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SUMMARYState-of-the-art Ca2+ imaging studies that monitor large-scale neural dynamics can produce video datasets ~10 terabytes or more in total size, roughly comparable to ~10,000 Hollywood films. Processing such data volumes requires automated, general-purpose and fast computational methods for cell identification that are robust to a wide variety of noise sources. We introduce EXTRACT, an algorithm that is based on robust estimation theory and uses graphical processing units (GPUs) to extract neural dynamics in computing times up to 10-times faster than imaging durations. We validated EXTRACT on simulated and experimental data and processed 94 public datasets from the Allen Institute Brain Observatory in one day. Showcasing its superiority over past cell-sorting methods at removing noise contaminants, neural activity traces from EXTRACT allow more accurate decoding of animal behavior. Overall, EXTRACT provides neuroscientists with a powerful computational tool matched to the present challenges of neural Ca2+ imaging studies in behaving animals.

Natural language generation (NLG) is a critical component in conversational systems, owing to its role of formulating a correct and natural text response. Traditionally, NLG components have been deployed using template-based solutions. Although neural network solutions recently developed in the research community have been shown to provide several benefits, deployment of such model-based solutions has been challenging due to high latency, correctness issues, and high data needs. In this paper, we present approaches that have helped us deploy data-efficient neural solutions for NLG in conversational systems to production. We describe a family of sampling and modeling techniques to attain production quality with light-weight neural network models using only a fraction of the data that would be necessary otherwise, and show a thorough comparison between each. Our results show that domain complexity dictates the appropriate approach to achieve high data efficiency. Finally, we distill the lessons from our experimental findings into a list of best practices for production-level NLG model development, and present them in a brief runbook. Importantly, the end products of all of the techniques are small sequence-to-sequence models (2Mb) that we can reliably deploy in production., Accepted for publication in COLING 2020

Microgrids are becoming an important aspect of Smart Grid technology that can offer increased energy security, efficiency, and reliability. The design, development, and analysis of microgrid systems demand an effective modeling and simulation approach. This paper presents a streamlined, modelbased design approach that was used to design the microgrid system as part of the Department of Energy funded demonstration project called West Virginia Super Circuit. The proposed modeling and simulation plan includes econometric, load flow, short circuit, protection and coordination, and dynamic stability analyses with each serving a distinct purpose in the comprehensive microgrid design process. This paper focuses on the dynamic stability analysis that was used to study the dynamic behavior of the microgrid for vulnerable situations — e.g., faults, generation loss, and step increase/decrease in loads — in order to propose remedies to these vulnerabilities through design modifications. The simulation platform was developed using Matlab/Simpower and CYME software.

Human Papillomavirus (HPV) infection has been recognized as the main etiologic factor in the development of various cancers including penile, vulva, oropharyngeal and cervical cancers. In the development of cancer, persistent HPV infections induce E6 and E7 oncoproteins, which promote cell proliferation and carcinogenesis resulting elevated levels of host antibodies (e.g., anti-HPV16 E7 antibody). Currently, these cancers are clinically diagnosed using invasive biopsy-based tests, which are performed only in centralized labs by experienced clinical staff using time-consuming and expensive tools and technologies. Therefore, these obstacles constrain their utilization at primary care clinics and in remote settings, where resources are limited. Here, we present a rapid, inexpensive, reliable, easy-to-use, customized immunoassay platform following a microfluidic filter device to detect and quantify anti-HPV16 E7 antibodies from whole blood as a non-invasive assisting technology for diagnosis of HPV-associated malignancies, especially, at primary healthcare and remote settings. The platform can detect and quantify anti-HPV16 E7 antibody down to 2.87 ng/mL. We further validated our immunoassay in clinical patient samples and it provided significantly high responses as compared to control samples. Thus, it can be potentially implemented as a pretesting tool to identify high-risk groups for broad monitoring of HPV-associated cancers in resource-constrained settings.

PubMed: 30740513, Neutrophils have a critical role in regulating the immune system. The immune system is compromised during chemotherapy, increasing infection risks and imposing a need for regular monitoring of neutrophil counts. Although commercial hematology analyzers are currently used in clinical practice for neutrophil counts, they are only available in clinics and hospitals, use large blood volumes, and are not available at the point of care (POC). Additionally, phlebotomy and blood processing require trained personnel, where patients are often admitted to hospitals when the infections are at late stage due to lack of frequent monitoring. Here, a reliable method is presented that selectively captures and quantifies white blood cells (WBCs) and neutrophils from a finger prick volume of whole blood by integrating microfluidics with high-resolution imaging algorithms. The platform is compact, portable, and easy to use. It captures and quantifies WBCs and neutrophils with high efficiency (> 95%) and specificity (> 95%) with an overall 4.2% bias compared to standard testing. The results from a small cohort of patients (N = 11 healthy, N = 5 lung and kidney cancer) present a unique disposable cell counter, demonstrating the ability of this tool to monitor neutrophil and WBC counts within clinical or in resource-constrained environments.

This paper proposes a novel optimization technique using Linear Programming (LP) method to solve the optimal scheduling problem of Distributed Energy Resources (DERs) including Battery Energy Storage Systems (BESS). The proposed optimization technique has a dual objective function of economics and peak-shaving. Optimization of the BESS operation is a complex problem as it has special constraints such as Depth of Discharge (DOD) requirements, State of Charge (SOC) limitations, Charge and Discharge rates, etc. The proposed LP method would be a useful tool for distribution operators to optimally dispatch the distributed resources in their systems. The derived charge/discharge schedules of BESS ensure maximum energy arbitrage revenue. The proposed optimization technique is tested on a taxonomy feeder developed by Pacific Northwest National Laboratory (PNNL) with a fictitious microgrid that consists of DERs including a BESS. The optimization problem is modeled and solved using the Matlab optimization toolbox with industry standard load and electricity price data.

Determination of the optimum number and placement of sectionalizing switches in Distribution Automation (DA) feeders is a very challenging task. This is an important step in the feasibility process of DA projects and one has to consider the trade-off between reliability and economics to arrive at the answer. This paper presents a novel iterative approach based on the relative reduction in the normalized customer interruption costs for the optimal switch number and placement problem. An iterative algorithm is constructed which minimizes the total interruption costs at each step of the analysis to arrive at the solution. The proposed approach isolates the impacts of varying switch investment and customer interruption costs that are based on surveys. The proposed method is implemented on IEEE 34-bus and 123-bus test feeders. The mathematical model is developed in Matlab and the results show the computational robustness and efficacy of the solution.

The research was performed under the scope of the effects of Tahirova Agricultural Facility, which was named as Anadolu Etap Tahirova Farm after it was rented and which is located within the borders of Gönen district which is bound to city of Balıkesir in Southern Marmara section of Marmara region. In the research, apart from the field studies which were conducted in and around the facility, survey method (which is one of the quantitative research methods) was also used. With the help of the survey which was applied to 84 farmers from surrounding villages the benefits of the facility for the agricultural life of surrounding villages and the expectations of farmers were determined. Apart from this, the studies of the facility and the agricultural policies that will be followed and investments that will be made were determined within the frame of interview technique applied to Anadolu Etap Tahirova Farm representative after the rental of the facility. Before it was in the course of rental, it took on the tasks of fulfilling the demands of regional farmers by raising certificated seed and breeding animals and conveying the modern agricultural techniques to farmers. The facility decided to concentrate only on fruit growing after rental, Araştırma, Marmara Bölgesi Güney Marmara Bölümü'nde Balıkesir İli'ne bağlı Gönen ilçesi sınırları içinde bulunan Tahirova Tarım İşletmesi, kiralandıktan sonraki adıyla Anadolu Etap Tahirova Çiftliği'nin etkileri kapsamında yapılmıştır. Araştırmada, işletme ve çevresinde yürütülen arazi çalışmaları yanında nicel araştırma yöntemlerinden anket metodu kullanılmıştır. İşletme çevresi köylerde 84 çiftçiye uygulanan anket ile İşletmenin çevre köyler tarımsal yaşamına sağladığı yararlar ve çiftçilerin beklentileri tespit edilmiştir. Bunun yanı sıra işletmenin kiralanması sonrasında Anadolu Etap Tahirova Çiftliği yetkilisi ile görüşme tekniği çerçevesinde işletmenin çalışmaları ve takip edeceği tarım politikaları ve yatırımları belirlenmiştir. Tahirova Tarım İşletmesi kiralanma sürecine girmeden önce sertifikalı tohumluk ve damızlık hayvan yetiştirerek bulunduğu bölge çiftçisi taleplerini karşılama ve modern tarım tekniklerini çiftçiye aktarma gibi görevler üstlenmiştir. İşletme, kiralanması sonrasında sadece meyvecilik faaliyetlerine yoğunlaşma kararı almıştır

Fault Location, Isolation, and Restoration (FLIR) is the key concept of Distribution Automation (DA) in today's Smart Grid arena. A novel hybrid Multi Agent System (MAS) fault restoration solution has been proposed in this paper that uses both centralized and decentralized concepts for fault restoration on feeders with Microgrids. The main goal is to automatically reenergize un-faulted portions of feeders and restore power to as much as customers as possible while maintaining system constraints like voltage limits and loading levels. The proposed MAS concept is designed to be implemented on a couple of circuits in the territory of local utility, Mon Power, under the efforts of a DOE demonstration project named West Virginia Super Circuit (WVSC). The real distribution network is simulated in CYMDIST and Matlab/Simpower whereas the MAS is designed in Java Agent Development Framework (JADE). Simulation results show promising advantages of using proposed MAS solution for fault restoration using Microgrids.