Representing numerosity through vibration patterns

It can be useful to display information about numerosity haptically. For instance, to display the time of day or distances when visual or auditory feedback is not possible or desirable. Here, we investigated the possibility of displaying numerosity information by means of a sequence of vibration pulses. From previous studies on numerosity perception in vision, haptics and audition it is known that numerosity judgment can be facilitated by grouping. Therefore, we investigated whether perception of the number of vibration pulses in a sequence can be facilitated by temporally grouping the pulses. We found that indeed temporal grouping can lead to considerably smaller errors and lower error rates indicating that this facilitated the task, but only when participants knew in advance whether the pulses would be temporally grouped. When grouped and ungrouped series of pulses were presented randomly interleaved, there was no difference in performance. This means that temporally grouping vibration sequences can allow the sequence to be displayed at a faster rate while it remains possible to perceive the number of vibration pulses accurately if the users is aware of the temporal grouping

Using multiple classifiers for predicting the risk of endovascular aortic aneurysm repair re-intervention through hybrid feature selection.

Feature selection is essential in medical area; however, its process becomes complicated with the presence of censoring which is the unique character of survival analysis. Most survival feature selection methods are based on Cox's proportional hazard model, though machine learning classifiers are preferred. They are less employed in survival analysis due to censoring which prevents them from directly being used to survival data. Among the few work that employed machine learning classifiers, partial logistic artificial neural network with auto-relevance determination is a well-known method that deals with censoring and perform feature selection for survival data. However, it depends on data replication to handle censoring which leads to unbalanced and biased prediction results especially in highly censored data. Other methods cannot deal with high censoring. Therefore, in this article, a new hybrid feature selection method is proposed which presents a solution to high level censoring. It combines support vector machine, neural network, and K-nearest neighbor classifiers using simple majority voting and a new weighted majority voting method based on survival metric to construct a multiple classifier system. The new hybrid feature selection process uses multiple classifier system as a wrapper method and merges it with iterated feature ranking filter method to further reduce features. Two endovascular aortic repair datasets containing 91% censored patients collected from two centers were used to construct a multicenter study to evaluate the performance of the proposed approach. The results showed the proposed technique outperformed individual classifiers and variable selection methods based on Cox's model such as Akaike and Bayesian information criterions and least absolute shrinkage and selector operator in p values of the log-rank test, sensitivity, and concordance index. This indicates that the proposed classifier is more powerful in correctly predicting the risk of re-intervention enabling doctor in selecting patients' future follow-up plan

Predicting the duration of reach-to-grasp movements to objects with asymmetric contact surfaces.

The duration of reach-to-grasp movements is influenced by the size of the contact surfaces, such that grasping objects with smaller contact surface areas takes longer. But what is the influence of asymmetric contact surfaces? In Experiment 1a, participants reached-to-lift wooden blocks off a table top, with the contact locations for the thumb and index finger varying in surface size. The time taken to lift the block was driven primarily by the thumb contact surface, which showed a larger effect size for the dependent variable of movement duration than the index finger's contact surface. In Experiment 1b participants reached-to-grasp (but not lift) the blocks. The same effect was found with duration being largely driven by contact surface size for the thumb. Experiment 2 tested whether this finding generalised to movements towards conical frusta grasped in a different plane mounted off the table top. Experiment 2 showed that movement duration again was dictated primarily by the size of the thumb's contact surface. The thumb contact surface was the visible surface in experiments 1 and 2 so Experiment 3 explored grasping when the index finger's contact surface was visible (participants grasped the frusta with the index finger at the top). An interaction between thumb and finger surface size was now found to determine movement duration. These findings provide the first empirical report of the impact of asymmetric contact surfaces on prehension, and may have implications for scientists who wish to model reach-to-grasp behaviours

Monoclonal antibody Py recognizes neurofilament heavy chain and is a selective marker for large diameter neurons in the brain Brain Structure and Function

Almost 30 years ago, the monoclonal antibody Py was developed to detect pyramidal neurons in the CA3 region of the rat hippocampus. The utility of this antibody quickly expanded when several groups discovered that it could be used to identify very specific populations of neurons in the normal, developing, and diseased or injured central nervous system. Despite this body of literature, the identity of the antigen that the Py antibody recognizes remained elusive. Here, immunoprecipitation experiments from the adult rat cortex identified the Py antigen as neurofilament heavy chain (NF-H). Double immunolabeling of sections through the rat brain using Py and NF-H antibodies confirmed the identity of the Py antigen, and reveal that Py/NF-H+ neurons appear to share the feature of being particularly large in diameter. These include the neurons of the gigantocellular reticular formation, pyramidal neurons of layers II/III and V of the cortex, cerebellar Purkinje neurons as well as CA3 pyramidal neurons. Taken together, this finding gives clarity to past work using the monoclonal Py antibody, and immediately expands our understanding of the importance of NF-H in neural development, functioning, and disease

The axial ratio of hcp iron at the conditions of the Earth's inner core

We present ab initio calculations of the high-temperature axial c/a ratio of hexagonal-close-packed (hcp) iron at Earth's core pressures, in order to help interpret the observed seismic anisotropy of the inner core. The calculations are based on density functional theory, which is known to predict the properties of high-pressure iron with good accuracy. The temperature dependence of c/a is determined by minimising the Helmholtz free energy at fixed volume and temperature, with thermal contributions due to lattice vibrations calculated using harmonic theory. Anharmonic corrections to the harmonic predictions are estimated from calculations of the thermal average stress obtained from ab initio molecular dynamics simulations of hcp iron at the conditions of the inner core. We find a very gradual increase of axial ratio with temperature. This increase is much smaller than found in earlier calculations, but is in reasonable agreement with recent high-pressure, high-temperature diffraction measurements. This result casts doubt on an earlier interpretation of the seismic anisotropy of the inner core

Liquid-gas phase transition in hot asymmetric nuclear matter with density-dependent relativistic mean-field models

The liquid-gas phase transition in hot asymmetric nuclear matter is studied within density-dependent relativistic mean-field models where the density dependence is introduced according to the Brown-Rho scaling and constrained by available data at low densities and empirical properties of nuclear matter. The critical temperature of the liquid-gas phase transition is obtained to be 15.7 MeV in symmetric nuclear matter falling on the lower edge of the small experimental error bars. In hot asymmetric matter, the boundary of the phase-coexistence region is found to be sensitive to the density dependence of the symmetry energy. The critical pressure and the area of phase-coexistence region increases clearly with the softening of the symmetry energy. The critical temperature of hot asymmetric matter separating the gas phase from the LG coexistence phase is found to be higher for the softer symmetry energy.Comment: To be published in Phys. Lett.

Circulating vitamin D, vitamin D-related genetic variation, and risk of fatal prostate cancer in the National Cancer Institute Breast and Prostate Cancer Cohort Consortium

BACKGROUND: Evidence from experimental animal and cell line studies supports a beneficial role for vitamin D in prostate cancer (PCa). Although the results from human studies have been mainly null for overall PCa risk, there may be a benefit for survival. This study assessed the associations of circulating 25-hydroxyvitamin D (25(OH)D) and common variations in key vitamin D-related genes with fatal PCa. METHODS: In a large cohort consortium, 518 fatal cases and 2986 controls with 25(OH)D data were identified. Genotyping information for 91 single-nucleotide polymorphisms (SNPs) in 7 vitamin D-related genes (vitamin D receptor, group-specific component, cytochrome P450 27A1 [CYP27A1], CYP27B1, CYP24A1, CYP2R1, and retinoid X receptor α) was available for 496 fatal cases and 3577 controls. Unconditional logistic regression was used to calculate odds ratios (ORs) and 95% confidence intervals (CIs) for the associations of 25(OH)D and SNPs with fatal PCa. The study also tested for 25(OH)D-SNP interactions among 264 fatal cases and 1169 controls. RESULTS: No statistically significant relationship was observed between 25(OH)D and fatal PCa (OR for extreme quartiles, 0.86; 95% CI, 0.65-1.14; P for trend = .22) or the main effects of the SNPs and fatal PCa. There was evidence suggesting that associations of several SNPs, including 5 related to circulating 25(OH)D, with fatal PCa were modified by 25(OH)D. Individually, these associations did not remain significant after multiple testing; however, the P value for the set-based test for CYP2R1 was .002. CONCLUSIONS: Statistically significant associations were not observed for either 25(OH)D or vitamin D-related SNPs with fatal PCa. The effect modification of 25(OH)D associations by biologically plausible genetic variation may deserve further exploration

Differentiating between Hemorrhagic Infarct and Parenchymal Intracerebral Hemorrhage

Differentiating hemorrhagic infarct from parenchymal intracerebral hemorrhage can be difficult. The immediate and long-term management of the two conditions are different and hence the importance of accurate diagnosis. Using a series of intracerebral hemorrhage cases presented to our stroke unit, we aim to highlight the clues that may be helpful in distinguishing the two entities. The main clue to the presence of hemorrhagic infarct on computed tomography scan is the topographic distribution of the stroke. Additional imaging modalities such as computed tomography angiogram, perfusion, and magnetic resonance imaging may provide additional information in differentiating hemorrhagic infarct from primary hemorrhages

Feature selection through validation and un-censoring of endovascular repair survival data for predicting the risk of re-intervention

Background: Feature selection (FS) process is essential in the medical area as it reduces the effort and time needed for physicians to measure unnecessary features. Choosing useful variables is a difficult task with the presence of censoring which is the unique characteristic in survival analysis. Most survival FS methods depend on Cox's proportional hazard model; however, machine learning techniques (MLT) are preferred but not commonly used due to censoring. Techniques that have been proposed to adopt MLT to perform FS with survival data cannot be used with the high level of censoring. The researcher's previous publications proposed a technique to deal with the high level of censoring. It also used existing FS techniques to reduce dataset dimension. However, in this paper a new FS technique was proposed and combined with feature transformation and the proposed uncensoring approaches to select a reduced set of features and produce a stable predictive model. Methods: In this paper, a FS technique based on artificial neural network (ANN) MLT is proposed to deal with highly censored Endovascular Aortic Repair (EVAR). Survival data EVAR datasets were collected during 2004 to 2010 from two vascular centers in order to produce a final stable model. They contain almost 91% of censored patients. The proposed approach used a wrapper FS method with ANN to select a reduced subset of features that predict the risk of EVAR re-intervention after 5 years to patients from two different centers located in the United Kingdom, to allow it to be potentially applied to cross-centers predictions. The proposed model is compared with the two popular FS techniques; Akaike and Bayesian information criteria (AIC, BIC) that are used with Cox's model. Results: The final model outperforms other methods in distinguishing the high and low risk groups; as they both have concordance index and estimated AUC better than the Cox's model based on AIC, BIC, Lasso, and SCAD approaches. These models have p-values lower than 0.05, meaning that patients with different risk groups can be separated significantly and those who would need re-intervention can be correctly predicted. Conclusion: The proposed approach will save time and effort made by physicians to collect unnecessary variables. The final reduced model was able to predict the long-term risk of aortic complications after EVAR. This predictive model can help clinicians decide patients' future observation plan