3D Aligned Nanofiber Scaffold Fabrication with Trench-Guided Electrospinning for Cardiac Tissue Engineering.

Constructing three-dimensional (3D) aligned nanofiber scaffolds is significant for the development of cardiac tissue engineering, which is promising in the field of drug discovery and disease mechanism study. However, the current nanofiber scaffold preparation strategy, which mainly includes manual assembly and hybrid 3D printing, faces the challenge of integrated fabrication of morphology-controllable nanofibers due to its cross-scale structural feature. In this research, a trench-guided electrospinning (ES) strategy was proposed to directly fabricate 3D aligned nanofiber scaffolds with alternative ES and a direct ink writing (DIW) process. The electric field effect of DIW poly(dimethylsiloxane) (PDMS) side walls on guiding whipping ES nanofibers was investigated to construct trench design rules. It was found that the width/height ratio of trenches greatly affected the nanofiber alignment, and the trench width/height ratio of 1.5 provided the nanofiber alignment degree over 60%. As a proof of principle, 3D nanofiber scaffolds with controllable porosity (60-80%) and alignment (30-60%) were fabricated. The effect of the scaffolds was verified by culturing human-induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs), which resulted in the uniform 3D distribution of aligned hiPSC-CMs with ∼1000 µm thickness. Therefore, this printing strategy shows great potential for the efficient engineered tissue construction.

A Refined Hot Melt Printing Technique with Real-Time CT Imaging Capability.

Personalised drug delivery systems with the ability to offer real-time imaging and control release are an advancement in diagnostic and therapeutic applications. This allows for a tailored drug dosage specific to the patient with a release profile that offers the optimum therapeutic effect. Coupling this application with medical imaging capabilities, real-time contrast can be viewed to display the interaction with the host. Current approaches towards such novelty produce a drug burst release profile and contrasting agents associated with side effects as a result of poor encapsulation of these components. In this study, a 3D-printed drug delivery matrix with real-time imaging is engineered. Polycaprolactone (PCL) forms the bulk structure and encapsulates tetracycline hydrochloride (TH), an antibiotic drug and Iron Oxide Nanoparticles (IONP, Fe3O4), a superparamagnetic contrasting agent. Hot melt extrusion (HME) coupled with fused deposition modelling (FDM) is utilised to promote the encapsulation of TH and IONP. The effect of additives on the formation of micropores (10-20 µm) on the 3D-printed surface was investigated. The high-resolution process demonstrated successful encapsulation of both bioactive and nano components to present promising applications in drug delivery systems, medical imaging and targeted therapy.

Learning Spherical Convolution for 360° Recognition.

While 360° cameras offer tremendous new possibilities in vision, graphics, and augmented reality, the spherical images they produce make visual recognition non-trivial. Ideally, 360° imagery could inherit the deep convolutional neural networks (CNNs) already trained with great success on perspective projection images. However, spherical images cannot be projected to a single plane without significant distortion, and existing methods to transfer CNNs from perspective to spherical images introduce significant computational costs and/or degradations in accuracy. We propose to learn a Spherical Convolution Network (SphConv) that translates a planar CNN to the equirectangular projection of 360° images. Given a source CNN for perspective images as input, SphConv learns to reproduce the flat filter outputs on 360° data, sensitive to the varying distortion effects across the viewing sphere. The key benefits are 1) efficient and accurate recognition for 360° images, and 2) the ability to leverage powerful pre-trained networks for perspective images. We further proposes two instantiation of SphConv-Spherical Kernel learns location dependent kernels on the sphere for SphConv, and Kernel Transformer Network learns a functional transformation that generates SphConv kernels from the source CNN. Among the two variants, Kernel Transformer Network has a much lower memory footprint at the cost of higher computational overhead. Validating our approach with multiple source CNNs and datasets, we show that SphConv using KTN successfully preserves the source CNN's accuracy, while offering efficiency, transferability, and scalability to typical image resolutions. We further introduce a spherical Faster R-CNN model based on SphConv and show that we can learn a spherical object detector without any object annotation in 360° images.

Learning Compressible 360 ° Video Isomers.

Standard video encoders developed for conventional narrow field-of-view video are widely applied to 360° video as well, with reasonable results. However, while this approach commits arbitrarily to a projection of the spherical frames, we observe that some orientations of a 360° video, once projected, are more compressible than others. We introduce an approach to predict the sphere rotation that will yield the maximal compression rate. Given video clips in their original encoding, a convolutional neural network learns the association between a clip's visual content and its compressibility at different rotations of a cubemap projection. Given a novel video, our learning-based approach efficiently infers the most compressible direction in one shot, without repeated rendering and compression of the source video. We validate our idea on thousands of video clips and multiple popular video codecs. The results show that this untapped dimension of 360° compression has substantial potential-"good" rotations are typically 8-18 percent more compressible than bad ones, and our learning approach can predict them reliably 78 percent of the time.

10-Gbit/s direct modulation of a TO-56-can packed 600-µm long laser diode with 2% front-facet reflectance.

A 600-µm long-cavity laser diode with a front-facet reflectance of 2% is demonstrated as a colorless OC-192 transmitter for the future DWDM-PON, which is packed in a TO-56-can package of only 4-GHz frequency bandwidth but can be over-bandwidth modulated with 10-Gbit/s non-return-to-zero data-stream. The coherent injection-locking successfully suppresses its side-mode intensity and noise floor level, which further improves its modulation throughput at higher frequencies. With increasing the coherent injection-locking power from -12 to -3 dBm, the side-mode suppression ratio significantly increases from 39 to 50 dB, which also suppresses the frequency chirp from -12 to -4 GHz within a temporal range of 150 ps. The dense but weak longitudinal modes (with 0.6-nm spacing) in the long-cavity laser diode suppresses to one single-mode in a 100-GHz wide DWDM channel for carrying the OC-192 data at 9.953 Gbit/s. Such an over-bandwidth modulated laser diode still exhibits an on/off extinction ratio of 6.68 dB and a signal-to-noise ratio of 4.96 dB, which can provide a back-to-back receiving power sensitivity of -12.2 dBm at BER of 10⁻9. After 25-km DSF transmission of the OOK data-stream at a bit rate up to 10 Gbit/s, the receiving power sensitivity is -10.1 dBm at a requested BER of 10⁻9.

Assessment of density functional methods with correct asymptotic behavior.

Long-range corrected (LC) hybrid functionals and asymptotically corrected (AC) model potentials are two distinct density functional methods with correct asymptotic behavior. They are known to be accurate for properties that are sensitive to the asymptote of the exchange-correlation potential, such as the highest occupied molecular orbital energies and Rydberg excitation energies of molecules. To provide a comprehensive comparison, we investigate the performance of the two schemes and others on a very wide range of applications, including asymptote problems, self-interaction-error problems, energy-gap problems, charge-transfer problems and many others. The LC hybrid scheme is shown to consistently outperform the AC model potential scheme. In addition, to be consistent with the molecules collected in the IP131 database [Y.-S. Lin, C.-W. Tsai, G.-D. Li and J.-D. Chai, J. Chem. Phys., 2012, 136, 154109], we expand the EA115 and FG115 databases to include, respectively, the vertical electron affinities and fundamental gaps of the additional 16 molecules and develop a new database, AE113 (113 atomization energies), consisting of accurate reference values for the atomization energies of the 113 molecules in IP131. These databases will be useful for assessing the accuracy of density functional methods.

Formation of biodegradable microcapsules utilizing 3D, selectively surface-modified PDMS microfluidic devices.

We have successfully demonstrated the formation of biodegradable microcapsules utilizing PDMS double-emulsification devices. Specially designed 3D PDMS microchannels with surfaces selectively modified by a self-aligned photografting process are employed to generate monodisperse water-in-organic-solvent-in-water (W/O/W) emulsions in a controlled manner. Mainly by varying the outer and inner fluid flow-rates, the dimensions of resulting double emulsions can be adjusted as desired. Meanwhile, biodegradable materials are dissolved in the middle organic solvent (in this work ethyl acetate is used), and solidified into microcapsules once the solvent is extracted. In the prototype demonstration, microcapsules made up of poly(L-lactic acid), trilaurin, and phosphocholine were successfully fabricated. In addition, it was also demonstrated that gamma-Fe(2)O(3) nanoparticles can be simultaneously embedded into the microcapsules, which consequently become responsive to electromagnetic stimulation. As such, the presented PDMS microfluidic devices could potentially serve as versatile encapsulation apparatus, and the fabricated biodegradable microcapsules could function as controlled delivery systems, which are desired for a variety of biological and pharmaceutical applications.

Iron-oxide embedded solid lipid nanoparticles for magnetically controlled heating and drug delivery.

This paper presents the development of magnetic lipid nanoparticles that could serve as controlled delivery vehicles for releasing encapsulated drugs in a desired manner. The nanoparticles are composed of multiple drugs in lipid matrices, which are solid at body temperature and melt around 45 degrees C to 55 degrees C. In addition, super-paramagnetic gamma-Fe2O3 particles with sizes ranging from 5 to 25 nm are surface modified and dispersed uniformly in the lipid nanoparticles. In the prototype demonstration, lipid nanoparticles with average sizes between 100 and 180 nm were fabricated by high-pressure homogenization at elevated temperatures. When exposed to an alternating magnetic field of 60 kA/m at 25 kHz, a solution containing 2 g/L encapsulated gamma-Fe2O3 particles showed a temperature increase from 37 degrees C to 50 degrees C in 20 min. Meanwhile, the dissipated heat melted the surrounding lipid matrices and resulted in an accelerated release of the encapsulated drugs. Within 20 min, approximately 35% of the encapsulated drug molecules were released from the lipid nanoparticles through diffusion. As such, the presented lipid nanoparticles enable a new scheme that combines magnetic control of heating and drug delivery, which could greatly enhance the performance of encapsulated drugs.

A self-adaptive fluidic probe for electrical caries detection.

This paper presents a miniature fluidic probe that is capable of self-adapting its shape to teeth and cooperating with electrical devices to detect dental caries by sensing the variation in electrical- impedance. The fluidic probe, whose liquid tip spontaneously spreads on hydrophilic tooth surface and into underlying caries, is employed to create intimate electrical contact for impedance sensing. A tubular air sleeve shaped by the probe casing is applied around the liquid tip to insulate it from surrounding saliva, and to regulate its spreading. In addition, a friction damper is integrated to stabilize the actuation of fluidic probe. In the prototype demonstration, un-restored, extracted premolar teeth were investigated and the results indicated >20-fold impedance differences between sound and carious teeth, by which caries could be identified in a consistent manner. Furthermore, the fluidic probe has been applied to detect approximal caries, which locates in the contacting surfaces of two adjacent teeth and is difficult to detect by most available schemes. As such, the proposed self-adaptive fluidic probe could readily serve as a diagnostic tool, which is critical to caries prevention and various dental-care applications.