Miracle-dz/UniFAR-FTUs huggingface dataset:
Ultrastructural Localization of the Serotonin Transporter in Limbic and Motor Compartments of the Nucleus Accumbens

Extracellular levels of serotonin [5-hydroxytryptamine (5-HT)] in the nucleus accumbens (NAc) can influence both cognitive and motor functions involving extensive connections with the frontal cortex. The 5-HT levels reflect vesicular release and plasmalemmal reuptake through the serotonin transporter (SERT). We used electron microscopic immunocytochemistry to determine the sites for SERT activation in the limbic shell and motor-associated core of the rat NAc. Of the SERT-immunoreactive profiles in each region, >90% were serotonergic axons and axon terminals; the remainder were nonserotonergic dendrites and glia. Axonal SERT immunogold labeling was seen mainly at nonsynaptic sites on plasma membranes and often near 5-HT-containing large dense core vesicles (DCVs). SERT-labeled axonal profiles were larger and had a higher numerical density in the shell versus the core but showed no regional differences in their content of SERT immunogold particles. In contrast, immunoreactive dendrites had a lower numerical density in the shell than in the core. SERT labeling in dendrites was localized to segments of plasma membrane near synaptic contacts from unlabeled terminals and/or dendritic appositions. Our results suggest that in the NAc (1) reuptake into serotonergic axons is most efficient after exocytotic release from DCVs, and (2) increased 5-HT release without concomitant increase in SERT expression in individual axons may contribute to higher extracellular levels of serotonin in the shell versus the core. These findings also indicate that SERT may play a minor substrate-dependent role in serotonin uptake or channel activity in selective nonserotonergic neurons and glia in the NAc.
Enhanced Greedy Routing with Anti-Void Traversal for Wireless Sensor Networks

-- In this paper the solution to the void problem is taken up as the issue. This situation which exists in the currently existing greedy routing algorithms has been studied for the wireless sensor networks. The GAR protocol is a new protocol proposed here to guarantee the delivery of packets and excessive consumption of control overheads is resolved. This protocol is a combination of the GF algorithm and the RUT scheme. To enhance this protocol’s functionality we go in for three mechanisms that can also be implemented in this project. The hop count reduction (HCR) scheme is utilized as a short-cutting technique to reduce the routing hops by listening to the neighbour’s traffic, the intersection navigation (IN) mechanism is proposed to obtain the best rolling direction for boundary traversal with the adoption of shortest path criterion. These three schemes are incorporated within the GAR protocol to further enhance the routing performance with reduced communication overhead. The proofs of correctness for the GAR scheme are also given in this paper.
Development, Implementation and Testing of a Discourse Model for Newspaper Texts

Texts of a particular type evidence a discernible, predictable schema. These schemata can be delineated, and as such provide models of their respective text-types which are of use in automatically structuring texts. We have developed a Text Structurer module which recognizes text-level structure for use within a larger information retrieval system to delineate the discourse-level organization of each document's contents. This allows those document components which are more likely to contain the type of information suggested by the user's query to be selected for higher weighting. We chose newspaper text as the first text type to implement. Several iterations of manually coding a randomly chosen sample of newspaper articles enabled us to develop a newspaper text model. This process suggested that our intellectual decomposing of texts relied on six types of linguistic information, which were incorporated into the Text Structurer module. Evaluation of the results of the module led to a revision of the underlying text model and of the Text Structurer itself.
Model-based Object-oriented Requirement Engineering and its Support to Software Documents Integration

Abstract Maintaining uniformity of software requirement documents with artifacts from other phases of software life cycle is a very important, however, still a difficult and time-consuming task. Most requirement documents were written in ambiguous natural language which is less formal and imprecise. Without proper modeling, the requirement knowledge is laboriously captured and thus the following formal integration with other artifacts is infeasible. In this paper, we propose a Model-based Object-oriented approach for Requirement Engineering to support and improve the maintenance and consistency of software requirement documents, as well as the consistency with other artifacts through software life cycle. By applying modeling and Object-Oriented technologies to requirement, the domain knowledge can be captured in a well-defined model. The completeness, consistency, traceability and reusability of requirement and its integration with the artifacts of other phases thus can be improved. Maintaining uniformity of software requirement documents with artifacts from other phases of software life cycle is a very important, however, still a difficult and time-consuming task. Most requirement documents were written in ambiguous natural language which is less formal and imprecise. Without proper modeling, the requirement knowledge is laboriously captured and thus the following formal integration with other artifacts is infeasible. In this paper, we propose a Model-based Object-oriented approach for Requirement Engineering to support and improve the maintenance and consistency of software requirement documents, as well as the consistency with other artifacts through software life cycle. By applying modeling and Object-Oriented technologies to requirement, the domain knowledge can be captured in a well-defined model. The completeness, consistency, traceability and reusability of requirement and its integration with the artifacts of other phases thus can be improved.
Meta-Analysis of Randomised Clinical Trials Comparing Idarubicin + Cytarabine with Daunorubicin + Cytarabine as the Induction Chemotherapy in Patients with Newly Diagnosed Acute Myeloid Leukaemia

Background To determine whether the use of idarubicin+cytarabine (IA) is more effective than the use of daunorubicin+cytarabine (DA) as induction chemotherapy for patients with newly diagnosed acute myeloid leukaemia. Methods A computer-based search was performed. Randomised trials comparing IA with DA as induction therapy for newly diagnosed AML were included in this meta-analysis. The primary outcome of interest for our analysis was survival (disease-free survival, event-free survival and overall survival); the secondary endpoint was complete remission. Results Ten trials with 4,060 patients were eligible for this meta-analysis. Our pooled results suggest that IA is associated with a significant advantage in CR (RR = 1·23; 95% CI = 1·07–1·41, p = 0.004), EFS (HR = 0·64; 95% CI = 0·45–0·91, p = 0.013), and OS (HR = 0·88; 95% CI = 0·81–0·95, p = 0.02) but not in DFS (HR = 0·90; 95% CI = 0·80–1·00, p = 0.06). In the subgroup analysis, age had a significant interaction with OS and CR benefits. Conclusion Our analysis indicated that IA could improve the duration of overall survival compared to DA as induction therapy for young patients with newly diagnosed AML. Further study is needed to determine whether IA can produce clinical benefits in selected genetic or molecular subgroups of young AML patients.
Efficient similarity search on massive gene data based on cloud computing

As biomedical data grows rapidly nowadays, we are going to study how to efficiently apply cloud computing into biomedical problem. In this paper, the process of solving biomedical problem based on cloud computing is presented. First, the biomedical issue would be focus on the gene match and generation of large amount of the emulated data from real data. Second, a mathematical model is proposed to describe the problem. Third, the mathematical model is implemented into our MapReduce model, which means simulating the biomedical application rested on cloud computing. Finally, some experiments are conducted to test the performance of cloud computing on this issue. Besides, at the end of the paper, some advices are given to doctors and biomedical scientists.
White Cells Facilitate Opposite- and Same-Sex Mating of Opaque Cells in Candida albicans

Modes of sexual reproduction in eukaryotic organisms are extremely diverse. The human fungal pathogen Candida albicans undergoes a phenotypic switch from the white to the opaque phase in order to become mating-competent. In this study, we report that functionally- and morphologically-differentiated white and opaque cells show a coordinated behavior during mating. Although white cells are mating-incompetent, they can produce sexual pheromones when treated with pheromones of the opposite mating type or by physically interacting with opaque cells of the opposite mating type. In a co-culture system, pheromones released by white cells induce opaque cells to form mating projections, and facilitate both opposite- and same-sex mating of opaque cells. Deletion of genes encoding the pheromone precursor proteins and inactivation of the pheromone response signaling pathway (Ste2-MAPK-Cph1) impair the promoting role of white cells (MTL a) in the sexual mating of opaque cells. White and opaque cells communicate via a paracrine pheromone signaling system, creating an environment conducive to sexual mating. This coordination between the two different cell types may be a trade-off strategy between sexual and asexual lifestyles in C. albicans.
Crime and Town Centers: Are Downtowns More Dangerous than Suburban Shopping Nodes?

The perception of high crime rates in downtowns has hindered the revitalization of downtown shopping districts and adjacent residential areas. This paper presents a better methodology for measuring crime in commercial shopping districts, replacing the conventional method of quoting crimes per 100,000 residences with a measure that more accurately reflects oneâ€™s chance for being a crime victim. This new measurement is used to address the question of whether the downtown shopping districts of Los Angeles and San Diego are as dangerous as two of their most competitive suburban shopping areasâ€”Santa Monica and Fashion Valley. The findings indicate that actual crime rates in both downtown Los Angeles and downtown San Diego are in fact lower that those of their suburban counterparts.
A scalable approach for Contradiction Detection driven by Opinion mining

In this paper we address the problem of identifying contradictions by opinion mining across documents. Our approach involves opinion extraction and storage by processing natural language documents such as reviews, news etc. and aims the identification of contradictory opinions related to the same target expressed by the same holder or by different holders. By matching the structured representations of opinions we identify a potential inconsistency occurring in two documents that is signaled and further analysis is applied to confirm/infirm the contradiction. Moreover, communities are detected both on individual opinions and social data. Thus, the (in)consistency might be tracked for the holder as a member of a community, as well as for the holder as an individual. We addressed scalability by designing a cloud-based storage infrastructure and an efficient indexing system that allows for fast retrieval and matching of structured representations.
Employee perceptions of share ownership schemes: an empirical study

The objective of this study was to investigate some of the problems associated with the introduction and successful management of Employee Share Ownership Schemes (ESOPs). An ESOP is a participative management approach that appeals to some of the needs of both employees and management. The study considered how perceptions of ESOPs can be influenced and managed through managerial antecedents such as trust, empowerment and communication, and empirically measures their impact on outcome variables such as organisational commitment. The empirical findings revealed that if employees have positive perceptions of their firm’s ESOP (the value and benefit for them) they are more likely to be committed to their firm (organizational commitment) and organizational commitment has been shown to lead to a host of benefits for the firm.
Dimerization of a Phage-Display Selected Peptide for Imaging of αvβ6- Integrin: Two Approaches to the Multivalent Effect

The integrin αvβ6 is an emerging biomarker for non-small cell lung cancer (NSCLC). An αvβ6-binding peptide was previously selected from a phage-displayed peptide library. Here, we utilize a multivalent design to develop a peptidic probe for positron emission tomography (PET) imaging of αvβ6+ NSCLC tumors. Multimeric presentation of this peptide, RGDLATLRQL, on a bifunctional copper chelator was achieved using two approaches: dimerization of the peptide followed by conjugation to the chelator (H2-D10) and direct presentation of two copies of the peptide on the chelator scaffold (H2-(M10)2). Binding affinities of the divalent peptide conjugates are four-fold higher than their monovalent counterpart (H2-M10), suggestive of multivalent binding. PET imaging using the bivalent 64Cu-labeled conjugates showed rapid and persistent accumulation in αvβ6+ tumors. By contrast, no significant accumulation was observed in αvβ6- tumors. Irrespective of the dimerization approach, all divalent probes showed three-fold higher tumor uptake than the monovalent probe, indicating the role of valency in signal enhancement. However, the divalent probes have elevated uptake in non-target organs, especially the kidneys. To abrogate nonspecific uptake, the peptide's N-terminus was acetylated. The resultant bivalent probe, 64Cu- AcD10, showed drastic decrease of kidney accumulation while maintaining tumor uptake. In conclusion, we developed an αvβ6-integrin specific probe with optimized biodistribution for noninvasive PET imaging of NSCLC. Further, we have demonstrated that use of multivalent scaffolds is a plausible method to improve library selected peptides, which would be suboptimal or useless otherwise, for imaging probe development.
The university's role in economic development: from research to outreach

The future of our global economy will be in knowledge- and technology-base economic development. Remaining competitive in a global economy will require the ability to harness the research and educational resources of higher educaiton institutions. Simultaneously, state and land-grant universities are seeking to reinvent their outreach missions with a major focus on providing direct services for local and regional economic development. This volume offers models the academy can use to become an engine of economic development.This is the 97th issue of the quarterly journal "New Directions for Higher Education. "
Accelerating parallel particle swarm optimization via GPU

Particle swarm optimization (PSO) is a population-based stochastic and derivative-free method that has been used to solve various optimization problems due to its simplicity and efficiency. While solving high-dimensional or complicated problems, PSO requires a large number of particles to explore the problem domains and consequently introduces high computational costs. In this paper, we focus on the acceleration of PSO for solving box-constrained, load-balanced optimization problems by parallelization on a graphics processing unit (GPU). We propose a GPU-accelerated PSO (GPSO) algorithm by using a thread pool model and implement GPSO on a GPU. Numerical results show that the GPU architecture fits the PSO framework well by reducing computational timing, achieving high parallel efficiency and finding better optimal solutions by using a large number of particles. For example, while solving the 100-dimensional test problems with 65,536 (16×212) particles, GPSO has achieved up to 280X and 83X speedups on a NVIDIA Tesla C1060 1.30 GHz GPU relative to an Intel Xeon-X5450 3.00 GHz central processing unit running in single- and quad-core mode, respectively. GPSO provides a promising method for tackling high-dimensional and difficult optimization problems using a low-cost and many-core GPU system.
Improving English-Vietnamese Word Alignment Using Translation Model

Word alignment for a parallel corpus is the connection between the words/phrases in source language and the words/phrases in target language. The alignment result is an important input for many natural language processing applications. In this paper, we propose an approach to improve the English-Vietnamese word alignment result by using the alignment frequency that is presented in the translation model of SMT (Statistical Machine Translation). We also indicate 5 common error types of English-Vietnamese word alignment and propose the heuristic patterns to discover the alignment errors. The experimental results show the improvement compared to the result of GIZA++.
Density Based Clustering Scheme Using Dynamic Dissimilarity Measures

 Abstract — Clustering methods are used support estimates of a data distribution have newly attracted much attention because of their ability to generate cluster boundaries of arbitrary shape and to contract with outliers efficiently. This paper proposes, Density based clustering using dynamic dissimilarity measure based on a dynamical system associated with Density estimating functions. Hypothetical basics of the proposed measure are developed and applied to construct a clustering method that can efficiently partition the whole data space. Clustering based on the proposed dissimilarity measure is robust to handle large amount of data and able to estimate the number of clusters automatically by avoid overlap. The dissimilarity values are evaluated and clustering process is carried out with the density values.
Modeling hysteresis, creep, and dynamic effects for piezoactuator-based nano-positioning systems

In this paper, a model characterizing hysteresis, creep, and dynamic effects is proposed for simulation and control system design of piezoactuator-based nano-positioning system. Fractional-order integrator is employed to characterize the creep behavior at low frequencies. Maxwell resistive capacitor (MRC) model is used to capture the rate-independent hysteresis lying in the electric domain between the applied voltage and the surface charge. The remaining high-frequency dynamics, including the dynamics of the mechanical structure, the voltage amplifier, and the capacitive sensor, are described by a high-order linear system. The proposed model is verified experimentally.
Using Electromagnetic Input for Multi-User or Two-Handed Spatial Gestural Interaction based on the Digital Compass

Multiple researchers recently proposed the use of the digital compass embedded in mobile devices for touchless interaction in the 3D space around them. These methods overcome several limits imposed by other interaction techniques and were evaluated for a variety of uses. However, they do not support collaborative settings and are prone to dynamic noise caused by external conditions, as with most other sensor-based interaction techniques. In this paper, we propose the use of frequency-modulated electromagnets as an input medium for magnetic interaction to overcome its various constraints and further enable multi-user and two-handed input. Furthermore, we demonstrated the hardware design specifications of a novel input device, referred to as electromagnetic stylus, which is prototyped to conduct a user-study on the proposed method. Experimental results indicate that gestures performed simultaneously by four electromagnetic styli can accurately be recognized using a single magnetic field sensor, and dynamic noises can be substantially reduced.
An approach to improve semantics in Smart Spaces using reactive fuzzy rules

Human behaviour representation and modelling in Smart Spaces are crucial tasks in Ambient Intelligence environments. A problem found among existing technologies is that the environment sensor data is always provided as crisp events. However, to model behaviour in reactive Smart Spaces we need to identify user patterns, which are not always performed in the same way or happening at the same time/frequency. Hence, the handling of imprecision is essential to model human behaviour in a realistic way. In order to connect these two paradigms, we suggest a combined architecture to fill the gap between quantitative Smart Space architectures (which provide raw crisp events) and qualitative aspects such as fuzzy reasoning and learning algorithms, that extract intelligence from data. With this aim, we propose to use Semantic Web principles of independence and interoperability to build an integrated framework to take advantage of the benefits of both crisp and fuzzy models. Our contribution in this work is a proof of concept to demonstrate how a hybrid architecture, with a reactive rule-based subscription mechanism, can empower users to model everyday activities semantically or control what happens on their surroundings, among other advantages.
Secure Network Authentication with PassText

Network security partially depends on reliable user authentication; unfortunately currently used passwords are not completely secure. We have designed an authentication schema with a password, which is easy to remember and can be relatively quickly provided to the system, while at the same time remaining impossible to break with brute force alone. We have also proposed a novel measure of password length and compared password spaces of many popular alphanumeric and graphical authentication schemas against the one proposed in this paper
Heat Kernel Smoothing and its Application to Cortical Manifolds

In brain imaging analysis, there is a need for analyzing data collected on the cortical surface of the human brain. Gaussian kernel smoothing ha s been widely used in this area in conjunction with random field theory for analy zing data residing in Euclidean spaces. The Gaussian kernel is isotropic in Eucli dian space so it assigns the same weights to observations equal distance apart. Howe ver, when we smooth data residing on a curved surface, it fails to be isotropic. O n the curved surface, a straight line between two points is not the shortest distan ce so one may assign smaller weights to closer observations. For this reason smo othing data residing on manifolds requires constructing a kernel that is isotrop ic along the geodesic curves. With this motivation in mind, we construct the kerne l of a heat equation on manifolds that should be isotropic in the local conformal coordinates and develop a framework for heat kernel smoothing and statistical inference is performed on manifolds. As an illustration, we apply our approach in co mparing the cortical thickness of autistic children to that of normal children.