Predicting The Structure And Selectivity Of Coiled-Coil Proteins

A coiled-coil protein structure consists of two (in coiled-coil dimers) or more interacting α-helical strands that together form a left-handed supercoil structure. Many coiled-coil proteins are involved in significant biological functions such as the regulation of gene expression, known as transcription factors. Also coiled-coil structures entail unique mechanical properties critical to the function and integrity of various motor proteins, cytoskeletal filaments and extra-cellular matrix proteins. Engineering these transcription factors is also expected to create more efficient and practical solutions to treat neurodegenerative diseases such as Alzheimer\u27s disease (AD), Parkinson\u27s disease (PD), Huntington\u27s disease (HD), amyotrophic lateral sclerosis (ALS) and prion diseases, which are increasingly being realized to have common cellular and molecular mechanisms including protein aggregation. The main objectives of our work are: a) to develop a model to predict the propensity of a protein sequence to form an isolated coiled-coil structure, and b) to investigate the selectivity of coiled-coils by studying protein-protein interactions. Control over protein-protein interaction specificity has a wide range of applications in synthetic biology such as protein labeling and purification (as high-specificity affinity tags or cognate pairs), drugs and toxin delivery and disease modulation. In naturally occurring proteins, specificity is achieved via a complex balance of various molecular-level energetic and entropic interactions. Such complexity makes any specificity prediction from the primary sequence data an extremely complicated task. Possibly, one of the simplest and most studied protein-protein interactions exists in coiled-coil structures

Optimization of the Capsule Production Stochastic Cycle Time

Manufacturing speed is one of the most important factors in pharmaceutical production, since the drug excipient is sensitive and its exposure to light and temperature should be controlled. Therefore, by minimizing the manufacturing cycle time, the quality of product can be improved. This process also results in minimizing the cost of manufacturing such as working hours, human resource, energy consumption and overhead cost while increasing the system productivity. In this study, using a stochastic dynamic programming method, the stochastic manufacturing cycle time of pharmaceutical product in a plant with process layout and concurrent machines is minimized. The result of this study has been compared to simulation modeling of the process

A Comparison of 940 nm Diode Laser and Cryosurgery With Liquid Nitrogen in the Treatment of Gingival Physiologic Hyperpigmentation Using Split Mouth Technique: 12 Months Follow Up

Introduction: Gingival hyperpigmentation is excessive deposition of melanin pigments in the epithelium of gingiva which affects facial esthetics. Various surgical methods for gingival depigmentation have been used to treat the darkened color of pigmented gingiva. This study compared the use of 940 nm diode laser and liquid nitrogen cryosurgery in the treatment of gingival physiologic hyperpigmentation in terms of gingival depigmentation, postoperative pain, healing duration, pigmentation recurrence, and patients’ satisfaction.Methods: Fifteen systemically healthy patients (11 females and 4 males; 17-35 years of age) with bilateral gingival physiologic hyperpigmentation were enrolled in this split-mouth randomized study. Maxillary anterior labial gingiva of each patient was divided into left and right halves, and each half was randomly depigmented by either laser or cryosurgery. Patients were given questionnaires to evaluate the procedures and were followed up in 3, 7, 10, 17 and 21 days postoperatively for the assessment of gingival healing and 1, 3, 6 and 12 months after the treatments to detect any sign of pigmentation recurrence.Results: The severity of post-op pain measured by visual analogue scale (VAS) was mild to average and showed no significant difference between the 2 modalities (P > 0.05). There was no considerable swelling or hemorrhage after the treatment procedures and the healing duration was significantly shorter in laser (P < 0.05). The degree of pigmentation in all gingival sites treated by laser reached and remained at zero until the last follow up (1 year) and reached zero in 9 out of 15 cryosurgery-treated sites. All patients were completely satisfied with the laser, and 9 out of 15 were completely satisfied with cryosurgery. No pigmentation recurrence was observed during any follow-up periods.Conclusion: Removal of gingival physiologic hyperpigmentation by laser therapy and cryotherapy was effective and safe. The efficiency of the laser was better than cryotherapy

A Deep Learning-to-learning Based Control system for renewable microgrids

In terms of microgrids (MGs) operation, optimal control and management are vital issues that must be addressed carefully. This paper proposes a practical framework for the optimal energy management and control of renewable MGs considering energy storage (ES) devices, wind turbines, and microturbines. Due to the non-linearity and complexity of operation problems in MGs, it is vital to use an accurate and robust optimization technique to control the power flow of units efficiently. To this end, in the proposed framework, teacher learning-based optimization (TLBO) is utilized to solve the power flow dispatch in the system efficiently. Moreover, a novel hybrid deep learning model based on principal component analysis (PCA), convolutional neural networks (CNN), and bidirectional long short-term memory (BLSTM) is proposed to address the short-term wind power forecasting problem. The feasibility and performance of the proposed framework and the effect of wind power forecasting on operation efficiency are examined using the IEEE 33-bus test system. Also, the Australian Woolnorth wind site data is utilized as a real-world dataset to evaluate the performance of the forecasting model. The results show that the proposed framework can be used to schedule MGs in the best way possible.© 2023 The Authors. IET Renewable Power Generation published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.fi=vertaisarvioitu|en=peerReviewed

The Relationship between Blood Lead Levels and Clinical Features among Multiple Sclerosis Patients in Isfahan, Iran

Lead (Pb) is one of the most likely toxicants that could be potentially a risk factor in the development of Multiple Sclerosis (MS) through changes in the immune system. The purpose of the study was to describe the clinical features of MS in general and for sub-groups stratified for gender, age, residence, disease duration, disability degree, clinical diagnosis in Isfahan, Iran and also, to elucidate the relationship between the blood Pb level and the development of MS. Blood samples of 48 patients (20 to 57 years) were selected from the department of neurology of the Kashani hospital in Isfahan, Iran and were analyzed for Pb using anodic stripping voltammetry (ASV). The clinical and demographic characteristics in our study were similar to those in other reports. The blood Pb level in 70.83% of the total population wa

Estimation of CO2 solubility in aqueous solutions of commonly used blended amines: Application to optimised greenhouse gas capture

One of the key concerns in the 21st century, alongside the growing population, is the increase in energy consumption and the resulting global warming. The impact of CO2, a prominent greenhouse gas, has garnered significant attention in the realm of CO2 capture and gas purification. CO2 absorption can be enhanced by introducing some additives into the aqueous solution. In this study, the accuracies of some of the most up-to-date computational approaches are investigated. The employed machine learning methods are hybrid-adaptive neuro-fuzzy inference system (Hybrid-ANFIS), particle swarm optimization-adaptive neuro-fuzzy inference system (PSO-ANFIS), least-squares support vector machines (LSSVM) and genetic algorithm-radial basis function (GA-RBF). The developed models were used in estimating the solubility of CO2 in binary and ternary amines aqueous solutions. i.e. blends of monoethanolamine (MEA), triethanolamine (TEA), aminomethyl propanol (AMP), and methyldiethanolamine (MDEA). This modeling study was undertaken over relatively significant ranges of CO2 loading (mole of CO2/mole of solution) as a function of input parameters, which are 0.4–2908 kPa for pressure, 303–393.15 K for temperature, 36.22–68.89 g/mol for apparent molecular weight, and 30–55 wt % for total concentration. In this work, the validity of approaches based on different statistical graphs was investigated, and it was observed that the developed methods, especially the GA-RBF model, are highly accurate in estimating the data of interest. The obtained AARD% values for the developed models are 18.63, 8.25, 12.22, and 7.54 for Hybrid-ANFIS, PSO-ANFIS, LSSVM, and GA-RBF, respectively

A novel robust extended dissipativity state feedback control system design for interval type-2 fuzzy Takagi-Sugeno large-scale systems

Recently, systems have become large in their model and dynamic. To apply control algorithms, serious problems appear that need to be solved. Two significant problems are modelling the dynamics of the large-scale system and reducing effects of perturbations. In this paper, we use the advantage of large-scale systems modelling based on the type-2 fuzzy Takagi–Sugeno model to cover the uncertainties caused by large-scale systems modelling. The advantage of using membership function information is the reduction of conservatism resulting from stability analysis. Also, this paper uses the extended dissipativity robust control performance index to reduce the effect of external perturbations on the large-scale system, which is a generalization of [Formula: see text], [Formula: see text], passive and dissipativity performance indexes and control gains can be achieved through solving linear matrix inequalities (LMIs). Hence, the whole closed-loop system is asymptotically stable. Finally, the effectiveness of the proposed method is demonstrated by two practical examples

Multi-Echo Quantitative Susceptibility Mapping for Strategically Acquired Gradient Echo (STAGE) Imaging

Purpose: To develop a method to reconstruct quantitative susceptibility mapping (QSM) from multi-echo, multi-flip angle data collected using strategically acquired gradient echo (STAGE) imaging. Methods: The proposed QSM reconstruction algorithm, referred to as “structurally constrained Susceptibility Weighted Imaging and Mapping” scSWIM, performs an ℓ1 and ℓ2 regularization-based reconstruction in a single step. The unique contrast of the T1 weighted enhanced (T1WE) image derived from STAGE imaging was used to extract reliable geometry constraints to protect the basal ganglia from over-smoothing. The multi-echo multi-flip angle data were used for improving the contrast-to-noise ratio in QSM through a weighted averaging scheme. The measured susceptibility values from scSWIM for both simulated and in vivo data were compared to the: original susceptibility model (for simulated data only), the multi orientation COSMOS (for in vivo data only), truncated k-space division (TKD), iterative susceptibility weighted imaging and mapping (iSWIM), and morphology enabled dipole inversion (MEDI) algorithms. Goodness of fit was quantified by measuring the root mean squared error (RMSE) and structural similarity index (SSIM). Additionally, scSWIM was assessed in ten healthy subjects. Results: The unique contrast and tissue boundaries from T1WE and iSWIM enable the accurate definition of edges of high susceptibility regions. For the simulated brain model without the addition of microbleeds and calcium, the RMSE was best at 5.21ppb for scSWIM and 8.74ppb for MEDI thanks to the reduced streaking artifacts. However, by adding the microbleeds and calcium, MEDI’s performance dropped to 47.53ppb while scSWIM performance remained the same. The SSIM was highest for scSWIM (0.90) and then MEDI (0.80). The deviation from the expected susceptibility in deep gray matter structures for simulated data relative to the model (and for the in vivo data relative to COSMOS) as measured by the slope was lowest for scSWIM + 1%(−1%); MEDI + 2%(−11%) and then iSWIM −5%(−10%). Finally, scSWIM measurements in the basal ganglia of healthy subjects were in agreement with literature. Conclusion: This study shows that using a data fidelity term and structural constraints results in reduced noise and streaking artifacts while preserving structural details. Furthermore, the use of STAGE imaging with multi-echo and multi-flip data helps to improve the signal-to-noise ratio in QSM data and yields less artifacts

Diagnosing Parkinson's disease by combining neuromelanin and iron imaging features using an automated midbrain template approach

Background and purpose: Early diagnosis of Parkinson's disease (PD) is still a clinical challenge. Most previous studies using manual or semi-automated methods for segmenting the substantia nigra (SN) are time-consuming and, despite raters being well-trained, individual variation can be significant. In this study, we used a template-based, automatic, SN subregion segmentation pipeline to detect the neuromelanin (NM) and iron features in the SN and SN pars compacta (SNpc) derived from a single 3D magnetization transfer contrast (MTC) gradient echo (GRE) sequence in an attempt to develop a comprehensive imaging biomarker that could be used to diagnose PD. Materials and methods: A total of 100 PD patients and 100 age- and sex-matched healthy controls (HCs) were imaged on a 3T scanner. NM-based SN (SNNM) boundaries and iron-based SN (SNQSM) boundaries and their overlap region (representing the SNpc) were delineated automatically using a template-based SN subregion segmentation approach based on quantitative susceptibility mapping (QSM) and NM images derived from the same MTC-GRE sequence. All PD and HC subjects were evaluated for the nigrosome-1 (N1) sign by two raters independently. Receiver Operating Characteristic (ROC) analyses were performed to evaluate the utility of SNNM volume, SNQSM volume, SNpc volume and iron content with a variety of thresholds as well as the N1 sign in diagnosing PD. Correlation analyses were performed to study the relationship between these imaging measures and the clinical scales in PD. Results: In this study, we verified the value of the fully automatic template based midbrain deep gray matter mapping approach in differentiating PD patients from HCs. The automatic segmentation of the SN in PD patients led to satisfactory DICE similarity coefficients and volume ratio (VR) values of 0.81 and 1.17 for the SNNM, and 0.87 and 1.05 for the SNQSM, respectively. For the HC group, the average DICE similarity coefficients and VR values were 0.85 and 0.94 for the SNNM, and 0.87 and 0.96 for the SNQSM, respectively. The SNQSM volume tended to decrease with age for both the PD and HC groups but was more severe for the PD group. For diagnosing PD, the N1 sign performed reasonably well by itself (Area Under the Curve (AUC) = 0.783). However, combining the N1 sign with the other quantitative measures (SNNM volume, SNQSM volume, SNpc volume and iron content) resulted in an improved diagnosis of PD with an AUC as high as 0.947 (using an SN threshold of 50ppb and an NM threshold of 0.15). Finally, the SNQSM volume showed a negative correlation with the MDS-UPDRS III (R2 = 0.1, p = 0.036) and the Hoehn and Yahr scale (R2 = 0.04, p = 0.013) in PD patients. Conclusion: In summary, this fully automatic template based deep gray matter mapping approach performs well in the segmentation of the SN and its subregions for not only HCs but also PD patients with SN degeneration. The combination of the N1 sign with other quantitative measures (SNNM volume, SNQSM volume, SNpc volume and iron content) resulted in an AUC of 0.947 and provided a comprehensive set of imaging biomarkers that, potentially, could be used to diagnose PD clinically