Neural dynamics of reversal learning in the prefrontal cortex and recurrent neural networks.

In probabilistic reversal learning, the choice option yielding reward at higher probability switches at a random trial. To perform optimally in this task, one has to accumulate evidence across trials to infer the probability that a reversal has occurred. In this study, we investigated how this reversal probability is represented in cortical neurons by analyzing the neural activity in prefrontal cortex of monkeys and recurrent neural networks trained on the task. We found that neural trajectories encoding reversal probability had substantial dynamics associated with intervening behaviors necessary to perform the task. Furthermore, the neural trajectories were translated systematically in response to whether outcomes were rewarded, and their position in the neural subspace captured information about reward outcomes. These findings suggested that separable dynamic trajectories, instead of fixed points on a line attractor, provided a better description of neural representation of reversal probability. Near the behavioral reversal, in particular, the trajectories shifted monotonically across trials with stable ordering, representing varying estimates of reversal probability around the reversal point. Perturbing the neural trajectory of trained networks biased when the reversal trial occurred, showing the role of reversal probability activity in decision-making. In sum, our study shows that cortical neurons encode reversal probability in a family of dynamic neural trajectories that accommodate flexible behavior while maintaining separability to represent distinct probabilistic values.

3D chromatin architecture, BRD4, and Mediator have distinct roles in regulating genome-wide transcriptional bursting and gene network.

Discontinuous transcription is evolutionarily conserved and a fundamental feature of gene regulation; yet, the exact mechanisms underlying transcriptional bursting are unresolved. Analyses of bursting transcriptome-wide have focused on the role of cis-regulatory elements, but other factors that regulate this process remain elusive. We applied mathematical modeling to single-cell RNA sequencing data to infer bursting dynamics transcriptome-wide under multiple conditions to identify possible molecular mechanisms. We found that Mediator complex subunit 26 (MED26) primarily regulates frequency, MYC regulates burst size, while cohesin and Bromodomain-containing protein 4 (BRD4) can modulate both. Despite comparable effects on RNA levels among these perturbations, acute depletion of MED26 had the most profound impact on the entire gene regulatory network, acting downstream of chromatin spatial architecture and without affecting TATA box-binding protein (TBP) recruitment. These results indicate that later steps in the initiation of transcriptional bursts are primary nodes for integrating gene networks in single cells.

Heterotrimeric collagen helix with high specificity of assembly results in a rapid rate of folding.

The most abundant natural collagens form heterotrimeric triple helices. Synthetic mimics of collagen heterotrimers have been found to fold slowly, even compared to the already slow rates of homotrimeric helices. These prolonged folding rates are not understood. Here we compare the stabilities, specificities and folding rates of three heterotrimeric collagen mimics designed through a computationally assisted approach. The crystal structure of one ABC-type heterotrimer verified a well-controlled composition and register and elucidated the geometry of pairwise cation-π and axial and lateral salt bridges in the assembly. This collagen heterotrimer folds much faster (hours versus days) than comparable, well-designed systems. Circular dichroism and NMR data suggest the folding is frustrated by unproductive, competing heterotrimer species and these species must unwind before refolding into the thermodynamically favoured assembly. The heterotrimeric collagen folding rate is inhibited by the introduction of preformed competing triple-helical assemblies, which suggests that slow heterotrimer folding kinetics are dominated by the frustration of the energy landscape caused by competing triple helices.

Dysbiosis not observed in Canadian horse with free fecal liquid (FFL) using 16S rRNA sequencing.

Free Fecal Liquid (FFL), also termed Fecal Water Syndrome (FWS), is an ailment in horses characterized by variable solid and liquid (water) phases at defecation. The liquid phase can be excreted before, during, or after the solid defecation phase. While the underlying causes of FFL are unknown, hindgut dysbiosis is suggested to be associated with FFL. Three European studies investigated dysbiosis in horses with FFL using 16S rRNA sequencing and reported results that conflicted between each other. In the present study, we also used 16S rRNA sequencing to study the fecal microbial composition in 14 Canadian horses with FFL, and 11 healthy stable mate controls. We found no significant difference in fecal microbial composition between FFL and healthy horses, which further supports that dysbiosis is not associated with FFL.

Gradient-flow adaptive importance sampling for Bayesian leave one out cross-validation for sigmoidal classification models.

We introduce a set of gradient-flow-guided adaptive importance sampling (IS) transformations to stabilize Monte-Carlo approximations of point-wise leave one out cross-validated (LOO) predictions for Bayesian classification models. One can leverage this methodology for assessing model generalizability by for instance computing a LOO analogue to the AIC or computing LOO ROC/PRC curves and derived metrics like the AUROC and AUPRC. By the calculus of variations and gradient flow, we derive two simple nonlinear single-step transformations that utilize gradient information to shift a model's pre-trained full-data posterior closer to the target LOO posterior predictive distributions. In doing so, the transformations stabilize importance weights. Because the transformations involve the gradient of the likelihood function, the resulting Monte Carlo integral depends on Jacobian determinants with respect to the model Hessian. We derive closed-form exact formulae for these Jacobian determinants in the cases of logistic regression and shallow ReLU-activated artificial neural networks, and provide a simple approximation that sidesteps the need to compute full Hessian matrices and their spectra. We test the methodology on an n≪p dataset that is known to produce unstable LOO IS weights.

Interpretable (not just posthoc-explainable) medical claims modeling for discharge placement to reduce preventable all-cause readmissions or death.

We developed an inherently interpretable multilevel Bayesian framework for representing variation in regression coefficients that mimics the piecewise linearity of ReLU-activated deep neural networks. We used the framework to formulate a survival model for using medical claims to predict hospital readmission and death that focuses on discharge placement, adjusting for confounding in estimating causal local average treatment effects. We trained the model on a 5% sample of Medicare beneficiaries from 2008 and 2011, based on their 2009-2011 inpatient episodes (approximately 1.2 million), and then tested the model on 2012 episodes (approximately 400 thousand). The model scored an out-of-sample AUROC of approximately 0.75 on predicting all-cause readmissions-defined using official Centers for Medicare and Medicaid Services (CMS) methodology-or death within 30-days of discharge, being competitive against XGBoost and a Bayesian deep neural network, demonstrating that one need-not sacrifice interpretability for accuracy. Crucially, as a regression model, it provides what blackboxes cannot-its exact gold-standard global interpretation, explicitly defining how the model performs its internal "reasoning" for mapping the input data features to predictions. In doing so, we identify relative risk factors and quantify the effect of discharge placement. We also show that the posthoc explainer SHAP provides explanations that are inconsistent with the ground truth model reasoning that our model readily admits.

Tunable Macroscopic Alignment of Self-Assembling Peptide Nanofibers.

Progress in the design and synthesis of nanostructured self-assembling systems has facilitated the realization of numerous nanoscale geometries, including fibers, ribbons, and sheets. A key challenge has been achieving control across multiple length scales and creating macroscopic structures with nanoscale organization. Here, we present a facile extrusion-based fabrication method to produce anisotropic, nanofibrous hydrogels using self-assembling peptides. The application of shear force coinciding with ion-triggered gelation is used to kinetically trap supramolecular nanofibers into aligned, hierarchical macrostructures. Further, we demonstrate the ability to tune the nanostructure of macroscopic hydrogels through modulating phosphate buffer concentration during peptide self-assembly. In addition, increases in the nanostructural anisotropy of fabricated hydrogels are found to enhance their strength and stiffness under hydrated conditions. To demonstrate their utility as an extracellular matrix-mimetic biomaterial, aligned nanofibrous hydrogels are used to guide directional spreading of multiple cell types, but strikingly, increased matrix alignment is not always correlated with increased cellular alignment. Nanoscale observations reveal differences in cell-matrix interactions between variably aligned scaffolds and implicate the need for mechanical coupling for cells to understand nanofibrous alignment cues. In total, innovations in the supramolecular engineering of self-assembling peptides allow us to decouple nanostructure from macrostructure and generate a gradient of anisotropic nanofibrous hydrogels. We anticipate that control of architecture at multiple length scales will be critical for a variety of applications, including the bottom-up tissue engineering explored here.

Quantitative profiling of human translation initiation reveals regulatory elements that potently affect endogenous and therapeutically modified mRNAs.

mRNA therapeutics offer a potentially universal strategy for the efficient development and delivery of therapeutic proteins. Current mRNA vaccines include chemically modified nucleotides to reduce cellular immunogenicity. Here, we develop an efficient, high-throughput method to measure human translation initiation on therapeutically modified as well as endogenous RNAs. Using systems-level biochemistry, we quantify ribosome recruitment to tens of thousands of human 5' untranslated regions and identify sequences that mediate 250-fold effects. We observe widespread effects of coding sequences on translation initiation and identify small regulatory elements of 3-6 nucleotides that are sufficient to potently affect translational output. Incorporation of N1-methylpseudouridine (m1Ψ) selectively enhances translation by specific 5' UTRs that we demonstrate surpass those of current mRNA vaccines. Our approach is broadly applicable to dissect mechanisms of human translation initiation and engineer more potent therapeutic mRNAs. Highlights: Measurement of >30,000 human 5' UTRs reveals a 250-fold range of translation outputSystematic mutagenesis demonstrates the causality of short (3-6nt) regulatory elementsN1-methylpseudouridine alters translation initiation in a sequence-specific mannerOptimal modified 5' UTRs outperform those in the current class of mRNA vaccines.

Tunable Macroscopic Alignment of Self-Assembling Peptide Nanofibers.

Fibrous proteins that comprise the extracellular matrix (ECM) guide cellular growth and tissue organization. A lack of synthetic strategies able to generate aligned, ECM-mimetic biomaterials has hampered bottom-up tissue engineering of anisotropic tissues and led to a limited understanding of cell-matrix interactions. Here, we present a facile extrusion-based fabrication method to produce anisotropic, nanofibrous hydrogels using self-assembling peptides. The application of shear force coinciding with ion-triggered gelation is used to kinetically trap supramolecular nanofibers into aligned, hierarchical structures. We establish how modest changes in phosphate buffer concentration during peptide self-assembly can be used to tune their alignment and packing. In addition, increases in the nanostructural anisotropy of fabricated hydrogels are found to enhance their strength and stiffness under hydrated conditions. To demonstrate their utility as an ECM-mimetic biomaterial, aligned nanofibrous hydrogels are used to guide directional spreading of multiple cell types, but strikingly, increased matrix alignment is not always correlated with increased cellular alignment. Nanoscale observations reveal differences in cell-matrix interactions between variably aligned scaffolds and implicate the need for mechanical coupling for cells to understand nanofibrous alignment cues. In total, innovations in the supramolecular engineering of self-assembling peptides allow us to generate a gradient of anisotropic nanofibrous hydrogels, which are used to better understand directed cell growth.

Systemic Involvement in Immunoglobulin G4-Related Ophthalmic Disease.

BACKGROUND: Immunoglobulin G4-related ophthalmic disease (IgG4-ROD) poses clinical challenges due to its heterogeneous ocular and systemic manifestations. We aim to report the systemic involvement and the clinical, serological and radiological associations of a cohort of Chinese patients. METHODS: A territory-wide, biopsy-proven, Chinese cohort. A retrospective, masked chart review of medical records, orbital images, and histopathology reports. RESULTS: A total of 122 (65 male) patients with a follow-up of 81 ± 49 (24 to 84) months were reviewed. Ninety (74%) patients presented bilaterally. Subacute upper eyelid swelling was the commonest presentation (82/122, 67%). During follow-up, 91/122 patients (75%) underwent extra-orbital imaging including computer tomography (692 films), ultrasonography (182 films), magnetic resonance imaging (76 films) and whole body FDG-PET scan (33 films). Eighty-six (95%) of these 91 patients had extra-orbital involvement radiologically (2.7 ± 1.6 regions, range: 0 to 9). Lymph node was the most prevalent (N = 60,66%), followed by salivary gland (N = 51,56%), lung (N = 49,54%), kidney (N = 22, 24%), hepatobiliary tree (N = 18, 20%) and pancreas (N = 17, 19%). Other organs include thyroid, aorta, meninges/brain and skin. Twenty-eight (23%) patients had allergic diseases (19 asthma, 16 allergic rhinitis, and 6 eczemas). Fifty-seven (48%) patients had paranasal sinusitis. Serum eosinophilia was associated with a higher number (3.24 versus 2.52, P = 0.0304) of organ involvement. Patients with deep organ involvement was associated with a higher age of IgG4-ROD onset (70 ± 12 versus 56 ± 13, P < 0.0001). CONCLUSIONS: 95% of the patients who underwent systemic imaging in our cohort had systemic organ involvement. An early physicians' assessment and radiological imaging are recommended after the diagnosis of IgG4-ROD.

Stabilization of Synthetic Collagen Triple Helices: Charge Pairs and Covalent Capture.

Collagen mimetic peptides are composed of triple helices. Triple helical formation frequently utilizes charge pair interactions to direct protein assembly. The design of synthetic triple helices is challenging due to the large number of competing species and the overall fragile nature of collagen mimetics. A successfully designed triple helix incorporates both positive and negative criteria to achieve maximum specificity of the supramolecular assembly. Intrahelical charge pair interactions, particularly those involved in lysine-aspartate and lysine-glutamate pairs, have been especially successful both in driving helix specificity and for subsequent stabilization by covalent capture. Despite this progress, the important sequential and geometric relationships of charged residues in a triple helical context have not been fully explored for either supramolecular assembly or covalent capture stabilization. In this study, we compare the eight canonical axial and lateral charge pairs of lysine and arginine with glutamate and aspartate to their noncanonical, reversed charge pairs. These findings are put into the context of collagen triple helical design and synthesis.

A case of venous overload choroidopathy in the setting of superior vena cava syndrome.

Purpose: To report a case of overload venous choroidopathy in a patient with superior vena cava syndrome. Observations: A patient presented with episcleral vessel dilation, bilateral subretinal fluid accumulations in the maculae and unilateral serous choroidal detachment. He had a medical history of kidney transplantation and was on chronic corticosteroids. Separately he had also experienced recurrent bilateral innominate vein occlusion and superior vena cava stenosis, consistent with a diagnosis of superior vena cava syndrome. His presentation was further complicated by a retinal vein occlusion in the left eye which was treated with anti-vascular endothelial growth factor intravitreal injections. The bilateral subretinal fluid accumulations responded well to photodynamic therapy. Conclusions and Importance: We report a constellation of findings including venous overload choroidopathy and retinal vein occlusion as ocular manifestations of superior vena cava syndrome. The pathophysiologic changes leading to these findings could aid in the understanding of these related conditions.

Impact of the COVID-19 Pandemic on Outcomes for Patients with Lung Cancer Receiving Curative-intent Radiotherapy in the UK.

AIMS: Previous work found that during the first wave of the COVID-19 pandemic, 34% of patients with lung cancer treated with curative-intent radiotherapy in the UK had a change to their centre's usual standard of care treatment (Banfill et al. Clin Oncol 2022;34:19-27). We present the impact of these changes on patient outcomes. MATERIALS AND METHODS: The COVID-RT Lung database was a prospective multicentre UK cohort study including patients with stage I-III lung cancer referred for and/or treated with radical radiotherapy between April and October 2020. Data were collected on patient demographics, radiotherapy and systemic treatments, toxicity, relapse and death. Multivariable Cox and logistic regression were used to assess the impact of having a change to radiotherapy on survival, distant relapse and grade ≥3 acute toxicity. The impact of omitting chemotherapy on survival and relapse was assessed using multivariable Cox regression. RESULTS: Patient and follow-up forms were available for 1280 patients. Seven hundred and sixty-five (59.8%) patients were aged over 70 years and 603 (47.1%) were female. The median follow-up was 213 days (119, 376). Patients with stage I-II non-small cell lung cancer (NSCLC) who had a change to their radiotherapy had no significant increase in distant relapse (P = 0.859) or death (P = 0.884); however, they did have increased odds of grade ≥3 acute toxicity (P = 0.0348). Patients with stage III NSCLC who had a change to their radiotherapy had no significant increase in distant relapse (P = 0.216) or death (P = 0.789); however, they did have increased odds of grade ≥3 acute toxicity (P < 0.001). Patients with stage III NSCLC who had their chemotherapy omitted had no significant increase in distant relapse (P = 0.0827) or death (P = 0.0661). CONCLUSION: This study suggests that changes to radiotherapy and chemotherapy made in response to the COVID-19 pandemic did not significantly affect distant relapse or survival. Changes to radiotherapy, namely increased hypofractionation, led to increased odds of grade ≥3 acute toxicity. These results are important, as hypofractionated treatments can help to reduce hospital attendances in the context of potential future emergency situations.

A mathematical model of SARS-CoV-2 immunity predicts paxlovid rebound.

Nirmatrelvir/ritonavir (Paxlovid), an oral antiviral medication targeting SARS-CoV-2, remains an important treatment for COVID-19. Initial studies of nirmatrelvir/ritonavir were performed in SARS-CoV-2 unvaccinated patients without prior confirmed SARS-CoV-2 infection; however, most individuals have now either been vaccinated and/or have experienced SARS-CoV-2 infection. After nirmatrelvir/ritonavir became widely available, reports surfaced of "Paxlovid rebound," a phenomenon in which symptoms (and SARS-CoV-2 test positivity) would initially resolve, but after finishing treatment, symptoms and test positivity would return. We used a previously described parsimonious mathematical model of immunity to SARS-CoV-2 infection to model the effect of nirmatrelvir/ritonavir treatment in unvaccinated and vaccinated patients. Model simulations show that viral rebound after treatment occurs only in vaccinated patients, while unvaccinated (SARS-COV-2 naïve) patients treated with nirmatrelvir/ritonavir do not experience any rebound in viral load. This work suggests that an approach combining parsimonious models of the immune system could be used to gain important insights in the context of emerging pathogens.

Distributing task-related neural activity across a cortical network through task-independent connections.

Task-related neural activity is widespread across populations of neurons during goal-directed behaviors. However, little is known about the synaptic reorganization and circuit mechanisms that lead to broad activity changes. Here we trained a subset of neurons in a spiking network with strong synaptic interactions to reproduce the activity of neurons in the motor cortex during a decision-making task. Task-related activity, resembling the neural data, emerged across the network, even in the untrained neurons. Analysis of trained networks showed that strong untrained synapses, which were independent of the task and determined the dynamical state of the network, mediated the spread of task-related activity. Optogenetic perturbations suggest that the motor cortex is strongly-coupled, supporting the applicability of the mechanism to cortical networks. Our results reveal a cortical mechanism that facilitates distributed representations of task-variables by spreading the activity from a subset of plastic neurons to the entire network through task-independent strong synapses.

Multiple models for outbreak decision support in the face of uncertainty.

Policymakers must make management decisions despite incomplete knowledge and conflicting model projections. Little guidance exists for the rapid, representative, and unbiased collection of policy-relevant scientific input from independent modeling teams. Integrating approaches from decision analysis, expert judgment, and model aggregation, we convened multiple modeling teams to evaluate COVID-19 reopening strategies for a mid-sized United States county early in the pandemic. Projections from seventeen distinct models were inconsistent in magnitude but highly consistent in ranking interventions. The 6-mo-ahead aggregate projections were well in line with observed outbreaks in mid-sized US counties. The aggregate results showed that up to half the population could be infected with full workplace reopening, while workplace restrictions reduced median cumulative infections by 82%. Rankings of interventions were consistent across public health objectives, but there was a strong trade-off between public health outcomes and duration of workplace closures, and no win-win intermediate reopening strategies were identified. Between-model variation was high; the aggregate results thus provide valuable risk quantification for decision making. This approach can be applied to the evaluation of management interventions in any setting where models are used to inform decision making. This case study demonstrated the utility of our approach and was one of several multimodel efforts that laid the groundwork for the COVID-19 Scenario Modeling Hub, which has provided multiple rounds of real-time scenario projections for situational awareness and decision making to the Centers for Disease Control and Prevention since December 2020.