Precursor- route ZnO films from mixed casting solvent for high performance aqueous electrolyte- gated transistors

We significantly improved the properties of semiconducting zinc oxide (ZnO) films resulting from the thermal conversion of a soluble precursor, zinc acetate (ZnAc), by using a mixed casting solvent for the precursor. ZnAc dissolves more readily in a 1:1 mix of ethanol (EtOH) and acetone than in either pure EtOH, pure acetone, or pure isopropanol, and ZnO films converted from mixed solvent cast ZnAc are more homogeneous. When gated with a biocompatible electrolyte, phosphate buffered saline (PBS), ZnO thin film transistors (TFTs) derived from mixed solvent cast ZnAc give 7 times larger field effect current than similar films derived from ZnAc cast from pure EtOH. Sheet resistance at VG = VD = 1V is 18 kΩ/▢, lower than for any organic TFT, and lower than for any water- gated ZnO TFT, reported to date

Fetal brain tissue annotation and segmentation challenge results.

In-utero fetal MRI is emerging as an important tool in the diagnosis and analysis of the developing human brain. Automatic segmentation of the developing fetal brain is a vital step in the quantitative analysis of prenatal neurodevelopment both in the research and clinical context. However, manual segmentation of cerebral structures is time-consuming and prone to error and inter-observer variability. Therefore, we organized the Fetal Tissue Annotation (FeTA) Challenge in 2021 in order to encourage the development of automatic segmentation algorithms on an international level. The challenge utilized FeTA Dataset, an open dataset of fetal brain MRI reconstructions segmented into seven different tissues (external cerebrospinal fluid, gray matter, white matter, ventricles, cerebellum, brainstem, deep gray matter). 20 international teams participated in this challenge, submitting a total of 21 algorithms for evaluation. In this paper, we provide a detailed analysis of the results from both a technical and clinical perspective. All participants relied on deep learning methods, mainly U-Nets, with some variability present in the network architecture, optimization, and image pre- and post-processing. The majority of teams used existing medical imaging deep learning frameworks. The main differences between the submissions were the fine tuning done during training, and the specific pre- and post-processing steps performed. The challenge results showed that almost all submissions performed similarly. Four of the top five teams used ensemble learning methods. However, one team's algorithm performed significantly superior to the other submissions, and consisted of an asymmetrical U-Net network architecture. This paper provides a first of its kind benchmark for future automatic multi-tissue segmentation algorithms for the developing human brain in utero

Implementation of Modified GSO Based Magic Cube Keys Generation in Cryptography

Over the last few decades, tremendous and exponential expansion in digital contents together with their applications has emerged. The Internet represents the essential leading factor for this expansion, which provides low-cost communication tools worldwide. However, the main drawback of the Internet is related to security problems. In order to provide secure communication, enormous efforts have been spent in the cryptographic field. Recently, cryptographic algorithms have become essential for increasing information safety. However, these algorithms require random keys and can be regarded as compromised when the random keys are cracked via the attackers. Therefore, it is substantial that the generation of keys should be random and hard to crack. In this paper, this is guaranteed via one of the most efficient nature-inspired algorithms emerged by inspiring the movements of stars, galaxies, and galaxy superclusters in the cosmos that can be utilized with a mathematical model (magic cube) for generating hardly cracking random number keys. In the proposed cryptographic system, the Modified Galactic Swarm Optimization (GSO) algorithm has been utilized in which every row and column of magic cube faces are randomly rotated until reaching the optimal face, and the optimal random elements are selected as optimal key from the optimal face. The generated optimized magic cube keys are used with several versions of RC6 algorithms to encrypt various secret texts. Furthermore, these generated keys are also used for encrypting images using the logical XOR operation. The obtained results of NIST tests proved that the generated keys are random and uncorrelated. Moreover, the security of the proposed cryptographic system was prove

Tenogenic differentiation of human embryonic stem cells

Tendon healing is complex to manage because of the limited regeneration capacity of tendon tissue; stem cell-based tissue engineering approaches may provide alternative healing strategies. We sought to determine whether human embryonic stem cells (hESC) could be induced to differentiate into tendon-like cells by the addition of exogenous bone morphogenetic protein (BMP)12 (growth differentiation factor[GDF]7) and BMP13 (GDF6). hESC (SHEF-1) were maintained with or without BMP12/13 supplementation, or supplemented with BMP12/13 and the Smad signaling cascade blocking agent, dorsomorphin. Primary rat tenocytes were included as a positive control in immunocytochemistry analysis. A tenocyte-like elongated morphology was observed in hESC after 40-days continuous supplementation with BMP12/13 and ascorbic acid (AA). These cells displayed a tenomodulin expression pattern and morphology consistent with that of the primary tenocyte control. Analysis of tendon-linked gene transcription in BMP12/13 supplemented hESC demonstrated consistent expression of COL1A2, COL3A1, DCN, TNC, THBS4, and TNMD levels. Conversely, when hESCs were cultured in the presence of BMP12/13 and dorsomorphin COL3A1, DCN, and TNC gene expression and tendon matrix formation were inhibited. Taken together, we have demonstrated that hESCs are responsive to tenogenic induction via BMP12/13 in the presence of AA. The directed in vitro generation of tenocytes from pluripotent stem cells may facilitate the development of novel repair approaches for this difficult to heal tissue

Morphological and Thermal Properties of Poly(Vinyl Alcohol)/Layered Double Hydroxide Hybrid Nanocomposite Fibers

Nanolayered particulate of Zn-based layered double hydroxide (LDH) was prepared by a low temperature greener sol-gel method. X-ray diffraction (XRD) studies were performed on the particles annealed at different temperatures. Hexagonal crystal structure of the as-grown LDH particulates was observed. The crystal structure was modified to tetragonal structure of layered double oxide (LDO) on annealing at 250°C. Rietveld fittings showed a collapse of interlayer separation distance along the preferred orientation of the LDH particles as a result of heat treatment. Further, LDH particles were used as fillers of electrospun poly(vinyl alcohol) (PVA) fibers. Heat treatment of the polymer fibers was also performed at different temperatures, and thermal changes were studied by thermogravimetric analysis (TGA), Raman spectroscopy, and scanning electron microscopy (SEM) techniques. Improved interaction of fibers with LDH nanoparticles was observed and ascribed to LDH-related LDO phase transformation at higher temperature. Thermal mechanisms of the rapid weight loss in filled fibers were discussed in comparison to the pure PVA fiber losses. Experimental Raman frequencies of the composite fibers were compared with the calculated Raman modes of the enol and ZnO monomers. The molecular vibration frequencies were found to differ significantly due to heat treatment. Finally, the role filler in the faster and greener thermal decomposition of polymeric fibers was also discussed in the present work

An EXAFS study of the structure of the Zn1-xBexSe alloy system

International audienceZn1-xBexSe is an interesting II-VI semiconductor showing unusual phonon behavior for 0.1

3D printed biocompatible enclosures for an implantable DBS microdevice

A number of methods have been used to make electronic medical microdevices biocompatible. This paper presents a novel approach for design and fabrication of biocompatible silicone enclosures for implantable medical microdevices. The approach involves design and formation of a 3D model of the enclosure using a computer-aided design software tool, followed by 3D printing of the enclosures using a bioplotter. Three different implantable enclosure designs are presented. The fabrication of the three enclosures is given. An evaluation of the suitability of the enclosures for implantation of a deep brain stimulation microdevice is discussed through submersion and operation tests. The evaluation results are presented and discussed