Adipose tissue is distributed in diverse locations throughout the human body. Not much is known about the extent to which anatomically distinct adipose depots are functionally distinct, specialized organs, nor whether depot-specific characteristics result from intrinsic developmental programs, as opposed to reversible physiological responses to differences in tissue microenvironment. We used DNA microarrays to compare mRNA expression patterns of isolated human adipocytes and cultured adipose stem cells, before and after ex vivo adipocyte differentiation, from seven anatomically diverse adipose tissue depots. Adipocytes from different depots display distinct gene expression programs, which are most closely shared with anatomically related depots. mRNAs whose expression differs between anatomically diverse groups of depots (e.g., subcutaneous vs. internal) suggest important functional specializations. These depot-specific differences in gene expression were recapitulated when adipocyte progenitor cells from each site were differentiated ex vivo, suggesting that progenitor cells from specific anatomic sites are deterministically programmed to differentiate into depot-specific adipocytes. Many developmental transcription factors show striking depot-specific patterns of expression, suggesting that adipocytes in each anatomic depot are programmed during early development in concert with anatomically related tissues and organs. Our results support the hypothesis that adipocytes from different depots are functionally distinct and that their depot-specific specialization reflects distinct developmental programs.

In the context of Industrial Internet of Thing (IIoT) applications, network administrators must use their available bandwidth to both identify best 5G NR (New Radio) configuration and best transmission schemes. In this paper we derive optimal channel access and packet replication schemes for Ultra Reliable Low Latency Communications (URLLC) traffic in 5G and Beyond networks. Based on typical system configurations, we identify three main regimes, characterized by the delay and the consequent presence or absence of feedback received from the network. In particular, we identify (a) extreme situations where there is only one time slot opportunity for packet transmission with possible replications in the frequency dimension, (b) blind repeated replication with several transmission slots opportunities, with no received ACK before the delay expiration, and (c) far-sighted scenarios where, additionally, some ACKs may be received, acknowledging older replicas of the packet. We propose adapted replication models for each radio scenario and develop corresponding mathematical models from which the transmission schemes may be optimized. The proposed policies are semi-distributed, in the sense that the optimal policy is communicated to each device by the network, that then applies it autonomously in order to respect the stringent timing constraints of URLLC. We then show the corresponding system dimensioning for achieving the target reliability and identify the best NR configurations to be deployed in the controlled industrial environments.

Animal agriculture contributes significantly to global warming through ongoing emissions of the potent greenhouse gases methane and nitrous oxide, and displacement of biomass carbon on the land used to support livestock. However, because estimates of the magnitude of the effect of ending animal agriculture often focus on only one factor, the full potential benefit of a more radical change remains underappreciated. Here we quantify the full “climate opportunity cost” of current global livestock production, by modeling the combined, long-term effects of emission reductions and biomass recovery that would be unlocked by a phaseout of animal agriculture. We show that, even in the absence of any other emission reductions, persistent drops in atmospheric methane and nitrous oxide levels, and slower carbon dioxide accumulation, following a phaseout of livestock production would, through the end of the century, have the same cumulative effect on the warming potential of the atmosphere as a 25 gigaton per year reduction in anthropogenic CO2emissions, providing half of the net emission reductions necessary to limit warming to 2°C. The magnitude and rapidity of these potential effects should place the reduction or elimination of animal agriculture at the forefront of strategies for averting disastrous climate change.

Many Industrial Internet of Things (IIoT) use cases are characterized by a large number of users transmitting sporadically packets to a central controller. The transmitted packets have to be conveyed within a very short time so that it is not possible to make per-packet resource reservation, and contention-based access is needed, which is conflicting with the very stringent reliability constraints. In order to achieve both reliability and latency constraints, we consider in this paper the combination of contention-based access with blind replication of packets. Knowing the limited, but large, number of potential users in the system, we propose a semi-centralized channel access scheme where each user is pre-allocated positions for its replicas in case he has a packet to convey. We show, using coding theory, how to design sequences for users so that the number of collisions is minimized. We further exploit our pre-allocation scheme to develop a successive interference cancellation method where the base station tries to decode a packet based on the knowledge of the already decoded colliding packet. We show that the proposed schemes succeed to attain very low loss rates with low resource reservation.

We investigate in this paper uplink multiple transmission schemes for 5G Ultra-Reliable Low Latency Communications (URLLC) traffic. The URLLC class of services has been defined for applications requiring extremely stringent latency and reliability. We show that, in systems with episodic traffic and many users compared with the number of transmission resources, randomly transmitting multiple copies of a packet allows to meet the URLLC requirements. We develop analytical models for the packet loss rate for two contention based multiple transmission schemes and show that one dominates the other in the parameter range for which the URLLC requirements are met. We then showon a possible radio setting for 5G, an example of radio resource dimensioning for different user traffic levels and we illustrate how the latency constraint may limit the allowable traffic for a given radio bandwidth.

Cloud RAN (C-RAN) is a very promising architecture for future mobile network deployment, where the cloud-centric approach is useful in improving total processing load. In this context, radio and baseband network functions processing pose interesting problems that we try to expose and solve in this paper. A novel architecture for C-RAN and a first modeling of the system are proposed. Furthermore, we study the impact of many radio parameters on the processing time. Moreover, a mathematical and a deep learning model are proposed and evaluated for processing time prediction. Results show the feasibility of the proposed approaches.

Low latency targets for ultra-reliable low latency communications (URLLC) may be conflicting with their stringent reliability requirements due to the need for re-transmissions. We explore, in this paper, the different resource allocation schemes for transmissions and re-transmissions depending on the requirements of the underlying service and on the traffic characteristics, focusing on the Industrial Internet of Things. We, namely, consider schemes with individual reservation versus a pool of contention-based reserved resources. We provide novel resource allocation schemes for initial transmissions and re-transmissions and derive corresponding analytical models for loss rates. We then show how to set the system parameters that allow meeting the URLLC requirements with low resource consumption.

Wilms tumor 1 (WT1) is a zinc finger transcriptional regulator, and has been implicated as both a tumor suppressor and oncogene in various malignancies. Mutations in the DNA-binding domain of the WT1 gene are described in 10-15% of normal-karyotype AML (NK-AML) in pediatric and adult patients. Similar WT1 mutations have been reported in adult patients with myelodysplastic syndrome (MDS). WT1 mutations have been independently associated with treatment failure and poor prognosis in NK-AML. Internal tandem duplication (ITD) mutations of FMS-like tyrosine kinase 3 (FLT3) commonly co-occur with WT1-mutant AML, suggesting a cooperative role in leukemogenesis. The functional role of WT1 mutations in hematologic malignancies appears to be complex and is not yet fully elucidated. Here, we describe the hematologic phenotype of a knock-in mouse model of a Wt1 mutation (R394W), described in cases of human leukemia. We show that Wt1 +/R394W mice develop MDS which becomes 100% penetrant in a transplant model, exhibit an aberrant expansion of myeloid progenitor cells, and demonstrate enhanced self-renewal of hematopoietic progenitor cells in vitro. We crossbred Wt1 +/R394W mice with knock-in Flt3 +/ITD mice, and show that mice with both mutations (Flt3 +/ITD/Wt1 +/R394W) develop a transplantable MDS/MPN, with more aggressive features compared to either single mutant mouse model.

The dependence of the conformation of the S-adenosylmethionine (SAM) II riboswitch on the concentration of added Mg(2+) ions and SAM, individually and in mixtures, was monitored by circular dichroism (CD) spectroscopy and by measurement of the diffusion coefficient. The results are analyzed in the context of two complementary quantitative models, both of which are consistent with a single underlying physical model. Magnesium binding sites in the open state have an affinity on average higher than the affinity of those in the compact state, but formation of the compact state is accompanied by an increase in the number of binding sites. Consequently, at low Mg(2+) concentrations, Mg(2+) binds preferentially to the open state, favoring its formation, but at high concentrations, Mg(2+) binds preferentially to the compact state. The affinity of the riboswitch for SAM increases drastically with an increased level of binding of Mg(2+) to the compact pseudoknot conformation. The effect of increasing concentrations of trimethylamine N-oxide (TMAO), a well-studied molecular crowding agent, on the conformation of the riboswitch and its affinity for SAM were also monitored by CD spectroscopy and measurement of diffusion. In the absence of added Mg(2+), high concentrations of TMAO were found to induce a conformational change compatible with the formation of the pseudoknot form but have only a small effect on the affinity of the RNA for SAM.

Circular RNAs comprise a poorly understood new class of noncoding RNA. In this study, we used a combination of targeted deletion, high-resolution splicing detection, and single-cell sequencing to deeply probe ASXL1 circular splicing. We found that efficient circular splicing required the canonical transcriptional start site and inverted AluSx elements. Sequencing-based interrogation of isoforms after ASXL1 overexpression identified promiscuous linear splicing between all exons, with the two most abundant non-canonical linear products skipping the exons that produced the circular isoforms. Single-cell sequencing revealed a strong preference for either the linear or circular ASXL1 isoforms in each cell, and found the predominant exon skipping product is frequently co-expressed with its reciprocal circular isoform. Finally, absolute quantification of ASXL1 isoforms confirmed our findings and suggests that standard methods overestimate circRNA abundance. Taken together, these data reveal a dynamic new view of circRNA genesis, providing additional framework for studying their roles in cellular biology.

The clinically most relevant indications for coronary CT angiography are presented.

The first demonstration that macromolecules could be evolved in a test tube was reported twenty-five years ago. That breakthrough meant that billions of years of chance discovery and refinement could be compressed into a few weeks, and provided a powerful tool that now dominates all aspects of protein engineering. A challenge has been to extend this scientific advance into synthetic chemical space: to enable the directed evolution of abiotic molecules. The problem has been tackled in many ways. These include expanding the natural genetic code to include unnatural amino acids, engineering polyketide and polypeptide synthases to produce novel products, and tagging combinatorial chemistry libraries with DNA. Importantly, there is still no small-molecule analog of directed protein evolution, i.e. a substantiated approach for optimizing complex (≥ 10^9 diversity) populations of synthetic small molecules over successive generations. We present a key advance towards this goal: a tool for genetically-programmed synthesis of small-molecule libraries from large chemical alphabets. The approach accommodates alphabets that are one to two orders of magnitude larger than any in Nature, and facilitates evolution within the chemical spaces they create. This is critical for small molecules, which are built up from numerous and highly varied chemical fragments. We report a proof-of-concept chemical evolution experiment utilizing an outsized genetic code, and demonstrate that fitness traits can be passed from an initial small-molecule population through to the great-grandchildren of that population. The results establish the practical feasibility of engineering synthetic small molecules through accelerated evolution.

Reprogramming of a gene's expression pattern by acquisition and loss of sequences recognized by specific regulatory RNA binding proteins may be a major mechanism in the evolution of biological regulatory programs. We identified that RNA targets of Puf3 orthologs have been conserved over 100-500 million years of evolution in five eukaryotic lineages. Focusing on Puf proteins and their targets across 80 fungi, we constructed a parsimonious model for their evolutionary history. This model entails extensive and coordinated changes in the Puf targets as well as changes in the number of Puf genes and alterations of RNA binding specificity including that: 1) Binding of Puf3 to more than 200 RNAs whose protein products are predominantly involved in the production and organization of mitochondrial complexes predates the origin of budding yeasts and filamentous fungi and was maintained for 500 million years, throughout the evolution of budding yeast. 2) In filamentous fungi, remarkably, more than 150 of the ancestral Puf3 targets were gained by Puf4, with one lineage maintaining both Puf3 and Puf4 as regulators and a sister lineage losing Puf3 as a regulator of these RNAs. The decrease in gene expression of these mRNAs upon deletion of Puf4 in filamentous fungi (N. crassa) in contrast to the increase upon Puf3 deletion in budding yeast (S. cerevisiae) suggests that the output of the RNA regulatory network is different with Puf4 in filamentous fungi than with Puf3 in budding yeast. 3) The coregulated Puf4 target set in filamentous fungi expanded to include mitochondrial genes involved in the tricarboxylic acid (TCA) cycle and other nuclear-encoded RNAs with mitochondrial function not bound by Puf3 in budding yeast, observations that provide additional evidence for substantial rewiring of post-transcriptional regulation. 4) Puf3 also expanded and diversified its targets in filamentous fungi, gaining interactions with the mRNAs encoding the mitochondrial electron transport chain (ETC) complex I as well as hundreds of other mRNAs with nonmitochondrial functions. The many concerted and conserved changes in the RNA targets of Puf proteins strongly support an extensive role of RNA binding proteins in coordinating gene expression, as originally proposed by Keene. Rewiring of Puf-coordinated mRNA targets and transcriptional control of the same genes occurred at different points in evolution, suggesting that there have been distinct adaptations via RNA binding proteins and transcription factors. The changes in Puf targets and in the Puf proteins indicate an integral involvement of RNA binding proteins and their RNA targets in the adaptation, reprogramming, and function of gene expression.

Characterization of the molecular attributes and spatial arrangements of cells and features within complex human tissues provides a critical basis for understanding processes involved in development and disease. Moreover, the ability to automate steps in the analysis and interpretation of histological images that currently require manual inspection by pathologists could revolutionize medical diagnostics. Toward this end, we developed a new imaging approach called multidimensional microscopic molecular profiling (MMMP) that can measure several independent molecular properties in situ at subcellular resolution for the same tissue specimen. MMMP involves repeated cycles of antibody or histochemical staining, imaging, and signal removal, which ultimately can generate information analogous to a multidimensional flow cytometry analysis on intact tissue sections. We performed a MMMP analysis on a tissue microarray containing a diverse set of 102 human tissues using a panel of 15 informative antibody and 5 histochemical stains plus DAPI. Large-scale unsupervised analysis of MMMP data, and visualization of the resulting classifications, identified molecular profiles that were associated with functional tissue features. We then directly annotated H&E images from this MMMP series such that canonical histological features of interest (e.g. blood vessels, epithelium, red blood cells) were individually labeled. By integrating image annotation data, we identified molecular signatures that were associated with specific histological annotations and we developed statistical models for automatically classifying these features. The classification accuracy for automated histology labeling was objectively evaluated using a cross-validation strategy, and significant accuracy (with a median per-pixel rate of 77% per feature from 15 annotated samples) for de novo feature prediction was obtained. These results suggest that high-dimensional profiling may advance the development of computer-based systems for automatically parsing relevant histological and cellular features from molecular imaging data of arbitrary human tissue samples, and can provide a framework and resource to spur the optimization of these technologies.