Active Object Search Exploiting Probabilistic Object–Object Relations

\u3cp\u3eThis paper proposes a probabilistic object-object relation based approach for an active object search. An important role of mobile robots will be to perform object-related tasks and active object search strategies deal with the non-trivial task of finding an object in unstructured and dynamically changing environments. This work builds further upon an existing approach exploiting probabilistic object-room relations for selecting the room in which an object is expected to be. Learnt object-object relations allow to search for objects inside a room via a chain of intermediate objects. Simulations have been performed to investigate the effect of the camera quality on path length and failure rate. Furthermore, a comparison is made with a benchmark algorithm based the same prior knowledge but without using a chain of intermediate objects. An experiment shows the potential of the proposed approach on the AMIGO robot.\u3c/p\u3

Prediabetes Is Associated With Structural Brain Abnormalities:The Maastricht Study

OBJECTIVE Structural brain abnormalities are key risk factors for brain diseases, such as dementia, stroke, and depression, in type 2 diabetes. It is unknown whether structural brain abnormalities already occur in prediabetes. Therefore, we investigated whether both prediabetes and type 2 diabetes are associated with lacunar infarcts (LIs), white matter hyperintensities (WMHs), cerebral microbleeds (CMBs), and brain atrophy. RESEARCH DESIGN and METHODS We used data from 2,228 participants (1,373 with normal glucose metabolism [NGM], 347 with prediabetes, and 508 with type 2 diabetes (oversampled); mean age 59.2 6 8.2 years; 48.3% women) of the Maastricht Study, a population-based cohort study. Diabetes status was determined with an oral glucose tolerance test. Brain imaging was performed with 3 Tesla MRI. Results were analyzed with multivariable logistic and linear regression analyses. RESULTS Prediabetes and type 2 diabetes were associated with the presence of LIs (odds ratio 1.61 [95% CI 0.98-2.63] and 1.67 [1.04-2.68], respectively; P trend = 0.027), larger WMH (b 0.07 log10-transformed mL [log-mL] [95% CI 0.00-0.15] and 0.21 log-mL [0.14-0.28], respectively; P trend <0.001), and smaller white matter volumes (b 24.0 mL [27.3 to 20.6] and 27.2 mL [210.4 to 24.0], respectively; P trend <0.001) compared with NGM. Prediabetes was not associated with gray matter volumes or the presence of CMBs. CONCLUSIONS Prediabetes is associated with structural brain abnormalities, with further deterioration in type 2 diabetes. These results indicate that, in middle-aged populations, structural brain abnormalities already occur in prediabetes, which may suggest that the treatment of early dysglycemia may contribute to the prevention of brain diseases

Development of prediction models for upper and lower respiratory and gastrointestinal tract infections using social network parameters in middle-aged and older persons -The Maastricht Study.

The ability to predict upper respiratory infections (URI), lower respiratory infections (LRI), and gastrointestinal tract infections (GI) in independently living older persons would greatly benefit population and individual health. Social network parameters have so far not been included in prediction models. Data were obtained from The Maastricht Study, a population-based cohort study (N = 3074, mean age (±s.d.) 59·8 ± 8·3, 48·8% women). We used multivariable logistic regression analysis to develop prediction models for self-reported symptomatic URI, LRI, and GI (past 2 months). We determined performance of the models by quantifying measures of discriminative ability and calibration. Overall, 953 individuals (31·0%) reported URI, 349 (11·4%) LRI, and 380 (12·4%) GI. The area under the curve was 64·7% (95% confidence interval (CI) 62·6-66·8%) for URI, 71·1% (95% CI 68·4-73·8) for LRI, and 64·2% (95% CI 61·3-67·1%) for GI. All models had good calibration (based on visual inspection of calibration plot, and Hosmer-Lemeshow goodness-of-fit test). Social network parameters were strong predictors for URI, LRI, and GI. Using social network parameters in prediction models for URI, LRI, and GI seems highly promising. Such parameters may be used as potential determinants that can be addressed in a practical intervention in older persons, or in a predictive tool to compute an individual's probability of infections

Using monocular SLAM for position-based visual control

Most of the existing visual control algorithms make use of pairwise geometry constraints to define the relation between the control input of the robot and the dynamics of tracked features in an image. The assumption is that feature correspondences will be available between the current image and the goal image, which do not always hold. For example, if a non-holonomic robot has to turn a large angle to reach a goal image, it most surely will lose track of this goal image. As another limitation, the use of pairwise geometry needs to change its underlying model depending on the geometric configuration of the current pair of frames –usually, from fundamental to homography matrix. In order to cope with these two limitations, this paper proposes the use of geometric maps from SLAM (Simultaneous Localization and Mapping) for visual control. A SLAM map summarizes feature tracks by registering them, along with the camera position, in a 3D common reference frame. Even when a feature goes out of sight and the track is lost; it remains registered in the 3D scene and hence usable for the control. Using a map also makes the control independent of the geometric configuration of two particular frames. As a proof of concept, we present two experiments: In the first one, a low-cost robot (build with Lego NXT and equipped with a 320x240 black-and-white camera) navigates around an object only relying on monocular information and even when the object comes out of view in the first frames of the input image sequence. In the second one, the robot is able to go back to an initial position without presenting degeneracies

Active object search exploiting probabilistic object-object relations

\u3cp\u3eThis paper proposes a probabilistic object-object relation based approach for an active object search. An important role of mobile robots will be to perform object-related tasks and active object search strategies deal with the non-trivial task of finding an object in unstructured and dynamically changing environments. This work builds further upon an existing approach exploiting probabilistic object-room relations for selecting the room in which an object is expected to be. Learnt object-object relations allow to search for objects inside a room via a chain of intermediate objects. Simulations have been performed to investigate the effect of the camera quality on path length and failure rate. Furthermore, a comparison is made with a benchmark algorithm based the same prior knowledge but without using a chain of intermediate objects. An experiment shows the potential of the proposed approach on the AMIGO robot.\u3c/p\u3

Development of prediction models for upper and lower respiratory and gastrointestinal tract infections using social network parameters in middle-aged and older persons: The Maastricht Study

The ability to predict upper respiratory infections (URI), lower respiratory infections (LRI), and gastrointestinal tract infections (GI) in independently living older persons would greatly benefit population and individual health. Social network parameters have so far not been included in prediction models. Data were obtained from The Maastricht Study, a population-based cohort study (N = 3074, mean age (+/- s.d.) 59.8 +/- 8.3, 48.8% women). We used multivariable logistic regression analysis to develop prediction models for self-reported symptomatic URI, LRI, and GI (past 2 months). We determined performance of the models by quantifying measures of discriminative ability and calibration. Overall, 953 individuals (31.0%) reported URI, 349 (11.4%) LRI, and 380 (12.4%) GI. The area under the curve was 64.7% (95% confidence interval (CI) 62.6-66.8%) for URI, 71.1% (95% CI 68.4-73.8) for LRI, and 64.2% (95% CI 61.3-67.1%) for GI. All models had good calibration (based on visual inspection of calibration plot, and Hosmer-Lemeshow goodness-of-fit test). Social network parameters were strong predictors for URI, LRI, and GI. Using social network parameters in prediction models for URI, LRI, and GI seems highly promising. Such parameters may be used as potential determinants that can be addressed in a practical intervention in older persons, or in a predictive tool to compute an individual's probability of infections

Communication with Older, Seriously Ill Patients

This article aims to provide more insight into effective communication with older people with serious illness and their surrogates/caregivers. To do so, if focusses on specific skills in three core functions of communication: (i) empathic behavior, (ii) information provision (iii) enabling decision making. Empathy is always important and can be provided using ‘NURSE’, meanwhile assuring a continued relationship. As older people’s abilities for information processing decreases, the importance of tailoring information is highlighted, using approaches as ‘SPIKES’ or ‘Ask-tell-Ask’ and providing chunks of information, while empathy also facilitates information processing. Eliciting patients’ goals of care, with or without the help of surrogates, is important to come to effective decision making. Surrogates need assistance when making decisions for patients while they also have their own caregiver needs for support and information. Lastly, several suggestions to ensure patients’ and caregivers’ needs are being met are made, with the aim to improve communication in challenging and uncertain times. (aut. ref.

Functional brain networks are altered in type 2 diabetes and pre-diabetes signs for compensation of cognitive decrements? - The Maastricht Study: signs for compensation of cognitive decrements? - The Maastricht Study

Type 2 diabetes is associated with cognitive decrements, accelerated cognitive decline, and increased risk for dementia. Participants with the metabolic syndrome, a major risk factor for diabetes, may display comparable cognitive decrements as seen in type 2 diabetes. Currently, the impact of (pre-)diabetes on cognition and the underlying organization of functional brain networks still remain to be elucidated. This study was designed to investigate whether functional brain networks are affected in type 2 diabetes and pre-diabetes. Forty-seven participants with diabetes, 47 pre-diabetic participants, and 45 control participants underwent detailed cognitive testing and 3-Tesla resting state functional MRI. Graph theoretical network analysis was performed to investigate alterations in functional cerebral networks. Participants with diabetes displayed altered network measures, characterized by a higher normalized cluster coefficient and higher local efficiency compared with controls. The network measures of the pre-diabetic participants fell between those of the diabetes and control participants. Lower processing speed was associated with shorter path length and higher global efficiency. To conclude, participants with type 2 diabetes have altered functional brain networks. This alteration is already apparent in the pre-diabetic stage to a somewhat lower level, hinting at functional reorganization of the cerebral networks as compensatory mechanism for cognitive decrements