Vertical transport and electroluminescence in InAs/GaSb/InAs structures: GaSb thickness and hydrostatic pressure studies

We have measured the current-voltage (I-V) of type II InAs/GaSb/InAs double heterojunctions (DHETs) with 'GaAs like' interface bonding and GaSb thickness between 0-1200 \AA. A negative differential resistance (NDR) is observed for all DHETs with GaSb thickness $>$ 60 \AA below which a dramatic change in the shape of the I-V and a marked hysteresis is observed. The temperature dependence of the I-V is found to be very strong below this critical GaSb thickness. The I-V characteristics of selected DHETs are also presented under hydrostatic pressures up to 11 kbar. Finally, a mid infra-red electroluminescence is observed at 1 bar with a threshold at the NDR valley bias. The band profile calculations presented in the analysis are markedly different to those given in the literature, and arise due to the positive charge that it is argued will build up in the GaSb layer under bias. We conclude that the dominant conduction mechanism in DHETs is most likely to arise out of an inelastic electron-heavy-hole interaction similar to that observed in single heterojunctions (SHETs) with 'GaAs like' interface bonding, and not out of resonant electron-light-hole tunnelling as proposed by Yu et al. A Zener tunnelling mechanism is shown to contribute to the background current beyond NDR.Comment: 8 pages 12 fig

Evolutionary characterization of lung adenocarcinoma morphology in TRACERx

Lung adenocarcinomas (LUADs) display a broad histological spectrum from low-grade lepidic tumors through to mid-grade acinar and papillary and high-grade solid, cribriform and micropapillary tumors. How morphology reflects tumor evolution and disease progression is poorly understood. Whole-exome sequencing data generated from 805 primary tumor regions and 121 paired metastatic samples across 248 LUADs from the TRACERx 421 cohort, together with RNA-sequencing data from 463 primary tumor regions, were integrated with detailed whole-tumor and regional histopathological analysis. Tumors with predominantly high-grade patterns showed increased chromosomal complexity, with higher burden of loss of heterozygosity and subclonal somatic copy number alterations. Individual regions in predominantly high-grade pattern tumors exhibited higher proliferation and lower clonal diversity, potentially reflecting large recent subclonal expansions. Co-occurrence of truncal loss of chromosomes 3p and 3q was enriched in predominantly low-/mid-grade tumors, while purely undifferentiated solid-pattern tumors had a higher frequency of truncal arm or focal 3q gains and SMARCA4 gene alterations compared with mixed-pattern tumors with a solid component, suggesting distinct evolutionary trajectories. Clonal evolution analysis revealed that tumors tend to evolve toward higher-grade patterns. The presence of micropapillary pattern and ‘tumor spread through air spaces’ were associated with intrathoracic recurrence, in contrast to the presence of solid/cribriform patterns, necrosis and preoperative circulating tumor DNA detection, which were associated with extra-thoracic recurrence. These data provide insights into the relationship between LUAD morphology, the underlying evolutionary genomic landscape, and clinical and anatomical relapse risk

Whole-genome sequencing reveals host factors underlying critical COVID-19

Critical COVID-19 is caused by immune-mediated inflammatory lung injury. Host genetic variation influences the development of illness requiring critical care1 or hospitalization2,3,4 after infection with SARS-CoV-2. The GenOMICC (Genetics of Mortality in Critical Care) study enables the comparison of genomes from individuals who are critically ill with those of population controls to find underlying disease mechanisms. Here we use whole-genome sequencing in 7,491 critically ill individuals compared with 48,400 controls to discover and replicate 23 independent variants that significantly predispose to critical COVID-19. We identify 16 new independent associations, including variants within genes that are involved in interferon signalling (IL10RB and PLSCR1), leucocyte differentiation (BCL11A) and blood-type antigen secretor status (FUT2). Using transcriptome-wide association and colocalization to infer the effect of gene expression on disease severity, we find evidence that implicates multiple genes—including reduced expression of a membrane flippase (ATP11A), and increased expression of a mucin (MUC1)—in critical disease. Mendelian randomization provides evidence in support of causal roles for myeloid cell adhesion molecules (SELE, ICAM5 and CD209) and the coagulation factor F8, all of which are potentially druggable targets. Our results are broadly consistent with a multi-component model of COVID-19 pathophysiology, in which at least two distinct mechanisms can predispose to life-threatening disease: failure to control viral replication; or an enhanced tendency towards pulmonary inflammation and intravascular coagulation. We show that comparison between cases of critical illness and population controls is highly efficient for the detection of therapeutically relevant mechanisms of disease

Discriminative joint non-negative matrix factorization for human action classification

This paper describes a supervised classification approach based on non-negative matrix factorization (NMF). Our classification framework builds on the recent expansions of non-negative matrix factorization to multiview learning, where the primary dataset benefits from auxiliary information for obtaining shared and meaningful spaces. For discrimination, we utilize data categories in a supervised manner as an auxiliary source of information in order to learn co-occurrences through a common set of basis vectors. We demonstrate the efficiency of our algorithm in integrating various image modalities for enhancing the overall classification accuracy over different benchmark datasets. Our evaluation considers two challenging image datasets of human action recognition. We show that our algorithm achieves superior results over state-of-the-art in terms of efficiency and overall classification accuracy

Learning spatial interest regions from videos to inform action recognition in still images

Common approaches to human action recognition from images rely on local descriptors for classification. Typically, these descriptors are computed in the vicinity of key points which either result from running a key point detector or from dense or random sampling of pixel coordinates. Such key points are not a-priori related to human activities and thus of limited information with regard to action recognition. In this paper, we propose to identify action-specific key points in images using information available from videos. Our approach does not require manual segmentation or templates but applies non-negative matrix factorization to optical flow fields extracted from videos. The resulting basisflows are found to to be indicative of action specific image regions and therefore allow for an informed sampling of key points. We also present a generative model that allows for characterizing joint distributions of regions of interest and a human actions. In practical experiments, we determine correspondences between regions of interest that were automatically learned from videos and manually annotated locations of human body parts available from independent benchmark image data sets. We observe high correlations between learned interest regions and body parts most relevant for different actions

The future role of forensic radiology — scope and challenges

[No abstract available

Human milk lactose, insulin, and glucose relative to infant body composition during exclusive breastfeeding

Human milk (HM) components may influence infant growth and development. This study aimed to investigate relationships between infant body composition (BC) and HM lactose, insulin, and glucose (concentrations and calculated daily intakes (CDI)) as well as 24-h milk intake and maternal BC at 3 months postpartum. HM samples were collected at 2 months postpartum. Infant and maternal BC was assessed with bioimpedance spectroscopy. Statistical analysis used linear regression accounting for infant birth weight. 24-h milk intake and CDI of lactose were positively associated with infant anthropometry, lean body mass and adiposity. Higher maternal BC measures were associated with lower infant anthropometry, z-scores, lean body mass, and adiposity. Maternal characteristics including BC and age were associated with concentrations and CDI of HM components, and 24-h milk intake. In conclusion, 24-h intake of HM and lactose as well as maternal adiposity are related to development of infant BC