Integration of local elements into a coherent global form is a fundamental aspect of visual object recognition. How the different hierarchically organized stages of visual analysis develop in order to support object representation in infants remains unknown. The aim of this study was to investigate structural encoding of natural images in 4- to 6-month-old infants and adults. We used the steady-state visual evoked potential (ssVEP) technique to measure cortical responses specific to the global structure present in object and face images, and assessed whether differential responses were present for these image categories. This study is the first to apply the ssVEP method to high-level vision in infants. Infants and adults responded to the structural relations present in both image categories, and topographies of the responses differed based on image category. However, while adult responses to face and object structure were localized over occipitotemporal scalp areas, only infant face responses were distributed over temporal regions. Therefore, both infants and adults show object category specificity in their neural responses. The topography of the infant response distributions indicates that between 4 and 6 months of age, structure encoding of faces occurs at a higher level of processing than that of objects.
Cognitive theories on deception posit that lying requires more cognitive resources than telling the truth. In line with this idea, it has been demonstrated that deceptive responses are typically associated with increased response times and higher error rates compared to truthful responses. Although the cognitive cost of lying has been assumed to be resistant to practice, it has recently been shown that people who are trained to lie can reduce this cost. In the present study (n = 42), we further explored the effects of practice on one’s ability to lie by manipulating the proportions of lie and truth-trials in a Sheffield lie test across three phases: Baseline (50% lie, 50% truth), Training (frequent-lie group: 75% lie, 25% truth; control group: 50% lie, 50% truth; and frequent-truth group: 25% lie, 75% truth), and Test (50% lie, 50% truth). The results showed that lying became easier while participants were trained to lie more often and that lying became more difficult while participants were trained to tell the truth more often. Furthermore, these effects did carry over to the test phase, but only for the specific items that were used for the training manipulation. Hence, our study confirms that relatively little practice is enough to alter the cognitive cost of lying, although this effect does not persist over time for non-practiced items.
The left ventricle (LV) of mammals with Situs Solitus (SS, normal organ arrangement) displays hardly any interindividual variation in myofiber pattern and experimentally determined torsion. SS LV myofiber pattern has been suggested to result from adaptive myofiber reorientation, in turn leading to efficient pump and myofiber function. Limited data from the Situs Inversus Totalis (SIT, a complete mirror image of organ anatomy and position) LV demonstrated an essential different myofiber pattern, being normal at the apex but mirrored at the base. Considerable differences in torsion patterns in between human SIT LVs even suggest variation in myofiber pattern among SIT LVs themselves. We addressed whether different myofiber patterns in the SIT LV can be predicted by adaptive myofiber reorientation and whether they yield similar pump and myofiber function as in the SS LV. With a mathematical model of LV mechanics including shear induced myofiber reorientation, we predicted myofiber patterns of one SS and three different SIT LVs. Initial conditions for SIT were based on scarce information on the helix angle. The transverse angle was set to zero. During reorientation, a non-zero transverse angle developed, pump function increased, and myofiber function increased and became more homogeneous. Three continuous SIT structures emerged with a different location of transition between normal and mirrored myofiber orientation pattern. Predicted SIT torsion patterns matched experimentally determined ones. Pump and myofiber function in SIT and SS LVs are similar, despite essential differences in myocardial structure. SS and SIT LV structure and function may originate from same processes of adaptive myofiber reorientation.
Obesity may increase heart failure risk through cardiac remodeling. Cross-sectional associations between adiposity and cardiac structure and function have been elucidated, but the impact of longitudinal changes in adiposity on cardiac remodeling is less well understood.
Reduced libido is widely considered the most prominent symptomatic reflection of low testosterone (T) levels in men. Testosterone deficiency (TD) afflicts approximately 30% of men aged 40-79 years. This study seeks to evaluate the effect of a new natural compound “tradamixina "in order to improve male sexual function in elderly men, particularly libido and possible erectile dysfunction, versus administration of tadalafil 5 mg daily.
Finite simple groups are the building blocks of finite symmetry. The effort to classify them precipitated the discovery of new examples, including the monster, and six pariah groups which do not belong to any of the natural families, and are not involved in the monster. It also precipitated monstrous moonshine, which is an appearance of monster symmetry in number theory that catalysed developments in mathematics and physics. Forty years ago the pioneers of moonshine asked if there is anything similar for pariahs. Here we report on a solution to this problem that reveals the O'Nan pariah group as a source of hidden symmetry in quadratic forms and elliptic curves. Using this we prove congruences for class numbers, and Selmer groups and Tate-Shafarevich groups of elliptic curves. This demonstrates that pariah groups play a role in some of the deepest problems in mathematics, and represents an appearance of pariah groups in nature.Classifying groups is an important challenge in mathematics and has led to the identification of groups which do not belong to the main families. Here Duncan et al. introduce a type of moonshine which is a connection between these groups, number theory and potentially physics.
Phytochrome photoreceptors in plants and microorganisms switch photochromically between two states, controlling numerous important biological processes. Although this phototransformation is generally considered to involve rotation of ring D of the tetrapyrrole chromophore, Ulijasz et al. (2010, Nature 463, 250-254) proposed that the A-ring rotates instead. Here, we apply MAS NMR to the two parent states of following studies of the 23-kDa GAF-domain fragment of phytochrome from Synechococcus OS-B'. Major changes occur at the A-ring covalent linkage to the protein as well as at the protein residue contact of ring D. Conserved contacts associated with the A-ring nitrogen rule out an A-ring photoflip, whereas loss of contact of the D-ring nitrogen to the protein implies movement of ring D. Although none of the methine bridges showed a chemical shift change comparable to those characteristic of the D-ring photoflip in canonical phytochromes, denaturation experiments showed conclusively that the same occurs in Synechococcus OS-B' phytochrome upon photoconversion. The results are consistent with the D-ring being strongly tilted in both states and the C15=C16 double bond undergoing a Z/E isomerization upon light absorption. More subtle changes are associated with the A-ring linkage to the protein. Our findings thus disprove A-ring rotation and are discussed in relation to the position of the D-ring, photoisomerization and photochromicity in the phytochrome family.
This study was aimed to introduce a novel entry point for pedicle screw fixation in the thoracic spine and compare it with the traditional entry point. A novel entry point was found with the aim of improving accuracy, safety and stability of pedicle screw technique based on anatomical structures of the spine. A total of 76 pieces of normal thoracic CT images at the transverse plane and the thoracic pedicle anatomy of 6 cadaveric specimens were recruited. Transverse pedicle angle (TPA), screw length, screw placement accuracy rate and axial pullout strength of the two different entry point groups were compared. There were significant differences in the TPA, screw length, and the screw placement accuracy rate between the two groups (P<0.05). The maximum axial pullout strength of the novel entry point group was slightly larger than that of the traditional group. However, the difference was not significant (P>0.05). The novel entry point significantly improved the accuracy, stability and safety of pedicle screw placement. With reference to the advantages above, the new entry point can be used for spinal internal fixations in the thoracic spine.
Dense active matter, from bacterial suspensions and microtubule bundles driven by motor proteins to cellular monolayers and synthetic Janus particles, is characterized by mesoscale turbulence, which is the emergence of chaotic flow structures. By immersing an ordered array of symmetric rotors in an active fluid, we introduce a microfluidic system that exploits spontaneous symmetry breaking in mesoscale turbulence to generate work. The lattice of rotors self-organizes into a spin state where neighboring discs continuously rotate in permanent alternating directions due to combined hydrodynamic and elastic effects. Our virtual prototype demonstrates a new research direction for the design of micromachines powered by the nematohydrodynamic properties of active turbulence.
Deep learning allows computational models that are composed of multiple processing layers to learn representations of data with multiple levels of abstraction. These methods have dramatically improved the state-of-the-art in speech recognition, visual object recognition, object detection and many other domains such as drug discovery and genomics. Deep learning discovers intricate structure in large data sets by using the backpropagation algorithm to indicate how a machine should change its internal parameters that are used to compute the representation in each layer from the representation in the previous layer. Deep convolutional nets have brought about breakthroughs in processing images, video, speech and audio, whereas recurrent nets have shone light on sequential data such as text and speech.