Photo-triggered Phase Transition of Crystals and Photoactuation.

Photo-triggered phase transition is a new type of phase transition in which a photochromic crystal with a thermal phase transition transforms into an identical high-temperature phase in a temperature region lower than the thermal phase transition temperature upon light irradiation. Here, we report a second crystal that exhibits a photo-triggered phase transition, thereby demonstrating that the photo-triggered phase transition is a general phenomenon that occurs in crystals. When the chiral salicylidenephenylethylamine crystal was irradiated with ultraviolet (UV) light, the photo-triggered phase transition occurred in the temperature range -30 to -10 °C. The photo-triggered phase transition is induced by local stress due to trans-keto molecules produced by photoisomerization near the irradiated surface. Crystal cantilevers exhibited stepwise bending by the combination of the photo-triggered phase transition and photoisomerization. Alternate irradiation with UV and visible light achieved locomotion of single crystals driven by repeated stepwise bending. Finally, a detailed comparison of photo-triggered and non-photo-triggered phase transition crystals revealed that a sufficient molecular conformation change in affordable crystal voids, smooth photoisomerization, and most likely a chiral molecular arrangement are required for inducing the photo-triggered phase transition.

Automated characterization and detection of fibromyalgia using slow wave sleep EEG signals with glucose pattern and D'hondt pooling technique.

Fibromyalgia is a soft tissue rheumatism with significant qualitative and quantitative impact on sleep macro and micro architecture. The primary objective of this study is to analyze and identify automatically healthy individuals and those with fibromyalgia using sleep electroencephalography (EEG) signals. The study focused on the automatic detection and interpretation of EEG signals obtained from fibromyalgia patients. In this work, the sleep EEG signals are divided into 15-s and a total of 5358 (3411 healthy control and 1947 fibromyalgia) EEG segments are obtained from 16 fibromyalgia and 16 normal subjects. Our developed model has advanced multilevel feature extraction architecture and hence, we used a new feature extractor called GluPat, inspired by the glucose chemical, with a new pooling approach inspired by the D'hondt selection system. Furthermore, our proposed method incorporated feature selection techniques using iterative neighborhood component analysis and iterative Chi2 methods. These selection mechanisms enabled the identification of discriminative features for accurate classification. In the classification phase, we employed a support vector machine and k-nearest neighbor algorithms to classify the EEG signals with leave-one-record-out (LORO) and tenfold cross-validation (CV) techniques. All results are calculated channel-wise and iterative majority voting is used to obtain generalized results. The best results were determined using the greedy algorithm. The developed model achieved a detection accuracy of 100% and 91.83% with a tenfold and LORO CV strategies, respectively using sleep stage (2 + 3) EEG signals. Our generated model is simple and has linear time complexity.

Negative to positive axial thermal expansion switching of an organic crystal: contribution to multistep photoactuation.

Materials displaying negative thermal expansion (NTE), in contrast to typical materials with positive thermal expansion (PTE), are attractive for both fundamental research and practical applications, including the development of composites with near-zero thermal expansion. A recent data mining study revealed that approximately 34% of organic crystals may present NTE, indicating that NTE in organic crystals is much more common than generally believed. However, organic crystals that switch from NTE to PTE or vice versa have rarely been reported. Here, we report the crystal of N-3,5-di-tert-butylsalicylide-3-nitroaniline in the enol form (enol-1) as the first organic crystal in which the axial thermal expansion changes from negative to positive at around room temperature. When heated, the crystal shrinks along the a-axis below 30 °C and then it expands above 30 °C. Geometric calculations revealed that below 30 °C, the decrease in the tilt angle of the molecule exceeds the increase in the interplanar distance, causing NTE, whereas above 30 °C, the increase in the interplanar distance outweighs the decrease in the tilt angle, resulting in PTE. By combining photoisomerisation and the NTE-PTE switching induced by the photothermal effect, multistep crystal photoactuation was achieved. Moreover, actuation switching of the same crystal sample by changing atmosphere temperature was realised by utilising the NTE-PTE change. Such NTE-PTE switching without a thermal phase transition provides not only new insight into organic crystals but also a new strategy for designing crystal actuators.

Mechanical properties and peculiarities of molecular crystals.

In the last century, molecular crystals functioned predominantly as a means for determining the molecular structures via X-ray diffraction, albeit as the century came to a close the response of molecular crystals to electric, magnetic, and light fields revealed that the physical properties of molecular crystals were as rich as the diversity of molecules themselves. In this century, the mechanical properties of molecular crystals have continued to enhance our understanding of the colligative responses of weakly bound molecules to internal frustration and applied forces. Here, the authors review the main themes of research that have developed in recent decades, prefaced by an overview of the particular considerations that distinguish molecular crystals from traditional materials such as metals and ceramics. Many molecular crystals will deform themselves as they grow under some conditions. Whether they respond to intrinsic stress or external forces or interactions among the fields of growing crystals remains an open question. Photoreactivity in single crystals has been a leading theme in organic solid-state chemistry; however, the focus of research has been traditionally on reaction stereo- and regio-specificity. However, as light-induced chemistry builds stress in crystals anisotropically, all types of motions can be actuated. The correlation between photochemistry and the responses of single crystals-jumping, twisting, fracturing, delaminating, rocking, and rolling-has become a well-defined field of research in its own right: photomechanics. The advancement of our understanding requires theoretical and high-performance computations. Computational crystallography not only supports interpretations of mechanical responses, but predicts the responses itself. This requires the engagement of classical force-field based molecular dynamics simulations, density functional theory-based approaches, and the use of machine learning to divine patterns to which algorithms can be better suited than people. The integration of mechanics with the transport of electrons and photons is considered for practical applications in flexible organic electronics and photonics. Dynamic crystals that respond rapidly and reversibly to heat and light can function as switches and actuators. Progress in identifying efficient shape-shifting crystals is also discussed. Finally, the importance of mechanical properties to milling and tableting of pharmaceuticals in an industry still dominated by active ingredients composed of small molecule crystals is reviewed. A dearth of data on the strength, hardness, Young's modulus, and fracture toughness of molecular crystals underscores the need for refinement of measurement techniques and conceptual tools. The need for benchmark data is emphasized throughout.

Photothermally induced natural vibration for versatile and high-speed actuation of crystals.

The flourishing field of soft robotics requires versatile actuation methodology. Natural vibration is a physical phenomenon that can occur in any material. Here, we report high-speed bending of anisole crystals by natural vibration induced by the photothermal effect. Rod-shaped crystal cantilevers undergo small, fast repetitive bending (~0.2°) due to natural vibration accompanied by large photothermal bending (~1°) under ultraviolet light irradiation. The natural vibration is greatly amplified by resonance upon pulsed light irradiation at the natural frequency to realise high frequency (~700 Hz), large bending (~4°), and high energy conversion efficiency from light to mechanical energy. The natural vibration is induced by the thermal load generated by the temperature gradient in the crystal due to the photothermal effect. The bending behaviour is successfully simulated using finite element analysis. Any light-absorbing crystal can be actuated by photothermally induced natural vibration. This finding of versatile crystal actuation can lead to the development of soft robots with high-speed and high-efficient actuation capabilities.

A Novel In Vitro Membrane Permeability Methodology Using Three-dimensional Caco-2 Tubules in a Microphysiological System Which Better Mimics In Vivo Physiological Conditions.

The aim of this study was to develop an in vitro drug permeability methodology which mimics the gastrointestinal environment more accurately than conventional 2D methodologies through a three-dimensional (3D) Caco-2 tubules using a microphysiological system. Such a system offers significant advantages, including accelerated cellular polarization and more accurate mimicry of the in vivo environment. This methodology was confirmed by measuring the permeability of propranolol as a model compound, and subsequently applied to those of solifenacin and bile acids for a comprehensive understanding of permeability for the drug product in the human gastrointestinal tract. To protect the Caco-2 tubules from bile acid toxicity, a mucus layer was applied on the surface of Caco-2 tubules and it enables to use simulated intestinal fluid. The assessment using propranolol reproduced results equivalent to those obtained from conventional methodology, while that using solifenacin indicated fluctuations in the permeability of solifenacin due to various factors, including interaction with bile acids. We therefore suggest that this model will serve as an alternative testing system for measuring drug absorption in an environment closely resembling that of the human gastrointestinal tract.

Photothermally Driven High-Speed Crystal Actuation and Its Simulation.

Mechanically responsive crystals have been increasingly explored, mainly based on photoisomerization. However, photoisomerization has some disadvantages for crystal actuation, such as a slow actuation speed, no actuation of thick crystals, and a narrow wavelength range. Here we report photothermally driven fast-bending actuation and simulation of a salicylideneaniline derivative crystal with an o-amino substituent in enol form. Under ultraviolet (UV) light irradiation, these thin (<20 µm) crystals bent but the thick (>40 µm) crystals did not due to photoisomerization; in contrast, thick crystals bent very quickly (in several milliseconds) due to the photothermal effect, even by visible light. Finally, 500 Hz high-frequency bending was achieved by pulsed UV laser irradiation. The generated photothermal energy was estimated based on the photodynamics using femtosecond transient absorption. Photothermal bending is caused by a nonsteady temperature gradient in the thickness direction due to the heat conduction of photothermal energy generated near the crystal surface. The temperature gradient was calculated based on the one-dimensional nonsteady heat conduction equation to simulate photothermally driven crystal bending successfully. Most crystals that absorb light have their own photothermal effects. It is expected that the creation and design of actuation of almost all crystals will be possible via the photothermal effect, which cannot be realized by photoisomerization, and the potential and versatility of crystals as actuation materials will expand in the near future.

Wide-area multilayered self-assembly of fluorapatite nanorods vertically oriented on a substrate as a non-classical crystal growth.

Oriented attachment of homogeneously shaped nanoblocks, such as nanocubes and nanorods, is attracting attention as a fundamental process of non-classical crystal growth to produce specific ordered architectures of functional materials. Although lateral alignments of horizontally oriented nanorod are commonly observed at the air-liquid and liquid-solid interfaces in dispersion systems, the accumulation of vertically oriented nanorods on a substrate has rarely been produced in a wide area over a millimeter-sized flat surface. Here, we achieved homogeneous stacking of vertical fluorapatite nanorods with a large aspect ratio (∼6) in a toluene-hexane mixture system through a gradual decrease in the dispersibility. Micrometer-thick flat films in which the c direction of fluorapatite nanorods was arranged perpendicularly to the surface were deposited on a substrate with a diameter of over 20 mm. The wide-area accumulation of vertical nanorods occurs through the self-assembly of laterally arranged clusters of nanorods covered with a stabilizing agent and subsequent gentle sedimentation on the substrate surface.

Characterization of fibromyalgia using sleep EEG signals with nonlinear dynamical features.

Fibromyalgia is an intense musculoskeletal pain causing sleep, fatigue, and mood problems. Sleep studies have suggested that 70%-80% of fibromyalgia patients complain of non-restorative sleep. The abnormalities in sleep have been implicated as both a cause and effect of the disease. In this paper, the electroencephalogram (EEG) signals of sleep stages 2 and 3 are used to classify the normal and fibromyalgia classes automatically. We have used various nonlinear parameters, namely sample entropy (SampEn), fractal dimension (FD), higher order spectra (HOS), largest Lyapunov exponent (LLE), Kolmogorov complexity (KC), Hurst exponent (HE), energy, and power in various frequency bands from the EEG signals. Then these features are subjected to Student's t-test to select the clinically significant features, and are classified using the support vector machine (SVM) classifier. Our proposed method can classify normal and fibromyalgia subjects using the stage 2 sleep EEG signals with an accuracy of 96.15%, sensitivity and specificity of 96.88% and 95.65%, respectively. Performance of the developed system can be improved further by adding more subjects in each class, and can be employed for clinical use.

Preparation of 99mTc-Labeled Mannan-S-Cysteine and Effect of Molecular Size of Mannan on Its Biodistribution.

Macrophage mannose receptor (MMR/CD206) is a promising target for the detection and identification of sentinel lymph node (SLN). MMR-targeting probes have been developed using mannosylated dextran, however, impairment of efficient targeting of SLN was often caused because of retention of injection site in which macrophages and dendritic cells exist. In this study, we prepared new MMR-targeting probes from yeast mannan (85 kDa), and its bioditribution was investigated. In-vivo evaluation showed that 11.9% of injected dose of 99mTc-labeled mannan-S-cysteines (99mTc-MSCs) was accumulated in popliteal lymph node (the SLN in this model), however, significant level of radioactivity (approximately 80%) was remained in injection site. Interestingly, 99mTc-labeled low molecular weight mannan-S-cysteine mannan (99mTc-LSC) prepared from 50 and 25 kDa mannan showed a decreased specific accumulation of 99mTc-LSC in the popliteal lymph node, while the radioactivity at the injection site remained unchanged. These results suggest that the molecular size, or nature/shape of the sugar chain is important for the specific accumulation of 99mTc-MSC in popliteal lymph node.

Automated detection and classification of liver fibrosis stages using contourlet transform and nonlinear features.

BACKGROUND AND OBJECTIVE: Liver fibrosis is a type of chronic liver injury that is characterized by an excessive deposition of extracellular matrix protein. Early detection of liver fibrosis may prevent further growth toward liver cirrhosis and hepatocellular carcinoma. In the past, the only method to assess liver fibrosis was through biopsy, but this examination is invasive, expensive, prone to sampling errors, and may cause complications such as bleeding. Ultrasound-based elastography is a promising tool to measure tissue elasticity in real time; however, this technology requires an upgrade of the ultrasound system and software. In this study, a novel computer-aided diagnosis tool is proposed to automatically detect and classify the various stages of liver fibrosis based upon conventional B-mode ultrasound images. METHODS: The proposed method uses a 2D contourlet transform and a set of texture features that are efficiently extracted from the transformed image. Then, the combination of a kernel discriminant analysis (KDA)-based feature reduction technique and analysis of variance (ANOVA)-based feature ranking technique was used, and the images were then classified into various stages of liver fibrosis. RESULTS: Our 2D contourlet transform and texture feature analysis approach achieved a 91.46% accuracy using only four features input to the probabilistic neural network classifier, to classify the five stages of liver fibrosis. It also achieved a 92.16% sensitivity and 88.92% specificity for the same model. The evaluation was done on a database of 762 ultrasound images belonging to five different stages of liver fibrosis. CONCLUSIONS: The findings suggest that the proposed method can be useful to automatically detect and classify liver fibrosis, which would greatly assist clinicians in making an accurate diagnosis.

Computer-aided diagnosis of glaucoma using fundus images: A review.

BACKGROUND AND OBJECTIVES: Glaucoma is an eye condition which leads to permanent blindness when the disease progresses to an advanced stage. It occurs due to inappropriate intraocular pressure within the eye, resulting in damage to the optic nerve. Glaucoma does not exhibit any symptoms in its nascent stage and thus, it is important to diagnose early to prevent blindness. Fundus photography is widely used by ophthalmologists to assist in diagnosis of glaucoma and is cost-effective. METHODS: The morphological features of the disc that is characteristic of glaucoma are clearly seen in the fundus images. However, manual inspection of the acquired fundus images may be prone to inter-observer variation. Therefore, a computer-aided detection (CAD) system is proposed to make an accurate, reliable and fast diagnosis of glaucoma based on the optic nerve features of fundus imaging. In this paper, we reviewed existing techniques to automatically diagnose glaucoma. RESULTS: The use of CAD is very effective in the diagnosis of glaucoma and can assist the clinicians to alleviate their workload significantly. We have also discussed the advantages of employing state-of-art techniques, including deep learning (DL), when developing the automated system. The DL methods are effective in glaucoma diagnosis. CONCLUSIONS: Novel DL algorithms with big data availability are required to develop a reliable CAD system. Such techniques can be employed to diagnose other eye diseases accurately.

Automated seizure prediction.

In the past two decades, significant advances have been made on automated electroencephalogram (EEG)-based diagnosis of epilepsy and seizure detection. A number of innovative algorithms have been introduced that can aid in epilepsy diagnosis with a high degree of accuracy. In recent years, the frontiers of computational epilepsy research have moved to seizure prediction, a more challenging problem. While antiepileptic medication can result in complete seizure freedom in many patients with epilepsy, up to one-third of patients living with epilepsy will have medically intractable epilepsy, where medications reduce seizure frequency but do not completely control seizures. If a seizure can be predicted prior to its clinical manifestation, then there is potential for abortive treatment to be given, either self-administered or via an implanted device administering medication or electrical stimulation. This will have a far-reaching impact on the treatment of epilepsy and patient's quality of life. This paper presents a state-of-the-art review of recent efforts and journal articles on seizure prediction. The technologies developed for epilepsy diagnosis and seizure detection are being adapted and extended for seizure prediction. The paper ends with some novel ideas for seizure prediction using the increasingly ubiquitous machine learning technology, particularly deep neural network machine learning.

Parkinson's disease: Cause factors, measurable indicators, and early diagnosis.

Parkinson's disease (PD) is a neurodegenerative disease of the central nervous system caused due to the loss of dopaminergic neurons. It is classified under movement disorder as patients with PD present with tremor, rigidity, postural changes, and a decrease in spontaneous movements. Comorbidities including anxiety, depression, fatigue, and sleep disorders are observed prior to the diagnosis of PD. Gene mutations, exposure to toxic substances, and aging are considered as the causative factors of PD even though its genesis is unknown. This paper reviews PD etiologies, progression, and in particular measurable indicators of PD such as neuroimaging and electrophysiology modalities. In addition to gene therapy, neuroprotective, pharmacological, and neural transplantation treatments, researchers are actively aiming at identifying biological markers of PD with the goal of early diagnosis. Neuroimaging modalities used together with advanced machine learning techniques offer a promising path for the early detection and intervention in PD patients.

Biotransformation of (+)-Carvone and (-)-Carvone by the Common Cutworm Spodoptera litura Larvae.

Biotransformation of (+)- and (-)-carvone (1 and 2) by the larvae of common cutworm (Spodoptera litura) has been investigated. (+)-Carvone was transformed to (+)-(4S)-10-hydroxycarvone (1-1), (+)-(4S)-7- hydroxycarvone (1-2), and (-)-(4S)-8,9-dihydroxy-8,9-dihydrocarvone (1-3). (-)-Carvone (2) was transformed to (-)-(4R)-10-hydroxycarvone (2-1), (-)-(4R)-7-hydroxycarvone (2-2), (+)-(4R)-8,9-dihydroxy-8,9- dihydrocarvone (2-3), and (-)-(2R,4R)-10-hydroxycarveol (2-4). The results indicate that the main metabolic reaction of carvones by S. litura larvae is oxidation at vinyl group (C-8 and C-9).

Deep learning for healthcare applications based on physiological signals: A review.

BACKGROUND AND OBJECTIVE: We have cast the net into the ocean of knowledge to retrieve the latest scientific research on deep learning methods for physiological signals. We found 53 research papers on this topic, published from 01.01.2008 to 31.12.2017. METHODS: An initial bibliometric analysis shows that the reviewed papers focused on Electromyogram(EMG), Electroencephalogram(EEG), Electrocardiogram(ECG), and Electrooculogram(EOG). These four categories were used to structure the subsequent content review. RESULTS: During the content review, we understood that deep learning performs better for big and varied datasets than classic analysis and machine classification methods. Deep learning algorithms try to develop the model by using all the available input. CONCLUSIONS: This review paper depicts the application of various deep learning algorithms used till recently, but in future it will be used for more healthcare areas to improve the quality of diagnosis.

Automated EEG-based screening of depression using deep convolutional neural network.

In recent years, advanced neurocomputing and machine learning techniques have been used for Electroencephalogram (EEG)-based diagnosis of various neurological disorders. In this paper, a novel computer model is presented for EEG-based screening of depression using a deep neural network machine learning approach, known as Convolutional Neural Network (CNN). The proposed technique does not require a semi-manually-selected set of features to be fed into a classifier for classification. It learns automatically and adaptively from the input EEG signals to differentiate EEGs obtained from depressive and normal subjects. The model was tested using EEGs obtained from 15 normal and 15 depressed patients. The algorithm attained accuracies of 93.5% and 96.0% using EEG signals from the left and right hemisphere, respectively. It was discovered in this research that the EEG signals from the right hemisphere are more distinctive in depression than those from the left hemisphere. This discovery is consistent with recent research and revelation that the depression is associated with a hyperactive right hemisphere. An exciting extension of this research would be diagnosis of different stages and severity of depression and development of a Depression Severity Index (DSI).

Automated characterization of cardiovascular diseases using relative wavelet nonlinear features extracted from ECG signals.

Cardiovascular diseases (CVDs) are the leading cause of deaths worldwide. The rising mortality rate can be reduced by early detection and treatment interventions. Clinically, electrocardiogram (ECG) signal provides useful information about the cardiac abnormalities and hence employed as a diagnostic modality for the detection of various CVDs. However, subtle changes in these time series indicate a particular disease. Therefore, it may be monotonous, time-consuming and stressful to inspect these ECG beats manually. In order to overcome this limitation of manual ECG signal analysis, this paper uses a novel discrete wavelet transform (DWT) method combined with nonlinear features for automated characterization of CVDs. ECG signals of normal, and dilated cardiomyopathy (DCM), hypertrophic cardiomyopathy (HCM) and myocardial infarction (MI) are subjected to five levels of DWT. Relative wavelet of four nonlinear features such as fuzzy entropy, sample entropy, fractal dimension and signal energy are extracted from the DWT coefficients. These features are fed to sequential forward selection (SFS) technique and then ranked using ReliefF method. Our proposed methodology achieved maximum classification accuracy (acc) of 99.27%, sensitivity (sen) of 99.74%, and specificity (spec) of 98.08% with K-nearest neighbor (kNN) classifier using 15 features ranked by the ReliefF method. Our proposed methodology can be used by clinical staff to make faster and accurate diagnosis of CVDs. Thus, the chances of survival can be significantly increased by early detection and treatment of CVDs.

Towards precision medicine: from quantitative imaging to radiomics.

Radiology (imaging) and imaging-guided interventions, which provide multi-parametric morphologic and functional information, are playing an increasingly significant role in precision medicine. Radiologists are trained to understand the imaging phenotypes, transcribe those observations (phenotypes) to correlate with underlying diseases and to characterize the images. However, in order to understand and characterize the molecular phenotype (to obtain genomic information) of solid heterogeneous tumours, the advanced sequencing of those tissues using biopsy is required. Thus, radiologists image the tissues from various views and angles in order to have the complete image phenotypes, thereby acquiring a huge amount of data. Deriving meaningful details from all these radiological data becomes challenging and raises the big data issues. Therefore, interest in the application of radiomics has been growing in recent years as it has the potential to provide significant interpretive and predictive information for decision support. Radiomics is a combination of conventional computer-aided diagnosis, deep learning methods, and human skills, and thus can be used for quantitative characterization of tumour phenotypes. This paper discusses the overview of radiomics workflow, the results of various radiomics-based studies conducted using various radiological images such as computed tomography (CT), magnetic resonance imaging (MRI), and positron-emission tomography (PET), the challenges we are facing, and the potential contribution of radiomics towards precision medicine.

Application of stacked convolutional and long short-term memory network for accurate identification of CAD ECG signals.

Coronary artery disease (CAD) is the most common cause of heart disease globally. This is because there is no symptom exhibited in its initial phase until the disease progresses to an advanced stage. The electrocardiogram (ECG) is a widely accessible diagnostic tool to diagnose CAD that captures abnormal activity of the heart. However, it lacks diagnostic sensitivity. One reason is that, it is very challenging to visually interpret the ECG signal due to its very low amplitude. Hence, identification of abnormal ECG morphology by clinicians may be prone to error. Thus, it is essential to develop a software which can provide an automated and objective interpretation of the ECG signal. This paper proposes the implementation of long short-term memory (LSTM) network with convolutional neural network (CNN) to automatically diagnose CAD ECG signals accurately. Our proposed deep learning model is able to detect CAD ECG signals with a diagnostic accuracy of 99.85% with blindfold strategy. The developed prototype model is ready to be tested with an appropriate huge database before the clinical usage.