HDSDF: Hybrid Directional and Signed Distance Functions for Fast Inverse Rendering

Implicit neural representations of 3D shapes form strong priors that areuseful for various applications, such as single and multiple view 3Dreconstruction. A downside of existing neural representations is that theyrequire multiple network evaluations for rendering, which leads to highcomputational costs. This limitation forms a bottleneck particularly in thecontext of inverse problems, such as image-based 3D reconstruction. To addressthis issue, in this paper (i) we propose a novel hybrid 3D objectrepresentation based on a signed distance function (SDF) that we augment with adirectional distance function (DDF), so that we can predict distances to theobject surface from any point on a sphere enclosing the object. Moreover, (ii)using the proposed hybrid representation we address the multi-view consistencyproblem common in existing DDF representations. We evaluate our novel hybridrepresentation on the task of single-view depth reconstruction and show thatour method is several times faster compared to competing methods, while at thesame time achieving better reconstruction accuracy.<br

i3DMM: Deep Implicit 3D Morphable Model of Human Heads

We present the first deep implicit 3D morphable model (i3DMM) of full heads. Unlike earlier morphable face models it not only captures identity-specific geometry, texture, and expressions of the frontal face, but also models the entire head, including hair. We collect a new dataset consisting of 64 people with different expressions and hairstyles to train i3DMM. Our approach has the following favorable properties: (i) It is the first full head morphable model that includes hair. (ii) In contrast to mesh-based models it can be trained on merely rigidly aligned scans, without requiring difficult non-rigid registration. (iii) We design a novel architecture to decouple the shape model into an implicit reference shape and a deformation of this reference shape. With that, dense correspondences between shapes can be learned implicitly. (iv) This architecture allows us to semantically disentangle the geometry and color components, as color is learned in the reference space. Geometry is further disentangled as identity, expressions, and hairstyle, while color is disentangled as identity and hairstyle components. We show the merits of i3DMM using ablation studies, comparisons to state-of-the-art models, and applications such as semantic head editing and texture transfer. We will make our model publicly available

A robust tool for discriminative analysis and feature selection in paired samples impacts the identification of the genes essential for reprogramming lung tissue to adenocarcinoma

<p>Abstract</p> <p>Background</p> <p>Lung cancer is the leading cause of cancer deaths in the world. The most common type of lung cancer is lung adenocarcinoma (AC). The genetic mechanisms of the early stages and lung AC progression steps are poorly understood. There is currently no clinically applicable gene test for the early diagnosis and AC aggressiveness. Among the major reasons for the lack of reliable diagnostic biomarkers are the extraordinary heterogeneity of the cancer cells, complex and poorly understudied interactions of the AC cells with adjacent tissue and immune system, gene variation across patient cohorts, measurement variability, small sample sizes and sub-optimal analytical methods. We suggest that gene expression profiling of the primary tumours and adjacent tissues (PT-AT) handled with a rational statistical and bioinformatics strategy of biomarker prediction and validation could provide significant progress in the identification of clinical biomarkers of AC. To minimise sample-to-sample variability, repeated multivariate measurements in the same object (organ or tissue, e.g. PT-AT in lung) across patients should be designed, but prediction and validation on the genome scale with small sample size is a great methodical challenge.</p> <p>Results</p> <p>To analyse PT-AT relationships efficiently in the statistical modelling, we propose an Extreme Class Discrimination (ECD) feature selection method that identifies a sub-set of the most discriminative variables (e.g. expressed genes). Our method consists of a paired Cross-normalization (CN) step followed by a modified sign Wilcoxon test with multivariate adjustment carried out for each variable. Using an Affymetrix U133A microarray paired dataset of 27 AC patients, we reviewed the global reprogramming of the transcriptome in human lung AC tissue versus normal lung tissue, which is associated with about 2,300 genes discriminating the tissues with 100% accuracy. Cluster analysis applied to these genes resulted in four distinct gene groups which we classified as associated with (i) up-regulated genes in the mitotic cell cycle lung AC, (ii) silenced/suppressed gene specific for normal lung tissue, (iii) cell communication and cell motility and (iv) the immune system features. The genes related to mutagenesis, specific lung cancers, early stage of AC development, tumour aggressiveness and metabolic pathway alterations and adaptations of cancer cells are strongly enriched in the AC PT-AT discriminative gene set. Two AC diagnostic biomarkers SPP1 and CENPA were successfully validated on RT-RCR tissue array. ECD method was systematically compared to several alternative methods and proved to be of better performance and as well as it was validated by comparison of the predicted gene set with literature meta-signature.</p> <p>Conclusions</p> <p>We developed a method that identifies and selects highly discriminative variables from high dimensional data spaces of potential biomarkers based on a statistical analysis of paired samples when the number of samples is small. This method provides superior selection in comparison to conventional methods and can be widely used in different applications. Our method revealed at least 23 hundreds patho-biologically essential genes associated with the global transcriptional reprogramming of human lung epithelium cells and lung AC aggressiveness. This gene set includes many previously published AC biomarkers reflecting inherent disease complexity and specifies the mechanisms of carcinogenesis in the lung AC. SPP1, CENPA and many other PT-AT discriminative genes could be considered as the prospective diagnostic and prognostic biomarkers of lung AC.</p

Novel insights into the epidemiology of epidermolysis bullosa (EB) from the Dutch EB Registry:EB more common than previously assumed?

Background Epidermolysis bullosa (EB) is a heterogeneous group of rare and incurable genetic disorders characterized by fragility of the skin and mucosae, resulting in blisters and erosions. Several epidemiological studies in other populations have been carried out, reporting varying and sometimes inconclusive figures, highlighting the need for standardized epidemiological analyses in well-characterized cohorts. Objectives To evaluate the epidemiological data on EB in the Netherlands, extracted from the molecularly well-characterized cohort in the Dutch EB Registry. Methods In this observational study all EB-patients that were based in the Netherlands and captured in the Dutch EB Registry between 1988 and 2018 were included. The epidemiological outcomes were based on complete diagnostic data (clinical features, immunofluorescence, electron microscopy and mutation analysis), with longitudinal follow-up. Results A total of 464 EB-patients (287 families) were included. The incidence and point-prevalence of EB in the Netherlands were 41.3 per million live births and 22.4 per million population, respectively. EB Simplex (EBS), Junctional EB (JEB), Dystrophic EB (DEB) and Kindler EB were diagnosed in 45.7%, 18.8%, 34.7% and 0.9% of the EB-patients, respectively, with an incidence and point-prevalence of 17.5 and 11.9 (EBS), 9.3 and 2.1 (JEB), 14.1 and 8.3 (DEB), 0.5 and 0.2 (Kindler EB). In 90.5% of the EB-patients the diagnosis was genetically confirmed. During the investigated time period 73 EB-patients died, 72.6% of whom as a direct consequence of their EB. Conclusion The epidemiological outcomes of EB in the Netherlands are high, attributed to a high detection rate in a well-organized set-up, indicating that EB might be more common than previously assumed. These epidemiological data help to understand the extensive need for (specialized) medical care of EB-patients and is invaluable for the design and execution of therapeutic trials. This study emphasizes the importance of thorough reporting systems and registries worldwide

Expression profile of nuclear receptors upon epstein — barr virus induced b cell transformation

Background: Infection of human B cells with Epstein—Barr virus (EBV) induces metabolic activation, morphological transformation, cell proliferation and eventual immortalization. Aim: To identify the nuclear receptors, which are the cellular interaction partners of EBNAs, that will help to elucidate the mechanism of B cell transformation. Methods: We have compared the nuclear receptor profile in the naïve and EBV-transformed B-lymphocytes, using TaqMan LDA microfluidic card technology. Results: Out of 48 nuclear receptor, 17 showed differential expression at the mRNA level. The expression of 5 genes was elevated in EBV-transformed cells, whereas 12 genes were downregulated in lymphoblastoid cells (LCLs). 7 genes were studied at the protein level; 2 genes were up regulated (Nr2F2 and RARA) and 4 genes were down regulated (ERB, NUR77, PPARG, and VDR) in LCLs. Conclusion: The nuclear receptor profiling on EBV infected B cells showed alterations of nuclear receptors expression at both mRNA and protein levels compared with non infected peripheral blood cells. Further analysis on a possible role of each nuclear receptor in EBV induced cell transformation should be performed

Human Factors Aspects of the Transfer of Control from the Driver to the Automated Highway System

DTFH-61-92-C-00100The third in a series of experiments exploring human factors issues related to the Automated Highway System (AHS) investigated the transfer of control from the driver of a vehicle entering an automated lane to the AHS. Twenty-four drivers aged between 25 and 34 years drove in the Iowa Driving Simulator--a moving base hexapod platform containing a mid-sized sedan with a 3.35-rad (180 deg) projection screen to the front and a 1.13-rad (60 deg) screen to the rear. The experiment focused on a generic AHS configuration in which the left lane was reserved for automated vehicles, the center and right lanes were reserved for unautomated vehicles, and in which there was no transition lane and no barrier. The driver took the simulator vehicle onto a freeway, moved to the center lane, and then, after receiving an "Enter" command, drove into an automated lane and transferred control to the AHS. Then, the AHS moved the vehicle into the lead position of the string of vehicles approaching it from behind. RESULTS: The entering response time, lane-change time, entering exposure time, and string-joining time data were used to determine the minimum inter-string gap required to enable the driver's vehicle to enter the automated lane without causing a delay to the string it joins. The required minimum inter-string gap varied with the design velocity and the method of transferring control. With the partially automated transfer method, the required minimum inter-string gap time increased from 1.14 s for the 104.7-km/h (65-mi/h) design velocity, through 3.38 s for the 128.8-km/h (80-mi/h) design velocity, to 7.33 s for the 153.0-km/h (95-mi/h) design velocity. The hourly capacity when the design velocity is 104.7 km/h (65 mi/h) is likely to be four times greater than when the design velocity is 153.0 km/h (95 mi/h) (the hourly capacity for the latter would be only slightly more than the traffic flow that could be achieved without an AHS). It is not the design velocity of 104.7 km/h (65 mi/h) per se that produces the higher capacity--it is the relatively low velocity differential between the design velocity and the speed limit in the unautomated lanes. If the transfer of control from the driver to the AHS were to occur before the driver moved into the automated lane, the required minimum inter-string gap times should be reduced--a possibility that is being investigated in the next in the experimental series. No collisions occurred, suggesting that the drivers were able to join the automated lane safely--a suggestion reinforced by the responses to a questionnaire indicating that the drivers felt safe and believed they controlled the vehicle well during the entry maneuver

Assessing copy number aberrations and copy neutral loss of heterozygosity across the genome as best practice: An evidence based review of clinical utility from the cancer genomics consortium (CGC) working group for myelodysplastic syndrome, myelodysplastic/myeloproliferative and myeloproliferative neoplasms

Multiple studies have demonstrated the utility of chromosomal microarray (CMA) testing to identify clinically significant copy number alterations (CNAs) and copy-neutral loss-of-heterozygosity (CN-LOH) in myeloid malignancies. However, guidelines for integrating CMA as a standard practice for diagnostic evaluation, assessment of prognosis and predicting treatment response are still lacking. CMA has not been recommended for clinical work-up of myeloid malignancies by the WHO 2016 or the NCCN 2017 guidelines but is a suggested test by the European LeukaemiaNet 2013 for the diagnosis of primary myelodysplastic syndrome (MDS). The Cancer Genomics Consortium (CGC) Working Group for Myeloid Neoplasms systematically reviewed peer-reviewed literature to determine the power of CMA in (1) improving diagnostic yield, (2) refining risk stratification, and (3) providing additional genomic information to guide therapy. In this manuscript, we summarize the evidence base for the clinical utility of array testing in the workup of MDS, myelodysplastic/myeloproliferative neoplasms (MDS/MPN) and myeloproliferative neoplasms (MPN). This review provides a list of recurrent CNAs and CN-LOH noted in this disease spectrum and describes the clinical significance of the aberrations and how they complement gene mutation findings by sequencing. Furthermore, for new or suspected diagnosis of MDS or MPN, we present suggestions for integrating genomic testing methods (CMA and mutation testing by next generation sequencing) into the current standard-of-care clinical laboratory testing (karyotype, FISH, morphology, and flow)

Cardiomyopathy in patients with epidermolysis bullosa simplex with mutations in KLHL24

Dominant mutations in the KLHL24 gene, encoding for kelch-like protein 24, have been implicated in the pathogenesis of epidermolysis bullosa simplex (EBS). So far, 26 patients from different ethnicities have been reported and all of them harboured a heterozygous KLHL24 start-codon mutation, with c.1A>G;p.Met1? being the most prevalent.1-3 Through this report, we aimed to expand the phenotypic spectrum by incorporating additional findings, in particular, dilated cardiomyopathy, seen in a Dutch family. This article is protected by copyright. All rights reserved

Assessing copy number abnormalities and copy-neutral loss-of-heterozygosity across the genome as best practice in diagnostic evaluation of acute myeloid leukemia: An evidence-based review from the cancer genomics consortium (CGC) myeloid neoplasms working group

Structural genomic abnormalities, including balanced chromosomal rearrangements, copy number gains and losses and copy-neutral loss-of-heterozygosity (CN-LOH) represent an important category of diagnostic, prognostic and therapeutic markers in acute myeloid leukemia (AML). Genome-wide evaluation for copy number abnormalities (CNAs) is at present performed by karyotype analysis which has low resolution and is unobtainable in a subset of cases. Furthermore, examination for possible CN-LOH in leukemia cells is at present not routinely performed in the clinical setting. Chromosomal microarray (CMA) analysis is a widely available assay for CNAs and CN-LOH in diagnostic laboratories, but there are currently no guidelines how to best incorporate this technology into clinical testing algorithms for neoplastic diseases including AML. The Cancer Genomics Consortium Working Group for Myeloid Neoplasms performed an extensive review of peer-reviewed publications focused on CMA analysis in AML. Here we summarize evidence regarding clinical utility of CMA analysis in AML extracted from published data, and provide recommendations for optimal utilization of CMA testing in the diagnostic workup. In addition, we provide a list of CNAs and CN-LOH regions which have documented clinical significance in diagnosis, prognosis and treatment decisions in AML