The Voluntary Adjustment of Railroad Obligations

Automatic memory management techniques eliminate many programming errors that are both hard to find and to correct. However, these techniques are not yet used in embedded systems with hard realtime applications. The reason is that current methods for automatic memory management have a number of drawbacks. The two major ones are: (1) not being able to always guarantee short real-time deadlines and (2) using large amounts of extra memory. Memory is usually a scarce resource in embedded applications. In this paper we present a new technique, Real-Time Reference Counting (RTRC) that overcomes the current problems and makes automatic memory management attractive also for hard real-time applications. The main contribution of RTRC is that often all memory can be used to store live objects. This should be compared to a memory overhead of about 500% for garbage collectors based on copying techniques and about 50% for garbage collectors based on mark-and-sweep techniques

Do robotic and non-robotic arm movement training drive motor recovery after stroke by a common neural mechanism? Experimental evidence and a computational model.

Different dose-matched, upper extremity rehabilitation training techniques, including robotic and non-robotic techniques, can result in similar improvement in movement ability after stroke, suggesting they may elicit a common drive for recovery. Here we report experimental results that support the hypothesis of a common drive, and develop a computational model of a putative neural mechanism for the common drive. We compared weekly motor control recovery during robotic and unassisted movement training techniques after chronic stroke (n = 27), as assessed with quantitative measures of strength, speed, and coordination. The results showed that recovery in both groups followed an exponential time course with a time constant of about 4-5 weeks. Despite the greater range and speed of movement practiced by the robot group, motor recovery was very similar between the groups. The premise of the computational model is that improvements in motor control are caused by improvements in the ability to activate spared portions of the damaged corticospinal system, as learned by a biologically plausible search algorithm. Robot-assisted and unassisted training would in theory equally drive this search process

Artificial Intelligence

Contains research objectives and reports on five research projects.Computation Center, M.I.T

Differential expression of selected histone modifier genes in human solid cancers.

BACKGROUND: Post-translational modification of histones resulting in chromatin remodelling plays a key role in the regulation of gene expression. Here we report characteristic patterns of expression of 12 members of 3 classes of chromatin modifier genes in 6 different cancer types: histone acetyltransferases (HATs)- EP300, CREBBP, and PCAF; histone deacetylases (HDACs)- HDAC1, HDAC2, HDAC4, HDAC5, HDAC7A, and SIRT1; and histone methyltransferases (HMTs)- SUV39H1and SUV39H2. Expression of each gene in 225 samples (135 primary tumours, 47 cancer cell lines, and 43 normal tissues) was analysedby QRT-PCR, normalized with 8 housekeeping genes, and given as a ratio by comparison with a universal reference RNA. RESULTS: This involved a total of 13,000 PCR assays allowing for rigorous analysis by fitting a linear regression model to the data. Mutation analysis of HDAC1, HDAC2, SUV39H1, and SUV39H2 revealed only two out of 181 cancer samples (both cell lines) with significant coding-sequence alterations. Supervised analysis and Independent Component Analysis showed that expression of many of these genes was able to discriminate tumour samples from their normal counterparts. Clustering based on the normalized expression ratios of the 12 genes also showed that most samples were grouped according to tissue type. Using a linear discriminant classifier and internal cross-validation revealed that with as few as 5 of the 12 genes, SIRT1, CREBBP, HDAC7A, HDAC5 and PCAF, most samples were correctly assigned. CONCLUSION: The expression patterns of HATs, HDACs, and HMTs suggest these genes are important in neoplastic transformation and have characteristic patterns of expression depending on tissue of origin, with implications for potential clinical application.RIGHTS : This article is licensed under the BioMed Central licence at http://www.biomedcentral.com/about/license which is similar to the 'Creative Commons Attribution Licence'. In brief you may : copy, distribute, and display the work; make derivative works; or make commercial use of the work - under the following conditions: the original author must be given credit; for any reuse or distribution, it must be made clear to others what the license terms of this work are

Increasing cardiovascular medication adherence:A Medical Research Council complex mHealth intervention mixed-methods feasibility study to inform global practice

AimsTo evaluate a mHealth intervention to increase medication adherence among Iranian coronary heart disease patients.DesignQuantitative-dominant mixed-methods study.Data SourceIranian coronary heart disease patients’ responses and most recent clinical documents as well as responses from Iranian cardiac nurses who participated in this study.MethodsThe study was conducted between September 2015–April 2016 drawing on the Medical Research Council's Framework. Phase one comprised of a patients’ survey and focus groups with cardiac nurses. The automated short message service reminder was piloted in phase two. We recruited 78 patients and randomized to receive either 12-week daily reminders or usual care. The primary outcome was the effect on medication adherence; secondary outcomes were self-efficacy, ejection fraction, functional capacity, readmission rate and quality of life.ResultsFeasibility was evidenced by high ownership of mobile phones and high interest in receiving reminders. Participants in the intervention group showed significantly higher medication adherence compared with the control group.ConclusionThe mHealth intervention was well accepted and feasible with early evidence of effectiveness that needs to be confirmed in a fully powered future randomized clinical trial

Gray matter volume reduction in rostral middle frontal gyrus in patients with chronic schizophrenia

The dorsolateral prefrontal cortex (DLPFC) is a brain region that has figured prominently in studies of schizophrenia and working memory, yet the exact neuroanatomical localization of this brain region remains to be defined. DLPFC primarily involves the superior frontal gyrus and middle frontal gyrus (MFG). The latter, however is not a single neuroanatomical entity but instead is comprised of rostral (anterior, middle, and posterior) and caudal regions. In this study we used structural MRI to develop a method for parcellating MFG into its component parts. We focused on this region of DLPFC because it includes BA46, a region involved in working memory. We evaluated volume differences in MFG in 20 patients with chronic schizophrenia and 20 healthy controls. Mid-rostral MFG (MR-MFG) was delineated within the rostral MFG using anterior and posterior neuroanatomical landmarks derived from cytoarchitectonic definitions of BA46. Gray matter volumes of MR-MFG were then compared between groups, and a significant reduction in gray matter volume was observed (p b 0.008), but not in other areas of MFG (i.e., anterior or posterior rostral MFG, or caudal regions of MFG). Our results demonstrate that volumetric alterations in MFG gray matter are localized exclusively to MR-MFG. 3D reconstructions of the cortical surface made it possible to follow MFG into its anterior part, where other approaches have failed. This method of parcellation offers a more precise way of measuring MR-MFG that will likely be important in further documentation of DLPFC anomalies in schizophrenia

Classification of Foetal Distress and Hypoxia Using Machine Learning Approaches

© 2018, Springer International Publishing AG, part of Springer Nature. Foetal distress and hypoxia (oxygen deprivation) is considered as a serious condition and one of the main factors for caesarean section in the obstetrics and Gynecology department. It is the third most common cause of death in new-born babies. Many foetuses that experienced some sort of hypoxic effects can develop series risks including damage to the cells of the central nervous system that may lead to life-long disability (cerebral palsy) or even death. Continuous labour monitoring is essential to observe the foetal well being. Foetal surveillance by monitoring the foetal heart rate with a cardiotocography is widely used. Despite the indication of normal results, these results are not reassuring, and a small proportion of these foetuses are actually hypoxic. In this paper, machine-learning algorithms are utilized to classify foetuses which are experiencing oxygen deprivation using PH value (a measure of hydrogen ion concentration of blood used to specify the acidity or alkalinity) and Base Deficit of extra cellular fluid level (a measure of the total concentration of blood buffer base that indicates the metabolic acidosis or compensated respiratory alkalosis) as indicators of respiratory and metabolic acidosis, respectively, using open source partum clinical data obtained from Physionet. Six well know machine learning classifier models are utilised in our experiments for the evaluation; each model was presented with a set of selected features derived from the clinical data. Classifier’s evaluation is performed using the receiver operating characteristic curve analysis, area under the curve plots, as well as the confusion matrix. Our simulation results indicate that machine-learning algorithms provide viable methods that could delivery improvements over conventional analysis