Exploring Language-Independent Emotional Acoustic Features via Feature Selection

We propose a novel feature selection strategy to discover language-independent acoustic features that tend to be responsible for emotions regardless of languages, linguistics and other factors. Experimental results suggest that the language-independent feature subset discovered yields the performance comparable to the full feature set on various emotional speech corpora.Comment: 15 pages, 2 figures, 6 table

Emotional State Categorization from Speech: Machine vs. Human

This paper presents our investigations on emotional state categorization from speech signals with a psychologically inspired computational model against human performance under the same experimental setup. Based on psychological studies, we propose a multistage categorization strategy which allows establishing an automatic categorization model flexibly for a given emotional speech categorization task. We apply the strategy to the Serbian Emotional Speech Corpus (GEES) and the Danish Emotional Speech Corpus (DES), where human performance was reported in previous psychological studies. Our work is the first attempt to apply machine learning to the GEES corpus where the human recognition rates were only available prior to our study. Unlike the previous work on the DES corpus, our work focuses on a comparison to human performance under the same experimental settings. Our studies suggest that psychology-inspired systems yield behaviours that, to a great extent, resemble what humans perceived and their performance is close to that of humans under the same experimental setup. Furthermore, our work also uncovers some differences between machine and humans in terms of emotional state recognition from speech.Comment: 14 pages, 15 figures, 12 table

A Review on Classification of White Blood Cells Using Machine Learning Models

The machine learning (ML) and deep learning (DL) models contribute to exceptional medical image analysis improvement. The models enhance the prediction and improve the accuracy by prediction and classification. It helps the hematologist to diagnose the blood cancer and brain tumor based on calculations and facts. This review focuses on an in-depth analysis of modern techniques applied in the domain of medical image analysis of white blood cell classification. For this review, the methodologies are discussed that have used blood smear images, magnetic resonance imaging (MRI), X-rays, and similar medical imaging domains. The main impact of this review is to present a detailed analysis of machine learning techniques applied for the classification of white blood cells (WBCs). This analysis provides valuable insight, such as the most widely used techniques and best-performing white blood cell classification methods. It was found that in recent decades researchers have been using ML and DL for white blood cell classification, but there are still some challenges. 1) Availability of the dataset is the main challenge, and it could be resolved using data augmentation techniques. 2) Medical training of researchers is recommended to help them understand the structure of white blood cells and select appropriate classification models. 3) Advanced DL networks such as Generative Adversarial Networks, R-CNN, Fast R-CNN, and faster R-CNN can also be used in future techniques.Comment: 23 page

How growing tumour impacts intracranial pressure and deformation mechanics of brain

Brain is an actuator for control and coordination. When a pathology arises in cranium, it may leave a degenerative, disfiguring and destabilizing impact on brain physiology. However, the leading consequences of the same may vary from case to case. Tumour, in this context, is a special type of pathology which deforms brain parenchyma permanently. From translational perspective, deformation mechanics and pressures, specifically the intracranial cerebral pressure (ICP) in a tumour-housed brain, have not been addressed holistically in literature. This is an important area to investigate in neuropathy prognosis. To address this, we aim to solve the pressure mystery in a tumour-based brain in this study and present a fairly workable methodology. Using image-based finite-element modelling, we reconstruct a tumour-based brain and probe resulting deformations and pressures (ICP). Tumour is grown by dilating the voxel region by 16 and 30 mm uniformly. Cumulatively three cases are studied including an existing stage of the tumour. Pressures of cerebrospinal fluid due to its flow inside the ventricle region are also provided to make the model anatomically realistic. Comparison of obtained results unequivocally shows that as the tumour region increases its area and size, deformation pattern changes extensively and spreads throughout the brain volume with a greater concentration in tumour vicinity. Second, we conclude that ICP pressures inside the cranium do increase substantially; however, they still remain under the normal values (15 mmHg). In the end, a correlation relationship of ICP mechanics and tumour is addressed. From a diagnostic purpose, this result also explains why generally a tumour in its initial stage does not show symptoms because the required ICP threshold has not been crossed. We finally conclude that even at low ICP values, substantial deformation progression inside the cranium is possible. This may result in plastic deformation, midline shift etc. in the brain

Analysis of Growing Tumor on the Flow Velocity of Cerebrospinal Fluid in Human Brain Using Computational Modeling and Fluid-Structure Interaction

Cerebrospinal fluid (CSF) plays a pivotal role in normal functioning of Brain. Intracranial compartments such as blood, brain and CSF are incompressible in nature. Therefore, if a volume imbalance in one of the aforenoted compartments is observed, the other reaches out to maintain net change to zero. Whereas, CSF has higher compliance over long term. However, if the CSF flow is obstructed in the ventricles, this compliance may get exhausted early. Brain tumor on the other hand poses a similar challenge towards destabilization of CSF flow by compressing any section of ventricles thereby ensuing obstruction. To avoid invasive procedures to study effects of tumor on CSF flow, numerical-based methods such as Finite element modeling (FEM) are used which provide excellent description of underlying pathological interaction. A 3D fluid-structure interaction (FSI) model is developed to study the effect of tumor growth on the flow of cerebrospinal fluid in ventricle system. The FSI model encapsulates all the physiological parameters which may be necessary in analyzing intraventricular CSF flow behavior. Findings of the model show that brain tumor affects CSF flow parameters by deforming the walls of ventricles in this case accompanied by a mean rise of 74.23% in CSF flow velocity and considerable deformation on the walls of ventricles

Mechanism of the antidiabetic action of Nigella sativa and Thymoquinone: a review

IntroductionLong used in traditional medicine, Nigella sativa (NS; Ranunculaceae) has shown significant efficacy as an adjuvant therapy for diabetes mellitus (DM) management by improving glucose tolerance, decreasing hepatic gluconeogenesis, normalizing blood sugar and lipid imbalance, and stimulating insulin secretion from pancreatic cells. In this review, the pharmacological and pharmacokinetic properties of NS as a herbal diabetes medication are examined in depth, demonstrating how it counteracts oxidative stress and the onset and progression of DM.MethodsThis literature review drew on databases such as Google Scholar and PubMed and various gray literature sources using search terms like the etiology of diabetes, conventional versus herbal therapy, subclinical pharmacology, pharmacokinetics, physiology, behavior, and clinical outcomes.ResultsThe efficiency and safety of NS in diabetes, notably its thymoquinone (TQ) rich volatile oil, have drawn great attention from researchers in recent years; the specific therapeutic dose has eluded determination so far. TQ has anti-diabetic, anti-inflammatory, antioxidant, and immunomodulatory properties but has not proved druggable. DM’s intimate link with oxidative stress, makes NS therapy relevant since it is a potent antioxidant that energizes the cell’s endogenous arsenal of antioxidant enzymes. NS attenuates insulin resistance, enhances insulin signaling, suppresses cyclooxygenase-2, upregulates insulin-like growth factor-1, and prevents endothelial dysfunction in DM.ConclusionThe interaction of NS with mainstream drugs, gut microbiota, and probiotics opens new possibilities for innovative therapies. Despite its strong potential to treat DM, NS and TQ must be examined in more inclusive clinical studies targeting underrepresented patient populations

Assessment of ameliorative effect of Aab-e-Shifa polyherbal formulation in experimentally-induced wound in rabbits

Purpose: The aim of the current study is to evaluate the wound healing potential of a polyherbal formulation (PHF) Aab-e-Shifa in normal and diabetic albino rabbits. Methods: The activity of PHF application was evaluated in comparison to tetrachlorodecaoxide (TCDO) on experimentally-induced excision wound in the thigh of normal and diabetic rabbits under ketamine anesthesia. Preliminary phytochemical analysis, total phenolic contents, wound contraction, as well as toxicological and histopathological studies were also investigated. Results: PHF exhibited parallel (p < 0.05) activity for initial wound healing in both normal (48.07 %) and diabetic groups (36.32 %), when compared to their respective control groups. Phytochemical analysis showed the presence of high levels of total phenolic contents in Allilum sativum L. (54.25 ± 0.15 GAE mg/g), Curcuma longa L. (25.45 ± 0.48 GAE mg/g), Zingiber officinale Rosc. (29.08 ± 0.35 GAE mg/g) and some phytochemicals such as flavonoids, couramins, terpenoids in these plants. No adverse sign of PHF was observed when applied at a dose of 2000 - 3000 g on rabbit skin. Conclusion: Aab-e-Shifa has great potential in wound healing and may be used as an alternative treatment for the healing of wounds in animals. The phytochemicals present in PHF might play a role in the wound healing activity possibly due to their antioxidant potential. However, further detailed studies are required to buttress this

Detection of Paracetamol as substrate of the gut microbiome

Gut microbiome, a new organ; represent targets to alter pharmacokinetics of orally administered drugs. Recently, in vitro trials endorsed the idea that orally administered drugs interact and some of their quantity may be taken up by normal microbiome during transit through gut. Such transport mechanisms in microbiome may compete for drug with the host itself. Currently, no data confirms specific transport system for paracetamol uptake by gut microbiome. In vivo trial was conducted in normal healthy male rats (n=36). Paracetamol was administered orally in a single dose of 75mg/kg to isolate microbial mass after transit of 2, 3, 4, 5 and 6 hours post drug administration. Paracetamol absorbance by microbiome was pursued by injecting extracted microbial lysate in RP-HPLC-UV with C18 column under isocratic conditions at 207nm using acetonitrile and water (25:75 v/v) pH 2.50 as mobile phase. Paracetamol absorbance (14.10±0.75μg/mg of microbial mass) and percent dose recovery (13.16±0.55%) seen at transit of 4 hours was significantly higher (P<0.05) compared to other groups. Study confirms the hypothesis of homology between membrane transporters of the gut microbiome and intestinal epithelium. Orally administered drugs can be absorbed by gut microbes competitively during transit in small intestine and it varies at various transit times