Miracle-dz/UniFAR-FTUs huggingface dataset:
NSC23925, Identified in a High-Throughput Cell-Based Screen, Reverses Multidrug Resistance

Background Multidrug resistance (MDR) is a major factor which contributes to the failure of cancer chemotherapy, and numerous efforts have been attempted to overcome MDR. To date, none of these attempts have yielded a tolerable and effective therapy to reverse MDR; thus, identification of new agents would be useful both clinically and scientifically. Methodology/Principal Findings To identify small molecule compounds that can reverse chemoresistance, we developed a 96-well plate high-throughput cell-based screening assay in a paclitaxel resistant ovarian cancer cell line. Coincubating cells with a sublethal concentration of paclitaxel in combination with each of 2,000 small molecule compounds from the National Cancer Institute Diversity Set Library, we identified a previously uncharacterized molecule, NSC23925, that inhibits Pgp1 and reverses MDR1 (Pgp1) but does not inhibit MRP or BCRP-mediated MDR. The cytotoxic activity of NSC23925 was further evaluated using a panel of cancer cell lines expressing Pgp1, MRP, and BCRP. We found that at a concentration of >10 µM NSC23925 moderately inhibits the proliferation of both sensitive and resistant cell lines with almost equal activity, but its inhibitory effect was not altered by co-incubation with the Pgp1 inhibitor, verapamil, suggesting that NSC23925 itself is not a substrate of Pgp1. Additionally, NSC23925 increases the intracellular accumulation of Pgp1 substrates: calcein AM, Rhodamine-123, paclitaxel, mitoxantrone, and doxorubicin. Interestingly, we further observed that, although NSC23925 directly inhibits the function of Pgp1 in a dose-dependent manner without altering the total expression level of Pgp1, NSC23925 actually stimulates ATPase activity of Pgp, a phenomenon seen in other Pgp inhibitors. Conclusions/Significance The ability of NSC23925 to restore sensitivity to the cytotoxic effects of chemotherapy or to prevent resistance could significantly benefit cancer patients.
State estimation for a class of nonlinear systems

We propose a new type of Proportional Integral (PI) state observer for a class of nonlinear systems in continuous time which ensures an asymptotic stable convergence of the state estimates. Approximations of nonlinearity are not necessary to obtain such results, but the functions must be, at least locally, of the Lipschitz type. The obtained state variables are exact and robust against noise. Naslin’s damping criterion permits synthesizing gains in an algebraically simple and efficient way. Both the speed and damping of the observer response are controlled in this way. Model simulations based on a Sprott strange attractor are discussed as an example.
Lagrangian Heuristics Based on Disaggregated Bundle Methods for Hydrothermal Unit Commitment

The paper presents a simple and effective Lagrangian relaxation approach for the solution of the optimal short-term unit commitment problem in hydrothermal power generation systems. The proposed approach, based on a disaggregated Bundle method for the solution of the dual problem, with a new warm-starting procedure, achieves accurate solutions in few iterations. The adoption of a disaggregated Bundle method not only improves the convergence of the proposed approach but also provides information that are suitably exploited for generating a feasible solution of the primal problem and for obtaining an optimal hydro scheduling. A comparison between the proposed Lagrangian approach and other ones, based on subgradient and Bundle methods, is presented for a simple yet reasonable formulation of the hydrothermal unit commitment problem.
Fusing GNSS, Dead-Reckoning, and Enhanced Maps for Road Vehicle Lane-Level Navigation

Nowadays, it is common that road vehicle navigation systems employ maps to represent the vehicle positions in a local reference. The most usual process to do that consists in the estimation of the vehicle positioning by fusing the Global Navigation Satellite System (GNSS) and some other aiding sensors data, and the subsequent projection of these values on the map by applying map-matching techniques. However, it is possible to benefit from map information also during the process of fusing data for positioning. This paper presents an algorithm for lane-level road vehicle navigation that integrates GNSS, dead-reckoning (odometry and gyro), and map data in the fusion process. Additionally, the proposed method brings some benefits for map-matching at lane level because, on the one hand, it allows the tracking of multiple hypothesis and on the other hand, it provides probability values of lane occupancy for each candidate segment. To do this, a new paradigm that describes lanes as piece-wise sets of clothoids was applied in the elaboration of an enhanced map (Emap). Experimental results in real complex scenarios with multiple lanes show the suitability of the proposed algorithm for the problem under consideration, presenting better results than some state-of-the-art methods of the literature.
A constrained coefficient ica algorithm for group difference enhancement

Independent component analysis (ICA) is a statistical and computational technique for revealing hidden factors that underlie sets of signals. We propose an improved ICA framework for group data analysis by adding an adaptive constraint to the mixing coefficients, namely, constrained coefficients ICA (CCICA). The method is dedicated to identification and increasing the accuracy of components that show significant group differences reflected in the mixing coefficients. Performance of CCICA is assessed by simulations under different signal to noise ratios. An application to multitask functional magnetic resonance imaging analysis is conducted to illustrate the advantages of CCICA. It is shown that CCICA provides stable results and can estimate both the components and the mixing coefficients with a relatively high accuracy compared to Infomax, hence is a promising tool for the identification of biomarkers from brain imaging data.
A smart poultice with reconfigurable sensor array for wearable cardiac healthcare

A smart poultice with reconfigurable sensor array is implemented by using planar fashionable circuit board (P-FCB) technology for wearable cardiac healthcare. It contains 1) 25-electrode array either for vital signal sensing or for external data communication, 2) a thin flexible battery, 3) a fabric inductor, and 4) a fabric circuit board on which a low power silicon chip is directly integrated. Start/stop operation of the smart poultice is realized by inductive-coupled remote controller with 8b ID verification function and external low power data transaction is achieved by using duty cycled body channel communication.
Incremental translation using hierarchichal phrase-based translation system

Hierarchical phrase-based machine translation [1] (Hiero) is a prominent approach for Statistical Machine Translation usually comparable to or better than conventional phrase-based systems. But Hiero typically uses the CKY decoding algorithm which requires the entire input sentence before decoding begins, as it produces the translation in a bottom-up fashion. Left-to-right (LR) decoding [2] is a promising decoding algorithm for Hiero that produces the output translation in left to right order. In this paper we focus on simultaneous translation using the Hiero translation framework. In simultaneous translation, translations are generated incrementally as source language speech input is processed. We propose a novel approach for incremental translation by integrating segmentation and decoding in LR-Hiero. We compare two incremental decoding algorithms for LR-Hiero and present translation quality scores (BLEU) and the latency of generating translations for both decoders on audio lectures from the TED collection.
Gene expression of lactobacilli in murine forestomach biofilms

Lactobacilli populate the gastro‐intestinal tract of vertebrates, and are used in food fermentations and as probiotics. Lactobacilli are also major constituents of stable biofilms in the forestomach of rodents. In order to investigate the lifestyle of these biofilm lactobacilli in C57BL/6 mice, we applied metatranscriptomics to analyse gene expression (assessed by mRNA) and community composition (assessed by rRNA). Lactobacillales were the major biofilm inhabitants (62–82% of rRNA reads), followed by Clostridiales (8–31% of rRNA reads). To identify mRNA transcripts specific for the forestomach, we compared forestomach and hindgut metatranscriptomes. Gene expression of the biofilm microbiota was characterized by high abundance of transcripts related to glucose and maltose utilization, peptide degradation, and amino acid transport, indicating their major catabolic and anabolic pathways. The microbiota transcribed genes encoding pathways enhancing oxidative stress (glutathione synthesis) and acid tolerance. Various pathways, including metabolite formation (urea degradation, arginine pathway, γ‐aminobutyrate) and cell wall modification (DltA, cyclopropane‐fatty‐acyl‐phospholipid synthase), contributed to acid tolerance, as judged from the transcript profile. In addition, the biofilm microbiota expressed numerous genes encoding extracellular proteins involved in adhesion and/or biofilm formation (e.g. MucBP, glycosyl hydrolase families 68 and 70). This study shed light on the lifestyle and specific adaptations of lactobacilli in the murine forestomach that might also be relevant for lactobacilli biofilms in other vertebrates, including humans.
Plasmacytoid Dendritic Cells Are Crucial in Bifidobacterium adolescentis-Mediated Inhibition of Yersinia enterocolitica Infection

In industrialized countries bacterial intestinal infections are commonly caused by enteropathogenic Enterobacteriaceae. The interaction of the microbiota with the host immune system determines the adequacy of an appropriate response against pathogens. In this study we addressed whether the probiotic Bifidobacterium adolescentis is protective during intestinal Yersinia enterocolitica infection. Female C57BL/6 mice were fed with B. adolescentis, infected with Yersinia enterocolitica, or B. adolescentis fed and subsequently infected with Yersinia enterocolitica. B. adolescentis fed and Yersinia infected mice were protected from Yersinia infection as indicated by a significantly reduced weight loss and splenic Yersinia load when compared to Yersinia infected mice. Moreover, protection from infection was associated with increased intestinal plasmacytoid dendritic cell and regulatory T-cell frequencies. Plasmacytoid dendritic cell function was investigated using depletion experiments by injecting B. adolescentis fed, Yersinia infected C57BL/6 mice with anti-mouse PDCA-1 antibody, to deplete plasmacytoid dendritic cells, or respective isotype control. The B. adolescentis-mediated protection from Yersinia dissemination to the spleen was abrogated after plasmacytoid dendritic cell depletion indicating a crucial function for pDC in control of intestinal Yersinia infection. We suggest that feeding of B. adolescentis modulates the intestinal immune system in terms of increased plasmacytoid dendritic cell and regulatory T-cell frequencies, which might account for the B. adolescentis-mediated protection from Yersinia enterocolitica infection.
Defining Pathogenic Bacterial Species in the Genomic Era

Actual definitions of bacterial species are limited due to the current criteria of definition and the use of restrictive genetic tools. The 16S ribosomal RNA sequence, for example, has been widely used as a marker for phylogenetic analyses; however, its use often leads to misleading species definitions. According to the first genetic studies, removing a certain number of genes from pathogenic bacteria removes their capacity to infect hosts. However, more recent studies have demonstrated that the specialization of bacteria in eukaryotic cells is associated with massive gene loss, especially for allopatric endosymbionts that have been isolated for a long time in an intracellular niche. Indeed, sympatric free-living bacteria often have bigger genomes and exhibit greater resistance and plasticity and constitute species complexes rather than true species. Specialists, such as pathogenic bacteria, escape these bacterial complexes and colonize a niche, thereby gaining a species name. Their specialization allows them to become allopatric, and their gene losses eventually favor reductive genome evolution. A pathogenic species is characterized by a gene repertoire that is defined not only by genes that are present but also by those that are lacking. It is likely that current bacterial pathogens will disappear soon and be replaced by new ones that will emerge from bacterial complexes that are already in contact with humans.
New MRI , 18 F-DOPA and 11 C-( + )-α-dihydrotetrabenazine templates for Macaca fascicularis neuroimaging : Advantages to improve PET quanti fi cation

a Small Animal Imaging Research Unit, Center for Applied Medical Research (CIMA) and Clínica Universidad de Navarra, Pamplona, Spain b Department of Nuclear Medicine, Clínica Universidad de Navarra, Pamplona, Spain c Basal Ganglia and Movement Disorders Group, Neurosciences Division, CIMA, and Department of Neurology and Neurosurgery, Clínica Universidad de Navarra, Pamplona, Spain d Centro de Investigación Biomédica en Red sobre Enfermedades Neurodegenerativas (CIBERNED), Pamplona, Spain e Laboratory of Regenerative Therapy, Center for Applied Medical Research (CIMA) and Clínica Universidad de Navarra, Pamplona, Spain f Department of Radiology, Clínica Universidad de Navarra, Pamplona, Spain
Alarm calls and chick reactions in the moustached warbler,
 Acrocephalus melanopogon
 


As in many altricial species, adult moustached warblers alarm call more at the nest as the breeding season progresses. This study used the experimental human approach method as well as two predator types (plastic snake and taxidermic raptor) placed at the nest to test the anti-predator responses of chicks to parental alarm calls. The probability of chicks making anti-predator responses (ducking and jumping) was strongly correlated with the probability of adults giving alarm calls. Furthermore, chicks reacted selectively to different predator types, tending to remain in the nest in response to aerial predators and to jump from the nest in response to ground predators. A conceptual framework is presented identifying the age of chicks when alarm calls are first given for the brood value, vulnerability and chick reaction hypotheses. These were tested by comparing the intercept of the regression line for alarm calls with those predicted by each hypothesis. The results suggest that the anti-predator response of chicks is the proximate cue for adult alarm calls.
Automatic grasp planning using shape primitives

Automatic grasp planning for robotic hands is a difficult problem because of the huge number of possible hand configurations. However, humans simplify the problem by choosing an appropriate prehensile posture appropriate for the object and task to be performed. By modeling an object as a set of shape primitives, such as spheres, cylinders, cones and boxes, we can use a set of rules to generate a set of grasp starting positions and pregrasp shapes that can then be tested on the object model. Each grasp is tested and evaluated within our grasping simulator "GraspIt! ", and the best grasps are presented to the user. The simulator can also plan grasps in a complex environment involving obstacles and the reachability constraints of a robot arm.
Regularizing Recurrent Networks - On Injected Noise and Norm-based Methods

Advancements in parallel processing have lead to a surge in multilayer perceptrons' (MLP) applications and deep learning in the past decades. Recurrent Neural Networks (RNNs) give additional representational power to feedforward MLPs by providing a way to treat sequential data. However, RNNs are hard to train using conventional error backpropagation methods because of the difficulty in relating inputs over many time-steps. Regularization approaches from MLP sphere, like dropout and noisy weight training, have been insufficiently applied and tested on simple RNNs. Moreover, solutions have been proposed to improve convergence in RNNs but not enough to improve the long term dependency remembering capabilities thereof. In this study, we aim to empirically evaluate the remembering and generalization ability of RNNs on polyphonic musical datasets. The models are trained with injected noise, random dropout, norm-based regularizers and their respective performances compared to well-initialized plain RNNs and advanced regularization methods like fast-dropout. We conclude with evidence that training with noise does not improve performance as conjectured by a few works in RNN optimization before ours.
<inline-formula><tex-math notation="LaTeX">$V$</tex-math></inline-formula>–<inline-formula> <tex-math notation="LaTeX">${d}p/{d}v$</tex-math></inline-formula> Droop Control for PV Sources in DC Microgrids

Direct current (dc) microgrids are highly compatible with photovoltaic (PV) sources due to their dc nature. However, as PV penetration increases, conventional current-source control configurations tracking the maximum power point may encounter the overvoltage problem, among others. This paper presents a <inline-formula><tex-math notation="LaTeX">$V$ </tex-math></inline-formula>–<inline-formula><tex-math notation="LaTeX">${d}p/{d}v$</tex-math></inline-formula> droop control strategy, which enables the PV sources to work in the maximum power point tracking (MPPT) mode, as well as behave as voltage sources to participate in dc-bus voltage regulating. The <inline-formula> <tex-math notation="LaTeX">$V$</tex-math></inline-formula>–<inline-formula><tex-math notation="LaTeX">${d}p/{d}v$ </tex-math></inline-formula> droop method unifies MPPT and dc-bus voltage regulating in the sense of using the same control configuration, which avoids the controller reconfiguration during the PV mode switching. Further, the coordinated operation of multiple PV sources in the dc microgrid, including autonomous mode switching and primarily power sharing, is achieved by the proposed control strategy without any communication. Hardware-in-loop tests are presented to demonstrate the effectiveness of the proposed control scheme.
Social search in collaborative tagging networks: the role of ties

With the rise of the Web 2.0 online social networking has become a huge trend. On Facebook , Google+, etc. people connect with their friends or make new friends. They form new (indirect) connections by reading and adopting from other peoples’ blogs or tweets. Similarly, in collaborative tagging systems like Last.fm, Delicious, or Flickr people share bookmarks and tagged resources with friends or unknown, like-minded users. Just like in many off-line situations, it has been argued that the different kinds of online ties hold different potentials for information exchange and collaboration. In this work we study social ties in collaborative tagging systems – a prerequisite for successfully exploiting the different kinds of ties within social search in and built upon such systems. First, we focus on social tags as means for enhancing search even without explicitly given social connections: through better user profiling, richer resource descriptions, newly mined knowledge, or the recommendation of people with similar interests. In order to prove that tags are indeed a useful source of additional information, we analyze tag usage patterns in diverse tagging systems and discuss the implications for user profiling, search, and recommendation. Building upon the found characteristics we present approaches exploiting tags to enrich resources or user profiles with additional information – music moods and themes. Second, we examine existing friendship links in Last.fm contrasting online and “real-world” friends having coattended events. We investigate in depth similarity along such social links regarding demographics, network structure as well as taste in particular. While in platforms like Google+ or Facebook users still have to manually maintain circles or lists of close friends, family, etc., we are developing machine learning methods that successfully identify online and off-line friends of different strength automatically. Additional experiments on weak and strong ties in Wikipedia show that also the prediction of future behavior, here co-editing of articles, can benefit from considering social ties.
Low-power low-phase-noise differentially tuned quadrature VCO design in standard CMOS

This paper describes the design and optimization of VCOs with quadrature outputs. Systematic design of fully integrated LC-VCOs with a high inductance tank leads to a cross-coupled double core LC-VCO as the optimal solution in terms of power consumption. Furthermore, a novel fully differential frequency tuning concept is introduced to ease high integration. The concepts are verified with a 0.25-/spl mu/m standard CMOS fully integrated quadrature VCO for zero- or low-IF DCS1800, DECT, or GSM receivers. At 2.5-V power supply voltage and a total power dissipation of 20 mW, the quadrature VCO features a worst-case phase noise of -143 dBc/Hz at 3-MHz frequency offset over the tuning range. The oscillator is tuned from 1.71 to 1.99 GHz through a differential nMOS/pMOS varactor input.
Autonomous Visual Mapping and Exploration With a Micro Aerial Vehicle

Cameras are a natural fit for micro aerial vehicles (MAVs) due to their low weight, low power consumption, and two‐dimensional field of view. However, computationally‐intensive algorithms are required to infer the 3D structure of the environment from 2D image data. This requirement is made more difficult with the MAV's limited payload which only allows for one CPU board. Hence, we have to design efficient algorithms for state estimation, mapping, planning, and exploration. We implement a set of algorithms on two different vision‐based MAV systems such that these algorithms enable the MAVs to map and explore unknown environments. By using both self‐built and off‐the‐shelf systems, we show that our algorithms can be used on different platforms. All algorithms necessary for autonomous mapping and exploration run on‐board the MAV. Using a front‐looking stereo camera as the main sensor, we maintain a tiled octree‐based 3D occupancy map. The MAV uses this map for local navigation and frontier‐based exploration. In addition, we use a wall‐following algorithm as an alternative exploration algorithm in open areas where frontier‐based exploration under‐performs. During the exploration, data is transmitted to the ground station which runs large‐scale visual SLAM. We estimate the MAV's state with inertial data from an IMU together with metric velocity measurements from a custom‐built optical flow sensor and pose estimates from visual odometry. We verify our approaches with experimental results, which to the best of our knowledge, demonstrate our MAVs to be the first vision‐based MAVs to autonomously explore both indoor and outdoor environments.