Open-source data pipeline for street-view images: A case study on community mobility during COVID-19 pandemic.

Street View Images (SVI) are a common source of valuable data for researchers. Researchers have used SVI data for estimating pedestrian volumes, demographic surveillance, and to better understand built and natural environments in cityscapes. However, the most common source of publicly available SVI data is Google Street View. Google Street View images are collected infrequently, making temporal analysis challenging, especially in low population density areas. Our main contribution is the development of an open-source data pipeline for processing 360-degree video recorded from a car-mounted camera. The video data is used to generate SVIs, which then can be used as an input for longitudinal analysis. We demonstrate the use of the pipeline by collecting an SVI dataset over a 38-month longitudinal survey of Seattle, WA, USA during the COVID-19 pandemic. The output of our pipeline is validated through statistical analyses of pedestrian traffic in the images. We confirm known results in the literature and provide new insights into outdoor pedestrian traffic patterns. This study demonstrates the feasibility and value of collecting and using SVI for research purposes beyond what is possible with currently available SVI data. Our methods and dataset represent a first of its kind longitudinal collection and application of SVI data for research purposes. Limitations and future improvements to the data pipeline and case study are also discussed.

Posthurricane damage assessment using satellite imagery and geolocation features.

Gaining timely and reliable situation awareness after hazard events such as a hurricane is crucial to emergency managers and first responders. One effective way to achieve that goal is through damage assessment. Recently, disaster researchers have been utilizing imagery captured through satellites or drones to quantify the number of flooded/damaged buildings. In this paper, we propose a mixed-data approach, which leverages publicly available satellite imagery and geolocation features of the affected area to identify damaged buildings after a hurricane. The method demonstrated significant improvement from performing a similar task using only imagery features, based on a case study of Hurricane Harvey affecting Greater Houston area in 2017. This result opens door to a wide range of possibilities to unify the advancement in computer vision algorithms such as convolutional neural networks and traditional methods in damage assessment, for example, using flood depth or bare-earth topology. In this work, a creative choice of the geolocation features was made to provide extra information to the imagery features, but it is up to the users to decide which other features can be included to model the physical behavior of the events, depending on their domain knowledge and the type of disaster. The data set curated in this work is made openly available (DOI: 10.17603/ds2-3cca-f398).

Splitting Gaussian processes for computationally-efficient regression.

Gaussian processes offer a flexible kernel method for regression. While Gaussian processes have many useful theoretical properties and have proven practically useful, they suffer from poor scaling in the number of observations. In particular, the cubic time complexity of updating standard Gaussian process models can be a limiting factor in applications. We propose an algorithm for sequentially partitioning the input space and fitting a localized Gaussian process to each disjoint region. The algorithm is shown to have superior time and space complexity to existing methods, and its sequential nature allows the model to be updated efficiently. The algorithm constructs a model for which the time complexity of updating is tightly bounded above by a pre-specified parameter. To the best of our knowledge, the model is the first local Gaussian process regression model to achieve linear memory complexity. Theoretical continuity properties of the model are proven. We demonstrate the efficacy of the resulting model on several multi-dimensional regression tasks.

How did outdoor biking and walking change during COVID-19?: A case study of three U.S. cities.

A growing body of literature suggests that restrictive public health measures implemented to control COVID-19 have had negative impacts on physical activity. We examined how Stay Home orders in Houston, New York City, and Seattle impacted outdoor physical activity patterns, measured by daily bicycle and pedestrian count data. We assessed changes in activity levels between the period before and during Stay Home orders. Across all three cities, we found significant changes in bicycle and pedestrian counts from the period before to the period during Stay Home orders. The direction of change varied by location, likely due to differing local contexts and outbreak progression. These results can inform policy around the use of outdoor public infrastructure as the COVID-19 pandemic continues.

Daily Bicycle and Pedestrian Activity as an Indicator of Disaster Recovery: A Hurricane Harvey Case Study.

Changes in levels and patterns of physical activity might be a mechanism to assess and inform disaster recovery through the lens of wellbeing. However, few studies have examined disaster impacts on physical activity or the potential for physical activity to serve as an indicator of disaster recovery. In this exploratory study, we examined daily bicycle and pedestrian counts from four public bicycle/pedestrian trails in Houston, before and after Hurricane Harvey landfall, to assess if physical activity returned to pre-Harvey levels. An interrupted time series analysis was conducted to examine the immediate impact of Harvey landfall on physical activity; t-tests were performed to assess if trail usage returned to pre-Harvey levels. Hurricane Harvey was found to have a significant negative impact on daily pedestrian and bicycle counts for three of the four trails. Daily pedestrian and bicycle counts were found to return to pre-Harvey or higher levels at 6 weeks post-landfall at all locations studied. We discuss the potential for further research to examine the trends, feasibility, validity, and limitations of using bicycle and pedestrian use levels as a proxy for disaster recovery and wellbeing among affected populations.

Role of the ribosomal quality control machinery in nucleocytoplasmic translocation of polyQ-expanded huntingtin exon-1.

The subcellular localization of polyQ-expanded huntingtin exon1 (Httex1) modulates polyQ toxicity in models of Huntington's disease. Using genome-wide screens in a yeast model system, we report that the ribosome quality control (RQC) machinery, recently implicated in neurodegeneration, is a key determinant for the nucleocytoplasmic distribution of Httex1-103Q. Deletion of the RQC genes, LTN1 or RQC1, caused the accumulation of Httex1-103Q in the nucleus through a process that required the CAT-tail tagging activity of Rqc2 and transport via the nuclear pore complex. We provide evidence that nuclear accumulation of Httex1-103Q enhances its cytotoxicity, suggesting that the RQC machinery plays an important role in protecting cells against the adverse effects of polyQ expansion proteins.

Failure of RQC machinery causes protein aggregation and proteotoxic stress.

Translation of messenger RNAs lacking a stop codon results in the addition of a carboxy-terminal poly-lysine tract to the nascent polypeptide, causing ribosome stalling. Non-stop proteins and other stalled nascent chains are recognized by the ribosome quality control (RQC) machinery and targeted for proteasomal degradation. Failure of this process leads to neurodegeneration by unknown mechanisms. Here we show that deletion of the E3 ubiquitin ligase Ltn1p in yeast, a key RQC component, causes stalled proteins to form detergent-resistant aggregates and inclusions. Aggregation is dependent on a C-terminal alanine/threonine tail that is added to stalled polypeptides by the RQC component, Rqc2p. Formation of inclusions additionally requires the poly-lysine tract present in non-stop proteins. The aggregates sequester multiple cytosolic chaperones and thereby interfere with general protein quality control pathways. These findings can explain the proteotoxicity of ribosome-stalled polypeptides and demonstrate the essential role of the RQC in maintaining proteostasis.

Protein-based SERS technology monitoring the chemical reactivity on an α-synuclein-mediated two-dimensional array of gold nanoparticles.

The enhancement of weak Raman signals has been challenged to obtain high-quality signals of surface-enhanced Raman scattering (SERS). By employing the Parkinson's disease-related protein of α-synuclein, we introduce SERS-active gold nanoparticles (AuNPs) individually isolated with an ultrathin α-synuclein shell and their 2-D array into a tightly packed monolayer on a glass support, which permits a quantitative SERS measurement of phthalocyanine tetrasulfonate (PcTS), a chemical ligand of the pathological protein. Subsequently, the PcTS-bound SERS substrate was also shown to be capable of discriminating two biologically important metal ions of iron and copper by detecting copper ion to the sub-ppm level in a highly selective manner via the in situ chemical reaction of metal chelation to PcTS. The strategy of using the protein-based 2-D AuNP SERS platform, therefore, could be further developed into a custom-made protein-based biosensor system for the detection of not only specific chemical/biological ligands of the immobilized coat proteins but also their biochemical reactivities.

Mechanism of amyloidogenesis: nucleation-dependent fibrillation versus double-concerted fibrillation.

Amyloidogenesis defines a condition in which a soluble and innocuous protein turns to insoluble protein aggregates known as amyloid fibrils. This protein suprastructure derived via chemically specific molecular self-assembly process has been commonly observed in various neurodegenerative disorders such as Alzheimer's, Parkinson's, and Prion diseases. Although the major culprit for the cellular degeneration in the diseases remains unsettled, amyloidogenesis is considered to be etiologically involved. Recent recognition of fibrillar polymorphism observed mostly from in vitro amyloidogeneses may indicate that multiple mechanisms for the amyloid fibril formation would be operated. Nucleation-dependent fibrillation is the prevalent model for assessing the self-assembly process. Following thermodynamically unfavorable seed formation, monomeric polypeptides bind to the seeds by exerting structural adjustments to the template, which leads to accelerated amyloid fibril formation. In this review, we propose another in vitro model of amyloidogenesis named double-concerted fibrillation. Here, two consecutive assembly processes of monomers and subsequent oligomeric species are responsible for the amyloid fibril formation of alpha-synuclein, a pathological component of Parkinson's disease, following structural rearrangement within the oligomers which then act as a growing unit for the fibrillation. [BMB reports 2009; 42(9): 541-551].

Increased [PSI+] appearance by fusion of Rnq1 with the prion domain of Sup35 in Saccharomyces cerevisiae.

During propagation, yeast prions show a strict sequence preference that confers the specificity of prion assembly. Although propagations of [PSI(+)] and [RNQ(+)] are independent of each other, the appearance of [PSI(+)] is facilitated by the presence of [RNQ(+)]. To explain the [RNQ(+)] effect on the appearance of [PSI(+)], the cross-seeding model was suggested, in which Rnq1 aggregates act as imperfect templates for Sup35 aggregation. If cross-seeding events take place in the cytoplasm of yeast cells, the collision frequency between Rnq1 aggregates and Sup35 will affect the appearance of [PSI(+)]. In this study, to address whether cross-seeding occurs in vivo, a new [PSI(+)] induction method was developed that exploits a protein fusion between the prion domain of Sup35 (NM) and Rnq1. This fusion protein successfully joins preexisting Rnq1 aggregates, which should result in the localization of NM around the Rnq1 aggregates and hence in an increased collision frequency between NM and Rnq1 aggregates. The appearance of [PSI(+)] could be induced very efficiently, even with a low expression level of the fusion protein. This study supports the occurrence of in vivo cross-seeding between Sup35 and Rnq1 and provides a new tool that can be used to dissect the mechanism of the de novo appearance of prions.

Real-time analysis of amyloid fibril formation of alpha-synuclein using a fibrillation-state-specific fluorescent probe of JC-1.

alpha-Synuclein is a pathological component of PD (Parkinson's disease) by participating in Lewy body formation. JC-1 (5,5',6,6'-tetrachloro-1,1,3,3'-tetraethylbenzimidazolyl carbocyanine iodide) has been shown to interact with alpha-synuclein at the acidic C-terminal region with a K(d) of 2.6 microM. JC-1 can discriminated between the fibrillation states of alpha-synuclein (monomeric, oligomeric intermediate and fibrillar forms) by emitting the enhanced binding fluorescence of different colours at 590, 560 and 538 nm respectively with the common excitation at 490 nm. The fibrillation-state-specific interaction of JC-1 allowed us to perform real-time analyses of the alpha-synuclein fibrillation in the presence of iron as a fibrillation inducer, rifampicin as a fibrillation inhibitor, baicalein as a defibrillation agent and dequalinium as a protofibril inducer. In addition, various alpha-synuclein fibrils with different morphologies prepared with specific ligands such as metal ions, glutathione, eosin and lipids were monitored with their characteristic JC-1-binding fluorescence spectra. FRET (fluorescence resonance energy transfer) between thioflavin-T and JC-1 was also employed to specifically identify the amyloid fibrils of alpha-synuclein. Taken together, we have introduced JC-1 as a powerful and versatile probe to explore the molecular mechanism of the fibrillation process of alpha-synuclein in vitro. It could be also useful in high-throughput drug screening. The specific alpha-synuclein interaction of JC-1 would therefore contribute to our complete understanding of the molecular aetiology of PD and eventual development of diagnostic/therapeutic strategies for various alpha-synucleinopathies.

A novel fermentation/respiration switch protein regulated by enzyme IIAGlc in Escherichia coli.

The bacterial phosphoenolpyruvate:sugar phosphotransferase system regulates a variety of physiological processes as well as effecting sugar transport. The crr gene product (enzyme IIA(Glc) (IIA(Glc))) mediates some of these regulatory phenomena. In this report, we characterize a novel IIA(Glc)-binding protein from Escherichia coli extracts, discovered using ligand-fishing with surface plasmon resonance spectroscopy. This protein, which we named FrsA (fermentation/respiration switch protein), is the 47-kDa product of the yafA gene, previously denoted as "function unknown." FrsA forms a 1:1 complex specifically with the unphosphorylated form of IIA(Glc), with the highest affinity of any protein thus far shown to interact with IIA(Glc). Orthologs of FrsA have been found to exist only in facultative anaerobes belonging to the gamma-proteobacterial group. Disruption of frsA increased cellular respiration on several sugars including glucose, while increased FrsA expression resulted in an increased fermentation rate on these sugars with the concomitant accumulation of mixed-acid fermentation products. These results suggest that IIA(Glc) regulates the flux between respiration and fermentation pathways by sensing the available sugar species via a phosphorylation state-dependent interaction with FrsA.