Miracle-dz/UniFAR-FTUs huggingface dataset:
A Rat Model of Progressive Nigral Neurodegeneration Induced by the Parkinson's Disease-Associated G2019S Mutation in LRRK2

The G2019S mutation in the leucine-rich repeat kinase 2 (LRRK2) gene is the most common genetic cause of Parkinson's disease (PD), accounting for a significant proportion of both autosomal dominant familial and sporadic PD cases. Our aim in the present study is to generate a mammalian model of mutant G2019S LRRK2 pathogenesis, which reproduces the robust nigral neurodegeneration characteristic of PD. We developed adenoviral vectors to drive neuron-specific expression of full-length wild-type or mutant G2019S human LRRK2 in the nigrostriatal system of adult rats. Wild-type LRRK2 did not induce any significant neuronal loss. In contrast, under the same conditions and levels of expression, G2019S mutant LRRK2 causes a progressive degeneration of nigral dopaminergic neurons. Our data provide a novel rat model of PD, based on a prevalent genetic cause, that reproduces a cardinal feature of the disease within a rapid time frame suitable for testing of neuroprotective strategies.
Recent Developments in Fused Deposition Modeling-Based 3D Printing of Polymers and Their Composites

Abstract Fused deposition modeling (FDM) is one of the most widely used 3D printing techniques that utilizes polymers to create models, prototypes or even end products. Since 2009, the demand for FDM has been increasing at an incredible rate from one year to the next, and many experts believe that this technology has the potential to revolutionize manufacturing in many sectors. The main advantages of FDM technology are that the technique offers a simpler fabrication process and a more cost-effective method compared to other prominent 3D printing techniques, and yet, it is still capable of manufacturing complex geometries and cavities with reasonable dimensional accuracy. However, there are still some limitations and shortcomings that have been identified, especially pertaining to the lower mechanical properties exhibited in FDM parts compared to the parts produced by conventional methods such as injection and compression techniques. Therefore, this review article focused on recent developments and progress in the FDM technique in an attempt to improve the end performance of 3D-printed parts, along with current challenges and the future direction.
I-Deals: Idiosyncratic Terms in Employment Relationships

Idiosyncratic employment arrangements (i-deals) stand to benefit the individual employee as well as his or her employer. However, unless certain conditions apply, coworkers may respond negatively to these arrangements. We distinguish functional i-deals from their dysfunctional counterparts and highlight evidence of i-deals in previous organizational research. We develop propositions specifying both how ideals are formed and how they impact workers and coworkers. Finally, we outline the implications i-deals have for research and for managing contemporary employment relationships.
A Finite-State Library for NLP

A library of functions is described which use finite-state au tomata for compact storage and efficient usage of very large dictionaries and language mode ls. The library can be used to test whether a word is in a dictionary, to perform morpholo gical analysis, to construct perfect hash tables, and to construct and use very large lang uage models (such as models which employ bigram and trigram frequencies derived from ve ry large corpora). The library makes use of a standalone software package fsa (available at http://www.pg.gda.pl/ jandac/fsa.html) that is briefly described here as well.
Embedding the Semantic Knowledge in Convolution Kernels

Convolution kernels, such as tree kernel and subsequence kernel are useful for natural language processing tasks. However, most of them ignore the semantic knowledge. In order to solve the problem, this paper proposes a new method to embed the semantic knowledge into kernel calculation. The new method has been applied to extract the ORG-affiliation relation from Chinese texts and achieves an average F-measure of 82.1%. Comparing with feature-based method and the traditional Word-sequence kernel, it provides significant improvement.
Chapter 2 Analytical Approaches for Assessing Ecosystem Condition and Human Well-being Coordinating Lead

Coordinating Lead Authors: Ruth DeFries, Stefano Pagiola Lead Authors: W.L. Adamowicz, H. Resit Akçakaya, Agustin Arcenas, Suresh Babu, Deborah Balk, Ulisses Confalonieri, Wolfgang Cramer, Fander Falconı́, Steffen Fritz, Rhys Green, Edgar Gutiérrez-Espeleta, Kirk Hamilton, Racine Kane, John Latham, Emily Matthews, Taylor Ricketts, Tian Xiang Yue Contributing Authors: Neville Ash, Jillian Thönell Review Editors: Gerardo Ceballos, Sandra Lavorel, Gordon Orians, Stephen Pacala, Jatna Supriatna, Michael Young
Stabilize Sequence Learning with Recurrent Neural Networks by Forced Alignment

Cursive handwriting recognition is still a hot topic of research, especially for non-Latin scripts. One of the techniques which yields best recognition results is based on recurrent neural networks: with neurons modeled by long short-term memory (LSTM) cells, and alignment of label sequence to output sequence performed by a connectionist temporal classification (CTC) layer. However, network training is time consuming, unstable, and tends to over-adaptation. One of the reasons is the bootstrap process, which aligns the label data more or less randomly in early training iterations. This also leads to the fact that the emission peak positions within a character are located unpredictably. But positions near the center of a character are more desirable: In theory, they better model the properties of a character. The solution presented here is to guide the back-propagation training in early iterations: Character alignment is enforced by replacing the forward-backward alignment by fixed character positions: either pre-segmented, or equally distributed. After a number of guided iterations, training may be continued by standard dynamic alignment. A series of experiments is performed to answer some of these questions: Can peak positions be controlled in the long run? Can training iterations be reduced, getting results faster? Is training more stable? And finally: Do defined character position lead to better recognition performance?
CHF: A Scalable Topological Data Structure for Tetrahedral Meshes

This work introduces a scalable topological data structure for manifold tetrahedral meshes called Compact Half-Face (CHF). It provides a high degree of scalability, since it is able to optimize the memory consumption/execution time ratio for different applications and data by using features of its different levels. An object-oriented API using class inheritance and virtual instantiation enables a unique interface for each function at any level. CHF requires very few memory, is simple to implement and easy to use, since it substitutes pointers by container of integers and basic bit-wise rules.
A lumped model of the neural peripheral and central nervous auditory link - qualitative view I

This paper develops a simple lumped model of the auditory link between the cochlea and the central nervous system (CNS). The model is developed for one temporal (tonotopic) segment of the periphery. The model appears to approach that of a delay line resonator. The lumped model is derived from an expanded physiological model, which is still under development. The lumped model is capable of explaining many phenomena which are important aspects of hearing. The delay line re-circulates the signal within the system; it is a delayed feedback mechanism. This signal is non-linearly limited by various physiological limitations. These combine to explain many auditory phenomena such as the base mechanism of the cochlea amplifier phase locking and masking frequency spreading functions - to name a few.
CSF of ERP in Australia

This research investigates the Critical Success Factors and the impact they have on Enterprise Resource Planning implementations in Australia. This research uses qualitative data and specifically the case study methodology to examine the effects that these factors have on Enterprise Resource Planning implementations and ultimately how this affects the success and failure of an implementation. Data collected from interviews were analysed and a number of findings were drawn. The findings of this research will form a basis for future studies in this field, and will highlight areas where Enterprise Resource Planning implementations can be improved to ensure success.
Estimating Domain-Specific User Expertise for Answer Retrieval in Community Question-Answering Platforms

Community Question-Answering (CQA) platforms leverage the inherent wisdom of the crowd - enabling users to retrieve quality information from domain experts through natural language. An important and challenging task is to identify reliable and trusted experts on large popular CQA platforms. State-of-the-art graph-based approaches to expertise estimation consider only user-user interactions without taking the relative contribution of individual answers into account, while pairwise-comparison approaches consider only pairs involving the best-answerer of each question. This research argues that there is a need to account for the user's relative contribution towards solving the question when estimating user expertise and proposes a content-agnostic measure of user contributions. This addition is incorporated into a competition-based approach for ranking users' question answering ability. The paper analyses how improvements in user expertise estimation impact on applications in expert search and answer quality prediction. Experiments using the Yahoo! Chiebukuro data show encouraging performance improvements and robustness over state-of-the-art approaches.
Correction of the supratip deformity of the nose.

BACKGROUND A supratip deformity can develop either congenitally or after a poorly executed rhinoplasty, as a result of fullness or convexity just above the nasal tip. OBJECTIVE In this article, the authors describe the causes of the supratip deformity and present their technique for surgical correction. METHODS A retrospective chart review was conducted for 62 consecutive patients (24 primary rhinoplasty and 38 secondary rhinoplasty) treated between January 2005 and February 2010. The authors' approach to managing supratip deformity included a combination of wide undermining of the nasal skin through the open technique; resection of excessive soft tissue and the bony, cartilaginous dorsum; suturing to advance the supratip; and augmentation of the nasal tip and dorsum with cartilage, crushed cartilage, silicone implants, and/or septal extension grafts. RESULTS No infection or nasal bleeding was recorded during postoperative follow-up. The supratip and tip projection showed adequate shape in all cases. To assess satisfaction, all patients and 2 independent plastic surgeons were asked to rate the result on a 5-point scale. The average patient satisfaction rating was 4.2 and the average independent rating was 4.4 points. CONCLUSIONS Supratip deformities should be corrected according to their unique cause and shape. The authors' method of repairing these deformities has resulted in a low complication rate and yielded satisfactory results.
A STUDY ON IMPLICIT FEEDBACK IN MULTICRITERIA E-COMMERCE RECOMMENDER SYSTEM

Recommender systems are personalized intelligent systems capable of helping people to easily locate their relevant information through recommendations from a large repository of information. In order to provide personalized recommendations, the accurate modeling of user‘s preferences is required. Modeling of user‘s preferences needs their relevance feedback on the recommendations. The relevance feedback may be collected either explicitly or implicitly. The explicit relevance feedback introduces intrusiveness problem whereas the implicit feedback can be inferred from normal user-system interactions without disturbing the user. The users expect accuracy in recommendations and effortless assistance from the recommender systems. The multicriteria user preference ratings are useful to improve the accuracy of recommendations. However, collecting multiple ratings increases the cognitive load of the user. We believe that a combined, implicit relevance feedback and multicriteria user preference ratings, approach improve the accuracy in recommendations and eliminate the intrusiveness problem of recommender systems. In order to fulfill the above needs and to better understand the potential behind the implicit relevance feedback approach under multicriteria ratings context, this study focuses a new implicit-multicriteria combined recommendation approach. A Music recommender system is developed for this experiment to evaluate the recommendation accuracy of implicit and explicit feedback approaches under the user-based and item-based prediction algorithms against different data sparsity levels, training/test ratio and neighborhood sizes. Out of this experiment, the implicit ratings based prediction algorithms provide better performance than the explicit ratings based prediction algorithms with respect to all the three sensitive parameters. It is also observed that the proposed IB_PIR prediction algorithm computes better predictions than other prediction algorithms. Finally, we discuss the study‘s implications for theory and practice and conclude with many suggestions for future research on non-intrusive
PATHS: A System for Accessing Cultural Heritage Collections

This paper describes a system for navigating large collections of information about cultural heritage which is applied to Europeana, the European Library. Europeana contains over 20 million artefacts with meta-data in a wide range of European languages. The system currently provides access to Europeana content with meta-data in English and Spanish. The paper describes how Natural Language Processing is used to enrich and organise this meta-data to assist navigation through Europeana and shows how this information is used within the system.
Thingsonomy: Tackling Variety in Internet of Things Events

The Internet of Things (IoT) will connect billions of devices to the Internet and create a large-scale dynamic and open environment with high heterogeneity. To assure rapid adoption of IoT applications, application developers and users need to be abstracted from IoT infrastructure via scalable middleware. Event-processing systems have the potential to contribute in filling the gap between the IoT infrastructure and applications layers. Event processing follows a decoupled model of interaction in space, time, and synchronization. However, the dimension of semantic coupling still exists and poses a challenge to scalability in highly semantically heterogeneous and dynamic environments such as the IoT. Here, the authors describe an approach based on loosely coupled producers and consumers enabled with approximate semantic matching of events. They emphasize a practitioner perspective to IoT architectures for building software that can tackle heterogeneity of IoT events.
Using Treemap as a Generic Least Square Backend for 6-DOF SLAM

Treemap is a generic SLAM algorithm that has been successfully used to estimate extremely large 2D maps closing a loop over a million landmarks in 442ms. We are currently working on an open-source implementation that can handle most variants of SLAM. In this paper we show initial results demonstrating 6-DOF feature based SLAM and closing a simulated loop over 106657 3D features in 209ms.
The Hsp90 Co-Chaperone Sgt1 Governs Candida albicans Morphogenesis and Drug Resistance

The molecular chaperone Hsp90 orchestrates regulatory circuitry governing fungal morphogenesis, biofilm development, drug resistance, and virulence. Hsp90 functions in concert with co-chaperones to regulate stability and activation of client proteins, many of which are signal transducers. Here, we characterize the first Hsp90 co-chaperone in the leading human fungal pathogen, Candida albicans. We demonstrate that Sgt1 physically interacts with Hsp90, and that it governs C. albicans morphogenesis and drug resistance. Genetic depletion of Sgt1 phenocopies depletion of Hsp90, inducing yeast to filament morphogenesis and invasive growth. Sgt1 governs these traits by bridging two morphogenetic regulators: Hsp90 and the adenylyl cyclase of the cAMP-PKA signaling cascade, Cyr1. Sgt1 physically interacts with Cyr1, and depletion of either Sgt1 or Hsp90 activates cAMP-PKA signaling, revealing the elusive link between Hsp90 and the PKA signaling cascade. Sgt1 also mediates tolerance and resistance to the two most widely deployed classes of antifungal drugs, azoles and echinocandins. Depletion of Sgt1 abrogates basal tolerance and acquired resistance to azoles, which target the cell membrane. Depletion of Sgt1 also abrogates tolerance and resistance to echinocandins, which target the cell wall, and renders echinocandins fungicidal. Though Sgt1 and Hsp90 have a conserved impact on drug resistance, the underlying mechanisms are distinct. Depletion of Hsp90 destabilizes the client protein calcineurin, thereby blocking crucial responses to drug-induced stress; in contrast, depletion of Sgt1 does not destabilize calcineurin, but blocks calcineurin activation in response to drug-induced stress. Sgt1 influences not only morphogenesis and drug resistance, but also virulence, as genetic depletion of C. albicans Sgt1 leads to reduced kidney fungal burden in a murine model of systemic infection. Thus, our characterization of the first Hsp90 co-chaperone in a fungal pathogen establishes C. albicans Sgt1 as a global regulator of morphogenesis and drug resistance, providing a new target for treatment of life-threatening fungal infections.
LIFESTYLE PERCEPTIONS AMONG MIDDLE – AGED OBESE WOMEN

Background. The aim of the study was to establish the perceptions of lifestyles among middle-aged obese women. Methods. Data collection method was semi-structured face-to-face interview. Interview guide was based on short form 36 (SF-36), long form of IPAQ and previous research (Bukman et al., 2014). Results. All obese women of our study feel inferiority complex due to their physical appearance, feel dissatisfied with themselves trying to hide their appearance, discomfort, and disadvantages. They have a lot of health problems: joint pain, increased cholesterol level, increased blood pressure, heart beat rate, dyspnoea, as well as chronic diseases such as type 2 diabetes, thyroid gland dysfunction and sleep disorders. All the subjects both had poor eating habits and ate inadequate food. Some of the participants ate a lot before bedtime and skip breakfast. The physical activity is limited and reduced of all subjects. Conclusions. The relationship between excess weight and psychological well-being is complex, encompassing physical, social and psychological factors. All obese women of our study feel inferiority complex due to their physical appearance, dissatisfied with themselves. They complained of cardiovascular problems, sleep disorders and chronic diseases. The physical activity was limited and decreased. Also, such a great weight excess caused a discomfort in their daily life. Obese women in our study negatively view smoking effects, but they tolerate or even have positive attitudes to alcohol consumption, though they acknowledge that alcohol increases their weight. Malnutrition is most common problem of our respondents. They both had poor eating habits and ate inadequate food. Some of the participants ate a lot before bedtime and skip breakfast. A lot of participants reported about their negative attitude to their diet, but this attitude did not always act like a stimulus to change. Most of respondents could not find the time or motivation to engage in the adequate physical activity. They wanted to increase their physical activity, but found excuses not to do that – their excess of weight, lack of time, as well as laziness, passivity the main causes of their inactivity or poor physical activity.
Scientific Inquiry Perception and Ability of Pre-service Teachers

The research purpose was to assess the inquiry perception and ability of biology pre-service teachers. This research used mixed methods, with embedded design drawing on instruments adapted from the Principle of Scientific Inquiry – Teacher (PSI-T), inquiry ability test, and open questions list. Data analysis used descriptive statistics technique and independent sample t-test. The results showed high scientific inquiry perception and ability of pre-service teachers who used structure inquiry learning strategy in the classroom. There are no significant differences among scientific inquiry perception and the ability of pre-service teachers with respect to gender, but there are differences according to university. Pre-service teachers need to improve inquiry strategies in the classroom to the next levels, i.e., guided inquiry and open inquiry, which provides positive implications for students to be more independent and creative.
High-speed and low-power 2.5D I/O circuits for memory-logic-integration by through-silicon interposer

In this paper, two high-speed and low-power I/O circuits are developed using through-silicon-interposer (TSI) for 2.5D integration of multi-core processor and memory in 65 nm CMOS process. For a 3 mm TSI interconnection of transmission line (T-line), the first I/O circuit is a low-voltage-differential-signal (LVDS) buffer and the second one is a current-mode-logic (CML) buffer. To compensate the high-frequency loss from T-line, a pre-emphasis circuit is deployed in the LVDS buffer, and a wide-band inductor-matching is deployed in the CML buffer. Based on the post layout simulation results, the LVDS buffer can achieve 360 mV peak-to-peak differential output signal swing and 563 fs cycle-to-cycle jitter with 10 Gb/s bandwidth and 4.8 mW power consumption. The CML buffer can achieve 240 mV peak-to-peak differential output signal swing and 453 fs jitter with 12.8 Gb/s data-rate and 1.6 mA current consumption under 0.6 V ultra low-power supply.
Performance evaluation of Real-Time MAC-Layer Selfish Misbehavior Detection for Wireless Networks using analytical method

IEEE 802.11 is a set of Medium Access Layer (MAC) and physical layer specification for implementing Wireless Ad hoc networks. Contention issue in the ad hoc network is resolved using Carrier Sense Multiple Access/ Collision Avoidance (CSMA/CA) protocol. This aims to ensure the Fair Share (FS) of the network access. A malicious node can manipulate the backoff parameters and lead to unfair gain of large share of network access. Reason for such behavior is the distributed nature of the CSMA/CA protocol. For real time detection of a malicious node, a Markov chain-based analytical model is being used. A systematic study of the real-time performance of the FS detector is possible by this model. Despite of all its advantages there have been no preventive schemas introduced to defend against MAC layer misbehavior in wireless network. Adding to the challenge of identifying and isolating the malicious node from the network is the non-deterministic nature and distributed functionality of IEEE 802.11 based networks. This detection demands more accuracy as it is based on probability. In order to overcome the problem regarding the defensive mechanism and for better efficiency, in this paper a new technique is introduced called “Real-Time MAC-Layer Selfish Misbehavior Detection and Prevention Technique in for Wireless Networks”. Using this technique all the nodes in the network can be monitored. This implementation shows better efficiency as can be inferred from simulation results.