Miracle-dz/UniFAR-FTUs huggingface dataset:
Microsaccadic suppression of peripheral perceptual detection performance as a function of foveated visual image appearance

Microsaccades are known to be associated with a deficit in perceptual detection performance for brief probe flashes presented in their temporal vicinity. However, it is still not clear how such a deficit might depend on the visual environment across which microsaccades are generated. Here, and motivated by studies demonstrating an interaction between visual background image appearance and perceptual suppression strength associated with large saccades, we probed peripheral perceptual detection performance of human subjects while they generated microsaccades over three different visual backgrounds. Subjects fixated near the center of a low spatial frequency grating, a high spatial frequency grating, or a small white fixation spot over an otherwise gray background. When a computer process detected a microsaccade, it presented a brief peripheral probe flash at one of four locations (over a uniform gray background), and at different times. After collecting full psychometric curves, we found that both perceptual detection thresholds and slopes of psychometric curves were impaired for peripheral flashes in the immediate temporal vicinity of microsaccades, and they recovered with later flash times. Importantly, the threshold elevations, but not the psychometric slope reductions, were stronger for the white fixation spot than for either of the two gratings. Thus, like with larger saccades, microsaccadic suppression strength can show a certain degree of image-dependence. However, unlike with larger saccades, stronger microsaccadic suppression did not occur with low spatial frequency textures. This observation might reflect the different spatio-temporal retinal transients associated with the small microsaccades in our study versus larger saccades.
A Microphysiological HHT-on-a-Chip Platform Recapitulates Patient Vascular Lesions

Hereditary Hemorrhagic Telangiectasia (HHT) is a rare congenital disease in which fragile vascular malformations focally develop in multiple organs. These can be small (telangiectasias) or large (arteriovenous malformations, AVMs) and may rupture leading to frequent, uncontrolled bleeding. There are few treatment options and no cure for HHT. Most HHT patients are heterozygous for loss-of-function mutations for Endoglin (ENG) or Alk1 (ACVRL1), however, why loss of these genes manifests as vascular malformations remains poorly understood. To complement ongoing work in animal models, we have developed a microphysiological system model of HHT. Based on our existing vessel-on-a-chip (VMO) platform, our fully human cell-based HHT-VMO recapitulates HHT patient vascular lesions. Using inducible ACVRL1 (Alk1)-knockdown, we control timing and extent of endogenous Alk1 expression in primary human endothelial cells (EC) in the HHT-VMO. HHT-VMO vascular lesions develop over several days, and are dependent upon timing of Alk1 knockdown. Interestingly, in chimera experiments AVM-like lesions can be comprised of both Alk1-intact and Alk1-deficient EC, suggesting possible cell non-autonomous effects. Single cell RNA sequencing data are consistent with microvessel pruning/regression as contributing to AVM formation, while loss of PDGFB expression implicates mural cell recruitment. Finally, lesion formation is blocked by the VEGFR inhibitor pazopanib, mirroring the positive effects of this drug in patients. In summary, we have developed a novel HHT-on-a-chip model that faithfully reproduces HHT patient lesions and that is sensitive to a treatment effective in patients. The VMO-HHT can be used to better understand HHT disease biology and identify potential new HHT drugs. Significance This manuscript describes development of an organ-on-a-chip model of Hereditary Hemorrhagic Telangiectasia (HHT), a rare genetic disease involving development of vascular malformations. Our VMO-HHT model produces vascular malformations similar to those seen in human HHT patients, including small (telangiectasias) and large (arteriovenous malformations) lesions. We show that VMO-HHT lesions are sensitive to a drug, pazopanib, that appears to be effective in HHT human patients. We further use the VMO-HHT platform to demonstrate that there is a critical window during vessel formation in which the HHT gene, Alk1, is required to prevent vascular malformation. Lastly, we show that lesions in the VMO-HHT model are comprised of both Alk1-deficient and Alk1-intact endothelial cells.
Labeling Natural Killer cells with superparamagnetic iron oxide nanoparticles for detection by preclinical and clinical-scale magnetic particle imaging

Introduction Clinical adoption of NK cell immunotherapy is underway for medulloblastoma and osteosarcoma, however there is currently little feedback on cell fate after administration. We propose magnetic particle imaging (MPI) for the detection, localization, and quantification of VivoTrax-labeled NK cells. Methods Human-derived NK-92 cells were labeled by co-incubation with VivoTrax for 24 hours then the excess nanoparticles were washed with centrifugation. Cytolytic activity of labeled vs. unlabeled NK-92 cells was assessed after 4 hours of co- incubation with medulloblastoma cells (DAOY) or osteosarcoma cells (LM7 or OS17) using bioluminescent or GFP counts. Labeled NK-92 cells at two different doses (0.5 or 1 x 106) were administered to excised mouse brains (cerebellum), tibias, and lungs then imaged by 3D preclinical MPI (MOMENTUM imager) and localized relative to fiducial markers. NK-92 cells were imaged by clinical-scale MPI under development at Magnetic Insight Inc. Results NK-92 cells were labeled with an average of 3.17 pg Fe/cell with no measured effects on cell viability or cytolytic activity against 3 tumor cell lines. MPI signal was directly quantitative with the number of VivoTrax-labeled NK-92 cells, with preclinical limit of detection of 3.1 x 104 cells on MOMENTUM imager. Labeled NK-92 cells could be accurately localized in mouse brains, tibias, and lungs within < 1 mm of stereotactic injection coordinates with preclinical scanner. Feasibility for detection of a clinically relevant dose of 4 x 107 labeled NK-92 cells was demonstrated on clinical-scale MPI. Conclusion MPI can provide sensitive, quantitative, and accurate spatial information on NK cell delivery, showing its potential to resolve a significant unmet clinical need to track NK cell treatments in patients.
GABAergic amacrine cells balance biased chromatic information in the mouse retina

The retina extracts chromatic information present in an animal’s environment. In the mouse, the feed-forward, excitatory pathway through the retina is dominated by a chromatic gradient, with green and UV signals primarily processed in the dorsal and ventral retina, respectively. However, at the output of the retina, chromatic tuning is more mixed, suggesting that amacrine cells alter spectral tuning. We genetically targeted the population of 40+ GABAergic amacrine cell types and used two-photon calcium imaging to systematically survey chromatic responses in their dendritic processes. We found that amacrine cells show diverse chromatic responses in different spatial regions of their receptive fields and across the dorso-ventral axis of the retina. Compared to their excitatory inputs from bipolar cells, amacrine cells are less chromatically tuned and less likely to be colour-opponent. We identified 25 functional amacrine cell types that, in addition to their chromatic properties, exhibit distinctive achromatic receptive field properties. A combination of pharmacological interventions and a biologically-inspired deep learning model revealed how lateral inhibition and recurrent excitatory inputs shape chromatic properties of amacrine cells. Our data suggest that amacrine cells balance the strongly biased spectral tuning of excitation in the mouse retina and thereby support increased diversity in chromatic information of the retinal output.
Advancements in Cardiovascular Disease Detection: Leveraging Data Mining and Machine Learning

Cardiovascular disease (CVD) is a significant global health concern, requiring early detection and accurate prediction for effective intervention. Machine learning (ML) offers a data-driven approach to analyzing patient data, identifying complex patterns and predicting CVD risk factors like blood pressure (BP), cholesterol levels, and genetic predispositions. Our research aims to predict CVD presence using ML algorithms, leveraging the Heart Disease UCI dataset with 14 attributes and 303 instances. Extensive feature engineering enhanced model performance. We developed five models using Logistic Regression, K-Nearest Neighbors (KNN), Decision Tree Classifier, Support Vector Machine (SVM), and Random Forest Classifier, refining them with hyperparameter tuning. Results show substantial accuracy improvements post-tuning and feature engineering. ‘Logistic Regression’ achieved the highest accuracy at 93.44%, closely followed by ‘Support Vector Machine’ at 91.80%. Our findings emphasize the potential of ML in early CVD prediction, underlining its value in healthcare and proactive risk management. ML’s utilization for CVD risk assessment promises personalized healthcare, benefiting both patients and healthcare providers. This research showcases the practicality and effectiveness of ML-based CVD risk assessment, enabling early intervention, improving patient outcomes, and optimizing healthcare resource allocation.
Key link between iron and the size structure of three main mesoplanktonic groups (Crustaceans, Rhizarians, and colonial N2-fixers) in the Global Ocean

Size is commonly used as a master trait to characterize pelagic organisms as it affects a range of processes and impact marine biogeochemical cycles and services. Yet, a holistic understanding of what environmental factors shape size structure is lacking for most mesoplankton. As part of the Pelagic Size Structure database, we explore the linkages between environmental factors and global compilations of Rhizarian, colonial N2-fixer, and Crustacean size spectra measured from Underwater Vision Profilers or benchtop scanners. We found that iron, alongside temperature, plays a disproportionate role in shaping their spectral biogeography. Our results highlight the effect of dust on N2-fixers and Rhizarians while total iron, comprising organic and mineral compounds, explained most of the variance in Crustaceans size structure. Using machine learning models, we predicted their size structure at the global scale with relatively high R2 of 0.93, 0.84, and 0.66. We hope our predictions can support further assessment of their role in biogeochemical processes under present and future forcings.
Motor Hotspot Localization Based on Electroencephalography Using Convolutional Neural Network in Patients with Stroke

Background Although transcranial magnetic stimulation (TMS) is the optimal tool for identifying individual motor hotspots for transcranial electrical stimulation (tES), it requires a cumbersome procedure in which patients must visit the hospital each time and rely on expert judgment to determine the motor hotspot. Therefore, in previous study, we proposed electroencephalography (EEG)-based machine learning approach to automatically identify individual motor hotspots. In this study, we proposed an advanced EEG-based motor hotspot identification algorithm using a deep learning model and assessed its clinical feasibility and benefits by applying it to stroke patient EEGs. Methods EEG data were measured from thirty subjects as they performed a simple hand movement task. We utilized the five types of input data depending on the processing levels to assess the signal processing capability of our proposed deep learning model. The motor hotspot locations were estimated using a two-dimensional convolutional neural network (CNN) model. The error distance between the 3D coordinate information of the individual motor hotspots identified by the TMS (ground truth) and EEGs was calculated using the Euclidean distance. Additionally, we confirmed the clinical benefits of our proposed deep-learning algorithm by applying the EEG of stroke patients. Results A mean error distance between the motor hotspot locations identified by TMS and our approach was 2.34 ± 0.19 mm when using raw data from only 9 channels around the motor area. When it was tested on stroke patients, the mean error distance was 1.77 ± 0.15 mm using only 5 channels around the motor area. Conclusion We have demonstrated that an EEG-based deep learning approach can effectively identify the individual motor hotspots. Moreover, we validated the clinical benefits of our algorithm by successfully implementing it in stroke patients. Our algorithm can be used as an alternative to TMS for identifying motor hotspots and maximizing rehabilitation effectiveness.
Distribution-driven disturbance of a molecular community: a theoretical framework

An important and understudied area in microbial ecology concerns the very large number of very rare species within an environment. Study of such species based on non-specific sequencing of environmental samples is practically difficult simply due to their rarity. A previous report demonstrated that pretreating samples with the photoactive dye PMA before subsequent PCR and sequencing can increase sensitivity to rare species. Here we propose a mathematical model based on equilibrium chemical thermodynamics that explains this effect and suggests a simple quantitative experiment.
Functional Diversification of Gene Duplicates under the Constraint of Protein Structure

Gene differentiation subsequent to duplication plays a pivotal role in the evolutionary process, facilitating the generation of new functional genes. This dynamic process involves alterations in gene characteristics including protein structure, expression patterns, cellular localization, and enzymatic activity, which collectively contribute to the advent of novel functions. However, there are still relatively few reports on how multiple genes differentiate after duplication. Here we show the functional diversification of Glycoside Hydrolase Family 50 (GH50) agarases in the deep-sea bacterium Agarivorans ablus JK6, uncovering the evolutionary mechanisms behind the emergence of new enzymatic functions post-duplication. By leveraging AlphaFold for precise protein structure prediction and integrating phylogenetic analysis with experimental methods, we shed light on the structural adaptations and sequence alterations driving these enzymes’ functional differentiation. Our findings establish a distinct link between structural changes and the creation of new hydrolysis products, underscoring the significance of gene duplication in broadening the enzymatic repertoire of organisms. Furthermore, the study elucidates how these adaptations contribute to the ecological success of A.ablus JK6, highlighting the role of gene duplication in expanding the enzymatic toolkit for environmental adaptation. Through exhaustive biochemical characterization and enzymatic activity assays, we furnish insights into the agarases’ adaptive evolution, providing a thorough perspective on how genetic and structural elements contribute to the biochemical diversity and ecological prowess of marine bacteria, and also suggests new pathways for the development of bioengineered enzymes with enhanced functionalities.
Layer 5 Martinotti cell activation reduces pyramidal cell population plasticity and improves fine motor function

During motor activity and motor learning, pyramidal cells in the motor cortex receive inputs from local interneurons as well as deeper structures. These signals funnel into pyramidal cells in the primary motor cortex that in turn feed commands to spinal circuits for execution. Here we show that layer 5 Chrna2 Martinotti cells receive direct input from thalamic nuclei, and that their genetic ablation results in disturbed fine motor functions. Activation of these layer 5 Martinotti cells during training reduces pyramidal cell plasticity, e.g. tuning, temporal patterning and assembly configuration. Moreover, in mice that had already learned a reach-and-grasp (prehension) task, Martinotti cell activation resulted in improved prehension. This work indicates that activation of layer 5 Martiotti cells reduces pyramidal cell assembly plasticity during learning, facilitating preservation of already acquired motor skills. Highlights Chrna2+ Martinotti cells (Mα2) in primary motor cortex layer 5 receive direct input from thalamic nuclei and substantia innominata. Overexcitation of Mα2 cells reduces plasticity in pyramidal cell assemblies and induces premature activation of leader pyramidal cells during relearning. Loss-of-function and activity modulation experiments establish a role for Mα2 cells in execution of fine motor functions.
Adaptive generalization in pollinators: hawkmoths increase fitness to long-tubed flowers, but secondary pollinators remain important

Selection on floral traits by animal pollinators is important in the evolution of flowering plants, yet whether floral divergence requires specialized pollination remains uncertain. Longer floral tubes, a trait associated with long-tongued pollinators, can also exclude other pollinators from accessing rewards, a potential mechanism for specialization. Across most of its range, Castilleja sessiliflora displays much longer corollas than most Castilleja species, though tube length varies geographically and correlates partially with hawkmoth visitation. To assess if long corolla tubes reflect adaptation to hawkmoth pollinators, we performed a day/night pollinator exclusion experiment in nine natural populations that varied in corolla length across the range of C. sessiliflora and short-tubed members of the parapatric C. purpurea complex. We compared the fitness contributions of nocturnal and diurnal visitors, revealing that long-tubed populations visited predominantly by hawkmoths experienced greater fruit set at night, in contrast with short-tubed populations or those visited mainly by diurnal pollinators. Next, leveraging a range-wide multi-year dataset of pollinator visitation to these species, we identify that hawkmoth visitation is associated with increased fitness in long-tubed populations overall, and that long tubes are associated with less diverse visitor assemblages. Thus, long corollas represent an adaptation to hawkmoth pollination at the exclusion of diverse pollinators. Nonetheless, while hawkmoths were scarce in the northern range, secondary diurnal pollinators contributed to fruit set across the range, providing reproductive assurance despite possible trait mismatch. This study illustrates adaptive generalization in pollination modes and that floral divergence may proceed along a continuum of generalized and specialized pollinator interactions.
IP6 and PF74 affect HIV-1 Capsid Stability through Modulation of Hexamer-Hexamer Tilt Angle Preference

The HIV-1 capsid is an irregularly shaped complex of about 1200 protein chains containing the viral genome and several viral proteins. Together, these components are the key to unlocking passage into the nucleus, allowing for permanent integration of the viral genome into the host cell genome. Recent interest into the role of the capsid in viral replication has been driven by the approval of the first-in-class drug lenacapavir, which marks the first drug approved to target a non-enzymatic HIV-1 viral protein. In addition to lenacapavir, other small molecules such as the drug-like compound PF74, and the anionic sugar inositolhexakisphosphate (IP6), are known to impact capsid stability, and although this is widely accepted as a therapeutic effect, the mechanisms through which they do so remain unknown. In this study, we employed a systematic atomistic simulation approach to study the impact of molecules bound to hexamers at the central pore (IP6) and the FG-binding site (PF74) on capsid oligomer dynamics, compared to apo hexamers and pentamers. We found that neither small molecule had a sizeable impact on the free energy of binding of the interface between neighboring hexamers but that both had impacts on the free energy profiles of performing angular deformations to the pair of oligomers akin to the variations in curvature along the irregular surface of the capsid. The IP6 cofactor, on one hand, stabilizes a pair of neighboring hexamers in their flattest configurations, whereas without IP6, the hexamers prefer a high tilt angle between them. On the other hand, having PF74 bound introduces a strong preference for intermediate tilt angles. These results suggest that structural instability is a natural feature of the HIV-1 capsid which is modulated by molecules bound in either the central pore or the FG-binding site. Such modulators, despite sharing many of the same effects on non-bonded interactions at the various protein-protein interfaces, have decidedly different effects on the flexibility of the complex. This study provides a detailed model of the HIV-1 capsid and its interactions with small molecules, informing structure-based drug design, as well as experimental design and interpretation.
Right-Adjoints for Datalog Programs

A Datalog program can be viewed as a syntactic specification of a mapping from database instances over some schema to database instances over another schema. We establish a large class of Datalog programs for which this mapping admits a (generalized) right-adjoint. We employ these results to obtain new insights into the existence of, and methods for constructing, homomorphism dualities within restricted classes of instances. From this, we derive new results regarding the existence of uniquely characterizing data examples for database queries in the presence of integrity constraints
Insights into Cellular Evolution: Temporal Deep Learning Models and Analysis for Cell Image Classification

Understanding the temporal evolution of cells poses a significant challenge in developmental biology. This study embarks on a comparative analysis of various machine-learning techniques to classify sequences of cell colony images, thereby aiming to capture dynamic transitions of cellular states. Utilizing transfer learning with advanced classification networks, we achieved high accuracy in single-timestamp image categorization. We introduce temporal models—LSTM, R-Transformer, and ViViT—to explore the effectiveness of integrating temporal features in classification, comparing their performance against non-temporal models. This research benchmarks various machine learning approaches in understanding cellular dynamics, setting a foundation for future studies to enhance our understanding of cellular developments with computational methods, contributing significantly to biological research advancements.