Production of aromatics by catalytic fast pyrolysis of cellulose in a bubbling fluidized bed reactor

Catalytic fast pyrolysis of cellulose was studied at 500°C using a ZSM‐5 catalyst in a bubbling fluidized bed reactor constructed from a 4.92‐cm ID pipe. Inert gas was fed from below through the distributor plate and from above through a vertical feed tube along with cellulose. Flowing 34% of the total fluidization gas through the feed tube led to the optimal mixing of the pyrolysis vapors into the catalyst bed, which experimentally corresponded to 29.5% carbon aromatic yield. Aromatic yield reached a maximum of 31.6% carbon with increasing gas residence time by changing the catalyst bed height. Increasing the hole‐spacing in the distributor plate was shown to have negligible effect on average bubble diameter and hence did not change the product distribution. Aromatic yields of up to 39.5% carbon were obtained when all studied parameters were optimized. © 2014 American Institute of Chemical Engineers AIChE J, 60: 1320–1335, 2014     

Atmospheric pressure photoionization mass spectrometry

Atmospheric pressure photoionization (APPI) is the last arrival in the family of atmospheric pressure ionization (API) methods to couple mass spectrometry (MS) to liquid-phase separation techniques. The basic idea was to further extend the fields of application of liquid chromatography (LC)-MS to those molecules that are not, or are poorly amenable, to electrospray (ESI) or APCI. The present review explores the literature. After a short introduction with an historical background and the premises for its development, we describe the technique, its physical principles, and the factors that affect its efficiency. The review also presents a survey of applications in different fields.     

Microscopic melanoma detection and classification: A framework of pixel‐based fusion and multilevel features reduction

The numbers of diagnosed patients by melanoma are drastic and contribute more deaths annually among young peoples. An approximately 192,310 new cases of skin cancer are diagnosed in 2019, which shows the importance of automated systems for the diagnosis process. Accordingly, this article presents an automated method for skin lesions detection and recognition using pixel‐based seed segmented images fusion and multilevel features reduction. The proposed method involves four key steps: (a) mean‐based function is implemented and fed input to top‐hat and bottom‐hat filters which later fused for contrast stretching, (b) seed region growing and graph‐cut method‐based lesion segmentation and fused both segmented lesions through pixel‐based fusion, (c) multilevel features such as histogram oriented gradient (HOG), speeded up robust features (SURF), and color are extracted and simple concatenation is performed, and (d) finally variance precise entropy‐based features reduction and classification through SVM via cubic kernel function. Two different experiments are performed for the evaluation of this method. The segmentation performance is evaluated on PH2, ISBI2016, and ISIC2017 with an accuracy of 95.86, 94.79, and 94.92%, respectively. The classification performance is evaluated on PH2 and ISBI2016 dataset with an accuracy of 98.20 and 95.42%, respectively. The results of the proposed automated systems are outstanding as compared to the current techniques reported in state of art, which demonstrate the validity of the proposed method.     

Automated techniques for blood vessels segmentation through fundus retinal images: A review

Retina is the interior part of human's eye, has a vital role in vision. The digital image captured by fundus camera is very useful to analyze the abnormalities in retina especially in retinal blood vessels. To get information of blood vessels through fundus retinal image, a precise and accurate vessels segmentation image is required. This segmented blood vessel image is most beneficial to detect retinal diseases. Many automated techniques are widely used for retinal vessels segmentation which is a primary element of computerized diagnostic systems for retinal diseases. The automatic vessels segmentation may lead to more challenging task in the presence of lesions and abnormalities. This paper briefly describes the various publicly available retinal image databases and various machine learning techniques. State of the art exhibited that researchers have proposed several vessel segmentation methods based on supervised and supervised techniques and evaluated their results mostly on publicly datasets such as digital retinal images for vessel extraction and structured analysis of the retina. A comprehensive review of existing supervised and unsupervised vessel segmentation techniques or algorithms is presented which describes the philosophy of each algorithm. This review will be useful for readers in their future research.     

Taxonomic study of subfamily Nepetoideae (Lamiaceae) by polynomorphological approach

The present study is insight into pollen morphology for characterizing species and their utility in the taxonomic separation of certain taxa of subfamily Nepetoideae (Lamiaceae) from Pakistan. The pollen micromorphology of 11 species of the Nepetoideae was analyzed and documented using light microscopy and scanning electron microscopy (SEM) for both qualitative and quantitative characteristics. Most species have hexazonocolpate pollen grains but trizonocolpate and tetrazonocolpate pollen with circular and oval amb were also rarely observed in Mentha spicata. The basic pollen shape in most of the studied species was subspheroidal but prolate grains were also observed in M. spicata, S. coccinea, and S. plebeia. The exine sculpturing of Nepetoideae pollen was taxonomically very informative particularly at subfamily level. Observations of exine sculpturing with SEM revealed various types of pollen grains: reticulate, bireticulate, microreticulate, perforate, aerolate, and gammate. The bireticulate type further subdivided into three subtypes based on the number of secondary lumina in each primary lumen and is characterized by varying characteristics of the secondary reticulum and primary muri. A significant variation was observed in colpus surface ornamentation. The maximum polar diameter was found in O. americanum (58 ± 5.8 μm) and the maximum equatorial diameter observed in O. basilicum (50.25 ± 1.37 μm). Pollen features of the studied species were discussed and compared based on the current taxonomical concepts. The results showed that pollen traits of the subfamily Nepetoideae was found significant to classify the taxa. Furthermore, pollen features provide additional evidence to distinguish macromorphologically similar taxa from each other.     

Plasmodium species aware based quantification of malaria parasitemia in light microscopy thin blood smear

Malaria is a serious worldwide disease, caused by a bite of a female Anopheles mosquito. The parasite transferred into complex life round in which it is grown and reproduces into the human body. The detection and recognition of Plasmodium species are possible and efficient through a process called staining (Giemsa). The staining process slightly colorizes the red blood cells (RBCs) but highlights Plasmodium parasites, white blood cells and artifacts. Giemsa stains nuclei, chromatin in blue tone and RBCs in pink color. It has been reported in numerous studies that manual microscopy is not a trustworthy screening technique when performed by nonexperts. Malaria parasites host in RBCs when it enters the bloodstream. This paper presents segmentation of Plasmodium parasite from the thin blood smear points on region growing and dynamic convolution based filtering algorithm. After segmentation, malaria parasite classified into four Plasmodium species: Plasmodium falciparum, Plasmodium ovale, Plasmodium vivax, and Plasmodium malaria. The random forest and K‐nearest neighbor are used for classification base on local binary pattern and hue saturation value features. The sensitivity for malaria parasitemia (MP) is 96.75% on training and testing of the proposed approach while specificity is 94.59%. Beside these, the comparisons of the two features are added to the proposed work for classification having sensitivity is 83.60% while having specificity is 94.90% through random forest classifier based on local binary pattern feature.     

Taxonomic importance of spore morphology in Thelypteridaceae from Northern Pakistan

Spore morphology of Thelypteridaceae species growing in Malakand Division, Northern Pakistan, was studied using both light microscopy and scanning electron microscopy. The taxa are Christella dentata and Glaphyropteridopsis erubescens in the subfamily Thelypteridoideae, and Phegopteris connectilis, Pseudophegopteris pyrrhorhachis, and Pseudophegopteris levingei in the subfamily Phegopteridoideae. The studied species exhibit differences in spore size, exospore thickness, color, and ornamentation. Spores of the studied species are monolete and medium‐sized, and shape is ellipsoidal in both polar and equatorial views. The average measurement of the polar diameter ranges from 27 μm to 31 μm, whereas in the equatorial direction it varied from 20 μm to 40 μm. The exospore thickness ranges from 1.2 μm to 2.4 μm. Reticulate, laevigate with microgranules, cristate, and coarsely echinate surface ornamentation are observed among the species. Multivariate analysis including unweighted pair group method with arithmetic mean and principal component analysis was used for the grouping and discrimination of species and genera.     

Machine learning for post-traumatic stress disorder identification utilizing resting-state functional magnetic resonance imaging

Early detection of post-traumatic stress disorder (PTSD) is essential for proper treatment of the patients to recover from this disorder. The aligned purpose of this study was to investigate the performance deviations in regions of interest (ROI) of PTSD than the healthy brain regions, to assess interregional functional connectivity and applications of machine learning techniques to identify PTSD and healthy control using resting-state functional magnetic resonance imaging (rs-fMRI). The rs-fMRI data of 10 ROI was extracted from 14 approved PTSD subjects and 14 healthy controls. The rs-fMRI data of the selected ROI were used in ANOVA to measure performance level and Pearson's correlation to investigate the interregional functional connectivity in PTSD brains. In machine learning approaches, the logistic regression, K-nearest neighbor (KNN), support vector machine (SVM) with linear, radial basis function, and polynomial kernels were used to classify the PTSD and control subjects. The performance level in brain regions of PTSD deviated as compared to the regions in the healthy brain. In addition, significant positive or negative functional connectivity was observed among ROI in PTSD brains. The rs-fMRI data have been distributed in training, validation, and testing group for maturity, implementation of machine learning techniques. The KNN and SVM with radial basis function kernel were outperformed for classification among other methods with high accuracies (96.6%, 94.8%, 98.5%) and (93.7%, 95.2%, 99.2%) to train, validate, and test datasets, respectively. The study's findings may provide a guideline to observe performance and functional connectivity of the brain regions in PTSD and to discriminate PTSD subject using only the suggested algorithms.     

Mass spectrometry in the diagnosis of thyroid disease and in the study of thyroid hormone metabolism

The importance of thyroid hormones in the regulation of development, growth, and energy metabolism is well known. Over the last decades, mass spectrometry has been extensively used to investigate thyroid hormone metabolism and to discover and characterize new molecules involved in thyroid hormones production, such as thyrotropin-releasing hormone. In the earlier period, the quantification methods, usually based on gas chromatography–mass spectrometry, were complicated and time consuming. They were mainly focused on basic research, and were not suitable for clinical diagnostics on a routine basis. The development of the modern mass spectrometers, mainly coupled to liquid chromatography, enabled simpler sample preparation procedures, and the accurate quantification of thyroid hormones, of their precursors, and of their metabolites in biological fluids, tissues, and cells became feasible. Nowadays, molecules of physiological and pathological interest can be assayed also for diagnostic purposes on a routine basis, and mass spectrometry is slowly entering the clinical laboratory. This review takes stock of the advancements in the field of thyroid metabolism that were carried out with mass spectrometry, with special focus on the use of this technique for the quantification of molecules involved in thyroid diseases.     

An improved strategy for skin lesion detection and classification using uniform segmentation and feature selection based approach

Melanoma is the deadliest type of skin cancer with highest mortality rate. However, the annihilation in early stage implies a high survival rate therefore, it demands early diagnosis. The accustomed diagnosis methods are costly and cumbersome due to the involvement of experienced experts as well as the requirements for highly equipped environment. The recent advancements in computerized solutions for these diagnoses are highly promising with improved accuracy and efficiency. In this article, we proposed a method for the classification of melanoma and benign skin lesions. Our approach integrates preprocessing, lesion segmentation, features extraction, features selection, and classification. Preprocessing is executed in the context of hair removal by DullRazor, whereas lesion texture and color information are utilized to enhance the lesion contrast. In lesion segmentation, a hybrid technique has been implemented and results are fused using additive law of probability. Serial based method is applied subsequently that extracts and fuses the traits such as color, texture, and HOG (shape). The fused features are selected afterwards by implementing a novel Boltzman Entropy method. Finally, the selected features are classified by Support Vector Machine. The proposed method is evaluated on publically available data set PH2. Our approach has provided promising results of sensitivity 97.7%, specificity 96.7%, accuracy 97.5%, and F‐score 97.5%, which are significantly better than the results of existing methods available on the same data set. The proposed method detects and classifies melanoma significantly good as compared to existing methods.