Miracle-dz/UniFAR-FTUs huggingface dataset:
Bilateral overactivation of the sensorimotor cortex in the unilateral rodent model of Parkinson's disease – a functional magnetic resonance imaging study

Functional magnetic resonance imaging (fMRI) is used to investigate the basal ganglia (BG)–cortex circuit using a rat model of Parkinson's disease (PD). The model involves a unilateral destruction of the right substantia nigra by intranigral injection of the dopaminergic neurotoxin 6‐hydroxydopamine. Volume of cortical activity was measured by the blood oxygenation level‐dependent contrast method while applying electrical forepaw stimulation. The main findings are the following. (i) Contrary to the predictions of the classic model but in line with recent experimental results (positron emission tomography, fMRI and electrophysiology), an increased cortical activity in the sensorimotor cortex of PD rats compared with sham‐operated or normal rats was found. (ii) A diffuse neuronal activity at large cortical areas that were not related directly to the stimulation used, was observed. (iii) No difference was found between the lesion and the nonlesion hemispheres when the left or the right forepaw was stimulated; both cortices show significant overactivation of the sensorimotor cortices in addition to diffuse cortical activation. The last finding could be explained by either corticocortical connections or by bilateral BG–cortex connections. These finding suggest that the mutual influence of the two hemispheres is important in the pathophysiology of the BG–cortex circuit and might be crucial in predicting treatments.
Effect of Soybean Hypocotyl Extract on Lipid Peroxidation in GK Rats

Vascular complications, as a consequence of atherosclerosis, are main causes of morbidity and mortality in patients with diabetes mellitus. There is increasing evidence that lipid peroxidation and oxidative modification of low density lipoprotein (LDL) is important in atherogenesis. In this study we investigated the effect of soybean hypocotyl extract (SHE), rich in isoflavones and saponins, on lipid peroxide (LPO) levels in liver, plasma and lipoproteins in GK diabetic rats, and its efficacy on the reduction of susceptibility of LDL and high density lipoprotein (HDL) to oxidation. The oxidative modification of LDL and HDL was determined with the lag time of copper ion-induced oxidation curve identified by the conjugated dienes. In SHE group which were fed diet containing 40 g/kg of SHE for 16 weeks, LPO levels in liver, plasma and HDL fraction were significantly decreased compared with the control group. The lag phage of LDL oxidation curve was prolonged noticeably by a mean of 27 min in SHE group as compared to the control group, indicating a reduced susceptibility to oxidation. The results suggest that intake of soybean hypocotyl extract might be useful for the prevention and treatment of diabetes mellitus and diabetes-associated diseases.
Towards Open Set Deep Networks

Deep networks have produced significant gains for various visual recognition problems, leading to high impact academic and commercial applications. Recent work in deep networks highlighted that it is easy to generate images that humans would never classify as a particular object class, yet networks classify such images high confidence as that given class - deep network are easily fooled with images humans do not consider meaningful. The closed set nature of deep networks forces them to choose from one of the known classes leading to such artifacts. Recognition in the real world is open set, i.e. the recognition system should reject unknown/unseen classes at test time. We present a methodology to adapt deep networks for open set recognition, by introducing a new model layer, OpenMax, which estimates the probability of an input being from an unknown class. A key element of estimating the unknown probability is adapting Meta-Recognition concepts to the activation patterns in the penultimate layer of the network. Open-Max allows rejection of "fooling" and unrelated open set images presented to the system, OpenMax greatly reduces the number of obvious errors made by a deep network. We prove that the OpenMax concept provides bounded open space risk, thereby formally providing an open set recognition solution. We evaluate the resulting open set deep networks using pre-trained networks from the Caffe Model-zoo on ImageNet 2012 validation data, and thousands of fooling and open set images. The proposed OpenMax model significantly outperforms open set recognition accuracy of basic deep networks as well as deep networks with thresholding of SoftMax probabilities.
AORS: Affinity-based outlier ranking score

Outlier ranking methods can provide a quantitative measure to evaluate the outlierness of data instances in data clustering and attract great interest in pattern recognition and data mining communities. However, it has been pointed out that the diverse scaling ranges of these scores bring difficulty to result interpretation. Moreover, popular outlier ranking scores based on simple distance measures might not accurately reflect the complex affinity among data points. In this paper, we propose a new outlier ranking method based on consensus affinity of a cluster ensemble. Two new outlier ranking scores generalized from well-known clustering evaluation measures, Rvv from the RAND measure and ARIvv from Adjusted Rand Index (ARI), are adopted for outlierness evaluation. Compared to other outlierness ranking measures, the two new measures have the desired bounds without additional transformations. Consistent with the improvement of Adjusted Rand Index (ARI) over RAND, we find that ARIvv also significantly outperforms Rvv. Benefiting from the consensus affinity of a cluster ensemble, our proposed method with the ARIvv score provides significant improvement beyond a number of competing algorithms on public UCI benchmark data sets. Studies with both theoretical analysis and experimental validation show the effectiveness of our proposed methods.
Robust multiagent plan generation and execution with decision theoretic planners

This thesis details research concerning the development of multiagent systems that can support robust goal achievement in realistic environments. Partially Observable Markov Decision Processes (POMDPs) are the de-facto technique for modeling environments with uncertainty. Traditional approaches to finding optimal policies for POMDPs are unable to scale to problems with more than tens of states. In many POMDP domains there is significant structure which, if exploited, can significantly reduce the computational requirements for generating policies. Run-time knowledge can also be used to further reduce computational requirements during policy generation. The main hypothesis of this research is that the exploitation of structure and runtime knowledge in the use of dynamic decision networks for POMDP planning will scale better than existing POMDP algorithms for policy generation, while still supporting generation of high quality policies. The scalability of algorithms which exploit domain structure and run-time knowledge in POMDPs is demonstrated by comparison to current exact and approximate approaches for finding POMDP solutions. To support social-level goals in a distributed environment, researchers have developed POMDP models encompassing multiagent beliefs and actions. These models are extremely computationally complex. Other popular approaches for supporting social goal achievement either do not fit well within frameworks for reasoning under uncertainty or do not balance individual and social goals well. A second hypothesis of this work is that socially-driven utility functions can be defined within a MDP framework and that agents planning with such a utility function can effectively balance social and individual goals while performing localized decision-theoretic planning. The performance of multiagent systems using a socially-driven utility function is compared to the performance of systems acting under alternative approaches to multiagent planning and execution.
The Human Ventromedial Prefrontal Cortex Is Critical for Transitive Inference

We hypothesized that the ventromedial pFC (vmPFC) is critical for making transitive inferences (e.g., the logical operation that if A > B and B > C, then A > C). To test this, participants with focal vmPFC damage, brain-damaged comparison participants, and neurologically normal participants completed a transitive inference task consisting an ordered set of arbitrary patterns. Participants first learned through trial-and-error the relationships of the patterns (e.g., Pattern A > Pattern B, Pattern B > Pattern C). After initial learning, participants were presented with novel pairings, some of which required transitive inference (e.g., Pattern A > Pattern C from the relationship above). We observed that vmPFC damage led to a specific deficit in transitive inference, suggesting that an intact vmPFC is necessary for making normal transitive inferences. Given the usefulness of transitivity in inferring social relationships, this deficit may be one of the basic features of social conduct problems associated with vmPFC damage.
Biodistribution of charged 17.1A photoimmunoconjugates in a murine model of hepatic metastasis of colorectal cancer

Optimizing photodynamic therapy involves attempting to increase both the absolute tumour content of photosensitizer and the selectivity between tumour and surrounding normal tissue. One reason why photodynamic therapy has not been considered suitable for treatment of metastatic tumours in the liver, is the poor selectivity of conventional photosensitizers for tumour compared to normal liver. This report details an alternative approach to increasing this selectivity by the use of antibody-targeted photosensitizers (or photoimmunoconjugates) to target intrahepatic tumours caused by human colorectal cancer cells in the nude mouse, and explores the role of molecular charge on the tumour-targeting efficiency of macromolecules. The murine monoclonal antibody 17.1A (which recognizes an antigen expressed on HT 29 cells) was used to prepare site-specific photoimmunoconjugates with the photosensitizer chlorine6. The conjugates had either a predominant cationic or anionic charge and were injected i.v. into tumour-bearing mice. Biodistribution 3 or 24 h later was measured by extraction of tissue samples and quantitation of chlorine6 content by fluorescence spectroscopy. The photoimmunoconjugates were compared to the polylysine conjugates in an attempt to define the effect of molecular charge as well as antibody targeting. The anionic 17.1A conjugate delivered more than twice as much photosensitizer to the tumour at 3 h than other species (5 times more than the cationic 17.1A conjugate) and had a tumour:normal liver ratio of 2.5. Tumour-to-liver ratios were greater than one for most compounds at 3 h but declined at 24 h. Tumour-to-skin ratios were high (> 38) for all conjugates but not for free chlorine6. Cationic species had a high uptake in the lungs compared to anionic species. The photoimmunoconjugates show an advantage over literature reports of other photosensitizers, which can result in tumour:normal liver ratios of less than 1. © 2000 Cancer Research Campaign http://www.bjcancer. com
A study of the challenges related to DIY assistive technology in the context of children with disabilities

The term Do It Yourself Assistive Technology (DIY-AT) refers to the creation and adaptation of AT by non-professionals, including people with disabilities and their families, friends and caregivers. Previous research has argued that the development of technologies and services that enable people to make their own DIY-AT will lead to the rapid and low cost development of assistive devices that are tailored to meet the complex needs of individual people with disabilities. We present the results of a qualitative study that explored challenges related to the process of making DIY-AT for children with disabilities. A series of eleven semi-structured interviews with a broad range of stakeholders involved in the current use, provision and adaptation of AT for children with disabilities revealed a number of challenges relating to the prevalence and scope of ongoing DIY-AT practice, barriers to participation, and the challenges faced by makers and users of DIY-AT.
Inward Motions of the Compact SiO Masers around VX Sagittarii

We report Very Long Baseline Array (VLBA) observations of 43 GHz v = 1, J = 1-0 SiO masers in the circumstellar envelope of the M-type semiregular variable star VX Sgr at three epochs during 1999 April-May. These high-resolution VLBA images reveal a persistent ringlike distribution of SiO masers with a projected radius of ≈3 stellar radii. The typical angular size of 0.5 mas for individual maser features was estimated from two-point correlation function analysis of maser spots. We found that the apparent size scale of maser features was distinctly smaller than that observed in the previous observations by comparing their fractions of the total power imaged. This change in the size scale of maser emission may be related to stellar activity that caused a large SiO flare during our observations. Our observations confirmed the asymmetric distribution of maser emission, but the overall morphology has changed significantly, with the majority of the masers clustering to the northeast of the star compared to the majority of the masers lying in the southwest in 1992. By identifying 42 matched maser features appearing in all three epochs, we determined the contraction of an SiO maser shell toward VX Sgr at a proper motion of -0.507 ± 0.069 mas yr-1, corresponding to a velocity of about 4 km s-1 at a distance of 1.7 kpc to VX Sgr. Such a velocity is on the order of the sound speed and can be easily explained by the gravitational infall of material from the circumstellar dust shell.
Papillomaviruses Causing Cancer: Evasion From Host-Cell Control in Early Events in Carcinogenesis

During the past 20 years, several types of human papillomaviruses (HPVs) have been identified that cause specific types of cancers. The etiology of cancer of the cervix has been linked to several types of HPV, with a high preponderance of HPV16. The role of these virus infections has been established 1) by the regular presence of HPV DNA in the respective tumor biopsy specimens, 2) by the demonstration of viral oncogene expression (E6 and E7) in tumor material, 3) by the identification of transforming properties of these genes, 4) by the requirement for E6 and E7 expression for maintaining the malignant phenotype of cervical carcinoma cell lines, 5) by the interaction of viral oncoproteins with growth-regulating host-cell proteins, and 6) by epidemiologic studies pointing to these HPV infections as the major risk factor for cervical cancer development. In addition to cancer of the cervix, a major proportion of anal, perianal, vulvar, and penile cancers appears to be linked to the same HPV infections. In addition, close to 20% of oropharyngeal cancers contain DNA from the same types of HPV. Recent evidence also points to a possible role of other HPV infections in squamous cell carcinomas of the skin. This review covers recent developments in understanding molecular mechanisms of HPV carcinogenesis, mainly discussing functions of viral oncoproteins and the regulation of viral oncogenes by host-cell factors. Modifications in host-cell genes, most likely engaged in the control of HPV gene expression in proliferating cells, emerge as important events in HPV-mediated carcinogenesis.
When Social Networking Turns to Social Overload: Explaining the stress, Emotional Exhaustion, and Quitting Behavior from Social Network sites' Users

This research responds to a current phenomenon that individuals experience fatigue, while using social network sites, such as Facebook, which original intend to provide hedonic value to users. To explain this current phenomenon, we propose and evaluate a research model based on the stress-strain-outcome model. Focal point is the stressor social overload, which induces feelings of being emotional exhausted. For that reason, some users of social network sites start to get dissatisfied and report an increasing discontinuous usage intention. In addition, the research article provides evidence for the fact that the effect of stress on the two outcome variables satisfaction and discontinuous usage intention is fully mediated through strain. This is validated with an empirical survey with 523 Facebook users. Several implications for technology adoption research are discussed.
QUANTITATIVE TUBE MODEL PREDICTIONS FOR THE LINEAR VISCOELASTICITY OF LINEAR POLYMERS

Tube models derived from the basic ideas of De Gennes and Doi & Edwards on reptation of entangled chains have made tremendous progress in the past 30 years and have now reached a quantitative level for the prediction of linear viscoelasticity of linear polymers. After a concise reminder of key experimental features of dynamic moduli, we first review the fundamental ingredients of quantitative tube models: reptation, contour length fluctuations and constraint release, as well as some important modifications that are proving essential in difficult cases, such as the “Struglinski- Graessley criterion” or the “constraint release Rouse” regime. The main models published since the origin of the theory are next briefly described and compared. They can be classified in two major categories, those derived from the Doi-Edwards theory built on the original reptation concept and those derived from the Milner-McLeish theory, which considers linear chains as two-arms stars able to reptate. We further review published comparisons between experimental data and model predictions for various systems, comprising monodisperse, polydisperse and bidisperse polymers. The latter are the most difficult to predict because they emphasize complex interactions between the relaxation mechanisms. Hence they highlight the current limitations of the models, in particular the influence of constraint release mechanisms on reptation and fluctuations.
Discriminative Training for Log-Linear Based SMT: Global or Local Methods

In statistical machine translation, the standard methods such as MERT tune a single weight with regard to a given development data. However, these methods suffer from two problems due to the diversity and uneven distribution of source sentences. First, their performance is highly dependent on the choice of a development set, which may lead to an unstable performance for testing. Second, the sentence level translation quality is not assured since tuning is performed on the document level rather than on sentence level. In contrast with the standard global training in which a single weight is learned, we propose novel local training methods to address these two problems. We perform training and testing in one step by locally learning the sentence-wise weight for each input sentence. Since the time of each tuning step is unnegligible and learning sentence-wise weights for the entire test set means many passes of tuning, it is a great challenge for the efficiency of local training. We propose an efficient two-phase method to put the local training into practice by employing the ultraconservative update. On NIST Chinese-to-English translation tasks with both medium and large scales of training data, our local training methods significantly outperform standard methods with the maximal improvements up to 2.0 BLEU points, meanwhile their efficiency is comparable to that of the standard methods.
A Formalization of Explanation-Based Macro-operator Learning

In spite of the popularity of Explanation-Based Learning (EBL), its theoretical basis is not well-understood. Using a generalization of Probably Approximately Correct (PAC) learning to problem solving domains, this paper formalizes two forms of Explanation-Based Learning of macro-operators and proves the sufficient conditions for their success. These two forms of EBL, called "Macro Caching" and "Serial Parsing," respectively exhibit two distinct sources of power or "bias": the sparseness of the solution space and the decomposability of the problem-space. The analysis shows that exponential speedup can be achieved when either of these biases is suitable for a domain. Somewhat surprisingly, it also shows that computing the preconditions of the macro-operators is not necessary to obtain these speedups. The the oretical results are confirmed by experiments in the domain of Eight Puzzle. Our work suggests that the best way to address the utility problem in EBL is to implement a bias which exploits the problem-space structure of the set of domains that one is interested in learning.
Interactions between the human gastrocnemius muscle and the Achilles tendon during incline, level and decline locomotion

SUMMARY Muscles are required to perform or absorb mechanical work under different conditions. However the ability of a muscle to do this depends on the interaction between its contractile components and its elastic components. In the present study we have used ultrasound to examine the length changes of the gastrocnemius medialis muscle fascicle along with those of the elastic Achilles tendon during locomotion under different incline conditions. Six male participants walked (at 5 km h-1) on a treadmill at grades of -10%, 0% and 10% and ran (at 10 km h-1) at grades of 0% and 10%, whilst simultaneous ultrasound, electromyography and kinematics were recorded. In both walking and running, force was developed isometrically; however, increases in incline increased the muscle fascicle length at which force was developed. Force was developed at shorter muscle lengths for running when compared to walking. Substantial levels of Achilles tendon strain were recorded in both walking and running conditions, which allowed the muscle fascicles to act at speeds more favourable for power production. In all conditions, positive work was performed by the muscle. The measurements suggest that there is very little change in the function of the muscle fascicles at different slopes or speeds, despite changes in the required external work. This may be a consequence of the role of this biarticular muscle or of the load sharing between the other muscles of the triceps surae.
Cluster-based Prediction of User Ratings for Stylistic Surface Realisation

Surface realisations typically depend on their target style and audience. A challenge in estimating a stylistic realiser from data is that humans vary significantly in their subjective perceptions of linguistic forms and styles, leading to almost no correlation between ratings of the same utterance. We address this problem in two steps. First, we estimate a mapping function between the linguistic features of a corpus of utterances and their human style ratings. Users are partitioned into clusters based on the similarity of their ratings, so that ratings for new utterances can be estimated, even for new, unknown users. In a second step, the estimated model is used to re-rank the outputs of a number of surface realisers to produce stylistically adaptive output. Results confirm that the generated styles are recognisable to human judges and that predictive models based on clusters of users lead to better rating predictions than models based on an average population of users.