Afghanistan's ethnic groups share a Y-chromosomal heritage structured by historical events

Afghanistan has held a strategic position throughout history. It has been inhabited since the Paleolithic and later became a crossroad for expanding civilizations and empires. Afghanistan's location, history, and diverse ethnic groups present a unique opportunity to explore how nations and ethnic groups emerged, and how major cultural evolutions and technological developments in human history have influenced modern population structures. In this study we have analyzed, for the first time, the four major ethnic groups in present-day Afghanistan: Hazara, Pashtun, Tajik, and Uzbek, using 52 binary markers and 19 short tandem repeats on the non-recombinant segment of the Y-chromosome. A total of 204 Afghan samples were investigated along with more than 8,500 samples from surrounding populations important to Afghanistan's history through migrations and conquests, including Iranians, Greeks, Indians, Middle Easterners, East Europeans, and East Asians. Our results suggest that all current Afghans largely share a heritage derived from a common unstructured ancestral population that could have emerged during the Neolithic revolution and the formation of the first farming communities. Our results also indicate that inter-Afghan differentiation started during the Bronze Age, probably driven by the formation of the first civilizations in the region. Later migrations and invasions into the region have been assimilated differentially among the ethnic groups, increasing inter-population genetic differences, and giving the Afghans a unique genetic diversity in Central Asia.     

Higher Dimensional Meta-State Analysis Reveals Reduced Resting fMRI Connectivity Dynamism in Schizophrenia Patients

Resting-state functional brain imaging studies of network connectivity have long assumed that functional connections are stationary on the timescale of a typical scan. Interest in moving beyond this simplifying assumption has emerged only recently. The great hope is that training the right lens on time-varying properties of whole-brain network connectivity will shed additional light on previously concealed brain activation patterns characteristic of serious neurological or psychiatric disorders. We present evidence that multiple explicitly dynamical properties of time-varying whole-brain network connectivity are strongly associated with schizophrenia, a complex mental illness whose symptomatic presentation can vary enormously across subjects. As with so much brain-imaging research, a central challenge for dynamic network connectivity lies in determining transformations of the data that both reduce its dimensionality and expose features that are strongly predictive of important population characteristics. Our paper introduces an elegant, simple method of reducing and organizing data around which a large constellation of mutually informative and intuitive dynamical analyses can be performed. This framework combines a discrete multidimensional data-driven representation of connectivity space with four core dynamism measures computed from large-scale properties of each subject’s trajectory, ie., properties not identifiable with any specific moment in time and therefore reasonable to employ in settings lacking inter-subject time-alignment, such as resting-state functional imaging studies. Our analysis exposes pronounced differences between schizophrenia patients (N_sz = 151) and healthy controls (N_hc = 163). Time-varying whole-brain network connectivity patterns are found to be markedly less dynamically active in schizophrenia patients, an effect that is even more pronounced in patients with high levels of hallucinatory behavior. To the best of our knowledge this is the first demonstration that high-level dynamic properties of whole-brain connectivity, generic enough to be commensurable under many decompositions of time-varying connectivity data, exhibit robust and systematic differences between schizophrenia patients and healthy controls.     

The effect of inlet flow profile and nozzle diameter on drug delivery to the maxillary sinus

In this paper, the effect of the turbulence and swirling of the inlet flow and the diameter of the nozzle on the flow characteristics and the particles' transport/deposition patterns in a realistic combination of the nasal cavity (NC) and the maxillary sinus (MS) were examined. A computational fluid dynamics (CFD) model was developed in ANSYS® Fluent using a hybrid Reynolds averaged Navier–Stokes–large-eddy simulation algorithm. For the validation of the CFD model, the pressure distribution in the NC was compared with the experimental data available in the literature. An Eulerian–Lagrangian approach was employed for the prediction of the particle trajectories using a discrete phase model. Different inlet flow conditions were investigated, with turbulence intensities of 0.15 and 0.3, and swirl numbers of 0.6 and 0.9 applied to the inlet flow at a flow rate of 7 L/min. Monodispersed particles with a diameter of 5 µm were released into the nostril for various nozzle diameters. The results demonstrate that the nasal valve plays a key role in nasal resistance, which damps the turbulence and swirl intensities of the inlet flow. Moreover, it was found that the effect of turbulence at the inlet of the NC on drug delivery to the MS is negligible. It was also demonstrated that increasing the flow swirl at the inlet and decreasing the nozzle diameter improves the total particle deposition more than threefold due to the generation of the centrifugal force, which acts on the particles in the nostril and vestibule. The results also suggest that the drug delivery efficiency to the MS can be increased by using a swirling flow with a moderate swirl number of 0.6. It was found that decreasing the nozzle diameter can increase drug delivery to the proximity of the ostium in the middle meatus by more than 45%, which subsequently increases the drug delivery to the MS. The results can help engineers design a nebulizer to improve the efficiency of drug delivery to the maxillary sinuses.

     

Nonlinear Strain Stiffening Is Not Sufficient to Explain How Far Cells Can?Feel on Fibrous Protein Gels

Recent observations suggest that cells on fibrous extracellular matrix materials sense mechanical signals over much larger distances than they do on linearly elastic synthetic materials. In this work, we systematically investigate the distance fibroblasts can sense a rigid boundary through fibrous gels by quantifying the spread areas of human lung fibroblasts and 3T3 fibroblasts cultured on sloped collagen and fibrin gels. The cell areas gradually decrease as gel thickness increases from 0 to 150 μm, with characteristic sensing distances of >65 μm below fibrin and collagen gels, and spreading affected on gels as thick as 150 μm. These results demonstrate that fibroblasts sense deeper into collagen and fibrin gels than they do into polyacrylamide gels, with the latter exhibiting characteristic sensing distances of <5 μm. We apply finite-element analysis to explore the role of strain stiffening, a characteristic mechanical property of collagen and fibrin that is not observed in polyacrylamide, in facilitating mechanosensing over long distances. Our analysis shows that the effective stiffness of both linear and nonlinear materials sharply increases once the thickness is reduced below 5 μm, with only a slight enhancement in sensitivity to depth for the nonlinear material at very low thickness and high applied traction. Multiscale simulations with a simplified geometry predict changes in fiber alignment deep into the gel and a large increase in effective stiffness with a decrease in substrate thickness that is not predicted by nonlinear elasticity. These results suggest that the observed cell-spreading response to gel thickness is not explained by the nonlinear strain-stiffening behavior of the material alone and is likely due to the fibrous nature of the proteins.     

Utrophin is lacking at the neuromuscular junctions in the extraocular muscles of normal cat: artefact or true?

Extraocular muscles (EOM) are typically spared in Duchenne muscular dystrophy. We hypothesized that this might be due to different patterns of utrophin expression. The expression of utrophin was examined in EOM of normal cats using immunohistochemical methods and Western blot. For detecting acetylcholine receptors (AChR), we used -bungarotoxin. Surprisingly, -bungarotoxin failed to stain the AChR and no expression of utrophin could be detected at the neuromuscular junctions. Our study could indicate that the expression of utrophin is dependent on the structure of the AChR.