Miracle-dz/UniFAR-FTUs huggingface dataset:
Theory of mind in youth and emerging adults with chromosome 22q11.2 deletion syndrome, with and without comorbid mood disorder

Mood disorders are some of the most commonly diagnosed psychiatric disorders in childhood and adolescence. Major Depressive Disorder (MDD) and Bipolar Disorder (BPD) have become more widely recognized in children and adolescents in recent years and are especially common in individuals with chromosome 22q11.2 deletion syndrome (22q11.2DS). Disruptions in social functioning are a common feature of mood disorders, including social withdrawal and loss of interest in activities that the individual typically experiences as pleasurable (anhedonia). Studies of individuals with 22q11.2DS also show they experience difficulty with social functioning, as well as social cognition, visuospatial, and executive functioning tasks. Studies of theory of mind (ToM) ability in youth and emerging adults with 22q11.2DS and comorbid mood disorder remain elusive, and a better understanding of whether or not social cognition plays a role in the development of mood disorders may help provide a target area for treatment. This study investigated the social, visuospatial, ToM, and executive functioning abilities of 15 youth and emerging adults with 22q11.2DS who met inclusion criteria for mood disorders and 46 individuals with 22q11.2DS with no mood disorder. These results were compared with the results from 22 unaffected sibling and 26 unaffected community controls. A multiple mediator model examining the contributions of visuospatial, social, ToM, and executive functioning to a diagnosis of mood disorder indicated that social functioning was the only significant mediator. Youth and emerging adults with 22q11.2DS and a comorbid mood disorder did not demonstrate greater deficits in visuospatial, ToM, or executive functioning skills relative to individuals with 22q11.2DS only. However, youth and emerging adults with 22q11.2DS and comorbid mood disorder did show greater social
Natural Language Video Description using Deep Recurrent Neural Networks

Abstract : For most people, watching a brief video and describing what happened (inwords) is an easy task. For machines, extracting the meaning from video pixelsand generating a sentence description is a very complex problem. The goal of myresearch is to develop models that can automatically generate natural language(NL) descriptions for events in videos. As a first step, this proposal presentsdeep recurrent neural network models for video to text generation. I build onrecent deep machine learning approaches to develop video description modelsusing a unified deep neural network with both convolutional and recurrentstructure. This technique treats the video domain as another language andtakes a machine translation approach using the deep network to translate videosto text. In my initial approach, I adapt a model that can learn on images andcaptions to transfer knowledge from this auxiliary task to generate descriptionsfor short video clips. Next, I present an end-to-end deep network that can jointlymodel a sequence of video frames and a sequence of words. The second part ofthe proposal outlines a set of models to significantly extend work in this area.Specifically, I propose techniques to integrate linguistic knowledge from plaintext corpora; and attention methods to focus on objects and track their interactionsto generate more diverse and accurate descriptions. To move beyondshort video clips, I also outline models to process multi-activity movie videos,learning to jointly segment and describe coherent event sequences. I proposefurther extensions to take advantage of movie scripts and subtitle informationto generate richer descriptions.
Generation of Induced Pluripotent Stem Cells from Human Nasal Epithelial Cells Using a Sendai Virus Vector

The generation of induced pluripotent stem cells (iPSCs) by introducing reprogramming factors into somatic cells is a promising method for stem cell therapy in regenerative medicine. Therefore, it is desirable to develop a minimally invasive simple method to create iPSCs. In this study, we generated human nasal epithelial cells (HNECs)-derived iPSCs by gene transduction with Sendai virus (SeV) vectors. HNECs can be obtained from subjects in a noninvasive manner, without anesthesia or biopsy. In addition, SeV carries no risk of altering the host genome, which provides an additional level of safety during generation of human iPSCs. The multiplicity of SeV infection ranged from 3 to 4, and the reprogramming efficiency of HNECs was 0.08–0.10%. iPSCs derived from HNECs had global gene expression profiles and epigenetic states consistent with those of human embryonic stem cells. The ease with which HNECs can be obtained, together with their robust reprogramming characteristics, will provide opportunities to investigate disease pathogenesis and molecular mechanisms in vitro, using cells with particular genotypes.
Efficient implementation of STDP rules on SpiNNaker neuromorphic hardware

Recent development of neuromorphic hardware offers great potential to speed up simulations of neural networks. SpiNNaker is a neuromorphic hardware and software system designed to be scalable and flexible enough to implement a variety of different types of simulations of neural systems, including spiking simulations with plasticity and learning. Spike-timing dependent plasticity (STDP) rules are the most common form of learning used in spiking networks. However, to date very few such rules have been implemented on SpiNNaker, in part because implementations must be designed to fit the specialized nature of the hardware. Here we explain how general STDP rules can be efficiently implemented in the SpiNNaker system. We give two examples of applications of the implemented rule: learning of a temporal sequence, and balancing inhibition and excitation of a neural network. Comparing the results from the SpiNNaker system to a conventional double-precision simulation, we find that the network behavior is comparable, and the final weights differ by less than 3% between the two simulations, while the SpiNNaker simulation runs much faster, since it runs in real time, independent of network size.
Mining Frequent Patterns and Prediction of User Behavior in Mobile Commerce

Mobile commerce has received a lot of interests from both the industry and academy. Among them, one of the active topic areas is the mining and prediction of users such as their movements and purchase transactions. In this paper we propose a novel framework, called Mobile Commerce Explorer for mining and prediction of mobile user movements and purchase transactions under the context of mobile commerce. The MCE framework consists of three major components:1) Similarity Inference Model (SIM) ,2)Personal Mobile Commerce Pattern Mine(PCMP-mine) algorithm for efficient discovery of mobile users and 3)Mobile Commerce Behavior Predictor(MCBP) for prediction of possible mobile user behaviors.
The ecological controls on the prevalence of candidate division TM7 in polar regions

The candidate division TM7 is ubiquitous and yet uncultured phylum of the Bacteria that encompasses a commonly environmental associated clade, TM7-1, and a “host-associated” clade, TM7-3. However, as members of the TM7 phylum have not been cultured, little is known about what differs between these two clades. We hypothesized that these clades would have different environmental niches. To test this, we used a large-scale global soil dataset, encompassing 223 soil samples, their environmental parameters and associated bacterial 16S rRNA gene sequence data. We correlated chemical, physical and biological parameters of each soil with the relative abundance of the two major classes of the phylum to deduce factors that influence the groups' seemingly ubiquitous nature. The two classes of the phylum (TM7-1 and TM7-3) were indeed distinct from each other in their habitat requirements. A key determinant of each class' prevalence appears to be the pH of the soil. The class TM7-1 displays a facultative anaerobic nature with correlations to more acidic soils with total iron, silicon, titanium and copper indicating a potential for siderophore production. However, the TM7-3 class shows a more classical oligotrophic, heterotroph nature with a preference for more alkaline soils, and a probable pathogenic role with correlations to extractable iron, sodium and phosphate. In addition, the TM7-3 was abundant in diesel contaminated soils highlighting a resilient nature along with a possible carbon source. In addition to this both classes had unique co-occurrence relationships with other bacterial phyla. In particular, both groups had opposing correlations to the Gemmatimonadetes phylum, with the TM7-3 class seemingly being outcompeted by this phylum to result in a negative correlation. These ecological controls allow the characteristics of a TM7 phylum preferred niche to be defined and give insight into possible avenues for cultivation of this previously uncultured group.
Integrin-mediated adhesion regulates membrane order

The properties of cholesterol-dependent domains (lipid rafts) in cell membranes have been controversial. Because integrin-mediated cell adhesion and caveolin both regulate trafficking of raft components, we investigated the effects of adhesion and caveolin on membrane order. The fluorescent probe Laurdan and two-photon microscopy revealed that focal adhesions are highly ordered; in fact, they are more ordered than caveolae or domains that stain with cholera toxin subunit B (CtxB). Membrane order at focal adhesion depends partly on phosphorylation of caveolin1 at Tyr14, which localizes to focal adhesions. Detachment of cells from the substratum triggers a rapid, caveolin-independent decrease in membrane order, followed by a slower, caveolin-dependent decrease that correlates with internalization of CtxB-stained domains. Endocytosed CtxB domains also become more fluid. Thus, membrane order is highly dependent on caveolae and focal adhesions. These results show that lipid raft properties are conferred by assembly of specific protein complexes. The ordered state within focal adhesions may have important consequences for signaling at these sites.
Improved Seventh-Order WENO Scheme

In this paper, an improved seventh-order WENO (WENO-Z7) scheme is suggested by extending the 5th-order WENO scheme of Borges et al[R. Borges, M. Carmona, B. Costa, W. S. Don, An improved weighted essentially non-oscillatory scheme for hyperbolic conservation laws, J. Comput. Phys. 227(2008) 3191-3211]. The su‐cient condition for seventh-order accuracy is described for the new smoothness indicator. The role of the parameter ", which is used to construct the weights of WENO schemes to prevent the denominator from being zero, is discussed, and an optimized value of " is suggested to improve the convergence and accuracy for practical applications.
Model-Based Optimization of Scaffold Geometry and Operating Conditions of Radial Flow Packed-Bed Bioreactors for Therapeutic Applications

Radial flow perfusion of cell-seeded hollow cylindrical porous scaffolds may overcome the transport limitations of pure diffusion and direct axial perfusion in the realization of bioengineered substitutes of failing or missing tissues. Little has been reported on the optimization criteria of such bioreactors. A steady-state model was developed, combining convective and dispersive transport of dissolved oxygen with Michaelis-Menten cellular consumption kinetics. Dimensional analysis was used to combine more effectively geometric and operational variables in the dimensionless groups determining bioreactor performance. The effectiveness of cell oxygenation was expressed in terms of non-hypoxic fractional construct volume. The model permits the optimization of the geometry of hollow cylindrical constructs, and direction and magnitude of perfusion flow, to ensure cell oxygenation and culture at controlled oxygen concentration profiles. This may help engineer tissues suitable for therapeutic and drug screening purposes.
Exploring the beneficial effects of cardiorespiratory fitness and exercise on cerebrovascular health in Huntington’s Disease: a cross-species approach

This thesis explores the benefits of physical activity on cerebrovascular health in healthy subjects and in Huntington’s Disease (HD), where cerebrovascular health is thought to be jeopardised. A cross-species approach was employed, to inform the relevance of MRI findings in humans, using histology and pre-clinical imaging. In Chapter 2, measurement of cerebrovascular markers using arterial spin labelling (ASL) MRI showed that arterial compliance and resting cerebral blood flow was lower in subjects with higher cardiorespiratory fitness, whilst differences in cerebrovascular reactivity (CVR) to a breath-hold task were not statistically significant. Measurements of vessel density in Chapter 3 showed that running mice had greater vessel density than non-running mice following a 6-week voluntary wheel running intervention, which may be attributed to a process of angiogenesis. Methods developed in healthy subjects were subsequently applied to investigate the cerebrovascular benefits of exercise in HD. In Chapter 4, pre-/early- symptomatic patients with HD were assessed for subtle differences in cerebrovascular health and whether this varied with cardiorespiratory fitness. Disease-related differences were observed in cognition, and ASL measures including resting CBF and CVR, but no clear relationship with fitness was observed. Preclinical imaging was used in Chapter 5 to measure longitudinal changes in resting CBF and CVR in a transgenic Q175 mouse model of HD, prior to behavioural deficits (preHD). Neither CBF or vessel density differed between preHD animals and controls, and did not appear altered by voluntary running.
Embedded target detection system based on raspberry pi system

This paper describes implementation of target detecting and tracking system, which can extract required information from images without need for an external processing unit and track the target, based on embedded system. The requirements of image capturing, target detection, allocate target position, and implementing based on raspberry pi system as embedded system on chip kit. Real time tracking is achieved using simple and fast color detection procedures based on frame differencing and camera motion compensation for estimate the motion vectors to control the pan tilt DC motors, which drive the camera to track the moving target in real-time.
When absorptive capacity meets institutions and (e)learners

E-learning was thought to be one of the fastest growing industries on both sides of the Atlantic and has been frequently heralded as a transforming influence on global corporate training and higher education. Despite such rhetoric, the adoption, diffusion and exploitation have been slower than anticipated. In this paper we attempt to explain why this might have been the case in Europe by drawing on an increasingly influential body of management literature on the absorptive capacity (ACAP) of organizations to acquire, assimilate and use new technologies and ideas. We supplement this work on absorptive capacity with two other streams of literature on learners and on the business systems or institutionalist perspective, which focuses on the embeddedness of unique organizational forms, ideas and human resource development approaches in particular national business systems. We develop a model of absorptive capacity for e-learning in organizations (ACAP for eL), which we argue has important theoretical implications for business and management academics in developing a model of technology transfer and diffusion, key lessons for HRD practitioners and politicians associated with furthering e-learning developments in their organizations, and also for policy makers at government level wishing to spread the e-learning message.
Examination of the Utility of the High Throughput In Vitro Metabolic Stability Assay to Estimate In Vivo Clearance in the Mouse

In vitro determination of metabolic stability is routinely used to assess the overall metabolic liability of com- pounds and for prioritization for in vivo studies. If in vitro metabolic stability data could be used to reliably predict in vivo clearance (CL), it would add significant value in the selection of compounds for in vivo pharmacokinetic and pharmacol- ogy studies. We have evaluated the utility of our in vitro metabolic stability screening assay to estimate in vivo CL in the mouse. The in vitro mouse clearances (CLin vitro) of 146 structurally diverse compounds with metabolic stabilities > 30 %, were compared to mouse in vivo CL data. Approximately 45 % of the compounds showed agreement between in vivo CL and predicted CLin vitro within a 2-fold error criteria. The correlation appeared worse when correction for the extent of in- corporation of plasma protein binding or both plasma and S9 bindings (i.e. ~14 % and~ 28 % agreement, respectively). Classification of the compounds into three groups based on in vivo CL ( 70 mL/min/kg) did not show any improvement between in vivo CL and predicted CLin vitro. The percentage of compounds fal- ling within the 2-fold error criteria for low CL, moderate CL and high CL groups were 54, 31 and 24 %, respectively. In conclusion, our analysis suggests that in vitro metabolic stability data, as routinely obtained in early ADME screening protocols, does not demonstrate a strong correlation with or predictivity for, absolute in vivo CL in the mouse.
Dynamics On and Of Complex Networks: Functional Communities and Epidemic Spreading

Title of dissertation: DYNAMICS ON AND OF COMPLEX NETWORKS: FUNCTIONAL COMMUNITIES AND EPIDEMIC SPREADING Sanjeev Kumar Chauhan Doctor of Philosophy, 2012 Dissertation directed by: Professor Michelle Girvan Department of Physics The work presented in this thesis focusses on two topics: functional communities and epidemic spreading on dynamic networks. The first part of the thesis focuses on a functionally-based definition of community structure for complex networks. In particular, we consider networks whose function is enhanced by the ability to synchronize and/or by resilience to node failures. For networks whose functional performance is dependent on these processes, we propose a method that divides a given network into communities based on maximizing a function of the largest eigenvalues of the adjacency matrices of the resulting communities. We also explore the differences between the partitions obtained by our function-based method and the structure-based modularity approach. A major finding is that, in many cases, modularity-based partitions do almost as well as the function-based method in finding functional communities, even though modularity does not specifically incorporate consideration of function. We also discuss the spectral properties of the networks with community structure, relevant for the case of functional communities studied in this thesis. In the second part of the thesis, we study a discrete time SIR model on dynamic networks. In our dynamic network model, we consider the case where the nodes in the network change their links both in response to the disease and also due to social dynamics. We assume that the individuals trying to make new connections mix randomly, and, with a certain probability, we also allow for the formation of new susceptible-infected links. We find that increasing the social mixing dynamics inhibits the disease’s ability to spread in certain cases. This occurs because susceptibles who randomly disconnect from infected individuals preferentially reconnect to other susceptibles, inhibiting the disease spread. Finally, we also extend our dynamic network model to take into account the case of hidden infection. Here we find that, as expected, the disease spreads more readily if there is an initial time period during which an individual is infectious but unaware of the infection. DYNAMICS ON AND OF COMPLEX NETWORKS: FUNCTIONAL COMMUNITIES AND EPIDEMIC SPREADING
Estimation of sinusoids in audio signals using an analysis-by-synthesis neural network

In this paper we present a new method for estimating the frequency, amplitude and phase of sinusoidal components in audio signals. An analysis-by-synthesis system of neural networks is used to extract the sinusoidal parameters from the signal spectrum at each window position of the short-term Fourier transform. The system attempts to find the set of sinusoids that best fits the spectral representation in a least-squares sense. Overcoming a significant limitation of the traditional approach in the art, preliminary detection and interpolation of spectral peaks is not necessary and the method works even when spectral peaks are not well resolved in frequency. This allows for shorter analysis windows and therefore better time resolution of the estimated sinusoidal parameters. Results have also shown robust performance in presence of high levels of additive noise, with signal-to-noise ratios as low as 0 dB.
Managing Sibling Conflict and the Relation between Mothers' Emotion Socialization Beliefs and Children's Coping with Peer Victimization

This study examined the degree to which children’s strategies for coping with peer victimization were related to their strategies for coping with sibling victimization. Also examined were the relations among mothers’ sibling conflict management strategies, their emotion socialization beliefs, and children’s coping with peer and sibling victimization. Data were obtained from 98 4 th grade children and their mothers. Results indicated that children’s peer victimization coping strategies were significantly related to their sibling victimization coping strategies. I found that mothers who value and accept children’s negative emotions were more likely to coach their children through sibling conflict. Unexpectedly, I found that strategies that involved mothers taking control of sibling conflict were positively related to children’s adaptive coping and negatively related to children’s maladaptive coping. The relation between taking control over sibling conflict and children’s maladaptive coping was stronger for mothers who were less likely to be dismissing of children’s emotions. Implications and directions for future investigation are
Visual sign information extraction and identification by deformable models for intelligent vehicles

This paper deals with the extraction of part of the visual information presented in streets, roads, and motorways. This information, provided by either traffic or road signs and route-guidance signs, is extremely important for safe and successful driving. An automatic system that is capable of extracting and identifying these signs automatically would help human drivers enormously; navigation would be easier and would allow him or her to concentrate on driving the vehicle. The system would indicate to the driver the presence of a sign in advance, so that some incorrect human decisions could be avoided. A deformable model scheme allows us to include the knowledge used while designing the signs in the algorithm and is used for their detection and identification. Two techniques to find the minimum in the energy function are shown: simulated annealing and genetic algorithms. Some problems are addressed, such as uncontrolled lighting conditions; occlusions; and variations in shape, size, and color.