Miracle-dz/UniFAR-FTUs huggingface dataset:
A novel platform for minimally invasive delivery of cellular therapy as a thin layer across the subretina for treatment of retinal degeneration

Incurable retinal degenerations affect millions worldwide. Stem cell transplantation rescued visual functions in animal models of retinal degeneration. In those studies cells were transplanted in subretinal "blebs", limited number of cells could be injected and photoreceptor rescue was restricted to areas in proximity to the injection sites. We developed a minimally-invasive surgical platform for drug and cell delivery in a thin layer across the subretina and extravascular spaces of the choroid. The novel system is comprised of a syringe with a blunt-tipped needle and an adjustable separator. Human bone marrow mesenchymal stem cells (hBM-MSCs) were transplanted in eyes of RCS rats and NZW rabbits through a longitudinal triangular scleral incision. No immunosuppressants were used. Retinal function was determined by electroretinogram analysis and retinal structure was determined by histological analysis and OCT. Transplanted cells were identified as a thin layer across the subretina and extravascular spaces of the choroid. In RCS rats, cell transplantation delayed photoreceptor degeneration across the entire retina and significantly enhanced retinal functions. No retinal detachment or choroidal hemorrhages were observed in rabbits following transplantation. This novel platform opens a new avenue for drug and cell delivery, placing the transplanted cells in close proximity to the damaged RPE and retina as a thin layer, across the subretina and thereby slowing down cell death and photoreceptor degeneration, without retinal detachment or choroidal hemorrhage. This new transplantation system may increase the therapeutic effect of other cell-based therapies and therapeutic agents. This study is expected to directly lead to phase I/II clinical trials for autologous hBM-MSCs transplantation in retinal degeneration patients.
SGPPI: structure-aware prediction of protein-protein interactions in rigorous conditions with graph convolutional network

While deep learning (DL)-based models have emerged as powerful approaches to predict protein-protein interactions (PPIs), the reliance on explicit similarity measures (e.g. sequence similarity and network neighborhood) to known interacting proteins makes these methods ineffective in dealing with novel proteins. The advent of AlphaFold2 presents a significant opportunity and also a challenge to predict PPIs in a straightforward way based on monomer structures while controlling bias from protein sequences. In this work, we established Structure and Graph-based Predictions of Protein Interactions (SGPPI), a structure-based DL framework for predicting PPIs, using the graph convolutional network. In particular, SGPPI focused on protein patches on the protein-protein binding interfaces and extracted the structural, geometric and evolutionary features from the residue contact map to predict PPIs. We demonstrated that our model outperforms traditional machine learning methods and state-of-the-art DL-based methods using non-representation-bias benchmark datasets. Moreover, our model trained on human dataset can be reliably transferred to predict yeast PPIs, indicating that SGPPI can capture converging structural features of protein interactions across various species. The implementation of SGPPI is available at https://github.com/emerson106/SGPPI.
Eigenvector representations of Northern Hemispheric fields

The best eigenvector representations of Northern Hemispheric fields of the height of the 1,000 mb surface and the 1,000-500 mb thickness are compared with those previously found for the 500 mb surface. It is shown that the use of the first 50 eigenvectors of the 500 mb fields, for all three types of field, produces a representation which is only marginally inferior to that obtainable by using three different sets of eigenvectors.
Clinical Manufacturing of Regulatory T Cell Products For Adoptive Cell Therapy and Strategies to Improve Therapeutic Efficacy

ABSTRACT Based on successes in preclinical animal transplant models, adoptive cell therapy (ACT) with regulatory T cells (Tregs) is a promising modality to induce allograft tolerance or reduce the use of immunosuppressive drugs to prevent rejection. Extensive work has been done in optimizing the best approach to manufacture Treg cell products for testing in transplant recipients. Collectively, clinical evaluations have demonstrated that large numbers of Tregs can be expanded ex vivo and infused safely. However, these trials have failed to induce robust drug-free tolerance and/or significantly reduce the level of immunosuppression needed to prevent solid organ transplant (SOTx) rejection. Improving Treg therapy effectiveness may require increasing Treg persistence or orchestrating Treg migration to secondary lymphatic tissues or places of inflammation. In this review, we describe current clinical Treg manufacturing methods used for clinical trials. We also highlight current strategies being implemented to improve delivered Treg ACT persistence and migration in preclinical studies.
Free bending vibrations of adhesively bonded orthotropic plates with a single lap joint

This study is concerned with the free bending vibrations of two rectangular, orthotropic plates connected by an adhesively bonded lap joint. The influence of shear deformation and rotatory inertia in plates are taken into account in the equations according to the Mindlin plate theory. The effects of both thickness and shear deformations in the thin adhesive layer are included in the formulation. Plates are assumed to have simply supported boundary conditions at two opposite edges. However, any boundary conditions can be prescribed at the other two edges. First, equations of motion at the overlap region are derived. Then, a Levy-type solution for displacements and stress resultants are used to formulate the problem in terms of a system of first order ordinary differential equations. A revised version of the Transfer Matrix Method together with the boundary and continuity conditions are used to obtain the frequency equation of the system. The natural frequencies and corresponding mode shapes are obtained for identical and dissimilar adherends with different boundary conditions. The effects of some parameters on the natural frequencies are studied and plotted.
Design Rules for Active Control of Narrowband Thermal Emission Using Phase-Change Materials

We propose an analytical framework to design actively tunable narrowband thermal emitters at infrared frequencies. We exemplify the proposed design rules using phase-change materials (PCM), considering dielectric-to-dielectric PCMs (e.g. GSST) and dielectric-to-metal PCMs (e.g. $\mathrm{VO_2}$). Based on these, we numerically illustrate near-unity ON-OFF switching and arbitrarily large spectral shifting between two emission wavelengths, respectively. The proposed systems are lithography-free and consist of one or several thin emitter layers, a spacer layer which includes the PCM, and a back reflector. Our model applies to normal incidence, though we show that the behavior is essentially angle-independent. The presented formalism is general and can be extended to \textit{any} mechanism that modifies the optical properties of a material, such as electrostatic gating or thermo-optical modulation.
Modeling of Chemical Vapor Infiltration Using Boundary Singularity Method

Boundary Singularity Method (BSM) was used to model Chemical Vapor Infiltration (CVI) in a fibrous preform. Straight, long fibers of varying cross-sectional geometry, representing fibers of a preform, were placed within a domain of a pre-determined size. The preparation of dense fiber-reinforced Silicon-Carbon (SiC) composites was considered as a representative of CVI methodology, where methyl-trichlorosilane (MTS) was used as both the silicon and carbon donor for the silicon carbide matrix. Concentrations of MTS were then set at the domain boundaries, and the domain was gradually infiltrated with MTS as time progressed. The concentration of MTS at the surface of the preform fibers was calculated using the adopted BSM. For quasi-equilibrium considered, the reaction rate at solid surface is equal to the diffusion rate towards the surface. The Robin or third type boundary condition, which is a linear combination of the values of a function and the values of its derivative on the boundary of the domain, are developed and implemented to BSM. From the fibers surface concentrations obtained by BSM, deposition rates were calculated, and the geometry was updated to reflect the fiber growth during the time step, therefore, the fiber size growth and pore filling was modeled over time. The BSM analysis was verified by comparisons to a known analytical solution of concentric cylinders with a concentration set at the outer cylinder and a reaction at the inner. BSM solutions were also compared to experimental data as well as computational results obtained by a Level-Set Method (LSM). Obtained dynamics of pore size and location will help to evaluate quality of material manufactured by CVI. Porosity transients were obtained to show the relation between initial and current porosities as time progresses.
Multi-baseline UKF phase unwrapping algorithm via joint phase slope estimator

: This paper presents an improved phase unwrapping algo-rithm by combining an unscented KALMAN ﬁlter (UKF) and a joint phase slope estimator and a conventional path-following strategy. This algorithm is able to accurately unwrap seriously noisy wrapped phase images by applying the UKF to perform phase noise suppression and phase unwrapping at the same time. Results obtained from synthetic data validate the eﬀectiveness of the proposed method.
Online switching control with stability and regret guarantees

This paper considers online switching control with a finite candidate controller pool, an unknown dynamical system, and unknown cost functions. The candidate controllers can be unstabilizing policies. We only require at least one candidate controller to satisfy certain stability properties, but we do not know which one is stabilizing. We design an online algorithm that guarantees finite-gain stability throughout the duration of its execution. We also provide a sublinear policy regret guarantee compared with the optimal stabilizing candidate controller. Lastly, we numerically test our algorithm on quadrotor planar flights and compare it with a classical switching control algorithm, falsification-based switching, and a classical multi-armed bandit algorithm, Exp3 with batches.
Differentially private confidence intervals for proportions under stratified random sampling

Confidence intervals are a fundamental tool for quantifying the uncertainty of parameters of interest. With the increase of data privacy awareness, developing a private version of confidence intervals has gained growing attention from both statisticians and computer scientists. Differential privacy is a state-of-the-art framework for analyzing privacy loss when releasing statistics computed from sensitive data. Recent work has been done around differentially private confidence intervals, yet to the best of our knowledge, rigorous methodologies on differentially private confidence intervals in the context of survey sampling have not been studied. In this paper, we propose three differentially private algorithms for constructing confidence intervals for proportions under stratified random sampling. We articulate two variants of differential privacy that make sense for data from stratified sampling designs, analyzing each of our algorithms within one of these two variants. We establish analytical privacy guarantees and asymptotic properties of the estimators. In addition, we conduct simulation studies to evaluate the proposed private confidence intervals, and two applications to the 1940 Census data are provided.
Self-supervised learning for a nonlinear inverse problem with forward operator involving an unknown function arising in Photoacoustic Tomography

In this article, we are concerned with a nonlinear inverse problem with a forward operator involving an unknown function. The problem arises in diverse applications and is challenging in the presence of an unknown function, which makes it ill-posed. Additionally, the nonlinear nature of the problem makes it difficult to use traditional methods, and thus, the study addresses a simplified version of the problem by either linearizing it or assuming knowledge of the unknown function. Here, we propose self-supervised learning to directly tackle a nonlinear inverse problem involving an unknown function. In particular, we focus on an inverse problem derived in photoacoustic tomograpy (PAT), which is a hybrid medical imaging with high resolution and contrast. PAT can be modeled based on the wave equation. The measured data provide the solution to an equation restricted to surface and initial pressure of an equation that contains biological information on the object of interest. The speed of a sound wave in the equation is unknown. Our goal is to determine the initial pressure and the speed of the sound wave simultaneously. Under a simple assumption that sound speed is a function of the initial pressure, the problem becomes a nonlinear inverse problem involving an unknown function. The experimental results demonstrate that the proposed framework performs successfully.
causalgraph: A Python Package for Modeling, Persisting and Visualizing Causal Graphs Embedded in Knowledge Graphs

This paper describes a novel Python package, named causalgraph, for modeling and saving causal graphs embedded in knowledge graphs. The package has been designed to provide an interface between causal disciplines such as causal discovery and causal inference. With this package, users can create and save causal graphs and export the generated graphs for use in other graph-based packages. The main advantage of the proposed package is its ability to facilitate the linking of additional information and metadata to causal structures. In addition, the package offers a variety of functions for graph modeling and plotting, such as editing, adding, and deleting nodes and edges. It is also compatible with widely used graph data science libraries such as NetworkX and Tigramite and incorporates a specially developed causalgraph ontology in the background. This paper provides an overview of the package's main features, functionality, and usage examples, enabling the reader to use the package effectively in practice.
Polar Coded Merkle Tree: Mitigating Data Availability Attacks in Blockchain Systems Using Informed Polar Code Design

Data availability (DA) attack is a well-known problem in certain blockchains where users accept an invalid block with unavailable portions. Previous works have used LDPC and 2-D Reed Solomon (2D-RS) codes with Merkle trees to mitigate DA attacks. These codes perform well across various metrics such as DA detection probability and communication cost. However, these codes are difﬁcult to apply to blockchains with large blocks due to large decoding complexity and coding fraud proof size (2D-RS codes), and intractable code guarantees for large code lengths (LDPC codes). In this paper, we focus on large block size applications and address the above challenges by proposing the novel Polar Coded Merkle Tree (PCMT): a Merkle tree encoded using the encoding graph of polar codes. We provide a specialized polar code design algorithm called Sampling Efﬁcient Freezing and an algorithm to prune the polar encoding graph. We demonstrate that the PCMT built using the above techniques results in a better DA detection probability and communication cost compared to LDPC codes, has a lower coding fraud proof size compared to LDPC and 2D-RS codes, provides tractable code guarantees at large code lengths (similar to 2D-RS codes), and has comparable decoding complexity to 2D-RS and LDPC codes.
Generative Logic with Time: Beyond Logical Consistency and Statistical Possibility

This paper gives a simple theory of inference to logically reason symbolic knowledge fully from data over time. We take a Bayesian approach to model how data causes symbolic knowledge. Probabilistic reasoning with symbolic knowledge is modelled as a process of going the causality forwards and backwards. The forward and backward processes correspond to an interpretation and inverse interpretation of formal logic, respectively. The theory is applied to a localisation problem to show a robot with broken or noisy sensors can efficiently solve the problem in a fully data-driven fashion.
Fast integrated optical switching by the protein bacteriorhodopsin

State-of-the-art photonic integration technology is ready to provide the passive elements of optical integrated circuits, based either on silicon, glass or plastic materials. The bottleneck is to find the proper nonlinear optical (NLO) materials in waveguide-based integrated optical circuits for light-controlled active functions. Recently, we proposed an approach where the active role is performed by the chromoprotein bacteriorhodopsin as an NLO material, that can be combined with appropriate integrated optical devices. Here we present data supporting the possibility of switching based on a fast photoreaction of bacteriorhodopsin. The results are expected to have important implications for photonic switching technology.
"This Applies to the Real World": Student Perspectives on Integrating Ethics into a Computer Science Assignment

There is a growing movement in undergraduate computer science (CS) programs to embed ethics across CS classes rather than relying solely on standalone ethics courses. One strategy is creating assignments that encourage students to reflect on ethical issues inherent to the code they write. Building off prior work that has surveyed students after doing such assignments in class, we conducted focus groups with students who reviewed a new introductory ethics-based CS assignment. In this experience report, we present a case study describing our process of designing an ethics-based assignment and proposing the assignment to students for feedback. Participants in our focus groups not only shared feedback on the assignment, but also on the integration of ethics into coding assignments in general, revealing the benefits and challenges of this work from a student perspective. We also generated novel ethics-oriented assignment concepts alongside students. Deriving from tech controversies that participants felt most affected by, we created a bank of ideas as a starting point for further curriculum development.
Lagrangian dual method for solving stochastic linear quadratic optimal control problems with terminal state constraints

A stochastic linear quadratic (LQ) optimal control problem with a pointwise linear equality constraint on the terminal state is considered. A strong Lagrangian duality theorem is proved under a uniform convexity condition on the cost functional and a surjectivity condition on the linear constraint mapping. Based on the Lagrangian duality, two approaches are proposed to solve the constrained stochastic LQ problem. First, a theoretical method is given to construct the closed-form solution by the strong duality. Second, an iterative algorithm, called augmented Lagrangian method (ALM), is proposed. The strong convergence of the iterative sequence generated by ALM is proved. In addition, some sufficient conditions for the surjectivity of the linear constraint mapping are obtained.
Predicting Surface Texture in Steel Manufacturing at Speed

Control of the surface texture of steel strip during the galvanizing and temper rolling processes is essential to satisfy customer requirements and is conventionally measured post-production using a stylus. In-production laser reflection measurement is less consistent than physical measurement but enables real time adjustment of processing parameters to optimize product surface characteristics. We propose the use of machine learning to improve accuracy of the transformation from inline laser reflection measurements to a prediction of surface properties. In addition to accuracy, model evaluation speed is important for fast feedback control. The ROCKET model is one of the fastest state of the art models, however it can be sped up by utilizing a GPU. Our contribution is to implement the model in PyTorch for fast GPU kernel transforms and provide a soft version of the Proportion of Positive Values (PPV) nonlinear pooling function, allowing gradient flow. We perform timing and performance experiments comparing the implementations.
Eigenvectors of graph Laplacians: a landscape

We review the properties of eigenvectors for the graph Laplacian matrix, aiming at predicting a specific eigenvalue/vector from the geometry of the graph. After considering classical graphs for which the spectrum is known, we focus on eigenvectors that have zero components and extend the pioneering results of Merris (1998) on graph transformations that preserve a given eigenvalue λ or shift it in a simple way. These transformations enable us to obtain eigenvalues/vectors combinatorially instead of numerically; in particular we show that graphs having eigenvalues λ = 1, 2, . , 6 up to six vertices can be obtained from a short list of graphs. For the converse problem of a λ subgraph G of a λ graph G”, we prove results and conjecture that G and G” are connected by two of the simple transformations described above.
SpaceTx: A Roadmap for Benchmarking Spatial Transcriptomics Exploration of the Brain

Mapping spatial distributions of transcriptomic cell types is essential to understanding the brain, with its exceptional cellular heterogeneity and the functional significance of its spatial organization. Spatial transcriptomics techniques are hoped to accomplish these measurements, but each method uses different experimental and computational protocols, with different trade-offs and optimizations. In 2017, the SpaceTx Consortium was formed to compare these methods and determine their suitability for large-scale spatial transcriptomic atlases. SpaceTx work included progress in tissue processing, taxonomy development, gene selection, image processing and data standardization, cell segmentation, cell type assignments, and visualization. Although the landscape of experimental methods has changed dramatically since the beginning of SpaceTx, the need for quantitative and detailed benchmarking of spatial transcriptomics methods in the brain is still unmet. Here, we summarize the work of SpaceTx and highlight outstanding challenges as spatial transcriptomics grows into a mature field. We also discuss how our progress provides a roadmap for benchmarking spatial transcriptomics methods in the future. Data and analyses from this consortium, along with code and methods are publicly available at https://spacetx.github. io/.