Flash-Point prediction for binary partially miscible aqueous-organic mixtures

Flash point is the most important variable used to characterize fire and explosion hazard of liquids. Herein, partially miscible mixtures are presented within the context of liquid-liquid extraction processes and heterogeneous distillation processes. This paper describes development of a model for predicting the flash point of binary partially miscible mixtures of aqueous-organic system. To confirm the predictive efficiency of the derived flash points, the model was verified by comparing the predicted values with the experimental data for the studied mixtures: water + 1-butanol; water + 2-butanol; water + isobutanol; water + 1-pentanol; and, water + octane. Results reveal that immiscibility in the two liquid phases should not be ignored in the prediction of flash point. Overall, the predictive results of this proposed model describe the experimental data well when using the LLE and VLE parameters to estimate sequentially the span of two liquid phases and the flash point, respectively. Potential application for the model concerns the assessment of fire and explosion hazards, and the development of inherently safer designs for chemical processes containing binary partially miscible mixtures of aqueous-organic system

Presence of virus neutralizing antibodies in cerebral spinal fluid correlates with non-lethal rabies in dogs.

BACKGROUND: Rabies is traditionally considered a uniformly fatal disease after onset of clinical manifestations. However, increasing evidence indicates that non-lethal infection as well as recovery from flaccid paralysis and encephalitis occurs in laboratory animals as well as humans. METHODOLOGY/PRINCIPAL FINDINGS: Non-lethal rabies infection in dogs experimentally infected with wild type dog rabies virus (RABV, wt DRV-Mexico) correlates with the presence of high level of virus neutralizing antibodies (VNA) in the cerebral spinal fluid (CSF) and mild immune cell accumulation in the central nervous system (CNS). By contrast, dogs that succumbed to rabies showed only little or no VNA in the serum or in the CSF and severe inflammation in the CNS. Dogs vaccinated with a rabies vaccine showed no clinical signs of rabies and survived challenge with a lethal dose of wild-type DRV. VNA was detected in the serum, but not in the CSF of immunized dogs. Thus the presence of VNA is critical for inhibiting virus spread within the CNS and eventually clearing the virus from the CNS. CONCLUSIONS/SIGNIFICANCE: Non-lethal infection with wt RABV correlates with the presence of VNA in the CNS. Therefore production of VNA within the CNS or invasion of VNA from the periphery into the CNS via compromised blood-brain barrier is important for clearing the virus infection from CNS, thereby preventing an otherwise lethal rabies virus infection

Benchmarking of eight recurrent neural network variants for breath phase and adventitious sound detection on a self-developed open-access lung sound database-HF_Lung_V1

A reliable, remote, and continuous real-time respiratory sound monitor with automated respiratory sound analysis ability is urgently required in many clinical scenarios-such as in monitoring disease progression of coronavirus disease 2019-to replace conventional auscultation with a handheld stethoscope. However, a robust computerized respiratory sound analysis algorithm has not yet been validated in practical applications. In this study, we developed a lung sound database (HF_Lung_V1) comprising 9,765 audio files of lung sounds (duration of 15 s each), 34,095 inhalation labels, 18,349 exhalation labels, 13,883 continuous adventitious sound (CAS) labels (comprising 8,457 wheeze labels, 686 stridor labels, and 4,740 rhonchi labels), and 15,606 discontinuous adventitious sound labels (all crackles). We conducted benchmark tests for long short-term memory (LSTM), gated recurrent unit (GRU), bidirectional LSTM (BiLSTM), bidirectional GRU (BiGRU), convolutional neural network (CNN)-LSTM, CNN-GRU, CNN-BiLSTM, and CNN-BiGRU models for breath phase detection and adventitious sound detection. We also conducted a performance comparison between the LSTM-based and GRU-based models, between unidirectional and bidirectional models, and between models with and without a CNN. The results revealed that these models exhibited adequate performance in lung sound analysis. The GRU-based models outperformed, in terms of F1 scores and areas under the receiver operating characteristic curves, the LSTM-based models in most of the defined tasks. Furthermore, all bidirectional models outperformed their unidirectional counterparts. Finally, the addition of a CNN improved the accuracy of lung sound analysis, especially in the CAS detection tasks.Comment: 48 pages, 8 figures. To be submitte

Primary malignant tumors of the small intestine

From 1967 through 1987, 43 of primary malignant tumors of the small intestine were experienced at the First Department of Surgery, Nagasaki University Hospital and affiliated hospital, and clinically analysed. 1) Carcinoma, leiomyosarcoma and malignant lymphoma occupied one third in number. The preferable location of carcinomas and malignant lymphomas was lower part of the small bowel although that of leiomyosarcoma was upper part. 2) Diagnosis was mainly made by means of laparotomy which was carried out by clinical signs of obstruction or peritonitis. poor prognosis attributed to extension of a disease to nodes and liver. An early and accurate diagnosis of small bowel tumors is necessary for improving the survival rate

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

Retrospective evaluation of whole exome and genome mutation calls in 746 cancer samples

Funder: NCI U24CA211006Abstract: The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) curated consensus somatic mutation calls using whole exome sequencing (WES) and whole genome sequencing (WGS), respectively. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, which aggregated whole genome sequencing data from 2,658 cancers across 38 tumour types, we compare WES and WGS side-by-side from 746 TCGA samples, finding that ~80% of mutations overlap in covered exonic regions. We estimate that low variant allele fraction (VAF < 15%) and clonal heterogeneity contribute up to 68% of private WGS mutations and 71% of private WES mutations. We observe that ~30% of private WGS mutations trace to mutations identified by a single variant caller in WES consensus efforts. WGS captures both ~50% more variation in exonic regions and un-observed mutations in loci with variable GC-content. Together, our analysis highlights technological divergences between two reproducible somatic variant detection efforts

Design and Implementation of an Adaptive Decision Feedback qualizer for Wireless LAN

隨著通訊技術的成長以及區域網路的進步,資料傳輸的速率要求和通訊的品質變得愈來愈重要了。在數位通訊系統中有幾個重要因素影響著通訊速度及品質。其中一項重要的影響因素就是多重路徑效應所引起的選擇性衰落及碼間干擾。為了解決碼間干擾效應，一個有效且夠強大的選擇就是等化器。為求架構的精簡及線性穩定上的需求，我們使用最小方均根演算法去實現這等化器。由於收斂速度的要求，我們使用決策回授等化器架構來做設計。其中有四個主要部分，包含兩個有限脈衝濾波器即前饋式及回授式濾波器、係數調整器及決策器。此可適性回授等化器只用了兩個三階的有限脈衝濾波器。它可以4us裡訓練訊號至正確。在這次設計我們使用了台灣積體電路公司0.35 2P4M製程技術。此可適性回授等化器在3.3V的供應電壓下消耗必v為54.99mW。With the growth of the communication technology and Local Area Network (LAN), communication quality has become more and more important. In digital communication systems, there are many factors that affect the communication quality. One of the main factor decreasing system performances is inter-symbol interference (ISI) which come from the multi-path effect. To eliminate the ISI problem, we use an equalizer to solve this problem. In order to achieve linearity and stability, we use a Least-Mean-Square (LMS) algorithm to implement the equalizer. And due to the speed issue and convergence of recovery, we use a decision feedback equalizer architecture for this design. It consists of four main parts: two FIR filters, a Feed-Forward filter and a Feed-Back filter, and a block of update coefficients, and a slicer. We implement this decision feedback equalizer (DFE) by using two 3-tap FIR filters. It can also train the signal to correct in 44 chips (about 4μs). In this design, we use the TSMC CMOS 0.35μm 2P4M process technology. The DFE consumes 53.99 mW with a 3.3V supply voltage.TABLE OF CONTENTS ABSTRACT …………………………………………………………………… i LIST OF FIGURES …………………………………………………….....… v LIST OF TABLES …………………………………………………………..… ix CHAPTER 1. INTRODUCTION ……………………………………………… 1 1.1 Background ….………………..………………………………… 1 1.2 Organization of This Thesis .……………………………….…… 1 CHAPTER 2. OVERVIEW OF HIGH-SPEED WIRELESS LAN ……….. 3 2.1 A Brief History of Wireless Communication Systems ….……... 4 2.2 Digital Communication System ……………………………….. 5 2.3 Introduction to the IEEE 802.11b ……………………………… 6 2.4 Direct Spread Spectrum Technology and Modulation ………… 8 2.5 Multi-Path Modeling ……………………………………………… 12 2.5.1 IEEE 802.11 Channel Model ………………………… 12 2.5.2 Saleh and Valenzuela Channel Model ……........…… 13 CHAPTER 3. BASEBAND PROCESSOR ……………………………… 15 3.1 Introduction to Transmitter Architecture ………………………… 17 3.2 Modulation Technology ………………………………………… 19 3.2.1 Differential Binary Phase Shift Keying ……………… 19 3.2.2 Differential Quadrature Phase Shift Keying ………… 20 3.2.3 Complementary Coke Keying ………………………… 21 3.3 Introduction of Receiver Architecture ………………………… 24 CHAPTER 4. DECISION FEEDBACK EQUALIZER BASED ON LMS ALGORITHM …………………………………………..……………… 31 4.1 Algorithms for Adaptive Filter …………………………………… 32 4.2 Least-Mean-Square Algorithm ……………………………… 33 4.3 Introduction of Digital Filter …………………………………… 36 4.4 Adaptive Decision Feedback Equalizer ………………………… 40 4.4.1 Introduction to Adaptive Filter ………………………… 40 4.4.2 Structure of Decision Feedback Equalizer ……………… 43 CHAPTER 5. SIMULATION AND IMPLEMENTATION OF DFE ………… 47 5.1 Simulation of DFE ……………………………………………… 48 5.2 Implementation of DFE ………………………………………… 53 CHAPTER 6. CONCLUSION ……………………………………………… 5