Miracle-dz/UniFAR-FTUs huggingface dataset:
Mathematical Modeling of Human Glioma Growth Based on Brain Topological Structures: Study of Two Clinical Cases

Gliomas are the most common primary brain tumors and yet almost incurable due mainly to their great invasion capability. This represents a challenge to present clinical oncology. Here, we introduce a mathematical model aiming to improve tumor spreading capability definition. The model consists in a time dependent reaction-diffusion equation in a three-dimensional spatial domain that distinguishes between different brain topological structures. The model uses a series of digitized images from brain slices covering the whole human brain. The Talairach atlas included in the model describes brain structures at different levels. Also, the inclusion of the Brodmann areas allows prediction of the brain functions affected during tumor evolution and the estimation of correlated symptoms. The model is solved numerically using patient-specific parametrization and finite differences. Simulations consider an initial state with cellular proliferation alone (benign tumor), and an advanced state when infiltration starts (malign tumor). Survival time is estimated on the basis of tumor size and location. The model is used to predict tumor evolution in two clinical cases. In the first case, predictions show that real infiltrative areas are underestimated by current diagnostic imaging. In the second case, tumor spreading predictions were shown to be more accurate than those derived from previous models in the literature. Our results suggest that the inclusion of differential migration in glioma growth models constitutes another step towards a better prediction of tumor infiltration at the moment of surgical or radiosurgical target definition. Also, the addition of physiological/psychological considerations to classical anatomical models will provide a better and integral understanding of the patient disease at the moment of deciding therapeutic options, taking into account not only survival but also life quality.
Dynamic Length Packet Size Communication Technique for Underwater Sensor Networks

Greatly unexplored vastness of the ocean attracts human beings to discover knowledge in deep water. Underwater environment is a big challenge for human beings because of changeable and unknown conditions. A highly precise, real time and continuous monitoring system is extremely important for underwater aquatic surveillance, river and sea pollution discovery, oceanographic data compilation and commercial exploitation of the aquatic environment. Underwater Wireless Sensor Networks (UWSNs) with acoustic channel have better performance than radio and optics in underwater environment for interactive communication. However, current techniques suffer from several limitations due to unique challenges of underwater environment such as propagation delay, absorption loss and low bandwidth. Reliability is a major requirement in developing a communication technique that provides effective communication in terms of timeliness of data, better utilization of the channel and bandwidth. In this paper, we propose a naturally adaptive communication technique with an availability of bandwidth to transfer variable length packets. The packet size is determined by parameters including temperature, pressure, salinity and available bandwidth of acoustic channel.
Effects of Hepatitis B Virus S Protein Exposure on Sperm Membrane Integrity and Functions

Background Hepatitis B is a public health problem worldwide. Viral infection can affect a man's fertility, but only scant information about the influence of hepatitis B virus (HBV) infection on sperm quality is available. The purpose of this study was to investigate the effect of hepatitis B virus S protein (HBs) on human sperm membrane integrity and functions. Methods/Principal Findings Reactive oxygen species (ROS), lipid peroxidation (LP), total antioxidant capacity (TAC) and phosphatidylserine (PS) externalization were determined. The terminal deoxynucleotidyl transferase-mediated dUTP nick end labeling (TUNEL) assays and flow cytometric analyses were performed. (1) After 3 h incubation with 25 µg/ml of HBs, the average rates of ROS positive cells, annexin V–positive/propidium iodide (PI)-negative cells, Caspases-3,-8,-9 positive cells and TUNEL-positive cells were significantly increased in the test groups as compared to those in the control groups, while TAC level was decreased when compared with the control. The level of malondialdehyde (MDA) in the sperm cells exposed to 50 µg/ml of HBs for 3 h was significantly higher than that in the control (P<0.05–0.01). (2) HBs increased the MDA levels and the numbers of ROS positive cells, annexin V–positive/PI-negative cells, caspases-3, -8, -9 positive cells and TUNEL-positive cells in a dose-dependent manner. (3) HBs monoclonal antibody (MAb) and N-Acetylcysteine (NAC) reduced the number of ROS-positive sperm cells. (4) HBs decreased the TAC levels in sperm cells in a dose-dependent manner. Conclusion HBs exposure could lead to ROS generation, lipid peroxidation, TAC reduction, PS externalization, activation of caspases, and DNA fragmentation, resulting in increased apoptosis of sperm cells and loss of sperm membrane integrity and causing sperm dysfunctions.
Reprogramming protein kinase substrate specificity through synthetic mutations

Protein kinase specificity is largely imparted through substrate binding pocket motifs. Missense mutations in these regions are frequently associated with human disease, and in some cases can alter substrate specificity. However, current efforts at decoding the influence of mutations on substrate specificity have been focused on disease-associated mutations. Here, we adapted the Proteomic Peptide Library (ProPeL) approach for determining kinase specificity to the task of exploring structure-function relationships in kinase specificity by interrogating the effects of synthetic mutation. We established a specificity model for the wild-type DYRK1A kinase with unprecedented resolution. Using existing crystallographic and sequence homology data, we rationally designed mutations that precisely reprogrammed the DYRK1A kinase at the P+1 position to mimic the substrate preferences of a related kinase, CK II. This study illustrates a new synthetic biological approach to reprogram kinase specificity by design, and a powerful new paradigm to investigate structure-function relationships underpinning kinase substrate specificity.
Scalable Mobile Image Recognition for Real-Time Video Annotation

Traditional AR frameworks for gaming and advertising focus on tracking 2D static targets. This limits the plausible use of this solutions to certain application cases like brochures or posters, but deprives their use for dynamically changing 2D targets, such as video walls or electronic billboards used in advertising.In this demo, we show how to use a rapid, fully mobile image recognition system to introduce AR in videos playing on TV sets or other dynamic screens, without the need to alter or modify the content for trackability. Our approach uses a scalable and fully mobile concept, which requires a database with a very small memory footprint on mobiles for a video or even a collection of videos.The feasibility of the approach is demonstrated on over 16 hours of video from a popular TV series, indexing into the video and giving accurate time codes and full 6DOF tracking for AR augmentations.
Subjective Assessment of H.264 Compressed Stereoscopic Video

The tremendous growth in 3D (stereo) imaging and display technologies has led to stereoscopic content (video and image) becoming increasingly popular. However, both the subjective and the objective evaluation of stereoscopic video content has not kept pace with the rapid growth of the content. Further, the availability of standard stereoscopic video databases is also quite limited. In this work, we attempt to alleviate these shortcomings. We present a stereoscopic video database and its subjective evaluation. We have created a database containing a set of 144 distorted videos. We limit our attention to H.264 compression artifacts. The distorted videos were generated using 6 uncompressed pristine videos of left and right views originally created by Goldmann et al. at EPFL [1]. Further, 19 subjects participated in the subjective assessment task. Based on the subjective study, we have formulated a relation between the 2D and stereoscopic subjective scores as a function of compression rate and depth range. We have also evaluated the performance of popular 2D and 3D image/video quality assessment (I/VQA) algorithms on our database.
Hybrid Probabilistic Relational Models for System Quality Analysis

The formalism Probabilistic Relational Models (PRM) couples discrete Bayesian Networks with a modeling formalism similar to UML class diagrams and has been used for architecture analysis. PRMs are well-suited to perform architecture analysis with respect to system qualities since they support both modeling and analysis within the same formalism. A particular strength of PRMs is the ability to perform meaningful analysis of domains where there is a high level of uncertainty, as is often the case when performing system quality analysis. However, the use of discrete Bayesian networks in PRMs complicates the analysis of continuous phenomena. The main contribution of this paper is the Hybrid Probabilistic Relational Models (HPRM) formalism which extends PRMs to enable continuous analysis thus extending the applicability for architecture analysis and especially for trade-off analysis of system qualities. HPRMs use hybrid Bayesian networks which allow combinations of discrete and continuous variables. In addition to presenting the HPRM formalism, the paper contains an example which details the use of HPRMs for architecture trade-off analysis.
Impact of sea surface temperature anomaly on two global warming periods in the 20th century over the arid central Asia

Based on the gridded monthly air temperature data and the sea surface temperature (SST) data, the influence of SST change on two global warning periods in the 20th century over the arid central Asia is analyzed. The results show that the SST anomaly produces obvious effect on temperature. The winter temperature of the arid central Asia is much warmer (cooler) when high positive (negative) SST anomaly occurs in the equatorial central-eastern Pacific (ECEP) from autumn to winter. The summer temperature of the arid central Asia is much warmer (cooler) when higher (lower) summer SST occurs in the ECEP, the “warm pool”, the Kuroshio area and the westerly drift zone. The arid central Asia made different responses to the two warming periods from the 1920s to the 1940s and from the 1970s to the 1990s, in close dependence on the anomalies of SST controlling the arid area at the two time intervals. Study shows that the impacts of global warming in the 1920s–1940s and 1970s–1990s differ upon the arid central Asia, resulting in its different responses thereto.
Nicotinic modulation of serotonergic activity in the dorsal raphe nucleus

Abstract Cholinergic signaling mediated by nicotinic receptors has been associated to a large number of physiological and behavioral processes such as learning, memory, attention, food-intake and mood disorders. Although it is well established that many nicotinic actions are mediated through an increase in serotonin (5-HT) release, the physiological mechanisms by which nicotine produces these effects are still unclear. The dorsal raphe nucleus (DRN) contains the major amount of 5-HT neurons projecting to different parts of the brain. DRN also contains nicotinic acetylcholine receptors (nAChRs) located at somatic and presynaptic elements. Nicotine produces both inhibitory and excitatory effects on different subpopulations of 5-HT DRN neurons. In this review, we describe the presynaptic and postsynaptic mechanisms by which nicotine increases the excitability of DRN neurons as well as the subtypes of nAChRs involved. We also describe the inhibitory effects of nicotine and the role of 5-HT1A receptors in this effect. These nicotinic actions modulate the activity of different neuronal subpopulations in the DRN, changing the 5-HT tone in the brain areas where these groups of neurons project. Some of the physiological implications of nicotine-induced 5-HT release are discussed.
PREFER: a system for the efficient execution of multi-parametric ranked queries

Users often need to optimize the selection of objects by appropriately weighting the importance of multiple object attributes. Such optimization problems appear often in operations' research and applied mathematics as well as everyday life; e.g., a buyer may select a home as a weighted function of a number of attributes like its distance from office, its price, its area, etc. We capture such queries in our definition of preference queries that use a weight function over a relation's attributes to derive a score for each tuple. Database systems cannot efficiently produce the top results of a preference query because they need to evaluate the weight function over all tuples of the relation. PREFER answers preference queries efficiently by using materialized views that have been pre-processed and stored. We first show how the result of a preference query can be produced in a pipelined fashion using a materialized view. Then we show that excellent performance can be delivered given a reasonable number of materialized views and we provide an algorithm that selects a number of views to precompute and materialize given space constraints. We have implemented the algorithms proposed in this paper in a prototype system called PREFER, which operates on top of a commercial database management system. We present the results of a performance comparison, comparing our algorithms with prior approaches using synthetic datasets. Our results indicate that the proposed algorithms are superior in performance compared to other approaches, both in preprocessing (preparation of materialized views) as well as execution time.
A machine learning system for human-in-the-loop video surveillance

We propose a novel human-in-the-loop surveillance system that continuously learns the properties of objects that are interesting for a human operator. The interesting objects are automatically learned by tracking the eye gaze positions of the operator while he or she monitors the surveillance video. The system automatically detects interesting objects in the surveillance video and forms a new synthetic video that contains interesting objects at earlier positions in the time dimension. The operator always views this synthetically formed video which makes manual video retrieval tasks more convenient. Sensitivity to operator interests and interest changes are other major advantages. We tested our system both on synthetic and real videos, which are provided as supplementary materials [1]. The results show the effectiveness of the proposed system.
Throughput Guarantees for TCP Flows in a Network Based on Packet Classes Using Edge-to-Edge Per Flow Measurements

TCP flows generated by applications such as the Web or FTP require a minimum network throughput and can also benefit from an extra throughput due to their rate-adaptive algorithms. To build a guaranteed minimum throughput service, the authors propose a scheme with Admission Control (AC) using a small set of packet classes in a core-stateless network. At the ingress of the network, each flow packet is marked as one of the set of classes, and within the network, each class is assigned a different discarding priority. The AC method is based on edge-to-edge per flow measurements and requires flows to be sent at a minimum rate. The scheme is able to provide different minimum throughput to different users and protection against nonresponsive sources. The authors evaluate the scheme through simulation using different traffic loads consisting of TCP flows that carry files of varying sizes. In the simulation, TCP uses a new algorithm that forces the source to keep the short-term sending rate above a desired minimum rate. The authors study the influence of several parameters on the performance of the scheme in different network topologies. The results show that the scheme guarantees the requested throughput to accepted flows and achieves a high utilization of network resources, similar to the ideal results of a classical hop-by-hop AC.
Connoisseurs of Care? Unannounced Standardized Patients’ Ratings of Physicians

Background:Patient satisfaction surveys can be informative, but bias and poor response rates may limit their utility as stable measures of physician performance. Using unannounced standardized patients (SPs) may overcome some of these limitations because their experience and training make them able judges of physician behavior. Objectives:We sought to understand the reliability of unannounced SPs in rating primary care physicians when covertly presenting as real patients. Study Design:Data from 2 studies (Patient Centered Communication [PCC]; Social Influences in Practice [SIP]) were included. For the PCC study, 5 SPs made 192 visits to 96 physicians; for the SIP study, 18 SPs made 292 visits to 146 physicians. SPs visits to physicians were randomized, thus avoiding mutual selection bias. Each SP rated 16 to 38 physicians on interpersonal skills (autonomy support: PCC, SIP), technical skills (information gathering: SIP-only), and overall satisfaction (SIP-only). We evaluated SP evaluation consistency (physician vs. total variance ρ), and SPs’ overall satisfaction with specific dimensions of physician performance. Results:Scale reliability varied from 0.71 to 0.92. Physician rhos (95% confidence intervals) for autonomy support were 0.40 (0.22–0.58; PCC) and 0.30 (0.14–0.45; SIP); information gathering rho was 0.46 (0.33–0.59; SIP). Overall SP satisfaction rho was 0.47 (0.34–0.60; SIP). SPs varied significantly in adjusted overall satisfaction levels, but not other dimensions. Conclusions:These analyses provide some evidence that medical connoisseurship can be learned. When adequately sampled by trained SPs, some physician skills can be reliably measured in community practice settings.
An efficient framework for business software development

Businesses operate today in an increasingly dynamic environment through a worldwide network of information exchanges and business transactions. The Semantic Web - an extension of the current Web - which comprises ontologies and XML-related technologies, provides a shared and standardized vocabulary that can be communicated among automatic and human agents to share information and knowledge. Consider that an electronic business is a set of business processes governed by business rules that run over the Internet. These business rules play a significant role in determining the success or failure of a business as a whole. The goal of this paper is to introduce a framework that is designed to build business systems faster and which can more easily accommodate changes within a business. The advantages of this framework arise from its unity and simplicity, and especially its ability to effectively change, add, and/or delete business rules.
A multi-dimensional visualization method combining MDS and SVM

Human can only feel spatial information within three-dimensional space. However, there are more than three attributes in economic statistical data and other data sets generally. When studying the inherent structural characteristics of these data such as clustering and distribution, researchers need to reduce multi-dimensional information to three-dimensional space or less to achieve multi-dimensional visualization. There are many dimension reduction methods, whose results are different from each other because of different mathematics theories and application ranges. In the paper, authors analyze economic statistical data of Sichuan province in 2007 by using Multidimensional Scaling (MDS) which is a nonlinear method and Support Vector Machines (SVM) which is a supervised classification method. The classification result of MDS is consistent with the status of economic development of Sichuan in general, but details of the result cannot be verified itself; the output results of SVM by selecting different kernel functions are very similar to the classification result of MDS, which can validate these results. And considering the advantages and the solid mathematical theory, authors believe that the combination of these two methods is scientific.
Counting people in crowds with a real-time network of simple image sensors

Estimating the number of people in a crowded environment is a central task in civilian surveillance. Most vision-based counting techniques depend on detecting individuals in order to count, an unrealistic proposition in crowded settings. We propose an alternative approach that directly estimates the number of people. In our system, groups of image sensors segment foreground objects from the background, aggregate the resulting silhouettes over a network, and compute a planar projection of the scene's visual hull. We introduce a geometric algorithm that calculates bounds on the number of persons in each region of the projection, after phantom regions have been eliminated. The computational requirements scale well with the number of sensors and the number of people, and only limited amounts of data are transmitted over the network. Because of these properties, our system runs in real-time and can be deployed as an untethered wireless sensor network. We describe the major components of our system, and report preliminary experiments with our first prototype implementation.
Follicle Development of Xenotransplanted Sheep Ovarian Tissue into Male and Female Immunodeficient Rats

Background This study aimed to assess follicle survival after xenotransplantation of sheep ovarian tissue into male and female immunodeficient rats. We evaluated the effects of gonadotropin treatment on follicular development in the transplanted tissue. Materials and Methods In this experimental study, sheep ovarian cortical strips were transplanted into the neck back muscles of 8 male and 8 female immunodeficient, castrated rats. Fourteen days after surgery, each rat was treated with human menopausal gonadotropin (hMG) for 9 weeks. One day after the last injection, ovarian tissues were removed and fixed for histology assessment. Histology analyses were performed before and after grafting. Estradiol (E2) levels were measured before and after gonadectomy, and at the end of the experiment. The control group consisted of 7 male and 7 female noncastrated/non-grafted rats and the sham group comprised 7 male and 7 female castrated/ non-grafted rats for comparison of serum E2 concentrations. Results The percentage of primordial follicles decreased after transplantation in male (25.97%) and female (24.14%) rats compared to the control group (ovarian tissue nongrafted; 37.51%). Preantral follicles increased in the male (19.5%) and female (19.49%) transplanted rats compared to the control group (11.4%). Differences in antral follicles between male (0.06 ± 0.0%) and female (0.06 ± 0.0%) rats were not noticeable compared to control (1.25 ± 0.0%) rats. We observed a significantly higher percent of mean E2 secretion in grafted males compared to grafted females (P˂0.05). Conclusion Despite significant differences in E2 secretion between xenografted male and female rats, we observed no statistical differences in terms of follicular development.