Associations between Computationally Derived Parent Emotional Sentiment Scores and Child ADHD and ODD Over Time.

Family emotional climate is often assessed as expressed emotion (EE) using the five-minute speech sample (FMSS). Parent EE is related to child externalizing behavior, but the relationship with ADHD apart from externalizing is unclear. We report the largest ADHD-non-ADHD study of EE to date, introduce computational scoring of the FMSS to assay parent negative sentiment, and use this to evaluate reciprocal parent-child effects over time in ADHD while considering comorbid ODD. Parents of 810 children (nADHD = 509), aged 7-13 years old, completed the FMSS at three points. The FMSS was expert-coded for EE-Criticism at Time 1 and Time 2, negative sentiment was scored at all three time points. Sentiment and EE-Criticism were moderately correlated (r =.39, p <.001, 95% CI [0.32, 0.46]), and each was similarly correlated with baseline ADHD symptoms (r's range 0.31-0.33, p <.001) and ODD symptoms (r(ODD-EE) = 0.35, p <.001; r(ODD-sentiment = 0.28, p <.001). A longitudinal, cross-lagged panel model revealed that increases over time in parental negative sentiment scores led to increased ODD symptoms. Parent sex (namely fathers, but not mothers) showed an interaction effect of sentiment with ADHD. ADHD and ODD are independently and jointly associated with parental EE-Criticism and negative sentiment assessed by the FMSS cross-sectionally. A recursive effects model is supported for ODD, but for ADHD effects depend on which parent is assessed. For fathers, ADHD was related to negative sentiment in complex manners but for mothers, negative sentiment was related primarily to ODD.

MIM-CyCIF: masked imaging modeling for enhancing cyclic immunofluorescence (CyCIF) with panel reduction and imputation.

Cyclic Immunofluorescence (CyCIF) can quantify multiple biomarkers, but panel capacity is limited by technical challenges. We propose a computational panel reduction approach that can impute the information content from 25 markers using only 9 markers, learning co-expression and morphological patterns while concurrently increasing speed and panel content and decreasing cost. We demonstrate strong correlations in predictions and generalizability across breast and colorectal cancer, illustrating applicability of our approach to diverse tissue types.

Ultra high content analyses of circulating and tumor associated hybrid cells reveal phenotypic heterogeneity.

Persistently high, worldwide mortality from cancer highlights the unresolved challenges of disease surveillance and detection that impact survival. Development of a non-invasive, blood-based biomarker would transform survival from cancer. We demonstrate the functionality of ultra-high content analyses of a newly identified population of tumor cells that are hybrids between neoplastic and immune cells in patient matched tumor and peripheral blood specimens. Using oligonucleotide conjugated antibodies (Ab-oligo) permitting cyclic immunofluorescence (cyCIF), we present analyses of phenotypes among tumor and peripheral blood hybrid cells. Interestingly, the majority of circulating hybrid cell (CHC) subpopulations were not identified in tumor-associated hybrids. These results highlight the efficacy of ultra-high content phenotypic analyses using Ab-oligo based cyCIF applied to both tumor and peripheral blood specimens. The combination of a multiplex phenotypic profiling platform that is gentle enough to analyze blood to detect and evaluate disseminated tumor cells represents a novel approach to exploring novel tumor biology and potential utility for developing the population as a blood-based biomarker in cancer.

MIM-CyCIF: Masked Imaging Modeling for Enhancing Cyclic Immunofluorescence (CyCIF) with Panel Reduction and Imputation.

CyCIF can quantify multiple biomarkers, but panel capacity is limited by technical challenges. We propose a computational panel reduction approach that can impute the information content from 25 markers using only 9 markers, learning co-expression and morphological patterns while concurrently increasing speed and panel content and decreasing cost. We demonstrate strong correlations in predictions and generalizability across breast and colorectal cancer, illustrating applicability of our approach to diverse tissue types.

MIM-CyCIF: Masked Imaging Modeling for Enhancing Cyclic Immunofluorescence (CyCIF) with Panel Reduction and Imputation.

CyCIF quantifies multiple biomarkers, but panel capacity is compromised by technical challenges including tissue loss. We propose a computational panel reduction, inferring surrogate CyCIF data from a subset of biomarkers. Our model reconstructs the information content from 25 markers using only 9 markers, learning co-expression and morphological patterns. We demonstrate strong correlations in predictions and generalizability across breast and colorectal cancer tissue microarrays, illustrating broader applicability to diverse tissue types.

SEG: Segmentation Evaluation in absence of Ground truth labels.

Identifying individual cells or nuclei is often the first step in the analysis of multiplex tissue imaging (MTI) data. Recent efforts to produce plug-and-play, end-to-end MTI analysis tools such as MCMICRO1- though groundbreaking in their usability and extensibility - are often unable to provide users guidance regarding the most appropriate models for their segmentation task among an endless proliferation of novel segmentation methods. Unfortunately, evaluating segmentation results on a user's dataset without ground truth labels is either purely subjective or eventually amounts to the task of performing the original, time-intensive annotation. As a consequence, researchers rely on models pre-trained on other large datasets for their unique tasks. Here, we propose a methodological approach for evaluating MTI nuclei segmentation methods in absence of ground truth labels by scoring relatively to a larger ensemble of segmentations. To avoid potential sensitivity to collective bias from the ensemble approach, we refine the ensemble via weighted average across segmentation methods, which we derive from a systematic model ablation study. First, we demonstrate a proof-of-concept and the feasibility of the proposed approach to evaluate segmentation performance in a small dataset with ground truth annotation. To validate the ensemble and demonstrate the importance of our method-specific weighting, we compare the ensemble's detection and pixel-level predictions - derived without supervision - with the data's ground truth labels. Second, we apply the methodology to an unlabeled larger tissue microarray (TMA) dataset, which includes a diverse set of breast cancer phenotypes, and provides decision guidelines for the general user to more easily choose the most suitable segmentation methods for their own dataset by systematically evaluating the performance of individual segmentation approaches in the entire dataset.

How Cerium and Lanthanum as Coproducts Promote Stable Rare Earth Production and New Alloys.

The largest outputs of rare earth mining are the low-value byproducts cerium and lanthanum, which burden rare earth supply chains because they must be separated from more desirable rare earths used in magnet production. Promoting demand for cerium and lanthanum can potentially diversify the economics of rare earth mining and improve supply chain stability for all rare earth elements. A promising avenue for increasing byproduct rare earth element demand is their use in aluminum alloys; an application for cerium and lanthanum offering multiple benefits to manufacturing such as energy reduction and improved throughput. Experimental materials science and economic implications of Al-rare earth element alloys will be discussed. We show that Al-La/Ce alloys have elevated mechanical strength compared to more traditional aluminum alloys, in some formulations can be used without heat treatment, and possess a highly castable eutectic microstructure. This report presents the use of cerium and lanthanum in aluminum alloys as an example of how supply chain focused approaches to technological development can benefit stakeholders at every step in production.

Operando measurement of lattice strain in internal combustion engine components by neutron diffraction.

Engineering neutron diffraction can nondestructively and noninvasively probe stress, strain, temperature, and phase evolutions deep within bulk materials. In this work, we demonstrate operando lattice strain measurement of internal combustion engine components by neutron diffraction. A modified commercial generator engine was mounted in the VULCAN diffractometer at the Spallation Neutron Source, and the lattice strains in both the cylinder block and head were measured under static nonfiring conditions as well as steady state and cyclic transient operation. The dynamic temporal response of the lattice strain change during transient operation was resolved in two locations by asynchronous stroboscopic neutron diffraction. We demonstrated that operando neutron measurements can allow for understanding of how materials behave throughout operational engineering devices. This study opens a pathway for the industrial and academic communities to better understand the complexities of material behavior during the operation of internal combustion engines and other real-scale devices and systems and to leverage techniques developed here for future investigations of numerous new platforms and alloys.