Intestinal blood flow in patients with chronic heart failure: A link with bacterial growth, gastrointestinal symptoms, and cachexia

Background: Blood flow in the intestinal arteries is reduced in patients with stable heart failure (HF) and relates to gastrointestinal (GI) symptoms and cardiac cachexia. Objectives: The aims of this study were to measure arterial intestinal blood flow and assess its role in juxtamucosal bacterial growth, GI symptoms, and cachexia in patients with HF. Methods: A total of 65 patients and 25 controls were investigated. Twelve patients were cachectic. Intestinal blood flow and bowel wall thickness were measured using ultrasound. GI symptoms were documented. Bacteria in stool and juxtamucosal bacteria on biopsies taken during sigmoidoscopy were studied in a subgroup by fluorescence in situ hybridization. Serum lipopolysaccharide antibodies were measured. Results: Patients showed 30% to 43% reduced mean systolic blood flow in the superior and inferior mesenteric arteries and celiac trunk (CT) compared with controls (p < 0.007 for all). Cachectic patients had the lowest blood flow (p < 0.002). Lower blood flow in the superior mesenteric artery and CT was correlated with HF severity (p < 0.04 for all). Patients had more feelings of repletion, flatulence, intestinal murmurs, and burping (p < 0.04). Burping and nausea or vomiting were most severe in patients with cachexia (p < 0.05). Patients with lower CT blood flow had more abdominal discomfort and immunoglobulin A–antilipopolysaccharide (r = 0.76, p < 0.02). Antilipopolysaccharide response was correlated with increased growth of juxtamucosal but not stool bacteria. Patients with intestinal murmurs had greater bowel wall thickness of the sigmoid and descending colon, suggestive of edema contributing to GI symptoms (p < 0.05). In multivariate regression analysis, lower blood flow in the superior mesenteric artery, CT (p < 0.04), and inferior mesenteric artery (p = 0.056) was correlated with the presence of cardiac cachexia. Conclusions: Intestinal blood flow is reduced in patients with HF. This may contribute to juxtamucosal bacterial growth and GI symptoms in patients with advanced HF complicated by cachexia

COLTRANE: ConvolutiOnaL TRAjectory NEtwork for Deep Map Inference

The process of automatic generation of a road map from GPS trajectories, called map inference, remains a challenging task to perform on a geospatial data from a variety of domains as the majority of existing studies focus on road maps in cities. Inherently, existing algorithms are not guaranteed to work on unusual geospatial sites, such as an airport tarmac, pedestrianized paths and shortcuts, or animal migration routes, etc. Moreover, deep learning has not been explored well enough for such tasks. This paper introduces COLTRANE, ConvolutiOnaL TRAjectory NEtwork, a novel deep map inference framework which operates on GPS trajectories collected in various environments. This framework includes an Iterated Trajectory Mean Shift (ITMS) module to localize road centerlines, which copes with noisy GPS data points. Convolutional Neural Network trained on our novel trajectory descriptor is then introduced into our framework to detect and accurately classify junctions for refinement of the road maps. COLTRANE yields up to 37% improvement in F1 scores over existing methods on two distinct real-world datasets: city roads and airport tarmac.Comment: BuildSys 201

AI is a viable alternative to high throughput screening: a 318-target study

: High throughput screening (HTS) is routinely used to identify bioactive small molecules. This requires physical compounds, which limits coverage of accessible chemical space. Computational approaches combined with vast on-demand chemical libraries can access far greater chemical space, provided that the predictive accuracy is sufficient to identify useful molecules. Through the largest and most diverse virtual HTS campaign reported to date, comprising 318 individual projects, we demonstrate that our AtomNet® convolutional neural network successfully finds novel hits across every major therapeutic area and protein class. We address historical limitations of computational screening by demonstrating success for target proteins without known binders, high-quality X-ray crystal structures, or manual cherry-picking of compounds. We show that the molecules selected by the AtomNet® model are novel drug-like scaffolds rather than minor modifications to known bioactive compounds. Our empirical results suggest that computational methods can substantially replace HTS as the first step of small-molecule drug discovery

An improved search algorithm for optimal multiple-sequence alignment

Multiple sequence alignment (MSA) is a ubiquitous problem in computational biology. Although it is NP-hard to find an optimal solution for an arbitrary number of sequences, due to the importance of this problem researchers are trying to push the limits of exact algorithms further. Since MSA can be cast as a classical path finding problem, it is attracting a growing number of AI researchers interested in heuristic search algorithms as a challenge with actual practical relevance. In this paper, we first review two previous, complementary lines of research. Based on Hirschberg’s algorithm, Dynamic Programming needs O(kN k−1) space to store both the search frontier and the nodes needed to reconstruct the solution path, for k sequences of length N. Best first search, on the other hand, has the advantage of bounding the search space that has to be explored using a heuristic. However, it is necessary to maintain all explored nodes up to the final solution in order to prevent the search from re-expanding them at higher cost. Earlier approaches to reduce the Closed list are either incompatible with pruning methods for the Open list, or must retain at least the boundary of the Close

Towards Learning Adaptive Workload Maps

One approach to mitigate the risks of driver distraction is to build an in-vehicle service manager component that is aware of the attentional requirements of the current and of upcoming traffic situations. This component will rely on technologies for personalized driver workload prediction, based on an enhanced digital map, and/or on sensors for physiological and behavioral workload correlates. In this report, we address first results of our approach towards the following questions: • According to our experiments, what method is best for online/predictive workload estimation? • Which sensors are most suitable? • How do physiological measurements and subjective rating correlate? • Which proportion of workload can be statically predicted (based on map features alone)? • How do workload patterns differ between drivers? • How dynamic is workload (how long does an influence persist)? • Which factors (percentage) influence workload?

Predicting Driving Speed using Neural Networks

Predicting the speed of a vehicle for a future point on the road ahead is an important subtask of many advanced safety systems. We propose a two-stage neural net approach: first, (a small number of) characteristics of the overall speed distribution at a given location are estimated from road features alone. Second, for the case of a particular trip the speed at the current location, together with the speed characteristics output by the first stage for both the current and a future location, is used to predict the speed at the latter. Our approach parallels the previous empirical constant-percentile approach. It achieves nearly the same predictive accuracy, while at the same time reduces the data requirement to a feasible amount and additionally is able to generalize to extreme speeds not previously seen in the training set

External A*

In this paper we study External A*, a variant of the internal A* algorithm that employs external memory. The approach applies to implicit, undirected, unweighted state space problem graphs with consistent estimates. The complexity of the External algorithm is almost linear in external sorting time and accumulates to O(sort(|E|) + scan(|V I/Os, where V and E are the set of nodes and edges in the explored portion of the state space graph. Given that delayed duplicate elimination has to be performed, the established bound is I/O optimal. In di#erence to the internal design in the construction we exploit memory locality to allow block rather than random access. The algorithmic design refers to external shortest path search in explicit graphs and extends the strategy of delayed duplicate detection recently suggested for breadth-first search to best-first search. We conduct experiments with sliding-tile puzzle instances

Proceedings of the Eighteenth International Conference on Machine Learning, 2001, p. 577–584. Constrained K-means Clustering with Background Knowledge

Clustering is traditionally viewed as an unsupervised method for data analysis. However, in some cases information about the problem domain is available in addition to the data instances themselves. In this paper, we demonstrate how the popular k-means clustering algorithm can be profitably modified to make use of this information. In experiments with artificial constraints on six data sets, we observe improvements in clustering accuracy. We also apply this method to the real-world problem of automatically detecting road lanes from GPS data and observe dramatic increases in performance. 1

Data Mining and Knowledge Discovery, 9, 59–87, 2004 c ○ 2004 Kluwer Academic Publishers. Manufactured in The Netherlands. Mining GPS Traces for Map Refinement

Abstract. Despite the increasing popularity of route guidance systems, current digital maps are still inadequate for many advanced applications in automotive safety and convenience. Among the drawbacks are the insufficient accuracy of road geometry and the lack of fine-grained information, such as lane positions and intersection structure. In this paper, we present an approach to induce high-precision maps from traces of vehicles equipped with differential GPS receivers. Since the cost of these systems is rapidly decreasing and wireless technology is advancing to provide the communication infrastructure, we expect that in the next few years large amounts of car data will be available inexpensively. Our approach consists of successive processing steps: individual vehicle trajectories are divided into road segments and intersections; a road centerline is derived for each segment; lane positions are determined by clustering the perpendicular offsets from it; and the transitions of traces between segments are utilized in the generation of intersection models. This paper describes an approach to this complex data-mining task in a contiguous manner. Among the new contributions are a spatial clustering algorithm for inferring the connectivity structure, more powerful lane finding algorithms that are able to handle lane splits and merges, and an approach to inferring detailed intersection models