Engineering vascularized and innervated bone biomaterials for improved skeletal tissue regeneration

Blood vessels and nerve fibers are distributed throughout the entirety of skeletal tissue, and play important roles during bone development and fracture healing by supplying oxygen, nutrients, and cells. However, despite the successful development of bone mimetic materials that can replace damaged bone from a structural point of view, most of the available bone biomaterials often do not induce sufficient formation of blood vessels and nerves. In part, this is due to the difficulty of integrating and regulating multiple tissue types within artificial materials, which causes a gap between native skeletal tissues. Therefore, understanding the anatomy and underlying interaction mechanisms of blood vessels and nerve fibers in skeletal tissue is important to develop biomaterials that can recapitulate its complex microenvironment. In this perspective, we highlight the structure and osteogenic functions of the vascular and nervous systems in bone, in a coupled manner. In addition, we discuss important design criteria for engineering vascularized, innervated, and neurovascularized bone implant materials, as well as recent advances in the development of such biomaterials. We expect that bone implant materials with neurovascularized networks can more accurately mimic native skeletal tissue and improve the regeneration of bone tissue.     

Enhanced dielectric constant and structural transformation in Fe-doped hydroxyapatite synthesized by wet chemical method

We report the synthesis of single-phase Fe-doped hydroxyapatite (HAp) [Ca_10?xFe _x (PO₄)₆(OH)₂ (0.0?≤?x?≤?0.3)] and enhanced dielectric constant of HAp with Fe doping. Rietveld analysis shows the change in x-axis-oriented lattice constant a in Fe-doped x?=?0.1 and 0.3 compositions in comparison with parent HAp, while z-axis-oriented lattice constant c does not show any considerable change. Analysis of absorbance data shows two new symmetric stretching peaks for Fe-doped x?=?0.1 and x?=?0.3 compositions, which are not present in parent HAp. Magnetic measurements show paramagnetic behaviour of all Fe-doped samples at 300 K. Fe-doped Ca_9.9Fe_0.1(PO₄)₆(OH)₂ composition shows increase in impedance in the presence of 500 Oersted (Oe) applied magnetic field in comparison with impedance in the absence of magnetic field. Ca_9.9Fe_0.1(PO₄)₆(OH)₂ composition shows increase in dielectric constant in comparison with parent HAp in frequency range 5–35 MHz. Fe-doped Ca_9.9Fe_0.1(PO₄)₆(OH)₂ composition shows?~?970% colossal magnetoimpedance at 100 Hz and?~?200% at 20 MHz frequency.     

Stable Light‐Emitting Diodes Using Phase‐Pure Ruddlesden–Popper Layered Perovskites

State‐of‐the‐art light‐emitting diodes (LEDs) are made from high‐purity alloys of III–V semiconductors, but high fabrication cost has limited their widespread use for large area solid‐state lighting. Here, efficient and stable LEDs processed from solution with tunable color enabled by using phase‐pure 2D Ruddlesden–Popper (RP) halide perovskites with a formula (CH₃(CH₂)₃NH₃)₂(CH₃NH₃)_n?1Pb_nI_3n+1 are reported. By using vertically oriented thin films that facilitate efficient charge injection and transport, efficient electroluminescence with a radiance of 35 W Sr^?1 cm^?2 at 744 nm with an ultralow turn‐on voltage of 1 V is obtained. Finally, operational stability tests suggest that phase purity is strongly correlated to stability. Phase‐pure 2D perovskites exhibit >14 h of stable operation at peak operating conditions with no droop at current densities of several Amperes cm^?2 in comparison to mixtures of 2D/3D or 3D perovskites, which degrade within minutes.     

Single-sensor hand-vein multimodal biometric recognition using multiscale deep pyramidal approach

Biometrics has emerged as a powerful technology for person authentication in various scenarios including forensic and civilian applications. Deployment of biometric solutions that use cues from multiple modalities enhances the reliability and robustness of authentication necessary to meet the increasingly stringent security requirements. However, there are two drawbacks typically associated with multimodal biometrics. Firstly, the image acquisition process in such systems is not very user-friendly, primarily due to the time and effort required to capture biometric samples belonging to multiple modalities. Secondly, the overall cost is higher as they employ multiple biometric sensors. To overcome these drawbacks, we employ a single NIR sensor-based image acquisition in the proposed approach for hand-vein recognition. From the input hand image, a palm-vein and four finger-vein subimages are extracted. These images are then enhanced by CLAHE and transformed into illumination invariant representation using center-symmetric local binary pattern (CS-LBP). Further, a hierarchical non-rigid matching technique inspired by the architecture of deep convolutional networks is employed for matching the CS-LBP features. Finally, weighted sum rule-based matching score-level fusion is performed to combine the palm-vein and the four finger-vein modalities. A set of rigorous experiments has been performed on an in-house database collected from the left and right hands of 185 subjects and the publicly available CASIA dataset. The proposed approach achieves equal error rates of 0.13% and 1.21%, and rank-1 identification rates of 100% and 100% on the in-house and CASIA datasets, respectively. Additionally, we compare the proposed approach with the state-of-the-art techniques proposed for vascular biometric recognition in the literature. The important findings are (1) the proposed approach outperforms all the existing techniques considered in this study, (2) the fusion of palm-vein and finger-vein modalities consistently leads to better performance for all the feature extraction techniques considered in this work. (3) Furthermore, our experimental results also suggest that considering the constituent palm-vein and finger-vein images instead of the entire hand-vein images achieves better performance.     

Improving UAV imaging quality by optical sensor fusion: an initial study

The most frequent application of unmanned aerial vehicle (UAVs) is to collect optical colour images from an area of interest. Thus, high spatial resolution colour images with high amount of signal to noise ratio (SNR) are of great importance in UAV applications. Currently, most UAVs use single sensor colour filter array (CFA) cameras for image collection, within which the Bayer-pattern sensors are the most frequently used ones. Due to the limitations of the CFAs, the quality (in terms of spatial resolution, SNR, and sharpness) of UAV colour images is not optimal. In this article, a sensor fusion solution is proposed to improve the quality of UAV imaging. In the proposed solution, a high-resolution colour (HRC) Bayer-pattern sensor is replaced by a dual camera set containing a panchromatic (Pan) sensor, with the same pixel size and a Bayer-pattern colour (or a four-band multi-spectral) sensor with larger pixel size; the resulting images of the dual camera set are then fused. The enlarged pixel size of the colour sensor provides a higher SNR for the cost of lower spatial resolution. However, the accompanied Pan sensor provides single band images with high SNR and high spatial resolution. Fusing the images of the dual camera set generates colour (or MS) images with high spatial resolution, SNR, and sharpness compensating for the major problems of the Bayer-pattern filters.

This replacement solution is initially tested in a laboratory experiment. The results of quality assessments show that the SNR is increased by 2–3 times, the sharpness is improved by around 2 times, and the spatial resolution is increased up to the level of the pan images, while the colour errors remained almost as low as the original colour images. In addition, image classification capability of the images is examined using two methods: Support Vector Machine (SVM) and Maximum Likelihood (ML). The results of image classification also confirmed around 20–40% increase in accuracy. Therefore, the proposed sensor fusion can be a good alternative for UAV colour sensors.     

2-D DIGE identification of differentially expressed heterogeneous nuclear ribonucleoproteins and transcription factors during neural differentiation of human embryonic stem cells

Neural stem cells (NSC) are progenitors that can give rise to all neural lineages. They are found in specific niches of fetal and adult brains and grow in vitro as non-adherent colonies, the neurospheres. These cells express the intermediate filament nestin, commonly considered an NSC marker. NSC can be derived as neurospheres from human embryonic stem cells (hESC). The mechanisms of cellular programming that hESC undergo during differentiation remain obscure. To investigate the commitment process of hESC during directed neural differentiation, we compared the nuclear proteomes of hESC and hESC-derived neurospheres. We used 2-D DIGE to conduct a quantitative comparison of hESC and NSC nuclear proteins and detected 1521 protein spots matched across three gels. Statistical analysis (ANOVA n = 3 with false discovery correction) revealed that only 2.1% of the densitometric signal was significantly changed. The ranges of average ratios varied from 1.2- to 11-fold at a statistically significant p-value <0.05. MS/MS identified 15 regulated proteins previously shown to be involved in chromatin remodeling, mRNA processing and gene expression regulation. Notably, three members of the heterogeneous nuclear ribonucleoprotein family (AUF-1, and FBP-1 and FBP-2) register a 54, 70 and 99% increased expression, highlighting them as potential markers for NSC in vitro derivation. By contrast, Cpsf-6 virtually disappears with differentiation with an 11-fold drop in NSC, highlighting this protein as a novel marker for undifferentiated ESC.     

Efficient Ruddlesden–Popper Perovskite Light‐Emitting Diodes with Randomly Oriented Nanocrystals

Ruddlesden–Popper phase (RP‐phase) perovskites that consist of 2D perovskite slabs interleaved with bulky organic ammonium (OA) are favorable for light‐emitting diodes (LEDs). The critical limitation of LED applications is that the insulating OA arranged in a preferred orientation limits charge transport. Therefore, the ideal solution is to achieve a randomly connected structure that can improve charge transport without hampering the confinement of the electron–hole pair. Here, a structurally modulated RP‐phase metal halide perovskite (MHP), (PEA)₂(CH₃NH₃)_m?1Pb_mBr_3m+1 is introduced to make the randomly oriented RP‐phase unit and ensure good connection between them by applying modified nanocrystal pinning, which leads to an increase in the efficiency of perovskite LEDs (PeLEDs). The randomly connected RP‐phase MHP forces contact between inorganic layers and thereby yields efficient charge transport and radiative recombination. Combined with an optimal dimensionality, (PEA)₂(CH₃NH₃)₂Pb₃Br₁₀, the structurally modulated RP‐phase MHP exhibits increased photoluminescence quantum efficiency, from 0.35% to 30.3%, and their PeLEDs show a 2,018 times higher current efficiency (20.18 cd A^?1) than in the 2D PeLED (0.01 cd A^?1) and 673 times than in the 3D PeLED (0.03 cd A^?1) using the same film formation process. This approach provides insight on how to solve the limitation of RP‐phase MHP for efficient PeLEDs.     

Physical Layer Security for Wireless Powered Massive MIMO Decode and Forward Relay Systems with Hardware Impairments: Performance Analysis

In this paper, the issue of secrecy capacity of wireless powered massive MIMO dual hop relay system with a single antenna eavesdropper having non ideal hardware is addressed. The relay harvests energy in a proportionate manner and passes it to destination through beamforming with classical decode and forward relaying protocol. The relay has no channel state information (CSI) of passive eavesdropper but has CSI of the legitimate channel. The work presented in this paper focuses on the analysis of the difference in system performance with ideal and non ideal hardware, bounded by strict outage probability. The performance (in terms of secrecy outage capacity) is studied with hardware impairments (HWIs) defined for all network elements, i.e., source, relay, destination and passive eavesdropper. It is also observed that compared to ideal hardware, there is significant degradation in performance due to HWIs.

     

A systematic review on deep learning architectures and applications

The amount of digital data in the universe is growing at an exponential rate, doubling every 2 years, and changing how we live in the world. The information storage capacity and data requirement crossed the zettabytes. With this level of bombardment of data on machine learning techniques, it becomes very difficult to carry out parallel computations. Deep learning is broadening its scope and gaining more popularity in natural language processing, feature extraction and visualization, and almost in every machine learning trend. The purpose of this study is to provide a brief review of deep learning architectures and their working. Research papers and proceedings of conferences from various authentic resources (Institute of Electrical and Electronics Engineers, Wiley, Nature, and Elsevier) are studied and analyzed. Different architectures and their effectiveness to solve domain specific problems are evaluated. Various limitations and open problems of current architectures are discussed to provide better insights to help researchers and student to resume their research on these issues. One hundred one articles were reviewed for this meta‐analysis of deep learning. From this analysis, it is concluded that advanced deep learning architectures are combinations of few conventional architectures. For example, deep belief network and convolutional neural network are used to build convolutional deep belief network, which has higher capabilities than the parent architectures. These combined architectures are more robust to explore the problem space and thus can be the answer to build a general‐purpose architecture.     

Shock Exfoliation of Graphene Fluoride in Microwave

An unprecedented microwave‐based strategy is developed to facilitate solid‐phase, instantaneous delamination and decomposition of graphite fluoride (GF) into few‐layer, partially fluorinated graphene. The shock reaction occurs (and completes in few seconds) under microwave irradiation upon exposing GF to either “microwave‐induced plasma” generated in vacuum or “catalyst effect” caused by intense sparking of graphite at ambient conditions. A detailed analysis of the structural and compositional transformations in these processes indicates that the GF experiences considerable exfoliation and defluorination, during which sp²‐bonded carbon is partially recovered despite significant structural defects being introduced. The exfoliated fluorinated graphene shows excellent electrochemical performance as anode materials in potassium ion batteries and as catalysts for the conversion of O₂ to H₂O₂. This simple and scalable method requires minimal energy input and does not involve the use of other chemicals, which is attractive for extensive research in fluorine‐containing graphene and its derivatives in laboratories and industrial applications.