OBJECTIVE: To develop a novel particle-based in silico MR model and demonstrate applications of this model to signal mechanisms which are affected by the spatial organization of particles, including metabolic reaction kinetics, microstructural effects on diffusion, and radiofrequency (RF) refocusing effects in gradient-echo sequences.
METHODS: The model was developed by integrating a forward solution of the Bloch equations with a Brownian dynamics simulator. Simulation configurations were then designed to model MR signal dynamics of interest, with a primary focus on hyperpolarized 13C MRI methods. Phantom scans and spectrophotometric assays were conducted to validate model results in vitro.
RESULTS: The model accurately reproduced the reaction kinetics of enzyme-mediated conversion of pyruvate to lactate. When varying proportions of restrictive structure were added to the reaction volume, nonlinear changes in the reaction rate measured in vitro were replicated in silico. Modeling of RF refocusing effects characterized the degree of diffusion-weighted contribution from preserved residual magnetization in nonspoiled gradient-echo sequences.
CONCLUSIONS: These results show accurate reproduction of a range of MR signal mechanisms, establishing the model\'s capability to investigate the multifactorial signal dynamics such as those underlying hyperpolarized 13C MRI data.

Intrinsic cortical activity forms traveling waves that modulate sensory-evoked responses and perceptual sensitivity. These intrinsic traveling waves (iTWs) may arise from the coordination of synaptic activity through long-range feature-dependent horizontal connectivity within cortical areas. In a spiking network model that incorporates feature-selective patchy connections, we observe iTW motifs that result from shifts in excitatory/inhibitory balance as action potentials traverse these patchy connections. To test whether feature-selective motifs occur in vivo, we examined data recorded in the middle temporal visual area (Area MT) of marmosets performing a visual detection task. We find that some iTWs form motifs that are feature selective, exhibiting direction-selective modulations in spiking activity. Further, motifs modulate the gain of target-evoked responses and perceptual sensitivity if the target matches the preference of the motif. These results suggest that iTWs are shaped by the patchy horizontal fiber projections in the cortex and can regulate neural and perceptual sensitivity in a feature-selective manner.

Theoretical computational models are widely used to describe latent cognitive processes. However, these models do not equally explain data across participants, with some individuals showing a bigger predictive gap than others. In the current study, we examined the use of theory-independent models, specifically recurrent neural networks (RNNs), to classify the source of a predictive gap in the observed data of a single individual. This approach aims to identify whether the low predictability of behavioral data is mainly due to noisy decision-making or misspecification of the theoretical model. First, we used computer simulation in the context of reinforcement learning to demonstrate that RNNs can be used to identify model misspecification in simulated agents with varying degrees of behavioral noise. Specifically, both prediction performance and the number of RNN training epochs (i.e., the point of early stopping) can be used to estimate the amount of stochasticity in the data. Second, we applied our approach to an empirical dataset where the actions of low IQ participants, compared with high IQ participants, showed lower predictability by a well-known theoretical model (i.e., Daw\'s hybrid model for the two-step task). Both the predictive gap and the point of early stopping of the RNN suggested that model misspecification is similar across individuals. This led us to a provisional conclusion that low IQ subjects are mostly noisier compared to their high IQ peers, rather than being more misspecified by the theoretical model. We discuss the implications and limitations of this approach, considering the growing literature in both theoretical and data-driven computational modeling in decision-making science.

Residual nonvisible bladder cancer after proper treatment caused by technological and therapeutic limitations is responsible for tumor relapse and progression. This study aimed to demonstrate the feasibility of a solution for simultaneous detection and treatment of bladder cancer lesions smaller than one millimeter. The α5β1 integrin was identified as a specific marker in 81% of human high-grade nonmuscle invasive bladder cancers and used as a target for the delivery of targeted gold nanorods (GNRs). In a preclinical model of orthotopic bladder cancer expressing the α5β1 integrin, the photoacoustic imaging of targeted GNRs visualized lesions smaller than one millimeter, and their irradiation with continuous laser was used to induce GNR-assisted hyperthermia. Necrosis of the tumor mass, improved survival, and computational modeling were applied to demonstrate the efficacy and safety of this solution. Our study highlights the potential of the GNR-assisted theranostic strategy as a complementary solution in clinical practice to reduce the risk of nonvisible residual bladder cancer after current treatment. Further validation through clinical studies will support the findings of the present study.

OBJECTIVE: For prosthesis users, sensory feedback that appears to come from the missing limb can improve function, confidence, and phantom limb pain. Numerous pre-clinical studies have considered stimulation via penetrating microelectrodes at the dorsal root ganglion (DRG) as a potential approach for somatosensory neuroprostheses. However, to develop clinically translatable neuroprosthetic devices, a less invasive approach, such as stimulation via epineural macroelectrodes, would be preferable. This work explores the feasibility of using such electrodes to deliver focal sensory feedback by examining the mechanisms of selective activation in response to stimulation via epineural electrodes compared with penetrating electrodes. Approach: We developed computational models of the DRG, representing the biophysical properties of the DRG and surrounding tissue to evaluate neural responses to stimulation via penetrating microelectrodes and epineural macroelectrodes. To assess the role of properties such as neuron morphology and spatial arrangement we designed three models, including one that contained only axons (axon only), one with pseudounipolar neurons arranged randomly (random), and one with pseudounipolar neurons placed according to a realistic spatial distribution (realistic). Main results: Our models demonstrate that activation in response to stimulation via epineural electrodes in a realistic model is commonly initiated in the axon initial segment adjacent to the cell body, whereas penetrating electrodes commonly elicit responses in t-junctions and axons. Moreover, we see a wider dynamic range for epineural electrodes compared with penetrating electrodes. This difference appears to be driven by the spatial organization and neuron morphology of the realistic DRG. Significance: We demonstrate that the anatomical features of the DRG make it a potentially effective target for epineural stimulation to deliver focal sensations from the limbs. Specifically, we show that epineural stimulation at the DRG can be highly selective thanks to the neuroanatomical arrangement of the DRG, making this a promising approach for future neuroprosthetic development. .

Early number skills represent critical milestones in children\'s cognitive development and are shaped over years of interacting with quantities and numerals in various contexts. Several connectionist computational models have attempted to emulate how certain number concepts may be learned, represented, and processed in the brain. However, these models mainly used highly simplified inputs and focused on limited tasks. We expand on previous work in two directions: First, we train a model end-to-end on video demonstrations in a synthetic environment with multimodal visual and language inputs. Second, we use a more holistic dataset of 35 tasks, covering enumeration, set comparisons, symbolic digits, and seriation. The order in which the model acquires tasks reflects input length and variability, and the resulting trajectories mostly fit with findings from educational psychology. The trained model also displays symbolic and non-symbolic size and distance effects. Using techniques from interpretability research, we investigate how our attention-based model integrates cross-modal representations and binds them into context-specific associative networks to solve different tasks. We compare models trained with and without symbolic inputs and find that the purely non-symbolic model employs more processing-intensive strategies to determine set size.

UNASSIGNED: The electrophysiological mechanism connecting mitral valve prolapse (MVP), premature ventricular complexes and life-threatening ventricular arrhythmia is unknown. A common hypothesis is that stretch activated channels (SACs) play a significant role. SACs can trigger depolarizations or shorten repolarization times in response to myocardial stretch. Through these mechanisms, pathological traction of the papillary muscle (PM), as has been observed in patients with MVP, may induce irregular electrical activity and result in reentrant arrhythmia.
UNASSIGNED: Based on a patient with MVP and mitral annulus disjunction, we modeled the effect of excessive PM traction in a detailed medical image-derived ventricular model by activating SACs in the PM insertion region. By systematically varying the onset of SAC activation following sinus pacing, we identified vulnerability windows for reentry with 1 ms resolution. We explored how reentry was affected by the SAC reversal potential ( E SAC ) and the size of the region with simulated stretch (SAC region). Finally, the effect of global or focal fibrosis, modeled as reduction in tissue conductivity or mesh splitting (fibrotic microstructure), was investigated.
UNASSIGNED: In models with healthy tissue or fibrosis modeled solely as CV slowing, we observed two vulnerable periods of reentry: For E SAC of -10 and -30 mV, SAC activated during the T-wave could cause depolarization of the SAC region which lead to reentry. For E SAC of -40 and -70 mV, SAC activated during the QRS complex could result in early repolarization of the SAC region and subsequent reentry. In models with fibrotic microstructure in the SAC region, we observed micro-reentries and a larger variability in which times of SAC activation triggered reentry. In these models, 86% of reentries were triggered during the QRS complex or T-wave. We only observed reentry for sufficiently large SAC regions ( > = 8 mm radius in models with healthy tissue).
UNASSIGNED: Stretch of the PM insertion region following sinus activation may initiate ventricular reentry in patients with MVP, with or without fibrosis. Depending on the SAC reversal potential and timing of stretch, reentry may be triggered by ectopy due to SAC-induced depolarizations or by early repolarization within the SAC region.

Pyrone-2,4-dicarboxylic acid (PDC) is a valuable polymer precursor that can be derived from the microbial degradation of lignin. The key enzyme in the microbial production of PDC is CHMS dehydrogenase, which acts on the substrate 4-carboxy-2-hydroxymuconate-6-semialdehyde (CHMS). We present the crystal structure of CHMS dehydrogenase (PmdC from Comamonas testosteroni) bound to the cofactor NADP, shedding light on its three-dimensional architecture, and revealing residues responsible for binding NADP. Using a combination of structural homology, molecular docking, and quantum chemistry calculations we have predicted the binding site of CHMS. Key histidine residues in a conserved sequence are identified as crucial for binding the hydroxyl group of CHMS and facilitating dehydrogenation with NADP. Mutating these histidine residues results in a loss of enzyme activity, leading to a proposed model for the enzyme\'s mechanism. These findings are expected to help guide efforts in protein and metabolic engineering to enhance PDC yields in biological routes to polymer feedstock synthesis.

UNASSIGNED: Food allergy is a prevalent disease in the U.S., affecting nearly 30 million people. The primary management strategy for this condition is food avoidance, as limited treatment options are available. The elevation of pathologic IgE and over-reactive mast cells/basophils is a central factor in food allergy anaphylaxis. This study aims to comprehensively evaluate the potential therapeutic mechanisms of a small molecule compound called formononetin in regulating IgE and mast cell activation.
UNASSIGNED: In this study, we determined the inhibitory effect of formononetin on the production of human IgE from peripheral blood mononuclear cells of food-allergic patients using ELISA. We also measured formononetin\'s effect on preventing mast cell degranulation in RBL-2H3 and KU812 cells using beta-hexosaminidase assay. To identify potential targets of formononetin in IgE-mediated diseases, mast cell disorders, and food allergies, we utilized computational modeling to analyze mechanistic targets of formononetin from various databases, including SEA, Swiss Target Prediction, PubChem, Gene Cards, and Mala Cards. We generated a KEGG pathway, Gene Ontology, and Compound Target Pathway Disease Network using these targets. Finally, we used qRT-PCR to measure the gene expression of selected targets in KU812 and U266 cell lines.
UNASSIGNED: Formononetin significantly decreased IgE production in IgE-producing human myeloma cells and PBMCs from food-allergic patients in a dose-dependent manner without cytotoxicity. Formononetin decreased beta-hexosaminidase release in RBL-2H3 cells and KU812 cells. Formononetin regulates 25 targets in food allergy, 51 in IgE diseases, and 19 in mast cell diseases. KEGG pathway and gene ontology analysis of targets showed that formononetin regulated disease pathways, primary immunodeficiency, Epstein-Barr Virus, and pathways in cancer. The biological processes regulated by formononetin include B cell proliferation, differentiation, immune response, and activation processes. Compound target pathway disease network identified NFKB1, NFKBIA, STAT1, STAT3, CCND1, TP53, TYK2, and CASP8 as the top targets regulated at a high degree by formononetin. TP53, STAT3, PTPRC, IL2, and CD19 were identified as the proteins mostly targeted by formononetin. qPCR validated genes of Formononetin molecular targets of IgE regulation in U266 cells and KU812 cells. In U266 cells, formononetin was found to significantly increase the gene expression of NFKBIA, TP53, and BCL-2 while decreasing the gene expression of BTK TYK, CASP8, STAT3, CCND1, STAT1, NFKB1, IL7R. In basophils KU812 cells, formononetin significantly increased the gene expression of NFKBIA, TP53, and BCL-2 while decreasing the gene expression of BTK, TYK, CASP8, STAT3, CCND1, STAT1, NFKB1, IL7R.
UNASSIGNED: These findings comprehensively present formononetin\'s mechanisms in regulating IgE production in plasma cells and degranulation in mast cells.

The use of convolutional neural networks can revolutionize XRD analysis by significantly reducing processing times. Demonstration against synthetic and real mineral mixture data provide a first assessment of the accuracy of such methods.