Design of a Highly Portable Data Logging Embedded System for Naturalistic Motorcycle Study

According to Motorcycle Industrial Council (MIC), in USA the number of owned motorcycle increased during last few years and most likely will keep increasing. However, the number of the deadly crash accidents associated with motorcycles is on the rise. Although MIC doesn\u27t explain why the accident rate has increased, the unprotected motorcyclist gear can be one of the reasons. The most recent National Highway Traffic Safety Administration (NHTSA) annual report stated that its data analyses are based on their experiences and the best judgment is not based on solid scientific experiment [3]. Thus, building a framework for the data acquisition about the motorcyclist environment is a first step towards decreasing motorcyclist crashes. There are a few naturalistic motorcycle studies reported in the literature. The naturalistic motorcycle study also identifies the behaviors and environmental crash hazards. The primary objective of this thesis work is to design a highly portable data logging embedded system for naturalistic motorcycle study with capability of collecting many types of data such as images, speed, acceleration, time, location, distance approximation, etc. This thesis work is the first phase (of three phases) of a naturalistic motorcycle study project. The second phase is to optimize system area, form factor, and power consumption. The third phase will be concerned with aggressive low power design and energy harvesting. The proposed embedded system design is based on an Arduino microcontroller. A whole suite of Arduino based prototype boards, sensor boards, support software, and user forum is available. The system is high portable with capability to store up to eight (8) hours of text/image data during a one month study period. We have successfully designed and implemented the system and performed three trial runs. The data acquired has been validated and found to be accurate

Classification of nucleic acid amplification on ISFET arrays using spectrogram-based neural networks.

The COVID-19 pandemic has highlighted a significant research gap in the field of molecular diagnostics. This has brought forth the need for AI-based edge solutions that can provide quick diagnostic results whilst maintaining data privacy, security and high standards of sensitivity and specificity. This paper presents a novel proof-of-concept method to detect nucleic acid amplification using ISFET sensors and deep learning. This enables the detection of DNA and RNA on a low-cost and portable lab-on-chip platform for identifying infectious diseases and cancer biomarkers. We show that by using spectrograms to transform the signal to the time-frequency domain, image processing techniques can be applied to achieve the reliable classification of the detected chemical signals. Transformation to spectrograms is beneficial as it makes the data compatible with 2D convolutional neural networks and helps gain significant performance improvement over neural networks trained on the time domain data. The trained network achieves an accuracy of 84% with a size of 30kB making it suitable for deployment on edge devices. This facilitates a new wave of intelligent lab-on-chip platforms that combine microfluidics, CMOS-based chemical sensing arrays and AI-based edge solutions for more intelligent and rapid molecular diagnostics

Long-Short Term Memory for an Effective Short-Term Weather Forecasting Model Using Surface Weather Data

Part 7: Deep Learning - Convolutional ANNInternational audienceNumerical Weather Prediction (NWP) requires considerable computer power to solve complex mathematical equations to obtain a forecast based on current weather conditions. In this article, we propose a lightweight data-driven weather forecasting model by exploring state-of-the-art deep learning techniques based on Artificial Neural Network (ANN). Weather information is captured by time-series data and thus, we explore the latest Long Short-Term Memory (LSTM) layered model, which is a specialised form of Recurrent Neural Network (RNN) for weather prediction. The aim of this research is to develop and evaluate a short-term weather forecasting model using the LSTM and evaluate the accuracy compared to the well-established Weather Research and Forecasting (WRF) NWP model. The proposed deep model consists of stacked LSTM layers that uses surface weather parameters over a given period of time for weather forecasting. The model is experimented with different number of LSTM layers, optimisers, and learning rates and optimised for effective short-term weather predictions. Our experiment shows that the proposed lightweight model produces better results compared to the well-known and complex WRF model, demonstrating its potential for efficient and accurate short-term weather forecasting

Changes in CD4+ cells’ miRNA expression following exposure to HIV-1

Background: MiRNAs inhibit HIV-1 expression by either modulating host innate immunity or by directly interfering with viral mRNAs. Here, we investigated the miRNA profile that discriminates different classes of HIV-1 infected patients from multiple exposed uninfected individuals. Methods: The expression levels of 377 miRNAs were selectively analyzed in CD4+ cells isolated from whole blood of HIV-1 \ue9lite LTNP (\ue9LTNP), naive, and multiply exposed uninfected individuals (MEU). MiRNA extraction was performed by the mirVana miRNA Isolation Kit (Ambion) and their expression was subsequently examined by real-time PCR-based arrays. The expression of miRNAs was also determined in primary culture of CD4+T cells and monocyte-macrophages infected in vitro by R5 strains. Expression of Dicer and Drosha was evaluated by real-time PCR. Results: We only considered miRNAs that were expressed in the 70% of patients of at least one class and varied by at least 1 log10 from healthy controls. Out of 377 miRNAs, 26 were up-regulated, while 88 were down-regulated. Statistical analysis showed that 21 miRNAs significantly differentiated \ue9LTNP from MEU and 23 miRNAs distinguished naive from MEU, while only 1 (miR-155) discriminated \ue9LTNP from naive. By hierarchical clustering of the miRNAs according to patient class, \ue9LTNP clustered with naive whereas all MEU subjects grouped together. The Dicer and Drosha expression in the patient classes correlated with miRNA profile changes. Among miRNAs differentially expressed in patient classes, 32 were detected in in vitro infection model: the most of the up-regulated miRNAs were expressed in monocyte-macrophages, whereas the most of the down-regulated miRNAs were expressed in T lymphocytes. Conclusions: These findings support that miRNA profile could be the result not only of a productive infection, but also of the exposure to HIV products that leave a signature in immune cells. These data provide some intriguing issues relative to the development of HIV vaccines targeting viral proteins

TORC1 Determines Fab1 Lipid Kinase Function at Signaling Endosomes and Vacuoles

Organelles of the endomembrane system maintain their identity and integrity during growth or stress conditions by homeostatic mechanisms that regulate membrane flux and biogenesis. At lysosomes and endosomes, the Fab1 lipid kinase complex and the nutrient-regulated target of rapamycin complex 1 (TORC1) control the integrity of the endolysosomal homeostasis and cellular metabolism. Both complexes are functionally connected as Fab1-dependent generation of PI(3,5)P2 supports TORC1 activity. Here, we identify Fab1 as a target of TORC1 on signaling endosomes, which are distinct from multivesicular bodies, and provide mechanistic insight into their crosstalk. Accordingly, TORC1 can phosphorylate Fab1 proximal to its PI3P-interacting FYVE domain, which causes Fab1 to shift to signaling endosomes, where it generates PI(3,5)P2. This, in turn, regulates (1) vacuole morphology, (2) recruitment of TORC1 and the TORC1-regulatory Rag GTPase-containing EGO complex to signaling endosomes, and (3) TORC1 activity. Thus, our study unravels a regulatory feedback loop between TORC1 and the Fab1 complex that controls signaling at endolysosomes

Number of apoptotic cells as a prognostic marker in invasive breast cancer

Apoptosis plays an important role in tumorigenesis. Tumour growth is determined by the rate of cell proliferation and cell death. We counted the number of apoptotic cells in haematoxylin and eosin (H&E)-stained tumour sections in series of 172 grade I and II invasive breast cancers with long-term follow-up. The number of apoptotic cells in ten high-power fields were converted to the number of apoptotic cells per mm2to obtain the apoptotic index (AI). The AI showed a positive correlation to the mitotic activity index (MAI) (P = 0.0001), histological grade (P< 0.0001) and worse tumour differentiation. Patients with high AI showed shorter overall survival than patients with low AI in the total group as well as in the lymph node-positive group. Tumour size, MAI, lymph node status and AI were independent prognostic indicators in multivariate analysis. The AI was shown to be of additional prognostic value to the MAI in the total patients group as well as in the lymph node-positive group. The correlation between the AI and the MAI points to linked mechanisms of apoptosis and proliferation. Since apoptotic cells can be counted with good reproducibility in H&E-stained tumour sections, the AI may be used as an additional prognostic indicator in invasive breast cancer. © 2000 Cancer Research Campaig

The Rho GDI Rdi1 regulates Rho GTPases by distinct mechanisms

© 2008 by The American Society for Cell Biology. Under the License and Publishing Agreement, authors grant to the general public, effective two months after publication of (i.e.,. the appearance of) the edited manuscript in an online issue of MBoC, the nonexclusive right to copy, distribute, or display the manuscript subject to the terms of the Creative Commons–Noncommercial–Share Alike 3.0 Unported license (http://creativecommons.org/licenses/by-nc-sa/3.0).The small guanosine triphosphate (GTP)-binding proteins of the Rho family are implicated in various cell functions, including establishment and maintenance of cell polarity. Activity of Rho guanosine triphosphatases (GTPases) is not only regulated by guanine nucleotide exchange factors and GTPase-activating proteins but also by guanine nucleotide dissociation inhibitors (GDIs). These proteins have the ability to extract Rho proteins from membranes and keep them in an inactive cytosolic complex. Here, we show that Rdi1, the sole Rho GDI of the yeast Saccharomyces cerevisiae, contributes to pseudohyphal growth and mitotic exit. Rdi1 interacts only with Cdc42, Rho1, and Rho4, and it regulates these Rho GTPases by distinct mechanisms. Binding between Rdi1 and Cdc42 as well as Rho1 is modulated by the Cdc42 effector and p21-activated kinase Cla4. After membrane extraction mediated by Rdi1, Rho4 is degraded by a novel mechanism, which includes the glycogen synthase kinase 3β homologue Ygk3, vacuolar proteases, and the proteasome. Together, these results indicate that Rdi1 uses distinct modes of regulation for different Rho GTPases.Deutsche Forschungsgemeinschaf

The association between prehospital care and in-hospital treatment decisions in acute stroke: a cohort study.

BACKGROUND: Hospital prealerting in acute stroke improves the timeliness of subsequent treatment, but little is known about the impact of prehospital assessments on in-hospital care. OBJECTIVE: Examine the association between prehospital assessments and notification by emergency medical service staff on the subsequent acute stroke care pathway. METHODS: This was a cohort study of linked patient medical records. Consenting patients with a diagnosis of stroke were recruited from two urban hospitals. Data from patient medical records were extracted and entered into a Cox regression analysis to investigate the association between time to CT request and recording of onset time, stroke recognition (using the Face Arm Speech Test (FAST)) and sending of a prealert message. RESULTS: 151 patients (aged 71±15 years) travelled to hospital via ambulance and were eligible for this analysis. Time of symptom onset was recorded in 61 (40%) cases, the FAST test was positive in 114 (75%) and a prealert message was sent in 65 (44%). Following adjustment for confounding, patients who had time of onset recorded (HR 0.73, 95% CI 0.52 to 1.03), were FAST-positive (HR 0.54, 95% CI 0.37 to 0.80) or were prealerted (HR 0.26, 95% CI 0.18 to 0.38), were more likely to receive a timely CT request in hospital. CONCLUSIONS: This study highlights the importance of hospital prealerting, accurate stroke recognition, and recording of onset time. Those not recognised with stroke in a prehospital setting appear to be excluded from the possibility of rapid treatment in hospital, even before they have been seen by a specialist