Peptide Cross-Linked Poly(2-oxazoline) as a Sensor Material for the Detection of Proteases with a Quartz Crystal Microbalance

Inflammatory conditions are frequently accompanied by increased levels of active proteases, and there is rising interest in methods for their detection to monitor inflammation in a point of care setting. In this work, new sensor materials for disposable single-step protease biosensors based on poly(2-oxazoline) hydrogels cross-linked with a protease-specific cleavable peptide are described. The performance of the sensor material was assessed targeting the detection of matrix metalloproteinase-9 (MMP-9), a protease that has been shown to be an indicator of inflammation in multiple sclerosis and other inflammatory conditions. Films of the hydrogel were formed on gold-coated quartz crystals using thiol–ene click chemistry, and the cross-link density was optimized. The degradation rate of the hydrogel was monitored using a quartz crystal microbalance (QCM) and showed a strong dependence on the MMP-9 concentration. A concentration range of 0–160 nM of MMP-9 was investigated, and a lower limit of detection of 10 nM MMP-9 was determined

Multiple Projection Optical Diffusion Tomography with Plane Wave Illumination

We describe a new data collection scheme for optical diffusion tomography in which plane wave illumination is combined with multiple projections in the slab imaging geometry. Multiple projection measurements are performed by rotating the slab around the sample. The advantage of the proposed method is that the measured data can be much more easily fitted into the dynamic range of most commonly used detectors. At the same time, multiple projections improve image quality by mutually interchanging the depth and transverse directions, and the scanned (detection) and integrated (illumination) surfaces. Inversion methods are derived for image reconstructions with extremely large data sets. Numerical simulations are performed for fixed and rotated slabs

Automated Prediction of CMEs Using Machine Learning of CME – Flare Associations

YesIn this work, machine learning algorithms are applied to explore the relation between significant flares and their associated CMEs. The NGDC flares catalogue and the SOHO/LASCO CMEs catalogue are processed to associate X and M-class flares with CMEs based on timing information. Automated systems are created to process and associate years of flares and CMEs data, which are later arranged in numerical training vectors and fed to machine learning algorithms to extract the embedded knowledge and provide learning rules that can be used for the automated prediction of CMEs. Different properties are extracted from all the associated (A) and not-associated (NA) flares representing the intensity, flare duration, duration of decline and duration of growth. Cascade Correlation Neural Networks (CCNN) are used in our work. The flare properties are converted to numerical formats that are suitable for CCNN. The CCNN will predict if a certain flare is likely to initiate a CME after input of its properties. Intensive experiments using the Jack-knife techniques are carried out and it is concluded that our system provides an accurate prediction rate of 65.3%. The prediction performance is analysed and recommendation for enhancing the performance are provided

<記録II>ハミル館一〇〇年の歩み : 1918～2018

Renewable energy systems are of importance as being modular, nature-friendly and domestic. Among the renewable energy systems, a great deal of research has been conducted especially on photovoltaic, wind energy and fuel cell in the recent years. One of the hybrid renewable energy systems consisting of 5 kWp photovoltaic panels, 800 Wp wind turbines and 2.4 kWp fuel cell modules was installed at Clean Energy House (CEH), Pamukkale University in Denizli, Turkey. To protect this laboratory, a "Lightning Protection System" was installed at the CEH. In this study, design and installation processes of a lightning protection system for the hybrid renewable energy system at the CEH are considered. III. 7, bibl. 15 (in English; abstracts in English and Lithuanian)

Quantum gravity phenomenology at the dawn of the multi-messenger era—A review

The exploration of the universe has recently entered a new era thanks to the multi-messenger paradigm, characterized by a continuous increase in the quantity and quality of experimental data that is obtained by the detection of the various cosmic messengers (photons, neutrinos, cosmic rays and gravitational waves) from numerous origins. They give us information about their sources in the universe and the properties of the intergalactic medium. Moreover, multi-messenger astronomy opens up the possibility to search for phenomenological signatures of quantum gravity. On the one hand, the most energetic events allow us to test our physical theories at energy regimes which are not directly accessible in accelerators; on the other hand, tiny effects in the propagation of very high energy particles could be amplified by cosmological distances. After decades of merely theoretical investigations, the possibility of obtaining phenomenological indications of Planck-scale effects is a revolutionary step in the quest for a quantum theory of gravity, but it requires cooperation between different communities of physicists (both theoretical and experimental). This review, prepared within the COST Action CA18108 “Quantum gravity phenomenology in the multi-messenger approach”, is aimed at promoting this cooperation by giving a state-of-the art account of the interdisciplinary expertise that is needed in the effective search of quantum gravity footprints in the production, propagation and detection of cosmic messengers

Solar flare prediction using advanced feature extraction, machine learning and feature selection

YesNovel machine-learning and feature-selection algorithms have been developed to study: (i) the flare prediction capability of magnetic feature (MF) properties generated by the recently developed Solar Monitor Active Region Tracker (SMART); (ii) SMART's MF properties that are most significantly related to flare occurrence. Spatio-temporal association algorithms are developed to associate MFs with flares from April 1996 to December 2010 in order to differentiate flaring and non-flaring MFs and enable the application of machine learning and feature selection algorithms. A machine-learning algorithm is applied to the associated datasets to determine the flare prediction capability of all 21 SMART MF properties. The prediction performance is assessed using standard forecast verification measures and compared with the prediction measures of one of the industry's standard technologies for flare prediction that is also based on machine learning - Automated Solar Activity Prediction (ASAP). The comparison shows that the combination of SMART MFs with machine learning has the potential to achieve more accurate flare prediction than ASAP. Feature selection algorithms are then applied to determine the MF properties that are most related to flare occurrence. It is found that a reduced set of 6 MF properties can achieve a similar degree of prediction accuracy as the full set of 21 SMART MF properties

Neuroendocrine tumors presenting with thyroid gland metastasis: a case series

<p>Abstract</p> <p>Introduction</p> <p>Autopsy series have shown that metastasis to the thyroid gland has occurred in up to 24% of patients who have died of cancer. Neuroendocrine tumors may metastasize to thyroid gland.</p> <p>Case presentations</p> <p>Case 1 was a 17-year-old Turkish woman who was referred from our Endocrinology Department for a thyroidectomy for treatment of neuroendocrine tumor metastasis. She was treated with a bilateral total thyroidectomy. Histopathological examination results were consistent with a neuroendocrine tumor; neoplastic cells showed strong immunoreactivity to chromogranin A and synaptophysin, but the immunohistochemical profile was inconsistent with medullary thyroid carcinoma in that the tumor was negative for calcitonin, carcinoembryonic antigen, and thyroid transcription factor-1.</p> <p>Case 2 was a 54-year-old Turkish woman who presented with a 3-cm nodule on her right thyroid lobe. She had undergone surgery for a right lung mass four years previously. After a right pneumonectomy, thymectomy and lymph node dissection, a typical carcinoid tumor was diagnosed. Under ultrasonographic guidance, fine needle aspiration biopsy of her right thyroid pole nodule was performed and the biopsy was compatible with a neuroendocrine tumor metastasis. She was treated with a bilateral total thyroidectomy. Histopathological examination indicated three nodular lesions, 5 cm and 0.4 cm in diameter in her right lobe and 0.1 cm in diameter in her left lobe. The tumors were consistent with a neuroendocrine phenotype, showing strong immunoreactivity to chromogranin A and synaptophysin.</p> <p>Conclusion</p> <p>Thyroid nodules detected during follow-up of neuroendocrine tumor patients should be thoroughly investigated. A fine needle aspiration biopsy of the thyroid confirms the diagnosis in most cases and leads to appropriate management of those patients and may prevent unnecessary treatment approaches.</p

Genetic and phenotypic characterization of NKX6‐2‐related spastic ataxia and hypomyelination

Background and purpose Hypomyelinating leukodystrophies are a heterogeneous group of genetic disorders with a wide spectrum of phenotypes and a high rate of genetically unsolved cases. Bi‐allelic mutations in NKX6‐2 were recently linked to spastic ataxia 8 with hypomyelinating leukodystrophy. Methods Using a combination of homozygosity mapping, exome sequencing, and detailed clinical and neuroimaging assessment a series of new NKX6‐2 mutations in a multicentre setting is described. Then, all reported NKX6‐2 mutations and those identified in this study were combined and an in‐depth analysis of NKX6‐2‐related disease spectrum was provided. Results Eleven new cases from eight families of different ethnic backgrounds carrying compound heterozygous and homozygous pathogenic variants in NKX6‐2 were identified, evidencing a high NKX6‐2 mutation burden in the hypomyelinating leukodystrophy disease spectrum. Our data reveal a phenotype spectrum with neonatal onset, global psychomotor delay and worse prognosis at the severe end and a childhood onset with mainly motor phenotype at the milder end. The phenotypic and neuroimaging expression in NKX6‐2 is described and it is shown that phenotypes with epilepsy in the absence of overt hypomyelination and diffuse hypomyelination without seizures can occur. Conclusions NKX6‐2 mutations should be considered in patients with autosomal recessive, very early onset of nystagmus, cerebellar ataxia with hypotonia that rapidly progresses to spasticity, particularly when associated with neuroimaging signs of hypomyelination. Therefore, it is recommended that NXK6‐2 should be included in hypomyelinating leukodystrophy and spastic ataxia diagnostic panels