Miracle-dz/UniFAR-FTUs huggingface dataset:
Natural Killer Cells Are Characterized by the Concomitantly Increased Interferon-γ and Cytotoxicity in Acute Resolved Hepatitis B Patients

Natural killer (NK) cells are abundant in the liver and have been implicated in inducing hepatocellular damage in patients with chronic hepatitis B virus (HBV) infection. However, the role of NK cells in acute HBV infection remains to be elucidated. We comprehensively characterized NK cells and investigated their roles in HBV clearance and liver pathology in 19 chronic hepatitis B (CHB) patients and 21 acute hepatitis B (AHB) patients as well as 16 healthy subjects. It was found that NKp46+ NK cells were enriched in the livers of AHB and CHB patients. We further found that peripheral NK cells from AHB patients expressed higher levels of activation receptors and lower levels of inhibitory receptors than those from CHB patients and HC subjects, thus displaying the increased cytolytic activity and interferon-γ production. NK cell activation levels were also correlated positively with serum alanine aminotransferase levels and negatively with plasma HBV DNA levels in AHB patients, which is further confirmed by the longitudinal follow-up of AHB patients. Serum pro-inflammatory cytokine and chemokine levels were also increased in AHB patients as compared with CHB and HC subjects. Thus, the concomitantly increased interferon-γ and cytotoxicity of NK cells were associated with liver injury and viral clearance in AHB patients.
The Partially Observable Hidden Markov Model with Application to Keystroke Biometrics

This work introduces the partially observable hidden Markov model (POHMM), a generalization of the hidden Markov model (HMM) in which the underlying system state is partially observable through event metadata at each time step. Whereas in a HMM, the hidden state is inferred through the observed values, the hidden state in a POHMM is inferred through both the observed values and a partially observed state. Despite an explosion in the number of model parameters, parameter estimation can still be performed in time that scales linearly with the number of observations as demonstrated by the derived parameter estimation algorithm. Marginal distributions of the model act as a fallback mechanism when novel sequences are encountered in likelihood estimation. A parameter smoothing technique is used to account for missing data during parameter estimation and simultaneously reduce the degrees of freedom of the model to avoid overfitting, especially for a small number of observations. The POHMM is applied to keystroke time intervals, in which the user can be in either an active or passive hidden state, and the keyboard key names partially reveal the underlying system state. The proposed model is shown to consistently outperform the standard HMM in keystroke biometric identification and verification tasks for various types of keystroke input and is generally preferred over the HMM in a Monte Carlo goodness of fit test.
The Krein Matrix and an Interlacing Theorem Shamuel Auyeung

Consider the linear general eigenvalue problem Ay = λBy , where A and B are both invertible and Hermitian N × N matrices. In this paper we construct a set of meromorphic functions, the Krein eigenvalues, whose zeros correspond to the real eigenvalues of the general eigenvalue problem. The Krein eigenvalues are generated by the Krein matrix, which is constructed through projections on the positive and negative eigenspaces of B . The number of Krein eigenvalues depends on the number of negative eigenvalues for B . These constructions not only allow for us to determine solutions to the general eigenvalue problem, but also to determine the Krein signature for each real eigenvalue. Furthermore, by applying our formulation to the simplest case of the general eigenvalue problem (where B has one negative eigenvalue), we are able to show an interlacing theorem between the eigenvalues for the general problem and the eigenvalues of A.
A toolset for the development of mixed-criticality partitioned systems

The development of mixed-criticality virtualized multi-core systems poses new challenges that are being subject of active research work. There is an additional complexity: it is now required to identify a set of partitions, and allocate applications to partitions. In this job, a number of issues have to be considered, such as the criticality level of the application, security and dependability requirements, time requirements granularity, etc. MultiPARTES [11] toolset relies on Model Driven Engineering (MDE), which is a suitable approach in this setting, as it helps to bridge the gap between design issues and partitioning concerns. MDE is changing the way systems are developed nowadays, reducing development time. In general, modelling approaches have shown their benefits when applied to embedded systems. These benefits have been achieved by fostering reuse with an intensive use of abstractions, or automating the generation of boiler-plate code.
How Stable Is Stable? Function versus Community Composition

ABSTRACT The microbial community dynamics of a functionally stable, well-mixed, methanogenic reactor fed with glucose were analyzed over a 605-day period. The reactor maintained constant pH and chemical oxygen demand removal during this period. Thirty-six rrn clones from each of seven sampling events were analyzed by amplified ribosomal DNA restriction analysis (ARDRA) for the Bacteria andArchaea domains and by sequence analysis of dominant members of the community. Operational taxonomic units (OTUs), distinguished as unique ARDRA patterns, showed reproducible distribution for three sample replicates. The highest diversity was observed in the Bacteria domain. The 16S ribosomal DNABacteria clone library contained 75 OTUs, with the dominant OTU accounting for 13% of the total clones, but just 21Archaea OTUs were found, and the most prominent OTU represented 50% of the clones from the respective library. Succession in methanogenic populations was observed, and two periods were distinguished: in the first, Methanobacterium formicicumwas dominant, and in the second, Methanosarcina mazei and aMethanobacterium bryantii-related organism were dominant. Higher variability in Bacteria populations was detected, and the temporal OTU distribution suggested a chaotic pattern. Although dominant OTUs were constantly replaced from one sampling point to the next, phylogenetic analysis indicated that inferred physiologic changes in the community were not as dramatic as were genetic changes. Seven of eight dominant OTUs during the first period clustered with the spirochete group, although a cyclic pattern of substitution occurred among members within this order. A more flexible community structure characterized the second period, since a sequential replacement of aEubacterium-related organism by an unrelated deep-branched organism and finally by a Propionibacterium-like species was observed. Metabolic differences among the dominant fermenters detected suggest that changes in carbon and electron flow occurred during the stable performance and indicate that an extremely dynamic community can maintain a stable ecosystem function.
miR172b Controls the Transition to Autotrophic Development Inhibited by ABA in Arabidopsis

Seedling establishment is a critical phase in the life of plants when they are the most vulnerable to environment. Growth arrest at post-germinative stage under stress is the major adaptive strategy to help germinating seedlings to survive a spectrum of stressful conditions. ABA signaling is the key pathway to control stress-induced developmental arrest. However, mechanisms controlling the phase transition under abiotic stress are not fully understood. Here, we described miR172b as a new key regulator controlling transition of germinating seedlings from heterotrophic to autotrophic growth under osmotic stress in Arabidopsis. We showed that miR172b and its target SNZ were co-expressed during early seedling development. Expression of miR172b and SNZ was low after radicle emergence and sharply increased at the checkpoint to autotrophic development under normal conditions. Interestingly, activation of miR172b and SNZ was completely abolished by ABA and osmotic stress. miR172b overexpression and snz-1 exhibited increased sensitivity to ABA and osmotic stress during specific post-germinative stage, and resulted in higher expression of ABI3, ABI5 and downstream genes, such as Em6 and RAB18, than wild type under ABA treatment. Our results revealed that miR172b is a critical regulator specifically controlling cotyledon greening during post-germinative growth by directly targeting SNZ under ABA treatment and osmotic stress.
Progress in Linear Programming-Based Algorithms for Integer Programming: An Exposition

This paper is about modeling and solving mixed integer programming (MIP) problems. In the last decade, the use of mixed integer programming models has increased dramatically. Fifteen years ago, mainframe computers were required to solve problems with a hundred integer variables. Now it is possible to solve problems with thousands of integer variables on a personal computer and obtain provably good approximate solutions to problems such as set partitioning with millions of binary variables. These advances have been made possible by developments in modeling, algorithms, software, and hardware. This paper focuses on effective modeling, preprocessing, and the methodologies of branch-and-cut and branch-and-price, which are the techniques that make it possible to treat problems with either a very large number of constraints or a very large number of variables. We show how these techniques are useful in important application areas such as network design and crew scheduling. Finally, we discuss the relatively new research areas of parallel integer programming and stochastic integer programming.
On the Design of the Semantic P2P System for Music Recommendation

The pervasive use of Peer-to-Peer (P2P) systems and the growing demand on personalization for consumers has made future business focus on the niche market instead of the mass market. The recommender system, which is able to timely select data of the interest to each individual user, has become the key to any successful business. However, currently most recommendation systems are based on a centralized architecture; nonetheless, they are not suitable for P2P environments. In this article, we propose a distributed semantic P2P overlay, which can provide music search and recommendation services by considering both of user preference and diversity of interests. We then propose a 3C hybrid recommendation procedure that adapts three traditional filtering techniques to fitting the requirements in a distributed semantic overlay. First, we choose a set of proper meta-data to represent a music object and use them to construct the characteristic-vector-based content filter. Second, a dominant attribute, which is one of the attributes in the characteristic vector of a music object, is used to build the profile of a peer. With the idea of the social network, a P2P profile-based collaborative filter is proposed. Finally, we explore the item-to-item relationship to construct a history-based cooperative filter. We use simulations and a real database called Audio Scrobbler, which tracks users’ listening habits, to evaluate the performance of the recommendation system. The results demonstrate the effectiveness of the proposed approach compared with existing recommendation systems.
RATES OF FITNESS DECLINE AND REBOUND SUGGEST PERVASIVE EPISTASIS

Unraveling the factors that determine the rate of adaptation is a major question in evolutionary biology. One key parameter is the effect of a new mutation on fitness, which invariably depends on the environment and genetic background. The fate of a mutation also depends on population size, which determines the amount of drift it will experience. Here, we manipulate both population size and genotype composition and follow adaptation of 23 distinct Escherichia coli genotypes. These have previously accumulated mutations under intense genetic drift and encompass a substantial fitness variation. A simple rule is uncovered: the net fitness change is negatively correlated with the fitness of the genotype in which new mutations appear—a signature of epistasis. We find that Fisher's geometrical model can account for the observed patterns of fitness change and infer the parameters of this model that best fit the data, using Approximate Bayesian Computation. We estimate a genomic mutation rate of 0.01 per generation for fitness altering mutations, albeit with a large confidence interval, a mean fitness effect of mutations of −0.01, and an effective number of traits nine in mutS− E. coli. This framework can be extended to confront a broader range of models with data and test different classes of fitness landscape models.
Flow-aggregation Accelerating Strategy for TCP Traffic

A great number of researches on network flow characteristics show a large proportion of the network flows are single-packet flows. However, almost all existing flow termination strategies have no optimization for single-packet flows, so the efficiency of flow-aggregation is lower. Based on in-depth study of flow characteristics and TCP protocol specifications, we find the packet status, packet arrival interval and SYN packet size can identify single-packet flows accurately, and then propose a flow-aggregation accelerating strategy for TCP traffic that aims to quickly identify single-packet flows. We build efficiency model and accuracy model to compare our strategy performance with others and make a lot of experiments on the traces collected from a main channel in the CERNET during the latest five years. The results prove our strategy can greatly improve the efficiency of flow-aggregation at the cost of very little loss of accuracy
Query expansion by spatial co-occurrence for image retrieval

The well-known bag-of-features (BoF) model is widely utilized for large scale image retrieval. However, BoF model lacks the spatial information of visual words, which is informative for local features to build up meaningful visual patches. To compensate for the spatial information loss, in this paper, we propose a novel query expansion method called Spatial Co-occurrence Query Expansion (SCQE), by utilizing the spatial co-occurrence information of visual words mined from the database images to boost the retrieval performance. In offline phase, for each visual word in the vocabulary, we treat the visual words that are frequently co-occurred with it in the database images as neighbors, base on which a spatial co-occurrence graph is built. In online phase, a query image can be expanded with some spatial co-occurred but unseen visual words according to the spatial co-occurrence graph, and the retrieval performance can be improved by expanding these visual words appropriately. Experimental results demonstrate that, SCQE achieves promising improvements over the typical BoF baseline on two datasets comprising 5K and 505K images respectively.
An Improved Cooperative Quantum Particle Swarm Optimization Algorithm for Function Optimization

Based on the PSO, co-evolution and quantum evolution, this paper proposes an improved cooperative quantum particle swarm optimization (ICQPSO) algorithm. In this algorithm, a new definition of Q-bit expression called quantum angle is proposed and all sub-swarms use the optimized cooperation mode, which not only ensures the convergence rate, but also avoids plunging into local optimum. Meanwhile, a comprehensive learning is introduced to strengthen the diversity of population and prevents the stagnation. On this basis, a disturbance mechanism is added, which is furthermore to avoid plunging into local optimum. The new algorithm is tested by four typical functions. Results of simulation experiments show that new algorithm conquers the stagnation effectively, improves the global convergence ability and has better optimization performance than traditional Quantum Genetic Algorithm.
Functional hierarchy underlies preferential connectivity disturbances in schizophrenia

Significance Schizophrenia is linked to widespread neuronal-level changes causing cortical excitation-inhibition imbalance. However, functional neuroimaging reveals preferential association network dysconnectivity. Therefore, a tension exists between two competing frameworks: global versus localized neural dysfunction in schizophrenia. To link these levels of analysis, this study initially simulated cellular-level glutamatergic deficits, generating network-level predictions of cortical imbalance. Schizophrenia results revealed widespread hyperconnectivity in line with model predictions, yet effects were most profound within association networks. These clinical effects were computationally captured by considering a pre-existing functional cortical hierarchy between association and sensory regions. This study reveals that widespread cellular deficits in schizophrenia can give rise to network-preferential disruptions observed in neuroimaging as an emergent property of pre-existing functional differences between cortical areas. Schizophrenia may involve an elevated excitation/inhibition (E/I) ratio in cortical microcircuits. It remains unknown how this regulatory disturbance maps onto neuroimaging findings. To address this issue, we implemented E/I perturbations within a neural model of large-scale functional connectivity, which predicted hyperconnectivity following E/I elevation. To test predictions, we examined resting-state functional MRI in 161 schizophrenia patients and 164 healthy subjects. As predicted, patients exhibited elevated functional connectivity that correlated with symptom levels, and was most prominent in association cortices, such as the fronto-parietal control network. This pattern was absent in patients with bipolar disorder (n = 73). To account for the pattern observed in schizophrenia, we integrated neurobiologically plausible, hierarchical differences in association vs. sensory recurrent neuronal dynamics into our model. This in silico architecture revealed preferential vulnerability of association networks to E/I imbalance, which we verified empirically. Reported effects implicate widespread microcircuit E/I imbalance as a parsimonious mechanism for emergent inhomogeneous dysconnectivity in schizophrenia.
PEA - a Personal Email Assistant with Evolutionary Adaptation

In this paper we present P E A , a P ersonal Email Assistant, which lters incoming emails and ranks them according to their relevance. We provide tools for the acquisition of individual user models, which m a y consist of several prooles to map various interest domains of the user. In order to respond promptly to the shifts of interests of a user, we apply evolutionary algorithms to support an adaptive e n vironment that constantly adjusts the user model to improve the quality of relevance assessment. As second adaptive component w e m a k e use of a monitoring module that records all activities of the user. By means of a classiier system we model the behavior of the user to predict future actions, which results rst in suggestions to the user and later in automatically performed tasks. Additional features of the system include the segmentation of lengthy emails, eecient treatment of duplicate or new versions of messages, cross-language lter-ing, and the extraction of relevant information by using templates learned from examples.
An Iterative Information-Theoretic approach to estimate traffic matrix

Traffic matrices are very essential for many network engineering tasks: for instance, load balancing, capacity planning, routing protocol configuration. However, measuring these traffic matrices directly is difficult and costly. Hence many methods have been proposed to estimate these traffic matrices based on link load measurements and other more easily available data. This paper presents an iterative algorithm to estimate traffic matrix without differentiating the access links and the peering links and our algorithm get a similar performance with the Minimal Mutual Information method which requires that difference. Experiments on real backbone network data have also demonstrated that our algorithm is accurate and robust to measurement noise.
The Golgin Family of Coiled-Coil Tethering Proteins

The golgins are a family of predominantly coiled-coil proteins that are localized to the Golgi apparatus. Golgins are present in all eukaryotes, suggesting an evolutionary conserved function. Golgins are anchored to the Golgi membrane by their carboxy terminus and are predicted to adopt an extended conformation that projects into the surrounding cytoplasm. This arrangement is ideal for the capture or tethering of nearby membranes or cytoskeletal elements. Golgin-mediated tethering is thought to be important for vesicular traffic at the Golgi apparatus, the maintenance of Golgi architecture, as well as the positioning of the Golgi apparatus within cells. In addition to acting as tethers, some golgins can also sequester various factors at the Golgi membrane, allowing for the spatiotemporal regulation of downstream cellular functions. Although it is now established that golgins are membrane and cytoskeleton tethers, the mechanisms underlying tethering remain poorly defined. Moreover, the importance of golgin-mediated tethering in a physiological context remains to be fully explored. This review will describe our current understanding of golgin function, highlighting recent progress that has been made, and goes on to discuss outstanding questions and potential avenues for future research with regard to this family of conserved Golgi-associated proteins.
Thoracoscopic segmentectomy for T1 classification of non-small cell lung cancer: a single center experience.

OBJECTIVES Segmentectomy is one of the treatment options for small-sized non-small cell lung cancer (NSCLC). Although growing results support the feasibility and efficacy, it still remains unclear in segmentectomy. The International Association for the Study of Lung Cancer recommended a revised classification of TNM staging in 2009 (the seventh edition) and multidisciplinary classification of adenocarcinoma. We report here the outcome of totally thoracoscopic segmentectomy and lobectomy for T factor and adenocarcinoma. METHODS Ninety patients with Stage IA NSCLC underwent thoracoscopic segmentectomy between September 2003 and June 2011. A total of 124 patients were referred as a control group to compare the peri-operative outcome, local recurrence rate and survival. These survivals were analysed using the Kaplan-Meier method with the log-rank test and propensity score analyses. RESULTS The peri-operative outcome, including operative time, blood loss, duration of chest tube drainage and length of hospital stay, was not significantly different between groups. The number of dissected lymph nodes with segmentectomy was less than that with lobectomy. Morbidity and mortality were not significantly different between groups. Seven patients relapsed in each group and propensity score analysis in disease-free and overall survivals showed no differences between two groups in Stage IA. Subclass analyses revealed that disease-free and overall survivals in T1a and T1b were not significantly different between the two groups. CONCLUSIONS Our study demonstrated that thoracoscopic segmentectomy was feasible with regard to peri-operative and oncological outcomes for Stage IA NSCLC, especially T1a and carefully selected T1b descriptor.