Excitatory and Inhibitory Subnetworks Are Equally Selective during Decision-Making and Emerge Simultaneously during Learning

Inhibitory neurons, which play a critical role in decision-making models, are often simplified as a single pool of non-selective neurons lacking connection specificity. This assumption is supported by observations in the primary visual cortex: inhibitory neurons are broadly tuned in vivo and show non-specific connectivity in slice. The selectivity of excitatory and inhibitory neurons within decision circuits and, hence, the validity of decision-making models are unknown. We simultaneously measured excitatory and inhibitory neurons in the posterior parietal cortex of mice judging multisensory stimuli. Surprisingly, excitatory and inhibitory neurons were equally selective for the animal’s choice, both at the single-cell and population level. Further, both cell types exhibited similar changes in selectivity and temporal dynamics during learning, paralleling behavioral improvements. These observations, combined with modeling, argue against circuit architectures assuming non-selective inhibitory neurons. Instead, they argue for selective subnetworks of inhibitory and excitatory neurons that are shaped by experience to support expert decision-making

Behavioral immune landscapes of inflammation.

Transcriptional or proteomic profiling of individual cells have revolutionized interpretation of biological phenomena by providing cellular landscapes of healthy and diseased tissues. These approaches, however, fail to describe dynamic scenarios in which cells can change their biochemical properties and downstream “behavioral” outputs every few seconds or minutes. Here, we used 4D live imaging to record tens to hundreds of morpho-kinetic parameters describing the dynamism of individual leukocytes at sites of active inflammation. By analyzing over 100,000 reconstructions of cell shapes and tracks over time, we obtained behavioral descriptors of individual cells and used these high-dimensional datasets to build behavioral landscapes. These landscapes recognized leukocyte identities in the inflamed skin and trachea, and inside blood vessels uncovered a continuum of neutrophil states, including a large, sessile state that was embraced by the underlying endothelium and associated with pathogenic inflammation. Behavioral in vivo screening of thousands of cells from 24 different mouse mutants identified the kinase Fgr as a driver of this pathogenic state, and genetic or pharmacological interference of Fgr protected from inflammatory injury. Thus, behavioral landscapes report unique biological properties of dynamic environments at high cellular, spatial and temporal resolution.pre-print4302 K

The Hyper Suprime-Cam Software Pipeline

In this paper, we describe the optical imaging data processing pipeline developed for the Subaru Telescope's Hyper Suprime-Cam (HSC) instrument. The HSC Pipeline builds on the prototype pipeline being developed by the Large Synoptic Survey Telescope's Data Management system, adding customizations for HSC, large-scale processing capabilities, and novel algorithms that have since been reincorporated into the LSST codebase. While designed primarily to reduce HSC Subaru Strategic Program (SSP) data, it is also the recommended pipeline for reducing general-observer HSC data. The HSC pipeline includes high level processing steps that generate coadded images and science-ready catalogs as well as low-level detrending and image characterizations.Comment: 39 pages, 21 figures, 2 tables. Submitted to Publications of the Astronomical Society of Japa

Mathematical modeling of neuronal dynamics during disease

We currently do not understand how neuronal activity leads to cognition. However, we can observe how neuronal activity changes as cognition becomes abnormal. Brain diseases tell us what aspects of neuronal functioning are necessary to maintain brain function and cognition. An essential step to understanding brain function is knowing how to fix it as it becomes dysfunctional. However, studying brain diseases in humans can be challenging because these conditions often span years or decades, making longitudinal studies difficult. Additionally, researchers are restricted to noninvasive measurement methods when studying human subjects. As a result, neuroscience is relying increasingly on quantitative sciences to find patterns in large and complex datasets. Mathematical modeling has become an essential tool to assimilate biological theories and test them in light of experimental data. In this thesis, we study the mathematical modeling of brain diseases. We cover various aspects of modeling, such as developing and analyzing new model formulations, simulating large-scale mathematical models of the human brain, and fitting them to data. First, we integrate mathematical models of Alzheimer’s disease progression and neuronal activity, showing that toxic proteins may cause alterations in brain activity consistent with clinical observations. Second, we develop a model for how neuronal activity affects disease progression, demonstrating the pivotal role neuronal activity plays in shaping disease trajectories. Third, we fit a model for brain-wide neuronal activity to brain cancer patients, discovering significant alterations in brain dynamics. Overall, we develop and analyze mathematical models to study brain diseases and their impact on neuronal activity, demonstrating the benefit of mathematical modeling in studying the mechanisms of brain disease

Optimal Design and Operation of Heat Exchanger Network

Heat exchanger networks (HENs) are the backbone of heat integration due to their ability in energy and environmental managements. This thesis deals with two issues on HENs. The first concerns with designing of economically optimal Heat exchanger network (HEN) whereas the second focus on optimal operation of HEN in the presence of uncertainties and disturbances within the network. In the first issue, a pinch technology based optimal HEN design is firstly implemented on a 3–streams heat recovery case study to design a simple HEN and then, a more complex HEN is designed for a coal-fired power plant retrofitted with CO2 capture unit to achieve the objectives of minimising energy penalty on the power plant due to its integration with the CO2 capture plant. The benchmark in this case study is a stream data from (Khalilpour and Abbas, 2011). Improvement to their work includes: (1) the use of economic data to evaluate achievable trade-offs between energy, capital and utility cost for determination of minimum temperature difference; (2) redesigning of the HEN based on the new minimum temperature difference and (3) its comparison with the base case design. The results shows that the energy burden imposed on the power plant with CO2 capture is significantly reduced through HEN leading to utility cost saving maximisation. The cost of addition of HEN is recoverable within a short payback period of about 2.8 years. In the second issue, optimal HEN operation considering range of uncertainties and disturbances in flowrates and inlet stream temperatures while minimizing utility consumption at constant target temperatures based on self-optimizing control (SOC) strategy. The new SOC method developed in this thesis is a data-driven SOC method which uses process data collected overtime during plant operation to select control variables (CVs). This is in contrast to the existing SOC strategies in which the CV selection requires process model to be linearized for nonlinear processes which leads to unaccounted losses due to linearization errors. The new approach selects CVs in which the necessary condition of optimality (NCO) is directly approximated by the CV through a single regression step. This work was inspired by Ye et al., (2013) regression based globally optimal CV selection with no model linearization and Ye et al., (2012) two steps regression based data-driven CV selection but with poor optimal results due to regression errors in the two steps procedures. The advantage of this work is that it doesn’t require evaluation of derivatives hence CVs can be evaluated even with commercial simulators such as HYSYS and UNISIM from among others. The effectiveness of the proposed method is again applied to the 3-streams HEN case study and also the HEN for coal-fired power plant with CO2 capture unit. The case studies show that the proposed methodology provides better optimal operation under uncertainties when compared to the existing model-based SOC techniques

Methods for analysis of deep sequencing data from mixtures of Plasmodium falciparum clones or stage-specific transcriptomes

Malaria is a life-threatening infectious disease caused by Plasmodium parasites transmitted to humans through bites of infected Anopheles mosquitos. An estimated 445,000 people die every year by an infection with Plasmodium parasites, most of them children living in sub-Saharan Africa. As a result of increased malaria control, the mortality was greatly reduced in the last decades. To develop new tools for elimination and to evaluate the impact of control, a good understanding of the epidemiology and biology of malaria parasites is required. Studies of infection and transmission dynamics of Plasmodium parasites were greatly improved by distinguishing individual parasite clones and monitoring their infection dynamics over time. In regions with high transmission of Plasmodium parasites, individuals are often infected with several clones concurrently. Individual parasites clones can be identified by genotyping. The current standard method used for genotyping is amplification of highly length-polymorphic merozoite surface protein 2 (msp2) or other antigen genes followed by sizing of the amplicon by capillary electrophoresis (CE). The sensitivity to detect low-abundant clones (minority clones) of msp2-CE genotyping is however limited, resulting in an underestimation of multiplicity of infection (MOI). A shortfall of this genotyping method is that frequency of individual clones within a sample cannot be determined. This urges the search for new genotyping methods that rely on sequencing of genomic fragments with extensive single nucleotide polymorphism (SNP). Improvement in next generation sequencing (NGS) technologies permitted the use of amplicon sequencing (Amp-Seq) in epidemiological studies. Genotyping by amplicon sequencing has a higher sensitivity to detect minority clones, can quantify the frequency of each clone within a sample, and allows the use of SNP polymorphic markers. In the frame of this thesis, a new Amp-Seq genotyping assay was developed, including known SNP polymorphic markers, and novel marker ‘cpmp’, as well as a bioinformatic analysis workflow. This genotyping assay was applied on field samples from a longitudinal study conducted in Papua New Guinea. A comparison to msp2-CE genotyping confirmed the higher sensitivity to detect minority clones by Amp-Seq genotyping method and showed a significant underestimation of MOI by classical size polymorphic marker. However, no significant increase in molecular force of infection (molFOI), i.e. number of new infections per individual per year, was observed. Quantification of the frequency of individual clones in longitudinal samples permitted to infer multi-locus haplotypes. Multi-locus haplotypes increased discriminatory power of genotyping and robustly distinguished new infections from those detected in an individual earlier. For calculating the density of clones from multi-clone infections the within-host clone frequency is multiplied by parasitaemia of this infection determined by quantitative PCR. Density of individual parasites clones in multi-clone infections over time is a new parameter for epidemiological studies. It will permit to study the dynamics, and thus fitness, of parasite clones exposed to within-host competition or to acquired natural immunity. NGS also gained great importance in gene expression studies of Plasmodium parasites in patient samples. Transcriptome studies are complicated by the mixture of different developmental stages present concurrently in samples collected from patients. Even in in vitro cultured samples after tight synchronisation or enrichment of a specific developmental stage, small fractions of other development stages are still found. This problem is of particular relevance for P. vivax, as the absence of continuous in vitro culture so far has hampered the study of isolated parasite stages. For example, the transcriptome of P. vivax gametocytes, one of the stages found in peripheral blood and infective to mosquitos, has not yet been described. A solution for differentiating mixed transcription may come from deconvolution methods, which either infer the stage proportion in samples or stage-specific transcriptome signatures. A large selection of different deconvolution methods has been developed for the analysis of heterogeneous tissues, e.g. cancer tissues or hematopoietic cell, but these methods have rarely been applied to mixed stages of malaria parasites. The best suited combination of normalisation and deconvolution methods for analysis of RNA sequencing (RNA-Seq) data from mixed-stage samples of Plasmodium parasites was evaluated based on experimentally mixed highly synchronised blood stages. Normalisation by count per million and deconvolution with a negative binomial regression model followed by selection of genes with significant fold change resulted in the best agreement with transcriptomes as observed in single stages. This strategy can easily be transferred to Plasmodium field samples with known stage proportions. This analysis performed in cultured parasites of defined mixed stages served as proof-of-concept and confirmed that identification of stage-specific genes is feasible also in field samples, notably in species that cannot be cultivated, such as P. vivax. NGS permits fundamentally new approaches to study Plasmodium parasites. This thesis presents a novel marker and data analysis platform for highly sensitive P. falciparum genotyping. Furthermore, a best practice workflow was identified to infer stage-specific gene expression from parasite infections consisting of mixed developmental stages. This provides a crucial tool for the analysis of gene expression data generated from Plasmodium field samples

New geophysics study programs at the University of Helsinki

Peer reviewe

Petrophysical properties of the Kylylahti Cu-Au-Zn sulphide mineralization and its host rocks

Non peer reviewe

Aeronautical engineering: A continuing bibliography with indexes, supplement 100

This bibliography lists 295 reports, articles, and other documents introduced into the NASA Scientific and Technical Information System in August 1978