Forecasting bicycle traffic in cities

In this project the task is to predict bicycle theft and bicycle traffic in a city using machine learning methods. The project proposal was given in collaboration with BikeFinder AS, a Petter Stordalen"s #Strawberry Million” award winning company established in 2015. Bicycle theft is a problem in many places around the world and one of the objectives in this thesis is to help preventing it, based on data science analysis and machine learning methods applied on existing data. Predicting bicycle traffic as well as analyzing the factors that might affect traffic is another important goal for this thesis. However, throughout the project it is expected to work on various other steps such as gathering the relevant data, pre-processing, evaluating and comparing methods and results. It is also important to optimize and improve the performance of the methods to achieve as accurate results as possible. Lastly, interpreting the results, and solving the questions asked in the thesis. The project has been solved by first, gathering BikeFinder theft and traffic data, Stavanger weather conditions data, Rogaland Police District bike theft reports data and data from the bike counting sensors in the city of Stavanger. Secondly, various steps of preprocessing has been done on the data according to the use cases. Afterwards, machine learning method evaluations and comparisons, using a neutral and larger dataset, Chicago crime dataset was accomplished. Thereafter, applying the best performing methods on the theft and traffic datasets, as well as forecasting bike theft and traffic has been achieved. Finally, results interpretation and discussion on the findings of the project. The findings in this project reflects that bike theft and bike traffic can be predicted using machine learning methods on BikeFinder data. Furthermore, other factors such as weather conditions do affect bike traffic as well as improves the performances of bike traffic predictions. The results of the project provide useful insight to multiple parties and can be used to help preventing bike theft as well as providing suggestions for city planning improvements

Cold Storage Effects on Fitness of the Whitefly Parasitoids Encarsia sophia and Eretmocerus hayati

Successful biological control of the whitefly Bemisia tabaci involves the mass rearing of biocontrol agents in large numbers for field release. Cold storage of the biocontrol agents is often necessary to provide a sufficient number of biocontrol agents during an eventual pest outbreak. In this study, the fitness of two whitefly parasitoids Encarsia sophia Girault and Dodd (Hymenoptera: Aphelinidae) and Eretmocerus hayati Zolnerowich and Rose (Hymenoptera: Aphelinidae) was evaluated under fluctuating cold storage temperatures. The emergence rate of old pupae of either species was not affected when stored at 12, 10, 8 and 6 °C for 1 week. Cold storage had no effect on the longevity of the emerging adult En. sophia except young pupae stored at 4 °C, while Er. hayati was negatively affected after 2 weeks of storage time at all temperatures. Parasitism by adults emerging from older pupae stored at 12 °C for 1 week was equivalent to the control. Combined with the results for the emergence time, we suggest that the old pupal stage of En. sophia and Er. hayati could be stored at 12 and 10 °C, respectively (transferred every 22 h to 26 ± 1 °C for 2 h), for 1 week, with no or little adverse effect.National Natural Science Foundation of China (NSFC) (31672087); National Key Research and Development Project of China (2017YFC1200600, 2016YFC1201200); International Science & Technology Cooperation Program of China (2015DFG32300); Shenzhen Science and Technology Program (KQTD20180411143628272)info:eu-repo/semantics/publishedVersio

Bacillus subtilis SbcC protein plays an important role in DNA inter-strand cross-link repair

BACKGROUND: Several distinct pathways for the repair of damaged DNA exist in all cells. DNA modifications are repaired by base excision or nucleotide excision repair, while DNA double strand breaks (DSBs) can be repaired through direct joining of broken ends (non homologous end joining, NHEJ) or through recombination with the non broken sister chromosome (homologous recombination, HR). Rad50 protein plays an important role in repair of DNA damage in eukaryotic cells, and forms a complex with the Mre11 nuclease. The prokaryotic ortholog of Rad50, SbcC, also forms a complex with a nuclease, SbcD, in Escherichia coli, and has been implicated in the removal of hairpin structures that can arise during DNA replication. Ku protein is a component of the NHEJ pathway in pro- and eukaryotic cells. RESULTS: A deletion of the sbcC gene rendered Bacillus subtilis cells sensitive to DNA damage caused by Mitomycin C (MMC) or by gamma irradiation. The deletion of the sbcC gene in a recN mutant background increased the sensitivity of the single recN mutant strain. SbcC was also non-epistatic with AddAB (analog of Escherichia coli RecBCD), but epistatic with RecA. A deletion of the ykoV gene encoding the B. subtilis Ku protein in a sbcC mutant strain did not resulted in an increase in sensitivity towards MMC and gamma irradiation, but exacerbated the phenotype of a recN or a recA mutant strain. In exponentially growing cells, SbcC-GFP was present throughout the cells, or as a central focus in rare cases. Upon induction of DNA damage, SbcC formed 1, rarely 2, foci on the nucleoids. Different to RecN protein, which forms repair centers at any location on the nucleoids, SbcC foci mostly co-localized with the DNA polymerase complex. In contrast to this, AddA-GFP or AddB-GFP did not form detectable foci upon addition of MMC. CONCLUSION: Our experiments show that SbcC plays an important role in the repair of DNA inter-strand cross-links (induced by MMC), most likely through HR, and suggest that NHEJ via Ku serves as a backup DNA repair system. The cell biological experiments show that SbcC functions in close proximity to the replication machinery, suggesting that SbcC may act on stalled or collapsed replication forks. Our results show that different patterns of localization exist for DNA repair proteins, and that the B. subtilis SMC proteins RecN and SbcC play distinct roles in the repair of DNA damage

Mutation of A DNA Repair Enzyme Causes Lupus in Mice

A replication study of a previous genome-wide association study (GWAS) suggested that a SNP linked to the POLβ gene is associated with systemic lupus erythematosus (SLE). This SNP is correlated with decreased expression of Pol β, a key enzyme in the base excision repair (BER) pathway. To determine whether decreased Pol β activity results in SLE, we constructed a mouse model of POLβ that encodes an enzyme with slow DNA polymerase activity. We show that mice expressing this hypomorphic POLβ allele develop an autoimmune pathology that strongly resembles SLE. Of note, the mutant mice have shorter immunoglobulin heavy-chain junctions and somatic hypermutation is dramatically increased. These results demonstrate that decreased Pol β activity during the generation of immune diversity leads to lupus-like disease in mice, and suggest that decreased expression of Pol β in humans is an underlying cause of SLE

Mapping local patterns of childhood overweight and wasting in low- and middle-income countries between 2000 and 2017

A double burden of malnutrition occurs when individuals, household members or communities experience both undernutrition and overweight. Here, we show geospatial estimates of overweight and wasting prevalence among children under 5 years of age in 105 low- and middle-income countries (LMICs) from 2000 to 2017 and aggregate these to policy-relevant administrative units. Wasting decreased overall across LMICs between 2000 and 2017, from 8.4% (62.3 (55.1–70.8) million) to 6.4% (58.3 (47.6–70.7) million), but is predicted to remain above the World Health Organization’s Global Nutrition Target of <5% in over half of LMICs by 2025. Prevalence of overweight increased from 5.2% (30 (22.8–38.5) million) in 2000 to 6.0% (55.5 (44.8–67.9) million) children aged under 5 years in 2017. Areas most affected by double burden of malnutrition were located in Indonesia, Thailand, southeastern China, Botswana, Cameroon and central Nigeria. Our estimates provide a new perspective to researchers, policy makers and public health agencies in their efforts to address this global childhood syndemic

The evolving SARS-CoV-2 epidemic in Africa: Insights from rapidly expanding genomic surveillance

INTRODUCTION Investment in Africa over the past year with regard to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) sequencing has led to a massive increase in the number of sequences, which, to date, exceeds 100,000 sequences generated to track the pandemic on the continent. These sequences have profoundly affected how public health officials in Africa have navigated the COVID-19 pandemic. RATIONALE We demonstrate how the first 100,000 SARS-CoV-2 sequences from Africa have helped monitor the epidemic on the continent, how genomic surveillance expanded over the course of the pandemic, and how we adapted our sequencing methods to deal with an evolving virus. Finally, we also examine how viral lineages have spread across the continent in a phylogeographic framework to gain insights into the underlying temporal and spatial transmission dynamics for several variants of concern (VOCs). RESULTS Our results indicate that the number of countries in Africa that can sequence the virus within their own borders is growing and that this is coupled with a shorter turnaround time from the time of sampling to sequence submission. Ongoing evolution necessitated the continual updating of primer sets, and, as a result, eight primer sets were designed in tandem with viral evolution and used to ensure effective sequencing of the virus. The pandemic unfolded through multiple waves of infection that were each driven by distinct genetic lineages, with B.1-like ancestral strains associated with the first pandemic wave of infections in 2020. Successive waves on the continent were fueled by different VOCs, with Alpha and Beta cocirculating in distinct spatial patterns during the second wave and Delta and Omicron affecting the whole continent during the third and fourth waves, respectively. Phylogeographic reconstruction points toward distinct differences in viral importation and exportation patterns associated with the Alpha, Beta, Delta, and Omicron variants and subvariants, when considering both Africa versus the rest of the world and viral dissemination within the continent. Our epidemiological and phylogenetic inferences therefore underscore the heterogeneous nature of the pandemic on the continent and highlight key insights and challenges, for instance, recognizing the limitations of low testing proportions. We also highlight the early warning capacity that genomic surveillance in Africa has had for the rest of the world with the detection of new lineages and variants, the most recent being the characterization of various Omicron subvariants. CONCLUSION Sustained investment for diagnostics and genomic surveillance in Africa is needed as the virus continues to evolve. This is important not only to help combat SARS-CoV-2 on the continent but also because it can be used as a platform to help address the many emerging and reemerging infectious disease threats in Africa. In particular, capacity building for local sequencing within countries or within the continent should be prioritized because this is generally associated with shorter turnaround times, providing the most benefit to local public health authorities tasked with pandemic response and mitigation and allowing for the fastest reaction to localized outbreaks. These investments are crucial for pandemic preparedness and response and will serve the health of the continent well into the 21st century

Molecular Mechanisms of H. pylori-Induced DNA Double-Strand Breaks

Infections contribute to carcinogenesis through inflammation-related mechanisms. H. pylori infection is a significant risk factor for gastric carcinogenesis. However, the molecular mechanism by which H. pylori infection contributes to carcinogenesis has not been fully elucidated. H. pylori-associated chronic inflammation is linked to genomic instability via reactive oxygen and nitrogen species (RONS). In this article, we summarize the current knowledge of H. pylori-induced double strand breaks (DSBs). Furthermore, we provide mechanistic insight into how processing of oxidative DNA damage via base excision repair (BER) leads to DSBs. We review recent studies on how H. pylori infection triggers NF-&kappa;B/inducible NO synthase (iNOS) versus NF-&kappa;B/nucleotide excision repair (NER) axis-mediated DSBs to drive genomic instability. This review discusses current research findings that are related to mechanisms of DSBs and repair during H. pylori infection

Forecasting bicycle traffic in cities

In this project the task is to predict bicycle theft and bicycle traffic in a city using machine learning methods. The project proposal was given in collaboration with BikeFinder AS, a Petter Stordalen"s #Strawberry Million” award winning company established in 2015. Bicycle theft is a problem in many places around the world and one of the objectives in this thesis is to help preventing it, based on data science analysis and machine learning methods applied on existing data. Predicting bicycle traffic as well as analyzing the factors that might affect traffic is another important goal for this thesis. However, throughout the project it is expected to work on various other steps such as gathering the relevant data, pre-processing, evaluating and comparing methods and results. It is also important to optimize and improve the performance of the methods to achieve as accurate results as possible. Lastly, interpreting the results, and solving the questions asked in the thesis. The project has been solved by first, gathering BikeFinder theft and traffic data, Stavanger weather conditions data, Rogaland Police District bike theft reports data and data from the bike counting sensors in the city of Stavanger. Secondly, various steps of preprocessing has been done on the data according to the use cases. Afterwards, machine learning method evaluations and comparisons, using a neutral and larger dataset, Chicago crime dataset was accomplished. Thereafter, applying the best performing methods on the theft and traffic datasets, as well as forecasting bike theft and traffic has been achieved. Finally, results interpretation and discussion on the findings of the project. The findings in this project reflects that bike theft and bike traffic can be predicted using machine learning methods on BikeFinder data. Furthermore, other factors such as weather conditions do affect bike traffic as well as improves the performances of bike traffic predictions. The results of the project provide useful insight to multiple parties and can be used to help preventing bike theft as well as providing suggestions for city planning improvements