Miracle-dz/UniFAR-FTUs huggingface dataset:
Selective Glucocorticoid Receptor Agonists for the Treatment of Inflammatory Bowel Disease: Studies in Mice with Acute Trinitrobenzene Sulfonic Acid Colitis

Despite being a mainstay of inflammatory bowel disease (IBD) therapy, glucocorticoids (GCs) still carry significant risks with respect to unwanted side effects. Alternative drugs with a more favorable risk/benefit ratio than common GCs are thus highly desirable for the management of IBD. New and supposedly selective glucocorticoid receptor (GR) agonists (SEGRAs), with dissociated properties, have been described as promising candidates for circumventing therapeutic problems while still displaying full beneficial anti-inflammatory potency. Here, we report on compound A [CpdA; (2-((4-acetophenyl)-2-chloro-N-methyl)ethylammonium-chloride)] and N-(4-methyl-1-oxo-1H-2,3-benzoxazine-6-yl)-4-(2,3-dihydrobenzofuran-7-yl)-2-hydroxy-2-(trifluoromethyl)-4-methylpentanamide (ZK216348), two GR agonists for the treatment of experimental colitis. Their therapeutic and anti-inflammatory effects were tested in the acute trinitrobenzene sulfonic acid-mediated colitis model in mice against dexamethasone (Dex). In addition to their influence on immunological pathways, a set of possible side effects, including impact on glucose homeostasis, steroid resistance, and induction of apoptosis, was surveyed. Our results showed that, comparable with Dex, treatment with CpdA and ZK216348 reduced the severity of wasting disease, macroscopic and microscopic damage, and colonic inflammation. However, both SEGRAs exhibited no GC-associated diabetogenic effects, hypothalamic pituitary adrenal axis suppression, or development of glucocorticoid resistance. In addition, CpdA and ZK216348 showed fewer transactivating properties and successfully dampened T helper 1 immune response. Unlike ZK216348, the therapeutic benefit of CpdA was lost at higher doses because of toxic apoptotic effects. In conclusion, both SEGRAs acted as potent anti-inflammatory agents with a significantly improved profile compared with classic GCs. Although CpdA revealed a narrow therapeutic window, both GR agonists might be seen as a starting point for a future IBD treatment option.
Optimal Registration Of Aliased Images Using Variable Projection With Applications To Super-Resolution

Accurate registration of images is the most important and challenging aspect of multiframe image restoration problems such as super-resolution. The accuracy of super-resolution algorithms is quite often limited by the ability to register a set of low-resolution images. The main challenge in registering such images is the presence of aliasing. In this paper, we analyse the problem of jointly registering a set of aliased images and its relationship to super-resolution. We describe a statistically optimal approach to multiframe registration which exploits the concept of variable projections to achieve very efficient algorithms. Finally, we demonstrate how the proposed algorithm offers accurate estimation under various conditions when standard approaches fail to provide sufficient accuracy for super-resolution.
Hyperspectral band selection based on evolutionary optimization

A hyperspectral image consists of a series of spectral bands which has brought great challenges to image processing and analysis. To alleviate the curse of dimensionality, band selection is therefore applied to the hyperspectral images. In this paper, a two-step method is proposed for band selection. In the first step, the band selection is converted to a global optimization problem and tackled by evolutionary optimization. To this end, a new fitness function is designed as the optimization objective and a differential evolution (DE) algorithm is employed to optimize the objective and find the optimal bands. In the second step, a simplified optimum idea factor (SOIF) is used for a fine selection. The K-nearest neighbor(KNN) and support vector machine (SVM) classifiers are then used to evaluate the obtained bands. The experiment on the AVIRIS images demonstrates that our approach is more effective than some state-of-the-art methods.
Anterior approach for major right hepatic resection for large hepatocellular carcinoma.

OBJECTIVE To report the surgical and long-term outcomes of major right hepatic resection for large hepatocellular carcinoma (HCC) using the anterior approach compared with the conventional approach. SUMMARY BACKGROUND DATA Great difficulty can be encountered during major right hepatic resection for large HCC using the conventional approach. Forceful retraction during mobilization of the tumor might result in serious complications, including dissemination of cancer cells, iatrogenic tumor rupture, and excessive bleeding, leading to unfavorable surgical and long-term outcomes. METHODS In patients who had large HCC at the right lobe of liver and underwent major hepatic resection, the technique of anterior approach was used. After hilar control of the inflow blood vessels and without prior mobilization of the right lobe of liver and the tumor, parenchymal transection was performed using an ultrasonic dissector from the anterior surface of the liver until the anterior surface of the inferior vena cava was exposed. All venous tributaries, including the right hepatic vein, were controlled before the right lobe of liver was mobilized. Surgical and long-term outcomes were analyzed retrospectively and compared with patients who underwent surgery using the conventional approach. RESULTS From 1989 to 1997, the anterior approach was used for major right hepatic resection in 54 patients with HCC of 5 cm or more in diameter. When compared with the 106 patients with similar clinical parameters who underwent hepatic resection using the conventional approach during the same period, the patients in the anterior approach group had significantly less intraoperative blood loss and blood transfusion, a lower hospital death rate, a lower incidence of pulmonary metastases, and a better median disease-free survival and median overall cumulative survival. CONCLUSION The anterior approach is the preferred technique for major right hepatic resection for large HCC because it resulted in improved surgical and survival outcomes compared with the conventional approach.
Environment mismatch compensation using average eigenspace for speech recognition

The performance of speech recognition systems is adversely affected by mismatch in training and testing environmental conditions. In addition to test data from noisy environments, there are scenarios where the training data itself is noisy. Speech enhancement techniques which solely focus on finding a clean speech estimate from the noisy signal are not effective here. Model adaptation techniques may also be ineffective due to the dynamic nature of the environment. In this paper, we propose a method for mismatch compensation between training and testing environments using the ”average eigenspace” approach when the mismatch is non-stationary. There is no need for explicit adaptation data as the method works on incoming test data to find the compensatory transform. This method is different from traditional signal-noise subspace filtering techniques where the dimensionality of the clean signal space is assumed to be less than the noise space and noise affects all dimensions to the same extent. We evaluate this approach on two corpora which are collected from real car environments: CU-Move and UTDrive. Using Sphinx, a relative reduction of 40-50% is achieved in WER compared to the baseline system. The method also results in a reduction in the dimensionality of the feature vectors allowing for a more compact set of acoustic models in the phoneme space.
Research Article Reduced Representation Libraries from Dna Pools Analysed with next Generation Semiconductor Based-sequencing to Identify Snps in Extreme and Divergent Pigs for Back Fat Thickness

The aim of this study was to identify single nucleotide polymorphisms (SNPs) that could be associated with back fat thickness (BFT) in pigs. To achieve this goal, we evaluated the potential and limits of an experimental design that combined several methodologies. DNA samples from two groups of Italian Large White pigs with divergent estimating breeding value (EBV) for BFT were separately pooled and sequenced, after preparation of reduced representation libraries (RRLs), on the Ion Torrent technology. Taking advantage from SNAPE for SNPs calling in sequenced DNA pools, 39,165 SNPs were identified; 1/4 of them were novel variants not reported in dbSNP. Combining sequencing data with Illumina PorcineSNP60 BeadChip genotyping results on the same animals, 661 genomic positions overlapped with a good approximation of minor allele frequency estimation. A total of 54 SNPs showing enriched alleles in one or in the other RRLs might be potential markers associated with BFT. Some of these SNPs were close to genes involved in obesity related phenotypes.
[The Duke Health Profile (DUKE)].

The Duke Health Profile is a 17-item generic questionnaire instrument designed to measure adult self-reported functional health status quantitatively during a one-week time window. It is appropriate for both patient and non-patient adult populations. It can be self-administered by the individual respondent or administered by another person. The administration time is less than five minutes. It is crucial that each question is answered. There are 11 scales. Six scales (i.e., physical health, mental health, social health, general health, perceived health, self-esteem) measure function, with high scores indicating better health. Five scales (i.e., anxiety, depression, anxiety-depression, pain disability) measure dysfunction, with high scores indicating greater dysfunction. Most extensive use has been in family practice patients with the broadest spectrum of diagnoses, but it has also been used in patient populations with specific diagnoses such as insulin-dependent diabetes mellitus, endstage renal disease, ischemic disease, and impotence. Both internal consistency (Cronbach's alpha) and temporal stability (test-retest) testing have supported reliability of the DUKE. Validity has been supported for the DUKE scales by: (a) comparison of the DUKE scores with scores of other health measures for the same patients, (b) comparison of DUKE scores between patient groups having different clinical diagnostic profiles and severity of illness, (c) prediction of health-related outcomes by DUKE scores. Convergent and discriminant validity have been shown when comparing with other instruments.
Mining Audit Data to Build Intrusion Detection Models

In this paper we discuss a data mining framework for constructing intrusion detection models. The key ideas are to mine system audit data for consistent and useful patterns of program and user behavior, and use the set of relevant system features presented in the patterns to compute (inductively learned) classifiers that can recognize anomalies and known intrusions. Our past experiments showed that classifiers can be used to detect intrusions, provided that sufficient audit data is available for training and the right set of system features are selected. We propose to use the association rules and frequent episodes computed from audit data as the basis for guiding the audit data gathering and feature selection processes. We modify these two basic algorithms to use axis attribute(s) as a form of item constraints to compute only the relevant ("useful") patterns, and an iterative level-wise approximate mining procedure to uncover the low frequency (but important) patterns. We report our experiments in using these algorithms on real-world audit data.
Incidence of Type 2 Diabetes in Individuals With Central Obesity in a Rural Japanese Population

According to the new International Diabetes Federation (IDF) definition of metabolic syndrome, for a person to be defined as having metabolic syndrome they must have central obesity defined by waist circumference (1). In the definition, there are some ethnic or country-specific differences in the cutoff points of waist circumference, and Japanese cutoff points have been separately established in the IDF definition (≥85 cm for men and ≥90 cm for women). The Japanese Society of Internal Medicine and eight related scientific societies have also jointly announced new Japanese criteria of metabolic syndrome using the same cutoff points of waist circumference (2). However, the impact of central obesity using the cutoff points as a risk of type 2 diabetes is not known. In this study, we investigated the incidence of type 2 diabetes in citizens of two rural communities in Japan to determine the relationship between type 2 diabetes and central obesity, and we also investigated the independent effects of central obesity compared with those of overall obesity. The subjects were 348 men and 523 women selected from 938 citizens who had undergone medical examinations in the towns of Tanno and Sobetsu, Hokkaido, both in 1994 and 2003 or 2004. The following participants in medical examinations in 1994 were excluded: those with missing data on blood …
Neural network setups for a precise detection of the many-body localization transition: Finite-size scaling and limitations

Determining phase diagrams and phase transitions semi-automatically using machine learning has received a lot of attention recently, with results in good agreement with more conventional approaches in most cases. When it comes to more quantitative predictions, such as the identification of universality class or precise determination of critical points, the task is more challenging. As an exacting test-bed, we study the Heisenberg spin-1/2 chain in a random external field that is known to display a transition from a many-body localized to a thermalizing regime, which nature is not entirely characterized. We introduce different neural network structures and dataset setups to achieve a finite-size scaling analysis with the least possible physical bias (no assumed knowledge on the phase transition and directly inputing wave-function coefficients), using state-of-the-art input data simulating chains of sizes up to L=24. In particular, we use domain adversarial techniques to ensure that the network learns scale-invariant features. We find a variability of the output results with respect to network and training parameters, resulting in relatively large uncertainties on final estimates of critical point and correlation length exponent which tend to be larger than the values obtained from conventional approaches. We put the emphasis on interpretability throughout the paper and discuss what the network appears to learn for the various used architectures. Our findings show that a it quantitative analysis of phase transitions of unknown nature remains a difficult task with neural networks when using the minimally engineered physical input.
Mechanical design of a compact Serial Variable Stiffness Actuator (SVSA) based on lever mechanism

Compliant actuator is widely accepted for physical human-robot interaction due to its safety aspect, dynamic performance improvements and energy saving abilities. In this paper, based on the variable ratio lever mechanism, a new kind of Serial Variable Stiffness Actuator (SVSA) is proposed by using an Archimedean Spiral Relocation Mechanism (ASRM) to change the position of the pivot, implementing large range of adjustable stiffness. The ASRM introduced here makes the SVSA design has continuous stiffness adjustment ability and simply mechanical structure. Within the Variable Stiffness Mechanism (VSM), two linear springs are assembled antagonistically on a spring shaft. Their displacements are perpendicular to the output link to transmit the spring force more efficiently. Stiffness modeling and analysis of the SVSA are carried out to cover large deflection angle. The physical implementation of the SVSA shows that the output stiffness of the VSM is changed from 1.72 to 150.56 Nm/rad using a linear spring with stiffness 1882 N/m, working range covered from 0 to 360 °. Control experiments also proved the wide range of stiffness adjustment ability of the SVSA.
Evaluation of optimum path forest classifier for pedestrian detection

Machine learning (ML) and image processing techniques have been applied together to various scenarios for the development of Intelligent Vehicles. Among these scenarios, pedestrian detection has received growing interest in recent years, since high concern for safety applications in traffic has arisen. Several ML methods were successfully applied to solve this problem. However, because pedestrian detection is in general computationally intensive, a good trade off between accuracy and processing time is desirable, particularly if the methods are directed to real-time applications. Optimum Path Forest (OPF) classifier is a recently developed non-parametric classifier method. This work contribution is the performance assessment of a novel OPF application to pedestrian detection. Results have shown that it is fast and competitive against established methods and a viable alternative to be considered for machine learning and pedestrian detection applications.
Glycolytic enzymes localize to ribonucleoprotein granules in Drosophila germ cells, bind Tudor and protect from transposable elements

Germ cells give rise to all cell lineages in the next‐generation and are responsible for the continuity of life. In a variety of organisms, germ cells and stem cells contain large ribonucleoprotein granules. Although these particles were discovered more than 100 years ago, their assembly and functions are not well understood. Here we report that glycolytic enzymes are components of these granules in Drosophila germ cells and both their mRNAs and the enzymes themselves are enriched in germ cells. We show that these enzymes are specifically required for germ cell development and that they protect their genomes from transposable elements, providing the first link between metabolism and transposon silencing. We further demonstrate that in the granules, glycolytic enzymes associate with the evolutionarily conserved Tudor protein. Our biochemical and single‐particle EM structural analyses of purified Tudor show a flexible molecule and suggest a mechanism for the recruitment of glycolytic enzymes to the granules. Our data indicate that germ cells, similarly to stem cells and tumor cells, might prefer to produce energy through the glycolytic pathway, thus linking a particular metabolism to pluripotency.
An Accurate Metalloprotein-Specific Scoring Function and Molecular Docking Program Devised by a Dynamic Sampling and Iteration Optimization Strategy

Metalloproteins, particularly zinc metalloproteins, are promising therapeutic targets, and recent efforts have focused on the identification of potent and selective inhibitors of these proteins. However, the ability of current drug discovery and design technologies, such as molecular docking and molecular dynamics simulations, to probe metal-ligand interactions remains limited because of their complicated coordination geometries and rough treatment in current force fields. Herein we introduce a robust, multiobjective optimization algorithm-driven metalloprotein-specific docking program named MpSDock, which runs on a scheme similar to consensus scoring consisting of a force-field-based scoring function and a knowledge-based scoring function. For this purpose, in this study, an effective knowledge-based zinc metalloprotein-specific scoring function based on the inverse Boltzmann law was designed and optimized using a dynamic sampling and iteration optimization strategy. This optimization strategy can dynamically sample and regenerate decoy poses used in each iteration step of refining the scoring function, thus dramatically improving both the effectiveness of the exploration of the binding conformational space and the sensitivity of the ranking of the native binding poses. To validate the zinc metalloprotein-specific scoring function and its special built-in docking program, denoted MpSDockZn, an extensive comparison was performed against six universal, popular docking programs: Glide XP mode, Glide SP mode, Gold, AutoDock, AutoDock4Zn, and EADock DSS. The zinc metalloprotein-specific knowledge-based scoring function exhibited prominent performance in accurately describing the geometries and interactions of the coordination bonds between the zinc ions and chelating agents of the ligands. In addition, MpSDockZn had a competitive ability to sample and identify native binding poses with a higher success rate than the other six docking programs.
Flow-Data Compressibility Changes During Internet Work Outbreaks

During outbreaks of fast Internet worms the characteristics of network ﬂow data from backbone networks changes. We have observed that in particular source and destination IP and port ﬁelds undergo compressibility changes, that are characteristic for the scanning strategy of the observed worm. In this paper we present measurements done on a medium sized Swiss Internet backbone (SWITCH, AS559) during the outbreak of the Blaster and Witty Internet worms and attempt to give a ﬁrst explanation for the observed behaviour. We also discuss the impact of sampled versus full ﬂow data and diﬀerent compression algorithms. This is work in progress. In particular the details of what exactly causes the observed eﬀects are still preliminary and under ongoing investigation
Cloud-based orthognathic surgical planning platform

Multi-disciplinary technologies are currently involved in orthognathic and dental surgery. By using 3D and CT scans, the surgery can be planned beforehand by making use of 3D image processing, visualization and planning tools. With 3D printing, accurate splints and wafers can be generated for the surgery. Nowadays, these tools are on-premises software and this makes it very hard for collaboration between several specialists. Therefore, we researched the possibility to create an online cloud-based platform to run the currently used surgical planning tools. We achieved multiple two-factor authentication user logins, simultaneous surgical planning sessions and lightweight multi-platform support.
Conflict Resolution in Online Databases *

Shared online databases, such as Google Fusion Tables or Quickbase, allow non-programmer community members to collaboratively maintain and browse data. While community members may believe in conflicting facts (due to conflicting sources, measurements or opinions), current online databases do not yet offer support for the management of data conflicts. Ricolla is a novel online database that treats data conflicts as first-class citizens. Unlike prior work in uncertain databases, which was made to provide a database back-end to application logic, Ricolla is tuned to the requirements of the online database paradigm, allowing intuitive visualization of conflicts and collaborative data editing/conflict resolution. The proposed end-to-end system makes the following contributions: a) an online database paradigm that captures conflicts, allowing data query and update, while enabling personalized, “as-you-go” conflict resolution, b) a data model and corresponding generic user interface for explaining the conflicts to the users in a simple and compact form that is aligned with the requirements of online databases; we formalize the “simplicity” and “compactness” requirements and show that Ricolla strikes a novel point in the tradeoff between simplicity, compactness and expressive power, c) personalized conflict resolution via resolution actions that allow each user to resolve individual data conflicts, and via a resolution policy language for performing a set of resolution actions based on high-level criteria (e.g., give priority to data based on their value, provenance or timestamps), and d) a set of algorithms for implementing the system on top of an RDBMS.