Deep Multi-Spectral Registration Using Invariant Descriptor Learning

In this paper, we introduce a novel deep-learning method to align cross-spectral images. Our approach relies on a learned descriptor which is invariant to different spectra. Multi-modal images of the same scene capture different signals and therefore their registration is challenging and it is not solved by classic approaches. To that end, we developed a feature-based approach that solves the visible (VIS) to Near-Infra-Red (NIR) registration problem. Our algorithm detects corners by Harris and matches them by a patch-metric learned on top of CIFAR-10 network descriptor. As our experiments demonstrate we achieve a high-quality alignment of cross-spectral images with a sub-pixel accuracy. Comparing to other existing methods, our approach is more accurate in the task of VIS to NIR registration

On the validity of the centrality hypothesis in cross-sectional between-subject networks of psychopathology

Background In the network approach to psychopathology, psychiatric disorders are considered networks of causally active symptoms (nodes), with node centrality hypothesized to reflect symptoms’ causal influence within a network. Accordingly, centrality measures have been used in numerous network-based cross-sectional studies to identify specific treatment targets, based on the assumption that deactivating highly central nodes would proliferate to other nodes in the network, thereby collapsing the network structure and alleviating the overall psychopathology (i.e., the centrality hypothesis). Methods Here, we summarize three types of evidence pertaining to the centrality hypothesis in psychopathology. First, we discuss the validity of the theoretical assumptions underlying the centrality hypothesis in psychopathology. We then summarize the methodological aspects of extant studies using centrality measures as predictors of symptom change following treatment, while delineating their main findings and several of their limitations. Finally, using a specific dataset of 710 treatment-seeking patients with posttraumatic stress disorder (PTSD) as an example, we empirically examine node centrality as a predictor of therapeutic change, replicating the approach taken by previous studies, while addressing some of their limitations. Specifically, we investigated whether three pre-treatment centrality indices (strength, predictability, and expected influence) were significantly correlated with the strength of the association between a symptom’s change and the change in the severity of all other symptoms in the network from pre- to post-treatment (Δnode-Δnetwork association). Using similar analyses, we also examine the predictive validity of two simple non-causal node properties (mean symptom severity and infrequency of symptom endorsement). Results Of the three centrality measures, only expected influence successfully predicted how strongly changes in nodes/symptoms were associated with change in the remainder of the nodes/symptoms. Importantly, when excluding the amnesia node, a well-documented outlier in the phenomenology of PTSD, none of the tested centrality measures predicted symptom change. Conversely, both mean symptom severity and infrequency of symptom endorsement, two standard non-network-derived indices, were found to be more predictive than expected influence and remained significantly predictive also after excluding amnesia from the network analyses. Conclusions The centrality hypothesis in its current form is ill-defined, showing no consistent supporting evidence in the context of cross-sectional, between-subject networks

Human histone H1 variants impact splicing outcome by controlling RNA polymerase II elongation

Histones shape chromatin structure and the epigenetic landscape. H1, the most diverse histone in the human genome, has 11 variants. Due to the high structural similarity between the H1s, their unique functions in transferring information from the chromatin to mRNA-processing machineries have remained elusive. Here, we generated human cell lines lacking up to five H1 subtypes, allowing us to characterize the genomic binding profiles of six H1 variants. Most H1s bind to specific sites, and binding depends on multiple factors, including GC content. The highly expressed H1.2 has a high affinity for exons, whereas H1.3 binds intronic sequences. H1s are major splicing regulators, especially of exon skipping and intron retention events, through their effects on the elongation of RNA polymerase II (RNAPII). Thus, H1 variants determine splicing fate by modulating RNAPII elongation.The research was funded by the Israel Science Foundation (ISF 671/18, ISF 142/13, and ISF 2417/20); the Israel Cancer Research Foundation (ICRF PG-18-105, PG-20-104); and the United States – Israel Binational Science Foundation (BSF 2017086). V.R.R. was supported by Edmond J. Safra Bioinformatics Center fellowship at Tel Aviv University.Peer reviewe

Scalable whole-exome sequencing of cell-free DNA reveals high concordance with metastatic tumors

Whole-exome sequencing of cell-free DNA (cfDNA) could enable comprehensive profiling of tumors from blood but the genome-wide concordance between cfDNA and tumor biopsies is uncertain. Here we report ichorCNA, software that quantifies tumor content in cfDNA from 0.1× coverage whole-genome sequencing data without prior knowledge of tumor mutations. We apply ichorCNA to 1439 blood samples from 520 patients with metastatic prostate or breast cancers. In the earliest tested sample for each patient, 34% of patients have ≥10% tumor-derived cfDNA, sufficient for standard coverage whole-exome sequencing. Using whole-exome sequencing, we validate the concordance of clonal somatic mutations (88%), copy number alterations (80%), mutational signatures, and neoantigens between cfDNA and matched tumor biopsies from 41 patients with ≥10% cfDNA tumor content. In summary, we provide methods to identify patients eligible for comprehensive cfDNA profiling, revealing its applicability to many patients, and demonstrate high concordance of cfDNA and metastatic tumor whole-exome sequencing

The “Facebook-self”: characteristics and psychological predictors of false self-presentation on Facebook

In this study we present and empirically examine a new phenomenon related to social networking sites, such as Facebook, the “false Facebook-self.” Arguably false self-presentation on Facebook is a growing phenomenon, and in extreme cases; i.e., when ones Facebook image substantially deviates from their true image, it may serve as a gateway behavior to more problematic behaviors which may lead to psychological problems and even pathologies. In this study we show that certain users are more vulnerable to such false self-presentation than others. The study involved 258 Facebook users. Applying ANOVA and SEM analyses we show that users with low self-esteem and low trait authenticity are more likely than others to present a Facebook-self which deviates from their true selves. These social-interaction-related traits are influenced by one’s upbringing and the anxious and avoidant attachment styles he or she has developed. Several cases (7.5%) with large gaps between the true and false Facebook-self were detected, which implies that future research should consider the adverse consequences and treatments of high levels of false Facebook-self

Data_Sheet_1_The “Facebook-self”: characteristics and psychological predictors of false self-presentation on Facebook.DOCX

<p>In this study we present and empirically examine a new phenomenon related to social networking sites, such as Facebook, the “false Facebook-self.” Arguably false self-presentation on Facebook is a growing phenomenon, and in extreme cases; i.e., when ones Facebook image substantially deviates from their true image, it may serve as a gateway behavior to more problematic behaviors which may lead to psychological problems and even pathologies. In this study we show that certain users are more vulnerable to such false self-presentation than others. The study involved 258 Facebook users. Applying ANOVA and SEM analyses we show that users with low self-esteem and low trait authenticity are more likely than others to present a Facebook-self which deviates from their true selves. These social-interaction-related traits are influenced by one’s upbringing and the anxious and avoidant attachment styles he or she has developed. Several cases (7.5%) with large gaps between the true and false Facebook-self were detected, which implies that future research should consider the adverse consequences and treatments of high levels of false Facebook-self.</p