Quality Checks Research Articles

Multi-source data modelling and graph neural networks for predictive quality

State-of-the-art predictive quality (PQ) applications use machine learning and deep learning methods to learn patterns and classify a product's quality. Models typically estimate quality control labels based on process data, such as machine control and sensor data, substituting time-consuming manual quality checks. Most current advancements in the field of PQ focus on machine-level quality prediction. The quality of the final product is, however, influenced by each manufacturing process of the production line. Performing PQ at the production line level involves challenges such as merging multiple data sources and considering process interdependencies. This paper thus explores a graph representation of manufacturing data and the use of graph neural networks to classify products' quality and develop a predictive quality solution. The graph representation of the data tackles the mentioned challenges and allows PQ at the production line level. Experiments have shown promising results for an assembly line use case.

Lean/Sizzurp Ingredients, Use, and Coping With Mental Health Symptoms.

The substance combination of codeine and promethazine, commonly termed lean/sizzurp, has been identified as a method that some individuals use to cope with PTSD and other mental health symptomology. A sample of 1423 adults with self-reported past year lean use was recruited from substance-related Reddit pages to complete a survey about lean, including information about using lean to cope with emotions, thoughts, or feelings. To be included in the sample, persons needed to: (1) be ⩾18 years old, (2) report past year lean use, (3) complete lean use screeners, and (4) pass data quality checks (eg, bot detection). As Reddit is an online forum, no geographic restrictions were placed on study participation. Data on demographic characteristics, lean use, and mental health disorder symptomology were captured from participants. Logistic regression models included anxiety, depression, and trauma as independent variables along with covariates to examine using lean to cope with emotions, thoughts, or feelings in the past 30 days. Most participants were male (n = 1102; 77.4%), with an average age of 26.9 (SD = 5.2) years. Most participants used included codeine as an ingredient in lean (n = 1060; 74.5%); promethazine was added as an ingredient by 31.7% of the sample (n = 451), and the combination of codeine and promethazine was included as ingredients by 13.5% (n = 192) of the sample. Participants with anxiety, lifetime trauma exposure, and who were female had increased odds of using lean to cope with emotions, thoughts, or feelings in the past 30 days. Those with depression and unstable housing exhibited decreased odds of using lean to cope with emotions, thoughts, or feelings in the past 30 days. This study recruited persons via social media to learn more about lean use, especially lean use to cope with mental health symptoms; future population-level studies are needed.

Hydro-biological assessment of sustainable desert Rain-Fed ponds of Western Rajasthan using Physico-chemical characteristics and Phyto-planktonic indices

The present study reports the water quality of small rain-fed water reservoirs of Nagaur district, Rajasthan, India. These surface water bodies are called Talab and Nadis in the local language and play a significant role in fulfilling rural domestic water demands throughout the year from July2020-June 2021. The microfloral investigations of these ponds were neglected in the past. So, investigation is needed for water quality checks to improve the use of storage water strategy. The water samples were collected from July 2020 to June 2021 from selected four sampling ponds located in and around Nagaur city in nearby villages for Physico-chemical and phytoplankton analysis once in a month. Physical and chemical parameter were determined such as odour, pH, Total Dissolved Solid (TDS), Total Hardness, carbonate and bi-carbonate hardness and Total Alkalinity with organic parameter BOD, COD and organic matter by following the standard method of A.P.H.A., The Bureau of Indian Standards' IS:10500 drinking water standard was used to compare the outcomes (B.I.S. 2012). Palmer Pollution Index for genera was also employed by using algal and phytoplankton identification. It was concluded from the physicochemical and phytoplanktonic analysis results that among all sampling sites one of the urban sampling Talab (Jada Talab) located in Nagaur city showed deteriorated water quality but, all village ponds satisfactory water quality was recorded in rainy and winter but in summer phytoplankton analysis indicated poor water quality. The present study will be helpful in the future framework for the improved understanding of hydro-biological research and improvement in sustainable water management strategies in old water conservation rain-fed ponds of Western Rajasthan.

Population data quality checks: Romanian adult deaths and lives, an evaluation

<abstract> <p>This article investigates the quality of the Romanian population data for the years 1966–2018. The following age heaping measures are being used, each with its recommended population age span: Whipple, Myers, Bachi, UNASA, Kannisto, Coale and Li. In addition, the Kannisto measure of old age overstatement is used. Our analysis corroborates the results of our measures with the demographic characteristic of the Romanian population. We aim to establish whether; (a) the data spanning the time interval 1966–1989 is more accurate than the data collected during the 1990–2018 interval, (b) the Romanian data is at least of comparable quality with the tier 2 HMD data and/or (c) the Romanian data is not grossly inaccurate and therefore suitable for research. We find support for (a) and, cautioning against the live data for the time interval 2002–2018, for (b) and (c). Limitations aside (no through overstatement or cohort analysis), this is the first study to attempt such evaluation and hopefully not the last.</p> </abstract>

ESTABLISHMENT OF A BIOINFORMATICS PIPELINE FOR THE DETECTION OF PATHOGENIC BACTERIA

Next-generation sequencing-based methods using partial 16S rRNA gene amplicons are extensively applied today in studies of the plant metagenomes. NGS sequencing creates huge sets of raw data making analysis a challenging task. Lack of computational and bioinformatics knowledge and tools for analyzing high throughput data to interpret correct biological variations is a major problem. In addition, downstream analysis of NGS data with the available bioinformatics platforms create various challenges in inferring microbial composition. The available commercial software are expensive and individual opensource tools are usually operate stand alone as they are not combined for a user-friendly workflow. Therefore, beginners in bioinformatics might find analysis procedures are complicated, expensive, and timeconsuming with the associated learning. In the present study, a bioinformatics pipeline is developed to analyze the 16S rDNA amplicons of plant metagenome. Microbial DNA was extracted from imported seed potato tubers. Extracted DNA was sequenced using Ion Torrent Next Generation Sequencing technology by amplifying 400 bp V1-V2 region of 16S rRNA gene for the detection of bacterial pathogens. The pipeline was built by stringing together many command line tools; Quality checking of raw fastq data using FastQC, trimming of low-quality data with Trimmomatric, alignment of trimmed data using the BWA-MEM algorithm, removal of duplicate reads with Picard Mark Duplicates tool and finally generation of the phylogenetic tree and taxonomic profile with MEGAN 5. The developed pipeline was used to analyze the 16S rRNA next generation sequences and the reliability of the results has been checked with the use of mock communities for validation. The pipeline often can be executed on laptop sized machines to obtain the output in a couple of hours enabling easy access for the researchers.

G-SAIP: Graphical Sequence Alignment Through Parallel Programming in the Post-Genomic Era.

A common task in bioinformatics is to compare DNA sequences to identify similarities between organisms at the sequence level. An approach to such comparison is the dot-plots, a 2-dimensional graphical representation to analyze DNA or protein alignments. Dot-plots alignment software existed before the sequencing revolution, and now there is an ongoing limitation when dealing with large-size sequences, resulting in very long execution times. High-Performance Computing (HPC) techniques have been successfully used in many applications to reduce computing times, but so far, very few applications for graphical sequence alignment using HPC have been reported. Here, we present G-SAIP (Graphical Sequence Alignment in Parallel), a software capable of spawning multiple distributed processes on CPUs, over a supercomputing infrastructure to speed up the execution time for dot-plot generation up to 1.68× compared with other current fastest tools, improve the efficiency for comparative structural genomic analysis, phylogenetics because the benefits of pairwise alignments for comparison between genomes, repetitive structure identification, and assembly quality checking.

Machine learning models to predict micronutrient profile in food after processing

The information on nutritional profile of cooked foods is important to both food manufacturers and consumers, and a major challenge to obtaining precise information is the inherent variation in composition across biological samples of any given raw ingredient. The ideal solution would address precision and generability, but the current solutions are limited in their capabilities; analytical methods are too costly to scale, retention-factor based methods are scalable but approximate, and kinetic models are bespoke to a food and nutrient. We provide an alternate solution that predicts the micronutrient profile in cooked food from the raw food composition, and for multiple foods. The prediction model is trained on an existing food composition dataset and has a 31% lower error on average (across all foods, processes and nutrients) than predictions obtained using the baseline method of retention-factors. Our results argue that data scaling and transformation prior to training the models is important to mitigate any yield bias. This study demonstrates the potential of machine learning methods over current solutions, and additionally provides guidance for the future generation of food composition data, specifically for sampling approach, data quality checks, and data representation standards.

Application of Radio Frequency Identification as a Motorcycle Quality Check Card

Application of Radio Frequency Identification as a Motorcycle Quality Check Card

Estimating Geophysical Parameters Using Petrophysical Algorithm to Enhance Hydrocarbon Recovery in Lokaka Field, Niger Delta, Nigeria

Adoption of Enhanced Oil Recovery (EOR) to boost the hydrocarbon saturation (Sh) of reservoirs has caught the interests of many researchers in Geosciences. Evidence from literature shows that both primary and secondary recovery methods have failed to account for about 60% hydrocarbon (HC) that is trapped in the reservoirs and getting to discover large productive new fields has become a herculean task. This study identified the fluid nature and boundaries of reservoirs using some relevant geophysical (petrophysical) parameters and reservoir rocks physical features such as shale volume (Vsh), permeability (K), water and hydrocarbon saturation (Sw& Sh). Petrophysical data were sourced from the data bank of the Department of Geology, Obafemi Awolowo University, Ile-Ife, Nigeria. Analysis of data was done using the PETREL 2010 and OpendTect 4.6.0 versions for quality checking, delineation of identified reservoirs, fluid contacts demarcation and fluid types’ determination. The interpreted data were thereafter loaded into Microsoft Excel environment in order to adopt suitable statistical relations for the estimation of Vsh, K, Sw and Sh. Exploration of about 59.4% HC with NaOH, 64.5% HC with KOH, 69.5% HC with NH4OH and 78.5% HC with LiOH were discovered after the (EOR) flooding process. Comparison of the Vsh, K, Sw and Sh values before EOR with the values after EOR further showed that the reservoirs produced more HC with EOR. This study concluded that more hydrocarbon saturation can be achieved from reservoirs when EOR is carried out.

Developing a regulatory model for product quality assurance in Nigeria’s local industries

This paper presents a comprehensive regulatory model for enhancing product quality assurance within Nigeria's local industries. As Nigeria's industrial sector continues to expand, the need for robust quality assurance mechanisms has become increasingly critical. Current challenges include inconsistent quality standards, inadequate enforcement of regulations, and limited industry compliance, which undermine product reliability and consumer trust. This study proposes a regulatory framework designed to address these issues by integrating international best practices with localized strategies. The proposed model emphasizes three core components: regulatory standards, enforcement mechanisms, and industry collaboration. First, the model advocates for the establishment of clear, standardized quality benchmarks tailored to the unique needs of local industries. By adopting globally recognized standards and adapting them to local contexts, the model aims to enhance the consistency and reliability of products manufactured in Nigeria. Second, the model outlines a multi-tiered enforcement strategy, including regular inspections, certification processes, and penalties for non-compliance. It suggests leveraging technological advancements, such as digital tracking systems and automated quality checks, to improve monitoring efficiency and reduce the potential for corruption. Third, the model highlights the importance of fostering collaboration between regulatory bodies, industry stakeholders, and consumer organizations. By promoting dialogue and cooperation, the model seeks to create a supportive ecosystem where best practices are shared, and feedback mechanisms are established to continuously refine quality standards. The regulatory model presented herein is designed to be dynamic and adaptable, capable of evolving in response to emerging industry trends and technological advancements. It aims to not only address current quality assurance challenges but also to set a foundation for sustained industrial growth and consumer protection. By implementing this model, Nigeria's local industries can achieve higher standards of product quality, enhance consumer confidence, and contribute to the broader goal of economic development.

Association of COVID-19 Vaccination Rates of Staff and COVID-19 Illness and Death Among Residents and Staff in US Nursing Homes

It is important to understand the association between staff vaccination rates and adverse COVID-19 outcomes in nursing homes. To assess the extent to which staff vaccination was associated with preventing COVID-19 cases and deaths among residents and staff in nursing homes. This longitudinal cohort study used data on COVID-19 outcomes in Medicare- and Medicaid-certified nursing homes in the US between May 30, 2021, and January 30, 2022. Participants included the residents of 15 042 US nursing homes that reported COVID-19 data to the Centers for Disease Control and Prevention and passed Centers for Medicare & Medicaid Services data quality checks in the National Healthcare Safety Network. Weekly staff vaccination rates. Main outcomes are weekly COVID-19 cases and deaths among residents and weekly COVID-19 cases among staff. The treatment variable is the primary 2-dose staff vaccination rate in each facility each week. In the primary analysis of 15 042 nursing homes before the Omicron variant wave (May 30 to December 5, 2021) using fixed effects of facility and week, increasing weekly staff vaccination rates by 10 percentage points was associated with 0.13 (95% CI, -0.20 to -0.10) fewer weekly COVID-19 cases per 1000 residents, 0.02 (95% CI, -0.03 to -0.01) fewer weekly COVID-19 deaths per 1000 residents, and 0.03 (95% CI, -0.04 to -0.02) fewer weekly COVID-19 staff cases. In the secondary analysis of the Omicron wave (December 5, 2021, to January 30, 2022), increasing staff vaccination rates were not associated with lower rates of adverse COVID-19 outcomes in nursing homes. The findings of this cohort study suggest that before the Omicron variant wave, increasing staff vaccination rates was associated with lower incidence of COVID-19 cases and deaths among residents and staff in US nursing homes. However, as newer, more infectious and transmissible variants of the virus emerged, the original 2-dose regimen of the COVID-19 vaccine as recommended in December 2020 was no longer associated with lower rates of adverse COVID-19 outcomes in nursing homes. Policy makers may want to consider longer-term policy options to increase the uptake of booster doses among staff in nursing homes.

Health record hiccups-5,526 real-world time series with change points labelled by crowdsourced visual inspection.

Large routinely collected data such as electronic health records (EHRs) are increasingly used in research, but the statistical methods and processes used to check such data for temporal data quality issues have not moved beyond manual, ad hoc production and visual inspection of graphs. With the prospect of EHR data being used for disease surveillance via automated pipelines and public-facing dashboards, automation of data quality checks will become increasingly valuable. We generated 5,526 time series from 8 different EHR datasets and engaged >2,000 citizen-science volunteers to label the locations of all suspicious-looking change points in the resulting graphs. Consensus labels were produced using density-based clustering with noise, with validation conducted using 956 images containing labels produced by an experienced data scientist. Parameter tuning was done against 670 images and performance calculated against 286 images, resulting in a final sensitivity of 80.4% (95% CI, 77.1%-83.3%), specificity of 99.8% (99.7%-99.8%), positive predictive value of 84.5% (81.4%-87.2%), and negative predictive value of 99.7% (99.6%-99.7%). In total, 12,745 change points were found within 3,687 of the time series. This large collection of labelled EHR time series can be used to validate automated methods for change point detection in real-world settings, encouraging the development of methods that can successfully be applied in practice. It is particularly valuable since change point detection methods are typically validated using synthetic data, so their performance in real-world settings cannot be assumed to be comparable. While the dataset focusses on EHRs and data quality, it should also be applicable in other fields.

Quality checks procedure for erroneous measurements detection of spectral components of solar irradiances

The shortwave solar radiation that arrives under the Earth's surface is spectrally composed of the ultraviolet, photosynthetically active, and near-infrared components. Having information about the availability of these parameters in different locations allows public agencies and scientific institutions to plan, execute, and manage energy projects in Brazil. Therefore, this information can be obtained from weather measurements with wide temporal and spatial coverage. However, the solar radiation measuring process is not an easy task, due to the uncertainties caused by instrumentation and operational problems. Thus, the aim of this study was to implement a procedure for erroneous measurements detection of ultraviolet, photosynthetically active, and near-infrared solar irradiances, collected in Botucatu (SP) - Brazil during the years 2001 to 2006, in sub-hourly temporal resolution (5 minutes in Wm-2), to certify the quality of the obtained measured values. For this purpose, analyzes were carried out by two quality check analyzes, the first one to verify whether the measurements were within physically possible limits and the second one whether they were within extremely rare but accepted limits. It was possible to identify that the near-infrared solar irradiance had the highest number of erroneous measurements flagged (0.310% of the total values), followed by photosynthetically active (0.162% of the total values) and, with less flags, from ultraviolet (0.047% of the total values). Measurements of spectral components of solar irradiances that were not flagged as erroneous by the quality checks procedure are considered valid, and can be used in future scientific investigations.

“After viral load testing, I get my results so I get to know which path my life is taking me”: qualitative insights on routine centralized and point-of-care viral load testing in western Kenya from the Opt4Kids and Opt4Mamas studies

BackgroundViral suppression (VS) is a marker of effective HIV therapy, and viral load (VL) testing is critical for treatment monitoring, especially in high-risk groups such as children and pregnant/postpartum women. Although routine VL testing, via centralized laboratory networks, was implemented in Kenya starting in 2014, optimization and sustainable scale up of VL testing are still needed.MethodsWe conducted a mixed methods study to evaluate the impact of higher frequency, point-of-care (POC) VL testing in optimizing VS among children and pregnant/postpartum women on antiretroviral treatment (ART) in five HIV treatment facilities in western Kenya in the Opt4Kids and Opt4Mamas studies. We conducted 68 key informant interviews (KIIs) from December 2019 to December 2020 with children and pregnant women living with HIV, child caregivers, providers, laboratory/facility leadership, and county- or national-level policymakers. Our KII guide covered the following domains: (1) barriers and facilitators to ART use and VS, (2) literacy and experiences with VL in routine care and via study, and (3) opinions on how to scale up VL testing for optimal programmatic use. We used inductive coding and thematic analysis to identify dominant themes with convergent and divergent subthemes.ResultsThree main themes regarding VL testing emerged from our analysis. (1) Key informants uniformly contrasted POC VL testing’s faster results turnaround, higher accessibility, and likely cost-effectiveness against centralized VL testing. (2) Key informants also identified areas of improvement for POC VL testing in Kenya, such as quality control, human resource and infrastructure capacity, supply chain management, and integration of VL testing systems. (3) To enable successful scale-up of VL testing, key informants proposed expanding the POC VL testing scheme, electronic medical records systems, conducting quality checks locally, capacity building and developing strong partnerships between key stakeholders.ConclusionThe more accessible, decentralized model of POC VL testing was deemed capable of overcoming critical challenges associated with centralized VL testing and was considered highly desirable for optimizing VS for children and pregnant/postpartum women living with HIV. While POC VL testing has the potential to improve VS rates among these populations, additional research is needed to develop strategies for ensuring the sustainability of POC VL testing programs.Trial registrationNCT03820323, 29/01/2019

Absolute Densities of Breeding Birds in Estonian Forests: A Synthesis

This paper synthesizes all available quantitative information on total breeding densities of Estonian forest bird assemblages, as estimated in territory mapping studies since the 1950s. There have been two approaches, one based on multiple visits (typically 7–8), and the other of reduced effort, developed for long-term monitoring. Compared to these mapping methods, line (strip) transects give ca. 30% lower estimates; they also vary widely. After quality checking, a total of 90 mapping estimates were extracted. The estimates were then integrated into ‘typical densities’ by habitat types, after critically assessing data quality and bias. ‘Typical density’ is conceptualized as a range of common density values in actual topographic, soil, forest-age and landscape conditions, under minor recent thinning influence. It also considers 15–20% underestimation of the territory mapping, which was demonstrated using a case study on post-breeding nest searching of three thrush (Turdus spp.) species. The Estonian forest bird densities show monotonous increases along the stand age, following three alternative shapes related to site productivity: a major increase within 100 years in the most productive forests, a delayed increase (also post 100 years) in pine forests of medium productivity, and slow and slight increases at low-productivity sites. Such density estimates can serve as a basis to analyse deviations in particular conditions and be integrated into land-use scenario-modelling tools across landscapes. It is important to keep the territory mapping approaches to bird census in active use and development, since several issues in conservation and ecosystem functioning cannot be addressed by relative assessments.

Efektifitas Pengadaan Bahan Makanan dalam Penentuan Harga Jual

This research focuses on the effectiveness of food procurement in determining the selling price at FPBS Bali hotel. Food procurement that affects the variance between standard food costs and actual food costs will affect the profit of the food and beverage department. This research uses a qualitative descriptive method, namely research that in the process uses systematic techniques to understand, reduce, process, and draw conclusions from qualitative data collected based on interviews, and observations. The results showed that: the purchasing department purchases food ingredients not competitively but based on supplier loyalty. Receiving food ingredients lacks quality and quantity checks in accordance with the standard purchase specifications set by the hotel. Food storage activities have not fully run properly such as not paying attention to temperature and the use of the FIFO method. The recording of food expenditure from the store has not been maximum using the storeroom requisition (SR). Determination of the selling price of food mark-up pricing approach, namely setting the price of the product by taking into account all material costs and the expected profit level has not been used for the entire food menu.

Assessing Associations Between COVID-19 Symptomology and Adverse Outcomes After Piloting Crowdsourced Data Collection: Cross-sectional Survey Study.

Crowdsourcing is a useful way to rapidly collect information on COVID-19 symptoms. However, there are potential biases and data quality issues given the population that chooses to participate in crowdsourcing activities and the common strategies used to screen participants based on their previous experience. The study aimed to (1) build a pipeline to enable data quality and population representation checks in a pilot setting prior to deploying a final survey to a crowdsourcing platform, (2) assess COVID-19 symptomology among survey respondents who report a previous positive COVID-19 result, and (3) assess associations of symptomology groups and underlying chronic conditions with adverse outcomes due to COVID-19. We developed a web-based survey and hosted it on the Amazon Mechanical Turk (MTurk) crowdsourcing platform. We conducted a pilot study from August 5, 2020, to August 14, 2020, to refine the filtering criteria according to our needs before finalizing the pipeline. The final survey was posted from late August to December 31, 2020. Hierarchical cluster analyses were performed to identify COVID-19 symptomology groups, and logistic regression analyses were performed for hospitalization and mechanical ventilation outcomes. Finally, we performed a validation of study outcomes by comparing our findings to those reported in previous systematic reviews. The crowdsourcing pipeline facilitated piloting our survey study and revising the filtering criteria to target specific MTurk experience levels and to include a second attention check. We collected data from 1254 COVID-19-positive survey participants and identified the following 6 symptomology groups: abdominal and bladder pain (Group 1); flu-like symptoms (loss of smell/taste/appetite; Group 2); hoarseness and sputum production (Group 3); joint aches and stomach cramps (Group 4); eye or skin dryness and vomiting (Group 5); and no symptoms (Group 6). The risk factors for adverse COVID-19 outcomes differed for different symptomology groups. The only risk factor that remained significant across 4 symptomology groups was influenza vaccine in the previous year (Group 1: odds ratio [OR] 6.22, 95% CI 2.32-17.92; Group 2: OR 2.35, 95% CI 1.74-3.18; Group 3: OR 3.7, 95% CI 1.32-10.98; Group 4: OR 4.44, 95% CI 1.53-14.49). Our findings regarding the symptoms of abdominal pain, cough, fever, fatigue, shortness of breath, and vomiting as risk factors for COVID-19 adverse outcomes were concordant with the findings of other researchers. Some high-risk symptoms found in our study, including bladder pain, dry eyes or skin, and loss of appetite, were reported less frequently by other researchers and were not considered previously in relation to COVID-19 adverse outcomes. We demonstrated that a crowdsourced approach was effective for collecting data to assess symptomology associated with COVID-19. Such a strategy may facilitate efficient assessments in a dynamic intersection between emerging infectious diseases, and societal and environmental changes.

Assessment of Satellite AOD during the 2020 Wildfire Season in the Western U.S.

Satellite remote sensing of aerosol optical depth (AOD) is essential for detection, characterization, and forecasting of wildfire smoke. In this work, we evaluate the AOD (550 nm) retrievals during the extreme wildfire events over the western U.S. in September 2020. Three products are analyzed, including the Moderate-resolution Imaging Spectroradiometers (MODIS) Multi-Angle Implementation of Atmospheric Correction (MAIAC) product collections C6.0 and C6.1, and the NOAA-20 Visible Infrared Imaging Radiometer (VIIRS) AOD from the NOAA Enterprise Processing System (EPS) algorithm. Compared with the Aerosol Robotic Network (AERONET) data, all three products show strong linear correlations with MAIAC C6.1 and VIIRS presenting overall low bias (<0.06). The accuracy of MAIAC C6.1 is found to be substantially improved with respect to MAIAC C6.0 that drastically underestimated AOD over thick smoke, which validates the effectiveness of updates made in MAIAC C6.1 in terms of an improved representation of smoke aerosol optical properties. VIIRS AOD exhibits comparable uncertainty with MAIAC C6.1 with a slight tendency of increased positive bias over the AERONET AOD range of 0.5–3.0. Averaging coincident retrievals from MAIAC C6.1 and VIIRS provides a lower root mean square error and higher correlation than for the individual products, motivating the benefit of blending these datasets. MAIAC C6.1 and VIIRS are further compared to provide insights on their retrieval strategy. When gridded at 0.1° resolution, MAIAC C6.1 and VIIRS provide similar monthly AOD distribution patterns and the latter exhibits a slightly higher domain average. On daily scale, over thick plumes near fire sources, MAIAC C6.1 reports more valid retrievals where VIIRS tends to have retrievals designated as low or medium quality, which tends to be due to internal quality checks. Over transported smoke near scattered clouds, VIIRS provides better retrieval coverage than MAIAC C6.1 owing to its higher spatial resolution, pixel-level processing, and less strict cloud masking. These results can be used as a guide for applications of satellite AOD retrievals during wildfire events and provide insights on future improvement of retrieval algorithms under heavy smoke conditions.

Bulk and single cell transcriptomic analysis of different dementia types in human hippocampus

AbstractBackgroundAlzheimer’s disease (AD) is characterized by cognitive deterioration and extracellular amyloid beta deposits and neurofibrillary tangles in the brain. These may occur in tandem with markers of Lewy body (LB) and vascular dementia (VaD). Studying bulk and single‐cell expression profiles for these disorders may identify factors shared between and unique to AD and other dementias.MethodBulk RNA‐sequencing was performed using 210 human postmortem hippocampal region samples obtained from brains donated to the BU Alzheimer Disease Research Center. After quality checks (QC), 191 samples classified as AD+LB (n=43), AD+VaD (n=54), AD alone (n=73), and controls (n=17) were included in the study. Analysis of expression differences between each of these AD groups and controls was performed using limma software with models including covariates for age, sex, RNA quality, plate, and two surrogate variables to adjust for unknown variation. Weighted gene co‐expression network analysis (WGCNA) was used to identify clusters of co‐expressed genes. Additionally, high quality single‐nuclei RNA sequencing data (snRNA‐seq) was obtained from 8 of the 210 hippocampal samples with an average of 6000 cells detected per sample and 1800 genes per cell after QC. This snRNA‐seq data was integrated with the bulk data using Scissor to identify cell populations positively and negatively associated with AD.Result481 genes were differentially expressed between AD cases and controls. Upregulated genes in AD subjects were enriched for cilia function and morphological development and downregulated genes in AD subjects were enriched for mitochondrial energy production. A WGCNA module strongly associated with ciliary function was found to be overrepresented in AD (and especially AD+VaD) samples compared to controls. Multiple brain cell types were detected from our snRNA‐seq data. Of these, a subpopulation of ependymal cells related to the ciliary WGCNA module was positively associated with AD status.ConclusionOur results suggest that different sets of co‐expressed genes in hippocampus distinguish AD brains with and without pathological hallmarks related to other forms of dementia, and brains without AD pathology. This study also provides preliminary evidence for the involvement of ependymal cell populations in AD

Early infestation volatile biomarkers of fruit fly Bactrocera dorsalis (Hendel) ovipositional activity in mango (Mangifera indica L.)

Infestation of agricultural commodities by insect pests results in significant economic, import and export, food safety, and invasive insect introduction issues for growers, consumers, and inspectors. The Oriental fruit fly (Bactrocera dorsalis) is considered a highly invasive insect pest with populations reported in more than 60 countries, with prevalent distributions in Asia and Africa. B. dorsalis is phytophagous with a host range encompassing hundreds of fruits and vegetables. Damage to the fruit or vegetable is inflicted through oviposition and subsequent larval feeding resulting in spoilage. Early detection of insect pest infestations is a critical component for ensuring food safety as well as controlling introduction and spread of invasive insects. However, detection of ovipositional activity and early larval development is visually difficult, thus rapid and non-destructive detection often relies on odors associated with infestation. We investigated the odors of mangoes (Mangifera indica L.) infested with B. dorsalis and compared the volatile profiles of infested mangoes to non-infested and mechanically damaged mangoes 24 h post-infestation. GC-MS and multivariate analyses provided the identification of eleven compounds unique to infested mangoes compared to mechanically damaged or non-infested fruit. Results indicated compositional and quantitative differentiation of volatile profiles among treatments for detection of infested fruit at quality checks or points of commerce.