Strike a happy medium: the effect of it knowledge on venture capitalists' overconfidence in it investments

In this article, the effect of IT knowledge on the overconfidence of venture capitalists (VCs) in their IT investments is examined. Our findings show that the effect of IT knowledge on overconfidence is nonlinear. VCs with moderate levels of IT knowledge are least overconfident. At the same time, VCs with moderate levels of IT knowledge are most resistant to the biasing effects of past successes. Past failures show a negative association with overconfidence independent of the level of the VC's IT knowledge. Finally, the negative association between stakes and VC overconfidence is stronger with greater levels of IT knowledge. These results shed light on the highly disputed role of IT knowledge in the domain of IT investments.info:eu-repo/semantics/publishedVersio

ATG proteins mediate efferocytosis and suppress inflammation in mammary involution.

Involution is the process of post-lactational mammary gland regression to quiescence and it involves secretory epithelial cell death, stroma remodeling and gland repopulation by adipocytes. Though reportedly accompanying apoptosis, the role of autophagy in involution has not yet been determined. We now report that autophagy-related (ATG) proteins mediate dead cell clearance and suppress inflammation during mammary involution. In vivo, Becn1(+/-) and Atg7-deficient mammary epithelial cells (MECs) produced 'competent' apoptotic bodies, but were defective phagocytes in association with reduced expression of the MERTK and ITGB5 receptors, thus pointing to defective apoptotic body engulfment. Atg-deficient tissues exhibited higher levels of involution-associated inflammation, which could be indicative of a tumor-modulating microenvironment, and developed ductal ectasia, a manifestation of deregulated post-involution gland remodeling. In vitro, ATG (BECN1 or ATG7) knockdown compromised MEC-mediated apoptotic body clearance in association with decreased RAC1 activation, thus confirming that, in addition to the defective phagocytic processing reported by other studies, ATG protein defects also impair dead cell engulfment. Using two different mouse models with mammary gland-associated Atg deficiencies, our studies shed light on the essential role of ATG proteins in MEC-mediated efferocytosis during mammary involution and provide novel insights into this important developmental process. This work also raises the possibility that a regulatory feedback loop exists, by which the efficacy of phagocytic cargo processing in turn regulates the rate of engulfment and ultimately determines the kinetics of phagocytosis and dead cell clearance

Infant hip screening using multi-class ultrasound scan segmentation

Developmental dysplasia of the hip (DDH) is a condition in infants where the femoral head is incorrectly located in the hip joint. We propose a deep learning algorithm for segmenting key structures within ultrasound images, employing this to calculate Femoral Head Coverage (FHC) and provide a screening diagnosis for DDH. To our knowledge, this is the first study to automate FHC calculation for DDH screening. Our algorithm outperforms the international state of the art, agreeing with expert clinicians on 89.8% of our test images

Infant hip screening using multi-class ultrasound scan segmentation

Developmental dysplasia of the hip (DDH) is a condition in infants where the femoral head is incorrectly located in the hip joint. We propose a deep learning algorithm for segmenting key structures within ultrasound images, employing this to calculate Femoral Head Coverage (FHC) and provide a screening diagnosis for DDH. To our knowledge, this is the first study to automate FHC calculation for DDH screening. Our algorithm outperforms the international state of the art, agreeing with expert clinicians on 89.8% of our test images.Comment: Four page pape

Rotational imaging optical coherence tomography for full-body mouse embryonic imaging

Optical coherence tomography (OCT) has been widely used to study mammalian embryonic development with the advantages of high spatial and temporal resolutions and without the need for any contrast enhancement probes. However, the limited imaging depth of traditional OCT might prohibit visualization of the full embryonic body. To overcome this limitation, we have developed a new methodology to enhance the imaging range of OCT in embryonic day (E) 9.5 and 10.5 mouse embryos using rotational imaging. Rotational imaging OCT (RI-OCT) enables full-body imaging of mouse embryos by performing multiangle imaging. A series of postprocessing procedures was performed on each cross-section image, resulting in the final composited image. The results demonstrate that RI-OCT is able to improve the visualization of internal mouse embryo structures as compared to conventional OCT

A deep learning pipeline for automatized assessment of spinal MRI

Background This work evaluates the feasibility, development, and validation of a machine learning pipeline that includes all tasks from MRI input to the segmentation and grading of the intervertebral discs in the lumbar spine, offering multiple different radiological gradings of degeneration as quantitative objective output. Methods The pipelines’ performance was analysed on 1′000 T2-weighted sagittal MRI. Binary outputs were assessed with the harmonic mean of precision and recall (DSC) and the area under the precision-recall curve (AUC-PR). Multi-class output scores were averaged and complemented by the Top-2 categorical accuracy. The processing success rate was evaluated on 10′053 unlabelled MRI scans of lumbar spines. Results The midsagittal plane selection achieved an DSC of 74,80% ± 2,99% and an AUC-PR score of 81.71% ± 2.72% (96.91% Top-2 categorical accuracy). The segmentation network obtained a DSC of 91.80% ± 0.44%. The Pfirrmann grading of intervertebral discs in the midsagittal plane was classified with a DSC of 64.08% ± 3.29% and an AUC-PR score of 68.25% ± 6.00% (91.65% Top-2 categorical accuracy). Disc herniations achieved a DSC of 61.57% ± 3.39% and an AUC-PR score of 66.86% ± 5.03%. The cranial endplate defects reached a DSC of 49.76% ± 3.45% and 52.36% ± 1.98% AUC-PR (slightly superior predictions of caudal endplate defect). The binary classifications for the caudal Schmorl's nodes obtained a DSC of 91.58% ± 2.25% with an AUC-PR metric of 96.69% ± 1.58% (similar performance for cranial Schmorl's nodes). Spondylolisthesis was classified with a DSC of 89.03% ± 2.42% and an AUC-PR score of 95.98% ± 1.82%. Annular Fissures were predicted with a DSC of 78.09% ± 7.21% and an AUC-PR score of 86.31% ± 7.45%. Intervertebral disc classifications in the parasagittal plane achieved an equivalent performance. The pipeline successfully processed 98.53% of the provided sagittal MRI scans. Conclusions The present deep learning framework has the potential to aid the quantitative evaluation of spinal MRI for an array of clinically established grading systems. + Graphical abstrac