL1 and progesterone receptor, which alters the expression of contractile and metabolic proteins. smtnl1−/− mice are metabolically less efficient and show impaired glucose tolerance. Pregnancy antagonizes these effects by inducing metabolic activity and increasing glucose tolerance. Our results suggest that SMTNL1 has a role in mediating the actions of steroid hormones to promote fiber switching in skeletal muscle during pregnancy. Our findings also bear on the management of gestational diabetes that develops as a complication of pregnancy in ∼4% of women.
Bromodomain 4 activation predicts breast cancer survival

Previous work identified the Rap1 GTPase-activating protein Sipa1 as a germ-line-encoded metastasis modifier. The bromodomain protein Brd4 physically interacts with and modulates the enzymatic activity of Sipa1. In vitro analysis of a highly metastatic mouse mammary tumor cell line ectopically expressing Brd4 demonstrates significant reduction of invasiveness without altering intrinsic growth rate. However, a dramatic reduction of tumor growth and pulmonary metastasis was observed after s.c. implantation into mice, implying that activation of Brd4 may somehow be manipulating response to tumor microenvironment in the in vivo setting. Further in vitro analysis shows that Brd4 modulates extracellular matrix gene expression, a class of genes frequently present in metastasis-predictive gene signatures. Microarray analysis of the mammary tumor cell lines identified a Brd4 activation signature that robustly predicted progression and/or survival in multiple human breast cancer datasets analyzed on different microarray platforms. Intriguingly, the Brd4 signature also almost perfectly matches a molecular classifier of low-grade tumors. Taken together, these data suggest that dysregulation of Brd4-associated pathways may play an important role in breast cancer progression and underlies multiple common prognostic signatures.
A guided tour into subcellular colocalization analysis in light microscopy

It is generally accepted that the functional compartmentalization of eukaryotic cells is reflected by the differential occurrence of proteins in their compartments. The location and physiological function of a protein are closely related; local information of a protein is thus crucial to understanding its role in biological processes. The visualization of proteins residing on intracellular structures by fluorescence microscopy has become a routine approach in cell biology and is increasingly used to assess their colocalization with well‐characterized markers. However, image‐analysis methods for colocalization studies are a field of contention and enigma. We have therefore undertaken to review the most currently used colocalization analysis methods, introducing the basic optical concepts important for image acquisition and subsequent analysis. We provide a summary of practical tips for image acquisition and treatment that should precede proper colocalization analysis. Furthermore, we discuss the application and feasibility of colocalization tools for various biological colocalization situations and discuss their respective strengths and weaknesses. We have created a novel toolbox for subcellular colocalization analysis under ImageJ, named JACoP, that integrates current global statistic methods and a novel object‐based approach.
Adaptive Central Pattern Generator based on Spiking Neural Networks

In this paper we present a new, adaptive model of the Central Pattern Generator (CPG) [1] based on Spiking Neural Networks (SNN) [2]. The model has the ability to learn the desired rhythmic patterns from demonstration. Central Pattern Generators (CPG) play the principle role in such processes as locomotion, breathing or heart-beating of animals. For this reason they are of great interest for scientists. Many models of CPGs have already been proposed [3]. However, most of these models are suitable only for the individually designated tasks. It is desirable to construct the generic CPG model with the universal ability to learn the task from the given, desired motor-patterns. An interesting approach to such a programmable CPG has been proposed in [4]. In that approach a CPG model has been based on Hopf oscillators. Here, we introduce a model of an adaptive CPG based on SNN. Our model can learn the desired motorpattern to be generated in each cycle of CPG operation. The learning process is performed according to the Remote Supervised Method (ReSuMe) 1 introduced in [2]. We describe our approach in an example of a CPG model with 2 outputs. This corresponds e.g. to the CPG generating the alternating flexor-extensor activity responsible for limb coordination during locomotion. Our model can be divided into two functional parts: rhythm and pattern generator [1] (Fig.1). The rhythm generator produces the basic oscillations and controls their frequency. This part consists of 2 reciprocally connected networks (A, B) of spiking neurons. The networks have the sparse recurrent organization. In our recent study we are concerned only with reproducing the desired shape of the motor-patterns. Hence, we assume that the parameters of the rhythm generator, such as the frequency of oscillations, the duty cycle and the phase relationship are not modified
Convex relaxation techniques for set-membership identification of LPV systems

Set-membership identification of single-input single-output linear parameter varying models is considered in the paper under the assumption that both the output and the scheduling parameter measurements are affected by bounded noise. First, we show that the problem of computing the parameter uncertainty intervals requires the solutions to a number of nonconvex optimization problems. Then, on the basis of the analysis of the regressor structure, we present some ad hoc convex relaxation schemes to compute parameter bounds by means of semidefinite optimization. Advantages of the new techniques with respect to previously published results are discussed both theoretically and by means of simulations.
Target localization via correlated link inference

We propose a new approach for target localization via correlated link inference. The proposed method takes advantage of the motion-induced difference of received signal strength(RSS) made in a wireless peer-to-peer network. By using the multipath channel model, the signal strength on a wireless link is largely dependent on the power contained in multipath components that travel through space containing moving targets. The effect of target obstruction is dominated by RSS attenuation and RSS enhancement. We locate targets by imaging both RSS attenuation and RSS enhancement. Experimental results with 14 nodes RF sensor network deployed in indoor office environment are presented.
Activation of AMPK/mTORC1-Mediated Autophagy by Metformin Reverses Clk1 Deficiency-Sensitized Dopaminergic Neuronal Death

The autophagy-lysosome pathway (ALP) plays a critical role in the pathology of Parkinson’s disease (PD). Clk1 (coq7) is a mitochondrial hydroxylase that is essential for coenzyme Q (ubiquinone) biosynthesis. We have reported previously that Clk1 regulates microglia activation via modulating microglia metabolic reprogramming, which contributes to dopaminergic neuronal survival. This study explores the direct effect of Clk1 on dopaminergic neuronal survival. We demonstrate that Clk1 deficiency inhibited dopaminergic neuronal autophagy in cultured MN9D dopaminergic neurons and in the substantia nigra pars compacta of Clk+/− mutant mice and consequently sensitized dopaminergic neuron damage and behavioral defects. These mechanistic studies indicate that Clk1 regulates the AMP-activated protein kinase (AMPK)/rapamycin complex 1 pathway, which in turn impairs the ALP and TFEB nuclear translocation. As a result, Clk1 deficiency promotes dopaminergic neuronal damage in vivo and in vitro, which ultimately contributes to sensitizing 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP)–induced dopaminergic neuronal death and behavioral impairments in Clk1-deficient mice. Moreover, we found that activation of autophagy by the AMPK activator metformin increases dopaminergic neuronal survival in vitro and in the MPTP-induced PD model in Clk1 mutant mice. These results reveal that Clk1 plays a direct role in dopaminergic neuronal survival via regulating ALPs that may contribute to the pathologic development of PD. Modulation of Clk1 activity may represent a potential therapeutic target for PD.
Automatic image annotation based on vocabulary prior probability

Automatic image annotation is an important and challenging task in computer vision. The existing models only use low-levels features of images to do the approximate calculation, without considering the influence of semantic information. This paper proposes a new automatic image annotation algorithm based on the vocabulary prior probability. It can solve the semantic gap to a certain extent. The algorithm is divided into two stages, first according to the existing generative model calculated the initial annotation word, and then calculated image similarity with considering the annotated words to improve the result of the annotation. The experiments over Corel5k images have shown the proposed method can effectively improve the rate of the annotation's accuracy and recall.
Rapid Aquaporin Translocation Regulates Cellular Water Flow

Background: Aquaporin channels ensure appropriate membrane permeability to water in all cells. Results: Following a hypotonic stimulus, subcellular localization of aquaporin 1 occurs via a mechanism dependent on transient receptor potential channels, extracellular calcium influx, calmodulin, and the phosphorylation of two threonines (157 and 239) of aquaporin 1. Conclusion: Rapid translocation of aquaporin 1 regulates membrane water permeability. Significance: This mechanism may serve as a prototype for the rapid regulation of aquaporin function. The control of cellular water flow is mediated by the aquaporin (AQP) family of membrane proteins. The structural features of the family and the mechanism of selective water passage through the AQP pore are established, but there remains a gap in our knowledge of how water transport is regulated. Two broad possibilities exist. One is controlling the passage of water through the AQP pore, but this only has been observed as a phenomenon in some plant and microbial AQPs. An alternative is controlling the number of AQPs in the cell membrane. Here, we describe a novel pathway in mammalian cells whereby a hypotonic stimulus directly induces intracellular calcium elevations through transient receptor potential channels, which trigger AQP1 translocation. This translocation, which has a direct role in cell volume regulation, occurs within 30 s and is dependent on calmodulin activation and phosphorylation of AQP1 at two threonine residues by protein kinase C. This direct mechanism provides a rationale for the changes in water transport that are required in response to constantly changing local cellular water availability. Moreover, because calcium is a pluripotent and ubiquitous second messenger in biological systems, the discovery of its role in the regulation of AQP translocation has ramifications for diverse physiological and pathophysiological processes, as well as providing an explanation for the rapid regulation of water flow that is necessary for cell homeostasis.