QUANTITATIVE INSIGHTS INTO DEVELOPMENTAL SIGNALS AND PHENOTYPES IN C. ELEGANS

Millions of users share their experiences, thoughts, and interests online, through social media sites (e.g., Twitter, Flickr, YouTube). As a result, these sites host a substantial number of user-contributed documents (e.g., textual messages, photographs, videos) for a wide variety of events (e.g., concerts, political demonstrations, earthquakes). In this dissertation, we present techniques for leveraging the wealth of available social media documents to identify and characterize events of different types and scale. By automatically identifying and characterizing events and their associated user-contributed social media documents, we can ultimately offer substantial improvements in browsing and search quality for event content. To understand the types of events that exist in social media, we first characterize a large set of events using their associated social media documents. Specifically, we develop a taxonomy of events in social media, identify important dimensions along which they can be categorized, and determine the key distinguishing features that can be derived from their associated documents. We quantitatively examine the computed features for different categories of events, and establish that significant differences can be detected across categories. Importantly, we observe differences between events and other non-event content that exists in social media. We use these observations to inform our event identification techniques. To identify events in social media, we follow two possible scenarios. In one scenario, we do not have any information about the events that are re ected in the data. In this scenario, we use an online clustering framework to identify these unknown events and their associated social media documents. To distinguish between event and non-event content, we develop event classification techniques that rely on a rich family of aggregate cluster statistics, including temporal, social, topical, and platform-centric characteristics. In addition, to tailor the clustering framework to the social media domain, we develop similarity metric learning techniques for social media documents, exploiting the variety of document context features, both textual and non-textual. In our alternative event identification scenario, the events of interest are known, through user-contributed event aggregation platforms (e.g., Last.fm events, EventBrite, Facebook events). In this scenario, we can identify social media documents for the known events by exploiting known event features, such as the event title, venue, and time. While this event information is generally helpful and easy to collect, it is often noisy and ambiguous. To address this challenge, we develop query formulation strategies for retrieving event content on different social media sites. Specifically, we propose a two-step query formulation approach, with a first step that uses highly specific queries aimed at achieving high-precision results, and a second step that builds on these high-precision results, using term extraction and frequency analysis, with the goal of improving recall. Importantly, we demonstrate how event-related documents from one social media site can be used to enhance the identification of documents for the event on another social media site, thus contributing to the diversity of information that we identify. The number of social media documents that our techniques identify for each event is potentially large. To avoid overwhelming users with unmanageable volumes of event information, we design techniques for selecting a subset of documents from the total number of documents that we identify for each event. Specifically, we aim to select high-quality, relevant documents that re ect useful event information. For this content selection task, we experiment with several centrality-based techniques that consider the similarity of each event-related document to the central theme of its associated event and to other social media documents that correspond to the same event. We then evaluate both the relative and overall user satisfaction with the selected social media documents for each event. The existing tools to find and organize social media event content are extremely limited. This dissertation presents robust ways to organize and filter this noisy but powerful event information. With our event identification, characterization, and content selection techniques, we provide new opportunities for exploring and interacting with a diverse set of social media documents that re ect timely and revealing event content. Overall, the work presented in this dissertation provides an essential methodology for organizing social media documents that re ect event information, towards improved browsing and search for social media event data.

Ad hoc wireless networks have emerged as a solution to providing ubiquitous, on-demand connectivity without the need for significant infrastructure deployment. In this thesis we address the privacy problems in two types of emerging wireless ad hoc networks, namely sensor and vehicular networks. Although the content of sensor messages describing “events of interest” may be encrypted to provide confidentiality, the context surrounding these events may also be sensitive and therefore should be protected from eavesdroppers. The source-location privacy problem occurs in sensor networks when adversaries use RF localization techniques to perform hop-by-hop traceback of messages to the source sensor’s location. Our work provides a formal model for this problem and examines the privacy characteristics of different sensor routing protocols. In order to provide efficient and private sensor communications, we devise new techniques to enhance source-location privacy that augment these routing protocols. Similarly, an adversary armed with knowledge of the network deployment, routing algorithms, and the data sink location can infer the temporal patterns and track the spatio-temporal evolution of a sensed event, by monitoring the packet arrivals at the sink. We introduce the temporal privacy problem for delay-tolerant sensor networks, provide an information theoretic formulation and propose adaptive buffering to obfuscate temporal information from the adversary. Sensor networks are also characterized by distinctive traffic patterns, wherein traffic mostly exists when events of interests occur. Due to the direct correlation between the type of event and size of data generated by it, an adversary observing a traffic burst can infer information about the type of event simply from the observed message size. We formulate this traffic privacy problem in terms of information entropy, present a quantifiable means to measure traffic privacy and propose solutions to enhance it. Vehicular ad hoc networks represent a promising new communication paradigm that can facilitate many new forms of automotive applications. We present a robust and efficient security and privacy framework, for such networks, that uses identity-based cryptography. We show that our framework provides authentication, confidentiality, non-repudiation and message-integrity. Further, it supports scalable, user-customizable privacy through short-lived, unforgeable, pseudonyms.

Early events in folding can determine if a protein is going to fold, misfold, or aggregate. Understanding these deterministic events is paramount for de novo protein engineering, the enhancement of biopharmaceutical stabilities, and understanding neurodegenerative diseases including amyotrophic lateral sclerosis and Alzheimer's disease. However, the physicochemical and structural biases within high energy states of protein biopolymers are poorly understood. A combined experimental and computational study was conducted on the small β/α-repeat protein CheY to determine the structural basis of its submillisecond misfolding reaction to an off-pathway intermediate. Using permutations, we were able to discriminate between the roles of two proposed mechanisms of folding; a nucleation condensation model, and a hydrophobic collapse model driven by the formation of clusters of isoleucine, leucine, and valine (ILV) residues. We found that by altering the ILV cluster connectivity we could bias the early folding events to either favor on or off-pathway intermediates. Structural biases were also experimentally observed in the unfolded state of a de novo designed synthetic β/α-repeat protein, Di-III_14. Although thermodynamically and kinetically 2-state, Di-III_14 has a well structured unfolded state that is only observable under native-favoring conditions. This unfolded state appears to retain native-like structure, consisting of a hydrophobic 7 core (69% ILV) stabilized by solvent exposed polar groups and long range electrostatic interactions. Together, these results suggest that early folding events are largely deterministic in these two systems. Generally, low contact order ILV clusters favor local compaction and, in specific cases, long range electrostatic interactions may have stabilizing effects in higher energy states.

This paper provides two puzzles for a theory of aspect. The first concerns the quirky behavior of the Russian imperfective with regard to its culmination properties: it seems to function like the perfect aspect in certain cases, but like the progressive in others. The other puzzle concerns how the Russian imperfective constrains the temporal location of a described event: it relates distinct event parts to a given temporal parameter. Which part is at play depends on how this parameter is specified. If it is specified by an adverbial, then an event is located in time. If it is specified by the discourse context, then a consequent state is located in time. I solve the former puzzle by appealing to the structure of atomic vs. non-atomic events and solve the latter by appealing to two temporal inputs required by an aspectual marker. These inputs reveal that aspectual meaning involves both temporal information and information about discourse connectivity.

This thesis investigates the spread and activity of the Internet over the years 2006-2012, an unprecedented period of growth in global internet connectivity. In stark contrast to previous general purpose technologies such as steam power or electrification the internet's diffusion process operates at a much faster pace. Whereas it took 100 years for the steam engine to reach saturation, and around 60 years for electrification, we estimate that the average time to saturation for the internet's expansion has been just 16 years (1% to 99%). Significantly, whilst the emergence of the steam engine necessitated the co-development of the engineering profession to install, use and maintain the engines, every participant in the Internet obtains (and extends) the knowledge required to utilise the internet for greater productivity. Moreover, each internet user, simply by their pattern of interaction with the internet reveals personal preferences and choices, and this, combined with the global reach and remarkably democratic foundations of the internet enables social science research of an unparalleled nature. I present a novel data set with corresponding methods to analyse the diffusion of the internet at a very disaggregated level and show it's relationship with economic progress, structural changes and innovation. The first two chapters of this thesis focuses on the extensive methods necessary to aggregate a trillion random Internet (IPv4, ICMP) probes and transform this information into useful data. This work yielded novel data join algorithms to optimise the joining process with distributed data flows. The resultant data set comprises accurately geo-located IP activity ('online'/'offline') aggregated at over 1600 locations (cities) at 15min intervals, over the period 2006-2012. The third chapter applies the data set to investigate biological, social and economic behaviour. The American Time Use Survey (ATUS), in combination with our Internet data, is used to upscale the time diary to cities worldwide. A novel machine learning method is presented to facilitate the process of predicting sleep start and sleep stop time over seven years around the globe. Furthermore, the dynamics of the Internet Protocol space regarding allocation challenges are overcome to derive comparable monthly Internet per capita measures and estimate country-specific diffusion properties. The globally consistent regional estimates are used to research the effect of the spread of the Internet on local economic outcomes. I find that the propagation of the Internet had positive effects on the economic performance of regions across industries. On the other hand, in industries where the spread of the Internet has lead to the possibility to outsource operation and results in a structural change, the spread of the Internet has adverse economic effects. The final chapter investigates how the free distribution of information over the Internet is seen as a threat by autocratic governments. First a method to identify events of Internet censorship through speed tampering is developed, and validated for Iran. Then, the method is applied to identify the limiting of the market for information during the Russian presidential election in 2012 using a difference in difference design. I find that on average the government candidate achieved a lower-bound estimate of 3.2 percentage point increase on Election Day due to subtle tampering of the Internet.

Particle based simulations are widely used in computer graphics. In this field, several recent results have improved the simulation itself or improved the tension of the final fluid surface. In current particle based implementations, the particle neighborhood is computed by considering the Euclidean distance between fluid particles only. Thus particles from different fluid components interact, which generates both local incorrect behavior in the simulation and blending artifacts in the reconstructed fluid surface. Our method introduces a better neighborhood computation for both the physical simulation and surface reconstruction steps. We track and store the local fluid topology around each particle using a graph structure. In this graph, only particles within the same local fluid component are neighbors and other disconnected fluid particles are inserted only if they come into contact. The graph connectivity also takes into account the asymmetric behavior of particles when they merge and split, and the fluid surface is reconstructed accordingly, thus avoiding their blending at distance before a merge. In the simulation, this neighborhood information is exploited for better controlling the fluid density and the force interactions at the vicinity of its boundaries. For instance, it prevents the introduction of collision events when two distinct fluid components are crossing without contact, and it avoids fluid interactions through thin waterproof walls. This leads to an overall more consistent fluid simulation and reconstruction.

Studies Reveal Rx for Doctors: A Daily Dose of Meditation

Successful drug development is a risky and lengthy process that can take over ten years and consume billions of dollars. Target discovery is a critical stage of drug development for the identification of key molecules and pathways that can be targeted by novel therapeutics to find more effective treatments. Due to the rapid development in artificial intelligence and machine learning techniques over the past decade, computational approaches have now emerged as powerful tools to unravel complex interactions within biological systems to identify novel therapeutic targets. In particular, modeling and analysis of intracellular molecular networks play a pivotal role in target discovery by enabling researchers to efficiently and simultaneously navigate massive amounts of biological data to identify potential therapeutic targets. Such technologies can significantly accelerate the prolonged process of development of innovative therapies for complex diseases. Besides highlighting the findings of the recently introduced extreme signaling failures in intracellular molecular networks, here we briefly review various methods for modeling and analysis of intracellular molecular networks and discuss how they can be utilized to predict potential drug targets within such complex signaling systems. Overall, this review emphasizes the significance of modeling and analysis of molecular networks for fast-tracking and rapid discovery of novel therapeutic targets; to pave the way for the development of more effective treatments.

BackgroundSufficient knowledge of molecular and genetic interactions, which comprise the entire basis of the functioning of living systems, is one of the necessary requirements for successfully answering almost any research question in the field of biology and medicine. To date, more than 24 million scientific papers can be found in PubMed, with many of them containing descriptions of a wide range of biological processes. The analysis of such tremendous amounts of data requires the use of automated text-mining approaches. Although a handful of tools have recently been developed to meet this need, none of them provide error-free extraction of highly detailed information.ResultsThe ANDSystem package was developed for the reconstruction and analysis of molecular genetic networks based on an automated text-mining technique. It provides a detailed description of the various types of interactions between genes, proteins, microRNA's, metabolites, cellular components, pathways and diseases, taking into account the specificity of cell lines and organisms. Although the accuracy of ANDSystem is comparable to other well known text-mining tools, such as Pathway Studio and STRING, it outperforms them in having the ability to identify an increased number of interaction types.ConclusionThe use of ANDSystem, in combination with Pathway Studio and STRING, can improve the quality of the automated reconstruction of molecular and genetic networks. ANDSystem should provide a useful tool for researchers working in a number of different fields, including biology, biotechnology, pharmacology and medicine.

Effects of sulfamethoxazole on nitrogen removal and molecular ecological network in integrated vertical-flow constructed wetland

Molecular crystals are solid and periodic materials. These entities may be regarded as supramolecular architectures and thus be described as molecular networks, which are infinite molecular assemblies possessing translational symmetry and composed of complementary or self-complementary tectons capable of mutual interactions. This way of analysing structural features of crystalline materials called molecular tectonics not only permits crystals to be described as supramolecular architectures generated through molecular recognition processes but also allows molecular networks to be conceived with predicted connectivity and thus dimensionality through the design of tectons. A formal description and analysis of molecular networks is given.

Safety is a major concern in the oil and gas industry due to accidents with the potential for catastrophic consequences. One of these was the Piper Alpha drilling platform disaster in 1988, which resulted in the loss of the Piper Alpha platform and the subsequently 167 lives; the worst disaster in the oil and gas industry. To foster a proactive approach in the prevention of workplace injuries, organisations have turned towards key predictors of safety, such as leadership. However, simply practising leadership behaviours is not enough to improve safety, especially in safety-critical organisations. An inability to move beyond the formal control inherent in traditional bureaucratic mindsets limits the applicability of mainstream leadership theories for safety-critical organisations. To address this challenge, this research study adopts a different paradigm for leadership, one which frames leadership as a complex interactive dynamic from which adaptive outcomes emerge as complexity science. Hence, the aim of this research study was to investigate the direct relationship between leadership behaviours and safety outcomes, and the mediating effect of complexity science principles. To accomplish this, a conceptual model was developed using theoretical foundation built from the literature review. The model comprises three main components: 1) leadership behaviours, 2) five complexity science principles (connectivity, interdependence, feedback, exploration-of-the-space-of-possibilities and co-evolution) and 3) three safety outcomes (safety participation, safety compliance and safety climate). These components were linked by the proposed hypotheses that were established based on assumed theoretical relationships. To assess and validate the conceptual model and test the hypotheses, a series of quantitative approaches were employed. A multivariate statistical approach was utilised for data collection through a questionnaire survey; 173 project managers participated in the survey. These individuals are responsible for emergency commands, as well as the day-to-day management of safety. Principal component analysis/Exploratory factor analysis was performed to identify the factor structures representing each underlying construct. Confirmatory factor analysis was conducted to validate the results obtained from the exploratory factor analysis, followed by multiple regression analysis. Next, structural equation modelling was applied to test the structural models for hypotheses testing. The regression analyses demonstrated that all three identified leadership behaviours — 1) empowering & engaging, 2) modelling & reinforcing, and 3) promoting & enabling — directly influenced the level of safety participation and safety compliance. Additionally, all five principles of complexity science mediated the relationship between empowering & engaging leadership behaviour, and safety participation and safety compliance. As for the hypothesised relationship between modelling & reinforcing leadership behaviour and safety outcomes, apart from the co-evolution principle, all other complexity science principles arbitrated the relationship with safety participation and safety compliance. Furthermore, the relationships between promoting & enabling leadership behaviour, and safety participation and safety compliance were mediated by all five principles of complexity science. Finally, structural equation modelling was used to refine and confirm the findings of the regression analysis, verifying both empowering & engaging, and promoting & enabling leadership behaviours have direct and indirect influences on safety participation and safety compliance levels as a result of all five complexity science principles. And established that modelling & reinforcing leadership behaviour has direct and indirect effects on safety participation and safety compliance levels under the influence of only four complexity science principles — namely, connectivity, interdependence, feedback and exploration-of-the-space-of-possibilities. On the basis of the research results, this study provides insight into the understanding of safety leadership by the empirical evidence that displayed the relationships between the three leadership behaviour constructs and their contribution to the effect of complexity science principles and safety outcomes within the oil and gas industry. From these results, the study provides a list of recommendations for research on the effect of leadership behaviours on complexity science principles and safety outcomes.

Flower patterning is determined by a complex molecular network but how this network functions remains to be elucidated. Here, we develop an integrative modeling approach that assembles heterogeneous data into a biologically coherent model to allow predictions to be made and inconsistencies among the data to be found. We use this approach to study the network underlying sepal development in the young flower of Arabidopsis thaliana. We constructed a digital atlas of gene expression and used it to build a dynamical molecular regulatory network model of sepal primordium development. This led to the construction of a coherent molecular network model for lateral organ polarity that fully recapitulates expression and interaction data. Our model predicts the existence of three novel pathways involving the HD-ZIP III genes and both cytokinin and ARGONAUTE family members. In addition, our model provides predictions on molecular interactions. In a broader context, this approach allows the extraction of biological knowledge from diverse types of data and can be used to study developmental processes in any multicellular organism.

BackgroundThe China-Myanmar border area is considered a hot spot of active HIV-1 recombination in Southeast Asia. To better understand the characteristics of HIV-1 transmission in this area, a cross-sectional HIV-1 molecular epidemiological survey was conducted in Baoshan Prefecture of Yunnan Province.MethodsIn total, 708 newly reported HIV-1 cases in Baoshan Prefecture from 2019 to 2020 were included in this study. HIV-1 gag, pol and env genes were sequenced, and the spatial and demographic distributions of HIV-1 genotypes were analyzed. The characteristics of HIV-1 transmission were investigated using the HIV-1 molecular network method.ResultsIn the 497 samples with genotyping results, 19 HIV-1 genotypes were found, with URFs being the predominant strains (30.2%, 150/497). The main circulating HIV-1 strains were mostly distributed in the northern area of Baoshan. URFs were more likely identified in Burmese individuals, intravenous drug users and those younger than 50 years old. CRF08_BC was more likely detected in farmers and those of Han ethnicity, CRF01_AE in the young and those of Han ethnicity, and CRF07_BC in the subpopulation with junior middle school education and higher. Moreover, CRF118_BC and CRF64_BC were more likely found in the subpopulation aged ≥40 years and ≥50 years, respectively. Among 480 individuals with pol sequence detection, 179 (37.3%) were grouped into 78 clusters, with Baoshan natives being more likely to be in the network. The proportion of the linked individuals showed significant differences when stratified by the regional origin, marital status, age and county of case reporting. In the molecular network, recent infections were more likely to occur among nonfarmers and individuals aged below 30 years.ConclusionsHIV-1 genetics has become complex in Baoshan. HIV-1 molecular network analysis provided transmission characteristics in the local area, and these findings provided information to prioritize transmission-reduction interventions.

Quality control of Radix Bupleuri (RB) can be challenging due to the complexity of origin, the similar morphological characteristics, and the diversity of the multiple components. In this study, an integrated strategy for extensive identification of metabolites in plants based on multiple data processing methods was proposed to distinguish four commercially available RB species. First, the pre-processed mass spectrometry data was uploaded to Global Natural Products Social Molecular Networking (GNPS) for spectral library search and molecular network analysis, which can effectively differentiate isomers and reduce molecular redundancy. Second, the possible cleavage mode was summarized from the characteristic MS/MS fragment ions of saikoside standard, and then the possible structure of saikoside in the sample was deduced according to the cleavage patterns. Third, collected all kinds of RB components reported in the literature and matched the information in the samples to obtain more comprehensive information about metabolites. Finally, chemical markers were found employing chemometrics. This strategy not only increases the variety and number of identified components, but also improves the accuracy of the data. Based on this strategy, a total of 132 components were identified from different species of RB, and 14 chemical constituents were considered to be potential chemical markers to distinguish four kinds of RB. Among them, saikogenin a, hydroxy-saikosaponin a, hydroxy-saikosaponin d, and rutinum were of great significance for identification. The method proposed in this study not only successfully identified and distinguished four species of RB, but also laid a good theoretical foundation for regulating the RB market. This strategy provides promising perspectives in the accurate analysis of the ingredients of traditional Chinese medicine.