Electrospun polyvinyl alcohol/carbon dioxide modified polyethyleneimine composite nanofiber scaffolds

A novel biocompatible polyvinyl alcohol/carbon dioxide modified polyethyleneimine (PVA/PEI-CO2) composite nanofiber was fabricated by a green and facile protocol, which reduces the cytotoxicity of PEI through the surface modification of the PEI with CO2. The 13C NMR spectrum, elemental analysis, and TGA show that CO2 has been incorporated in the PEI surface resulting in a relatively stable structure. The resulting PVA/PEI-CO2 composite nanofibers have been characterized by attenuated total reflection-Fourier transform infrared spectroscopy (ATR-FTIR), contact angle, and scanning electron microscopy (SEM). The results show that the average diameters of the nanofibers range from 265 ± 53 nm to 423 ± 80 nm. The cytotoxicity of PVA/PEI-CO2 composite nanofibers was assessed by cytotoxicity evaluation using the growth and cell proliferation of normal mice Schwann cells. SEM and the MTT assay demonstrated the promotion of cell growth and proliferation on the PVA/PEI-CO2 composite scaffold. It suggests that PEI-CO2 can have tremendous potential applications in biological material research

Oscillation Theorems for Second-Order Quasilinear Neutral Functional Differential Equations

New oscillation criteria are established for the second-order nonlinear neutral functional differential equations of the form (r(t)|z′(t)|α−1z′(t))’+f(t,x[σ(t)])=0, t≥t0, where z(t)=x(t)+p(t)x(τ(t)), p∈C1([t0,∞),[0,∞)), and α≥1. Our results improve and extend some known results in the literature. Some examples are also provided to show the importance of these results

Deep Learning-Based Automatic Diagnosis System for Developmental Dysplasia of the Hip

As the first-line diagnostic imaging modality, radiography plays an essential role in the early detection of developmental dysplasia of the hip (DDH). Clinically, the diagnosis of DDH relies on manual measurements and subjective evaluation of different anatomical features from pelvic radiographs. This process is inefficient and error-prone and requires years of clinical experience. In this study, we propose a deep learning-based system that automatically detects 14 keypoints from a radiograph, measures three anatomical angles (center-edge, T\"onnis, and Sharp angles), and classifies DDH hips as grades I-IV based on the Crowe criteria. Moreover, a novel data-driven scoring system is proposed to quantitatively integrate the information from the three angles for DDH diagnosis. The proposed keypoint detection model achieved a mean (95% confidence interval [CI]) average precision of 0.807 (0.804-0.810). The mean (95% CI) intraclass correlation coefficients between the center-edge, Tonnis, and Sharp angles measured by the proposed model and the ground-truth were 0.957 (0.952-0.962), 0.947 (0.941-0.953), and 0.953 (0.947-0.960), respectively, which were significantly higher than those of experienced orthopedic surgeons (p<0.0001). In addition, the mean (95% CI) test diagnostic agreement (Cohen's kappa) obtained using the proposed scoring system was 0.84 (0.83-0.85), which was significantly higher than those obtained from diagnostic criteria for individual angle (0.76 [0.75-0.77]) and orthopedists (0.71 [0.63-0.79]). To the best of our knowledge, this is the first study for objective DDH diagnosis by leveraging deep learning keypoint detection and integrating different anatomical measurements, which can provide reliable and explainable support for clinical decision-making

Mixture Selection, Mechanism Design, and Signaling

We pose and study a fundamental algorithmic problem which we term mixture selection, arising as a building block in a number of game-theoretic applications: Given a function $g$ from the $n$-dimensional hypercube to the bounded interval $[-1,1]$, and an $n \times m$ matrix $A$ with bounded entries, maximize $g(Ax)$ over $x$ in the $m$-dimensional simplex. This problem arises naturally when one seeks to design a lottery over items for sale in an auction, or craft the posterior beliefs for agents in a Bayesian game through the provision of information (a.k.a. signaling). We present an approximation algorithm for this problem when $g$ simultaneously satisfies two smoothness properties: Lipschitz continuity with respect to the $L^\infty$ norm, and noise stability. The latter notion, which we define and cater to our setting, controls the degree to which low-probability errors in the inputs of $g$ can impact its output. When $g$ is both $O(1)$-Lipschitz continuous and $O(1)$-stable, we obtain an (additive) PTAS for mixture selection. We also show that neither assumption suffices by itself for an additive PTAS, and both assumptions together do not suffice for an additive FPTAS. We apply our algorithm to different game-theoretic applications from mechanism design and optimal signaling. We make progress on a number of open problems suggested in prior work by easily reducing them to mixture selection: we resolve an important special case of the small-menu lottery design problem posed by Dughmi, Han, and Nisan; we resolve the problem of revenue-maximizing signaling in Bayesian second-price auctions posed by Emek et al. and Miltersen and Sheffet; we design a quasipolynomial-time approximation scheme for the optimal signaling problem in normal form games suggested by Dughmi; and we design an approximation algorithm for the optimal signaling problem in the voting model of Alonso and C\^{a}mara