The fission yeast FANCM ortholog directs non-crossover recombination during meiosis

Peer reviewedPostprin

Case report: Managing profound circulatory collapse post-atrial fibrillation ablation: a methodical approach

BACKGROUND: Circulatory collapse during/post-pulmonary vein (PV) isolation by cryo-balloon ablation is a Cardiology emergency that has multiple potential causes and requires a methodical investigative approach. Some of the complications that can arise include cardiac tamponade, bleeding/vascular injury, anaphylaxis, Addisonian crisis, acute pulmonary embolism, acute PV stenosis, oesophageal injury, and vagal reaction. CASE SUMMARY: Here, we present a case of a 76-year-old lady who developed profound circulatory collapse during an elective pulmonary vein isolation by cryo-balloon ablation for symptomatic paroxysmal atrial fibrillation (AF). Cardiac tamponade, bleeding/vascular injury, and other less common causes were excluded. She only responded transiently to fluid resuscitation and developed intermittent bradyarrhythmias and hypotension which responded to isoprenaline. She was discharged home at Day 3 post-AF ablation after remaining well and continued to do so at follow-up. DISCUSSION: Circulatory collapse during/post-PV cryo-balloon ablation is a Cardiology emergency that has multiple potential causes. The ganglionate plexi form part of the cardiac intrinsic autonomic nervous system (ANS) and are located close to the left atrial–PV junctions. The presence of vagal response has been observed to be a marker of ANS modulation although its significance on the long-term outcome post-ablation has yet to be elucidated. The true cause of our patient’s profound circulatory collapse is uncertain but a vital learning point in this case is the systematic exclusion of common and potentially life-threatening complications following AF ablation. A persistent vagal reaction secondary to PV cryo-balloon ablation can usually be managed with supportive medical therapy as demonstrated in our case

A Survey of Classification Methods

Classification may refer to categorization, the process in which ideas and objects are recognized, differentiated, and understood. There are many types of classification, researchers face a problem to choose a suitable method that give a good classification performance to solve their classification problems. In this paper, we present the basic classification techniques. Several major kinds of classification method including neural network, decision tree, Bayesian networks, support vector machine and k-nearest neighbor classifier. The goal of this survey is to provide a comprehensive review of the above different classification techniques

Linear and nonlinear arx model for intelligent pneumatic actuator systems

System modeling in describing the dynamic behavior of the system is very important and can be considered as a challenging problem in control systems engineering. This article presents the linear and nonlinear approaches using AutoRegressive with Exogenous Input (ARX) model structure for the modeling of position control of an Intelligent Pneumatic Actuator (IPA) system. The input and output data of the system were obtained from real-time experiment conducted while the linear and nonlinear mathematical models of the system were obtained using system identification (SI) technique. Best fit and Akaike’s criteria were used to validate the models. The results based on simulation reveals that nonlinear ARX (NARX) had the best performance for the modeling of position control of IPA system. The results show that nonlinear modeling is an effective way of analyzing and describing the dynamic behavior and characteristics of IPA system. This approach is also expected to be able to be applied to other systems. A future study exploring the execution of other model structures in demonstrating the position control of IPA system would be exceptionally intriguing

Effects of fuel ratio on performance and emission of diesel-compressed natural gas (CNG) dual fuel engine

Recent research breakthrough reveals that diesel-CNG dual fuel (DDF) combustion can potentially reduce exhaust emission of internal combustion engines. However, problem arises when knock phenomenon occurs producing high carbon monoxide (CO) and hydrocarbon (HC) emission due to uncontrolled blending ratio of diesel-CNG fuel on specific engine load. This study will determine the limit of dual fuel ratio before knock occurrence while analysing performance and exhaust emission of an engine operating with diesel and DDF fuel mode. A 2.5 litre 4-cylinder direct injection common-rail diesel engine was utilised as a test platform. The modelstested were 100% Diesel, 90% DDF, 80% DDF and 70% DDF, representing diesel to CNG mass ratio of 100:0, 90:10, 80:20 and 70:30 respectively. It was found that DDF engine performance was lower compared to diesel engine at 1500 rpm engine speed. At higher engine speed, the 70% DDF showed engine performance comparable to diesel engine. However, high HC emission with knock onset and a decrease of Nitrogen Oxide (NOX) emission were recorded. This study suggests the preferred limit of dual fuel ratio should not be lower than 70% DDF which will be able to operate at high engine speed without the occurrence of knock and poor exhaust emission

Physico-mechanical properties and bacterial adhesion of resin composite CAD/CAM blocks : an in-vitro study

The recent introduction of CAD/CAM technology has been strongly impacting the workflow in dental clinics and labs. Among the used CAD/CAM materials, resin composite CAD/CAM blocks offer several advantages. The aim of this study was to evaluate the physic

The L3Pilot Common Data Format - Enabling Efficient Automated Driving Data Analysis

Analyzing road-test data is important for developing automated vehicles. L3Pilot is a European pilot project on level 3 automation, including 34 partners among manufacturers, suppliers and research institutions. Targeting around 100 cars and 1000 test subjects, the project will generate large amounts of data. We present a data format, allowing efficient data collection, handling and analysis by multiple organizations. A project of the scope of L3Pilot involves various challenges. Data come from a multitude of heterogeneous sources and are processed by a variety of tools. Recorded data span all data types generated in various vehicular sensors/systems and are enriched with external data sources. Videos supplement time-series data as external files. Derived measures and performance indicators \u2013 required to answer research questions about effectiveness of automated driving \u2013 are processed by analysis partners and included for each test session. As a file format, we chose HDF5, which offers a data model and software libraries for storing and managing data. HDF5 is designed for flexible and efficient I/O and for high volume and complex data. The usage of different computing environments for specific tasks is facilitated by the portability that comes with the format. Portability is also important for exploiting the rising potential within artificial intelligence (e.g. automatic scene detection and video annotation). Based on lessons learned from past field tests, we defined a general frame for the common data format that is aligned with the data processing steps of FESTA \u201cV\u201d evaluation methodology. The definitions include representation of the source signals and a hierarchical structure for including multiple datasets that are gradually supplemented (post-processed or annotated) during the various analysis steps. By using the HDF5 format, analysis partners have the freedom to exploit their familiar tools: MATLAB, Java, Python, R, etc. First comparisons between time-series data in previous projects (e.g. AdaptIVe) and the proposed data format show a reduction in storage size of around 80 %, without losses in performance. Much of that is due to efficient internal compression and structuring of data. Considering the amount of objective data involved in automated driving, this leads to a great benefit, in terms of usability. This paper presents a compact, portable, and extensible format aimed at handling extremely large amounts of field test data collected in automated driving pilots. As a harmonized format between tens of organizations performing tests in the L3Pilot project, the proposed format has the potential to promote data sharing as well as development of common tools and gain popularity for use in other projects. The format is designed to allow efficient storing of data and its iterative processing with analysis and evaluation tools. The format also considers the requirements of AI tools supporting neural network training and use