Synthesis of α-glucan in mycobacteria involves a hetero-octameric complex of trehalose synthase TreS and Maltokinase Pep2

Recent evidence established that the cell envelope of Mycobacterium tuberculosis, the bacillus causing tuberculosis (TB), is coated by an α-glucan-containing capsule that has been implicated in persistence in a mouse infection model. As one of three known metabolic routes to α-glucan in mycobacteria, the cytoplasmic GlgE-pathway converts trehalose to α(1 → 4),α(1 → 6)-linked glucan in 4 steps. Whether individual reaction steps, catalyzed by trehalose synthase TreS, maltokinase Pep2, and glycosyltransferases GlgE and GlgB, occur independently or in a coordinated fashion is not known. Here, we report the crystal structure of M. tuberculosis TreS, and show by small-angle X-ray scattering and analytical ultracentrifugation that TreS forms tetramers in solution. Together with Pep2, TreS forms a hetero-octameric complex, and we demonstrate that complex formation markedly accelerates maltokinase activity of Pep2. Thus, complex formation may act as part of a regulatory mechanism of the GlgE pathway, which overall must avoid accumulation of toxic pathway intermediates, such as maltose-1-phosphate, and optimize the use of scarce nutrients.

An efficient neural network based method for medical image segmentation

The aim of this research is to propose a new neural network based method for medical image segmentation. Firstly, a modified self-organizing map (SOM) network, named moving average SOM (MA-SOM), is utilized to segment medical images. After the initial segmentation stage, a merging process is designed to connect the objects of a joint cluster together. A two-dimensional (2D) discrete wavelet transform (DWT) is used to build the input feature space of the network. The experimental results show that MA-SOM is robust to noise and it determines the input image pattern properly. The segmentation results of breast ultrasound images (BUS) demonstrate that there is a significant correlation between the tumor region selected by a physician and the tumor region segmented by our proposed method. In addition, the proposed method segments X-ray computerized tomography (CT) and magnetic resonance (MR) head images much better than the incremental supervised neural network (ISNN) and SOM-based methods.

A new nonparametric statistical approach to detect lumen and Media-Adventitia borders in intravascular ultrasound frames

Intravascular ultrasound (IVUS) imaging is widely known as a powerful interventional imaging modality for diagnosing atherosclerosis, and for treatment planning. In this regard, the detection of lumen and media-adventitia (MA) borders is considered to be a vital process. However, the manual detection of these two borders by the physician is cumbersome due to the large number of frames in a sequence. In addition, no approved universal automatic method has been presented so far due to the great diversity in the appearance of the coronary artery in the images acquired by different IVUS systems. To this end, the present study aimed to provide a new border search theory on the radial profile, based upon the nonparametric statistical approach, and to develop a generic and fully automatic three-step process for extracting the lumen and MA borders in IVUS frames based on the proposed theory. Thereafter, the proposed theory and three-step process were evaluated on synthetic images, as well as on a test set of standard publicly available images, respectively. The results showed that our three-step process could segment the borders with ≥0.82 and with ≥0.75 Jaccard measure (JM) to manual borders in IVUS frames acquired by the 20 MHz and 40 MHz probes, respectively. Based on the results, the lumen and MA borders can be extracted automatically, and the border extraction process can be implemented in parallel for a polar image due to the capability of the present proposed method to estimate the borders for each angle independently.

Improved healing of critical-size femoral defect in osteoporosis rat models using 3D elastin/polycaprolactone/nHA scaffold in combination with mesenchymal stem cells

Osteoporosis is a common bone disease that results in elevated risk of fracture, and delayed bone healing and impaired bone regeneration are implicated by this disease. In this study, Elastin/Polycaprolactone/nHA nanofibrous scaffold in combination with mesenchymal stem cells were used to regenerate bone defects. Cytotoxicity, cytocompatibility and cellular morphology were evaluated in vitro and observations revealed that an appropriate environment for cellular attachment, growth, migration, and proliferation is provided by this scaffold. At 3 months following ovariectomy (OVX), the rats were used as animal models with an induced critical size defect in the femur to evaluate the therapeutic potential of osteogenic differentiation of bone marrow mesenchymal stem cells (BM-MSCs) seeded on 3 dimension (3D) scaffolds. In this experimental study, 24 female Wistar rats were equally divided into three groups: Control, scaffold (non-seeded BM-MSC), and scaffold + cell (seeded BM-MSC) groups. 30 days after surgery, the right femur was removed, and underwent a stereological analysis and RNA extraction in order to examine the expression of Bmp-2 and Vegf genes. The results showed a significant increase in stereological parameters and expression of Bmp-2 and Vegf in scaffold and scaffold + cell groups compared to the control rats. The present study suggests that the use of the 3D Elastin/Polycaprolactone (PCL)/Nano hydroxyapatite (nHA) scaffold in combination with MSCs may improve the fracture regeneration and accelerates bone healing at the osteotomy site in rats.

A Vehicle Routing Problem with Time Windows and Workload Balancing for COVID-19 Testers: A Case Study

Due to the COVID-19 pandemic, laboratories have faced unprecedented demand for in-home delivery test services. This drastic demand increase requires a rapid reaction from laboratories to manage their testers in order to respond to the high demand volume and avoid unnecessary costs. This study provides an optimization model based on the vehicle routing problem with time windows by considering the testers' workload balancing to improve laboratories' assignment and routing policies. A medical lab that has faced this situation for its in-home test services is taken as a real-world case in the current study. A mixed-integer programming model is solved for small instances using the CPLEX solver, and an adaptive large neighborhood search algorithm is implemented for large instances. Ultimately, the obtained solutions are compared to the real-world implementation of the lab on a dataset of six consecutive days, and the results are further discussed.

An efficient hybrid filter-wrapper method based on improved Harris Hawks optimization for feature selection

Introduction

High-dimensional datasets often contain an abundance of features, many of which are irrelevant to the subject of interest. This issue is compounded by the frequently low number of samples and imbalanced class samples. These factors can negatively impact the performance of classification algorithms, necessitating feature selection before classification. The primary objective of feature selection algorithms is to identify a minimal subset of features that enables accurate classification.

Methods

In this paper, we propose a two-stage hybrid method for the optimal selection of relevant features. In the first stage, a filter method is employed to assign weights to the features, facilitating the removal of redundant and irrelevant features and reducing the computational cost of classification algorithms. A subset of high-weight features is retained for further processing in the second stage. In this stage, an enhanced Harris Hawks Optimization algorithm and GRASP, augmented with crossover and mutation operators from genetic algorithms, are utilized based on the weights calculated in the first stage to identify the optimal feature set.

Results

Experimental results demonstrate that the proposed algorithm successfully identifies the optimal subset of features.

Conclusion

The two-stage hybrid method effectively selects the optimal subset of features, improving the performance of classification algorithms on high-dimensional datasets. This approach addresses the challenges posed by the abundance of features, low number of samples, and imbalanced class samples, demonstrating its potential for application in various fields.

The Evaluation of Concentration of Harmful Chemical Pollutants in Fume Caused by Surgical Cauterization in Hospital Surgery Rooms Related to Pharmacological Aspects

Background: Harmful chemical pollutants of pharmaceutical origin present in the environmental pollutants especially surgical room may have the same toxic specifications of chemicals from industrial sources. The aim of this study was to evaluate the concentration of harmful chemical pollutants in fume caused by surgical cauterization in hospital surgery rooms. Methods: This is a quantitative, cross-sectional and practical study. The study population consisted of all personnel who are working in hospital surgery rooms. Samples were prepared according to NIOSH standard method. The tool for data collection is Occupational Exposure Limits booklet (OEL). Sample Size: 30 samples were required according to surgery room conditions. In order to evaluate and measure the concentration of workplace air pollutants and individual exposure to them, the individual sampling method is used compliant with the standard procedures of the NIOSH or OSHA organizations, that according to the type of material and for each material, a particular method is mentioned and has a specific code. In this study, NIOSH Organization Method No. 1501 was used. The data were analyzed based on the data obtained from sampling and comparing them with the allowed limits. Results: In this study, the organic aromatic hydrocarbons including, Benzene, Ethylbenzene, Toluene and Xylene were investigated and fortunately, in all samples, these pollutants are below the permitted level. Conclusion: According to the results, the measured concentration of pollutants are less than the allowed limit. It is also recommended that other pollutants be evaluated at any time in order to gain the necessary knowledge of the personnel exposure status.