Integration and Segregation in Audition and Vision

Perceptual systems can improve their performance by integrating relevant perceptual information and segregating away irrelevant information. Three studies exploring perceptual integration and segregation in audition and vision are reported in this thesis. In Chapter 1, we explore the role of similarity in informational masking. In informational masking tasks, listeners detect the presence of a signal tone presented simultaneously with a random-frequency multitone masker. Detection thresholds are high in the presence of an informational masker, even though listeners should be able to ignore the masker frequencies. The informational masker\u27s effect may be due to the similarity between signal and masker components. We used a behavioral measure to demonstrate that the amount of frequency change over time could be the stimulus dimension underlying the similarity effect. In Chapter 2, we report a set of experiments on the visual system\u27s ability to discriminate distributions of luminances. The distribution of luminances can serve as a cue to the presence of multiple illuminants in a scene. We presented observers with simple achromatic scenes with patches drawn from one or two luminance distributions. Performance depended on the number of patches from the second luminance distribution, as well as knowledge of the location of these patches. Irrelevant geometric cues, which we expected to negatively affect performance, did not have an effect. An ideal observer model and a classification analysis showed that observers successfully integrated information provided by the image photometric cues. In Chapter 3, we investigated the role of photometric and geometric cues in lightness perception. We rendered achromatic scenes that were consistent with two oriented background context surfaces illuminated by a light source with a directional component. Observers made lightness matches to tabs rendered at different orientations in the scene. We manipulated the photometric cues by changing the intensity of the illumination, and the geometric cues by changing the orientation of the context surfaces. Observers\u27 matches varied with both manipulations, demonstrating that observers used both types of cues to account for the illumination in the scene. The two types of cues were found to have independent effects on the lightness matches

Gene expression profiling of breast cancer survivability by pooled cDNA microarray analysis using logistic regression, artificial neural networks and decision trees

BACKGROUND: Microarray technology can acquire information about thousands of genes simultaneously. We analyzed published breast cancer microarray databases to predict five-year recurrence and compared the performance of three data mining algorithms of artificial neural networks (ANN), decision trees (DT) and logistic regression (LR) and two composite models of DT-ANN and DT-LR. The collection of microarray datasets from the Gene Expression Omnibus, four breast cancer datasets were pooled for predicting five-year breast cancer relapse. After data compilation, 757 subjects, 5 clinical variables and 13,452 genetic variables were aggregated. The bootstrap method, Mann–Whitney U test and 20-fold cross-validation were performed to investigate candidate genes with 100 most-significant p-values. The predictive powers of DT, LR and ANN models were assessed using accuracy and the area under ROC curve. The associated genes were evaluated using Cox regression. RESULTS: The DT models exhibited the lowest predictive power and the poorest extrapolation when applied to the test samples. The ANN models displayed the best predictive power and showed the best extrapolation. The 21 most-associated genes, as determined by integration of each model, were analyzed using Cox regression with a 3.53-fold (95% CI: 2.24-5.58) increased risk of breast cancer five-year recurrence… CONCLUSIONS: The 21 selected genes can predict breast cancer recurrence. Among these genes, CCNB1, PLK1 and TOP2A are in the cell cycle G2/M DNA damage checkpoint pathway. Oncologists can offer the genetic information for patients when understanding the gene expression profiles on breast cancer recurrence

Deconvolving the pre-Himalayan Indian margin – tales of crustal growth and destruction

The metamorphic core of the Himalaya is composed of Indian cratonic rocks with two distinct crustal affinities that are defined by radiogenic isotopic geochemistry and detrital zircon age spectra. One is derived predominantly from the Paleoproterozoic and Archean rocks of the Indian cratonic interior and is either represented as metamorphosed sedimentary rocks of the Lesser Himalayan Sequence (LHS) or as slices of the distal cratonic margin. The other is the Greater Himalayan Sequence (GHS) whose provenance is less clear and has an enigmatic affinity. Here we present new detrital zircon Hf analyses from LHS and GHS samples spanning over 1000 kilometers along the orogen that respectively show a striking similarity in age spectra and Hf isotope ratios. Within the GHS, the zircon age populations at 2800–2500 Ma, 1800 Ma, 1000 Ma and 500 Ma can be ascribed to various Gondwanan source regions; however, a pervasive and dominant Tonian age population (∼860–800 Ma) with a variably enriched radiogenic Hf isotope signature (εHf = 10 to -20) has not been identified from Gondwana or peripheral accreted terranes. We suggest this detrital zircon age population was derived from a crustal province that was subsequently removed by tectonic erosion. Substantial geologic evidence exists from previous studies across the Himalaya supporting the Cambro-Ordovician Kurgiakh Orogeny. We propose the tectonic removal of Tonian lithosphere occurred prior to or during this Cambro-Ordovician episode of orogenesis in a similar scenario as is seen in the modern Andean and Indonesian orogenies, wherein tectonic processes have removed significant portions of the continental lithosphere in a relatively short amount of time. This model described herein of the pre-Himalayan northern margin of Greater India highlights the paucity of the geologic record associated with the growth of continental crust. Although the continental crust is the archive of Earth history, it is vital to recognize the ways in which preservation bias and destruction of continental crust informs geologic models

A continuously benchmarked and crowdsourced challenge for rapid development and evaluation of models to predict COVID-19 diagnosis and hospitalization

Importance: Machine learning could be used to predict the likelihood of diagnosis and severity of illness. Lack of COVID-19 patient data has hindered the data science community in developing models to aid in the response to the pandemic. Objectives: To describe the rapid development and evaluation of clinical algorithms to predict COVID-19 diagnosis and hospitalization using patient data by citizen scientists, provide an unbiased assessment of model performance, and benchmark model performance on subgroups. Design, Setting, and Participants: This diagnostic and prognostic study operated a continuous, crowdsourced challenge using a model-to-data approach to securely enable the use of regularly updated COVID-19 patient data from the University of Washington by participants from May 6 to December 23, 2020. A postchallenge analysis was conducted from December 24, 2020, to April 7, 2021, to assess the generalizability of models on the cumulative data set as well as subgroups stratified by age, sex, race, and time of COVID-19 test. By December 23, 2020, this challenge engaged 482 participants from 90 teams and 7 countries. Main Outcomes and Measures: Machine learning algorithms used patient data and output a score that represented the probability of patients receiving a positive COVID-19 test result or being hospitalized within 21 days after receiving a positive COVID-19 test result. Algorithms were evaluated using area under the receiver operating characteristic curve (AUROC) and area under the precision recall curve (AUPRC) scores. Ensemble models aggregating models from the top challenge teams were developed and evaluated. Results: In the analysis using the cumulative data set, the best performance for COVID-19 diagnosis prediction was an AUROC of 0.776 (95% CI, 0.775-0.777) and an AUPRC of 0.297, and for hospitalization prediction, an AUROC of 0.796 (95% CI, 0.794-0.798) and an AUPRC of 0.188. Analysis on top models submitting to the challenge showed consistently better model performance on the female group than the male group. Among all age groups, the best performance was obtained for the 25- to 49-year age group, and the worst performance was obtained for the group aged 17 years or younger. Conclusions and Relevance: In this diagnostic and prognostic study, models submitted by citizen scientists achieved high performance for the prediction of COVID-19 testing and hospitalization outcomes. Evaluation of challenge models on demographic subgroups and prospective data revealed performance discrepancies, providing insights into the potential bias and limitations in the models

Physical and functional characterization of the genetic locus of IBtk, an inhibitor of Bruton's tyrosine kinase: evidence for three protein isoforms of IBtk

Bruton's tyrosine kinase (Btk) is required for B-cell development. Btk deficiency causes X-linked agammaglobulinemia (XLA) in humans and X-linked immunodeficiency (Xid) in mice. Btk lacks a negative regulatory domain and may rely on cytoplasmic proteins to regulate its activity. Consistently, we identified an inhibitor of Btk, IBtk, which binds to the PH domain of Btk and down-regulates the Btk kinase activity. IBtk is an evolutionary conserved protein encoded by a single genomic sequence at 6q14.1 cytogenetic location, a region of recurrent chromosomal aberrations in lymphoproliferative disorders; however, the physical and functional organization of IBTK is unknown. Here, we report that the human IBTK locus includes three distinct mRNAs arising from complete intron splicing, an additional polyadenylation signal and a second transcription start site that utilizes a specific ATG for protein translation. By northern blot, 5′RACE and 3′RACE we identified three IBTKα, IBTKβ and IBTKγ mRNAs, whose transcription is driven by two distinct promoter regions; the corresponding IBtk proteins were detected in human cells and mouse tissues by specific antibodies. These results provide the first characterization of the human IBTK locus and may assist in understanding the in vivo function of IBtk

The proteome of cytosolic lipid droplets isolated from differentiated Caco-2/TC7 enterocytes reveals cell-specific characteristics

Background information. Intestinal absorption of alimentary lipids is a complex process ensured by enterocytes and leading to TRL [TAG (triacylglycerol)-rich lipoprotein] assembly and secretion. The accumulation of circulating intestine-derived TRL is associated with atherosclerosis, stressing the importance of the control of postprandial hypertriglyceridaemia. During the postprandial period, TAGs are also transiently stored as CLDs (cytosolic lipid droplets) in enterocytes. As a first step for determining whether CLDs could play a role in the control of enterocyte TRL secretion, we analysed the protein endowment of CLDs isolated by sucrose-gradient centrifugation from differentiated Caco-2/TC7 enterocytes, the only human model able to secrete TRL in culture and to store transiently TAGs as CLDs when supplied with lipids. Cells were analysed after a 24 h incubation with lipid micelles and thus in a state of CLD-associated TAG mobilization

Impact of Resistant Starch on Body Fat Patterning and Central Appetite Regulation

Background: Adipose tissue patterning has a major influence on the risk of developing chronic disease. Environmental influences on both body fat patterning and appetite regulation are not fully understood. This study was performed to investigate the impact of resistant starch (RS) on adipose tissue deposition and central regulation of appetite in mice. Methodology and Principle Findings: Forty mice were randomised to a diet supplemented with either the high resistant starch (HRS), or the readily digestible starch (LRS). Using 1H magnetic resonance (MR) methods, whole body adiposity, intrahepatocellular lipids (IHCL) and intramyocellular lipids (IMCL) were measured. Manganese-enhanced MRI (MEMRI) was used to investigate neuronal activity in hypothalamic regions involved in appetite control when fed ad libitum. At the end of the interventional period, adipocytes were isolated from epididymal adipose tissue and fasting plasma collected for hormonal and adipokine measurement. Mice on the HRS and LRS diet had similar body weights although total body adiposity, subcutaneous and visceral fat, IHCL, plasma leptin, plasma adiponectin plasma insulin/glucose ratios was significantly greater in the latter group. Adipocytes isolated from the LRS group were significantly larger and had lower insulin-stimulated glucose uptake. MEMRI data obtained from the ventromedial and paraventricular hypothalamic nuclei suggests a satiating effect of the HRS diet despite a lower energy intake. Conclusion and Significance: Dietary RS significantly impacts on adipose tissue patterning, adipocyte morphology and metabolism, glucose and insulin metabolism, as well as affecting appetite regulation, supported by changes in neuronal activity in hypothalamic appetite regulation centres which are suggestive of satiation

Paradoxical Effects of Rapamycin on Experimental House Dust Mite-Induced Asthma

The mammalian target of rapamycin (mTOR) modulates immune responses and cellular proliferation. The objective of this study was to assess whether inhibition of mTOR with rapamycin modifies disease severity in two experimental murine models of house dust mite (HDM)-induced asthma. In an induction model, rapamycin was administered to BALB/c mice coincident with nasal HDM challenges for 3 weeks. In a treatment model, nasal HDM challenges were performed for 6 weeks and rapamycin treatment was administered during weeks 4 through 6. In the induction model, rapamycin significantly attenuated airway inflammation, airway hyperreactivity (AHR) and goblet cell hyperplasia. In contrast, treatment of established HDM-induced asthma with rapamycin exacerbated AHR and airway inflammation, whereas goblet cell hyperplasia was not modified. Phosphorylation of the S6 ribosomal protein, which is downstream of mTORC1, was increased after 3 weeks, but not 6 weeks of HDM-challenge. Rapamycin reduced S6 phosphorylation in HDM-challenged mice in both the induction and treatment models. Thus, the paradoxical effects of rapamycin on asthma severity paralleled the activation of mTOR signaling. Lastly, mediastinal lymph node re-stimulation experiments showed that treatment of rapamycin-naive T cells with ex vivo rapamycin decreased antigen-specific Th2 cytokine production, whereas prior exposure to in vivo rapamycin rendered T cells refractory to the suppressive effects of ex vivo rapamycin. We conclude that rapamycin had paradoxical effects on the pathogenesis of experimental HDM-induced asthma. Thus, consistent with the context-dependent effects of rapamycin on inflammation, the timing of mTOR inhibition may be an important determinant of efficacy and toxicity in HDM-induced asthma