Crosstalk Model Research Articles

Computational modeling of AMPK and mTOR crosstalk in glutamatergic synapse calcium signaling

Neuronal energy consumption is vital for information processing and memory formation in synapses. The brain consists of just 2% of the human body’s mass, but consumes almost 20% of the body’s energy budget. Most of this energy is attributed to active transport in ion signaling, with calcium being the canonical second messenger of synaptic transmission. Here, we develop a computational model of synaptic signaling resulting in the activation of two protein kinases critical in metabolic regulation and cell fate, AMP-Activated protein kinase (AMPK) and mammalian target of rapamycin (mTOR) and investigate the effect of glutamate stimulus frequency on their dynamics. Our model predicts that frequencies of glutamate stimulus over 10 Hz perturb AMPK and mTOR oscillations at higher magnitudes by up to 36% and change the area under curve (AUC) by 5%. This dynamic difference in AMPK and mTOR activation trajectories potentially differentiates high frequency stimulus bursts from basal neuronal signaling leading to a downstream change in synaptic plasticity. Further, we also investigate the crosstalk between insulin receptor and calcium signaling on AMPK and mTOR activation and predict that the pathways demonstrate multistability dependent on strength of insulin signaling and metabolic consumption rate. Our predictions have implications for improving our understanding of neuronal metabolism, synaptic pruning, and synaptic plasticity.

Abstract 3176: Modeling the complex multi-cellular ecosystem of the tumor microenvironment in colorectal cancer using multi-omics data

Abstract The high inter-tumoral phenotypic heterogeneity of colorectal cancer exists within the tumor microenvironment (TME), which ultimately contributes to overall tumor survival. Tumor cells, as the center of the tumor microenvironment, employ sophisticated signaling networks to alter the cell fate decisions between cancerous and non-cancerous cells. A complex network of interactions is the source of this intercellular communication, resulting in tumor growth and progression, drug resistance, and ineffective treatment response. Recent experimental developments in colorectal cancer (CRC) have shown that understanding various underlying mechanisms of tumor growth and progression requires an in-depth understanding of the complex interactions between tumor cells and their surrounding microenvironment. Here, we analyze single-cell RNA sequencing data in CRC and construct gene regulatory networks to define interactions and identify cell states within the TME. The gene regulatory networks are incorporated as a multi-cellular ecosystem representing an experiment-based computational model of phenotypic crosstalk within the TME of CRC. The model simulates and predicts the multi-cellular responses to colorectal cancer to probe and identify mechanisms of cell state evolution and decisions resulting from chemo-agents, used to target TME components. This research will further our understanding of the multi-cellular crosstalk underlying the mechanisms within the TME and facilitate the development and optimization of cancer therapies that inhibit tumor growth in CRC. Citation Format: Geena Ildefonso, Stacey Finley. Modeling the complex multi-cellular ecosystem of the tumor microenvironment in colorectal cancer using multi-omics data [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 3176.

Efficient Emulation of Computer Models Utilising Multiple Known Boundaries of Differing Dimension

Emulation has been successfully applied across a wide variety of scientific disciplines for efficiently analysing computationally intensive models. We develop known boundary emulation strategies which utilise the fact that, for many computer models, there exist hyperplanes in the input parameter space for which the model output can be evaluated far more efficiently, whether this be analytically or just significantly faster using a more efficient and simpler numerical solver. The information contained on these known hyperplanes, or boundaries, can be incorporated into the emulation process via analytical update, thus involving no additional computational cost. In this article, we show that such analytical updates are available for multiple boundaries of various dimensions. We subsequently demonstrate which configurations of boundaries such analytical updates are available for, in particular by presenting a set of conditions that such a set of boundaries must satisfy. We demonstrate the powerful computational advantages of the known boundary emulation techniques developed on both an illustrative low-dimensional simulated example and a scientifically relevant and high-dimensional systems biology model of hormonal crosstalk in the roots of an Arabidopsis plant.

Accurate holographic light potentials using pixel crosstalk modelling

Arbitrary light potentials have proven to be a valuable and versatile tool in many quantum information and quantum simulation experiments with ultracold atoms. Using a phase-modulating spatial light modulator (SLM), we generate arbitrary light potentials holographically with measured efficiencies between 15 and 40% and an accuracy of <2% root-mean-squared error. Key to the high accuracy is the modelling of pixel crosstalk of the SLM on a sub-pixel scale which is relevant especially for large light potentials. We employ conjugate gradient minimisation to calculate the SLM phase pattern for a given target light potential after measuring the intensity and wavefront at the SLM. Further, we use camera feedback to reduce experimental errors, we remove optical vortices and investigate the difference between the angular spectrum method and the Fourier transform to simulate the propagation of light. Using a combination of all these techniques, we achieved more accurate and efficient light potentials compared to previous studies, and generated a series of potentials relevant for cold atom experiments.

Upregulated GPRC5A disrupting the Hippo pathway promotes the proliferation and migration of pancreatic cancer cells via the cAMP-CREB axis

BackgroundPancreatic cancer has a high mortality rate worldwide, and is predicted to be third leading cause of death in the near future. However, the regulatory mechanisms that inhibit the progression of pancreatic cancer remain elusive. Currently, exploring the function and mechanisms of GPCRs (G-protein coupled receptors) is an important way to discover promising therapeutic targets for cancer.MethodsGPRC5A expression was measured using real-time quantitative PCR, immunohistochemistry and western blot assays. Cell proliferation and migration were assessed using CCK-8, clone formation, wound-healing and transwell assays. A cytosolic/nuclear distribution experiment was used to detect the protein location transfer. A xenograft model of pancreatic cancer was established to explore the role of GPRC5A in vivo.ResultsGPRC5A expression was increased in pancreatic cancer, and disruption of GPRC5A expression inhibited tumor growth in vivo. Mechanistically, GPRC5A positively regulated the transcription of YAP1 through cAMP-CREB signaling. Moreover, we show that the proliferation and migration induced by GPRC5A in pancreatic cancer could be rescued by inhibiting YAP1 expression.ConclusionsGPRC5A interacts with the Hippo pathway to promote the progression of pancreatic cancer. These findings reveal an important crosstalk model and provide potential targets for pancreatic cancer therapy.

Examining crosstalk between CD44 and integrins and its effects on cell migration and traction force in glioblastoma.

Cell migration is a major driver of invasion and metastasis during cancer progression. For cells to migrate, they utilize the actin-myosin cytoskeleton and adhesion molecules to interrogate the external environment and generate traction force. CD44 is an adhesion receptor that is overexpressed in glioblastoma and several other tumors. It plays a role in several processes that contribute to cancer metastasis such as: migration, proliferation, and invasion. CD44 primarily binds to hyaluronic acid in the extracellular matrix (ECM), while integrins primarily bind to collagen and fibronectin. Inside of the cell, integrins and CD44 use adaptor proteins to link to the actin cytoskeleton. Here, we explore the crosstalk between integrin and CD44-mediated cell mechanics. First, traction force microscopy was used on U251WT(WT) and U251 CD44KO glioma cells in integrin-binding conditions. Counterintuitively, we found that traction force in CD44KO cells were higher compared to WT. This supports the idea of crosstalk between CD44 and integrins, where if one is depleted, the other increases to compensate. We further tested this idea by measuring traction force for WT cells treated with IM7, a CD44 blocking antibody, to untreated WT cells. Upon blocking CD44, we consistently observe an increase in cell traction for IM7 treated cells compared to untreated. We also measured traction forces for CD44-binding conditions, by using IM7 as a coating on our polyacrylamide gels. When integrins were blocked on cells seeded onto IM7, traction force increased. We have also measured differences in cell migration under integrin vs CD44 binding conditions. Our data suggest a model of crosstalk between integrins and CD44 where signals can be sent either: CD44-to-integrin or integrin-to-CD44. The outcome of these studies may lead to novel avenues for multi-receptor targeting cancer therapies.

Simulation of substrate coupling for mobile communications SoC – A 20 GHz VCO case study

An innovative substrate coupling simulation methodology with on-the-fly extraction and validation of CMOS substrate parasitic elements, combining boundary element method (BEM) with a computational python based physical layout extraction and RC modeling method, is presented in this work. The proposed methodology is seamlessly integrated into the standard virtuoso based custom circuit design flow and allows the designer to extract fast and accurate a substrate RC mesh, valid from DC to GHz, enabling crosstalk impact estimation and gaining insight on substrate coupling effects from the early stage of the design process until the prototype phase. The proposed substrate crosstalk modeling flow is validated using a 20 GHz VCO circuit as a crosstalk victim. The VCO vehicle is designed using a 65 nm RFCMOS process and the respective RF devices of the specific PDK are used. The PCB transmission line and the package parasitics must be considered as to achieve more accurate and realistic simulation results. The simulation scheme is set using a layout footprint test case and two separate signal injection tap points, each at a certain distance from the VCO victim. This simulation scheme can provide various substrate signal crosstalk scenarios by injecting signals that consist of multiple frequencies into the substrate. Three simulation analysis types, Transient, QPSS and ENVLP, are performed as to be able to designate which analysis is the most suitable for crosstalk simulation in terms of simulation time, accuracy and compatibility. Finally, as a sanity check and methodology validation, crosstalk isolation trends with respect to the aggressor distance and the substrate signal frequency are extracted and compared with the State-of-the-Art results available in literature.

Lorenzetti Showers - A general-purpose framework for supporting signal reconstruction and triggering with calorimeters

Calorimeters play an important role in high-energy physics experiments. Their design includes electronic instrumentation, signal processing chain, computing infrastructure, and also a good understanding of their response to particle showers produced by the interaction of incoming particles. This is usually supported by full simulation frameworks developed for specific experiments so that their access is restricted to the collaboration members only. Such restrictions limit the general-purpose developments that aim to propose innovative approaches to signal processing, which may include machine learning and advanced stochastic signal processing models. This work presents the Lorenzetti Showers, a general-purpose framework that mainly targets supporting novel signal reconstruction and triggering strategies using segmented calorimeter information. This framework fully incorporates developments down to the signal processing chain level (signal shaping, energy estimation, and noise mitigation techniques) to allow advanced signal processing approaches in modern calorimetry and triggering systems. The developed framework is flexible enough to be extended in different directions. For instance, it can become a tool for the phenomenology community to go beyond the usual detector design and physics process generation approaches. Program summaryProgram Title: Lorenzetti ShowersCPC Library link to program files:https://doi.org/10.17632/sy64367452.1Developer's repository link:https://github.com/lorenzetti-hep/lorenzettiLicensing provisions: GPLv3Programming language: Python, C++.Nature of problem: In experimental high-energy physics, simulation is essential for experiment preparation, design and interpretations of ongoing acquisitions. Especially for calorimeters, an accurate simulation that can describe detector geometry, behavior to different physics processes and signal generation close to the readout electronics and data acquisition levels is required to properly develop signal processing and computational methods. Such detectors may face very challenging demands arising from the new designs, such as pileup mitigation and noise reduction tasks under unprecedented levels. In this sense, simulation requirements continuously increase in complexity and performance, because new physics searches require large datasets and accurate modeling to experimental effects.Solution method: The Lorenzetti Showers is an integrated software framework that provides complete calorimeter information close enough to the electronic readout chain. Thus, the proposed framework allows users to access cell readout values, configurable sensor pulse-shapes, crosstalk modeling, and different energy estimation methods. It aims at supporting designs that target low or high pileup operation conditions in an easy-to-use modular structure. The developed framework is based on Pythia 8 (particle generation) and Geant4 (interactions with the calorimeter technique under analysis). An efficient data recording structure was used to allow full access to the Lorenzetti Showers outputs. In summary, the Lorenzetti Showers tool provides to the scientific community a user-friendly, flexible, user-oriented, and low-level calorimeter simulation framework.Additional comments including restrictions and unusual features: The framework current version provides the implementation of a generic segmented calorimeter (electromagnetic and hadronic sections), which may be modified by the user, if desired. It allows the generation of particles interactions using Pythia 8 (native) or any generator compatible with the HepMC format (which may be integrated using an external input file) and propagation through a user-configurable calorimeter using Geant4.

PDAC-on-chip for in vitro modeling of stromal and pancreatic cancer cell crosstalk.

Pancreatic ductal adenocarcinoma (PDAC) mainly develops in the head of the pancreas, within the acino-ductal unit composed of acinar and ductal cells surrounded by pancreatic stellate cells (PSCs). PSCs strongly influence the tumor microenvironment by triggering an intense stromal deposition, which plays a key role in tumor progression and limits drug perfusion. We have developed a microfluidic in vitro model recreating the in vivo tumor-stroma crosstalk to replicate the steps of PDAC evolution towards the establishment of an efficient in vitro platform for innovative therapy validation. The multilayer PDAC-on-chip was designed to culture the PDAC cells and the PSCs embedded in a type I collagen gel in the top and bottom layers, respectively. The presence of a biomimetic nanofibrous membrane in the middle of the chip permits the control of interactions between the two cell lines and the easy analysis of the effects of the crosstalk on cell behavior. First, the PDAC-stromal cell relationship was evaluated under co-culture conditions on 24-well inserts including the PCL/Gel electrospun membrane. This simplified model shows that human fibroblasts change their morphology and secrete larger amounts of IL-6 cytokines in the presence of tumor cells, confirming the activation of stromal cells under co-culture. Then, the PDAC-on-chip system was validated by demonstrating that human fibroblasts seeded in a 3D collagen matrix in the bottom microchannel also change to a myofibroblast-like shape with increased expression of α-SMA and secrete larger amounts of IL-6 cytokines. This microfluidic system is suitable for the evaluation of drug efficacy and serves as a powerful tool for understanding the early evolution steps of PDAC.

COVID-19 plasma exosomes promote proinflammatory immune responses in peripheral blood mononuclear cells

Elevated serum cytokine production in COVID-19 patients is associated with disease progression and severity. However, the stimuli that initiate cytokine production in patients remain to be fully revealed. Virus-infected cells release virus-associated exosomes, extracellular vesicles of endocytic origin, into the blood to deliver viral cargoes able to regulate immune responses. Here, we report that plasma exosomes of COVID-19 patients contain SARS-CoV-2 double stranded RNA (dsRNA) and stimulate robust production of interleukin-6 (IL-6), IL-8, tumor necrosis factor-α (TNF-α), and other inflammatory cytokines and chemokines by human peripheral mononuclear cells. Exosome depletion abolished these stimulated responses. COVID-19 plasma exosomes induced proinflammatory responses in CD4+ T cells, CD8+ T cells, and CD14+ monocytes but not significantly in regulatory T cells, Th17 T cells, or central memory T cells. COVID-19 plasma exosomes protect the SARS-CoV-2 dsRNA cargo from RNase and deliver the dsRNA into recipient cells. These exosomes significantly increase expression of endosomal toll-like receptor 3 (TLR3), TLR7, TLR8, and TLR9 in peripheral T cells and monocytes. A pharmacological inhibitor of TLR3 considerably reduced cytokine and chemokine production by CD4+ and CD8+ T cells but not by CD14+ monocytes, highlighting divergent signaling pathways of immune cells in response to COVID-19 plasma exosomes. Our results identify a novel model of intercellular crosstalk following SARS-CoV-2 infection that evoke immune responses positioned to contribute to elevated cytokine production associated with COVID-19 progression, severity, and long-haul symptoms.

Abstract 633: Delineating tumor-neural communication in brain metastasis with spatiotemporal transcriptomic analysis and multi-cellular crosstalk modeling

Abstract Brain metastases are diagnosed in 20-40% of all cancer patients. Despite the advancements in neurosurgery, radiotherapy, and chemotherapy, one-year survival of metastatic brain tumor patients still within a single digit such that brain metastases constitute about 20% of annual cancer deaths. These facts clearly indicate a critical need for better understanding of mechanisms that promote the growth of brain metastases. Emerging data, including ours, indicate that different types of metastatic cancer cells leverage dissimilar sets of neurotransmitters to favor their outgrowth in the brain. However, there are over sixty types of neurotransmitters in the brain being synthesized and released by various types of neural cells, we need a systemic investigation of how they mediate the pathogenesis of brain metastases in concert. Since neurotransmitters, gliotransmitters, neurotrophic factors, and neural cytokines, called neuroactive substances, are the most abundant and key intercellular communication signals in the brain, we hypothesize that tumor cells leverage these neuroactive substances to assimilate into neural circuitry via communicating with neural cells including neurons, astrocytes, microglia, oligodendrocyte, and others, in promoting the vicious circle of tumor-neural crosstalk during brain metastasis development. By performing spatial transcriptomics analysis on mouse brain sections bearing metastatic tumors, we found that tumor lesions at different locations and even tumor-surrounding areas in the brain regions could be differentiated into different cellular clusters based on gene transcriptomic expressions. We further predicted several paracrine and autocrine signaling between and within tumors as well as tumor-surrounding neural niches in different tumor locations (e.g., centers or edges), of different tumor sizes, and at various tumor development stages, using our Cell-Cell Communication Explorer (CCC-Explorer) modeling software. Functional experiments were conducted to demonstrate that cell type-specific depletion of the secreted NSs deactivated the activities of corresponding receptors and downstream signaling in co-cultured metastatic brain tumor cells. Characterizing spatiotemporal tumor-neural crosstalk in the brain metastatic niche would provide a new strategy in the development of novel and effective therapeutics of brain metastases. Citation Format: Xin Wang, Ying Zhu, Jianting Sheng, Hong Zhao, Stephen Wong. Delineating tumor-neural communication in brain metastasis with spatiotemporal transcriptomic analysis and multi-cellular crosstalk modeling [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 633.

New shapemeter roll technology based on Fiber Bragg Grating technology for on-line flatness monitoring of thin cold rolled metal sheets

The shape of laminated products is a crucial parameter in high-speed and high-volume finishing lines where they must fulfill tight tolerances. Latent defects are detected by flatness rolls (or shapemeter rolls) inserted in the manufacturing line for online flatness control. A prototype of flatness roll has been developed with high spatial resolution (5 mm) and measurement capability over a thickness range of 0.1–1 mm to evaluate flatness and residual stresses of flat metal strip products manufactured by continuous cold rolling (ultra-thin sheets for packaging and automotive markets). The flatness roll prototype relies on pressure-induced strain sensing on segmented blades using Fiber Bragg Grating technology. Synchronous measurement of the flatness profile across strip width rejects disturbances of strip tension during rolling. We provide a proof-of-concept on pilot lines in both static and dynamic conditions (e.g., roll in rotation and in contact with a strip under tension) and the sensing mechanism shows a capability above 3. This combination of performances remains unique up to now. Interblade crosstalk appears to be an important issue. We propose a crosstalk model that agrees with experimental coupling coefficients. A crosstalk matrix inversion yields the distribution in contact pressure from strain data measured on the reverse side. Additional work will be required to use the flatness roll in industrial conditions.

A Dual-Air-Core Coil Sensor With Wide Bandwidth and Large Effective Area in Electromagnetic Exploration System

Air-core coil sensors are widely used in geophysical electromagnetic exploration because of their ease of production, high sensitivity, and broadband. In order to increase the bandwidth of the air-core coil without reducing the number of turns, a new type of dual-air-core coil sensor was proposed for the shallow exploration in the electromagnetic method. The design principle of the dual-air-core coil sensor was introduced, consisting of a low-bandwidth coil and a high-bandwidth coil. The output voltage of the high-frequency coil is used as a linear reference to obtain the amplitude-frequency response data of the low-frequency coil, and the fitting model is constructed to extract the equivalent inductance and distributed capacitance of the low-frequency coil, so as to realize the correction of the system response. In addition, the crosstalk model between air-core coils was established, and a method to suppress crosstalk was proposed. In order to prove the feasibility of the dual-air-core coil sensor, in-laboratory and field test platforms were set up. The experimental results show that the crosstalk between the coils can be eliminated by adjusting the relative position of the coil. The fitting method can effectively increase the bandwidth of the low-bandwidth coil, so that it exceeds the high-frequency coil used, and it is shown to have better performance. The effective area is 59.5 m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> , the amplitude bandwidth of −3 dB is 200 kHz, the phase bandwidth with the error less than ±5° is 220 kHz, and the noise level is 18 fT <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\cdot $ </tex-math></inline-formula> Hz <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$^{-1/2}$ </tex-math></inline-formula> at 1 kHz.

Modeling and Optimization of OAM-MIMO Communication Systems With Unaligned Antennas

The orbital angular momentum (OAM) wireless communication technique is emerging as one of potential techniques for the Sixth generation (6G) wireless communication system. The most advantage of OAM wireless communication technique is the natural orthogonality among different OAM states. However, one of the most disadvantages is the crosstalk among different OAM states which is widely caused by the atmospheric turbulence and the misalignment between the transmitting and receiving antennas. Considering the OAM-based multiple-input multiple-output (OAM-MIMO) transmission system with unaligned antennas, a new channel model is proposed for performance analysis. Moreover, the purity and crosstalk models of the OAM-MIMO transmission system with unaligned antennas are derived for the non-Kolmogorov turbulence. Furthermore, the error probability, bit error rate (BER) and capacity models are derived for OAM-MIMO transmission systems with unaligned antennas. To overcome the disadvantage caused by the unaligned antennas and non-Kolmogorov turbulence, a new optimization algorithm of OAM state interval is proposed to improve the capacity of the OAM-MIMO transmission system. Numerical results indicate that the capacity of OAM-MIMO transmission system is improved by the proposed optimization algorithm. Specifically, the capacity increment of the OAM-MIMO transmission system adopting the proposed optimization algorithm is up to 28.7% and 320.3% when the angle of deflection between the transmitting and receiving antennas is −24 dB and −5 dB, respectively.

Lung-brain 'cross-talk': systemic propagation of cytokines in the ARDS via the bloodstream using a blood transfusion model does not influence cerebral inflammatory response in pigs.

BackgroundInterorgan cross-talk describes the phenomenon in which a primarily injured organ causes secondary damage to a distant organ. This cross-talk is well known between the lung and brain. One theory suggests that the release and systemic distribution of cytokines via the bloodstream from the primarily affected organ sets in motion proinflammatory cascades in distant organs. In this study, we analysed the role of the systemic distribution of cytokines via the bloodstream in a porcine ARDS model for organ cross-talk and possible inflammatory changes in the brain.MethodsAfter approval of the State and Institutional Animal Care Committee, acute respiratory distress syndrome (ARDS) induction with oleic acid injection was performed in seven animals. Eight hours after ARDS induction, blood (35–40 ml kg–1) was taken from these seven ‘ARDS donor’ pigs. The collected ‘ARDS donor’ blood was transfused into seven healthy ‘ARDS-recipient’ pigs. Three animals served as a control group, and blood from these animals was transfused into three healthy pigs after an appropriate ventilation period. All animals were monitored for 8 h using advanced cardiorespiratory monitoring. Postmortem assessment included cerebral (hippocampal and cortex) mediators of early inflammatory response (IL-6, TNF-alpha, iNOS, sLCN-2), wet-to-dry ratio and lung histology. TNF-alpha serum concentration was measured in all groups.ResultsARDS was successfully induced in the ‘ARDS donor’ group, and serum TNF-alpha levels were elevated compared with the ‘ARDS-recipient’ group. In the ‘ARDS-recipient’ group, neither significant ARDS alterations nor upregulation of inflammatory mediators in the brain tissue were detected after high-volume random allogenic ‘ARDS-blood’ transfusion. The role of the systemic distribution of inflammatory cytokines from one affected organ to another could not be confirmed in this study.

H2O2/Ca2+/Zn2+ Complex Can Be Considered a "Collaborative Sensor" of the Mitochondrial Capacity?

In order to maintain a state of well-being, the cell needs a functional control center that allows it to respond to changes in the internal and surrounding environments and, at the same time, carry out the necessary metabolic functions. In this review, we identify the mitochondrion as such an “agora”, in which three main messengers are able to collaborate and activate adaptive response mechanisms. Such response generators, which we have identified as H2O2, Ca2+, and Zn2+, are capable of “reading” the environment and talking to each other in cooperation with the mitochondrion. In this manner, these messengers exchange information and generate a holistic response of the whole cell, dependent on its functional state. In this review, to corroborate this claim, we analyzed the role these actors, which in the review we call “sensors”, play in the regulation of skeletal muscle contractile capacities chosen as a model of crosstalk between Ca2+, Zn2+, and H2O2.

Possible kidney-lung cross-talk in COVID-19: in silico modeling of SARS-CoV-2 infection

BackgroundPublicly available genomics datasets have grown drastically during the past decades. Although most of these datasets were initially generated to answer a pre-defined scientific question, their repurposing can be useful when new challenges such as COVID-19 arise. While the establishment and use of experimental models of COVID-19 are in progress, the potential hypotheses for mechanisms of onset and progression of COVID-19 can be generated by using in silico analysis of known molecular changes during COVID-19 and targets for SARS-CoV-2 invasion.MethodsSelecting condition: COVID-19 infection leads to pneumonia and mechanical ventilation (PMV) and associated with acute kidney injury (AKI). There is increasing data demonstrating mechanistic links between AKI and lung injury caused by mechanical ventilation.Selecting targets: SARS-CoV-2 uses angiotensin-converting enzyme 2 (ACE2) and transmembrane protease serine 2 (TMPRSS2) for cell entry. We hypothesized that expression of ACE2 and TMPRSS2 would be affected in models of AKI and PMV. We therefore evaluated expression of ACE2 and TMPRSS2 as well as other novel molecular players of AKI and AKI-lung cross-talk in the publicly available microarray datasets GSE6730 and GSE60088, which represent gene expression of lungs and kidneys in mouse models of AKI and PMV, respectively.ResultsExpression of COVID-19 related genes ACE2 and TMPRSS2 was downregulated in lungs after 6 h of distant AKI effects. The expression of ACE2 decreased further after 36 h, while expression of TMPRSS2 recovered. In kidneys, both genes were downregulated by AKI, but not by distant lung injury. We also identified 53 kidney genes upregulated by PMV; and 254 lung genes upregulated by AKI, 9 genes of which were common to both organs. 3 of 9 genes were previously linked to kidney-lung cross-talk: Lcn2 (Fold Change (FC)Lung (L) = 18.6, FCKidney (K) = 6.32), Socs3 (FCL = 10.5, FCK = 10.4), Inhbb (FCL = 6.20, FCK = 6.17). This finding validates the current approach and reveals 6 new candidates, including Maff (FCL = 7.21, FCK = 5.98).ConclusionsUsing our in silico approach, we identified changes in COVID-19 related genes ACE2 and TMPRSS2 in traditional mouse models of AKI and kidney-lung cross-talk. We also found changes in new candidate genes, which could be involved in the combined kidney-lung injury during COVID-19.

A Single-Cell Omics Network Model of Cell Crosstalk during the Formation of Primordial Follicles

The fate of fetal germ cells (FGCs) in primordial follicles is largely determined by how they interact with the surrounding granulosa cells. However, the molecular mechanisms underlying this interactive process remain poorly understood. Here, we develop a computational model to characterize how individual genes program and rewire cellular crosstalk across FGCs and somas, how gene regulatory networks mediate signaling pathways that functionally link these two cell types, and how different FGCs diversify and evolve through cooperation and competition during embryo development. We analyze single-cell RNA-seq data of human female embryos using the new model, identifying previously uncharacterized mechanisms behind follicle development. The majority of genes (70%) promote FGC–soma synergism, only with a small portion (4%) that incur antagonism; hub genes function reciprocally between the FGC network and soma network; and germ cells tend to cooperate between different stages of development but compete in the same stage within a developmental embryo. Our network model could serve as a powerful tool to unravel the genomic signatures that mediate folliculogenesis from single-cell omics data.

Performance and characterization of the SPT-3G digital frequency-domain multiplexed readout system using an improved noise and crosstalk model

The third-generation South Pole Telescope camera (SPT-3G) improves upon its predecessor (SPTpol) by an order of magnitude increase in detectors on the focal plane. The technology used to read out and control these detectors, digital frequency-domain multiplexing (DfMUX), is conceptually the same as used for SPTpol, but extended to accommodate more detectors. A nearly 5× expansion in the readout operating bandwidth has enabled the use of this large focal plane, and SPT-3G performance meets the forecasting targets relevant to its science objectives. However, the electrical dynamics of the higher-bandwidth readout differ from predictions based on models of the SPTpol system due to the higher frequencies used and parasitic impedances associated with new cryogenic electronic architecture. To address this, we present an updated derivation for electrical crosstalk in higher-bandwidth DfMUX systems and identify two previously uncharacterized contributions to readout noise, which become dominant at high bias frequency. The updated crosstalk and noise models successfully describe the measured crosstalk and readout noise performance of SPT-3G. These results also suggest specific changes to warm electronics component values, wire-harness properties, and SQUID parameters, to improve the readout system for future experiments using DfMUX, such as the LiteBIRD space telescope.

Photon-Counting CT Reconstruction With a Learned Forward Operator

Photon-Counting CT is an emerging imaging technology that promises higher spatial resolution and the possibility for material decomposition in the reconstruction. A major difficulty in Photon-Counting CT is to efficiently model cross-talk between detectors. In this work, we accelerate image reconstruction tasks for Photon-Counting CT by modelling the cross-talk with an appropriately trained deep convolutional neural network. The main result relates to proving convergence when using such a learned cross-talk model in the context of second-order optimisation methods for spectral CT. Another is to evaluate the method through numerical experiments on small-scale CT acquisitions generated using a realistic physics model. Using the reconstruction with a full cross-talk model as ground truth, the learned cross-talk model results in a 20&#x00A0;dB increase in peak-signal-to noise ratio compared to ignoring cross-talk altogether. At the same time, it effectively cuts the computation time of the full cross-talk model in half. Furthermore, the learned cross-talk model generalises well to both unseen data and unseen detector settings. Our results indicate that such a partially learned forward operator is a suitable way of modelling data generation in Photon-Counting CT with a computational benefit that becomes more noticeable for realistic problem sizes.