True Interaction Research Articles

SIMVI reveals intrinsic and spatial-induced states in spatial omics data.

Spatial omics technologies enable the analysis of gene expression and interaction dynamics in relation to tissue structure and function. However, existing computational methods may not properly distinguish cellular intrinsic variability and intercellular interactions, and may thus fail to capture spatial regulations for further biological discoveries. Here, we present Spatial Interaction Modeling using Variational Inference (SIMVI), an annotation-free framework that disentangles cell intrinsic and spatial-induced latent variables for modeling gene expression in spatial omics data. We derive theoretical support for SIMVI in disentangling intrinsic and spatial-induced variations. By this disentanglement, SIMVI enables estimation of spatial effects (SE) at a single-cell resolution, and opens up various opportunities for novel downstream analyses. To demonstrate the potential of SIMVI, we applied SIMVI to spatial omics data from diverse platforms and tissues (MERFISH human cortex, Slide-seqv2 mouse hippocampus, Slide-tags human tonsil, spatial multiome human melanoma, cohort-level CosMx melanoma). In all tested datasets, SIMVI effectively disentangles variations and infers accurate spatial effects compared with alternative methods. Moreover, on these datasets, SIMVI uniquely uncovers complex spatial regulations and dynamics of biological significance. In the human tonsil data, SIMVI illuminates the cyclical spatial dynamics of germinal center B cells during maturation. Applying SIMVI to both RNA and ATAC modalities of the multiome melanoma data reveals potential tumor epigenetic reprogramming states. Application of SIMVI on our newly-collected cohort-level CosMx melanoma dataset uncovers space-and-outcome-dependent macrophage states and the underlying cellular communication machinery in the tumor microenvironments.

Generating Baikal-GVD high energy cascade-like neutrino events with a GEANT4-based simulation toolkit

Abstract Using GEANT4 and Cosmic Ray Monte Carlo (CRMC) software packages, we developed a new simulation toolkit for astrophysical neutrino telescopes. By configuring the Baikal-GVD detector and comparing the vertex position and direction of incident particles, as well as the channel-by-channel signals, to the events detected by Baikal-GVD, we successfully generated 13 high-energy cascades neutrino events with the toolkit. Our analysis revealed a systematic offset between the reconstructed shower position and the true interaction position, with a distance close to the scale of the shower maximum of -0.64±1.32 m. The results of the linear fit between real data and simulation demonstrate reliable outcomes, indicating that the simulation toolkit is capable of studying the performance of astrophysical neutrino telescopes.

A New Level of Connectivity- 5G Package Considerations

Understanding the intricacies of 5G and the various technology challenges leave most people confused especially when RF frequency spectrum is brought into the discussion. We hear today more and more about faster internet speeds as the key benefit but what does this really mean to most people and how will this really be different than 4G? For most, just downloading a video or meme may be good enough, so how will this next generation of 5G really impact our lives? A good way to understand the 5G revolution is by looking at a similarity to what is happening with artificial intelligence (AI). Prior to Deep Learning, most AI methods were not able to produce the results that could exceed the human error rate, but around 2015, this changed. Deep Learning leverages what is called a “neural network” or basically a system of nodes that perform calculations but more in a parallel format vs a serial approach. The evolution of Wide IO memory has helped to enable this neural network type approach of computing by storing the outputs of these calculations into a parallel node like format. 5G is like deep learning in that mMIMO (Massive Multiple in Multiple Out) essentially creates a parallel or array approach to RF signals when communicating from towers which allow for many more device connections. Fundamentally, this larger number of connections can help prevent reduced data rates due to traffic and can allow multiple paths for data transfer. Setting data transfer speed aside, the ability for more devices to be connected reliabily and even using each other for connections as well begins to bring a true cross-linked network that has not existed. The ability to handle many connections and even segment the type of connections depending on application space, begins to paint the picture of the future to come. To enable these new technical approaches requires some new thinking for IC Packaging as well. To understand the various RF frequencies, you need to understand how certain frequencies are being used today. When you think about AM/FM radio, these are in the Kilohertz/Megahertz ranges and generally their signals travel far- usually 10s to 100s of miles. Cellular phone technology starting with 1G, 2G, 3G are in the Mhz to GHz and generally the higher the frequency, the higher the data rates. The challenge is that as you move up in frequency, the signal does not travel as far due to the energy being absorbed into materials. For Sub6Ghz, this is not a problem but at higher mmwave Ghz ranges, it can be real challenge when the signal is being used for data transfer and not just a proximity sensor like what is being used in automotive. For frequencies at 20Gh and higher, these signals tend to get absorbed in solid materials such as walls and thus a direct line of sight is required for a reliable signal. This can pose major challenges when trying to roll out a full network to support 5G and would also require a lot of towers to support due to the short effective signal distance for a good signal strength (around 1000 ft or less). For dense central business districts or on commuter trains this may be ok but wide area rollout continues to be difficult. The IC Packaging challenges are even greater at these higher mmwave frequencies due to the material sensitivities to handle these very fast speeds. For a signal to travel at these speeds, they travel on the outside of the copper trace (Skin) and minor roughness of these traces can initiate a skin effect that causes signal loss. The package design considerations for Sub 6Ghz are very different than for mmWave frequencies and require a different approach. Today, most of the world has focused initially on the sub 6Ghz frequency spectrum to roll out their 5G networks. This is primarily due to the similar RF considerations for infrastructure, handsets and other devices when moving from 4G/LTE. The key benefit that keeps the industry working on mmWave is the much-improved speed and low latency that it will provide. These challenges will take time to resolve and research in new materials, new process techniques, tighter tolerance process requirements, true 3D interaction impacts, and other considerations will be required to create a successful solution. The complexity of mmWave has driven several unique packaging solutions, one disadvantage to not having any design/package related standards in place. Which package solutions will end up winning in this higher frequency area remains to be seen but RF test at the device level and the integrated final product level will be critical for validating their performance. This presentation will step through a background on the 5G market and will also dive a little deeper into both Sub6Ghz packaging solutions as well as mmWave considerations.

Dual perspective denoising model for session-based recommendation

Session-based recommendation predicts the next interaction option for an anonymous user based on his/her short-term behavior records. A key challenge faced by session-based recommendation is the processing of noise information. That is, the precise identification of noise items and noise connections. Existing research typically follows two assumptions: (1) the latest interaction item is directly treated as the user’s real-time preference; (2) the temporal order relationship between items is directly treated as the user’s true interaction intention conversion pattern. These strict assumptions ignore two facts: (1) the items that the user accidentally clicked on also exist in the interaction session. (2) the connection edges caused by noise items or user interaction intention drift are also fused into the explicit sequential structure. To address the above two issues, we propose a new Dual Perspective Denoising Model for session-based recommendation called DPDM. Specifically, DPDM mainly consists of two parts: Noise Item Recognition Network and Noise Structure Reconstruction Network. The Noise Item Recognition Network removes noise items from the session through a sparse attention mechanism and virtual target representation. The Noise Structure Reconstruction Network composed of Sequence Module and Co-occurrence Module is used to mine users’ real interaction intention conversion patterns. The Sequence Module captures sequence relationships after removing noise items. The Co-occurrence Module eliminates noise connections by optimizing explicit interaction sequences with L0 activation regularization. Extensive experiments were conducted on three representative real-world datasets, and DPDM achieved positive results, demonstrating the effectiveness of the components in the model.

Nutraceuticals known to promote hair growth do not interfere with the inhibitory action of tamoxifen in MCF7, T47D and BT483 breast cancer cell lines.

Hair loss/thinning is a common side effect of tamoxifen in estrogen receptor (ER) positive breast cancer therapy. Some nutraceuticals known to promote hair growth are avoided during breast cancer therapy for fear of phytoestrogenic activity. However, not all botanical ingredients have similarities to estrogens, and in fact, no information exists as to the true interaction of these ingredients with tamoxifen. Therefore, this study sought to ascertain the effect of nutraceuticals (+/- estrogen/tamoxifen), on proliferation of breast cancer cells and the relative expression of ERα/β. Kelp, Astaxanthin, Saw Palmetto, Tocotrienols, Maca, Horsetail, Resveratrol, Curcumin and Ashwagandha were assessed on proliferation of MCF7, T47D and BT483 breast cancer cell lines +/- 17β-estradiol and tamoxifen. Each extract was analysed by high performance liquid chromatography (HPLC) prior to use. Cellular ERα and ERβ expression was assessed by qRT-PCR and western blot. Changes in the cellular localisation of ERα:ERβ and their ratio following incubation with the nutraceuticals was confirmed by immunocytochemistry. Estradiol stimulated DNA synthesis in three different breast cancer cell lines: MCF7, T47D and BT483, which was inhibited by tamoxifen; this was mirrored by a specific ERa agonist in T47D and BT483 cells. Overall, nutraceuticals did not interfere with tamoxifen inhibition of estrogen; some even induced further inhibition when combined with tamoxifen. The ERα:ERβ ratio was higher at mRNA and protein level in all cell lines. However, incubation with nutraceuticals induced a shift to higher ERβ expression and a localization of ERs around the nuclear periphery. As ERα is the key driver of estrogen-dependent breast cancer, if nutraceuticals have a higher affinity for ERβ they may offer a protective effect, particularly if they synergize and augment the actions of tamoxifen. Since ERβ is the predominant ER in the hair follicle, further studies confirming whether nutraceuticals can shift the ratio towards ERβ in hair follicle cells would support a role for them in hair growth. Although more research is needed to assess safety and efficacy, this promising data suggests the potential of nutraceuticals as adjuvant therapy for hair loss in breast cancer patients receiving endocrine therapy.

Posterior inference of Hi-C contact frequency through sampling.

Hi-C is one of the most widely used approaches to study three-dimensional genome conformations. Contacts captured by a Hi-C experiment are represented in a contact frequency matrix. Due to the limited sequencing depth and other factors, Hi-C contact frequency matrices are only approximations of the true interaction frequencies and are further reported without any quantification of uncertainty. Hence, downstream analyses based on Hi-C contact maps (e.g., TAD and loop annotation) are themselves point estimations. Here, we present the Hi-C interaction frequency sampler (HiCSampler) that reliably infers the posterior distribution of the interaction frequency for a given Hi-C contact map by exploiting dependencies between neighboring loci. Posterior predictive checks demonstrate that HiCSampler can infer highly predictive chromosomal interaction frequency. Summary statistics calculated by HiCSampler provide a measurement of the uncertainty for Hi-C experiments, and samples inferred by HiCSampler are ready for use by most downstream analysis tools off the shelf and permit uncertainty measurements in these analyses without modifications.

Developing a Virtual Reality Simulation System for Preoperative Planning of Robotic-Assisted Thoracic Surgery.

Background. Robotic-assisted thoracic surgery (RATS) is now standard for lung cancer treatment, offering advantages over traditional methods. However, RATS's minimally invasive approach poses challenges like limited visibility and tactile feedback, affecting surgeons' navigation through com-plex anatomy. To enhance preoperative familiarization with patient-specific anatomy, we devel-oped a virtual reality (VR) surgical navigation system. Using head-mounted displays (HMDs), this system provides a comprehensive, interactive view of the patient's anatomy pre-surgery, aiming to improve preoperative simulation and intraoperative navigation. Methods. We integrated 3D data from preoperative CT scans into Perspectus VR Education software, displayed via HMDs for in-teractive 3D reconstruction of pulmonary structures. This detailed visualization aids in tailored preoperative resection simulations. During RATS, surgeons access these 3D images through Tile-ProTM multi-display for real-time guidance. Results. The VR system enabled precise visualization of pulmonary structures and lesion relations, enhancing surgical safety and accuracy. The HMDs offered true 3D interaction with patient data, facilitating surgical planning. Conclusions. VR sim-ulation with HMDs, akin to a robotic 3D viewer, offers a novel approach to developing robotic surgical skills. Integrated with routine imaging, it improves preoperative planning, safety, and accuracy of anatomical resections. This technology particularly aids in lesion identification in RATS, optimizing surgical outcomes.

Optimizing graphics for understanding real-scene using 3D image registration and tracking and mapping

A satisfactory graphic design and good-looking 3D models and environments are the backbones of a positive user experience, especially in Augmented Reality (AR) / Virtual Reality (VR) app development. Where these technologies is seen as the an excellent realm of human-computer interaction. The purpose is to fool the viewer by the seamless incorporation of simulated features. Every AR system relies on true interaction and three-dimensional registration to function properly. In this research, we present a strategy for real-world 3D image registration and tracking. The primary foci of this study are the first three stages: initial registrations and matrix acquisitions, road scene feature extraction, and virtual information registration. At initial registration, a rough virtual plane is estimated onto which the objects will be projected. To this, we propose YoloV3 for transferring features from a virtual to a real-world setting. The projection process concludes with a guess at the camera’s posture matrix. This tech is used in the vehicle’s head-up display to augment reality. The average time required to register a virtual item is 43 seconds. The final step in making augmented reality content is to merge the computer-generated images of virtual objects with real-world photographs in full colour. Our results indicate that this method is effective and precise for 3D photo registration but has the potential to dramatically increase the verisimilitude of AR systems.

Isotropic compression simulation of kaolinite using coarse-grained molecular dynamics

It is now viable to use coarse-grained molecular dynamics (CGMD) to model interacting clay particles in simulations of soil mechanics element tests. In CGMD, particle interactions are described by the Gay-Berne (GB) potential, which can approximate the potential energy between clay particles as a function of their separation and relative orientations; however, a previous study identified a significant shortcoming of the GB potential, which is that it lacks a local maximum called the “energy barrier” that the true interaction possesses at very close range. In this study, we propose a modified GB potential which can capture the energy barrier at and we use this new potential function to simulate the clay mineral kaolinite under isotropic compression. Our simulations show that the energy barrier is a crucial ingredient required to reproduce the elastoplastic behaviour observed in laboratory tests upon unloading from an isotropic normally consolidated state. Our data show that the difference in mechanical behaviour between normally consolidated clay and overconsolidated clay can be explained by the fact that, during the initial loading, some pairs of interacting particles surmount the energy barrier so that they then experience a large attractive force. Effectively, these particles become bounded and do not separate when the stress applied to the sample is released. The response of overconsolidated clay to applied stress is stiffer than that of normally consolidated clay because a larger proportion of the clay particle interactions exist in this bonded state.

Kinetic energy distributions of atomic ions from disintegration of argon containing nanoclusters in moderately intense nanosecond laser fields: Coulomb explosion or hydrodynamic expansion.

We report kinetic energies (KE) of multiply charged atomic ions (MCAI) from interactions of moderately intense nanosecond lasers at 532 nm with argon containing clusters, including neat and doped clusters with a trace amount of trichlorobenzene. We develop a mathematical method to retrieve speed and thereby kinetic energy information from analyzing the time-of-flight profiles of the MCAI. This method should be generally applicable in detections of energetic charged particles with high velocities, a realm where velocity map imaging is inadequate. From this analysis, we discover that the KE of MCAI from doped clusters demonstrates a quadratic dependence on the charge of the atomic ions, while for neat clusters, the dependence is cubic. This result confirms the nature of the cluster disintegration process to be dominated by Coulomb explosion. This result bears more similarity to reports from extreme vacuum ultraviolet (EUV) fields with similar intensities, than to reports from near infrared (NIR) intense laser fields. However, the charge state distribution from our experiment is the opposite: we observe more higher charge state ions than reported in EUV fields, and our charge state distribution is actually similar to those reported in NIR fields. We also report a significant effect of the external electric field on the charge state distribution of the atomic ions: the presence of an electric field can significantly increase the charge from the atomic ions, as shown by a three-fold reduction in the average kinetic energy per charge. Although molecular dynamics simulations have been implemented for experiments in the EUV and NIR, our results allude to the need of a concerted effort in this regime of moderately intense nanosecond laser fields. The significant decrease in charge state distribution and the significant increase in KE from doped clusters, compared with neat clusters, is a telltale sign that the true interaction time between the laser field and the cluster may be substantially shorter than the duration of the laser, a welcome relief for molecular dynamics simulations.

Studying Conversational Adjustments in Interaction: Beyond Acoustic Phonetic Changes.

We examined which measures of complexity are most informative when studying language produced in interaction. Specifically, using these measures, we explored whether native and nonnative speakers modified the higher level properties of their production beyond the acoustic-phonetic level based on the language background of their conversation partner. Using a subset of production data from the Wildcat Corpus that used Diapix, an interactive picture matching task, to elicit production, we compared English language production at the dyad and individual level across three different pair types: eight native pairs (English-English), eight mixed pairs (four English-Chinese and four English-Korean), and eight nonnative pairs (four Chinese-Chinese and four Korean-Korean). At both the dyad and individual levels, native speakers produced longer and more clausally dense speech. They also produced fewer silent pauses and fewer linguistic mazes relative to nonnative speakers. Speakers did not modify their production based on the language background of their interlocutor. The current study examines higher level properties of language production in true interaction. Our results suggest that speakers' productions were determined by their own language background and were independent of that of their interlocutor. Furthermore, these demonstrated promise for capturing syntactic characteristics of language produced in true dialogue. https://doi.org/10.23641/asha.24712956.

Physics Guided Machine Learning for Variational Multiscale Reduced Order Modeling

We propose a new physics guided machine learning (PGML) paradigm that leverages the variational multiscale (VMS) framework and available data to dramatically increase the accuracy of reduced order models (ROMs) at a modest computational cost. The hierarchical structure of the ROM basis and the VMS framework enable a natural separation of the resolved and unresolved ROM spatial scales. Modern PGML algorithms are used to construct novel models for the interaction among the resolved and unresolved ROM scales. Specifically, the new framework builds ROM operators that are closest to the true interaction terms in the VMS framework. Finally, machine learning is used to reduce the projection error and further increase the ROM accuracy. Our numerical experiments for a two-dimensional vorticity transport problem show that the novel PGML-VMS-ROM paradigm maintains the low computational cost of current ROMs while significantly increasing the ROM accuracy.

Misclassification Bias in the Assessment of Gene-by-Environment Interactions.

Misclassification bias is a common concern in epidemiologic studies. Despite strong bias on main effects, gene-environment interactions have been shown to be biased towards the null under gene-environment independence. In the context of a recent article examining the interaction between nerve agent exposure and paraoxonase-1 gene on Gulf War Illness, we aimed to assess the impact of recall bias-a common misclassfication bias-on the identification of gene-environment interactions when the independence assumption is violated. We derive equations to quantify the bias of the interaction, and numerically illustrate these results by simulating a case-control study of 1000 cases and 1000 controls. Simulation input parameters included exposure prevalence, strength of gene-environment dependence, strength of the main effect, exposure specificity among cases, and strength of the gene-environment interaction. We show that, even if gene-environment independence is violated, we can bound possible gene-environment interactions by knowing the strength and direction of the gene-environment dependence ( ) and the observed gene-environment interaction ( )-thus often still allowing for the identification of such interactions. Depending on whether is larger or smaller than the inverse of , is a lower (if ) or upper (if ) bound for the true interaction. In addition, the bias magnitude is somewhat predictable by examining other characteristics such as exposure prevalence, the strength of the exposure main effect, and directions of the recall bias and gene-environment dependence. Even if gene-environment dependence exists, we may still be able to identify gene-environment interactions even when misclassification bias is present.

When Criminals Abuse the Blockchain: Establishing Personal Jurisdiction in a Decentralised Environment

In August of 2022, the United States Department of Treasury sanctioned the virtual currency mixer Tornado Cash, an open-source and fully decentralised piece of software running on the Ethereum blockchain, subsequently leading to the arrest of one of its developers in the Netherlands. Not only was this the first time the Office of Foreign Assets Control (OFAC) extended its authority to sanction a foreign ‘person’ to software, but the decentralised nature of the software and global usage highlight the challenge of establishing jurisdiction over decentralised software and its global user base. The government claims jurisdiction over citizens, residents, and any assets that pass through the country’s territory. As a global financial center with most large tech companies, this often facilitates the establishment of jurisdiction over global conduct that passes through US servers. However, decentralised programs on blockchains with nodes located around the world challenge this traditional approach as either nearly all countries can claim jurisdiction over users, subjecting users to criminal laws in countries with which they have no true interaction, or they limit jurisdiction, thereby risking abuse by bad actors. This article takes a comparative approach to examine the challenges to establishing criminal jurisdiction on cryptocurrency-related crimes.

Computer Assisted Drug Design of AITC for Treatment of Cancer: A Combined Molecular Target Identification Study and Drug-Target Prediction

Drugs can influence the whole biological system by targeting interaction reactions. However, current databases have minimal drug target information and only a few are validated. This emerges a need to develop new drugs that predict true interaction pairs with high accuracy. Currently, network pharmacology has been used in identifying potential drug targets for allylisothiocyanate (AITC) and in predicting the spread of its activity toward bladder cancer whereas the mechanisms of isothiocyanates remain largely undefined. In this study, we present a computational method to predict targets for AITC. Results showed 13 differentially expressed hubs confirmed by Cytoscape (3.7.2). We further observed that the obtained targets were found to involve a type of Ontology of classification- molecular function, cellular component, biological process and protein class. Pathway enrichment was done by DAVID. This study identifies key genes and pathways involved in bladder cancer which helps us to understand the progression and molecular mechanism underlying bladder cancer. The obtained key genes may serve as active therapeutic targets for bladder cancer treatment. AITC may directly regulate several biological processes and their genes in bladder cancer.

Inference of gene-environment interaction from heterogeneous case-parent trios.

Introduction: In genetic epidemiology, log-linear models of population risk may be used to study the effect of genotypes and exposures on the relative risk of a disease. Such models may also include gene-environment interaction terms that allow the genotypes to modify the effect of the exposure, or equivalently, the exposure to modify the effect of genotypes on the relative risk. When a measured test locus is in linkage disequilibrium with an unmeasured causal locus, exposure-related genetic structure in the population can lead to spurious gene-environment interaction; that is, to apparent gene-environment interaction at the test locus in the absence of true gene-environment interaction at the causal locus. Exposure-related genetic structure occurs when the distributions of exposures and of haplotypes at the test and causal locus both differ across population strata. A case-parent trio design can protect inference of genetic main effects from confounding bias due to genetic structure in the population. Unfortunately, when the genetic structure is exposure-related, the protection against confounding bias for the genetic main effect does not extend to the gene-environment interaction term. Methods: We show that current methods to reduce the bias in estimated gene-environment interactions from case-parent trio data can only account for simple population structure involving two strata. To fill this gap, we propose to directly accommodate multiple population strata by adjusting for genetic principal components (PCs). Results and Discussion: Through simulations, we show that our PC adjustment maintains the nominal type-1 error rate and has nearly identical power to detect gene-environment interaction as an oracle approach based directly on population strata. We also apply the PC-adjustment approach to data from a study of genetic modifiers of cleft palate comprised primarily of case-parent trios of European and East Asian ancestry. Consistent with earlier analyses, our results suggest that the gene-environment interaction signal in these data is due to the self-reported European trios.

The Consequences of Model Misspecification for the Estimation of Nonlinear Interaction Effects

AbstractRecent research has shown that interaction effects may often be nonlinear (Hainmueller, Mummolo, and Xu [2019, Political Analysis 27, 163–192]). As standard interaction effect specifications assume a linear interaction effect, that is, the moderator conditions the effect at a constant rate, this can lead to bias. However, allowing nonlinear interaction effects, without accounting for other nonlinearities and nonlinear interaction effects, can also lead to biased estimates. Specifically, researchers can infer nonlinear interaction effects, even though the true interaction effect is linear, when variables used for covariate adjustment that are correlated with the moderator have a nonlinear effect upon the outcome of interest. We illustrate this bias with simulations and show how diagnostic tools recommended in the literature are unable to uncover the issue. We show how using the adaptive Lasso to identify relevant nonlinearities among variables used for covariate adjustment can avoid this issue. Moreover, the use of regularized estimators, which allow for a fuller set of nonlinearities, both independent and interactive, is more generally shown to avoid this bias and more general forms of omitted interaction bias.

Calculating the power to examine treatment-covariate interactions when planning an individual participant data meta-analysis of randomized trials with a binary outcome.

Before embarking on an individual participant data meta-analysis (IPDMA) project, researchers and funders need assurance it is worth their time and cost. This should include consideration of how many studies are promising their IPD and, given the characteristics of these studies, the power of an IPDMA including them. Here, we show how to estimate the power of a planned IPDMA of randomized trials to examine treatment-covariate interactions at the participant level (ie, treatment effect modifiers). We focus on a binary outcome with binary or continuous covariates, and propose a three-step approach, which assumes the true interaction size is common to all trials. In step one, the user must specify a minimally important interaction size and, for each trial separately (eg, as obtained from trial publications), the following aggregate data: the number of participants and events in control and treatment groups, the mean and SD for each continuous covariate, and the proportion of participants in each category for each binary covariate. This allows the variance of the interaction estimate to be calculated for each trial, using an analytic solution for Fisher's information matrix from a logistic regression model. Step 2 calculates the variance of the summary interaction estimate from the planned IPDMA (equal to the inverse of the sum of the inverse trial variances from step 1), and step 3 calculates the corresponding power based on a two-sided Wald test. Stata and R code are provided, and two examples given for illustration. Extension to allow for between-study heterogeneity is also considered.

Quantification of spatial subclonal interactions enhancing the invasive phenotype of pediatric glioma.

SummaryDiffuse midline gliomas (DMGs) are highly aggressive, incurable childhood brain tumors. They present a clinical challenge due to many factors, including heterogeneity and diffuse infiltration, complicating disease management. Recent studies have described the existence of subclonal populations that may co-operate to drive pro-tumorigenic processes such as cellular invasion. However, a precise quantification of subclonal interactions is lacking, a problem that extends to other cancers. In this study, we combine spatial computational modeling of cellular interactions during invasion with co-evolution experiments of clonally disassembled patient-derived DMG cells. We design a Bayesian inference framework to quantify spatial subclonal interactions between molecular and phenotypically distinct lineages with different patterns of invasion. We show how this approach could discriminate genuine interactions, where one clone enhanced the invasive phenotype of another, from those apparently only due to the complex dynamics of spatially restricted growth. This study provides a framework for the quantification of subclonal interactions in DMG.

A robust experimental and computational analysis framework at multiple resolutions, modalities and coverages.

The ability to study cancer-immune cell communication across the whole tumor section without tissue dissociation is needed, especially for cancer immunotherapy development, which requires understanding of molecular mechanisms and discovery of more druggable targets. In this work, we assembled and evaluated an integrated experimental framework and analytical process to enable genome-wide scale discovery of ligand-receptors potentially used for cellular crosstalks, followed by targeted validation. We assessed the complementarity of four different technologies: single-cell RNA sequencing and Spatial transcriptomic (measuring over >20,000 genes), RNA In Situ Hybridization (RNAscope, measuring 4-12 genes) and Opal Polaris multiplex protein staining (4-9 proteins). To utilize the multimodal data, we implemented existing methods and also developed STRISH (Spatial TRanscriptomic In Situ Hybridization), a computational method that can automatically scan across the whole tissue section for local expression of gene (e.g. RNAscope data) and/or protein markers (e.g. Polaris data) to recapitulate an interaction landscape across the whole tissue. We evaluated the approach to discover and validate cell-cell interaction in situ through in-depth analysis of two types of cancer, basal cell carcinoma and squamous cell carcinoma, which account for over 70% of cancer cases. We showed that inference of cell-cell interactions using scRNA-seq data can misdetect or detect false positive interactions. Spatial transcriptomics still suffers from misdetecting lowly expressed ligand-receptor interactions, but reduces false discovery. RNAscope and Polaris are sensitive methods for defining the location of potential ligand receptor interactions, and the STRISH program can determine the probability that local gene co-expression reflects true cell-cell interaction. We expect that the approach described here will be widely applied to discover and validate ligand receptor interaction in different types of solid cancer tumors.