Band structure, superconductivity and polytypism in AuSn4_4

The orthorhombic compound AuSn4 is compositionally similar to the Dirac node arc semimetal PtSn$_4$. AuSn$_4$ is, contrary to PtSn$_4$, superconducting with a critical temperature of T$_c$ = 2.35 K. Recent measurements present indications for quasi two-dimensional superconducting behavior in AuSn$_4$. Here we present measurements of the superconducting density of states and the band structure of AuSn$_4$ through Scanning Tunneling Microscopy (STM) and Angular Resolved Photoemission Spectroscopy (ARPES). The superconducting gap values in different portions of the Fermi surface are spread around {\Delta}0 = 0.4 meV, which is close to but somewhat larger than $\Delta =$ 1.76kBT$_c$ expected from BCS theory. We observe superconducting features in the tunneling conductance at the surface up to temperatures about 20% larger than bulk Tc. The band structure calculated with Density Functional Theory (DFT) follows well the results of ARPES. The crystal structure presents two possible stackings of Sn layers, giving two nearly degenerate polytypes. This makes AuSn$_4$ a rather unique case with a three dimensional electronic band structure but properties ressembling those of low dimensional layered compounds

Autocycle: Design, Construction, and Validation of an Autonomous Bicycle

Efficient urban transportation has time and time again proved to be a difficult problem to rectify. One modern solution is the bike-sharing system, where many bicycles are available either at hubs or spread across a city for short-term use. However, usage is limited to those located close enough to a bicycle hub that traveling to and from it is time-effective. As for hubless bike-shares, bicycles require redistribution over time to remain conveniently available to many. We propose the creation of an electric bicycle that can either be used by a cyclist manually or operated autonomously using locomotion, sensing, balance, and control systems. We have concluded that such a concept is possible and achievable, as we are making significant progress toward developing working prototypes for the self-stability and autonomous navigation of the Autocycle. Once those milestones are completed, we will integrate the two systems together in the final prototype. Our Undergraduate Research Day presentation will showcase the research and data we have collected up until this point and outline our future goals for the project. With the completion of this prototype, we want to show that such a bicycle could be implemented into a larger bike-sharing system that autonomously manages distribution and allows users to summon a bicycle to their location, expanding the range of use and encouraging environmentally-friendly transportation solutions in an urban setting.Gemston

An ontology-based segmentation scheme for tracking postnatal changes in the developing rodent brain with MRI

The postnatal period of neurodevelopment has been implicated in a number of brain disorders including autism and schizophrenia. Rodent models have proven to be invaluable in advancing our understanding of the human brain, and will almost certainly play a pivotal role in future studies on postnatal neurodevelopment. The growing field of magnetic resonance microscopy has the potential to revolutionize our understanding of neurodevelopment, if it can be successfully and appropriately assimilated into the vast body of existing neuroscience research. In this study, we demonstrate the utility of a developmental neuro-ontology designed specifically for tracking regional changes in MR biomarkers throughout postnatal neurodevelopment. Using this ontological classification as a segmentation guide, we track regional changes in brain volume in rats between postnatal day zero and postnatal day 80 and demonstrate differential growth rates in axial versus paraxial brain regions. Both the ontology and the associated label volumes are provided as a foundation for future MR-based studies of postnatal neurodevelopment in normal and disease states

When stakeholder representation leads to faultlines: a study of board service performance in social enterprises

Following the growing interest in sustainability and ethics, organizations are increasingly attentive to accountability toward stakeholders. Stakeholder representation, obtained by appointing board members representing different stakeholder groups, is suggested to be a good ethical practice. However, such representation may also have nefarious implications for board functioning. Particularly, it may result in strong faultline emergence, subsequently mitigating board performance. Our study aims at understanding the process through which faultlines affect board performance, and particularly the board service role through which the board is involved in providing counsel and strategic decision-making. We study the relationship between faultlines and board service performance in the particularly relevant context of social enterprises. We find that faultline strength is negatively related to board service performance and that this relationship is mediated by board task conflict. Furthermore, our study reveals the importance of clear and shared organizational goals in attenuating the negative effects of faultlines

Remodeling of the Cortical Structural Connectome in Posttraumatic Stress Disorder:Results from the ENIGMA-PGC PTSD Consortium

BACKGROUND: Posttraumatic stress disorder (PTSD) is accompanied by disrupted cortical neuroanatomy. We investigated alteration in covariance of structural networks associated with PTSD in regions that demonstrate the case-control differences in cortical thickness (CT) and surface area (SA). METHODS: Neuroimaging and clinical data were aggregated from 29 research sites in >1,300 PTSD cases and >2,000 trauma-exposed controls (age 6.2-85.2 years) by the ENIGMA-PGC PTSD working group. Cortical regions in the network were rank-ordered by effect size of PTSD-related cortical differences in CT and SA. The top-n (n = 2 to 148) regions with the largest effect size for PTSD > non-PTSD formed hypertrophic networks, the largest effect size for PTSD < non-PTSD formed atrophic networks, and the smallest effect size of between-group differences formed stable networks. The mean structural covariance (SC) of a given n-region network was the average of all positive pairwise correlations and was compared to the mean SC of 5,000 randomly generated n-region networks. RESULTS: Patients with PTSD, relative to non-PTSD controls, exhibited lower mean SC in CT-based and SA-based atrophic networks. Comorbid depression, sex and age modulated covariance differences of PTSD-related structural networks. CONCLUSIONS: Covariance of structural networks based on CT and cortical SA are affected by PTSD and further modulated by comorbid depression, sex, and age. The structural covariance networks that are perturbed in PTSD comport with converging evidence from resting state functional connectivity networks and networks impacted by inflammatory processes, and stress hormones in PTSD

A Comparison of Methods to Harmonize Cortical Thickness Measurements Across Scanners and Sites

Results of neuroimaging datasets aggregated from multiple sites may be biased by site-specific profiles in participants’ demographic and clinical characteristics, as well as MRI acquisition protocols and scanning platforms. We compared the impact of four different harmonization methods on results obtained from analyses of cortical thickness data: (1) linear mixed-effects model (LME) that models site-specific random intercepts (LME INT), (2) LME that models both site-specific random intercepts and age-related random slopes (LME INT+SLP), (3) ComBat, and (4) ComBat with a generalized additive model (ComBat-GAM). Our test case for comparing harmonization methods was cortical thickness data aggregated from 29 sites, which included 1,340 cases with posttraumatic stress disorder (PTSD) (6.2–81.8 years old) and 2,057 trauma-exposed controls without PTSD (6.3–85.2 years old). We found that, compared to the other data harmonization methods, data processed with ComBat-GAM was more sensitive to the detection of significant case-control differences (Χ 2(3) = 63.704, p < 0.001) as well as case-control differences in age-related cortical thinning (Χ 2(3) = 12.082, p = 0.007). Both ComBat and ComBat-GAM outperformed LME methods in detecting sex differences (Χ 2(3) = 9.114, p = 0.028) in regional cortical thickness. ComBat-GAM also led to stronger estimates of age-related declines in cortical thickness (corrected p-values < 0.001), stronger estimates of case-related cortical thickness reduction (corrected p-values < 0.001), weaker estimates of age-related declines in cortical thickness in cases than controls (corrected p-values < 0.001), stronger estimates of cortical thickness reduction in females than males (corrected p-values < 0.001), and stronger estimates of cortical thickness reduction in females relative to males in cases than controls (corrected p-values < 0.001). Our results support the use of ComBat-GAM to minimize confounds and increase statistical power when harmonizing data with non-linear effects, and the use of either ComBat or ComBat-GAM for harmonizing data with linear effects

The genetic architecture of the human cerebral cortex

The cerebral cortex underlies our complex cognitive capabilities, yet little is known about the specific genetic loci that influence human cortical structure. To identify genetic variants that affect cortical structure, we conducted a genome-wide association meta-analysis of brain magnetic resonance imaging data from 51,665 individuals. We analyzed the surface area and average thickness of the whole cortex and 34 regions with known functional specializations. We identified 199 significant loci and found significant enrichment for loci influencing total surface area within regulatory elements that are active during prenatal cortical development, supporting the radial unit hypothesis. Loci that affect regional surface area cluster near genes in Wnt signaling pathways, which influence progenitor expansion and areal identity. Variation in cortical structure is genetically correlated with cognitive function, Parkinson's disease, insomnia, depression, neuroticism, and attention deficit hyperactivity disorder

Intrusive Traumatic Re-Experiencing Domain: Functional Connectivity Feature Classification by the ENIGMA PTSD Consortium

Background: Intrusive traumatic re-experiencing domain (ITRED) was recently introduced as a novel perspective on posttraumatic psychopathology, proposing to focus research of posttraumatic stress disorder (PTSD) on the unique symptoms of intrusive and involuntary re-experiencing of the trauma, namely, intrusive memories, nightmares, and flashbacks. The aim of the present study was to explore ITRED from a neural network connectivity perspective. Methods: Data were collected from 9 sites taking part in the ENIGMA (Enhancing Neuro Imaging Genetics through Meta Analysis) PTSD Consortium (n = 584) and included itemized PTSD symptom scores and resting-state functional connectivity (rsFC) data. We assessed the utility of rsFC in classifying PTSD, ITRED-only (no PTSD diagnosis), and trauma-exposed (TE)–only (no PTSD or ITRED) groups using a machine learning approach, examining well-known networks implicated in PTSD. A random forest classification model was built on a training set using cross-validation, and the averaged cross-validation model performance for classification was evaluated using the area under the curve. The model was tested using a fully independent portion of the data (test dataset), and the test area under the curve was evaluated. Results: rsFC signatures differentiated TE-only participants from PTSD and ITRED-only participants at about 60% accuracy. Conversely, rsFC signatures did not differentiate PTSD from ITRED-only individuals (45% accuracy). Common features differentiating TE-only participants from PTSD and ITRED-only participants mainly involved default mode network–related pathways. Some unique features, such as connectivity within the frontoparietal network, differentiated TE-only participants from one group (PTSD or ITRED-only) but to a lesser extent from the other group. Conclusions: Neural network connectivity supports ITRED as a novel neurobiologically based approach to classifying posttrauma psychopathology