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Although nasopharyngeal samples have been considered the gold standard for COVID-19 testing, variability in viral load across different anatomical sites could cause nasopharyngeal samples to be less sensitive than saliva or nasal samples in certain cases. Self-collected samples have logistical advantages over nasopharyngeal samples, making them amenable to population-scale screening. To evaluate sampling alternatives for population screening, we collected nasopharyngeal, saliva, and nasal samples from two cohorts with varied levels and types of symptoms. In a mixed cohort of 60 symptomatic and asymptomatic participants, we found that saliva had 88% concordance with nasopharyngeal samples when tested in the same testing lab (n = 41) and 68% concordance when tested in different testing labs (n = 19). In a second cohort of 20 participants hospitalized for COVID-19, saliva had 74% concordance with nasopharyngeal samples tested in the same testing lab but detected virus in two participants that tested negative with nasopharyngeal samples on the same day. Medical record review showed that the saliva-based testing sensitivity was related to the timing of symptom onset and disease stage. We find that no sample site will be perfectly sensitive for COVID-19 testing in all situations, and the significance of negative results will always need to be determined in the context of clinical signs and symptoms. Saliva retained high clinical sensitivity for early-stage and presymptomatic COVID-19 while allowing easier collection, minimizing the exposure of health care workers, and need for personal protective equipment and making it a viable option for population-scale testing. IMPORTANCE Methods for COVID-19 detection are necessary for public health efforts to monitor the spread of disease. Nasopharyngeal samples have been considered the best approach for COVID-19 testing. However, alternative samples like self-collected saliva offer advantages for population-scale screening. Meta-analyses of recent studies suggest that saliva is useful for detecting SARS-CoV-2; however, differences in disease prevalence, sample collection, and analysis methods still confound strong conclusions on the utility of saliva compared to nasopharyngeal samples. Here, we find that the sensitivity of saliva testing is related to both the timing of the sample collection relative to symptom onset and the disease stage. Importantly, several clinical vignettes in our cohorts highlight the challenges of medical decision making with limited knowledge of the associations between laboratory test data and the natural biology of infection.

India being the second largest producer and importer of coal in the world, coal mining industry is one of the major contributors for the economic growth of the country. However, mining activities generate tremendous amount of dust and fines leading to air pollution, which in turn causes several health and environment hazards. Hence it is important to control the dust being generated by effective dust suppression mechanisms. One of the methods employed is sprinkling or spraying of water, which is done manually at most places. However, this method can lead to wastage of water if it is overdone or not effective enough if underdone. Automating the sprinkling systems is a way to overcome this issue. The paper proposes an automatic dust suppression system which activates sprinklers on a need-to-basis depending on the concentration of airborne dust. The system monitors the amount of coal-dust present in the atmosphere and activates the sprinklers when the dust content exceeds permissible safe limits. This paper discusses the intelligent dust suppression system consisting of a scalable network of dust sensors and wireless sensor nodes that are used to measure the dust levels at various locations of the coal mines or coal handling areas and activate the sprinklers based on area wise dust concentration.

Widespread high-throughput testing for identification of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection by RT-PCR has been a foundation in the response to the coronavirus disease 2019 (COVID-19) pandemic. Quality assurance metrics for these RT-PCR tests are still evolving as testing is widely implemented. As testing increases, it is important to understand performance characteristics and the errors associated with these tests. Herein, we investigate a high-throughput, laboratory-developed SARS-CoV-2 RT-PCR assay to determine whether modeling can generate quality control metrics that identify false-positive (FP) results due to contamination. This study reviewed repeated clinical samples focusing on positive samples that test negative on re-extraction and PCR, likely representing false positives. To identify and predict false-positive samples, we constructed machine learning–derived models based on the extraction method used. These models identified variables associated with false-positive results across all methods, with sensitivities for predicting FP results ranging between 67% and 100%. Application of the models to all results predicted a total FP rate of 0.08% across all samples, or 2.3% of positive results, similar to reports for other RT-PCR tests for RNA viruses. These models can predict quality control parameters, enabling laboratories to generate decision trees that reduce interpretation errors, allow for automated reflex testing of samples with a high FP probability, improve workflow efficiency, and increase diagnostic accuracy for patient care.

The COVID-19 global pandemic is an unprecedented health emergency. Insufficient access to testing has hampered effective public health interventions and patient care management in a number of countries. Furthermore, the availability of regulatory-cleared reagents has challenged widespread implementation of testing. We rapidly developed a qRT-PCR SARS-CoV-2 detection assay using a 384-well format and tested its analytic performance across multiple nucleic acid extraction kits. Our data shows robust analytic accuracy on residual clinical biospecimens. Limit of detection sensitivity and specificity was confirmed with currently available commercial reagents. Our methods and results provide valuable information for other high-complexity laboratories seeking to develop effective, local, laboratory-developed procedures with high-throughput capability to detect SARS-CoV-2.

Clinical laboratories have adopted next generation sequencing (NGS) as a gold standard for the diagnosis of hereditary disorders because of its analytic accuracy, high throughput, and potential for cost-effectiveness. We describe the implementation of a single broad-based NGS sequencing assay to meet the genetic testing needs at the University of Minnesota. A single hybrid capture library preparation was used for each test ordered, data was informatically blinded to clinically-ordered genes, and identified variants were reviewed and classified by genetic counselors and molecular pathologists. We performed 2509 sequencing tests from August 2012 till December 2017. The diagnostic yield has remained steady at 25%, but the number of variants of uncertain significance (VUS) included in a patient report decreased over time with 50% of the patient reports including at least one VUS in 2012 and only 22% of the patient reports reporting a VUS in 2017 (p = .002). Among the various clinical specialties, the diagnostic yield was highest in dermatology (60% diagnostic yield) and ophthalmology (42% diagnostic yield) while the diagnostic yield was lowest in gastrointestinal diseases and pulmonary diseases (10% detection yield in both specialties). Deletion/duplication analysis was also implemented in a subset of panels ordered, with 9% of samples having a diagnostic finding using the deletion/duplication analysis. We have demonstrated the feasibility of this broad-based NGS platform to meet the needs of our academic institution by aggregating a sufficient sample volume from many individually rare tests and providing a flexible ordering for custom, patient-specific panels. Keywords: Next generation sequencing, Molecular diagnostics, Panel testing, Diagnostic yield, Variants of uncertain significance, Copy number variation

The purpose of this study was to determine the level of heterogeneity in high grade serous ovarian cancer (HGSOC) by analyzing RNA expression in single epithelial and cancer associated stromal cells. In addition, we explored the possibility of identifying subgroups based on pathway activation and pre-defined signatures from cancer stem cells and chemo-resistant cells.A fresh, HGSOC tumor specimen derived from ovary was enzymatically digested and depleted of immune infiltrating cells. RNA sequencing was performed on 92 single cells and 66 of these single cell datasets passed quality control checks. Sequences were analyzed using multiple bioinformatics tools, including clustering, principle components analysis, and geneset enrichment analysis to identify subgroups and activated pathways. Immunohistochemistry for ovarian cancer, stem cell and stromal markers was performed on adjacent tumor sections.Analysis of the gene expression patterns identified two major subsets of cells characterized by epithelial and stromal gene expression patterns. The epithelial group was characterized by proliferative genes including genes associated with oxidative phosphorylation and MYC activity, while the stromal group was characterized by increased expression of extracellular matrix (ECM) genes and genes associated with epithelial-to-mesenchymal transition (EMT). Neither group expressed a signature correlating with published chemo-resistant gene signatures, but many cells, predominantly in the stromal subgroup, expressed markers associated with cancer stem cells.Single cell sequencing provides a means of identifying subpopulations of cancer cells within a single patient. Single cell sequence analysis may prove to be critical for understanding the etiology, progression and drug resistance in ovarian cancer.

The Internet Engineering Task Force (IETF) has set up the 6TiSCH Working Group (WG) to focus on enabling IPv6 over the Time Slotted Channel Hoping (TSCH) mode of the IEEE 802.15.4-2015 standard. This paper describes our Contiki implementation of the 6TiSCH operation sublayer, 6top, which is used for dynamic scheduling of bandwidth between the neighboring sensor nodes and facilitate the On the Fly (OTF) scheduling.

The increasing number of collisions at intersections calls for a reliable and safe, real time collision warning system that can alert the drivers about a potential collision. Although many kinds of collision warning systems are available, the problems related to acceleration change and curved road scenarios are to be considered to improve the accuracy of the system. In the proposed paper, an efficient intersection collision avoidance system for straight and curved roads are considered. The objective of the project is to predict the location of the vehicle 10 seconds in future. The location, speed and heading information of a vehicle are available through a GPS device. Accelerometer provides the acceleration/deceleration information of the vehicles. Kalman filter with GPS and accelerometer as inputs can be used to predict the future location of the vehicle. These predicted locations are checked to be on the road or not using GIS map. If off road, the locations are projected onto the road with the help of the coordinate data. A warning message in the form of alarms or lights are generated when the distance between the predicted locations of the host vehicle and target vehicle falls below a threshold value. Position information are exchanged between the vehicles using Dedicated Short Range Communication. The MATLAB simulation results validate our proposed system by reducing the prediction error with the combination of GIS and kalman filter. False alarms are reduced considerably in the proposed system for collision avoidance.

Technological advances allow genomic and transcriptomic analyses to be conducted at the single-cell level. Analysis of gene expression and DNA sequence at this detailed level could lead to prognostic and predictive biomarkers as well as an enhanced understanding of cancer cell and stromal subpopulations including stem cells and chemotherapy-resistant populations. This knowledge will improve our ability to implement a “precision medicine” approach to the treatment of women with ovarian cancer. We have initiated a project to prospectively study the transcriptome and DNA exome of freshly isolated, high-grade serous ovarian cancer solid tumor samples at the single-cell level. To date we have enrolled 8 patients and have initiated single-cell whole-exome DNA sequencing, using the Fluidigm C1 chip, and single-cell whole-transcriptome RNA sequencing, using the 10X Genomics platform. For comparison of RNA sequencing technologies, we previously performed RNA sequencing of a patient’s sample using the Fluidigm C1 chip. In addition to sequencing the primary tumor samples, we are concurrently attempting to establish patient-derived xenografts (PDXs) and cell lines for each patient. We have successfully generated PDX mice from the first patient and are currently treating a subset of the mice with carboplatin/paclitaxel. Patients will be followed prospectively and information on their clinical response will be incorporated into the analysis. Analysis of the RNA sequencing results from the first patient using the Fluidigm C1 platform identified several distinct populations of cells characterized by gene expression patterns that correlate with specific signaling pathways and disease states. We could clearly differentiate stromal cells from cancer epithelial cells based on gene expression patterns. Furthermore, using known functional markers, we could define subsets within each population, including three subgroups of cancer cells and four subgroups of stromal cells. Based on their gene expression patterns, we could determine the frequency of cancer epithelial cells compared to cancer cells undergoing epithelial-to-mesenchymal transition, as well as activated and nonactivated fibroblasts and myofibroblasts. We were also able to identify rare cells expressing stem cell markers. Using the 10X Genomics platform, we have completed RNA sequencing of five patients (~370 million reads/patient). We quantified gene expression on an average of 8,707 cells/patient and 1,723 genes/cell. Using graph-based clustering combined with the t-Distributed Stochastic Neighbor Embedding technique for high dimensionality reduction, we have identified approximately 9 to 15 subsets of cells within these cancer samples based on an unbiased analysis of their gene expression patterns. Using bioinformatic tools, including CellRanger, Seurat, Ingenuity Pathway Analysis, and Gene Set Enrichment Analysis, we can define several of these subsets based on known functional markers, including subsets of immune cells, stromal cells, and cancer epithelial cells. We can estimate the frequency of each subset and can also subdivide the groups using cell-type specific markers, for example, by distinguishing macrophages from dendritic cells within the immune subset. We will present unpublished data, including RNA sequencing and DNA exome sequencing of single cells on the first 8 enrolled patients. In addition to sequencing the primary tumor samples, we plan on performing single-cell sequencing of matched platinum-resistant tumors generated by treating the PDX mice with carboplatin and paclitaxel. We will also correlate presence and percentage of cell subpopulations with clinical outcomes of the patients. Our long-term goal is to use single-cell data as a prognostic biomarker for chemotherapy resistance as well as a tool for predicting effective therapeutic options, including targeted therapy and immune therapy. Citation Format: Boris J. Winterhoff, Christopher R. Clark, Sidharth Ramesh, Mihir Shetty, Locke Uppendahl, Amit Kumra Mitra, Attila Sebe, Martina Bazzaro, Melissa A. Geller, Juan E. Abrahante, Klein Molly, Raffaele Hellweg, Sally Mullany, Kenneth Beckman, Jerry Daniel, Timothy K. Starr. Single-cell sequencing as a prognostic and predictive tool for ovarian cancer therapy. [abstract]. In: Proceedings of the AACR Conference: Addressing Critical Questions in Ovarian Cancer Research and Treatment; Oct 1-4, 2017; Pittsburgh, PA. Philadelphia (PA): AACR; Clin Cancer Res 2018;24(15_Suppl):Abstract nr B59.

BACKGROUND Aberrant methylation of promoter-associated cystosine-guanine (CpG) islands is an epigenetic modification of DNA frequently observed in adult patients with acute lymphocytic leukemia (ALL). This epigenetic modification has been associated with gene silencing, malignant transformation, and aging. It is not known whether there are epigenetic differences between pediatric patients and adult patients with ALL. METHODS To investigate the methylation characteristics of pediatric patients with ALL and to determine whether DNA methylation can explain prognostic or biologic differences between pediatric and adult patients, the authors analyzed the methylation status of 7 promoter-associated CpG islands in 16 pediatric patients with ALL and compared them with the methylation characteristics of a cohort of adult patients with ALL. The genes analyzed included the estrogen receptor gene (ER), multidrug resistance gene 1 (MDR1), p15, C-ABL, CD10, p16, and p73. RESULTS The mean methylation densities of ER, MDR1, CD10, p15, and C-ABL were 25.4%, 16.4%, 5.23%, 4.24%, and 4%, respectively. P16 was methylated in 11.7% of patients, and p73 was methylated in 17.6% of patients. One patient (6.2%) had methylation of 0 genes, 15 patients (93.7%) had methylation of ≥ 1 gene, and 4 patients (25%) had methylation of 3–4 genes. Methylation of all these genes was < 2% (or methylation specific polymerase chain reaction negative) in nonneoplastic tissues. A significant inverse correlation was observed between methylation of CD10 and CD10 expression. No differences were observed between the methylation characteristics of pediatric patients and adult patients. CONCLUSIONS The results indicate that DNA methylation is common in pediatric patients with ALL and that methylation of the genes studied does not account for prognostic differences between pediatric patients and adult patients with ALL. Cancer 2003;97:695–702. © 2003 American Cancer Society. DOI 10.1002/cncr.11090

We performed RNA sequencing of single cells derived from a high grade serous ovarian cancer (HGSOC) specimen to determine the extent of heterogeneity and to determine if it was feasible to identify cancer stem cells or gene expression signatures of chemo resistance. To perform RNA sequencing we enzymatically digested a fresh specimen from an HGSOC derived from the ovary. Immune cells were depleted by flow cytometry and single cell sequencing was performed using the Fluidigm C1 chip in tandem with Illumina HiSeq 2500 sequencing. Multiple bioinformatics tools were used to identify subgroups and activated pathways. Immunohistochemistry was performed on an adjacent tumor section to analyze markers of epithelium, stroma and stem cells. We found that gene expression patterns in single cells could be used to separate cells into stroma-like and epithelial-like groups. Gene set enrichment analysis identified proliferative genesets (oxidative phosphorylation and MYC targets) associated with the epithelial-like cells while epithelial-to-mesenchymal-transition (EMT) genes associated with the stroma-like cells. Neither group was significantly associated with genesets derived from chemo-resistant cells. Using known marker analysis, we could identify a small percentage of cells that expressed ovarian cancer stem cell markers and we could group cells into functional categories. Using four molecular subtypes established from large-scale bulk sequencing studies we show that single cells from a single patient are heterogeneous and each molecular subtype is represented. In conclusion, we show the feasibility of performing single cell sequencing on an epithelial ovarian cancer and reveal a heterogeneous population of cells. Expanding these findings to a larger cohort of patients could allow for identification of targetable sub-populations of cells that were previously undetectable in studies that use bulk samples to interrogate the transcriptome and genome of ovarian cancer patients. Citation Format: Timothy K. Starr, Boris Winterhoff, Makayla Maile, Kenneth Beckman, Jerry Daniel, Melissa Geller, Martina Bazzaro, Molly Klein, Raffaele Hellweg, Juan Abrahante, Amit K. Mitra, Atilla Sebe, Sally A. Mullany. Single cell sequencing of high grade serous ovarian cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 5402. doi:10.1158/1538-7445.AM2017-5402

Control and Instrumentation Group at the Centre for Development of Advanced Computing (CDAC), Thiruvananthapuram, has deployed an integrated control and monitoring system at a 210 MW thermal power plant in India. This system has all the features of an advanced Supervisory Control and Data Acquisition System (SCADA) and in addition has support for web-based SCADA. This paper outlines and discusses the various hardware and software components used in this SCADA system. How these basic components are combined in realizing various subsystems of this SCADA like the Prediction based Superheater and Reheater Steam Temperature Control, Genetic Algorithm based Pulverized Coal Flow Soft Sensor and Wireless Monitoring of Cooling Water Pump House are also detailed. Various results obtained with this SCADA system in place are pondered upon. The main objective of this paper is to bring out the salient features of this SCADA system against the backdrop of a critical and dynamic environment as in a 210 MW coal-fired thermal power plant which expects maximum efficiency and minimum downtime from a SCADA system overseeing it.

In this paper we present the design and development of an industrial grade wireless base station for industrial monitoring and control using wireless sensor networks. This paper highlights the need for a base station for wireless sensor networks, the challenges in deploying in industrial environments, the requirements of the base station in addressing those challenges and the design of an industrial grade wireless base station. Although a wireless sensor node is efficient enough to act as a base station in a wireless sensor network their processing power and communication capabilities are limited since they are designed to work at very low power. A large scale wireless sensor network usually consists of thousands of motes which have low-memory, low processing power, and limited communication capacity. To collaboratively monitor environment and obtain global information, centralized data processing by Base Station is necessary. A Base Station is a powerful device which has much higher processing power, more memory and much higher communication capabilities. The Industrial Wireless Base Station (iWase) built around the powerful AT91SAM9XE512 controller has higher processing power and supports Ethernet (wired) and GSM/GPRS (wireless) communication in addition to IEEE 802.15.4 radio communication with sensor nodes. iWase is provided with an ingress protection of IP65, which facilitates operation in harsh outdoor environments. iWase also provides excellent operation in the industrial environment with an operating temperature range of −40 to +85°C.