Relation of Leptin, Ghrelin and Inflammatory Cytokines with Body Mass Index in Pulmonary Tuberculosis Patients with and without Type 2 Diabetes Mellitus

Background: Pulmonary tuberculosis (TB) patients often suffer from anorexia and poor nutrition, causing weight loss. The peptide hormones leptin and its counterpart ghrelin, acting in the regulation of food intake and fat utilization, play an important role in nutritional balance. This study aimed to investigate the association of blood concentrations of leptin, ghrelin and inflammatory cytokines with body mass index (BMI) in TB patients with and without type 2 diabetes mellitus (T2DM). Methods: BMI, biochemical parameters and plasma levels of leptin, ghrelin and inflammatory cytokines were measured before the start of treatment in 27 incident TB patients with T2DM, 21 TB patients and 23 healthy subjects enrolled in this study. Results: The levels of leptin were significantly higher in TB patients (35.2 +/- 19.1 ng/ml) than TB+T2DM (12.6 +/- 6.1 ng/ml) and control (16.1 +/- 11.1 ng/ml) groups. The level of ghrelin was significantly lower in TB (119.9 +/- 46.1 pg/ml) and non-significantly lower in TB+T2DM (127.7 +/- 38.6 pg/ml) groups than control (191.6 +/- 86.5 pg/ml) group. The levels of TNF-alpha were higher, while IFN-gamma and IL-6 levels were lower in patients than in the control group. Leptin showed a negative correlation with BMI in TB (r=-0.622, p0.05) groups, but negative correlation with BMI in the control (r=-0.693,

Enhancing Higher Order Question of Student Through Problem Based Learning at Grade X MIA 6 of SMA N 4 Surakarta

The research aims to enhance the Higher Order Question of student through problem based learning in Biology at Grade X MIA 6 of SMA N 4 Surakarta. The research was a four-cycle action research conducted in academic year 2014/2015. All questions were analyzed based on revised Bloom Taxonomy. Data were validated using triangulation method. The result of the research showed that problem based learning effectively enhance student\u27s High Order Question (C4-C6). The percentage of each High Order Question (C4-C6) in pre cycle were 0%. The percentage of C4 type question at first cycle (73,14%), second cycle (52,13%), third cycle (56,05%), and fourth cycle (58,42%). The percentage of each High Order Question (C4-C6) in pre cycle were 0%. The percentage of C5 type question at first cycle (18,37%), second cycle (9,57%), third cycle (10,30%), and fourth cycle (58,42%). The percentage of each High Order Question (C4-C6) in pre cycle were 0%. The percentage of C6 type question at first cycle (8,16%), second cycle (38,30%), third cycle (41,18%) and fourth cycle (25,74%)

The 2nd Workshop on Maritime Computer Vision (MaCVi) 2024

The 2nd Workshop on Maritime Computer Vision (MaCVi) 2024 addresses maritime computer vision for Unmanned Aerial Vehicles (UAV) and Unmanned Surface Vehicles (USV). Three challenges categories are considered: (i) UAV-based Maritime Object Tracking with Re-identification, (ii) USV-based Maritime Obstacle Segmentation and Detection, (iii) USV-based Maritime Boat Tracking. The USV-based Maritime Obstacle Segmentation and Detection features three sub-challenges, including a new embedded challenge addressing efficicent inference on real-world embedded devices. This report offers a comprehensive overview of the findings from the challenges. We provide both statistical and qualitative analyses, evaluating trends from over 195 submissions. All datasets, evaluation code, and the leaderboard are available to the public at https://macvi.org/workshop/macvi24.Comment: Part of 2nd Workshop on Maritime Computer Vision (MaCVi) 2024 IEEE Xplore submission as part of WACV 202

Nanoparticles for Applications in Cellular Imaging

In the following review we discuss several types of nanoparticles (such as TiO2, quantum dots, and gold nanoparticles) and their impact on the ability to image biological components in fixed cells. The review also discusses factors influencing nanoparticle imaging and uptake in live cells in vitro. Due to their unique size-dependent properties nanoparticles offer numerous advantages over traditional dyes and proteins. For example, the photostability, narrow emission peak, and ability to rationally modify both the size and surface chemistry of Quantum Dots allow for simultaneous analyses of multiple targets within the same cell. On the other hand, the surface characteristics of nanometer sized TiO2allow efficient conjugation to nucleic acids which enables their retention in specific subcellular compartments. We discuss cellular uptake mechanisms for the internalization of nanoparticles and studies showing the influence of nanoparticle size and charge and the cell type targeted on nanoparticle uptake. The predominant nanoparticle uptake mechanisms include clathrin-dependent mechanisms, macropinocytosis, and phagocytosis

Research on the effect of drilling fluid’s pH value on the coal’s wettability

Abstract Drilling fluid contacts with the surface of coal and fractures fully during the drilling process for CBM, its pH value has direct impact on the wettability of coal, affecting the seepage of CBM further. Therefore, the research about the effect of drilling fluid’s pH value on the coal’s wettability has important practical significance. The influence law of drilling fluid’s pH value on the coal’s wettability was studied through lots of experiments, the results showed that the wettability was related to the pH value of drilling fluid, decreases firstly, increases secondly and decreases at last, the coal’s hydrophilicity was the weakest at the pH value of 9, and the hydrophilicity of coal was weaken further after the addition of surfactants. The conclusions provide strong technical guidance for selecting drilling fluid and optimizing fluid performance, and it helps to protect the reservoir and increase CBM production

Toward Measuring Target Perception: First-Order and Second-Order Deep Network Pipeline for Classification of Fixation-Related Potentials

The topdown determined visual object perception refers to the ability of a person to identify a prespecified visual target. This paper studies the technical foundation for measuring the target-perceptual ability in a guided visual search task, using the EEG-based brain imaging technique. Specifically, it focuses on the feature representation learning problem for single-trial classification of fixation-related potentials (FRPs). The existing methods either capture only first-order statistics while ignoring second-order statistics in data, or directly extract second-order statistics with covariance matrices estimated with raw FRPs that suffer from low signal-to-noise ratio. In this paper, we propose a new representation learning pipeline involving a low-level convolution subnetwork followed by a high-level Riemannian manifold subnetwork, with a novel midlevel pooling layer bridging them. In this way, the discriminative power of the first-order features can be increased by the convolution subnetwork, while the second-order information in the convolutional features could further be deeply learned with the subsequent Riemannian subnetwork. In particular, the temporal ordering of FRPs is well preserved for the components in our pipeline, which is considered to be a valuable source of discriminant information. The experimental results show that proposed approach leads to improved classification performance and robustness to lack of data over the state-of-the-art ones, thus making it appealing for practical applications in measuring the target-perceptual ability of cognitively impaired patients with the FRP technique

A Finger-Shaped Tactile Sensor for Fabric Surfaces Evaluation by 2-Dimensional Active Sliding Touch

Sliding tactile perception is a basic function for human beings to determine the mechanical properties of object surfaces and recognize materials. Imitating this process, this paper proposes a novel finger-shaped tactile sensor based on a thin piezoelectric polyvinylidene fluoride (PVDF) film for surface texture measurement. A parallelogram mechanism is designed to ensure that the sensor applies a constant contact force perpendicular to the object surface, and a 2-dimensional movable mechanical structure is utilized to generate the relative motion at a certain speed between the sensor and the object surface. By controlling the 2-dimensional motion of the finger-shaped sensor along the object surface, small height/depth variation of surface texture changes the output charge of PVDF film then surface texture can be measured. In this paper, the finger-shaped tactile sensor is used to evaluate and classify five different kinds of linen. Fast Fourier Transformation (FFT) is utilized to get original attribute data of surface in the frequency domain, and principal component analysis (PCA) is used to compress the attribute data and extract feature information. Finally, low dimensional features are classified by Support Vector Machine (SVM). The experimental results show that this finger-shaped tactile sensor is effective and high accurate for discriminating the five textures