Resonance-Assisted Hydrogen Bond—Revisiting the Original Concept in the Context of Its Criticism in the Literature

In the presented research, we address the original concept of resonance-assisted hydrogen bonding (RAHB) by means of the many-body interaction approach and electron density delocalization analysis. The investigated molecular patterns of RAHBs are open chains consisting of two to six molecules in which the intermolecular hydrogen bond stabilizes the complex. Non-RAHB counterparts are considered to be reference systems. The results show the influence of the neighbour monomers on the unsaturated chains in terms of the many-body interaction energy contribution. Exploring the relation between the energy parameters and the growing number of molecules in the chain, we give an explicit extrapolation of the interaction energy and its components in the infinite chain. Electron delocalization within chain motifs has been analysed from three different points of view: three-body delocalization between C=C-C, two-body hydrogen bond delocalization indices and also between fragments (monomers). A many-body contribution to the interaction energy as well as electron density helps to establish the assistance of resonance in the strength of hydrogen bonds upon the formation of the present molecular chains. The direct relation between interaction energy and delocalization supports the original concept, and refutes some of the criticisms of the RAHB idea.

When Genetic and Environmental Factors Meet in Modelling Autism

<p>Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder, characterised by deficits in verbal and non-verbal communication, social interaction and repetitive behaviours (APA, 2013). The aetiology of ASD is mostly unknown, with continued research identifying a multitude of genetic and non-genetic factors. However, it is the interaction between environmental factors and the genetic background of an individual which leads to the development of ASD. There is an urgent need for improved animal models of ASD to further our understanding of the aetiology and particularly its pathophysiology, as this will aid in the development of much needed pharmaceutical treatments to alleviate the impact of adverse symptoms for individuals with ASD. Current animal models of ASD examine the genetic (e.g. serotonin transporter knock out rats) or the environmental (e.g. prenatal exposure to Valproate) contributions to the disorder, and very rarely a combination of the two. This thesis aimed to improve the Valproate (VPA) induced ASD animal model with a genetic × environmental interaction approach, as well as optimising chronic administration of the VPA to pregnant rats. To this aim, a non-invasive method of delivering VPA was used, which allowed genetically normal rats to voluntarily consume VPA throughout pregnancy. The prenatal exposure to VPA led to ASD-like behaviours in the offspring (communication delays, increased social behaviour, and social aversion). Next, rats with a genetic deficit in SERT (SERT+/-) exposed to VPA throughout gestation, with an optimised administration method using gelatine pellets, which allowed for voluntary non-invasive consumption, and a more accurate administration of increased VPA doses. Overall, the chronic prenatal exposure to VPA in SERT+/- rats led to a mild ASD-like phenotype, with rats exhibiting communication delays, abnormal play behaviour, disrupted social preference, and to some extent increased anxiety-like behaviour. The brains of the adult offspring were examined for neuronal changes in the GABA interneurons in brain regions associated with social behaviour (amygdala and hippocampus). However, no significant effects of prenatal VPA exposure, genotype, or sex were found. Thus, the variations GABAergic system is unlikely to underlie the earlier identified behavioural alterations. Ultimately, this thesis has furthered the VPA induced ASD animal model with a genetic × environmental interaction approach, as well as optimising the chronic administration method for pregnant rats.</p>

When Genetic and Environmental Factors Meet in Modelling Autism

<p>Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder, characterised by deficits in verbal and non-verbal communication, social interaction and repetitive behaviours (APA, 2013). The aetiology of ASD is mostly unknown, with continued research identifying a multitude of genetic and non-genetic factors. However, it is the interaction between environmental factors and the genetic background of an individual which leads to the development of ASD. There is an urgent need for improved animal models of ASD to further our understanding of the aetiology and particularly its pathophysiology, as this will aid in the development of much needed pharmaceutical treatments to alleviate the impact of adverse symptoms for individuals with ASD. Current animal models of ASD examine the genetic (e.g. serotonin transporter knock out rats) or the environmental (e.g. prenatal exposure to Valproate) contributions to the disorder, and very rarely a combination of the two. This thesis aimed to improve the Valproate (VPA) induced ASD animal model with a genetic × environmental interaction approach, as well as optimising chronic administration of the VPA to pregnant rats. To this aim, a non-invasive method of delivering VPA was used, which allowed genetically normal rats to voluntarily consume VPA throughout pregnancy. The prenatal exposure to VPA led to ASD-like behaviours in the offspring (communication delays, increased social behaviour, and social aversion). Next, rats with a genetic deficit in SERT (SERT+/-) exposed to VPA throughout gestation, with an optimised administration method using gelatine pellets, which allowed for voluntary non-invasive consumption, and a more accurate administration of increased VPA doses. Overall, the chronic prenatal exposure to VPA in SERT+/- rats led to a mild ASD-like phenotype, with rats exhibiting communication delays, abnormal play behaviour, disrupted social preference, and to some extent increased anxiety-like behaviour. The brains of the adult offspring were examined for neuronal changes in the GABA interneurons in brain regions associated with social behaviour (amygdala and hippocampus). However, no significant effects of prenatal VPA exposure, genotype, or sex were found. Thus, the variations GABAergic system is unlikely to underlie the earlier identified behavioural alterations. Ultimately, this thesis has furthered the VPA induced ASD animal model with a genetic × environmental interaction approach, as well as optimising the chronic administration method for pregnant rats.</p>

Leadership and Follower Voice: The Role of Inclusive Leadership and Group Faultlines in Promoting Collective Voice Behavior

We developed and tested a theoretical model to examine how and when inclusive leadership affected collective voice behavior in this study. We identified two voice-relevant mediators—group psychological safety (an emergent state) and information elaboration (a group process) to clarify the mechanisms between inclusive leadership and collective voice behavior. Further, according to the person–situation interaction approach, we brought the faultlines theory to the inclusive leadership literature and considered group faultlines as a positive moderator to maximize the effects of inclusive leadership. With a two-wave, two-source design, we collected data from 301 employees within 67 research and development groups in China. We found that inclusive leadership positively affected collective voice behavior through the mediating mechanisms of both group psychological safety and information elaboration. Additionally, this effect was stronger in high faultlines situation. The findings suggest that leaders should show inclusiveness to motivate collective voice, especially in groups with high faultlines.

Providing acceleration for mobile gaming as a service

<p>In recent years, the mobile gaming industry has made rapid progress. Developers are now producing numerous mobile games with increasingly immersive graphics. However, these resource-hungry applications inevitably keep pushing well beyond the hardware limits of mobile devices. The limitations causes two main challenging issues for mobile game players. First, limited computational capabilities of smart devices are preventing rich multimedia applications from running smoothly. Second, the minuscule touchscreens impede the players from smoothly interacting with devices as they can do with PCs. This thesis aims to address the two issues. Specifically, we implement two systems, one for the application accelerations via offloading and the other for alternative interaction approach for mobile gaming. We identify and describe the the challenging issues when developing the systems and describe our corresponding solutions. Regarding the first system, it is well recognized the performance of GPUs on mobile devices is the bottleneck of rich multimedia mobile applications such as 3D games and virtual reality. Previous attempts to tackle the issue mainly mirgate GPU computation to servers residing in remote datacenters. However, the costly network delay is especially undesirable for highly-interactive multimedia applications since a crisp response time is critical for user experience. In this thesis, we propose GBooster, a system that accelerates GPU-intensive mobile applications by transparently offloading GPU tasks onto neighboring multimedia devices such as SmartTV and Gaming Consoles. Specifically, GBooster intercepts and redirects system graphics calls by utilizing the Dynamic Linker Hooking technique, which requires no modification of the apps and mobile systems. Besides, GBooster intelligently switches between the low-power Bluetooth and the high-bandwidth WiFi interface to reduce energy consumption of network transmissions. We implemented the GBooster on the Android system and evauluate its performance. The results demonstrate that GBooster can boost applications' frame rates by up to {85\%}. In terms of power consumption, GBooster can achieve {70\%} energy saving compared with local execution. Second, we investigate the potential of built-in mobile device sensors to provide an alternative interaction approach for mobile gaming. We propose UbiTouch, a novel system that extends smartphones with virtual touchpads on desktops using built-in smartphone sensors. It senses a user's finger movement with a proximity and ambient light sensor whose raw sensory data from underlying hardware are strongly dependent on the finger's locations. UbiTouch maps the raw data into the finger's positions by utilizing Curvilinear Component Analysis and improve tracking accuracy via a particle filter. We have evaluate our system in three scenarios with different lighting conditions by five users. The results show that UbiTouch achieves centimetre-level localization accuracy and poses no significant impact on the battery life. We envisage that UbiTouch could support applications such as text-writing and drawing.</p>

Providing acceleration for mobile gaming as a service

<p>In recent years, the mobile gaming industry has made rapid progress. Developers are now producing numerous mobile games with increasingly immersive graphics. However, these resource-hungry applications inevitably keep pushing well beyond the hardware limits of mobile devices. The limitations causes two main challenging issues for mobile game players. First, limited computational capabilities of smart devices are preventing rich multimedia applications from running smoothly. Second, the minuscule touchscreens impede the players from smoothly interacting with devices as they can do with PCs. This thesis aims to address the two issues. Specifically, we implement two systems, one for the application accelerations via offloading and the other for alternative interaction approach for mobile gaming. We identify and describe the the challenging issues when developing the systems and describe our corresponding solutions. Regarding the first system, it is well recognized the performance of GPUs on mobile devices is the bottleneck of rich multimedia mobile applications such as 3D games and virtual reality. Previous attempts to tackle the issue mainly mirgate GPU computation to servers residing in remote datacenters. However, the costly network delay is especially undesirable for highly-interactive multimedia applications since a crisp response time is critical for user experience. In this thesis, we propose GBooster, a system that accelerates GPU-intensive mobile applications by transparently offloading GPU tasks onto neighboring multimedia devices such as SmartTV and Gaming Consoles. Specifically, GBooster intercepts and redirects system graphics calls by utilizing the Dynamic Linker Hooking technique, which requires no modification of the apps and mobile systems. Besides, GBooster intelligently switches between the low-power Bluetooth and the high-bandwidth WiFi interface to reduce energy consumption of network transmissions. We implemented the GBooster on the Android system and evauluate its performance. The results demonstrate that GBooster can boost applications' frame rates by up to {85\%}. In terms of power consumption, GBooster can achieve {70\%} energy saving compared with local execution. Second, we investigate the potential of built-in mobile device sensors to provide an alternative interaction approach for mobile gaming. We propose UbiTouch, a novel system that extends smartphones with virtual touchpads on desktops using built-in smartphone sensors. It senses a user's finger movement with a proximity and ambient light sensor whose raw sensory data from underlying hardware are strongly dependent on the finger's locations. UbiTouch maps the raw data into the finger's positions by utilizing Curvilinear Component Analysis and improve tracking accuracy via a particle filter. We have evaluate our system in three scenarios with different lighting conditions by five users. The results show that UbiTouch achieves centimetre-level localization accuracy and poses no significant impact on the battery life. We envisage that UbiTouch could support applications such as text-writing and drawing.</p>

Utilizing a Human–Computer Interaction Approach to Evaluate the Design of Current Pharmacogenomics Clinical Decision Support

A formal assessment of pharmacogenomics clinical decision support (PGx-CDS) by providers is lacking in the literature. The objective of this study was to evaluate the usability of PGx-CDS tools that have been implemented in a healthcare setting. We enrolled ten prescribing healthcare providers and had them complete a 60-min usability session, which included interacting with two PGx-CDS scenarios using the “Think Aloud” technique, as well as completing the Computer System Usability Questionnaire (CSUQ). Providers reported positive comments, negative comments, and suggestions for the two PGx-CDS during the usability testing. Most provider comments were in favor of the current PGx-CDS design, with the exception of how the genotype and phenotype information is displayed. The mean CSUQ score for the PGx-CDS overall satisfaction was 6.3 ± 0.95, with seven strongly agreeing and one strongly disagreeing for overall satisfaction. The implemented PGx-CDS at our institution was well received by prescribing healthcare providers. The feedback collected from the session will guide future PGx-CDS designs for our healthcare system and provide a framework for other institutions implementing PGx-CDS.

Distributed Multi-Agent Learning is More Effectively than Single-Agent

Interpretable distributed group intelligence techniques have emerged as an essential topic in artificial intelligence. The mathematical interpretability of prediction outcomes is critical for improving the reliability of machine learning, especially in random scenes. Although some experimental results published so far show that the prediction of group intelligence is better than individual intelligence, establishing a mathematical foundation for the superiority of distributed group intelligence is still a challenging problem for enhancing the interpretability of learning systems. Through the Radermacher complexity principle, we proved mathematically that the learning quality of group machine intelligence is better than its subset machine intelligence with a high probability, significantly better than any individual among them if the number of individuals in the group is large enough. We proposed a multi-agent distributed learning method for time series forecasting by incorporating multi-agent cooperation in cognitive processes into machine learning. In addition, since the way of cooperative interaction between multi-agent affects the training effect of the model, we provide a generalized interaction approach and prove its convergence. We conduct sufficient experiments on predicting time series for classically chaotic systems, and the results indicate that distributed group intelligence significantly improves the prediction accuracy of individual intelligence. The experiments result shows that the prediction error reduces substantially as the number of agents increases, confirming the theoretical accuracy and the model's validity. This work provides new ideas for theoretically exploring how group intelligence emerges.