Major depressive disorder (MDD) is common and disabling, but its neuropathophysiology remains unclear. Most studies of functional brain networks in MDD have had limited statistical power and data analysis approaches have varied widely. The REST-meta-MDD Project of resting-state fMRI (R-fMRI) addresses these issues. Twenty-five research groups in China established the REST-meta-MDD Consortium by contributing R-fMRI data from 1,300 patients with MDD and 1,128 normal controls (NCs). Data were preprocessed locally with a standardized protocol before aggregated group analyses. We focused on functional connectivity (FC) within the default mode network (DMN), frequently reported to be increased in MDD. Instead, we found decreased DMN FC when we compared 848 patients with MDD to 794 NCs from 17 sites after data exclusion. We found FC reduction only in recurrent MDD, not in first-episode drug-naïve MDD. Decreased DMN FC was associated with medication usage but not with MDD duration. DMN FC was also positively related to symptom severity but only in recurrent MDD. Exploratory analyses also revealed alterations in FC of visual, sensory-motor, and dorsal attention networks in MDD. We confirmed the key role of DMN in MDD but found reduced rather than increased FC within the DMN. Future studies should test whether decreased DMN FC mediates response to treatment. All R-fMRI indices of data contributed by the REST-meta-MDD consortium are being shared publicly via the R-fMRI Maps Project.

Thermal conductivity of nanocrystalline silicon by direct molecular dynamics simulation J. Appl. Phys. 112, 064305 (2012) Electrical and heat conduction mechanisms of GeTe alloy for phase change memory application J. Appl. Phys. 112, 053712 (2012) Thermal rectification and phonon scattering in silicon nanofilm with cone cavity J. Appl. Phys. 112, 054312 (2012) Analysis of the "3-Omega" method for substrates and thick films of anisotropic thermal conductivity This work describes an experimental study of thermal conductance across multiwalled carbon nanotube ͑CNT͒ array interfaces, one sided ͑Si-CNT-Ag͒ and two sided ͑Si-CNT-CNT-Cu͒, using a photoacoustic technique ͑PA͒. Well-anchored, dense, and vertically oriented multiwalled CNT arrays have been directly synthesized on Si wafers and pure Cu sheets using plasma-enhanced chemical vapor deposition. With the PA technique, the small interface resistances of the highly conductive CNT interfaces can be measured with accuracy and precision. In addition, the PA technique can resolve the one-sided CNT interface component resistances ͑Si-CNT and CNT-Ag͒ and the two-sided CNT interface component resistances ͑Si-CNT, CNT-CNT, and CNT-Cu͒ and can estimate the thermal diffusivity of the CNT layers. The thermal contact resistances of the one-and two-sided CNT interfaces measured using the PA technique are 15.8± 0.9 and 4.0± 0.4 mm 2 K/W, respectively, at moderate pressure. These results compare favorably with those obtained using a steady state, one-dimensional reference bar method; however, the uncertainty range is much narrower. The one-sided CNT thermal interface resistance is dominated by the resistance between the free CNT array tips and their opposing substrate ͑CNT-Ag͒, which is measured to be 14.0± 0.9 mm 2 K / W. The two-sided CNT thermal interface resistance is dominated by the resistance between the free tips of the mating CNT arrays ͑CNT-CNT͒, which is estimated to be 2.1± 0.4 mm 2 K/W.

The mechanisms of decomposition of a metal ͑nickel͒ during femtosecond laser ablation are studied using molecular dynamics simulations. It is found that phase explosion is responsible for gas bubble generation and the subsequent material removal at lower laser fluences. The phase explosion process occurs as combined results of heating, thermal expansion, and the propagation of tensile stress wave induced by the laser pulse. When the laser fluence is higher, it is revealed that critical point phase separation plays an important role in material removal.

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Aim To describe the experiences of frontline nurses who are working in critical care areas during the COVID‐19 pandemic with a focus on trauma and the use of substances as a coping mechanism. Design A qualitative study based on content analysis. Methods Data were collected from mid‐June 2020 to early September 2020 via an online survey. Nurses were recruited through the research webpage of the American Association of Critical Care Nurses as well as an alumni list from a large, public Midwest university. Responses to two open‐ended items were analysed: (1) personal or professional trauma the nurse had experienced; and (2) substance or alcohol use, or other mental health issues the nurse had experienced or witnessed in other nurses. Results For the item related to psychological trauma five themes were identified from 70 nurses’ comments: (1) Psychological distress in multiple forms; (2) Tsunami of death; (3) Torn between two masters; (4) Betrayal; and (5) Resiliency/posttraumatic growth through self and others. Sixty‐five nurses responded to the second item related to substance use and other mental health issues. Data supported three themes: (1) Mental health crisis NOW!!: ‘more stressed than ever and stretched thinner than ever’; (2) Nurses are turning to a variety of substances to cope; and (3) Weakened supports for coping and increased maladaptive coping due to ongoing pandemic. Conclusions This study brings novel findings to understand the experiences of nurses who care for patients with COVID‐19, including trauma experienced during disasters, the use of substances to cope and the weakening of existing support systems. Findings also reveal nurses in crisis who are in need of mental health services. Impact Support for nurses’ well‐being and mental health should include current and ongoing services offered by the organization and include screening for substance use issues.

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Aberrant topological organization of whole-brain networks has been inconsistently reported in studies of patients with major depressive disorder (MDD), reflecting limited sample sizes. To address this issue, we utilized a big data sample of MDD patients from the REST-meta-MDD Project, including 821 MDD patients and 765 normal controls (NCs) from 16 sites. Using the Dosenbach 160 node atlas, we examined whole-brain functional networks and extracted topological features (e.g., global and local efficiency, nodal efficiency, and degree) using graph theory-based methods. Linear mixed-effect models were used for group comparisons to control for site variability; robustness of results was confirmed (e.g., multiple topological parameters, different node definitions, and several head motion control strategies were applied). We found decreased global and local efficiency in patients with MDD compared to NCs. At the nodal level, patients with MDD were characterized by decreased nodal degrees in the somatomotor network (SMN), dorsal attention network (DAN) and visual network (VN) and decreased nodal efficiency in the default mode network (DMN), SMN, DAN, and VN. These topological differences were mostly driven by recurrent MDD patients, rather than first-episode drug naive (FEDN) patients with MDD. In this highly powered multisite study, we observed disrupted topological architecture of functional brain networks in MDD, suggesting both locally and globally decreased efficiency in brain networks.