Miracle-dz/UniFAR-FTUs huggingface dataset:
Chiba study of Mother and Children's Health (C-MACH): cohort study with omics analyses

Purpose Recent epidemiological studies have shown that environmental factors during the fetal period to early childhood might affect the risk of non-communicable diseases in adulthood. This is referred to as the developmental origins of health and disease (DOHaD) concept. The Chiba study of Mother and Children's Health (C-MACH) is a birth cohort study based on the DOHaD hypothesis and involves multiomics analysis. This study aims to explore the effects of genetic and environmental factors—particularly the fetal environment and postbirth living environment—on children's health, and to identify potential biomarkers for these effects. Participants The C-MACH consists of three hospital-based cohorts. The study participants are pregnant women at <13 weeks gestation. Women who underwent an examination in one of the three hospitals received an explanation of the study. The participants consented to completing questionnaire surveys and the collection and storage of biological and house/environmental samples. Participants were provided unique study numbers. All of the data and biological specimens will be stored in the Chiba University Center for Preventive Medical Sciences and Chiba University Center for Preventive Medical Sciences BioBank, respectively. Findings to date Consent to participate was obtained from 433 women. Of these women, 376 women completed questionnaires in the early gestational period. The mean age was 32.5 (4.4) years. The mean body mass index (BMI) was 21.1 (3.0) kg/m2. Before pregnancy, 72.3% of the women had a BMI of 18.5–24.9 kg/m2. During early pregnancy, 5.0% of the participants smoked. Future plans Primary outcomes are allergy, obesity, endocrine and metabolic disorders, and developmental disorders. Genome-level, metabolome-level, umbilical cord DNA methylation (epigenome), gut microbiota and environmental chemical exposure variables will be evaluated. We will analyse the relationships between the outcomes and analytical variables.
Modeling of tire friction force of vehicle considering nonholonomic constraints

Most motion planners for a vehicle often assume no-slipping and no-skidding in the context of nonholonomic motion planning. However, there actually exist slipping and skidding in practical situations and it is important to know the force between the tire and the contact plane considering slipping and skidding in the planning problem. In this paper, a Coulomb friction model of the tire friction force of vehicle is extended to nonholonomic constraints. In the modeling, the longitudinal and lateral tire friction forces are modeled corresponding to no slipping and no skidding constraints, respectively. A two directional model based on the maximum dissipation principle is also developed. Some typical movements of vehicles with the proposed friction force model are illustrated by simulations.
Generating Fuzzy Rules from Examples Using the Particle Swarm Optimization Algorithm

The use of fuzzy logic to solve control problems have been increasing considerably in the past years. The problem of generating desirable fuzzy rules is very important in the development of fuzzy systems. It is known that the fuzzy control rules for a control system is always built by designers with trial and error and based on their experience or some experiments. This paper presents a generation method of fuzzy rule by learning from examples using the Particle Swarm Optimization method (PSO). The proposed algorithm can obtain a set of fuzzy rules which cover the examples set in iterative process. The proposed method is tested with promising results.
Language Technology and Patients with Limited English

This paper describes a proposed framework for the use of language technology to provide computer-based help for patients with limited or no English. Aimed at users of the Health Services who are disadvantaged by their (lack of) linguistic skills, the system will assist the patient in different ways at different stages of their interactions with health-care providers. In its full conception it will embrace a wide range of NLP technologies. Although the research is based on the UK model of health-care provision, there are clear messages for anyone interested in language technology and under-resourced languages, whatever the application. Focusing on the GP’s clinic, it will provide a kind of FAQ help-desk and act as a kind of Receptionist to help determine whether the patient needs to see the GP or some other health-care specialist. If a GP consultation is indicated, the computer can be used for history note-taking. During the consultation itself, it can act as a mediator between the doctor and patient. Afterwards, in help-desk mode again, it can help the patient understand the diagnosis, any tests needed, and the proposed treatment regime. We propose in the first instance to develop systems aimed at Urduand Somali-speaking patients, focusing on respiratory problems (e.g. asthma).
A New general learning structure approach of Bayesian networks from data

Nowadays, Bayesian Networks (BNs) have constituted one of the most complete, self-sustained and coherent formalisms useful for knowledge acquisition, representation and application through computer systems. Yet, the learning of these BNs structures from data represents a problem classified at an NP-hard range of difficulty. As such, it has turned out to be the most exciting challenge in the learning machine area. In this context, the present work’s major objective lies in setting up a further solution conceived to be a remedy for the intricate algorithmic complexity problems imposed during the learning of BNstructure through a massively-huge data backlog. Our present work has been constructed according to the following framework; on a first place, we are going to proceed by defining BNs and their related problems of structurelearning from data. We, then, go on to propose a novel heuristic designed to reduce the algorithmic complexity without engendering any loss of information. Ultimately, our conceived approach will be tested on a car diagnosis as well as on a Lymphography diagnosis data-bases, while our achieved results would be discussed, along with an exposition of our conducted work’s interests as a closing step to this work.
Parents and schools : partners or protagonists?

Parents' relationships with schools are changing. No longer passive recipients of a bureaucratic allocation system they are increasingly making choices, negotiating and challenging authority - all with a wealth of information that would have been unthinkable in the recent past. This book explores the changing roles and relationships of parents, teachers and administrators and the consequences for children's education.
Therapeutic effect of a TM4SF5-specific peptide vaccine against colon cancer in a mouse model

Molecular-targeted therapy has gained attention because of its high efficacy and weak side effects. Previously, we confirmed that transmembrane 4 superfamily member 5 protein (TM4SF5) can serve as a molecular target to prevent or treat hepatocellular carcinoma (HCC). We recently extended the application of the peptide vaccine, composed of CpG-DNA, liposome complex, and TM4SF5 peptide, to prevent colon cancer in a mouse model. Here, we first implanted mice with mouse colon cancer cells and then checked therapeutic effects of the vaccine against tumor growth. Immunization with the peptide vaccine resulted in robust production of TM4SF5-specific antibodies, alleviated tumor growth, and reduced survival rate of the tumor-bearing mice. We also found that serum levels of VEGF were markedly reduced in the mice immunized with the peptide vaccine. Therefore, we suggest that the TM4SF5-specific peptide vaccine has a therapeutic effect against colon cancer in a mouse model. [BMB Reports 2014; 47(4): 215-220]
Scalable Critical-Path Based Performance Analysis

The critical path, which describes the longest execution sequence without wait states in a parallel program, identifies the activities that determine the overall program runtime. Combining knowledge of the critical path with traditional parallel profiles, we have defined a set of compact performance indicators that help answer a variety of important performance-analysis questions, such as identifying load imbalance, quantifying the impact of imbalance on runtime, and characterizing resource consumption. By replaying event traces in parallel, we can calculate these performance indicators in a highly scalable way, making them a suitable analysis instrument for massively parallel programs with thousands of processes. Case studies with real-world parallel applications confirm that - in comparison to traditional profiles - our indicators provide enhanced insight into program behavior, especially when evaluating partitioning schemes of MPMD programs.
Dynamics of multi-campaign propagation in online social networks

Ever since the advent of online social networking, people have been voluntarily posting and consuming information on the web. This new method to communicate digitally provides the means to spread information considerably far in a very short span of time with minimal resources. Social networks are increasingly being used to spread misinformation online due to low-costs in organizing grassroots of these campaigns. Our goal in this paper is to determine the efficiency with which campaigns can succeed in an online social network, efficiency represents the ease with which a campaign can triumph over other competing campaigns in a network. We model the information diffusion using Multi-Campaign Independent Cascade Model, and by applying node coercion and link cutting as campaign limiting strategies we ascertain how efficiently a campaign can succeed. The efficiency measure tackles the problem of determining the survivability of campaigns, which is used to ensure success or failure of a campaign using campaign limiting strategies.
Details on Logical Model and Logical Data Analysis Formalism and Set-up

The logical modelling step is performed to test whether the putative model topology (Fig.1B, main manuscript) is capable of inducing bistability and fulfilling all observations (constraints) given by the experimental knockout data (Fig.1A, main manuscript). To this end, the entire set of parametrizations (denoted K, see further) of regulatory functions for the components (YdaM, c-di-GMP, etc.) is scanned and it is tested whether each of these parametrizations K ∈ K can fulfil all observations from the biological knockout experiments. This model checking procedure then results in a set of valid parametrizations K *. Lastly, the set of valid parametrizations is further analysed, which allows for model simplifications and the derivation of parameter constraints for the subsequent time-continuous modelling step. In this section we provide the full formal description of the employed logical framework and a formal version of the logical model we built in the Methods section. We also list all formal properties, the full description of the results and lastly elaborate on the methods used for parameter set analysis. In the following we describe the logical modelling framework. While the notation is slightly modified to better suit our modelling aim, there is no practical difference to the standard framework of multi-valued logical models of René Thomas as described for example in [3]. State space We denote V to be a set of named components. In the main text we stated that components v are either on or off, except for YciR, which has three activity levels. In the following, we will identify the off state with the value 0 and the on state with 1. For YciR, the values 2 and 1 correspond to the c-di-GMP degradation activity and the YdaM/MlrA inhibition activity, respectively. More generally, each component can take integer values n ∈ [0, ρ(v)], where ρ : V → N + assigns a maximal activity level to each component, see Figure S1(a). Since the range of values for each component is finite, we can describe the set of all possible configurations of the system, the logical state space, as S = v∈V [0, ρ(v)]. The set S therefore represents all qualitatively different configurations of a system (combinations of logical values for the distinct species, e.g. on and off). In the following, s ∈ S refers to a particular state of the system and by s v = n we state that the component v of the state …
Inhibits, Calcium Signaling via SHP-2 Immunoreceptor that Enhances, Rather than Inhibition Motif Encoding Costimulatory Platelet Immunoreceptor Tyrosine-Based Cutting Edge: TREM-Like Transcript-1, a

To date, immunoreceptor tyrosine-based inhibition motifs (ITIMs) have been shown to mediate inhibitory properties. We report a novel triggering receptor expressed on myeloid cells (TREM) family member, TREM-like tran-script-1 (TLT1), which differs from the activating mem-bers because its cytoplasmic tail contains two ITIMs at Y245 and Y281. A TLT1 splice variant (TLT1sp) encodes a different cytoplasmic tail lacking ITIMs. Both isoforms are expressed in resting platelet (cid:1) -granules, which are up-regulated to the cell surface following activation. TLT1 recruited Src homology 2 domain-containing tyrosine phosphatase (SHP)-2 to the “classical” ITIM (Y281) but not the “nonclassical” ITIM (Y245). In con-trast to previously characterized ITIM receptors, TLT1 enhanced, rather than inhibited, Fc (cid:2) RI-mediated calcium signaling in rat basophilic leukemia cells, a property dependent on the SHP-2 recruiting classical Y281 ITIM. Therefore, TLT1 represents a new costimulatory ITIM immunoreceptor and is the second ITIM-bearing receptor to be identified in platelets after platelet endothelial cell adhesion molecule-1. The Journal of Immunology, 2004, 172: 5838–5842.
Underwater programmetry for archaeology and marine biology: 40 years of experience in Marseille, France

Since 19,3 archeology and computer science have developed close ties in Marseille. Two departments (computer science and archaeology) from the French National Centre for Scientific Research (CNRS) in Marseille started working together and laid the cornerstone of the Computer Applications and Quantitative Methods in Archaeology (CAA) community. Marseille also has the advantage of being located in a very interesting place on the Mediterranean Sea and being the home to several famous laboratories, such as the French Cultural Heritage Department (DRASSM) or private companies like COMER. In 1980 they performed a series of explorations of a deep-sea wreck with the help of COMER and DRASSM. More recently, ten years ago, the Centre d'Océanologie de Marseille (COM) started using underwater photogrammetry to survey and monitor red coral populations in situ. In this paper we present new advances in underwater photogrammetry for archaeology and marine biology based on forty years of experience. The survey described in this article does not only discuss the acquisition of 3D points in difficult conditions but also linking archaeological knowledge to the surveyed geometry. This approach needed to combine automatic data processing and offered the opportunity to experts, archaeologists or biologists, to insert knowledge in the process. After an introduction to the history of computer science and archaeology, we will present related work in underwater archaeology and marine biology. The last section is dedicated to two recent experiments in Marseille, based on recent developments in automatic photogrammetry: a World War II plane wreck, surveyed using both acoustic and optical sensors, and a survey used to monitor red coral growth over several years.
Energy Aware Transmission in Cellular Uplink with Clustered Base Station Cooperation

We provide an analytical formula to evaluate the performance of the uplink of planar cellular networks when joint processing is enabled among limited number of base stations in a generalised fading environment. Focusing on user transmission power allocation techniques to mitigate inter-cluster interference we investigate the system's spectral-energy efficiency trade-off. The paper addresses the gains in both cell throughput and transmissions energy efficiency due to the combined strategies of base station cooperation and user power management. We assess the effect of the propagation environment and of the key network design parameters of cooperation cluster size and inter-site distance on the overall performance providing numerical results for a real-world scenario.
Gesture recognition : control of a computer with natural head movements

The topic of this article is a basic research considering a humancomputer interaction. The system is still under construction, however its basis – facial features tracking and head pose estimation – is ready to use, thus it could bring a head gesture controlled system into reality. We present an approach to control applications with head movements. We construct an Active Appearance Model (AAM) for facial feature extraction. Based on the landmarks of AAM shape, the head pose is modelled in the three-dimensional space by three Euler angles of rotation around the three axes orthogonal to each other, and three orthogonal translation directions. Regarding all the above mentioned facts, we are capable of recognizing gestures like head pointing, nodding, and shaking, blinking, opening and closing mouth.
On Interoperability of Ontologies for Web-based Educational Systems

Interoperability between disparate systems in open, distributed environments has become the quest of many practitioners in a variety of flelds. Web-based educational systems are not an exception, but provide some unique characteristics. In this perspectives paper we argue for the role of multiple ontologies in support of Web-based educational systems and speculate on the efiorts involved in achieving interoperable systems. We draw our criticism from our involvement in interoperability tasks between ontologies for Semantic Web systems and elaborate on the role of communities of users in interoperability scenarios.
Exploiting Rich Syntactic Features for Hedge Detection and Scope Finding ∗

Hedge detection and scope finding are increasingly important tasks in information extraction, especially in the biomedical natural language processing community. In this paper, a novel approach detecting hedge cues and their scopes by sequence labeling is explored. It should be emphasized that syntactic dependencies are systematically exploited and effectively integrated by a large-scale feature selection procedure. Experimental results demonstrate that our method outperforms previous works, and the selected syntactic features effectively promote the performances in both tasks of hedge detection and scope finding.
An Ameliorated Methodology for the Abstraction of Usecases from software requirements specification

Information systems of business process are realized through software projects which use the specific software requirements specification (SRS) provided for them as the base. The SRS contains details of the information system through which appropriate software can be developed. The information systems are also viewed prospectively with different equiponderant pragmatics like work, work process or usecase. The usecase is one of the prime perspective views each of whose sum forms the information system. In this paper, an attempt is made to abstract usecases statements of all actors from unformatted unstructured SRS text. The stages involve moulding the SRS, creating control flow & data flow graphs, creating appropriate slicing criteria, creating actor & actors' interface attributes table, create slicing criteria for each usecase and then slice the relevant statements for each usecase. Here we have attempted to use Weiser modified algorithm [1] to abstract exact usecase from first cut usecase. The slicing criterion is obtained through the intersection of actor's interface attributes and the referenced / defined attributes present between two consecutive actors (first cut usecase). Attempts have been made to resolve synonyms & heteronyms present in the SRS. The correctness & completeness of the proposed methodology depends on the realization of actor & actors' interface attributes.
[The diagnostic usability of selected narrativity indices in stories about close relationships in the assessment of personality organization].

AIM The aim of this study was to test the usability of selected narrativity indices identified from autobiographical accounts of important relationships in an assessment of neurotic (NPO) and borderline personality organization (BPO). METHODS Narrativity indices, both particular and generalized, were used to predict personality organization levels. Indices were derived from two separate layers of analysis: 1) lexical indices were counted with computer assistance; 2) evocative/reception indices dealing with coherence of the story were assessed using the competent judges method. RESULTS It was found that the lexical narrativity index-the active "I"-was a good predictor of both BPO and NPO, while the human factor was a good predictor of BPO when low. Moreover, a generalized index was used to describe how stories are saturated with the narrativity indices of intentionality, concreteness, and active "I", but simultaneously deprived of human factor, and was found to be the best predictor of BPO. Furthermore, where the coherence of the story and of its subdimension (integration) rise, the probability of BPO diagnosis decreases. CONCLUSIONS This research provides support for the thesis that surface narrativity indices may predict deeper personality structure. Its results are justified in the light of Kernberg's theory, and have the potential to become a useful tool in clinical practice as a supplementary source of information in diagnostic and psychotherapeutic processes.
Ensemble methods with non-negative matrix factorization for non-payment prevention system

A well designed and reliable prevention system for non-payment event is very important for the telecom company. Monitoring is especially needed in case the client exceeds the level of his standard payments what can lead to his financial problems. In this paper, we propose a system describing client's behavior and informing about possible problems in advance. In this approach we apply novel ensemble methods to integrate information from many models predicting the customer's behaviour. The ensemble methods base on non-negative matrix factorization which allows identifying the fundamental prediction components. The practical experiment with prevention system confirmed that proposed procedure.
An optimization framework for practical multipath routing in wireless mesh networks

We consider wireless mesh networks, and exploit the inherent broadcast nature of wireless by making use of multipath routing. We present an optimization framework that enables us to derive optimal flow control, routing, scheduling, and rate adaptation schemes, where we use network coding to ease the routing problem. We prove optimality and derive a primal-dual algorithm that lays the basis for a practical protocol. Optimal MAC scheduling is difficult to implement, and we use random scheduling rather than optimal for comparisons. Under random scheduling, our protocol becomes fully decentralised. We use simulation to show on realistic topologies that we can achieve 20-200% throughput improvement compared to single path routing, and several times compared to a recent related opportunistic protocol (MORE).