Miracle-dz/UniFAR-FTUs huggingface dataset:
Vitamin A Controls the Presence of RORγ+ Innate Lymphoid Cells and Lymphoid Tissue in the Small Intestine

Changes in diet and microbiota have determining effects on the function of the mucosal immune system. For example, the active metabolite of vitamin A, retinoic acid (RA), has been described to maintain homeostasis in the intestine by its influence on both lymphocytes and myeloid cells. Additionally, innate lymphoid cells (ILCs), important producers of cytokines necessary for intestinal homeostasis, are also influenced by vitamin A in the small intestines. In this study, we show a reduction of both NCR− and NCR+ ILC3 subsets in the small intestine of mice raised on a vitamin A–deficient diet. Additionally, the percentages of IL-22–producing ILCs were reduced in the absence of dietary vitamin A. Conversely, mice receiving additional RA had a specific increase in the NCR− ILC3 subset, which contains the lymphoid tissue inducer cells. The dependence of lymphoid tissue inducer cells on vitamin A was furthermore illustrated by impaired development of enteric lymphoid tissues in vitamin A–deficient mice. These effects were a direct consequence of ILC-intrinsic RA signaling, because retinoic acid–related orphan receptor γt–Cre × RARα-DN mice had reduced numbers of NCR− and NCR+ ILC3 subsets within the small intestine. However, lymphoid tissue inducer cells were not affected in these mice nor was the formation of enteric lymphoid tissue, demonstrating that the onset of RA signaling might take place before retinoic acid–related orphan receptor γt is expressed on lymphoid tissue inducer cells. Taken together, our data show an important role for vitamin A in controlling innate lymphoid cells and, consequently, postnatal formed lymphoid tissues within the small intestines.
Representational Pattern of Discursive Hegemony

The paper aims to construct a practical representational pattern, which is to uncover the way the discursive hegemony exists in the content of text. The representational pattern will be embarked upon level by level, mainly from the linguistic perspective such as field of discourse, transitivity, and the choice and meaning of words. For the reason of non-discursive elements having determined effects in the formation of discursive hegemony, the paper will also explore hegemony beyond the linguistic perspective by means of the concept "discourse" constructed in Fairclough’s Critical Discourse Analysis model. In addition, a particular written text will be chosen to further testify the way of how hegemony is represented in a particular text or discourse.
Numerical Stability and Convergence Analysis of Geometric Constraint Enforcement in Dynamic Simulation Systems

The geometric constraint enforcement using Lagrange Multipliers is one of the popular methods to control the behavior and trajectories of dynamically simulated entities. Therefore, effective and efficient enforcement and proper integration of the geometric constraints is the key to a successful constraint management. This paper describes the formulation and integration of geometric constraints in a dynamic simulation and provides a guideline to choose a proper constraint enforcement method. In addition, the numerical stability and convergence analysis of two explicit and implicit constraint enforcement techniques are reported with respect to the step sizes of differential equations. Experiments show that our new implicit constraint enforcement technique demonstrates a superior stability over large time steps and fast system response compared to the explicit Baumgarte method. The implicit constraint enforcement method uses the future time step to estimate the correct magnitude of the constraint forces and doesn’t require problem dependent stabilization parameters as a second order state feedback term.
Virtually real: learning ethicality in an online fan community

In this paper, we offer a non-typical snapshot of interaction from the field of Reality TV fandom to explore new ways of conceiving the relationship between empathic response, ethical awareness and emergent possibilities for learning as conditioned by online participatory cultures. We provide a limit-case analysis of an instance of situated ethical action among members of the fan forum Television Without Pity (TWoP) to explore the context-sensitive and unpredictable nature of learning in fan communities characterized by collective (no one knows everything), participatory (everybody knows something), and emergent (arising in the interactions between the knower and the world-to-be-known) forms of knowledge. We suggest that learning in the context of this participatory fan culture involves an ongoing willingness to respond to triggers, make discernments and articulate perspectives within a virtually real community of others.
Designing Memory Subsystems Resilient to Process Variations

As technology scales, more sophisticated fabrication processes cause variations in many different parameters in the device. These variations could severely affect the performance of processors by making the latency of circuits less predictable and thus requiring conservative design approaches. In this paper, we use Monte-Carlo simulations in addition to worst-case circuit analysis to establish the overall delay due to process variations in a cache subsystem under both typical and worst-case conditions. The distribution of a cache critical-path-delay in the typical scenario was determined by performing Monte-Carlo simulations at different supply voltages, threshold voltages, and transistor lengths on a complete cache design. In addition to establishing the delay variation, we present an adaptive variable-cycle-latency cache architecture that mitigates the impact of process variations on access latency by closely following the typical latency behavior rather than assuming a conservative worst-case design-point. Simulation results show that our adaptive data cache can achieve a 9% to 21% performance improvement in a superscalar processor, on the SPEC2 000 applications studied, compared to a conventional design. Additional performance improvement potential exists in processors where the data cache access is on the critical path, by allowing a more aggressive clock rate.
Contract-based component system design

Component technology tries to solve many problems of today's software industry practice: the productivity and produced quality should be increased and a better infrastructure for maintenance of the products is promised. The integration of off-the-shelf components to build customized products allows us to source out the development of general purpose components. A crucial prerequisite for the intended scenario of component usage is their strong separation. Especially in a distributed environment, synchronization aspects are of great importance to identify a suitable architecture and to decide whether a component matches some requirements. The presented approach allows us to model the synchronization aspect of contracts in a flexible manner including a whole spectrum of different degrees of preciseness from declaration of abstraction barriers to complete synchronization specifications describing the explicit behavior. The used Petri net based OCoN behavior specification formalism is structurally embedded in the UML and supports analysis and design of component systems.
Engineered Luciferase Reporter from a Deep Sea Shrimp Utilizing a Novel Imidazopyrazinone Substrate

Bioluminescence methodologies have been extraordinarily useful due to their high sensitivity, broad dynamic range, and operational simplicity. These capabilities have been realized largely through incremental adaptations of native enzymes and substrates, originating from luminous organisms of diverse evolutionary lineages. We engineered both an enzyme and substrate in combination to create a novel bioluminescence system capable of more efficient light emission with superior biochemical and physical characteristics. Using a small luciferase subunit (19 kDa) from the deep sea shrimp Oplophorus gracilirostris, we have improved luminescence expression in mammalian cells ∼2.5 million-fold by merging optimization of protein structure with development of a novel imidazopyrazinone substrate (furimazine). The new luciferase, NanoLuc, produces glow-type luminescence (signal half-life >2 h) with a specific activity ∼150-fold greater than that of either firefly (Photinus pyralis) or Renilla luciferases similarly configured for glow-type assays. In mammalian cells, NanoLuc shows no evidence of post-translational modifications or subcellular partitioning. The enzyme exhibits high physical stability, retaining activity with incubation up to 55 °C or in culture medium for >15 h at 37 °C. As a genetic reporter, NanoLuc may be configured for high sensitivity or for response dynamics by appending a degradation sequence to reduce intracellular accumulation. Appending a signal sequence allows NanoLuc to be exported to the culture medium, where reporter expression can be measured without cell lysis. Fusion onto other proteins allows luminescent assays of their metabolism or localization within cells. Reporter quantitation is achievable even at very low expression levels to facilitate more reliable coupling with endogenous cellular processes.
A Novel Fusion Toxin Derived from an EpCAM-Specific Designed Ankyrin Repeat Protein Has Potent Antitumor Activity

Purpose: Designed ankyrin repeat proteins (DARPins) hold great promise as a new class of binding molecules to overcome the limitations of antibodies for biomedical applications. Here, we assessed the potential of an epithelial cell adhesion molecule (EpCAM)–specific DARPin (Ec4) for tumor targeting as a fusion toxin with Pseudomonas aeruginosa exotoxin A. Experimental design: DARPin Ec4 was genetically fused to a truncated form of Pseudomonas aeruginosa exotoxin A (ETA″) and expressed in Escherichia coli. The cytotoxicity of Ec4-ETA″ was measured against tumor cell lines of various histotypes in vitro. Tumor localization and antitumor activity were determined in mice bearing 2 different EpCAM-positive tumor xenografts. Results: Ec4-ETA″ expressed very well in soluble form in the cytoplasm of E. coli and yielded up to 40 mg after purification per liter of culture. The protein was monomeric and the disulfides of ETA″ formed spontaneously. Ec4-ETA″ bound to EpCAM with low nanomolar affinity, similar to free Ec4. Furthermore, it was highly cytotoxic against various EpCAM-positive tumor cell lines in vitro with IC50 values less than 0.005 pmol/L. This effect was competed by free Ec4, but not by unspecific DARPins. Upon systemic administration in athymic mice, Ec4-ETA″ efficiently localized to EpCAM-positive tumors to achieve maximum accumulation 48 to 72 hours after injection, whereas an irrelevant control fusion toxin did not accumulate. Tumor targeting with Ec4-ETA″ resulted in a strong antitumor response including complete regressions in some animals. Conclusions: Our data show for the first time the potential of DARPins for the generation of protein therapeutics for tumor targeting, and that Ec4-ETA″ deserves attention for clinical development. Clin Cancer Res; 17(1); 100–10. ©2010 AACR.
Investigation of cyber crime conducted by abusing weak or default passwords with a medium interaction honeypot

Recently, advances in cyber-physical systems and IoT led to an increase in devices connected to the internet. This rise of functionality also comes with an increased attack surface for cyber criminals. A proven method for forensic investigations of trends and developments in crimes conducted in the virtual world are honeypots. We set up a medium interaction honeypot offering telnet and SSH services. With this honeypot we captured data from attack sessions. This data was used for statistical and behavioural analysis, such as distributions of attacks and different attacker IPs, originating countries, employed anonymisation services, skill level of an adversary and commonly targeted embedded devices. Furthermore, machine learning techniques that are capable of identifying unique types of sessions based on issued commands and provided credentials are presented in this work. There are strong indicators that most of the traffic captured during our research is caused by botnet activities, which corresponds to findings of different research activities.
CALCULUS FOR FRACTIONAL DELAY EQUATIONS

Abstract. In this paper we study the existence of a unique solution to a general class of Young delay differential equations driven by a Hölder continuous function with parameter greater that 1/2 via the Young integration setting. Then some estimates of the solution are obtained, which allow to show that the solution of a delay differential equation driven by a fractional Brownian motion (fBm) with Hurst parameter H > 1/2 has a C∞-density. To this purpose, we use Malliavin calculus based on the Fréchet differentiability in the directions of the reproducing kernel Hilbert space associated with fBm.
Relationship between plasma LDL concentrations during treatment with pravastatin and recurrent coronary events in the Cholesterol and Recurrent Events trial.

BACKGROUND Although LDL lowering has been shown to reduce recurrent coronary events in patients with coronary heart disease, little direct information is available on the extent of LDL lowering required to achieve this outcome. METHODS AND RESULTS The Cholesterol and Recurrent Events (CARE) trial compared pravastatin and placebo in patients who had experienced myocardial infarction (MI) who had average concentrations of total cholesterol <240 mg/dL (baseline mean, 209 mg/dL) and LDL cholesterol (LDL) 115 to 174 mg/dL (mean, 139 mg/dL). Pravastatin reduced coronary death or recurrent MI by 24%. In multivariate analysis, the LDL concentration achieved during follow-up was a significant, although nonlinear, predictor of the coronary event rate (P=.007), whereas the extent of LDL reduction was not significant, whether expressed as an absolute amount (P=.97) or a percentage (P=.76). The coronary event rate declined as LDL decreased during follow-up from 174 to approximately 125 mg/dL, but no further decline was seen in the LDL range from 125 to 71 mg/dL. In multivariate analysis, triglyceride but not HDL concentrations during follow-up were weakly but significantly associated with the coronary event rate. CONCLUSIONS The LDL concentrations achieved during treatment with pravastatin or placebo were associated with reduction in coronary events down to an LDL concentration of approximately 125 mg/dL. LDL concentrations <125 mg/dL during treatment were not associated with further benefit. Absolute or percentage reduction in LDL had little relationship to coronary events.
Diagnosing Autism Spectrum Disorder from Brain Resting-State Functional Connectivity Patterns Using a Deep Neural Network with a Novel Feature Selection Method

The whole-brain functional connectivity (FC) pattern obtained from resting-state functional magnetic resonance imaging data are commonly applied to study neuropsychiatric conditions such as autism spectrum disorder (ASD) by using different machine learning models. Recent studies indicate that both hyper- and hypo- aberrant ASD-associated FCs were widely distributed throughout the entire brain rather than only in some specific brain regions. Deep neural networks (DNN) with multiple hidden layers have shown the ability to systematically extract lower-to-higher level information from high dimensional data across a series of neural hidden layers, significantly improving classification accuracy for such data. In this study, a DNN with a novel feature selection method (DNN-FS) is developed for the high dimensional whole-brain resting-state FC pattern classification of ASD patients vs. typical development (TD) controls. The feature selection method is able to help the DNN generate low dimensional high-quality representations of the whole-brain FC patterns by selecting features with high discriminating power from multiple trained sparse auto-encoders. For the comparison, a DNN without the feature selection method (DNN-woFS) is developed, and both of them are tested with different architectures (i.e., with different numbers of hidden layers/nodes). Results show that the best classification accuracy of 86.36% is generated by the DNN-FS approach with 3 hidden layers and 150 hidden nodes (3/150). Remarkably, DNN-FS outperforms DNN-woFS for all architectures studied. The most significant accuracy improvement was 9.09% with the 3/150 architecture. The method also outperforms other feature selection methods, e.g., two sample t-test and elastic net. In addition to improving the classification accuracy, a Fisher's score-based biomarker identification method based on the DNN is also developed, and used to identify 32 FCs related to ASD. These FCs come from or cross different pre-defined brain networks including the default-mode, cingulo-opercular, frontal-parietal, and cerebellum. Thirteen of them are statically significant between ASD and TD groups (two sample t-test p < 0.05) while 19 of them are not. The relationship between the statically significant FCs and the corresponding ASD behavior symptoms is discussed based on the literature and clinician's expert knowledge. Meanwhile, the potential reason of obtaining 19 FCs which are not statistically significant is also provided.
Information Extraction from Semi-structured WEB Page Based on DOM Tree and its Application in Scientific Literature Statistical Analysis System

To extract information automatically from semi-structured web pages, this paper puts forward a method named IESS for discovering the record model based on DOM and Maximal Similar Sub Tree, to identify records automatically and correctly when there are some differences in expression models of records that belong to the same type. To test the performance of the method, a scientific literature statistical analysis system is designed. The practice shows that users can quickly understand the distribution of papers in their retrieving field and grasp the importance with the help of the system.
Identifying Stagnation Periods in Software Evolution by Replaying Editing Operations

As managing software evolution becomes more important, many methods of development support using editing operations have been proposed. However, it is still difficult to extract truly valuable information, since massiveness of recorded operations grows with the development period. In this paper, we propose a mechanism for extracting useful information on stagnation of development from huge operation history. We have implemented this mechanism as a tool, which is called OperationReplayer. The tool allows a developer to investigate detected candidates of stagnation periods and replay operations belonging to the candidate through its graphical user interface. Through exploratory case studies using the tool, we found several problematic operations that adversely affect of the developer's development process.
Mapping the Intellectual Structure of Relationship between Usability of Information System and User Emotion

This paper conducts co-word analysis and social network analysis to review the research of relationship between usability of information system and user emotions. The results show that more and more studies focus on the topic which involves three perspectives, namely products design and evaluation, human-computer interaction design and evaluation, interface design and evaluation. The main issues include affective computing, user experience, aesthetics, affect, satisfaction, interface design, motivation, product evaluation, affective tutoring system and interaction design. However, these studies are within active and junior periods generally, and the concrete topics and issues are relatively decentralized.
Fast Motion Search Using Early Zero-block Detection

In this paper, we proposed an early zero-block detection algorithm for motion search in H.264/AVC to reduce the computation using a nearly sufficient condition for zero-block detection of 4 times 4 DCT coefficients after quantization. The early zero-block detection approach was applied to the hybrid UMHexagonS algorithm that has been adopted in JM reference software. The experimental results indicated that a significant improvement in computation reduction was achieved. The PSNR loss as well as bit-rate increment, however, is not negligible, especially for high QP or low bit-rate coding. In addition, the early detection algorithm is modified in the UMHexagonS algorithm to improve the coding performance. Experimental results show that great reduction in computation can be achieved while still maintaining high coding efficiency.
Infinite Latent SVM for Classification and Multi-task Learning

Unlike existing nonparametric Bayesian models, which rely solely on specially conceived priors to incorporate domain knowledge for discovering improved latent representations, we study nonparametric Bayesian inference with regularization on the desired posterior distributions. While priors can indirectly affect posterior distributions through Bayes' theorem, imposing posterior regularization is arguably more direct and in some cases can be much easier. We particularly focus on developing infinite latent support vector machines (iLSVM) and multi-task infinite latent support vector machines (MT-iLSVM), which explore the large-margin idea in combination with a nonparametric Bayesian model for discovering predictive latent features for classification and multi-task learning, respectively. We present efficient inference methods and report empirical studies on several benchmark datasets. Our results appear to demonstrate the merits inherited from both large-margin learning and Bayesian nonparametrics.