Water, Sanitation, and Hygiene (WaSH) insecurity in unhoused communities of Los Angeles, California

BACKGROUND

Access to water and sanitation is a basic human right; however, in many parts of the world, communities experience water, sanitation, and hygiene (WaSH) insecurity. While WaSH insecurity is prevalent in many low and middle-income countries, it is also a problem in high-income countries, like the United States, as is evident in vulnerable populations, including people experiencing homelessness. Limited knowledge exists about the coping strategies unhoused people use to access WaSH services. This study, therefore, examines WaSH access among unhoused communities in Los Angeles, California, a city with the second-highest count of unhoused people across the nation.

METHODS

We conducted a cross-sectional study using a snowball sampling technique with 263 unhoused people living in Skid Row, Los Angeles. We calculated frequencies and used multivariable models to describe (1) how unhoused communities cope and gain access to WaSH services in different places, and (2) what individual-level factors contribute to unhoused people's ability to access WaSH services.

RESULTS

Our findings reveal that access to WaSH services for unhoused communities in Los Angeles is most difficult at night. Reduced access to overnight sanitation resulted in 19% of the sample population using buckets inside their tents and 28% openly defecating in public spaces. Bottled water and public taps are the primary drinking water source, but 6% of the sample reported obtaining water from fire hydrants, and 50% of the population stores water for night use. Unhoused people also had limited access to water and soap for hand hygiene throughout the day, with 17% of the sample relying on hand sanitizer to clean their hands. Shower and laundry access were among the most limited services available, and reduced people's ability to maintain body hygiene practices and limited employment opportunities. Our regression models suggest that WaSH access is not homogenous among the unhoused. Community differences exist; the odds of having difficulty accessing sanitation services is two times greater for those living outside of Skid Row (Adj OR: 2.52; 95% CI: 1.08-6.37) and three times greater for people who have been unhoused for more than six years compared to people who have been unhoused for less than a year (Adj OR: 3.26; 95% CI: 1.36-8.07).

CONCLUSION

Overall, this study suggests a need for more permanent, 24-h access to WaSH services for unhoused communities living in Skid Row, including toilets, drinking water, water and soap for hand hygiene, showers, and laundry services.

Towards the automated large-scale reconstruction of past road networks from historical maps

Transportation infrastructure, such as road or railroad networks, represent a fundamental component of our civilization. For sustainable planning and informed decision making, a thorough understanding of the long-term evolution of transportation infrastructure such as road networks is crucial. However, spatially explicit, multi-temporal road network data covering large spatial extents are scarce and rarely available prior to the 2000s. Herein, we propose a framework that employs increasingly available scanned and georeferenced historical map series to reconstruct past road networks, by integrating abundant, contemporary road network data and color information extracted from historical maps. Specifically, our method uses contemporary road segments as analytical units and extracts historical roads by inferring their existence in historical map series based on image processing and clustering techniques. We tested our method on over 300,000 road segments representing more than 50,000 km of the road network in the United States, extending across three study areas that cover 42 historical topographic map sheets dated between 1890 and 1950. We evaluated our approach by comparison to other historical datasets and against manually created reference data, achieving F-1 scores of up to 0.95, and showed that the extracted road network statistics are highly plausible over time, i.e., following general growth patterns. We demonstrated that contemporary geospatial data integrated with information extracted from historical map series open up new avenues for the quantitative analysis of long-term urbanization processes and landscape changes far beyond the era of operational remote sensing and digital cartography.

Combining Remote-Sensing-Derived Data and Historical Maps for Long-Term Back-Casting of Urban Extents

Spatially explicit, fine-grained datasets describing historical urban extents are rarely available prior to the era of operational remote sensing. However, such data are necessary to better understand long-term urbanization and land development processes and for the assessment of coupled nature-human systems (e.g., the dynamics of the wildland-urban interface). Herein, we propose a framework that jointly uses remote-sensing-derived human settlement data (i.e., the Global Human Settlement Layer, GHSL) and scanned, georeferenced historical maps to automatically generate historical urban extents for the early 20th century. By applying unsupervised color space segmentation to the historical maps, spatially constrained to the urban extents derived from the GHSL, our approach generates historical settlement extents for seamless integration with the multitemporal GHSL. We apply our method to study areas in countries across four continents, and evaluate our approach against historical building density estimates from the Historical Settlement Data Compilation for the US (HISDAC-US), and against urban area estimates from the History Database of the Global Environment (HYDE). Our results achieve Area-under-the-Curve values > 0.9 when comparing to HISDAC-US and are largely in agreement with model-based urban areas from the HYDE database, demonstrating that the integration of remote-sensing-derived observations and historical cartographic data sources opens up new, promising avenues for assessing urbanization and long-term land cover change in countries where historical maps are available.

W-TSS: A Wavelet-Based Algorithm for Discovering Time Series Shapelets

Many approaches to time series classification rely on machine learning methods. However, there is growing interest in going beyond black box prediction models to understand discriminatory features of the time series and their associations with outcomes. One promising method is time-series shapelets (TSS), which identifies maximally discriminative subsequences of time series. For example, in environmental health applications TSS could be used to identify short-term patterns in exposure time series (shapelets) associated with adverse health outcomes. Identification of candidate shapelets in TSS is computationally intensive. The original TSS algorithm used exhaustive search. Subsequent algorithms introduced efficiencies by trimming/aggregating the set of candidates or training candidates from initialized values, but these approaches have limitations. In this paper, we introduce Wavelet-TSS (W-TSS) a novel intelligent method for identifying candidate shapelets in TSS using wavelet transformation discovery. We tested W-TSS on two datasets: (1) a synthetic example used in previous TSS studies and (2) a panel study relating exposures from residential air pollution sensors to symptoms in participants with asthma. Compared to previous TSS algorithms, W-TSS was more computationally efficient, more accurate, and was able to discover more discriminative shapelets. W-TSS does not require pre-specification of shapelet length.

Artificial Intelligence for Modeling Complex Systems: Taming the Complexity of Expert Models to Improve Decision Making

Major societal and environmental challenges involve complex systems that have diverse multi-scale interacting processes. Consider, for example, how droughts and water reserves affect crop production and how agriculture and industrial needs affect water quality and availability. Preventive measures, such as delaying planting dates and adopting new agricultural practices in response to changing weather patterns, can reduce the damage caused by natural processes. Understanding how these natural and human processes affect one another allows forecasting the effects of undesirable situations and study interventions to take preventive measures. For many of these processes, there are expert models that incorporate state-of-the-art theories and knowledge to quantify a system's response to a diversity of conditions. A major challenge for efficient modeling is the diversity of modeling approaches across disciplines and the wide variety of data sources available only in formats that require complex conversions. Using expert models for particular problems requires integration of models with third-party data as well as integration of models across disciplines. Modelers face significant heterogeneity that requires resolving semantic, spatiotemporal, and execution mismatches, which are largely done by hand today and may take more than 2 years of effort.

We are developing a modeling framework that uses artificial intelligence (AI) techniques to reduce modeling effort while ensuring utility for decision making. Our work to date makes several innovative contributions: (1) an intelligent user interface that guides analysts to frame their modeling problem and assists them by suggesting relevant choices and automating steps along the way; (2) semantic metadata for models, including their modeling variables and constraints, that ensures model relevance and proper use for a given decision-making problem; and (3) semantic representations of datasets in terms of modeling variables that enable automated data selection and data transformations. This framework is implemented in the MINT (Model INTegration) framework, and currently includes data and models to analyze the interactions between natural and human systems involving climate, water availability, agricultural production, and markets. Our work to date demonstrates the utility of AI techniques to accelerate modeling to support decision-making and uncovers several challenging directions for future work.

Applying Multivariate Segmentation Methods to Human Activity Recognition From Wearable Sensors' Data

Background

Time-resolved quantification of physical activity can contribute to both personalized medicine and epidemiological research studies, for example, managing and identifying triggers of asthma exacerbations. A growing number of reportedly accurate machine learning algorithms for human activity recognition (HAR) have been developed using data from wearable devices (eg, smartwatch and smartphone). However, many HAR algorithms depend on fixed-size sampling windows that may poorly adapt to real-world conditions in which activity bouts are of unequal duration. A small sliding window can produce noisy predictions under stable conditions, whereas a large sliding window may miss brief bursts of intense activity.

Objective

We aimed to create an HAR framework adapted to variable duration activity bouts by (1) detecting the change points of activity bouts in a multivariate time series and (2) predicting activity for each homogeneous window defined by these change points.

Methods

We applied standard fixed-width sliding windows (4-6 different sizes) or greedy Gaussian segmentation (GGS) to identify break points in filtered triaxial accelerometer and gyroscope data. After standard feature engineering, we applied an Xgboost model to predict physical activity within each window and then converted windowed predictions to instantaneous predictions to facilitate comparison across segmentation methods. We applied these methods in 2 datasets: the human activity recognition using smartphones (HARuS) dataset where a total of 30 adults performed activities of approximately equal duration (approximately 20 seconds each) while wearing a waist-worn smartphone, and the Biomedical REAl-Time Health Evaluation for Pediatric Asthma (BREATHE) dataset where a total of 14 children performed 6 activities for approximately 10 min each while wearing a smartwatch. To mimic a real-world scenario, we generated artificial unequal activity bout durations in the BREATHE data by randomly subdividing each activity bout into 10 segments and randomly concatenating the 60 activity bouts. Each dataset was divided into ~90% training and ~10% holdout testing.

Results

In the HARuS data, GGS produced the least noisy predictions of 6 physical activities and had the second highest accuracy rate of 91.06% (the highest accuracy rate was 91.79% for the sliding window of size 0.8 second). In the BREATHE data, GGS again produced the least noisy predictions and had the highest accuracy rate of 79.4% of predictions for 6 physical activities.

Conclusions

In a scenario with variable duration activity bouts, GGS multivariate segmentation produced smart-sized windows with more stable predictions and a higher accuracy rate than traditional fixed-size sliding window approaches. Overall, accuracy was good in both datasets but, as expected, it was slightly lower in the more real-world study using wrist-worn smartwatches in children (BREATHE) than in the more tightly controlled study using waist-worn smartphones in adults (HARuS). We implemented GGS in an offline setting, but it could be adapted for real-time prediction with streaming data.

Emerging trends in geospatial artificial intelligence (geoAI): potential applications for environmental epidemiology

Geospatial artificial intelligence (geoAI) is an emerging scientific discipline that combines innovations in spatial science, artificial intelligence methods in machine learning (e.g., deep learning), data mining, and high-performance computing to extract knowledge from spatial big data. In environmental epidemiology, exposure modeling is a commonly used approach to conduct exposure assessment to determine the distribution of exposures in study populations. geoAI technologies provide important advantages for exposure modeling in environmental epidemiology, including the ability to incorporate large amounts of big spatial and temporal data in a variety of formats; computational efficiency; flexibility in algorithms and workflows to accommodate relevant characteristics of spatial (environmental) processes including spatial nonstationarity; and scalability to model other environmental exposures across different geographic areas. The objectives of this commentary are to provide an overview of key concepts surrounding the evolving and interdisciplinary field of geoAI including spatial data science, machine learning, deep learning, and data mining; recent geoAI applications in research; and potential future directions for geoAI in environmental epidemiology.

Mining Public Datasets for Modeling Intra-City PM2.5 Concentrations at a Fine Spatial Resolution

Air quality models are important for studying the impact of air pollutant on health conditions at a fine spatiotemporal scale. Existing work typically relies on area-specific, expert-selected attributes of pollution emissions (e,g., transportation) and dispersion (e.g., meteorology) for building the model for each combination of study areas, pollutant types, and spatiotemporal scales. In this paper, we present a data mining approach that utilizes publicly available OpenStreetMap (OSM) data to automatically generate an air quality model for the concentrations of fine particulate matter less than 2.5 μm in aerodynamic diameter at various temporal scales. Our experiment shows that our (domain-) expert-free model could generate accurate PM2.5 concentration predictions, which can be used to improve air quality models that traditionally rely on expert-selected input. Our approach also quantifies the impact on air quality from a variety of geographic features (i.e., how various types of geographic features such as parking lots and commercial buildings affect air quality and from what distance) representing mobile, stationary and area natural and anthropogenic air pollution sources. This approach is particularly important for enabling the construction of context-specific spatiotemporal models of air pollution, allowing investigations of the impact of air pollution exposures on sensitive populations such as children with asthma at scale.

A Scalable Data Integration and Analysis Architecture for Sensor Data of Pediatric Asthma

According to the Centers for Disease Control, in the United States there are 6.8 million children living with asthma. Despite the importance of the disease, the available prognostic tools are not sufficient for biomedical researchers to thoroughly investigate the potential risks of the disease at scale. To overcome these challenges we present a big data integration and analysis infrastructure developed by our Data and Software Coordination and Integration Center (DSCIC) of the NIBIB-funded Pediatric Research using Integrated Sensor Monitoring Systems (PRISMS) program. Our goal is to help biomedical researchers to efficiently predict and prevent asthma attacks. The PRISMS-DSCIC is responsible for collecting, integrating, storing, and analyzing real-time environmental, physiological and behavioral data obtained from heterogeneous sensor and traditional data sources. Our architecture is based on the Apache Kafka, Spark and Hadoop frameworks and PostgreSQL DBMS. A main contribution of this work is extending the Spark framework with a mediation layer, based on logical schema mappings and query rewriting, to facilitate data analysis over a consistent harmonized schema. The system provides both batch and stream analytic capabilities over the massive data generated by wearable and fixed sensors.

Expression profile of metastasis-related genes in invasive oral cancers

We used suppression subtractive hybridization (SSH) and oligonucleotide microarray to differentiate expression profiles of metastasis-related genes and to evaluate their clinical significance in patients with invasive oral cancer (OCa). Overexpression of the specific genes was confirmed by reverse transcription-PCR (RT-PCR). Cells expressing the gene were identified by immunohistochemistry in pathology specimens. Clinical correlation and significance were analyzed statistically. Using these methods, we detected increased expressions of MMP-1, -3, -7, -9, -10 and interleukin (IL)-8 in invasive OCa. Moreover, our data showed that overexpressions of MMP-1, -3, -7, -10 and IL-8 were associated with reduced survival.

Water-soluble hexasulfobutyl [60] fullerene inhibits plasma lipid peroxidation by direct association with lipoproteins

Free radicals can induce lipid peroxidation, leading to the formation of atherosclerosis. A new class of water-soluble C60 derivative, hexasulfobutyl [60] fullerene [C60-(CH2CH2CH2CH2SO3Na)6; (FC4S)], comprising six sulfobutyl functional groups covalently bound on a C60 cage, is a potent free radical scavenger. This study sought to define the effect of FC4S in protecting plasma from peroxidation. At concentrations of 10-100 microM, FC4S efficiently protected plasma against Cu2+-induced oxidation, as shown by maintenance of apoprotein B integrity and decrease in oxidative products levels, conjugated diene, and thiobarbituric acid-reactive substances. Addition of FC4S to both plasma and isolated lipoproteins, including very low density, low-density, and high-density lipoproteins, resulted in an increased mobility of the lipoprotein on agarose gel electrophoresis. This was attributed to FC4S associating with the lipoproteins because of the negative charge of the sulfonate groups after hydrolysis in the electrophoretic buffer. When lipoprotein was oxidized by 2,2-azobis (2,4-dimethyl-valeronitrile), which produces peroxyl radicals within lipoprotein, but not in the aqueous phase, the FC4S still efficiently inhibited lipoprotein oxidation. These data substantiate that FC4S acts efficiently in protecting plasma lipid from oxidation by associating with lipoprotein to scavenge free radicals in both the aqueous and lipophilic phases.

Lidocaine-induced CNS toxicity--a case report

Lidocaine is the first local anesthetic of the amide type to be introduced to clinical practice. It is a versatile drug and in anesthesia, is the most commonly used local anesthetic because of its aptness of potency, rapid onset, moderate duration of action and topical activity. It is relatively safe and useful in many other clinical settings. Unfortunately, systemic intoxication and psychotic reaction associated with its use often occur because of its popularity and wider safety margin, for which guide in use is often ignored and overdose becomes commonplace. Moreover, due to its universality in use seldom reports have recently dealt with lidocaine, particularly regarding its toxic reaction. Here, we present a case of lidocaine intoxication occurring during circumcision for a reviewal of the problem. A healthy young male, weighing 65 kg, underwent circumcision for phimosis under penile block with 2% lidocaine which totaled 600 mg. Twenty minutes after injection the patient developed headache, tinnitus, visual and auditory disturbances. Muscle twitching over the mouth angles, trismus and rigidity of extremities were also noted. Later in the course he became restless, agitative, hallucinative, talkative, and verbose with repetitious words. The whole course of the disorder lasted about 5 h. It was believed that lidocaine-induced CNS intoxication, manifested by psychotic reaction broke out. Treatment with thiopental was not very impressive. Also, we took this opportunity to discuss and review the toxic reaction associated with the use of lidocaine, its risk factors, mechanism, treatment and prevention. The complicated associations of lidocaine-induced CNS toxic reaction with central control of behavior and the neurotransmitter systems (adrenergic, dopaminergic and serotonin) were also touched.

Neuromuscular interactions between succinylcholine and esmolol in the rat

To study the neuromuscular interactions between succinylcholine (Sch) and esmolol, we determined the dose-response relationship of Sch and the neuromuscular actions of the 3xED90 dose of Sch, both prior to and following esmolol pretreatment. Twenty rats were anaesthetized with urethane. Train-of-four stimulation was applied every 12 s to the sciatic nerve, and the electromyogram (EMG) of the tibialis anterior muscle was measured. The results showed that the potency of Sch decreased with esmolol pretreatment. The ED50 of Sch increased significantly, from 191 ug/kg to 227 ug/kg after esmolol infusion, p < 0.05. The duration of EMG depression achieved by the 3xED90 dose of Sch decreased significantly with esmolol pretreatment (12 min vs 14 min p < 0.05), and also the onset time was significantly longer (43 sec vs 28 sec, p < 0.05). There were no significant difference between groups with regard to the maximal block or recovery index. The results of two methods of study demonstrated that the pharmacological interaction between Sch and esmolol is antagonistic instead of potentiating.

[Incidence of venous air embolism in parturients during cesarean section with regional anesthesia]

The purpose of the study is to probe the situation of venous air embolism (VAE) and the accompanying complications occurring in Chinese parturients in Taiwan during Cesarean section. Sixty ASA physical status class I-II parturients who were subjected to cesarean section under regional anesthesia were evaluated. The sensor of the Doppler device was placed on the anterior chest to detect the rumbles of air when it came to pass, and simultaneously the signs and symptoms following VAE were observed. Our results demonstrated that the usual or normal Doppler heart sound changed in 38 parturients out of 60 (63.3%), and the alteration occurred very often when the uterus was being incised (81.6%), or sutured (97.4%), and concurred strong correlation with such signs and symptoms such as chest tightness or precordial pain (78.9%), shortness of breath (60.5%), and change of heart rate or blood pressure (86.8%). The method of anesthesia (spinal or epidural block) did not have effect on the occurrence of VAE, but different surgical approaches and different positions in which the patients were posed during operation did apparently bring about VAE of variable degree. Besides, supplying of oxygen could mitigate the symptoms produced by VAE. Consequently, the application of Doppler monitor during Cesarean section can detect VAE earlier and more efficiently and thus provides information timely treatment.

In vitro analysis of extracellular matrix production by porcine glomerular mesangial and vascular smooth muscle cells

Proliferation potential and extracellular matrix production were compared in cultured porcine glomerular mesangial cells and arterial and venous smooth muscle cells. Mesangial and arterial smooth muscle cells proliferated more rapidly than venous smooth muscle cells. In immunofluorescence studies, mesangial and arterial smooth muscle cells stained strongly for collagen types I, III, and V; venous smooth muscles showed weaker staining for collagens III and V. Total collagen synthesis in cultured mesangial and arterial smooth muscle cells was lower than in venous smooth muscle cells. Electrophoretic analysis showed type I collagen predominated in all cell types, although levels were highest in mesangial and arterial smooth muscle cells. Collagen V (alpha 3) occurred only in venous smooth muscle cells. Mesangial and arterial smooth muscle cells showed cellbound fibronectin and laminin, which also were secreted into the medium. Venous smooth muscle cells secreted fibronectin, but all laminin was cell bound. The findings suggest a strong similarity between mesangial and arterial smooth muscle cells.

[Glomerular ultrastructures and prognosis of the patients with urinary abnormalities found by school screening program]

Detailed histopathological study were performed and compared with clinical features in 120 children with serial renal biopsies who were found by school screening program. 41 cases (34.2%) of IgA nephropathy (IgAN), 26 cases (21.7%) of thin membrane disease (TMD) and 22 cases (18.3%) of normal glomeruli [( Normal]) accounted for 74.2% of all biopsies. 81 cases (67.5%) were revealed to be minor glomerular abnormalities by light microscopy and which contained 26 cases (32.1%) of TMD, 22 cases (27.2%) of [Normal] and 19 cases (23.4%) of IgAN. The frequency and the severity of proteinuria was significantly higher in IgAN than in TMD and [Normal] (P less than 0.01, P less than 0.05). Hematuria was significantly greater in [Normal] than in IgAN. In the 71 follow-up cases, no patient went to renal insufficiency, moreover, urinary abnormalities had disappeared in 25.4% of the patients including IgAN, TMD, [Normal], nonIgA proliferative glomerulonephritis, incomplete foot process disease and MPGN. [Normal] consisted of stationary or exercised urinary abnormality.

Immunocytochemistry of intermediate filaments in cultured arterial smooth muscle cells: differences in desmin and vimentin expression related to cell of origin and/or plating time

The objective of this study was to determine whether intermediate filament expression, including desmin and vimentin, in cultured smooth muscle cells (SMCs) is related to cyto-differentiation or proliferation. Using antibodies to desmin and vimentin, we studied by immunoperoxidase technique the distribution of these proteins in subcultured SMCs derived from porcine aorta and coronary artery. In addition, the proliferative potentiality of the cells was estimated by the incorporation of [3H]thymidine into DNA. The frequency of desmin-positive cells in coronary arterial SMCs of 3 and 6 population doubling levels was significantly higher as compared to findings with the aortic SMCs and depended on the plating time. No difference was evident at the 12 population doubling level. In contrast, vimentin was present in the majority of both aortic and coronary arterial SMCs. With regard to the localization of vimentin, two cell types were observed, one had reaction products to vimentin in both perinuclear and cell-peripheral areas (type-I cell), the other only in the cell-peripheral region (type-II cell). The relative proportion of the type-I and -II cells varied with the period of culture. Most of the SMCs showed the type-I cell on the first day and the number of type-II cells was increased on the sixth day. Quiescent SMCs in serum-free media had the same percentage of desmin-positive cells and frequency distribution of type-I and -II cells as did the proliferating SMCs incubated in media containing 5% serum. These results suggest that intermediate filament expression, including desmin and vimentin in cultured SMCs, is related to cell origin and/or plating time, but not to the proliferating activity, per se.