Constraining the Models' Response of Tropical Clouds to SST Forcings Using CALIPSO Observations

Here we present preliminary results from the analysis of the low cloud cover (LCC) and cloud radiative effect (CRE) interannual changes in response to sea surface temperature (SST) forcings in two GISS climate models, and 12 other climate models. We further classify them as a function of their ability to reproduce the vertical structure of the cloud response to SST change against 10 years of CALIPSO observations: the constrained models, which match the observation constraint, and the unconstrained models. The constrained models replicate the observed interannual LCC change particularly well (LCC(sub con)=-3.49 1.01 %/K vs. LCC(sub obs)=-3.59 0.28 %/K) as opposed to the unconstrained models, which largely underestimate it (LCC(sub unc) = -1.32 1.28 %/K). As a result, the amount of short-wave warming simulated by the constrained models (CRE(sub con)=2.60 1.13 W/m2/K) is in better agreement with the observations (CRE(sub obs)=3.05 0.28 W/m2/K) than the unconstrained models (CRE(sub con)=0.87 2.63 W/m2/K). Depending on the type of low cloud, the observed relationship between cloud/radiation and surface temperature varies. Over the stratocumulus regions, increasing SSTs generate higher cloud top height along with a large decrease of the cloud fraction below as opposed to a slight decrease of the cloud fraction at each level over the trade cumulus regions. Our results suggest that the models must generate sustainable stratocumulus decks and moist processes in the planetary boundary layer to reproduce these observed features. Future work will focus on defining a method to objectively discriminate these cloud types that can be applied consistently in both the observations and the models

The Cumulus and Stratocumulus CloudSat-CALIPSO Dataset (CASCCAD)

Low clouds continue to contribute greatly to the uncertainty in cloud feedback estimates. Depending on whether a region is dominated by cumulus (Cu) or stratocumulus (Sc) clouds, the interannual low-cloud feedback is somewhat different in both spaceborne and large-eddy simulation studies. Therefore, simulating the correct amount and variation of the Cu and Sc cloud distributions could be crucial to predict future cloud feedbacks. Here we document spatial distributions and profiles of Sc and Cu clouds derived from Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) and CloudSat measurements. For this purpose, we create a new dataset called the Cumulus And Stratocumulus CloudSat-CALIPSO Dataset (CASCCAD), which identifies Sc, broken Sc, Cu under Sc, Cu with stratiform outflow and Cu. To separate the Cu from Sc, we design an original method based on the cloud height, horizontal extent, vertical variability and horizontal continuity, which is separately applied to both CALIPSO and combined CloudSatCALIPSO observations. First, the choice of parameters used in the discrimination algorithm is investigated and validated in selected Cu, Sc and ScCu transition case studies. Then, the global statistics are compared against those from existing passive- and active-sensor satellite observations. Our results indicate that the cloud optical thickness as used in passive-sensor observations is not a sufficient parameter to discriminate Cu from Sc clouds, in agreement with previous literature. Using clustering-derived datasets shows better results although one cannot completely separate cloud types with such an approach. On the contrary, classifying Cu and Sc clouds and the transition between them based on their geometrical shape and spatial heterogeneity leads to spatial distributions consistent with prior knowledge of these clouds, from ground-based, ship-based and field campaigns. Furthermore, we show that our method improves existing ScCu classifications by using additional information on cloud height and vertical cloud fraction variation. Finally, the CASCCAD datasets provide a basis to evaluate shallow convection and stratocumulus clouds on a global scale in climate models and potentially improve our understanding of low-level cloud feedbacks. The CASCCAD dataset (Cesana, 2019, https://doi.org/10.5281/zenodo.2667637) is available on the Goddard Institute for Space Studies (GISS) website at https://data.giss.nasa.gov/clouds/casccad/ (last access: 5 November 2019) and on the zenodo website at https://zenodo.org/record/2667637 (last access: 5 November 2019)

Energy consumption of visual sensor networks: impact of spatio-temporal coverage

Wireless visual sensor networks (VSNs) are expected to play a major role in future IEEE 802.15.4 personal area networks (PANs) under recently established collision-free medium access control (MAC) protocols, such as the IEEE 802.15.4e-2012 MAC. In such environments, the VSN energy consumption is affected by a number of camera sensors deployed (spatial coverage), as well as a number of captured video frames of which each node processes and transmits data (temporal coverage). In this paper we explore this aspect for uniformly formed VSNs, that is, networks comprising identical wireless visual sensor nodes connected to a collection node via a balanced cluster-tree topology, with each node producing independent identically distributed bitstream sizes after processing the video frames captured within each network activation interval. We derive analytic results for the energy-optimal spatiooral coverage parameters of such VSNs under a priori known bounds for the number of frames to process per sensor and the number of nodes to deploy within each tier of the VSN. Our results are parametric to the probability density function characterizing the bitstream size produced by each node and the energy consumption rates of the system of interest. Experimental results are derived from a deployment of TelosB motes and reveal that our analytic results are always within 7%of the energy consumption measurements for a wide range of settings. In addition, results obtained via motion JPEG encoding and feature extraction on a multimedia subsystem (BeagleBone Linux Computer) show that the optimal spatiooral settings derived by our framework allow for substantial reduction of energy consumption in comparison with ad hoc settings

Unraveling the effect of proliferative stress in vivo in hematopoietic stem cell gene therapy mouse study

The hematopoietic system of patients enrolled in hematopoietic stem cells (HSC) gene therapy (GT) treatments is fully reconstituted upon autologous transplantation of engineered stem cells. HSCs highly proliferate up to full restoration of homeostasis and compete for niche homing and engraftment. The impact of the proliferation stress in HSC on genetic instability remains an open question that cured patients advocate for characterizing long-term safety and efficacy. The accumulation of somatic mutations has been widely used as a sensor of proliferative stress. Vector integration site (IS) can be used as a molecular tool for clonal identity, inherited by all HSC progeny, to uncover lineage dynamics in vivo at single-cell level. Here we characterized at single-clone granularity the proliferative stress of HSCs and their progeny over time by measuring the accumulation of mutations from the DNA of each IS. To test the feasibility of the approach, we set-up an experimental framework that combines tumor-prone Cdkn2a-/- and wild type (WT) mouse models of HSC-GT and molecular analyses on different hematopoietic cell lineages after transplantation of HSCs transduced with genotoxic LV (LV.SF.LTR) or GT-like non-genotoxic LV (SIN.LV.PGK). The Cdkn2a-/- mouse model provided the experimental conditions to detect the accumulation of somatic mutations, since the absence of p16INK4A and p19ARF enhances the proliferative potential of cells that have acquired oncogenic mutations. As expected, mice transplanted with Cdkn2a-/- Lin- cells marked with LV.SF.LTR (N=24) developed tumors significantly earlier compared to mock (N=20, p<0.0001), while mice treated with SIN. LV.PGK (N=23) did not. On the other side, mice that received WT Lin- cells treated with LV.SF.LTR (N=25) or SIN.LV.PGK (N=24) vector have not developed tumors. Given this scenario, we expect that Cdkn2a-/- Lin- cells transduced with LV.SF.LTR are associated with higher mutation rates compared to the SIN.LV.PGK group and wild type control mice. The composition of peripheral blood, lymphoid (B and T) and myeloid compartments was assessed by FACS on samples collected every 4 weeks and IS identification. More than 200,000 IS have been recovered. To identify the presence of somatic mutations, the genomic portions of sequencing reads flanking each different IS were analyzed with VarScan2. The accumulation rates of mutations have been evaluated by our new Mutation Index (MI) which normalizes the number of mutations by clones and coverage. Considering that a large portion of IS has been discarded since not covered by a minimum number of 5 unique reads (genomes), the remaining number of IS contained >90% of reads in each group. The MI increased over time in both LV.SF.LTR groups, with higher values for the Cdkn2a-/-. On the other hand, treatment with SIN.LV.PGK resulted in lower MI in both groups compared to LV.SF.LTR groups, reflecting the higher clonal composition of the cells treated with the SIN.LV.PGK and the phenomenon of insertional mutagenesis in the LV.SF.LTR. Moreover, the higher MI values of the SIN.LV.PGK Cdkn2a-/- group compared with the WT group proved the induction of DNA fragility. Our results showed that the analysis of the accumulation of somatic mutations at single clone unraveled HSC proliferation stress in vivo, combining for the first time the analysis of acquired mutations with IS. We are now applying our model to different clinical trials, and studying HSCs sub- clonal trees by symmetric divisions, previously indistinguishable by IS only. Our study will open the doors to in vivo long-term non-invasive studies of HSC stability in patients

Differences in visceral fat and fat bacterial colonization between ulcerative colitis and Crohn's disease. An in vivo and in vitro study

Crohn's disease (CD) is notably characterized by the expansion of visceral fat with small adipocytes expressing a high proportion of anti-inflammatory genes. Conversely, visceral fat depots in ulcerative colitis (UC) patients have never been characterized. Our study aims were a) to compare adipocyte morphology and gene expression profile and bacterial translocation in omental (OM) and mesenteric (MES) adipose tissue of patients with UC and CD, and b) to investigate the effect of bacterial infection on adipocyte proliferation in vitro. Specimens of OM and MES were collected from 11 UC and 11 CD patients, processed and examined by light microscopy. Gene expression profiles were evaluated in adipocytes isolated from visceral adipose tissue using microarray and RTqPCR validations. Bacteria within adipose tissue were immuno-detected by confocal scanning laser microscopy. Adipocytes were incubated with Enterococcus faecalis and cells counted after 24 h. Morphology and molecular profile of OM and MES revealed that UC adipose tissue is less inflamed than CD adipose tissue. Genes linked to inflammation, bacterial response, chemotaxis and angiogenesis were down-regulated in adipocytes from UC compared to CD, whereas genes related to metallothioneins, apoptosis pathways and growth factor binding were up-regulated. A dense perinuclear positivity for Enterococcus faecalis was detected in visceral adipocytes from CD, whereas positivity was weak in UC. In vitro bacterial infection was associated with a five-fold increase in the proliferation rate of OM preadipocytes. Compared to UC, visceral adipose tissue from CD is more inflamed and more colonized by intestinal bacteria, which increase adipocyte proliferation. The influence of bacteria stored within adipocytes on the clinical course of IBD warrants further investigation

Italian cardiovascular mortality charts of the CUORE Project: are they comparable with the SCORE charts?

Background The aim of this study was to build risk charts for the assessment of cardiovascular mortality of the CUORE project, an Italian longitudinal study, and to compare them with the systematic coronary risk evaluation (SCORE) project charts for low risk European countries. Design Random population samples enrolled in the 1980s and 1990s in Italy were included in the analysis: 7520 men and 13 127 women aged 35-69 years without previous cardiovascular events and with a mean follow-up period of 10 years for cardiovascular disease. ICD-9 codes of death certificates similar to those of the SCORE project were considered when they appear as first cause of death. Methods Sex-stratified Cox proportional hazard model including age, systolic blood pressure, ratio between total and HDL cholesterol, and smoking habit as risk factors was used to assess cardiovascular mortality. Results Analysis showed that all risk factors included in the model were statistically significant. The corresponding area under the receiver operating characteristic curve was 0.825 (95% confidence interval: 0.803-0.846) for men and 0.850 (0.823-0.877) for women. The CUORE project charts yielded similar results to the corresponding charts of the SCORE project: Lin's coefficient was 0.929 for men and 0.935 for women. Conclusion The comparison between CUORE and SCORE mortality risk charts shows that SCORE charts reflect quite well the Italian cardiovascular mortality and, correspondingly, Italian cohorts of the CUORE project are quite representative of European countries at low risk for cardiovascular mortality

Normalization of clonal diversity in gene therapy studies using shape constrained splines

Viral vectors are used to insert genetic material into semirandom genomic positions of hematopoietic stem cells which, after reinfusion into patients, regenerate the entire hematopoietic system. Hematopoietic cells originating from genetically modified stem cells will harbor insertions in specific genomic positions called integration sites, which represent unique genetic marks of clonal identity. Therefore, the analysis of vector integration sites present in the genomic DNA of circulating cells allows to determine the number of clones in the blood ecosystem. Shannon diversity index is adopted to evaluate the heterogeneity of the transduced population of gene corrected cells. However, this measure can be affected by several technical variables such as the DNA amount used and the sequencing depth of the library analyzed and therefore the comparison across samples may be affected by these confounding factors. We developed an advanced spline-regression approach that leverages on confounding effects to provide a normalized entropy index. Our proposed method was first validated and compared with two state of the art approaches in a specifically designed in vitro assay. Subsequently our approach allowed to observe the expected impact of vector genotoxicity on entropy level decay in an in vivo model of hematopoietic stem cell gene therapy based on tumor prone mice

HIV-1 mediated insertional activation of STAT5B promotes the formation of a viral reservoir in T regulatory cells

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Met exon 14 skipping: A case study for the detection of genetic variants in cancer driver genes by deep learning

Background: Disruption of alternative splicing (AS) is frequently observed in cancer and might represent an important signature for tumor progression and therapy. Exon skipping (ES) represents one of the most frequent AS events, and in non-small cell lung cancer (NSCLC) MET exon 14 skipping was shown to be targetable. Methods: We constructed neural networks (NN/CNN) specifically designed to detect MET exon 14 skipping events using RNAseq data. Furthermore, for discovery purposes we also developed a sparsely connected autoencoder to identify uncharacterized MET isoforms. Results: The neural networks had a Met exon 14 skipping detection rate greater than 94% when tested on a manually curated set of 690 TCGA bronchus and lung samples. When globally applied to 2605 TCGA samples, we observed that the majority of false positives was characterized by a blurry coverage of exon 14, but interestingly they share a common coverage peak in the second intron and we speculate that this event could be the transcription signature of a LINE1 (Long Interspersed Nuclear Element 1)-MET (Mesenchymal Epithelial Transition receptor tyrosine kinase) fusion. Conclusions: Taken together, our results indicate that neural networks can be an effective tool to provide a quick classification of pathological transcription events, and sparsely connected autoencoders could represent the basis for the development of an effective discovery tool