Miracle-dz/UniFAR-FTUs huggingface dataset:
Prognostic impact and implications of extracapsular lymph node involvement in colorectal cancer: a systematic review with meta-analysis.

BACKGROUND The extranodal extension (ENE) of nodal metastasis (i.e. the extension of tumor cells through the nodal capsule into the perinodal adipose tissue) has recently emerged as an important prognostic factor in different types of malignancies. However, the tumor-node-metastasis (TNM) staging system for colorectal cancer does not consider it as a prognostic parameter. Therefore, we conducted a systematic review and meta-analysis to determine the prognostic role of ENE in patients with lymph node-positive colorectal cancer. MATERIALS AND METHODS Two independent authors searched PubMed and SCOPUS until 7 January 2015 without language restrictions. Prospective studies reporting data on prognostic parameters in subjects with colorectal cancer, comparing participants with the presence of ENE (ENE+) versus only intranodal extension (ENE-) were eligible. Data were summarized using risk ratios (RRs) for the number of deaths/recurrences and hazard ratios (HRs) together with 95% confidence intervals (CIs) for time-dependent risk related to ENE+, adjusted for potential confounders. RESULTS Thirteen studies including 1336 patients were identified with a median follow-up of 4.7 years. ENE was associated with a higher T stage and tumor grading. In addition, ENE was associated with a significantly increased risk of all-cause mortality (RR = 1.75; 95% CI 1.42-2.16, P < 0.0001, I(2) = 60%; HR = 1.69, 95% CI 1.32-2.17, P < 0.0001, I(2) = 46%) and of recurrence of disease (RR = 2.07, 95% CI 1.65-2.61, P < 0.0001, I(2) = 47%; HR = 2.31, 95% CI 1.54-3.44, P < 0.0001, I(2) = 48%). CONCLUSIONS Based of these results, in colorectal cancer, ENE should be considered from the gross sampling to the pathology report, as well as in future oncologic staging systems.
Mixed Body/Hand Reference Frame for Reaching in 3D Space in Macaque Parietal Area PEc

Abstract The neural correlates of coordinate transformations from vision to action are expressed in the activity of posterior parietal cortex (PPC). It has been demonstrated that among the medial‐most areas of the PPC, reaching targets are represented mainly in hand‐centered coordinates in area PE, and in eye‐centered, body‐centered, and mixed body/hand‐centered coordinates in area V6A. Here, we assessed whether neurons of area PEc, located between V6A and PE in the medial PPC, encode targets in body‐centered, hand‐centered, or mixed frame of reference during planning and execution of reaching. We studied 104 PEc cells in 3 Macaca fascicularis. The animals performed a reaching task toward foveated targets located at different depths and directions in darkness, starting with the hand from 2 positions located at different depths, one next to the trunk and the other far from it. We show that most PEc neurons encoded targets in a mixed body/hand‐centered frame of reference. Although the effect of hand position was often rather strong, it was not as strong as reported previously in area PE. Our results suggest that area PEc represents an intermediate node in the gradual transformation from vision to action that takes place in the reaching network of the dorsomedial PPC.
Experimental metagenomics and ribosomal profiling of the human skin microbiome

The skin is the largest organ in the human body, and it is populated by a large diversity of microbes, most of which are co‐evolved with the host and live in symbiotic harmony. There is increasing evidence that the skin microbiome plays a crucial role in the defense against pathogens, immune system training and homoeostasis, and microbiome perturbations have been associated with pathological skin conditions. Studying the skin resident microbial community is thus essential to better understand the microbiome‐host crosstalk and to associate its specific configurations with cutaneous diseases. Several community profiling approaches have proved successful in unravelling the composition of the skin microbiome and overcome the limitations of cultivation‐based assays, but these tools remain largely inaccessible to the clinical and medical dermatology communities. The study of the skin microbiome is also characterized by specific technical challenges, such as the low amount of microbial biomass and the extensive human DNA contamination. Here, we review the available community profiling approaches to study the skin microbiome, specifically focusing on the practical experimental and analytical tools necessary to generate and analyse skin microbiome data. We describe all the steps from the initial samples collection to the final data interpretation, with the goal of enabling clinicians and researchers who are not familiar with the microbiome field to perform skin profiling experiments.
Changes in the Material Properties of Steel 1.4762 Depending on the Temperature

Abstract This paper presents the experimental results and analysis of the behaviour of steel 1.4762 at different temperatures. Tensile mechanical properties and creep behaviour were determined using uniaxial tests. Charpy impact test was used to determine impact energies at different temperatures. The mentioned mechanical properties are displayed as engineering stress–strain diagrams while material resistance to creep is presented in the form of creep curves. Based on Charpy impact energy an assessment of fracture toughness is given. Experimental results show that the values of mechanical properties are continuously decreased by increasing the temperature. After the temperature of 623 K (tensile strength: 468 MPa; yield strength: 298 MPa) the decrease of the ultimate strength and yield strength is especially emphasized and the difference between them becomes smaller. The creep resistance is considered quite small, except at low stress levels.
Perthes disease: current principles of diagnosis and treatment.

BACKGROUND Because the course of Legg-Calvé-Perthes disease (LCPD) is highly variable, its appropriate diagnostic evaluation and treatment are still debated. METHODS The authors selectively review the literature, present their own study findings, and discuss the guidelines of the German Society for Orthopedics and Orthopedic Surgery. RESULTS The main prognostic factors are the patient's age at the onset of the disease, the degree of limitation of range of motion, the extent of involvement of the femoral epiphysis, and any additional radiographic "head-at-risk" signs. Depending on the severity of the disease, the treatment options range from observation and frequent follow-up to reconstructive hip surgery. The goal of all treatments is to prevent a prearthrotic deformity and the ensuing premature coxarthrosis. This goal is best met by adherence to the principle of containment, i.e., the maintenance or restoration of joint congruence while biological plasticity is still present. CONCLUSIONS In view of the variable course of LCPD, the proper course of treatment must be determined individually in each case. Every child with LCPD must receive individually adapted treatment and continued follow-up into adulthood.
Model Counting Modulo Theories

This thesis is concerned with the quantitative assessment of security in software. More specifically, it tackles the problem of efficient computation of channel capacity, the maximum amount of confidential information leaked by software, measured in Shannon entropy or R\'{e}nyi's min-entropy. Most approaches to computing channel capacity are either efficient and return only (possibly very loose) upper bounds, or alternatively are inefficient but precise; few target realistic programs. In this thesis, we present a novel approach to the problem by reducing it to a model counting problem on first-order logic, which we name Model Counting Modulo Theories or #SMT for brevity. For quantitative security, our contribution is twofold. First, on the theoretical side we establish the connections between measuring confidentiality leaks and fundamental verification algorithms like Symbolic Execution, SMT solvers and DPLL. Second, exploiting these connections, we develop novel #SMT-based techniques to compute channel capacity, which achieve both accuracy and efficiency. These techniques are scalable to real-world programs, and illustrative case studies include C programs from Linux kernel, a Java program from a European project and anonymity protocols. For formal verification, our contribution is also twofold. First, we introduce and study a new research problem, namely #SMT, which has other potential applications beyond computing channel capacity, such as returning multiple-counterexamples for Bounded Model Checking or automated test generation. Second, we propose an alternative approach for Bounded Model Checking using classical Symbolic Execution, which can be parallelised to leverage modern multi-core and distributed architecture.
Cell Death-Associated Ribosomal RNA Cleavage in Postmortem Tissues and Its Forensic Applications

Estimation of postmortem interval (PMI) is a key issue in the field of forensic pathology. With the availability of quantitative analysis of RNA levels in postmortem tissues, several studies have assessed the postmortem degradation of constitutively expressed RNA species to estimate PMI. However, conventional RNA quantification as well as biochemical and physiological changes employed thus far have limitations related to standardization or normalization. The present study focuses on an interesting feature of the subdomains of certain RNA species, in which they are site-specifically cleaved during apoptotic cell death. We found that the D8 divergent domain of ribosomal RNA (rRNA) bearing cell death-related cleavage sites was rapidly removed during postmortem RNA degradation. In contrast to the fragile domain, the 5′ terminal region of 28S rRNA was remarkably stable during the postmortem period. Importantly, the differences in the degradation rates between the two domains in mammalian 28S rRNA were highly proportional to increasing PMI with a significant linear correlation observed in mice as well as human autopsy tissues. In conclusion, we demonstrate that comparison of the degradation rates between domains of a single RNA species provides quantitative information on postmortem degradation states, which can be applied for the estimation of PMI.
Cardiac rehabilitation delivery model for low-resource settings

Objective Cardiovascular disease is a global epidemic, which is largely preventable. Cardiac rehabilitation (CR) is demonstrated to be cost-effective and efficacious in high-income countries. CR could represent an important approach to mitigate the epidemic of cardiovascular disease in lower-resource settings. The purpose of this consensus statement was to review low-cost approaches to delivering the core components of CR, to propose a testable model of CR which could feasibly be delivered in middle-income countries. Methods A literature review regarding delivery of each core CR component, namely: (1) lifestyle risk factor management (ie, physical activity, diet, tobacco and mental health), (2) medical risk factor management (eg, lipid control, blood pressure control), (3) education for self-management and (4) return to work, in low-resource settings was undertaken. Recommendations were developed based on identified articles, using a modified GRADE approach where evidence in a low-resource setting was available, or consensus where evidence was not. Results Available data on cost of CR delivery in low-resource settings suggests it is not feasible to deliver CR in low-resource settings as is delivered in high-resource ones. Strategies which can be implemented to deliver all of the core CR components in low-resource settings were summarised in practice recommendations, and approaches to patient assessment proffered. It is suggested that CR be adapted by delivery by non-physician healthcare workers, in non-clinical settings. Conclusions Advocacy to achieve political commitment for broad delivery of adapted CR services in low-resource settings is needed.
Parallel program debugging by specification

Most message passing parallel programs employ logical process topologies with regular characteristics to support their computation. Since process topologies define the relationship between processes, they present an excellent opportunity for debugging. The primary benefit is that process behaviours can be correlated, allowing expected behaviour to be abstracted and identified, and undesirable behaviour reported. However, topology support is inadequate in most message passing parallel programming environments, including the popular Message Passing Interface (MPI) and the Parallel Virtual Machine (PVM). Programmers are forced to implement topology support themselves, increasing the possibility of introducing errors.
Workload Hidden Markov Model for Anomaly Detection

We present an approach to anomaly detection based on the construction of a Hidden Markov Model trained on processor workload data. Based on processor load measurements, a HMM is constructed as a model of the system normal behavior. Any observed sequence of processor load measurements that is unlikely generated by the HMM is then considered as an anomaly. We test our approach taking real data of a mail server processor load to construct a HMM and then we test it under several experimental conditions including a simulated DoS attacks. We show some evidence suggesting that this method could be successful to detect attacks or misuse that directly affects processor performance.
Decoding rule search domain in the left inferior frontal gyrus

Traditionally, the left hemisphere has been thought to extract mainly verbal patterns of information, but recent evidence has shown that the left Inferior Frontal Gyrus (IFG) is active during inductive reasoning in both the verbal and spatial domains. We aimed to understand whether the left IFG supports inductive reasoning in a domain-specific or domain-general fashion. To do this we used Multi-Voxel Pattern Analysis to decode the representation of domain during a rule search task. Thirteen participants were asked to extract the rule underlying streams of letters presented in different spatial locations. Each rule was either verbal (letters forming words) or spatial (positions forming geometric figures). Our results show that domain was decodable in the left prefrontal cortex, suggesting that this region represents domain-specific information, rather than processes common to the two domains. A replication study with the same participants tested two years later confirmed these findings, though the individual representations changed, providing evidence for the flexible nature of representations. This study extends our knowledge on the neural basis of goal-directed behaviors and on how information relevant for rule extraction is flexibly mapped in the prefrontal cortex.
Dual Graph Regularized NMF for Hyperspectral Unmixing

Hyperspectral unmixing is an important technique for estimating fraction of different land cover types from remote sensing imagery. In recent years, nonnegative matrix factorization (NMF) with various constraints have been introduced into hyperspectral unmixing. Among these methods, graph based constraint have been proved to be useful in capturing the latent manifold structure of the hyperspectral data in the feature space. In this paper, we propose to integrate graph-based constraints based on manifold assumption in feature spaces and consistency of spatial space to regularize the NMF method. Results on both synthetic and real data have validated the effectiveness of the proposed method.
Fabrication and characterization of silicon nitride nanopores

The fabrication of synthetic nanopores with dimensional and electrical properties similar to organic alpha-hemolysin (α-HL) nanopores is required for the development of a novel genotyping device. This thesis details the development of synthetic nanopores with diameters below 5 nm fabricated by sputtering a free standing silicon nitride membrane using a tightly focused electron beam. Nanometer control is achieved with sputtering rates of 0.5 – 0.75 nm/s. This technique is further extended to fabricate a proof-of-concept array of 44 sub-5 nm nanopores in a single membrane to enable the detection of unamplified genomic DNA with acceptable signal-to-noise. As-drilled inorganic nanopores have inferior electrical characteristics compared to α-HL. Careful study, however, revealed electrical noise sources that could be effectively reduced by chemical pretreatment of the pores and surface coating with poly-di-methyl-siloxane (PDMS). The chemical pretreatment targeted 1/f noise, while the PDMS reduced dielectric noise with an overall reduction in RMS current noise by a factor of 10. This resulted in processed nanopores with extremely favorable noise characteristics. These low noise silicon nitride nanopores were used to demonstrate single-molecule DNA translocation and probe capture with exceptional signal-to-noise ratios ranging from 40 – 150.
DLL: A Blazing Fast Deep Neural Network Library

Deep Learning Library (DLL) is a new library for machine learning with deep neural networks that focuses on speed. It supports feed-forward neural networks such as fully-connected Artificial Neural Networks (ANNs) and Convolutional Neural Networks (CNNs). It also has very comprehensive support for Restricted Boltzmann Machines (RBMs) and Convolutional RBMs. Our main motivation for this work was to propose and evaluate novel software engineering strategies with potential to accelerate runtime for training and inference. Such strategies are mostly independent of the underlying deep learning algorithms. On three different datasets and for four different neural network models, we compared DLL to five popular deep learning frameworks. Experimentally, it is shown that the proposed framework is systematically and significantly faster on CPU and GPU. In terms of classification performance, similar accuracies as the other frameworks are reported.
Human THO–Sin3A interaction reveals new mechanisms to prevent R‐loops that cause genome instability

R‐loops, formed by co‐transcriptional DNA–RNA hybrids and a displaced DNA single strand (ssDNA), fulfill certain positive regulatory roles but are also a source of genomic instability. One key cellular mechanism to prevent R‐loop accumulation centers on the conserved THO/TREX complex, an RNA‐binding factor involved in transcription elongation and RNA export that contributes to messenger ribonucleoprotein (mRNP) assembly, but whose precise function is still unclear. To understand how THO restrains harmful R‐loops, we searched for new THO‐interacting factors. We found that human THO interacts with the Sin3A histone deacetylase complex to suppress co‐transcriptional R‐loops, DNA damage, and replication impairment. Functional analyses show that histone hypo‐acetylation prevents accumulation of harmful R‐loops and RNA‐mediated genomic instability. Diminished histone deacetylase activity in THO‐ and Sin3A‐depleted cell lines correlates with increased R‐loop formation, genomic instability, and replication fork stalling. Our study thus uncovers physical and functional crosstalk between RNA‐binding factors and chromatin modifiers with a major role in preventing R‐loop formation and RNA‐mediated genome instability.
Supervised Local Descriptor Learning for Human Action Recognition

Local features have been widely used in computer vision tasks, e.g., human action recognition, but it tends to be an extremely challenging task to deal with large-scale local features of high dimensionality with redundant information. In this paper, we propose a novel fully supervised local descriptor learning algorithm called discriminative embedding method based on the image-to-class distance (I2CDDE) to learn compact but highly discriminative local feature descriptors for more accurate and efficient action recognition. By leveraging the advantages of the I2C distance, the proposed I2CDDE incorporates class labels to enable fully supervised learning of local feature descriptors, which achieves highly discriminative but compact local descriptors. The objective of our I2CDDE is to minimize the I2C distances from samples to their corresponding classes while maximizing the I2C distances to the other classes in the low-dimensional space. To further improve the performance, we propose incorporating a manifold regularization based on the graph Laplacian into the objective function, which can enhance the smoothness of the embedding by extracting the local intrinsic geometrical structure. The proposed I2CDDE for the first time achieves fully supervised learning of local feature descriptors. It significantly improves the performance of I2C-based methods by increasing the discriminative ability of local features while greatly reducing the computational burden by dimensionality reduction to handle large-scale data. We apply the proposed I2CDDE algorithm to human action recognition on four widely used benchmark datasets. The results have shown that I2CDDE can significantly improve I2C-based classifiers and achieves state-of-the-art performance.
Routing in the "Umbrella" Architecture

Routing in a Peer-to-Peer environment faces a number of challenges, mainly due to its distributed nature. In this paper we evaluate a new distributed hash table architecture that is able to provide efficient routing through a fixed-size table. By introducing a set of base algorithms, multiple replication schemas, virtual nodes and a variable repair mechanism, we are able to ensure successful lookups of published keywords. Along with theoretical analysis of our proposed work, we present extensive simulation results that testify and evaluate our protocol.
EAACI Guidelines on Allergen Immunotherapy: Allergic rhinoconjunctivitis

Allergic rhinoconjunctivitis (AR) is an allergic disorder of the nose and eyes affecting about a fifth of the general population. Symptoms of AR can be controlled with allergen avoidance measures and pharmacotherapy. However, many patients continue to have ongoing symptoms and an impaired quality of life; pharmacotherapy may also induce some side‐effects. Allergen immunotherapy (AIT) represents the only currently available treatment that targets the underlying pathophysiology, and it may have a disease‐modifying effect. Either the subcutaneous (SCIT) or sublingual (SLIT) routes may be used. This Guideline has been prepared by the European Academy of Allergy and Clinical Immunology's (EAACI) Taskforce on AIT for AR and is part of the EAACI presidential project “EAACI Guidelines on Allergen Immunotherapy. ” It aims to provide evidence‐based clinical recommendations and has been informed by a formal systematic review and meta‐analysis. Its generation has followed the Appraisal of Guidelines for Research and Evaluation (AGREE II) approach. The process included involvement of the full range of stakeholders. In general, broad evidence for the clinical efficacy of AIT for AR exists but a product‐specific evaluation of evidence is recommended. In general, SCIT and SLIT are recommended for both seasonal and perennial AR for its short‐term benefit. The strongest evidence for long‐term benefit is documented for grass AIT (especially for the grass tablets) where long‐term benefit is seen. To achieve long‐term efficacy, it is recommended that a minimum of 3 years of therapy is used. Many gaps in the evidence base exist, particularly around long‐term benefit and use in children.