Novel Algorithms for LDD Motif Search

Background: Motifs are crucial patterns that have numerous applications including the identification of transcription factors and their binding sites, composite regulatory patterns, similarity between families of proteins, etc. Several motif models have been proposed in the literature. The (l,d)-motif model is one of these that has been studied widely. However, this model will sometimes report too many spurious motifs than expected. We interpret a motif as a biologically significant entity that is evolutionarily preserved within some distance. It may be highly improbable that the motif undergoes the same number of changes in each of the species. To address this issue, in this paper, we introduce a new model which is more general than (l,d)-motif model. This model is called (l,d1,d2)-motif model (LDDMS) and is NP-hard as well. We present three elegant as well as efficient algorithms to solve the LDDMS problem, i.e., LDDMS1, LDDMS2 and LDDMS3. They are all exact algorithms. Results: We did both theoretical analyses and empirical tests on these algorithms. Theoretical analyses demonstrate that our algorithms have less computational cost than the pattern driven approach. Empirical results on both simulated datasets and real datasets show that each of the three algorithms has some advantages on some (l,d1,d2) instances. Conclusions: We proposed LDDMS model which is more practically relevant. We also proposed three exact efficient algorithms to solve the problem. Besides, our algorithms can be nicely parallelized. We believe that the idea in this new model can also be extended to other motif search problems such as Edit-distance-based Motif Search (EMS) and Simple Motif Search (SMS)

Behavior quantification as the missing link between fields: Tools for digital psychiatry and their role in the future of neurobiology

The great behavioral heterogeneity observed between individuals with the same psychiatric disorder and even within one individual over time complicates both clinical practice and biomedical research. However, modern technologies are an exciting opportunity to improve behavioral characterization. Existing psychiatry methods that are qualitative or unscalable, such as patient surveys or clinical interviews, can now be collected at a greater capacity and analyzed to produce new quantitative measures. Furthermore, recent capabilities for continuous collection of passive sensor streams, such as phone GPS or smartwatch accelerometer, open avenues of novel questioning that were previously entirely unrealistic. Their temporally dense nature enables a cohesive study of real-time neural and behavioral signals. To develop comprehensive neurobiological models of psychiatric disease, it will be critical to first develop strong methods for behavioral quantification. There is huge potential in what can theoretically be captured by current technologies, but this in itself presents a large computational challenge -- one that will necessitate new data processing tools, new machine learning techniques, and ultimately a shift in how interdisciplinary work is conducted. In my thesis, I detail research projects that take different perspectives on digital psychiatry, subsequently tying ideas together with a concluding discussion on the future of the field. I also provide software infrastructure where relevant, with extensive documentation. Major contributions include scientific arguments and proof of concept results for daily free-form audio journals as an underappreciated psychiatry research datatype, as well as novel stability theorems and pilot empirical success for a proposed multi-area recurrent neural network architecture.Comment: PhD thesis cop

Rice Improvement

This book is open access under a CC BY 4.0 license. By 2050, human population is expected to reach 9.7 billion. The demand for increased food production needs to be met from ever reducing resources of land, water and other environmental constraints. Rice remains the staple food source for a majority of the global populations, but especially in Asia where ninety percent of rice is grown and consumed. Climate change continues to impose abiotic and biotic stresses that curtail rice quality and yields. Researchers have been challenged to provide innovative solutions to maintain, or even increase, rice production. Amongst them, the ‘green super rice’ breeding strategy has been successful for leading the development and release of multiple abiotic and biotic stress tolerant rice varieties. Recent advances in plant molecular biology and biotechnologies have led to the identification of stress responsive genes and signaling pathways, which open up new paradigms to augment rice productivity. Accordingly, transcription factors, protein kinases and enzymes for generating protective metabolites and proteins all contribute to an intricate network of events that guard and maintain cellular integrity. In addition, various quantitative trait loci associated with elevated stress tolerance have been cloned, resulting in the detection of novel genes for biotic and abiotic stress resistance. Mechanistic understanding of the genetic basis of traits, such as N and P use, is allowing rice researchers to engineer nutrient-efficient rice varieties, which would result in higher yields with lower inputs. Likewise, the research in micronutrients biosynthesis opens doors to genetic engineering of metabolic pathways to enhance micronutrients production. With third generation sequencing techniques on the horizon, exciting progress can be expected to vastly improve molecular markers for gene-trait associations forecast with increasing accuracy. This book emphasizes on the areas of rice science that attempt to overcome the foremost limitations in rice production. Our intention is to highlight research advances in the fields of physiology, molecular breeding and genetics, with a special focus on increasing productivity, improving biotic and abiotic stress tolerance and nutritional quality of rice. ; Up-to-date contributions by experts from international research centers and universities Provides practical knowledge and strong scientific foundation on rice biotechnology All-in-one resource for current advances in rice breeding Open Acces

Rice Improvement

This book is open access under a CC BY 4.0 license. By 2050, human population is expected to reach 9.7 billion. The demand for increased food production needs to be met from ever reducing resources of land, water and other environmental constraints. Rice remains the staple food source for a majority of the global populations, but especially in Asia where ninety percent of rice is grown and consumed. Climate change continues to impose abiotic and biotic stresses that curtail rice quality and yields. Researchers have been challenged to provide innovative solutions to maintain, or even increase, rice production. Amongst them, the ‘green super rice’ breeding strategy has been successful for leading the development and release of multiple abiotic and biotic stress tolerant rice varieties. Recent advances in plant molecular biology and biotechnologies have led to the identification of stress responsive genes and signaling pathways, which open up new paradigms to augment rice productivity. Accordingly, transcription factors, protein kinases and enzymes for generating protective metabolites and proteins all contribute to an intricate network of events that guard and maintain cellular integrity. In addition, various quantitative trait loci associated with elevated stress tolerance have been cloned, resulting in the detection of novel genes for biotic and abiotic stress resistance. Mechanistic understanding of the genetic basis of traits, such as N and P use, is allowing rice researchers to engineer nutrient-efficient rice varieties, which would result in higher yields with lower inputs. Likewise, the research in micronutrients biosynthesis opens doors to genetic engineering of metabolic pathways to enhance micronutrients production. With third generation sequencing techniques on the horizon, exciting progress can be expected to vastly improve molecular markers for gene-trait associations forecast with increasing accuracy. This book emphasizes on the areas of rice science that attempt to overcome the foremost limitations in rice production. Our intention is to highlight research advances in the fields of physiology, molecular breeding and genetics, with a special focus on increasing productivity, improving biotic and abiotic stress tolerance and nutritional quality of rice. ; Up-to-date contributions by experts from international research centers and universities Provides practical knowledge and strong scientific foundation on rice biotechnology All-in-one resource for current advances in rice breeding Open Acces

New fish product ideas generated by European consumers

Food lifestyles are changing; people have less time to spend on food purchase and preparation, therefore leading to increasing demand for new food products. However, around 76% of new food products launched in the market fail within the first year (Nielsen, 2014). One of the most effective ways to enhance new products’ success in the market is by incorporating consumers’ opinions and needs during the New Product Development (NPD) process (Moon et al., 2018). This study aimed to explore the usefulness of a qualitative technique, focus groups, to generate new aquaculture fish product ideas as well as to identify the most relevant product dimensions affecting consumers’ potential acceptance.Peer ReviewedPostprint (published version