Ligamentous Augmentation to Prevent Proximal Junctional Kyphosis and Failure: A Biomechanical Cadaveric Study.

STUDY DESIGN: Biomechanical cadaveric study (level V). OBJECTIVE: To evaluate the effectiveness of polyethylene bands looped around the supra-adjacent spinous process (SP) or spinal lamina (SL) in providing strength to the cephalad unfused segment and reducing junctional stress. BACKGROUND: Proximal junctional kyphosis (PJK) is a pathologic kyphotic deformity adjacent to posterior spinal instrumentation after fusion constructs. Recent studies demonstrate a mismatch in stiffness between the instrumented construct and nonfused adjacent levels to be a causative factor in the development of PJK and proximal junction failure. To our knowledge, no biomechanical studies have addressed the effect of different methods of polyethylene band placement at the proximal junction. MATERIALS AND METHODS: Twelve fresh frozen cadavers were divided into 3 groups of 4: pedicle screw-based instrumentation from T10 to L5 ("control"), T10-L5 instrumentation with a polyethylene band to the T9 "SP," T10-L5 instrumentation with 2 polyethylene bands to the T9 "SL." Specimens were tested with an eccentric (10 mm anterior) load at 5 mm/min for 15 mm or until failure occurred. Failure was defined by the inflection point on the load versus deformation curves. Linear regression was utilized to evaluate the effect of augmentation on the load-to-failure. Significance was set at 0.05. RESULTS: Fractures occurred in all specimens tested. The mean peak load to failure was 2148 N (974-3322) for the SP group, and 1248 N (742-1754) for the control group (P > 0.05) and 1390 N (1080-2004) for the SL group. No difference existed between the control group and the SP group in terms of fracture level (P > 0.05). Net kyphotic angulation shows no differences among these 3 groups (P > 0.05). CONCLUSION: Although statistical significance was not achieved, ligament augmentation to the SP increased mean peak load-to-failure in a cadaveric PJK model.

Composite fault reconstruction and fault-tolerant control design for cyber-physical systems: An interval type-2 fuzzy approach.

This article scrutinizes the stabilization and fault reconstruction issues for interval type-2 fuzzy-based cyber-physical systems with actuator faults, deception attacks and external disturbances. The primary objective of this research is to formulate the learning observer system with the interval type-2 fuzzy technique that reconstructs the actuator faults as well as the immeasurable states of the addressed fuzzy based model. Further, the information of reconstructed actuator faults is incorporated in the developed controller with the imperfect premise variables for ensuring the stabilization of the system under consideration. At the same time, the H∞ technique is employed to reduce the impact of external disturbances in the considered model. In addition to that, the deception attacks are represented as a stochastic variable that satisfies the Bernoulli distributions. On the ground of this, a set of sufficient criteria is deduced in the context of linear matrix inequalities to affirm the stability of the addressed systems. Furthermore, the requisite gain matrices are computed by resolving the obtained linear matrix inequality based stability criteria. At last, two simulation examples, including the mass-spring-damper system are exhibited to demonstrate the usefulness of analytical findings of the developed strategy.

Disturbance rejection for multi-weighted complex dynamical networks with actuator saturation and deception attacks via hybrid-triggered mechanism.

In this work, we address hybrid-driven-based robust synchronization problem for multi-weighted complex dynamical networks with actuator saturation and deception attacks. The hybrid-triggered mechanism, which combines a switch between the event-triggered scheme and the time-triggered scheme, is often used to reduce the data transmission and the alleviate network burden. Further, the equivalent-input-disturbance technique is applied to eliminate the unknown disturbance effect of the addressed system. Moreover, a memory controller is designed under actuator saturation to ensure that the resultant augmented system is asymptotically synchronized even in the presence of deception attacks. Finally, three numerical examples are given to show the validity of the obtained theoretical results.

Prediction of COVID-19 growth and trend using machine learning approach.

The Covid-19 Corona Virus, also known as SARS-CoV-2, has wreaked havoc around the world, and the condition is only getting worse. It is a pandemic disease spreading from person-to-person every day. Therefore, it is important to keep track the number of patients being affected. The current system gives the computerized data in a collective way which is very difficult to analyze and predict the growth of disease in a particular area and in the world. Machine learning algorithms can be used to successfully map the disease and its progression to solve this problem. Machine Learning, a branch of computer science, is critical in correctly distinguishing patients with the condition by analyzing their chest X-ray photographs. Supervised Machine learning models with associated algorithms (like LR, SVR and Time series algorithms) to analyze data for regression and classification helps in training the model to predict the number of total number of global confirmed cases who will be prone to the disease in the upcoming days. In this proposed work, the overall dataset of the world is being collected, preprocessed and the number of confirmed cases up to a particular date are extracted which is given as the training set to the model. The model is being trained by supervised machine learning algorithms to predict the growth of cases in the upcoming days. The experimental setup with the above mentioned algorithms shows that Time series Holt's model outperforms Linear Regression and Support Vector Regression algorithms.

Nanotechnology- A ray of hope for heavy metals removal.

Environmental effects of heavy metal pollution are considered as a widespread problem throughout the world, as it jeopardizes human health and also reduces the sustainability of a cleaner environment. Removal of such noxious pollutants from wastewater is pivotal because it provides a propitious solution for a cleaner environment and water scarcity. Adsorption treatment plays a significant role in water remediation due to its potent treatment and low cost of adsorbents. In the last two decades, researchers have been highly focused on the modification of adsorption treatment by functionalized and surface-modified nanomaterials which has spurred intense research. The characteristics of nano adsorbents attract global scientists as it is also economically viable. This review shines its light on the functionalized nanomaterials application for heavy metals removal from wastewater and also highlights the importance of regeneration of nanomaterials in the view of visualizing the economic aspects along with a cleaner environment. The review also focused on the proper disposal of nanomaterials with crucial issues that persist in the adsorption process and also emphasize future research modification at a large-scale application in industries.

Influence of metal oxide catalyst on co-pyrolysis of biomass and COVID-19 waste.

The disposal of waste generated by the COVID-19 pandemic is still a challenge to the government in most countries. The present study shines its light on the catalytic effect of metal oxide on converting COVID-19 waste i.e. used face masks into valuable products through co-pyrolysis. The co-pyrolysis trial was carried out for a mixture of waste face mask (WFM) and Moringa oleifera (MO) biomass at a constant temperature of 450°C for 15 min of resident time. This investigation focuses on studying the catalytic effect of calcium oxide (CaO) on the by-products of the pyrolysis process. From the FT-IR studies, it is observed that the CaO catalyst assisted to reduce oxygen as well as sulphur and carboxylic acids in the bio-oil due to its strong basic nature. The FE-SEM images suggest the increase in porous structure with catalytic pyrolysis (CP) char compared to non-catalytic pyrolysis (NCP) char. The catalytic activity of CaO increased the alcoholic content with a reduction in aldehydes and ketones in the bio-oil. The addition of WFM to the biomass with CaO catalyst pyrolysis (CP) delivered a higher oil yield of 52% compared to non-catalytic pyrolysis (NCP).

Design of robust tracking and disturbance attenuation control for stochastic control systems.

The focus of current research is to address the problem of robust output tracking, input delay compensation and disturbance attenuation performance for a family of stochastic systems by implementing the improved-equivalent-input-disturbance (IEID) estimator and the extended Smith predictor (ESP) technique. By integrating the observer and IEID-estimator together with ESP, a new closed-loop configuration is presented. Then, Lyapunov based mean-square asymptotic stability criterion is obtained. According to attained stability criterion, an IEID and ESP based-controller is designed, which ultimately guarantees the exact output tracking. Simulation studies of numerical examples are offered to expose the authenticity of IEID and ESP-based controller. Further, the proposed outcomes in comparison with existing results are presented to demonstrate the efficaciousness of the established control procedure.

Composite fault-tolerant and anti-disturbance control for switched fuzzy stochastic systems.

This paper investigates the issue of fault-tolerant and anti-disturbance attenuation for a two-dimensional modified repetitive control system (2D MRCS) which is described by switched fuzzy systems with multiple disturbances. In particular, the multiple disturbances contain an exogenous disturbance and standard Wiener noise. Specifically, a generalized extended state observer (GESO) is incorporated with the 2D MRCS to estimate both fault and exogenous multiple disturbances so that the disturbances and faults can be attenuated in the control input. Further, the improved 2D MRCS relaxes the stability condition and provides an enhanced tracking performance. Based on the Lyapunov function approach, pole placement technique and average dwell time approach, the stability criteria for the considered system is developed in terms of linear matrix inequality (LMI). Then an algorithm for designing a GESO-based 2D MRC design is developed based on the obtained LMIs. Further, the results developed are validated in the simulation section through three numerical examples.

Liquid hot water as sustainable biomass pretreatment technique for bioenergy production: A review.

In recent years, lignocellulosic biomass has emerged as one of the most versatile energy sources among the research community for the production of biofuels and value-added chemicals. However, biomass pretreatment plays an important role in reducing the recalcitrant properties of lignocellulose, leading to superior quality of target products in bioenergy production. Among existing pretreatment techniques, liquid hot water (LHW) pretreatment has several outstanding advantages compared to others including minimum formation of monomeric sugars, significant removal of hemicellulose, and positive environmental impacts; however, several constraints of LHW pretreatment should be clarified. This contribution aims to provide a comprehensive analysis of reaction mechanism, reactor characteristics, influencing factors, techno-economic aspects, challenges, and prospects for LHW-based biomass pretreatment. Generally, LHW pretreatment could be widely employed in bioenergy processing from biomass, but circular economy-based advanced pretreatment techniques should be further studied in the future to achieve maximum efficiency, and minimum cost and drawbacks.

Equivalent-input-disturbance estimator-based event-triggered control design for master-slave neural networks.

This paper investigates the robust synchronization problem for a class of master-slave neural networks (MSNNs) subject to network-induced delays, unknown time-varying uncertainty, and exogenous disturbances. An equivalent-input-disturbance (EID) estimation technique is applied to compensate for the effects of unknown uncertainty and disturbances in the system output. In addition, to reduce the burden of the communication channel in the addressed MSNNs and improve the utilization of bandwidth an event-triggered control protocol is developed to obtain the synchronization of MSNNs. In particular, event-triggering conditions are verified periodically at every sampling instant in both sensors and actuators to avoid the Zeno behavior in the networks. By designing an appropriate low-pass filter in the EID estimator block, the accuracy of disturbance estimation performance is improved. Moreover, by concatenating the synchronization error, observer, and filter states as a single state vector, an augmented system is formulated. Then the tangible delay-dependent stability condition for that augmented system is established by employing the Lyapunov stability theory and reciprocally convex approach. Based on the feasible solutions of the derived stability conditions, the event-triggering parameters, controller, and observer gains are co-designed. Finally, two toy examples are given to illustrate the established theoretical findings.

MPTherm: database for membrane protein thermodynamics for understanding folding and stability.

The functions of membrane proteins (MPs) are attributed to their structure and stability. Factors influencing the stability of MPs differ from globular proteins due to the presence of membrane spanning regions. Thermodynamic data of MPs aid to understand the relationship among their structure, stability and function. Although a wealth of experimental data on thermodynamics of MPs are reported in the literature, there is no database available explicitly for MPs. In this work, we have developed a database for MP thermodynamics, MPTherm, which contains more than 7000 thermodynamic data from about 320 MPs. Each entry contains protein sequence and structural information, membrane topology, experimental conditions, thermodynamic parameters such as melting temperature, free energy, enthalpy etc. and literature information. MPTherm assists users to retrieve the data by using different search and display options. We have also provided the sequence and structure visualization as well as cross-links to UniProt and PDB databases. MPTherm database is freely available at http://www.iitm.ac.in/bioinfo/mptherm/. It is implemented in HTML, PHP, MySQL and JavaScript, and supports the latest versions of major browsers, such as Firefox, Chrome and Opera. MPTherm would serve as an effective resource for understanding the stability of MPs, development of prediction tools and identifying drug targets for diseases associated with MPs.

Disturbance rejection in fuzzy systems based on two dimensional modified repetitive-control.

This paper concerns with the issues of designing an improved-equivalent-input-disturbance (IEID) based robust two dimensional modified repetitive control (2D MRC) for a class of fuzzy systems in the presence of aperiodic disturbances. Specifically, IEID-estimator is implemented to the 2D MRC systems that estimates all types of disturbances and compensates them for assuring robust stability. In particular, the proposed 2D MRC system has two different type of behaviours such as continuous control and discrete learning independently. To obtain gains of the observer and the controller, an adequate set of robust stability conditions is derived in the form of a linear-matrix-inequalities. Finally, simulation results for three numerical examples are provided to depict the efficacy of the proposed control technique.

Computational approaches for identifying potential inhibitors on targeting protein interactions in drug discovery.

In the era of big data, the interplay of artificial and human intelligence is the demanding job to address the concerns involving exchange of decisions between both sides. Drug discovery is one of the key sources of the big data, which involves synergy among various computational methods to achieve a clinical success. Rightful acquisition, mining and analysis of the data related to ligand and targets are crucial to accomplish reliable outcomes in the entire process. Novel designing and screening tactics are necessary to substantiate a potent and efficient lead compounds. Such methods are emphasized and portrayed in the current review targeting protein-ligand and protein-protein interactions involved in various diseases with potential applications.

CPAD 2.0: a repository of curated experimental data on aggregating proteins and peptides.

The Curated Protein Aggregation Database (CPAD) is a manually curated and open-access database dedicated to providing comprehensive information related to mechanistic, kinetic and structural aspects of protein and peptide aggregation. The database has been updated to CPAD 2.0 by significantly expanding datasets and improving the user-interface. Key features of CPAD 2.0 are (i) 83,098 data points on aggregation kinetics experiments, (ii) 565 structures related to aggregation, which are classified into proteins, fibrils, and protein-ligand complexes, (iii) 2031 aggregating/non-aggregating peptides with pre-calculated aggregation properties, and (iv) 912 aggregation-prone regions in amyloidogenic proteins. This database will help the scientific community (a) by facilitating research leading to improved understanding of protein aggregation, (b) by helping develop, validate and benchmark mechanistic and kinetic models of protein aggregation, and (c) by assisting experimentalists with design of their investigations and dissemination of data generated by their studies. CPAD 2.0 can be accessed at https://web.iitm.ac.in/bioinfo2/cpad2/index.html.

Atrial fibrillation and preexcitation - A licence to kill.

Atrial fibrillation becomes a potentially lethal arrhythmia in the presence of preexcitation because the rapid ventricular activation can result in ventricular fibrillation. Fortunately, radiofrequency ablation is an effective treatment for these patients. Specific points of interest regarding this association are the mechanism of increased incidence of atrial fibrillation and the current management of patients presenting in atrial fibrillation. These are discussed in this editorial.

Consensus of uncertain multi-agent systems with input delay and disturbances.

In this paper, the problem of robust consensus for multi-agent systems affected by external disturbances is discussed. A novel consensus control is developed by using a feedback controller based on disturbance rejection and Smith predictor scheme. Specifically, the disturbance rejection performance of the uncertain multi-agent systems is improved according to the estimation of equivalent-input-disturbance and the effect of time delay in the control system is reduced via Smith predictor scheme by shifting the delay outside the feedback loop. Furthermore, by combining Lyapunov theory, matrix inequality techniques and properties of Kronecker product, a robust feedback controller for each agent is designed such that the desired consensus of the uncertain multi-agent systems affected by external disturbances can be ensured. Finally, to illustrate the validity of the designed control scheme, two numerical examples with simulation results are provided.

Synchronization of complex dynamical networks with random coupling delay and actuator faults.

This paper addresses the issue of passivity-based synchronization problem for a family of Markovian jump neutral complex dynamical networks (NCDNs) with coupling delay and actuator faults. Also, by considering the effect of random fluctuation in complex dynamical network systems, the occurrence of coupling delay are taken in terms of a stochastic distribution, which obeys the Bernoulli distribution. To handle the fault effects in actuators of proposed complex network systems, an actuator fault model is considered. The main objective of this paper is to develop a robust state feedback controller such that for all possible actuator failures and random coupling delays, all nodes of the proposed Markovian jump NCDNs is globally asymptotically synchronized to the reference node in mean square sense and guarantee the output strict passivity performance. By developing a suitable Lyapunov-Krasovskii functional and utilizing the Wirtinger-based integral inequality, the required a set of sufficient conditions for the synchronization of proposed system is established in form of linear matrix inequalities. Finally, three numerical examples including a 3-dimensional Lorenz chaotic model are provided to demonstrate the correctness and superiority of the proposed control scheme.

Estimation and disturbance rejection performance for fractional order fuzzy systems.

This paper gives attention to the issues of output tracking and disturbance rejection performance for a class of fractional order Takagi-Sugeno fuzzy systems in the presence of time-varying delay and unknown external disturbances. More specifically, a new configuration of a fractional order modified repetitive controller that incorporates an improved equivalent-input-disturbance estimator and gain fluctuations in its design is proposed to perform disturbance rejection for the addressed system. By introducing a continuous frequency distributed equivalent model and using the Lyapunov-Krasovskii stability theory, a new set of sufficient conditions ensuring robust asymptotic stability of the resulting closed-loop system is obtained in the framework of linear matrix inequalities. Finally, a numerical example is presented to validate the developed theoretical results, where it is shown that the obtained conditions could force the considered system output to exactly track the given any kind of reference signal by compensating the unknown external disturbance.

Influence of Amino Acid Mutations and Small Molecules on Targeted Inhibition of Proteins Involved in Cancer.

BACKGROUND: Protein-protein interactions (PPIs) are of crucial importance in regulating the biological processes of cells both in normal and diseased conditions. Significant progress has been made in targeting PPIs using small molecules and achieved promising results. However, PPI drug discovery should be further accelerated with better understanding of chemical space along with various functional aspects. OBJECTIVE: In this review, we focus on the advancements in computational research for targeted inhibition of protein-protein interactions involved in cancer. METHODS: Here, we mainly focused on two aspects: (i) understanding the key roles of amino acid mutations in epidermal growth factor receptor (EGFR) as well as mutation-specific inhibitors and (ii) design of small molecule inhibitors for Bcl-2 to disrupt PPIs. RESULTS: The paradigm of PPI inhibition to date reflect the certainty that inclination towards novel and versatile strategies enormously dictate the success of PPI inhibition. As the chemical space highly differs from the normal drug like compounds the lead optimization process has to be given the utmost priority to ensure the clinical success. Here, we provided a broader perspective on effect of mutations in oncogene EGFR connected to Bcl-2 PPIs and focused on the potential challenges. CONCLUSION: Understanding and bridging mutations and altered PPIs will provide insights into the alarming signals leading to massive malfunctioning of a biological system in various diseases. Finding rational elucidations from a pharmaceutical stand point will presumably broaden the horizons in future.