A Methodology for the Analysis of Fly Activity Data.

Experiments to learn about the effect of light, sex, and diet on the activity of flies generate great quantities of data that is necessary to analyze. Since different researches and students participate in the analysis of those experiments, it is convenient to have a methodology to analyze the experimental data using software so that the data can be analyzed in a uniform way. Being a double major in mathematics and biology, I am interested in:Deciding which statistical procedure to use to analyze the data so that the research questions of the researchers in biology are answered.To recommend how to implement those procedures using software in an efficient way.To write a prototype for the interpretation of the results.Those are the objectives of this work. In the thesis, we first applied two-way ANOVA to analyze the effect of two selected factors, sex (female and male) and diet (liver and non-liver), on the fly activity under dark condition and under light condition, respectively. Next, we employed the repeated measures to capture how fly activity changes over time (day in this case) and to relate the changes to the selected factors, sex and diet, also under dark condition and under light condition, respectively. Finally, we did a little research on the analysis of circadian rhythms and compared the results with that obtained from honey bee activity experiments carried out before

AlerTiger: Deep Learning for AI Model Health Monitoring at LinkedIn

Data-driven companies use AI models extensively to develop products and intelligent business solutions, making the health of these models crucial for business success. Model monitoring and alerting in industries pose unique challenges, including a lack of clear model health metrics definition, label sparsity, and fast model iterations that result in short-lived models and features. As a product, there are also requirements for scalability, generalizability, and explainability. To tackle these challenges, we propose AlerTiger, a deep-learning-based MLOps model monitoring system that helps AI teams across the company monitor their AI models' health by detecting anomalies in models' input features and output score over time. The system consists of four major steps: model statistics generation, deep-learning-based anomaly detection, anomaly post-processing, and user alerting. Our solution generates three categories of statistics to indicate AI model health, offers a two-stage deep anomaly detection solution to address label sparsity and attain the generalizability of monitoring new models, and provides holistic reports for actionable alerts. This approach has been deployed to most of LinkedIn's production AI models for over a year and has identified several model issues that later led to significant business metric gains after fixing

Rigorous assessment and integration of the sequence and structure based features to predict hot spots

Background Systematic mutagenesis studies have shown that only a few interface residues termed hot spots contribute significantly to the binding free energy of protein-protein interactions. Therefore, hot spots prediction becomes increasingly important for well understanding the essence of proteins interactions and helping narrow down the search space for drug design. Currently many computational methods have been developed by proposing different features. However comparative assessment of these features and furthermore effective and accurate methods are still in pressing need. Results In this study, we first comprehensively collect the features to discriminate hot spots and non-hot spots and analyze their distributions. We find that hot spots have lower relASA and larger relative change in ASA, suggesting hot spots tend to be protected from bulk solvent. In addition, hot spots have more contacts including hydrogen bonds, salt bridges, and atomic contacts, which favor complexes formation. Interestingly, we find that conservation score and sequence entropy are not significantly different between hot spots and non-hot spots in Ab+ dataset (all complexes). While in Ab- dataset (antigen-antibody complexes are excluded), there are significant differences in two features between hot pots and non-hot spots. Secondly, we explore the predictive ability for each feature and the combinations of features by support vector machines (SVMs). The results indicate that sequence-based feature outperforms other combinations of features with reasonable accuracy, with a precision of 0.69, a recall of 0.68, an F1 score of 0.68, and an AUC of 0.68 on independent test set. Compared with other machine learning methods and two energy-based approaches, our approach achieves the best performance. Moreover, we demonstrate the applicability of our method to predict hot spots of two protein complexes. Conclusion Experimental results show that support vector machine classifiers are quite effective in predicting hot spots based on sequence features. Hot spots cannot be fully predicted through simple analysis based on physicochemical characteristics, but there is reason to believe that integration of features and machine learning methods can remarkably improve the predictive performance for hot spots

Rigorous assessment and integration of the sequence and structure based features to predict hot spots

<p>Abstract</p> <p>Background</p> <p>Systematic mutagenesis studies have shown that only a few interface residues termed hot spots contribute significantly to the binding free energy of protein-protein interactions. Therefore, hot spots prediction becomes increasingly important for well understanding the essence of proteins interactions and helping narrow down the search space for drug design. Currently many computational methods have been developed by proposing different features. However comparative assessment of these features and furthermore effective and accurate methods are still in pressing need.</p> <p>Results</p> <p>In this study, we first comprehensively collect the features to discriminate hot spots and non-hot spots and analyze their distributions. We find that hot spots have lower relASA and larger relative change in ASA, suggesting hot spots tend to be protected from bulk solvent. In addition, hot spots have more contacts including hydrogen bonds, salt bridges, and atomic contacts, which favor complexes formation. Interestingly, we find that conservation score and sequence entropy are not significantly different between hot spots and non-hot spots in Ab+ dataset (all complexes). While in Ab- dataset (antigen-antibody complexes are excluded), there are significant differences in two features between hot pots and non-hot spots. Secondly, we explore the predictive ability for each feature and the combinations of features by support vector machines (SVMs). The results indicate that sequence-based feature outperforms other combinations of features with reasonable accuracy, with a precision of 0.69, a recall of 0.68, an F1 score of 0.68, and an AUC of 0.68 on independent test set. Compared with other machine learning methods and two energy-based approaches, our approach achieves the best performance. Moreover, we demonstrate the applicability of our method to predict hot spots of two protein complexes.</p> <p>Conclusion</p> <p>Experimental results show that support vector machine classifiers are quite effective in predicting hot spots based on sequence features. Hot spots cannot be fully predicted through simple analysis based on physicochemical characteristics, but there is reason to believe that integration of features and machine learning methods can remarkably improve the predictive performance for hot spots.</p

Four years of multi-modal odometry and mapping on the rail vehicles

Precise, seamless, and efficient train localization as well as long-term railway environment monitoring is the essential property towards reliability, availability, maintainability, and safety (RAMS) engineering for railroad systems. Simultaneous localization and mapping (SLAM) is right at the core of solving the two problems concurrently. In this end, we propose a high-performance and versatile multi-modal framework in this paper, targeted for the odometry and mapping task for various rail vehicles. Our system is built atop an inertial-centric state estimator that tightly couples light detection and ranging (LiDAR), visual, optionally satellite navigation and map-based localization information with the convenience and extendibility of loosely coupled methods. The inertial sensors IMU and wheel encoder are treated as the primary sensor, which achieves the observations from subsystems to constrain the accelerometer and gyroscope biases. Compared to point-only LiDAR-inertial methods, our approach leverages more geometry information by introducing both track plane and electric power pillars into state estimation. The Visual-inertial subsystem also utilizes the environmental structure information by employing both lines and points. Besides, the method is capable of handling sensor failures by automatic reconfiguration bypassing failure modules. Our proposed method has been extensively tested in the long-during railway environments over four years, including general-speed, high-speed and metro, both passenger and freight traffic are investigated. Further, we aim to share, in an open way, the experience, problems, and successes of our group with the robotics community so that those that work in such environments can avoid these errors. In this view, we open source some of the datasets to benefit the research community

NO SPACE LEFT BEHIND - Graduate Urban Design Studio - LANDARCH 606

The following report documents the work of the 2015 Spring Graduate Urban Design Studio course in the Department of Landscape Architecture and Regional Planning. This fourteen week studio focused on using tactical urbanism to engage Springfield’s Metro Center neighborhood with visions to revitalize the downtown core of this legacy city. In addition to completing the components of a traditional urban design studio (site analyses, schematic plans, spatial designs, and programming), the student teams also developed conceptual projects to immediately engage the public. These efforts culminated in a free afternoon walking tour throughout the Metro Center that presented several tactical interventions. These interactive, public installations illustrated potential future initiatives and brought attention to overlooked areas and assets. Throughout the semester the teams were challenged to continuously consider both the short-term impacts of their interventions and the long-term visions for the future of Springfield’s urban core. This strategy allowed students to develop a process-based approach to urban design. It provided opportunities to engage stakeholders and test new ideas with the public, rather than simply creating a master plan without any real world interactions with the city and its populace. STUDIO GOALS • To enliven public space in Springfield’s Metro Center through small-scale urban design interventions which illustrate and inform long-term plans • To activate residual spaces in the downtown with Tactical Urbanism By matching long-term visions for the Metro Center with short-term, site-specific tactical interventions the teams were able to deliver a variety of ideas in multiple formats. Furthermore, the experience of interacting with community members during the installation of projects added a great deal of meaning to the research and design process. The work explores a community service learning strategy within the framework of an urban design studio with the goal of revitalizing the city of Springfield, Massachusetts, through sustainable design, planning, and engagement with the community. The six design-team interventions are documented and published as team-authored videos: 1. Union Station Green Corridor Maozhu Mao, Yuqing Wu https://www.youtube.com/watch?v=EnE8Yt8x0Es&feature=youtu.be 2. (No) Vacancy Chris Counihan, Michalagh Stoddard, Ruoying Tang https://youtu.be/CapaQ9gDxeM 3. Extra Space, Active Street Yu Yu https://youtu.be/2pgYq5HLNHA 4. Spring into Art Emilie Jordao, Matt Hisle, Jing Wang https://youtu.be/Vf2yk6Se6rU 5. Urban Agua Kellie Fenton, James Prendergast, Nelle Ward https://www.youtube.com/watch?v=vk2PUJMdhTU&feature=youtu.be 6. Make the Connection Yue Li, Yanhua Lu, Yi Yang https://youtu.be/EvQD0QYV97

Are C-Reactive Protein Associated Genetic Variants Associated with Serum Levels and Retinal Markers of Microvascular Pathology in Asian Populations from Singapore?

Introduction:C-reactive protein (CRP) levels are associated with cardiovascular disease and systemic inflammation. We assessed whether CRP-associated loci were associated with serum CRP and retinal markers of microvascular disease, in Asian populations.Methods:Genome-wide association analysis (GWAS) for serum CRP was performed in East-Asian Chinese (N = 2,434) and Malays (N = 2,542) and South-Asian Indians (N = 2,538) from Singapore. Leveraging on GWAS data, we assessed, in silico, association levels among the Singaporean datasets for 22 recently identified CRP-associated loci. At loci where directional inconsistencies were observed, quantification of inter-ethnic linkage disequilibrium (LD) difference was determined. Next, we assessed association for a variant at CRP and retinal vessel traits [central retinal artery equivalent (CRAE) and central retinal vein equivalent (CRVE)] in a total of 24,132 subjects of East-Asian, South-Asian and European ancestry.Results:Serum CRP was associated with SNPs in/near APOE, CRP, HNF1A and LEPR (p-values ≤4.7×10-8) after meta-analysis of Singaporean populations. Using a candidate-SNP approach, we further replicated SNPs at 4 additional loci that had been recently identified to be associated with serum CRP (IL6R, GCKR, IL6 and IL1F10) (p-values ≤0.009), in the Singaporean datasets. SNPs from these 8 loci explained 4.05% of variance in serum CRP. Two SNPs (rs2847281 and rs6901250) were detected to be significant (p-value ≤0.036) but with opposite effect directions in the Singaporean populations as compared to original European studies. At these loci we did not detect significant inter-population LD differences. We further did not observe a significant association between CRP variant and CRVE or CRAE levels after meta-analysis of all Singaporean and European datasets (p-value >0.058).Conclusions:Common variants associated with serum CRP, first detected in primarily European studies, are also associated with CRP levels in East-Asian and South-Asian populations. We did not find a causal link between CRP and retinal measures of microvascular disease

Tumor Transcriptome Sequencing Reveals Allelic Expression Imbalances Associated with Copy Number Alterations

Due to growing throughput and shrinking cost, massively parallel sequencing is rapidly becoming an attractive alternative to microarrays for the genome-wide study of gene expression and copy number alterations in primary tumors. The sequencing of transcripts (RNA-Seq) should offer several advantages over microarray-based methods, including the ability to detect somatic mutations and accurately measure allele-specific expression. To investigate these advantages we have applied a novel, strand-specific RNA-Seq method to tumors and matched normal tissue from three patients with oral squamous cell carcinomas. Additionally, to better understand the genomic determinants of the gene expression changes observed, we have sequenced the tumor and normal genomes of one of these patients. We demonstrate here that our RNA-Seq method accurately measures allelic imbalance and that measurement on the genome-wide scale yields novel insights into cancer etiology. As expected, the set of genes differentially expressed in the tumors is enriched for cell adhesion and differentiation functions, but, unexpectedly, the set of allelically imbalanced genes is also enriched for these same cancer-related functions. By comparing the transcriptomic perturbations observed in one patient to his underlying normal and tumor genomes, we find that allelic imbalance in the tumor is associated with copy number mutations and that copy number mutations are, in turn, strongly associated with changes in transcript abundance. These results support a model in which allele-specific deletions and duplications drive allele-specific changes in gene expression in the developing tumor

International trade and firm performance

This dissertation is a collection of three essays that study the effect of opening to trade, especially opening to the import market, on firm performance. The first essay (Chapter 2) explores the link between innovation and import competition in China, a country that during the period we study (2000-2007) saw both a rapid increase in patenting and a lowering of import barriers due to accession to the WTO. Combining manufacturing firm survey data with customs and patent data, we find that import competition encouraged innovation, but only for the most productive firms. These top firms saw an increase in patenting rate of 3.6% for every percentage point drop in import tariffs. The result is quantitatively similar whether we use a sector-wide tariff on output or a weighted tariff at the firm level as a measure of import competition. Consistent with the main finding, top firms also feature increased R&D expenditures and an increase in domestic sales following import liberalization. To analyze the mechanism and welfare implications underlying our empirical findings about China, the second essay (Chapter 3 and 4) builds a model. Firms engage in monopolistic competition across varieties and neck-and-neck competition within each variety. An increase in the neck-and-neck competition reduces the expected profit of not innovating, thus encouraging firms to innovate more to escape the competition. We analyze the efficiency and utility implications using a simple version of the model in Chapter 4. The third essay (Chapter 5) examines the relationship between Canadian manufacturing firms' import behavior and their performance. The focus is on two aspects of import structure, input variety and the dynamics of import relationships. Firms importing more products from a larger set of suppliers tend to be larger, more productive, and more successful in export markets. Not only the number, but also the duration of supply relationships matters. Firms maintaining a higher share of continuous supply relationships also benefit in size and productivity. These results suggest that the breadth and depth of the import network are relevant factors for the performance of Canadian manufacturers.Arts, Faculty ofVancouver School of EconomicsGraduat

Research on the Gradual Process of the Metallization Structures and Mechanical Properties of Wood Veneer

In order to improve the mechanical properties of the wood surface and explore the mechanical effect of wood veneer surface metallization, the 31-year-old Pinus sylvestris is taken as the research object and Cu is deposited on the wood surface by magnetron sputtering to achieve wood veneer metallization. Based on X-ray diffraction (XRD) and nanoindentation, a research on the gradual process of the structures and mechanical properties of wood veneer metallization was carried out. The results indicate that wood veneer metallization does not affect the crystallization zone of wood, there are still wood cellulose characteristic peaks and the crystalline structure of the wood cellulose is not damaged; the thickness of the copper thin film increases with the increase of the deposition time, the cellulose characteristic peak strength gradually decrease, and the relative crystallinity also decreases; the characteristic diffraction peaks of Cu (111), Cu (200), and Cu (220) appear near the diffraction angle 2&#952; which is equal to 43.3&#176;, 50.4&#176;, and 74.1&#176;, and the diffraction peak intensity increases with increase of deposition time, the copper film of the metal wood veneer crystallizes well; the load&#8315;displacement of wood veneer decreases significantly with the increase of deposition time, while the moduli of elasticity and hardness increase rapidly. The load&#8315;displacement of the samples which were coated for 15 min decreased by 80%, while the moduli of elasticity and hardness of these samples increased by 24.1 times and 17.3 times, respectively. From the results of Scanning Electron Microscope (SEM) measurement of the metallization of wood veneer, it can be seen that the uniform and continuous copper film can be formed on the wood veneer surface by using the magnetron sputtering method. This paper provides a basis for wood veneer surface metallization, which is of great significance for the functional improvement of wood, the expansion of wood application fields, and the enhancement of added value