TRAF3 loss-of-function reveals the noncanonical NF-κB pathway as a therapeutic target in diffuse large B cell lymphoma.

Here, we report recurrent focal deletions of the chr14q32.31-32 locus, including TRAF3, a negative regulator of NF-κB signaling, in de novo diffuse large B cell lymphoma (DLBCL) (24/324 cases). Integrative analysis revealed an association between TRAF3 copy number loss with accumulation of NIK, the central noncanonical (NC) NF-κB kinase, and increased NC NF-κB pathway activity. Accordingly, TRAF3 genetic ablation in isogenic DLBCL model systems caused upregulation of NIK and enhanced NC NF-κB downstream signaling. Knockdown or pharmacological inhibition of NIK in TRAF3-deficient cells differentially impaired their proliferation and survival, suggesting an acquired onco-addiction to NC NF-κB. TRAF3 ablation also led to exacerbated secretion of the immunosuppressive cytokine IL-10. Coculturing of TRAF3-deficient DLBCL cells with CD8+ T cells impaired the induction of Granzyme B and interferon (IFN) γ, which were restored following neutralization of IL-10. Our findings corroborate a direct relationship between TRAF3 genetic alterations and NC NF-κB activation, and highlight NIK as a potential therapeutic target in a defined subset of DLBCL.

Bayesian Semiparametric Longitudinal Inverse-Probit Mixed Models for Category Learning.

Understanding how the adult human brain learns novel categories is an important problem in neuroscience. Drift-diffusion models are popular in such contexts for their ability to mimic the underlying neural mechanisms. One such model for gradual longitudinal learning was recently developed in Paulon et al. (J Am Stat Assoc 116:1114-1127, 2021). In practice, category response accuracies are often the only reliable measure recorded by behavioral scientists to describe human learning. Category response accuracies are, however, often the only reliable measure recorded by behavioral scientists to describe human learning. To our knowledge, however, drift-diffusion models for such scenarios have never been considered in the literature before. To address this gap, in this article, we build carefully on Paulon et al. (J Am Stat Assoc 116:1114-1127, 2021), but now with latent response times integrated out, to derive a novel biologically interpretable class of 'inverse-probit' categorical probability models for observed categories alone. However, this new marginal model presents significant identifiability and inferential challenges not encountered originally for the joint model in Paulon et al. (J Am Stat Assoc 116:1114-1127, 2021). We address these new challenges using a novel projection-based approach with a symmetry-preserving identifiability constraint that allows us to work with conjugate priors in an unconstrained space. We adapt the model for group and individual-level inference in longitudinal settings. Building again on the model's latent variable representation, we design an efficient Markov chain Monte Carlo algorithm for posterior computation. We evaluate the empirical performance of the method through simulation experiments. The practical efficacy of the method is illustrated in applications to longitudinal tone learning studies.

Vibration-Based SHM in the Synthetic Mooring Lines of the Semisubmersible OO-Star Wind Floater under Varying Environmental and Operational Conditions.

As the industry transitions toward Floating Offshore Wind Turbines (FOWT) in greater depths, conventional chain mooring lines become impractical, prompting the adoption of synthetic fiber ropes. Despite their advantages, these mooring lines present challenges in inspection due to their exterior jacket, which prevents visual assessment. The current study focuses on vibration-based Structural Health Monitoring (SHM) in FOWT synthetic mooring lines under uncertainty arising from varying Environmental and Operational Conditions (EOCs). Six damage detection methods are assessed, utilizing either multiple models or a single functional model. The methods are based on Vector Autoregressive (VAR) or Transmittance Function Autoregressive with exogenous input (TF-ARX) models. All methods are evaluated through a Monte Carlo study involving 1100 simulations, utilizing acceleration signals generated from a finite element model of the OO-Star Wind Floater Semi 10 MW wind turbine. With signals from only two measuring positions, the methods demonstrate excellent results, detecting the stiffness reduction of a mooring line at levels 10% through 50%. The methods are also tested for healthy cases, with those utilizing TF-ARX models achieving zero false alarms, even for EOCs not encountered in the training data.

Functional analysis reveals driver cooperativity and novel mechanisms in endometrial carcinogenesis.

High-risk endometrial cancer has poor prognosis and is increasing in incidence. However, understanding of the molecular mechanisms which drive this disease is limited. We used genetically engineered mouse models (GEMM) to determine the functional consequences of missense and loss of function mutations in Fbxw7, Pten and Tp53, which collectively occur in nearly 90% of high-risk endometrial cancers. We show that Trp53 deletion and missense mutation cause different phenotypes, with the latter a substantially stronger driver of endometrial carcinogenesis. We also show that Fbxw7 missense mutation does not cause endometrial neoplasia on its own, but potently accelerates carcinogenesis caused by Pten loss or Trp53 missense mutation. By transcriptomic analysis, we identify LEF1 signalling as upregulated in Fbxw7/FBXW7-mutant mouse and human endometrial cancers, and in human isogenic cell lines carrying FBXW7 mutation, and validate LEF1 and the additional Wnt pathway effector TCF7L2 as novel FBXW7 substrates. Our study provides new insights into the biology of high-risk endometrial cancer and suggests that targeting LEF1 may be worthy of investigation in this treatment-resistant cancer subgroup.

Formate Dehydrogenase Mimics as Catalysts for Carbon Dioxide Reduction.

Formate dehydrogenases (FDH) reversibly catalyze the interconversion of CO2 to formate. They belong to the family of molybdenum and tungsten-dependent oxidoreductases. For several decades, scientists have been synthesizing structural and functional model complexes inspired by these enzymes. These studies not only allow for finding certain efficient catalysts but also in some cases to better understand the functioning of the enzymes. However, FDH models for catalytic CO2 reduction are less studied compared to the oxygen atom transfer (OAT) reaction. Herein, we present recent results of structural and functional models of FDH.

Inspired by Nature-Functional Analogues of Molybdenum and Tungsten-Dependent Oxidoreductases.

Throughout the previous ten years many scientists took inspiration from natural molybdenum and tungsten-dependent oxidoreductases to build functional active site analogues. These studies not only led to an ever more detailed mechanistic understanding of the biological template, but also paved the way to atypical selectivity and activity, such as catalytic hydrogen evolution. This review is aimed at representing the last decade's progress in the research of and with molybdenum and tungsten functional model compounds. The portrayed systems, organized according to their ability to facilitate typical and artificial enzyme reactions, comprise complexes with non-innocent dithiolene ligands, resembling molybdopterin, as well as entirely non-natural nitrogen, oxygen, and/or sulfur bearing chelating donor ligands. All model compounds receive individual attention, highlighting the specific novelty that each provides for our understanding of the enzymatic mechanisms, such as oxygen atom transfer and proton-coupled electron transfer, or that each presents for exploiting new and useful catalytic capability. Overall, a shift in the application of these model compounds towards uncommon reactions is noted, the latter are comprehensively discussed.

Neuro-functional modeling of near-death experiences in contexts of altered states of consciousness.

Near-death experiences (NDEs) including out-of-body experiences (OBEs) have been fascinating phenomena of perception both for affected persons and for communities in science and medicine. Modern progress in the recording of changing brain functions during the time between clinical death and brain death opened the perspective to address and understand the generation of NDEs in brain states of altered consciousness. Changes of consciousness can experimentally be induced in well-controlled clinical or laboratory settings. Reports of the persons having experienced the changes can inform about the similarity of the experiences with those from original NDEs. Thus, we collected neuro-functional models of NDEs including OBEs with experimental backgrounds of drug consumption, epilepsy, brain stimulation, and ischemic stress, and included so far largely unappreciated data from fighter pilot tests under gravitational stress generating cephalic nervous system ischemia. Since we found a large overlap of NDE themes or topics from original NDE reports with those from neuro-functional NDE models, we can state that, collectively, the models offer scientifically appropriate causal explanations for the occurrence of NDEs. The generation of OBEs, one of the NDE themes, can be localized in the temporo-parietal junction (TPJ) of the brain, a multimodal association area. The evaluated literature suggests that NDEs may emerge as hallucination-like phenomena from a brain in altered states of consciousness (ASCs).

Bayesian Wavelet-packet Historical Functional Linear Models.

Historical Functional Linear Models (HFLM) quantify associations between a functional predictor and functional outcome where the predictor is an exposure variable that occurs before, or at least concurrently with, the outcome. Prior work on the HFLM has largely focused on estimation of a surface that represents a time-varying association between the functional outcome and the functional exposure. This existing work has employed frequentist and spline-based estimation methods, with little attention paid to formal inference or adjustment for multiple testing and no approaches that implement wavelet-bases. In this work, we propose a new functional regression model that estimates the time-varying, lagged association between a functional outcome and a functional exposure. Building off of recently developed function-on-function regression methods, the model employs a novel use the wavelet-packet decomposition of the exposure and outcome functions that allows us to strictly enforce the temporal ordering of exposure and outcome, which is not possible with existing wavelet-based functional models. Using a fully Bayesian approach, we conduct formal inference on the time-varying lagged association, while adjusting for multiple testing. We investigate the operating characteristics of our wavelet-packet HFLM and compare them to those of two existing estimation procedures in simulation. We also assess several inference techniques and use the model to analyze data on the impact of lagged exposure to particulate matter finer than 2.5µg, or PM2.5, on heart rate variability in a cohort of journeyman boilermakers during the morning of a typical day's shift.

A Functional Model for Unifying Brain Computer Interface Terminology.

Brain Computer Interface (BCI) technology is a critical area both for researchers and clinical practitioners. The IEEE P2731 working group is developing a comprehensive BCI lexicography and a functional model of BCI. The glossary and the functional model are inextricably intertwined. The functional model guides the development of the glossary. Terminology is developed from the basis of a BCI functional model. This paper provides the current status of the P2731 working group's progress towards developing a BCI terminology standard and functional model for the IEEE.

Conditionally Reprogrammed Human Normal Airway Epithelial Cells at ALI: A Physiological Model for Emerging Viruses.

Cancer cell lines have been used widely in cancer biology, and as biological or functional cell systems in many biomedical research fields. These cells are usually defective for many normal activities or functions due to significant genetic and epigenetic changes. Normal primary cell yields and viability from any original tissue specimens are usually relatively low or highly variable. These normal cells cease after a few passages or population doublings due to very limited proliferative capacity. Animal models (ferret, mouse, etc.) are often used to study virus-host interaction. However, viruses usually need to be adapted to the animals by several passages due to tropism restrictions including viral receptors and intracellular restrictions. Here we summarize applications of conditionally reprogrammed cells (CRCs), long-term cultures of normal airway epithelial cells from human nose to lung generated by conditional cell reprogramming (CR) technology, as an ex vivo model in studies of emerging viruses. CR allows to robustly propagate cells from non-invasive or minimally invasive specimens, for example, nasal or endobronchial brushing. This process is rapid (2 days) and conditional. The CRCs maintain their differentiation potential and lineage functions, and have been used for studies of adenovirus, rhinovirus, respiratory syncytial virus, influenza viruses, parvovirus, and SARS-CoV. The CRCs can be easily used for air-liquid interface (ALI) polarized 3D cultures, and these coupled CRC/ALI cultures mimic physiological conditions and are suitable for studies of viral entry including receptor binding and internalization, innate immune responses, viral replications, and drug discovery as an ex vivo model for emerging viruses.

Conditional cell reprogramming for modeling host-virus interactions and human viral diseases.

Conventional cancer and transformed cell lines are widely used in cancer biology and other fields within biology. These cells usually have abnormalities from the original tumor itself, but may also develop abnormalities due to genetic manipulation, or genetic and epigenetic changes during long-term passages. Primary cultures may maintain lineage functions as the original tissue types, yet they have a very limited life span or population doubling time because of the nature of cellular senescence. Primary cultures usually have very low yields, and the high variability from any original tissue specimens, largely limiting their applications in research. Animal models are often used for studies of virus infections, disease modeling, development of antiviral drugs, and vaccines. Human viruses often need a series of passages in vivo to adapt to the host environment because of variable receptors on the cell surface and may have intracellular restrictions from the cell types or host species. Here, we describe a long-term cell culture system, conditionally reprogrammed cells (CRCs), and its applications in modeling human viral diseases and drug discovery. Using feeder layer coculture in presence of Y-27632 (conditional reprogramming, CR), CRCs can be obtained and rapidly propagated from surgical specimens, core or needle biopsies, and other minimally invasive or noninvasive specimens, for example, nasal cavity brushing. CRCs preserve their lineage functions and provide biologically relevant and physiological conditions, which are suitable for studies of viral entry and replication, innate immune responses of host cells, and discovery of antiviral drugs. In this review, we summarize the applications of CR technology in modeling host-virus interactions and human viral diseases including severe acute respiratory syndrome coronavirus-2 and coronavirus disease-2019, and antiviral discovery.

Neural Network Model of Vestibular Nuclei Reaction to Onset of Vestibular Prosthetic Stimulation.

The vestibular system incorporates multiple sensory pathways to provide crucial information about head and body motion. Damage to the semicircular canals, the peripheral vestibular organs that sense rotational velocities of the head, can severely degrade the ability to perform activities of daily life. Vestibular prosthetics address this problem by using stimulating electrodes that can trigger primary vestibular afferents to modulate their firing rates, thus encoding head movement. These prostheses have been demonstrated chronically in multiple animal models and acutely tested in short-duration trials within the clinic in humans. However, mainly, due to limited opportunities to fully characterize stimulation parameters, there is a lack of understanding of "optimal" stimulation configurations for humans. Here, we model possible adaptive plasticity in the vestibular pathway. Specifically, this model highlights the influence of adaptation of synaptic strengths and offsets in the vestibular nuclei to compensate for the initial activation of the prosthetic. By changing the synaptic strengths, the model is able to replicate the clinical observation that erroneous eye movements are attenuated within 30 minutes without any change to the prosthetic stimulation rate. Although our model was only built to match this time point, we further examined how it affected subsequent pulse rate modulation (PRM) and pulse amplitude modulation (PAM). PAM was more effective than PRM for nearly all stimulation configurations during these acute tests. Two non-intuitive relationships highlighted by our model explain this performance discrepancy. Specifically, the attenuation of synaptic strengths for afferents stimulated during baseline adaptation and the discontinuity between baseline and residual firing rates both disproportionally boost PAM. Comodulation of pulse rate and amplitude has been experimentally shown to induce both excitatory and inhibitory eye movements even at high baseline stimulation rates. We also modeled comodulation and found synergistic combinations of stimulation parameters to achieve equivalent output to only amplitude modulation. This may be an important strategy to reduce current spread and misalignment. The model outputs reflected observed trends in clinical testing and aspects of existing vestibular prosthetic literature. Importantly, the model provided insight to efficiently explore the stimulation parameter space, which was helpful, given limited available patient time.

Assessment of alternative disposal methods to reduce greenhouse gas emissions from municipal solid waste in India.

Open dumping, the most commonly practiced method of solid waste disposal in Indian cities, creates serious environment and economic challenges, and also contributes significantly to greenhouse gas emissions. The present article attempts to analyse and identify economically effective ways to reduce greenhouse gas emissions from municipal solid waste. The article looks at the selection of appropriate methods for the control of methane emissions. Multivariate functional models are presented, based on theoretical considerations as well as the field measurements to forecast the greenhouse gas mitigation potential for all the methodologies under consideration. Economic feasibility is tested by calculating the unit cost of waste disposal for the respective disposal process. The purpose-built landfill system proposed by Yedla and Parikh has shown promise in controlling greenhouse gas and saving land. However, these studies show that aerobic composting offers the optimal method, both in terms of controlling greenhouse gas emissions and reducing costs, mainly by requiring less land than other methods.

Topological performance measures as surrogates for physical flow models for risk and vulnerability analysis for electric power systems.

Critical infrastructure systems must be both robust and resilient in order to ensure the functioning of society. To improve the performance of such systems, we often use risk and vulnerability analysis to find and address system weaknesses. A critical component of such analyses is the ability to accurately determine the negative consequences of various types of failures in the system. Numerous mathematical and simulation models exist that can be used to this end. However, there are relatively few studies comparing the implications of using different modeling approaches in the context of comprehensive risk analysis of critical infrastructures. In this article, we suggest a classification of these models, which span from simple topologically-oriented models to advanced physical-flow-based models. Here, we focus on electric power systems and present a study aimed at understanding the tradeoffs between simplicity and fidelity in models used in the context of risk analysis. Specifically, the purpose of this article is to compare performance estimates achieved with a spectrum of approaches typically used for risk and vulnerability analysis of electric power systems and evaluate if more simplified topological measures can be combined using statistical methods to be used as a surrogate for physical flow models. The results of our work provide guidance as to appropriate models or combinations of models to use when analyzing large-scale critical infrastructure systems, where simulation times quickly become insurmountable when using more advanced models, severely limiting the extent of analyses that can be performed.

PyramidalExplorer: A New Interactive Tool to Explore Morpho-Functional Relations of Human Pyramidal Neurons.

This work presents PyramidalExplorer, a new tool to interactively explore and reveal the detailed organization of the microanatomy of pyramidal neurons with functionally related models. It consists of a set of functionalities that allow possible regional differences in the pyramidal cell architecture to be interactively discovered by combining quantitative morphological information about the structure of the cell with implemented functional models. The key contribution of this tool is the morpho-functional oriented design that allows the user to navigate within the 3D dataset, filter and perform Content-Based Retrieval operations. As a case study, we present a human pyramidal neuron with over 9000 dendritic spines in its apical and basal dendritic trees. Using PyramidalExplorer, we were able to find unexpected differential morphological attributes of dendritic spines in particular compartments of the neuron, revealing new aspects of the morpho-functional organization of the pyramidal neuron.

Box-and-arrow explanations need not be more abstract than neuroscientific mechanism descriptions.

The nature of the relationship between box-and-arrow (BA) explanations and neuroscientific mechanism descriptions (NMDs) is a key foundational issue for cognitive science. In this article we attempt to identify the nature of the constraints imposed by BA explanations on the formulation of NMDs. On the basis of a case study about motor control, we argue that BA explanations and NMDs both identify regularities that hold in the system, and that these regularities place constraints on the formulation of NMDs from BA analyses, and vice versa. The regularities identified in the two kinds of explanation play a crucial role in reasoning about the relationship between them, and in justifying the use of neuroscientific experimental techniques for the empirical testing of BA analyses of behavior. In addition, we make claims concerning the similarities and differences between BA analyses and NMDs. First, we argue that both types of explanation describe mechanisms. Second, we propose that they differ in terms of the theoretical vocabulary used to denote the entities and properties involved in the mechanism and engaging in regular, mutual interactions. On the contrary, the notion of abstractness, defined as omission of detail, does not help to distinguish BA analyses from NMDs: there is a sense in which BA analyses are more detailed than NMDs. In relation to this, we also focus on the nature of the extra detail included in NMDs and missing from BA analyses, arguing that such detail does not always concern how the system works. Finally, we propose reasons for doubting that BA analyses, unlike NMDs, may be considered "mechanism sketches." We have developed these views by critically analyzing recent claims in the philosophical literature regarding the foundations of cognitive science.

Designing a curriculum for communication skills training from a theory and evidence-based perspective.

OBJECTIVE: Because quality health care delivery requires effective clinician-patient communication, successful training of health professionals requires communication skill curricula of the highest quality. Two approaches for developing medical communication curricula are a consensus approach and a theory driven approach. We propose a theory-driven, communication function framework for identifying important communication skills, one that is focused on the key goals and outcomes that need to be accomplished in clinical encounters. We discuss 7 communication functions important to medical encounters and the types of skills needed to accomplish each. DISCUSSION: The functional approach has important pedagogical implications including the importance of distinguishing the performance of a behavior (capacity) from the outcome of that behavior in context (effectiveness) and the recognition that what counts as effective communication depends on perspective (e.g., observer, patient). CONCLUSION: Consensus and theory-driven approaches to medical communication curricula are not necessarily contradictory and can be integrated to further enhance ongoing development and improvements in medical communication education. PRACTICE IMPLICATIONS: A functional approach should resonate with practicing clinicians and continuing education initiatives in that it is embraces the notion that competent communication is situation-specific as clinicians creatively use communicative skills to accomplish the key goals of the encounter.

Current applications of models of genetic effects with interactions across the genome.

Models of genetic effects integrate the action of genes, regulatory regions and interactions among alleles across the genome. Such theoretical frameworks are critical for applied studies in at least two ways. First, discovering genetic networks with specific effects underlying traits in populations requires the development of models that implement those effects as parameters-adjusting the implementation of epistasis parameters in genetic models has for instance been a requirement for properly testing for epistasis in gene-mapping studies. Second, studying the properties and implications of models of genetic effects that involve complex genetic networks has proven to be valuable, whether those networks have been revealed for particular organisms or inferred to be of interest from theoretical works and simulations. Here I review the current state of development and recent applications of models of genetic effects. I focus on general models aiming to depict complex genotype-to-phenotype maps and on applications of them to networks of interacting loci.