Human wetness perception of fabrics under dynamic skin contact

This experiment studied textile (surface texture, thickness) and non-textile (local skin temperature changes, stickiness sensation and fabric-to-skin pressure) parameters affecting skin wetness perception under dynamic interactions. Changes in fabric texture sensation between WET and DRY states and their effect on pleasantness were also studied. The surface texture of eight fabric samples, selected for their different structures, was determined from surface roughness measurements using the Kawabata Evaluation System. Sixteen participants assessed fabric wetness perception, at high pressure and low pressure conditions, stickiness, texture and pleasantness sensation on the ventral forearm. Differences in wetness perception (p < 0.05) were not determined by texture properties and/or texture sensation. Stickiness sensation and local skin temperature drop were determined as predictors of wetness perception (r2 = 0.89), and although thickness did not correlate with wetness perception directly, when combined with stickiness sensation it provided a similar predictive power (r2 = 0.86). Greater (p < 0.05) wetness perception responses at high pressure were observed compared with low pressure. Texture sensation affected pleasantness in DRY (r2 = 0.89) and WET (r2 = 0.93). In WET, pleasantness was significantly reduced (p < 0.05) compared to DRY, likely due to the concomitant increase in texture sensation (p < 0.05). In summary, under dynamic conditions, changes in stickiness sensation and wetness perception could not be attributed to fabric texture properties (i.e. surface roughness) measured by the Kawabata Evaluation System. In dynamic conditions thickness or skin temperature drop can predict fabric wetness perception only when including stickiness sensation data

Exome sequencing identifies NBEAL2 as the causative gene for gray platelet syndrome.

Gray platelet syndrome (GPS) is a predominantly recessive platelet disorder that is characterized by mild thrombocytopenia with large platelets and a paucity of α-granules; these abnormalities cause mostly moderate but in rare cases severe bleeding. We sequenced the exomes of four unrelated individuals and identified NBEAL2 as the causative gene; it has no previously known function but is a member of a gene family that is involved in granule development. Silencing of nbeal2 in zebrafish abrogated thrombocyte formation

Predicting the outcomes of organic reactions via machine learning: are current descriptors sufficient?

As machine learning/artificial intelligence algorithms are defeating chess masters and, most recently, GO champions, there is interest -and hope -that they will prove equally useful in assisting chemists in predicting outcomes of organic reactions. This paper demonstrates, however, that the applicability of machine learning to the problems of chemical reactivity over diverse types of chemistries remains limited -in particular, with the currently available chemical descriptors, fundamental mathematical theorems impose upper bounds on the accuracy with which raction yields and times can be predicted. Improving the performance of machine-learning methods calls for the development of fundamentally new chemical descriptors

Machine learning for molecular and materials science

Here we summarize recent progress in machine learning for the chemical sciences. We outline machine-learning techniques that are suitable for addressing research questions in this domain, as well as future directions for the field. We envisage a future in which the design, synthesis, characterization and application of molecules and materials is accelerated by artificial intelligence.</p

The eXtensible Dynamic Presentation Manager for content adaptation

Human Computer Interaction studies deals with systems and tools that are able to improve user experience during interaction with computer. For this purpose, modern web application are expected to supply multimodal and multi-channel access, adaptivity and transcoding features. We will present in this work the eXtensible Dynamic Presentation Manager (XDPM) which is a set of innovative tools that support the eXtensible Presentation Language (XPL) in the adaptation of contents to different working contexts. The adaptation is performed according to the delivery context information which have been formalized by means of a profiler system. A profile holds information about the specific access device, the user preferences and the device working conditions