Miracle-dz/UniFAR-FTUs huggingface dataset:
Comparison of the Gene Expression Profiles of Human Fetal Cortical Astrocytes with Pluripotent Stem Cell Derived Neural Stem Cells Identifies Human Astrocyte Markers and Signaling Pathways and Transcription Factors Active in Human Astrocytes

Astrocytes are the most abundant cell type in the central nervous system (CNS) and have a multitude of functions that include maintenance of CNS homeostasis, trophic support of neurons, detoxification, and immune surveillance. It has only recently been appreciated that astrocyte dysfunction is a primary cause of many neurological disorders. Despite their importance in disease very little is known about global gene expression for human astrocytes. We have performed a microarray expression analysis of human fetal astrocytes to identify genes and signaling pathways that are important for astrocyte development and maintenance. Our analysis confirmed that the fetal astrocytes express high levels of the core astrocyte marker GFAP and the transcription factors from the NFI family which have been shown to play important roles in astrocyte development. A group of novel markers were identified that distinguish fetal astrocytes from pluripotent stem cell-derived neural stem cells (NSCs) and NSC-derived neurons. As in murine astrocytes, the Notch signaling pathway appears to be particularly important for cell fate decisions between the astrocyte and neuronal lineages in human astrocytes. These findings unveil the repertoire of genes expressed in human astrocytes and serve as a basis for further studies to better understand astrocyte biology, especially as it relates to disease.
Generation of limb trajectories with a sequential network

The authors of this study focus on the representation and generation of unconstrained aiming movements of a limb by means of a neural network architecture. The goal is that of producing a time trajectory of a limb from a starting posture toward a target specified as a sensory stimulus. The velocity profile along the trajectory is bell-shaped as in most movements performed by biological systems. The generalization capabilities of the network are investigated as well as its internal organization. Two experiments are performed on the trained network to test its robustness to noise and its dynamical properties. <<ETX>>
Toward a Carbon Dioxide Neutral Industrial Park

The industrial park of Herdersbrug (Brugge, Flanders, Belgium) comprises 92 small and medium‐sized enterprises, a waste‐to‐energy incinerator, and a power plant (not included in the study) on its site. To study the carbon dioxide (CO2) neutrality of the park, we made a park‐wide inventory for 2007 of the CO2 emissions due to energy consumption (electricity and fossil fuel) and waste incineration, as well as an inventory of the existing renewable electricity and heat generation. The definition of CO2 neutrality in Flanders only considers CO2 released as a consequence of consumption or generation of electricity, not the CO2 emitted when fossil fuel is consumed for heat generation. To further decrease or avoid CO2 emissions, we project and evaluate measures to increase renewable energy generation.
uKeying: A Key Management Framework for Wireless Sensor Networks Utilizing a Unique Session Key

Key management is a core mechanism to ensure the security of applications and network services in wireless sensor networks. Key management includes two aspects: key distribution and key revocation. The goal of the key distribution is to establish the required keys between sensor nodes which must exchange data. Key revocation is used to remove compromised sensor nodes from the network. Although many key distribution schemes and key revocation schemes have been proposed in the literature, there is a lack of a framework which can integrate the schemes. In this paper, we propose a key management framework, uKeying, for wireless sensor networks using a globally distributed session key. uKeying includes three parts: a security mechanism to provide secrecy for the communication in the sensor network, an efficient session key distribution scheme, and a centralized key revocation scheme. The proposed framework does not depend on a specific key distribution scheme and can support many key distribution schemes. We further demonstrate how to use the framework to support secure group communication protocols in wireless sensor networks. Our analysis shows that the framework is secure, efficient, and extensible. The simulation and results reveal for the first time that a centralized key revocation scheme can also attain a high efficiency.
How is the ocean filled?

The ocean surface rapidly exchanges heat, freshwater, and gases with the atmosphere, but once water sinks into the ocean interior, the inherited properties of seawater are closely conserved. Previous water‐mass decompositions have described the oceanic interior as being filled by just a few different property combinations, or water masses. Here we apply a new inversion technique to climatological tracer distributions to find the pathways by which the ocean is filled from over 10,000 surface regions, based on the discretization of the ocean surface at 2° by 2° resolution. The volume of water originating from each surface location is quantified in a global framework, and can be summarized by the estimate that 15% of the surface area fills 85% of the ocean interior volume. Ranked from largest to smallest, the volume contributions scaled by surface area follow a power‐law distribution with an exponent of −1.09 ± 0.03 that appears indicative of the advective‐diffusive filling characteristics of the ocean circulation, as demonstrated using a simple model. This work quantifies the connection between the surface and interior ocean, allowing insight into ocean composition, atmosphere‐ocean interaction, and the transient response of the ocean to a changing climate.
TFMAC: Multi-channel MAC Protocol for Wireless Sensor Networks

In this paper we introduce TFMAC, a hybrid MAC protocol especially designed for wireless sensor networks in which each sensor node is equipped with a single half-duplex transceiver with multiple-frequency support. TFMAC incorporates multiple channels into a traditional TDMA scheme allowing different sensor nodes in a neighborhood to transmit on different channels simultaneously. Simulation results demonstrate that the proposed multiple-channel protocol provides higher maximum throughput and smaller average packet delay in respect to the basic single-channel TDMA scheme.
MERIT-Infonomics Research Memorandum series A Simple Endogenous Growth Model With Asymmetric Employment Opportunities by Skill

In this paper we present the outlines of an endogenous growth model that focuses on the labour marketand skill-aspects of economic policy measures that may have an impact on technological change, and hence on the long term effectiveness of the policy measures concerned. The link between skills and technology is two-fold. On the one hand, new technology is high-skilled intensive, while on the other hand, process R&D may actively change the skill-mix of existing production technologies in the direction of a more intensive use of least-cost production factors/skills. Hence, we endogenise both product R&D and process R&D decisions. The product R&D generates new varieties of goods with a higher quality than older varieties. New and older varieties are assumed to be imperfect substitutes, so that new varieties only gradually replace older varieties. Process R&D in turn is geared towards downscaling the skill-requirements of the jobs associated with producing the different varieties of output. Because high-skilled labour has different uses (it is an input to final output production, but also into product and process R&D activities), whereas low-skilled labour is used only in final output generation, we can show how various alternative policy measures may affect R&D decisions, hence growth performance, but also the distribution of income between skills. We also show that the promotion of process R&D in particular has beneficial effects both for the employment perspectives of lowskilled workers and for growth in general. In simulation experiments with the model we show that the model, even in its present state, is able to mimic the stylised facts reported by Acemoglu (1997), who observed for the US that an increase in the supply of high-skilled labour does not necessarily imply a fall in the relative wage rate of high-skilled workers in the long run. We show that the ensuing increase in R&D activity creates its own demand for high-skilled workers when new products arrive on the market that are high-skilled intensive during the first phase of their life-cycle, as we assume it to be the case. This in turn invokes endogenous process R&D reactions that change the long term composition of the demand for labour by skill and by sector. In various experiments we found that the model generates an interesting interplay between both types of R&D that may have important consequences for the distribution of income between skills, for growth and more generally for the design of economic policy. Adriaan van Zon, MERIT, Maastricht University Phone:+31 43 3883890, Fax:+31 43 3884905 e-mail: Adriaan.vanZon@merit.unimaas. nl
Named Entity Recognition in Bengali: A Multi-Engine Approach

This paper reports about a multi-engine approach for the development of a Named Entity Recognition (NER) system in Bengali by combining the classifiers such as Maximum Entropy (ME), Conditional Random Field (CRF) and Support Vector Machine (SVM) with the help of weighted voting techniques. The training set consists of approximately 272K wordforms, out of which 150K wordforms have been manually annotated with the four major named entity (NE) tags, namely Person name, Location name, Organization name and Miscellaneous name. An appropriate tag conversion routine has been defined in order to convert the 122K wordforms of the IJCNLP-08 NER Shared Task on South and South East Asian Languages (NERSSEAL)1 data into the desired forms. The individual classifiers make use of the different contextual information of the words along with the variety of features that are helpful to predict the various NE classes. Lexical context patterns, generated from an unlabeled corpus of 3 million wordforms in a semi-automatic way, have been used as the features of the classifiers in order to improve their performance. In addition, we propose a number of techniques to post-process the output of each classifier in order to reduce the errors and to improve the performance further. Finally, we use three weighted voting techniques to combine the individual models. Experimental results show the effectiveness of the proposed multi-engine approach with the overall Recall, Precision and F-Score values of 93.98%, 90.63% and 92.28%, respectively, which shows an improvement of 14.92% in F-Score over the best performing baseline SVM based system and an improvement http://ltrc.iiit. ac.in/ner-ssea-08
Changing student use and perceptions of learning technologies, 2002 - 2004

The experiences of 504 students were explored by survey in their first year and their third year of study at university. Compared with first year, student engagement in paid work increased significantly and the number of days spent on campus decreased significantly by third year, though the overall number of hours spent attending classes did not change significantly. There were no significant differences in student levels of satisfaction with teaching or their university experience overall between first year and third year. Student use of information and communication technologies and their perceived usefulness of those technologies in supporting learning increased significantly over the two year period. Gender and age differences were apparent in the use of technologies. Frequency of use of learning technologies correlated with perceived usefulness of the technology. However there was a pattern of volatility in student use of technology over time in which early adopters later abandoned the technology.
A discrete mean value of the derivative of the Riemann zeta function

In this article we compute a discrete mean value of the derivative of the Riemann zeta function. This mean value will be important for several applications concerning the size of $\zeta'(\rho)$ where $\zeta(s)$ is the Riemann zeta function and $\rho$ is a non-trivial zero of the Riemann zeta function.
Concatenated Quantum Codes

One of the main problems for the future of practical quantum computing is to stabilize the computation against unwanted interactions with the environment and imperfections in the applied operations. Existing proposals for quantum memories and quantum channels require gates with asymptotically zero error to store or transmit an input quantum state for arbitrarily long times or distances with fixed error. In this report a method is given which has the property that to store or transmit a qubit with maximum error $\epsilon$ requires gates with error at most $c\epsilon$ and storage or channel elements with error at most $\epsilon$, independent of how long we wish to store the state or how far we wish to transmit it. The method relies on using concatenated quantum codes with hierarchically implemented recovery operations. The overhead of the method is polynomial in the time of storage or the distance of the transmission. Rigorous and heuristic lower bounds for the constant $c$ are given.
Utilizing a geometry of context for enhanced implicit feedback

Implicit feedback algorithms utilize interaction between searchers and search systems to learn more about users' needs and interests than expressed in query statements alone. This additional information can be used to formulate improved queries or directly improve retrieval performance. In this paper we present a geometric framework that utilizes multiple sources of evidence present in this interaction context (e.g., display time, document retention) to develop enhanced implicit feedback models personalized for each user and tailored for each search task. We use rich interaction logs (and associated metadata such as relevance judgments), gathered during a longitudinal user study, as relevance stimuli to compare an implicit feedback algorithm developed using the framework with alternative algorithms. Our findings demonstrate both the effectiveness of our proposed algorithm and the potential value of incorporating multiple sources of interaction evidence when developing implicit feedback algorithms.
Quality of antiretroviral therapy in public health facilities in Nigeria and perceptions of end users.

AIM This paper describes perceptions of the end users on quality of antiretroviral therapy (ART) in public health facilities in Nigeria. BACKGROUND Health care services in Nigeria face challenges of meeting end users' requirements and expectations for quality ART service provision. METHOD A qualitative design was followed. Unstructured focus group discussions were conducted with end users (n = 64) in six locations across the six geopolitical zones of Nigeria. RESULTS The findings indicate that end users were satisfied with uninterrupted antiretroviral drug supplies, courtesy treatment, volunteerism of support group members and quality counselling services. CONCLUSION End users expect effective collaboration between healthcare providers and support group members, to enhance the quality of life of people living with HIV. IMPLICATIONS FOR NURSING MANAGEMENT A best practice guideline for the provision of end user focused ART service provision was developed for nurse managers.
Design of Automatic Database Schema Generator Based on XML Schema

With the recent expansion of e-commerce, B2B has surfaced as an area of substantial interest to the corporate world. B2B refers to economic transactions created among businesses through various networks, including the Internet. Currently, XML documents are partially used in e-commerce for exchanging information among businesses, and the core corporate systems are expected to be based on XML as web service becomes more prevalent. In turn, many companies are becoming competitively involved in developing XML DBMS. Conventional XML DBMS studies use XML DTD to express the XML document structure. Since XML DTD defines simple format expressions, there are numerous difficulties in defining the XML document structure. In order to overcome such issues, this paper proposes an automatic conversion mechanism and algorithm for the relational database schema using the XML schema, which as been selected as the W3C standard, in order to store XML data in the relational database generally used in data content storage based on the XML schema. Furthermore, a module will be develop for converting the XML schema design based on this approach into a relational database schema. This should allow efficient management of the XML data through the relational database.
Variant Ownership with Existential Types

We propose an ownership types system with existential quantiflcation of owners, similar to the existential quantiflcation of types in models of Java wildcards. This produces a system with variant ownership types. Using explicit existential types for variance is more uniform, less ad hoc, and easier to understand and reason about than previous solutions. Furthermore, we propose using both type and ownership parameters to increase the precision with which variant types can be specifled. 1. Background Ownership types (in all their various ∞avours, e.g., [4]) allow for the structuring of objects in the heap according to some tree or graph. Each object has an owner, denoted as part of the object’s type. A type may also be parameterised by contexts, which may be used as owners within the class deflnition. For example, consider the list class:
Solvent Microenvironments and Copper Binding Alters the Conformation and Toxicity of a Prion Fragment

The secondary structures of amyloidogenic proteins are largely influenced by various intra and extra cellular microenvironments and metal ions that govern cytotoxicity. The secondary structure of a prion fragment, PrP(111-126), was determined using circular dichroism (CD) spectroscopy in various microenvironments. The conformational preferences of the prion peptide fragment were examined by changing solvent conditions and pH, and by introducing external stress (sonication). These physical and chemical environments simulate various cellular components at the water-membrane interface, namely differing aqueous environments and metal chelating ions. The results show that PrP(111-126) adopts different conformations in assembled and non-assembled forms. Aging studies on the PrP(111-126) peptide fragment in aqueous buffer demonstrated a structural transition from random coil to a stable β-sheet structure. A similar, but significantly accelerated structural transition was observed upon sonication in aqueous environment. With increasing TFE concentrations, the helical content of PrP(111-126) increased persistently during the structural transition process from random coil. In aqueous SDS solution, PrP(111-126) exhibited β-sheet conformation with greater α-helical content. No significant conformational changes were observed under various pH conditions. Addition of Cu2+ ions inhibited the structural transition and fibril formation of the peptide in a cell free in vitro system. The fact that Cu2+ supplementation attenuates the fibrillar assemblies and cytotoxicity of PrP(111-126) was witnessed through structural morphology studies using AFM as well as cytotoxicity using MTT measurements. We observed negligible effects during both physical and chemical stimulation on conformation of the prion fragment in the presence of Cu2+ ions. The toxicity of PrP(111-126) to cultured astrocytes was reduced following the addition of Cu2+ ions, owing to binding affinity of copper towards histidine moiety present in the peptide.
SysPTM: A Systematic Resource for Proteomic Research on Post-translational Modifications*

With the rapid expansion of protein post-translational modification (PTM) research based on large-scale proteomic work, there is an increasing demand for a suitable repository to analyze PTM data. Here we present a curated, web-accessible PTM data base, SysPTM. SysPTM provides a systematic and sophisticated platform for proteomic PTM research equipped not only with a knowledge base of manually curated multi-type modification data but also with four fully developed, in-depth data mining tools. Currently, SysPTM contains data detailing 117,349 experimentally determined PTM sites on 33,421 proteins involving nearly 50 PTM types, curated from public resources including five data bases and four web servers and more than one hundred peer-reviewed mass spectrometry papers. Protein annotations including Pfam domains, KEGG pathways, GO functional classification, and ortholog groups are integrated into the data base. Four online tools have been developed and incorporated, including PTMBlast, to compare a user's PTM dataset with PTM data in SysPTM; PTMPathway, to map PTM proteins to KEGG pathways; PTMPhylog, to discover potentially conserved PTM sites; and PTMCluster, to find clusters of multi-site modifications. The workflow of SysPTM was demonstrated by analyzing an in-house phosphorylation dataset identified by MS/MS. It is shown that in SysPTM, the role of single-type and multi-type modifications can be systematically investigated in a full biological context. SysPTM could be an important contribution to modificomics research. SysPTM is freely available online at www.sysbio. ac.cn/SysPTM.
Using trajectory features for upper limb action recognition

There is growing interest in using low-cost wearable sensors to model limb movement in applications such as stroke rehabilitation and physiotherapy. This paper presents an algorithm for the detection and classification of arm motion in time series collected by wearable inertial sensors. High level arm trajectory features are obtained from raw sensor data using a sensor orientation tracking algorithm and an arm model. The features are then used in a clustering-based classifier. In the classifier training stage, features are clustered using the k-means algorithm, and a histogram of “key poses” is generated from the clustering as a template for each class. In the recognition stage, new data are segmented and matched to the templates. Experiments on human subjects show that by using trajectory features in the proposed approach, we can achieve higher accuracy than a range of benchmark non-temporal classifiers.
A flexible and efficient algorithm for regularized Marginal Fisher analysis

Marginal Fisher analysis (MFA) is a well-known linear dimensionality reduction method. However, MFA does not utilize the local diversity information of the training data, which will degrade its performance. In order to enhance the discriminant power of MFA, this paper considers introducing local variation quantity to enlarge the distances between local neighborhood embeddings and proposes a flexible and efficient implementation of MFA (F-MFA) within the regularization framework. Therefore, the discriminant structure and diversity of data are preserved in low-dimensional subspace. Computationally, F-MFA is formulated as a trace differential optimization problem which can completely avoids the singularity problem as it exists in MFA. Further, an efficient algorithm is developed for implementing F-MFA via QR-decomposition. Experimental results on four face data sets demonstrate the effectiveness of our approach.