Miracle-dz/UniFAR-FTUs huggingface dataset:
Scaling Laws for Ergodic Spectral Efficiency in MIMO Poisson Networks

In this paper, we examine the benefits of multiple antenna communication in random wireless networks, the topology of which is modeled by stochastic geometry. The setting is the Poisson bipolar model introduced in [1], which is a natural model for ad-hoc and device-to-device networks. The primary finding is that, with the knowledge of channel state information between a receiver and its associated transmitter, by zero-forcing successive interference cancellation, and for appropriate antenna configurations, the ergodic spectral efficiency can be made to scale linearly with both: 1) the minimum of the number of transmit and receive antennas and 2) the density of nodes. This scaling law is achieved by using the multiple transmit antennas to send multiple data streams (e.g., through an open-loop transmission method) and by exploiting the receive antennas to cancel interference. Furthermore, when a receiver is able to learn channel state information from a certain number of near interferers, higher scaling gains can be achieved when a successive interference cancellation method is used. Both results require rich scattering environments. A major implication of the derived scaling laws is that, under this scattering assumption, spatial multiplexing transmission methods are essential for obtaining better and eventually optimal scaling laws in random wireless networks with multiple antennas.
Circulating microRNAs as Specific Biomarkers for Breast Cancer Detection

Background We previously showed microRNAs (miRNAs) in plasma are potential biomarkers for colorectal cancer detection. Here, we aimed to develop specific blood-based miRNA assay for breast cancer detection. Methodology/Principal Findings TaqMan-based miRNA profiling was performed in tumor, adjacent non-tumor, corresponding plasma from breast cancer patients, and plasma from matched healthy controls. All putative markers identified were verified in a training set of breast cancer patients. Selected markers were validated in a case-control cohort of 170 breast cancer patients, 100 controls, and 95 other types of cancers and then blindly validated in an independent set of 70 breast cancer patients and 50 healthy controls. Profiling results showed 8 miRNAs were concordantly up-regulated and 1 miRNA was concordantly down-regulated in both plasma and tumor tissue of breast cancer patients. Of the 8 up-regulated miRNAs, only 3 were significantly elevated (p<0.0001) before surgery and reduced after surgery in the training set. Results from the validation cohort showed that a combination of miR-145 and miR-451 was the best biomarker (p<0.0001) in discriminating breast cancer from healthy controls and all other types of cancers. In the blind validation, these plasma markers yielded Receiver Operating Characteristic (ROC) curve area of 0.931. The positive predictive value was 88% and the negative predictive value was 92%. Altered levels of these miRNAs in plasma have been detected not only in advanced stages but also early stages of tumors. The positive predictive value for ductal carcinoma in situ (DCIS) cases was 96%. Conclusions These results suggested that these circulating miRNAs could be a potential specific biomarker for breast cancer screening.
Chitin-binding domains of Escherichia coli ChiA mediate interactions with intestinal epithelial cells in mice with colitis.

BACKGROUND & AIMS Inducible chitinase 3-like-1 is expressed by intestinal epithelial cells (IECs) and adheres to bacteria under conditions of inflammation. We performed a structure-function analysis of the chitin-binding domains encoded by the chiA gene, which mediates the pathogenic effects of adherent invasive Escherichia coli (AIEC). METHODS We created AIEC (strain LF82) with deletion of chiA (LF82-ΔchiA) or that expressed chiA with specific mutations. We investigated the effects of infecting different IEC lines with these bacteria compared with nonpathogenic E coli; chitinase activities were measured using the colloidal chitin-azure method. Colitis was induced in C57/Bl6 mice by administration of dextran sodium sulfate, and mice were given 10(8) bacteria for 15 consecutive days by gavage. Stool/tissue samples were collected and analyzed. RESULTS LF82-ΔchiA had significantly less adhesion to IEC lines than LF82. Complementation of LF82-ΔchiA with the LF82 chiA gene, but not chiA from nonpathogenic (K12) E coli, increased adhesion. We identified 5 specific polymorphisms in the chitin-binding domain of LF82 chiA (at amino acids 362, 370, 378, 388, and 548) that differ from chiA of K12 and were required for LF82 to interact directly with IECs. This interaction was mediated by an N-glycosylated asparagine in chitinase 3-like-1 (amino acid 68) on IECs. Mice infected with LF82, or LF82-ΔchiA complemented with LF82 chiA, developed more severe colitis after administration of dextran sodium sulfate than mice infected with LF82-ΔchiA or LF82 that expressed mutant forms of chiA. CONCLUSIONS AIEC adheres to an N-glycosylated chitinase 3-like-1 on IECs via the chitin-binding domain of chiA. This mechanism promotes the pathogenic effects of AIEC in mice with colitis.
Positive Unlabeled Learning for Data Stream Classification

Learning from positive and unlabeled examples (PU learning) has been investigated in recent years as an alternative learning model for dealing with situations where negative training examples are not available. It has many real world applications, but it has yet to be applied in the data stream environment where it is highly possible that only a small set of positive data and no negative data is available. An important challenge is to address the issue of concept drift in the data stream environment, which is not easily handled by the traditional PU learning techniques. This paper studies how to devise PU learning techniques for the data stream environment. Unlike existing data stream classification methods that assume both positive and negative training data are available for learning, we propose a novel PU learning technique LELC (PU Learning by Extracting Likely positive and negative micro-Clusters) for document classification. LELC only requires a small set of positive examples and a set of unlabeled examples which is easily obtainable in the data stream environment to build accurate classifiers. Experimental results show that LELC is a PU learning method that can effectively address the issues in the data stream environment with significantly better speed and accuracy on capturing concept drift than the existing state-of-the-art PU learning techniques.
53BP1 and USP28 mediate p53 activation and G1 arrest after centrosome loss or extended mitotic duration

Meitinger et al. perform a genome-wide CRISPR/Cas9 screen for centrinone resistance and identify a 53BP1-USP28 module as critical for communicating mitotic challenges to the p53 circuit and TRIM37 as an enforcer of the singularity of centrosome assembly.
Deep Brain Stimulation for Treatment-Refractory Depression

Abstract Depression has a high lifetime prevalence and recurrence rate, with more than one-third of affected patients experiencing treatment-refractory depression. These individuals should benefit from additional treatment options such as deep brain stimulation (DBS), a research-grade intervention. DBS is being investigated for its efficacy in treatment-refractory cases. We reviewed the English-language literature published between the years 2010 and 2015 regarding the utility of DBS for patients with treatment-refractory depression. The literature review revealed that most DBS research is open label, with few large randomized, placebo-controlled trials to confirm results. Long-term response rates with DBS were between 40% and 70%, with clinical effects depending on location of electrode placement. Improvement was documented to last for months to years. Although DBS is potentially efficacious and a relatively safe option for patients with treatment resistance, it is invasive, costly, and still considered experimental. Understanding of the neurobiology of depression, the mechanism of DBS action, and biomarkers that may predict patient response remains obscure. Future research should contain careful design, including homogenous inclusion criteria and characterization of pretreatment patient mood, somatic complaints, and cognition; consistent outcome measures; monitoring of depressive symptoms at different brain-positioning targets across an adequate time course; and records of stimulus parameters.
The hedonic brain: A functional neuroanatomy of human pleasure.

Pleasure is central to our lives and intimately linked to emotional and reward processing in the brain. In general, hedonic experience is arguably at the heart of what makes us human, but at the same time it is also one of the most important factors that is keeping us from staying healthy (Kringelbach, 2004b; Saper et al., 2002). Understanding the underlying brain mechanisms can therefore help us understand and potentially treat the serious problems of aL ective disorders, such as, for example unipolar depression, bipolar disorder, chronic pain, and the worldwide epidemic of obesity. The central premise of this chapter is that in order to more eL ectively treat aL ective disorders, we need to develop a better understanding of hedonic processing—that is the aL ective component of sensory processing—in the human brain (Kringelbach, 2005, 2009). Pleasure is here deG ned as one of the positive dimensions of the more general category of hedonic processing, which also includes other negative and unpleasant dimensions such as pain (see Leknes and Tracey, Chapter 19, this book). Importantly, malignant aL ective disorders such as depression, chronic pain, and eating disorders are characterized by the lowered or missing ability to experience pleasure, anhedonia. Thus, in order to help with these disorders, we will have to further our understanding of the cortical and subcortical mechanisms involved in pleasure and hedonic processing in general (Berridge and Kringelbach, 2008). This review explores the evidence for underlying brain mechanisms and principles of pleasure and hedonic processing in the human brain. This evidence comes from human neuroimaging, neuropsychology, and neurosurgery. In particular, this chapter concentrates on the evidence linking the human orbitofrontal cortex (OFC) to general hedonic processing (see Figure 12.1).
Increased Salience Network Activity in Patients With Insomnia Complaints in Major Depressive Disorder

Background Insomnia is one of the main symptom correlates of major depressive disorder (MDD), but the neural mechanisms underlying the multifaceted interplay between insomnia and depression are not fully understood. Materials and methods Patients with MDD and high insomnia (MDD-HI, n = 24), patients with MDD and low insomnia (MDD-LI, n = 37), and healthy controls (HCs, n = 51) were recruited to participate in the present study. The amplitude of low-frequency fluctuations (ALFF) during the resting state were compared among the three groups. Results We observed ALFF differences between the three groups in the right inferior frontal gyrus/anterior insula (IFG/AI), right middle temporal gyrus, left calcarine, and bilateral dorsolateral prefrontal cortex (dlPFC). Further region of interest (ROI) comparisons showed that the increases in the right IFG/AI reflected an abnormality specific to insomnia in MDD, while increases in the bilateral dlPFC reflected an abnormality specific to MDD generally. Increased ALFF in the right IFG/AI was also found to be correlated with sleep disturbance scores when regressing out the influence of the severity of anxiety and depression. Conclusion Our findings suggest that increased resting state ALLF in IFG/AI may be specifically related to hyperarousal state of insomnia in patients with MDD, independently of the effects of anxiety and depression.
Merlin-Arthur with efficient quantum Merlin and quantum supremacy for the second level of the Fourier hierarchy

We introduce a simple sub-universal quantum computing model, which we call the Hadamard-classical circuit with one-qubit (HC1Q) model. It consists of a classical reversible circuit sandwiched by two layers of Hadamard gates, and therefore it is in the second level of the Fourier hierarchy. We show that output probability distributions of the HC1Q model cannot be classically efficiently sampled within a multiplicative error unless the polynomial-time hierarchy collapses to the second level. The proof technique is different from those used for previous sub-universal models, such as IQP, Boson Sampling, and DQC1, and therefore the technique itself might be useful for finding other sub-universal models that are hard to classically simulate. We also study the classical verification of quantum computing in the second level of the Fourier hierarchy. To this end, we define a promise problem, which we call the probability distribution distinguishability with maximum norm (PDD-Max). It is a promise problem to decide whether output probability distributions of two quantum circuits are far apart or close. We show that PDD-Max is BQP-complete, but if the two circuits are restricted to some types in the second level of the Fourier hierarchy, such as the HC1Q model or the IQP model, PDD-Max has a Merlin-Arthur system with quantum polynomial-time Merlin and classical probabilistic polynomial-time Arthur.
Analytical workspace delineation of a translational underconstrained cable-based robot

This paper presents the analytical controllable workspace boundaries of a crane-type cable-based robot with purely translational motions. The underconstrained cable robot is composed of eight cables which support the weight of a mobile platform, restricting its motions into the vertical plane of a static platform. The analytic equations are developed so that its workspace fulfills the tensionable condition for a given set of minimum and maximum tension limits. The all-tensionable workspace is obtained by analyzing slackness conditions with constant orientation of the mobile platform and by solving the all-positive cable tension redundancy. The minimum two-norm cable tension formulation results in a quadratic equation which admits a minimum value by equaling its derivative to zero.
On scale invariant features and sequential Monte Carlo sampling for bronchoscope tracking

This paper presents an improved bronchoscope tracking method for bronchoscopic navigation using scale invariant features and sequential Monte Carlo sampling. Although image-based methods are widely discussed in the community of bronchoscope tracking, they are still limited to characteristic information such as bronchial bifurcations or folds and cannot automatically resume the tracking procedure after failures, which result usually from problematic bronchoscopic video frames or airway deformation. To overcome these problems, we propose a new approach that integrates scale invariant feature-based camera motion estimation into sequential Monte Carlo sampling to achieve an accurate and robust tracking. In our approach, sequential Monte Carlo sampling is employed to recursively estimate the posterior probability densities of the bronchoscope camera motion parameters according to the observation model based on scale invariant feature-based camera motion recovery. We evaluate our proposed method on patient datasets. Experimental results illustrate that our proposed method can track a bronchoscope more accurate and robust than current state-of-the-art method, particularly increasing the tracking performance by 38.7% without using an additional position sensor.
Dynamics of implicit operations and tameness of pseudovarieties of groups

This work gives a new approach to the construction of implicit operations. By considering higher-dimensional spaces of implicit operations and implicit operators between them, the projection of idempotents back to one-dimensional spaces produces implicit operations with interesting properties. Besides providing a wealth of examples of implicit operations which can be obtained by these means, it is shown how they can be used to deduce from results of Ribes and Zalesskiiˇ, Margolis, Sapir and Weil, and Steinberg that the pseudovariety of p-groups is tame. More generally, for a recursively enumerable extension closed pseudovariety of groups V, if it can be decided whether a finitely generated subgroup of the free group with the pro-V topology is dense, then V is tame.
B 7-2 gene silencing by lentivirus-mediated delivery of shRNA reduces progression of experimental lupus nephritis

Over-activation of B7/CD28 co-stimulatory signal is believed to play an important role in promoting hyperfunctional immune response which contributes to autoimmune diseases. In the present study, we generated the recombinant lentivirus transcribing short hairpin RNA (shRNA) specific for mouse B7-2 gene silencing and B7/CD28 signal inhibition to explore and evaluate a potential candidate approach to suppress B7/CD28 co-signal and ameliorate auto-immune diseases. A pristine-induced mouse model of lupus nephritis (LN) was generated to assess the effects of an immune intervention on the progression of systemic lupus erythematosus (SLE) and end-stage renal injury. Lentivirus-mediated B7-2 gene silencing attenuated the over-activity of splenic immune cells, including macrophages, dendritic cells, granulocytes, and B-cells in the experimental lupus nephritis model ten days after pristane was given. Production of auto-antibodies, e.g., antinuclear antibody (ANA), anti-double-strand-DNA antibody (antidsDNA), and development of proteinuria after treatment with B7-2 silencing lentivirus were lessened from the third or fourth month while auto-antibodies and proteinuria appeared to be decreased at the eighth month. In addition, secretion of interleukin-4 and interferon-γ cytokines, immune complexes accumulation/deposition in kidneys, and the renal inflammatory damage were relieved compared to those in mice of model group without B7-2 silencing lentivirus intervention. In conclusion, suppression of B7/CD28 signal by lentivirus-mediated B7-2 gene silencing can reduce the development of experimental lupus nephritis, suggesting it is a potentially useful strategy for decelerating the progression of lupus-like diseases.
Rotation Invariant Local Shape Descriptors for Classification of Archaeological 3 D Models Edgar

We introduce a method for estimation of rotation invariant local shape descriptors for 3D models. This method follows a successful idea commonly used to obtain rotation invariant descriptors in 2D images, and improves it by tackling the difficulty of the 3 degrees of freedom that exists in 3D models. Our method is simple, yet it achieves high levels of invariance after rotation transformations, and it produces short descriptors that can be efficiently used in several tasks. Such is the case of automatic classification of 3D surfaces with archaeological value, in which the proposed method attains state-of-the-art results in shorter times when compared with previous methods.
Bond dynamics, microstructure, and rheology of colloidal gels

The stability and shelf life of colloidal suspensions and gels is industrially relevant for manufacturing materials such as paints, foods, pharmaceuticals, and personal care products. Bulk rheological properties, such as viscosities, elastic moduli, and yield stresses of these materials, are influenced by the colloidal interactions and microstructure. Therefore, predicting these macroscopic properties is a multi-scale problem that needs to be addressed by studying the materials over a range of length scales. Model colloidal systems are often used for these studies because they provide a simplified set of materials in which the particle interactions, sedimentation, and the ability to image and manipulate the microstructure can be better controlled. Thus, this thesis introduces a new model colloidal gel for measuring particle interactions, microstructure, and rheology. The results from these measurements are analyzed to quantitatively predict the elastic modulus from the particle bond stiffness and microstructure. Many experimental model gels typically have a refractive index and density matched solvent that allow for confocal microscopy and rheometry experiments for studying the gel microstructure and bulk rheological properties of industrial relevance. In this work, a model gel is created by suspending fluorescent poly(12-hydroxysteric acid) (PHSA) stabilized poly(methyl methacrylate) (PMMA) particles in cyclohexane and cyclohexyl bromide with polystyrene depletant to induce an interparticle attraction. The solvent mixture is optimized in this depletion gel to provide the refractive index contrast needed for optical trapping without greatly diminishing the confocal imaging resolution. Optical trapping provides the ability to relate gel rheology to the dynamics of individual bonds and interparticle forces. Therefore, this model gel is unique because particle-level interactions, microstructure, and bulk rheology data can all be obtained from one set of materials.
Wagging more, barking less: Glucocorticoid and behavioral responses of shelter dogs to human interaction

4 Introduction.5 Methods.9 Subjects.10 Procedure.11 Analysis.13 Results.20 Discussion.27 Conclusion.35 Acknowledgements.36 References.38 Appendix.44
CNTFET modeling and low power SRAM cell design

This paper presents compact model for carbon nanotube field effect transistor (CNTFET) which is derived from electronic structure of carbon nanotube (CNT). Complete current transport model is developed from carrier concentration in CNT for different chirality. The model describes the variation of charge developed on CNT with gate voltage. I-V characteristics have been efficiently modeled and compact model is developed for HSPICE circuit simulations. Finally 6T static random access memory (SRAM) cell is designed with developed model and analysis is done for various performance metrics. Results show that CNTFET based 6T-SRAM consumes very less standby power with high static noise margins.
Complex-YOLO: Real-time 3D Object Detection on Point Clouds

Lidar based 3D object detection is inevitable for autonomous driving, because it directly links to environmental understanding and therefore builds the base for prediction and motion planning. The capacity of inferencing highly sparse 3D data in real-time is an ill-posed problem for lots of other application areas besides automated vehicles, e.g. augmented reality, personal robotics or industrial automation. We introduce Complex-YOLO, a state of the art real-time 3D object detection network on point clouds only. In this work, we describe a network that expands YOLOv2, a fast 2D standard object detector for RGB images, by a specific complex regression strategy to estimate multi-class 3D boxes in Cartesian space. Thus, we propose a specific Euler-Region-Proposal Network (E-RPN) to estimate the pose of the object by adding an imaginary and a real fraction to the regression network. This ends up in a closed complex space and avoids singularities, which occur by single angle estimations. The E-RPN supports to generalize well during training. Our experiments on the KITTI benchmark suite show that we outperform current leading methods for 3D object detection specifically in terms of efficiency. We achieve state of the art results for cars, pedestrians and cyclists by being more than five times faster than the fastest competitor. Further, our model is capable of estimating all eight KITTI-classes, including Vans, Trucks or sitting pedestrians simultaneously with high accuracy.