Vehicular Traffic Flow Analysis and Minimize the Vehicle Queue Waiting Time Using Signal Distribution Control Algorithm.

The real-time vehicular traffic system is an integral part of the urban vehicular traffic system, which provides effective traffic signal control for a large multifaceted traffic network and is a highly challenging distributed control problem. Coordinating vehicular traffic enables the network model to deliver an efficient service flow. Consider that there are four lanes of vehicular traffic in this situation, allowing parallel vehicle movements to occur without causing an accident. In this instance, the vehicular system's control parameters are time and vehicle volume. In this work, vehicular traffic flow is examined, and an algorithm to estimate vehicle waiting time in each direction is estimated. The effectiveness of the proposed vehicle traffic signal distribution control system by comparing the experimental results with a real-time vehicular traffic system is verified. This is also illustrated numerically.

Ultra-High Capacity Optical Satellite Communication System Using PDM-256-QAM and Optical Angular Momentum Beams.

Twisted light beams such as optical angular momentum (OAM) with numerous possible orthogonal states have drawn the prodigious contemplation of researchers. OAM multiplexing is a futuristic multi-access technique that has not been scrutinized for optical satellite communication (OSC) systems thus far, and it opens up a new window for ultra-high-capacity systems. This paper presents the 4.8 Tbps (5 wavelengths × 3 OAM beams × 320 Gbps) ultra-high capacity OSC system by incorporating polarization division multiplexed (PDM) 256-Quadrature amplitude modulation (256-QAM) and OAM beams. To realize OAM multiplexing, Laguerre Gaussian (LG) transverse mode profiles such as LG00, LG140, and LG400 were used in the proposed study. The effects of the receiver's digital signal processing (DSP) module were also investigated, and performance improvement was observed using DSP for its potential to compensate for the effects of dispersion, phase errors, and nonlinear effects using the blind phase search (BPS), Viterbi phase estimation (VPE), and the constant modulus algorithm (CMA). The results revealed that the proposed OAM-OSC system successfully covered the 22,000 km OSC link distance and, out of three OAM beams, fundamental mode LG00 offered excellent performance. Further, a detailed comparison of the proposed system and reported state-of-the-art schemes was performed.

Evaluation of the dosimetric and radiobiological parameters in four radiotherapy regimens for synchronous bilateral breast cancer.

This study is to investigate the optimal treatment option for synchronous bilateral breast cancer (SBBC) by comparing dosimetric and radiobiological parameters of intensity-modulated radiotherapy (IMRT) and volumetric modulated arc therapy (VMAT) plans using single and dual isocenters. Twenty patients with SBBC without lymph node involvement were selected retrospectively. Four treatment plans were generated for each patient using the Eclipse treatment planning system (Varian Medical System, Palo Alto, CA, USA) following two delivery techniques with two isocenter conditions-IMRT using a single isocenter (IMRT_Iso1), VMAT using a single isocenter (VMAT_Iso1), IMRT using dual isocenters (IMRT_Iso2), and VMAT using dual isocenters (VMAT_Iso2). A dose of 42.56 Gy in 16 fractions was prescribed for the planning target volume (PTV). All plans were calculated using the Acuros XB algorithm and a photon optimizer for a 6-MV beam of a Vital Beam linear accelerator. PTV-related dosimetric parameters were analyzed. Further, the homogeneity index, conformity index, and conformation number were computed to evaluate plan quality. Dosimetric parameters were also measured for the organs at risk (OARs). In addition, the equivalent uniform dose corresponding to an equivalent dose related to a reference of 2 Gy per fraction, the tumor control probability, and the normal tissue complication probability were calculated based on the dose-volume histogram to investigate the radiobiological impact on PTV and OARs. IMRT_Iso1 exhibited similar target coverage and a certain degree of dosimetric improvement in OAR sparing compared to the other techniques. It also exhibited some radiobiological improvement, albeit insignificant. Although IMRT_Iso1 significantly increased monitor unit compared to VMAT_Iso1, which is the best option in terms of delivery efficiency, there was only a 22% increase in delivery time. Therefore, in conclusion, IMRT_Iso1, the complete treatment of which can be completed using a single setup, is the most effective method for treating SBBC.

Performance Evaluation of Cooperative OMA and NOMA Systems in 6G Deployment Scenarios.

Optimization of the energy efficiency, fairness, and rates of the system is a vital part of communication systems. Multiple access techniques have a huge potential to enhance such performance parameters. This paper studies the performance of NOMA and OMA systems in a singular cell environment, where the cellular users are distributed randomly, and cooperative relays are considered for better system reliability. The relay nodes forward the signals to the cell-edge users. This paper considers a practical scenario where all the relay equipment is distributed with non-uniform battery power levels. The performance of OMA and NOMA schemes is compared based on the key performance indicators: sum rate, fairness, and energy efficiency. The fairness factor determines fairness in the allocation of resources to all the system's users. The performance of the two schemes is assessed in three deployment scenarios: urban, suburban, and rural scenarios. Through numerical results, it is proved that the performance of the NOMA dominates the OMA scheme.

Solutions of Detour Distance Graph Equations.

Graph theory is a useful mathematical structure used to model pairwise relations between sensor nodes in wireless sensor networks. Graph equations are nothing but equations in which the unknown factors are graphs. Many problems and results in graph theory can be formulated in terms of graph equations. In this paper, we solved some graph equations of detour two-distance graphs, detour three-distance graphs, detour antipodal graphs involving with the line graphs.

A Hash-Based Quantum-Resistant Chameleon Signature Scheme.

As a standard digital signature may be verified by anybody, it is unsuitable for personal or economically sensitive applications. The chameleon signature system was presented by Krawczyk and Rabin as a solution to this problem. It is based on a hash then sign model. The chameleon hash function enables the trapdoor information holder to compute a message digest collision. The holder of a chameleon signature is the recipient of a chameleon signature. He could compute collision on the hash value using the trapdoor information. This keeps the recipient from disclosing his conviction to a third party and ensures the privacy of the signature. The majority of the extant chameleon signature methods are built on the computationally infeasible number theory problems, like integer factorization and discrete log. Unfortunately, the construction of quantum computers would be rendered insecure to those schemes. This creates a solid requirement for construct chameleon signatures for the quantum world. Hence, this paper proposes a novel quantum secure chameleon signature scheme based on hash functions. As a hash-based cryptosystem is an essential candidate of a post-quantum cryptosystem, the proposed hash-based chameleon signature scheme would be a promising alternative to the number of theoretic-based methods. Furthermore, the proposed method is key exposure-free and satisfies the security requirements such as semantic security, non-transferability, and unforgeability.

Effect of tumor properties on energy absorption, temperature mapping, and thermal dose in 13.56-MHz radiofrequency hyperthermia.

Computational techniques can enhance personalized hyperthermia-treatment planning by calculating tissue energy absorption and temperature distribution. This study determined the effect of tumor properties on energy absorption, temperature mapping, and thermal dose distribution in mild radiofrequency hyperthermia using a mouse xenograft model. We used a capacitive-heating radiofrequency hyperthermia system with an operating frequency of 13.56 MHz for in vivo mouse experiments and performed simulations on a computed tomography mouse model. Additionally, we measured the dielectric properties of the tumors and considered temperature dependence for thermal properties, metabolic heat generation, and perfusion. Our results showed that dielectric property variations were more dominant than thermal properties and other parameters, and that the measured dielectric properties provided improved temperature-mapping results relative to the property values taken from previous study. Furthermore, consideration of temperature dependency in the bio heat-transfer model allowed elucidation of precise thermal-dose calculations. These results suggested that this method might contribute to effective thermoradiotherapy planning in clinics.

Investigation of blood biomarkers related to meat quality and quantity in Hanwoo steers.

OBJECTIVE: This study was conducted to investigate the correlation between blood components and carcass traits, and to find the biomarkers related to meat quality and quantity in Hanwoo steers. METHODS: One hundred twenty-six Hanwoo steers (8 to 9 months of age, body weight of 185.6±24.38 kg) were used to find the correlation between blood compositions and carcass traits. The steers were fed concentrate and rice straw (30 steers) or total mixed rations (96 steers) during the whole experimental period. Blood samples were collected from the jugular vein at the growing (8 to 12 months), fattening (13 to 23 months) and finishing phases (24 to 30 months). Steers were slaughtered at 30 to 31 months of age (body weight of 701.6±60.45 kg) and the carcass traits were evaluated. Blood metabolites and hormones were analyzed and the correlation coefficients and regression equations with carcass traits were determined. RESULTS: Average concentrations of retinol, leptin and insulin were 1.10 IU, 30.34 ng, and 35.35 ng per mL of blood plasma, respectively. Retinol has negative correlations (p<0.01) with insulin and leptin. Blood insulin and total protein decreased with the age of steers, but retinol, aspartic acid transaminase (AST), glucose, cholesterol and triglyceride increased. In the finishing phase, significant (p<0.01) negative correlations occurred between blood retinol content and marbling score, and between blood AST content and longissimus muscle area of 13th rib, and the following regression equations were obtained: Marbling score (1-9) = -0.009×retinol (IU/100 mL)+ 9.125 (R2 = 0.643), Longissimus muscle area (cm2) = -0.250× AST(U/L)+112.498 (R2 = 0.450). CONCLUSION: It is possible to make highly marbled beef by controlling the blood retinol content during the fattening and finishing phases of Hanwoo steers. Accordingly, blood retinol and AST could be biomarkers for determining beef quality and quantity, respectively, prior to slaughter.

Effects of feeding system on growth performance, plasma biochemical components and hormones, and carcass characteristics in Hanwoo steers.

OBJECTIVE: This study was conducted to compare growth performance, blood components and carcass traits by two feeding systems (concentrate with roughage separately [CON] vs total mixed ration [TMR]) in Hanwoo steers, and to learn the relationship between blood components during fattening or finishing phases and carcass traits in Hanwoo steers. METHODS: Sixty steers aged 8 months were allotted to two feeding systems and fed similar amounts of average dry matter and total digestible nutrient throughout whole experimental period according to each feeding program. Steers were weighed monthly, taken blood at the end of growing, fattening and finishing periods, and slaughtered at 30 month of age. RESULTS: Growing performance was higher (p<0.05) in the CON group compared to the TMR group during fattening and finishing periods. The CON group was lower (p<0.05) in blood aspartic acid transaminase, blood urea nitrogen and retinol levels during growing period, but higher in triglyceride and cholesterol levels during fattening and finishing periods compared to the TMR group. The CON group was greater (p<0.05) in rib-eye area, and lighter (p<0.05) red in meat color compared to the TMR group. In the correlation coefficients between blood components of steers and carcass traits, retinol had a negative (p<0.05) correlation with marbling score and rib-eye area. Leptin had a positive (p<0.05) correlation with back fat thickness. Blood cholesterol and triglyceride were positively (p<0.05) correlated with carcass weight and rib-eye area. CONCLUSION: Growth performance, carcass ribeye area and meat color showed a more desirable result in the CON compared to the TMR in Hanwoo steers. Assessing the accumulated data of carcass traits with blood components including hormones-particularly retinol, cholesterol, triglyceride, and leptin-during the fattening or finishing phases, it may be possible to find a biomarker for determining beef quality in living animals.

Myometrial relaxation of mice via expression of two pore domain acid sensitive K(+) (TASK-2) channels.

Myometrial relaxation of mouse via expression of two-pore domain acid sensitive (TASK) channels was studied. In our previous report, we suggested that two-pore domain acid-sensing K(+) channels (TASK-2) might be one of the candidates for the regulation of uterine circular smooth muscles in mice. In this study, we tried to show the mechanisms of relaxation via TASK-2 channels in marine myometrium. Isometric contraction measurements and patch clamp technique were used to verify TASK conductance in murine myometrium. Western blot and immunehistochemical study under confocal microscopy were used to investigate molecular identity of TASK channel. In this study, we showed that TEA and 4-AP insensitive non-inactivating outward K(+) current (NIOK) may be responsible for the quiescence of murine pregnant longitudinal myometrium. The characteristics of NIOK coincided with two-pore domain acid-sensing K(+) channels (TASK-2). NIOK in the presence of K(+) channel blockers was inhibited further by TASK inhibitors such as quinidine, bupivacaine, lidocaine, and extracellular acidosis. Furthermore, oxytocin and estrogen inhibited NIOK in pregnant myometrium. When compared to non-pregnant myometrium, pregnant myometrium showed stronger inhibition of NIOK by quinidine and increased immunohistochemical expression of TASK-2. Finally, TASK-2 inhibitors induced strong myometrial contraction even in the presence of L-methionine, a known inhibitor of stretch-activated channels in the longitudinal myometrium of mouse. Activation of TASK-2 channels seems to play an essential role for relaxing uterus during pregnancy and it might be one of the alternatives for preventing preterm delivery.

Biomechanical effect of altered lumbar lordosis on intervertebral lumbar joints during the golf swing: a simulation study.

Although the lumbar spine region is the most common site of injury in golfers, little research has been done on intervertebral loads in relation to the anatomical-morphological differences in the region. This study aimed to examine the biomechanical effects of anatomical-morphological differences in the lumbar lordosis on the lumbar spinal joints during a golf swing. The golf swing motions of ten professional golfers were analyzed. Using a subject-specific 3D musculoskeletal system model, inverse dynamic analyses were performed to compare the intervertebral load, the load on the lumbar spine, and the load in each swing phase. In the intervertebral load, the value was the highest at the L5-S1 and gradually decreased toward the T12. In each lumbar spine model, the load value was the greatest on the kypholordosis (KPL) followed by normal lordosis (NRL), hypolordosis (HPL), and excessive lordosis (EXL) before the impact phase. However, results after the follow-through (FT) phase were shown in reverse order. Finally, the load in each swing phase was greatest during the FT phase in all the lumbar spine models. The findings can be utilized in the training and rehabilitation of golfers to help reduce the risk of injury by considering individual anatomical-morphological characteristics.

Comparative analysis of the gazelle Optimizer and its variants.

The Gazelle Optimization Algorithm (GOA) is an innovative nature-inspired metaheuristic algorithm, designed to mimic the agile and efficient hunting strategies of gazelles. Despite its promising performance in solving complex optimization problems, there is still a significant scope for enhancing its efficiency and robustness. This paper introduces several novel variants of GOA, integrating adaptive strategy, Levy flight strategy, Roulette wheel selection strategy, and random walk strategy. These enhancements aim to address the limitations of the original GOA and improve its performance in diverse optimization scenarios. The proposed algorithms are rigorously tested on CEC 2014 and CEC 2017 benchmark functions, five engineering problems, and a Total Harmonic Distortion (THD) minimization problem. The results demonstrate the superior performance of the proposed variants compared to the original GOA, providing valuable insights into their applicability and effectiveness.

Metaheuristics based dimensionality reduction with deep learning driven false data injection attack detection for enhanced network security.

Recent sensor, communication, and computing technological advancements facilitate smart grid use. The heavy reliance on developed data and communication technology increases the exposure of smart grids to cyberattacks. Existing mitigation in the electricity grid focuses on protecting primary or redundant measurements. These approaches make certain assumptions regarding false data injection (FDI) attacks, which are inadequate and restrictive to cope with cyberattacks. The reliance on communication technology has emphasized the exposure of power systems to FDI assaults that can bypass the current bad data detection (BDD) mechanism. The current study on unobservable FDI attacks (FDIA) reveals the severe threat of secured system operation because these attacks can avoid the BDD method. Thus, a Data-driven learning-based approach helps detect unobservable FDIAs in distribution systems to mitigate these risks. This study presents a new Hybrid Metaheuristics-based Dimensionality Reduction with Deep Learning for FDIA (HMDR-DLFDIA) Detection technique for Enhanced Network Security. The primary objective of the HMDR-DLFDIA technique is to recognize and classify FDIA attacks in the distribution systems. In the HMDR-DLFDIA technique, the min-max scalar is primarily used for the data normalization process. Besides, a hybrid Harris Hawks optimizer with a sine cosine algorithm (hybrid HHO-SCA) is applied for feature selection. For FDIA detection, the HMDR-DLFDIA technique utilizes the stacked autoencoder (SAE) method. To improve the detection outcomes of the SAE model, the gazelle optimization algorithm (GOA) is exploited. A complete set of experiments was organized to highlight the supremacy of the HMDR-DLFDIA method. The comprehensive result analysis stated that the HMDR-DLFDIA technique performed better than existing DL models.

Enhancing human computer interaction with coot optimization and deep learning for multi language identification.

Human-Computer Interaction (HCI) is a multidisciplinary field focused on designing and utilizing computer technology, underlining the interaction interface between computers and humans. HCI aims to generate systems that allow consumers to relate to computers effectively, efficiently, and pleasantly. Multiple Spoken Language Identification (SLI) for HCI (MSLI for HCI) denotes the ability of a computer system to recognize and distinguish various spoken languages to enable more complete and handy interactions among consumers and technology. SLI utilizing deep learning (DL) involves using artificial neural networks (ANNs), a subset of DL models, to automatically detect and recognize the language spoken in an audio signal. DL techniques, particularly neural networks (NNs), have succeeded in various pattern detection tasks, including speech and language processing. This paper develops a novel Coot Optimizer Algorithm with a DL-Driven Multiple SLI and Detection (COADL-MSLID) technique for HCI applications. The COADL-MSLID approach aims to detect multiple spoken languages from the input audio regardless of gender, speaking style, and age. In the COADL-MSLID technique, the audio files are transformed into spectrogram images as a primary step. Besides, the COADL-MSLID technique employs the SqueezeNet model to produce feature vectors, and the COA is applied to the hyperparameter range of the SqueezeNet method. The COADL-MSLID technique exploits the SLID process's convolutional autoencoder (CAE) model. To underline the importance of the COADL-MSLID technique, a series of experiments were conducted on the benchmark dataset. The experimentation validation of the COADL-MSLID technique exhibits a greater accuracy result of 98.33% over other techniques.

Disposable on-chip microfluidic system for buccal cell lysis, DNA purification, and polymerase chain reaction.

This paper reports the development of a disposable, integrated biochip for DNA sample preparation and PCR. The hybrid biochip (25 × 45 mm) is composed of a disposable PDMS layer with a microchannel chamber and reusable glass substrate integrated with a microheater and thermal microsensor. Lysis, purification, and PCR can be performed sequentially on this microfluidic device. Cell lysis is achieved by heat and purification is performed by mechanical filtration. Passive check valves are integrated to enable sample preparation and PCR in a fixed sequence. Reactor temperature is needed to lysis and PCR reaction is controlled within ±1°C by PID controller of LabVIEW software. Buccal epithelial cell lysis, DNA purification, and SY158 gene PCR amplification were successfully performed on this novel chip. Our experiments confirm that the entire process, except the off-chip gel electrophoresis, requires only approximately 1 h for completion. This disposable microfluidic chip for sample preparation and PCR can be easily united with other technologies to realize a fully integrated DNA chip.

Emerging nano-scale delivery systems for the treatment of osteoporosis.

Osteoporosis is a pathological condition characterized by an accelerated bone resorption rate, resulting in decreased bone density and increased susceptibility to fractures, particularly among the elderly population. While conventional treatments for osteoporosis have shown efficacy, they are associated with certain limitations, including limited drug bioavailability, non-specific administration, and the occurrence of adverse effects. In recent years, nanoparticle-based drug delivery systems have emerged as a promising approach for managing osteoporosis. Nanoparticles possess unique physicochemical properties, such as a small size, large surface area-to-volume ratio, and tunable surface characteristics, which enable them to overcome the limitations of conventional therapies. These nanoparticles offer several advantages, including enhanced drug stability, controlled release kinetics, targeted bone tissue delivery, and improved drug bioavailability. This comprehensive review aims to provide insights into the recent advancements in nanoparticle-based therapy for osteoporosis. It elucidates the various types of nanoparticles employed in this context, including silica, polymeric, solid lipid, and metallic nanoparticles, along with their specific processing techniques and inherent properties that render them suitable as potential drug carriers for osteoporosis treatment. Furthermore, this review discusses the challenges and future suggestions associated with the development and translation of nanoparticle drug delivery systems for clinical use. These challenges encompass issues such as scalability, safety assessment, and regulatory considerations. However, despite these challenges, the utilization of nanoparticle-based drug delivery systems holds immense promise in revolutionizing the field of osteoporosis management by enabling more effective and targeted therapies, ultimately leading to improved patient outcomes.

Combinatorial Effect of Mesenchymal Stem Cells and Extracellular Vesicles in a Hydrogel on Cartilage Regeneration.

BACKGROUND: Mesenchymal stem cells (MSCs) are used for tissue regeneration due to their wide differentiation capacity and anti-inflammatory effects. Extracellular vesicles (EVs) derived from MSCs are also known for their regenerative effects as they contain nucleic acids, proteins, lipids, and cytokines similar to those of parental cells. There are several studies on the use of MSCs or EVs for tissue regeneration. However, the combinatorial effect of human MSCs (hMSCs) and EVs is not clear. In this study, we investigated the combinatorial effect of hMSCs and EVs on cartilage regeneration via co-encapsulation in a hyaluronic-acid (HA)-based hydrogel. METHODS: A methacrylic-acid-based HA hydrogel was prepared to encapsulate hMSCs and EVs in hydrogels. Through in vitro and in vivo analyses, we investigated the chondrogenic potential of the HA hydrogel-encapsulated with hMSCs and EVs. RESULTS: Co-encapsulation of hMSCs with EVs in the HA hydrogel increased the chondrogenic differentiation of hMSCs and regeneration of damaged cartilage tissue compared with that of the HA hydrogel loaded with hMSCs only. CONCLUSION: Co-encapsulation of hMSCs and EVs in the HA hydrogel effectively enhances cartilage tissue regeneration due to the combinatorial therapeutic effect of hMSCs and EVs. Thus, in addition to cartilage tissue regeneration for the treatment of osteoarthritis, this approach would be a useful strategy to improve other types of tissue regeneration.

Concurrent blockade of free radical and microsomal prostaglandin E synthase-1-mediated PGE2 production improves safety and efficacy in a mouse model of amyotrophic lateral sclerosis.

While free radicals and inflammation constitute major routes of neuronal injury occurring in amyotrophic lateral sclerosis (ALS), neither antioxidants nor non-steroidal anti-inflammatory drugs have shown significant efficacy in human clinical trials. We examined the possibility that concurrent blockade of free radicals and prostaglandin E(2) (PGE(2))-mediated inflammation might constitute a safe and effective therapeutic approach to ALS. We have developed 2-hydroxy-5-[2-(4-trifluoromethylphenyl)-ethylaminobenzoic acid] (AAD-2004) as a derivative of aspirin. AAD-2004 completely removed free radicals at 50 nM as a potent spin-trapping molecule and inhibited microsomal PGE(2) synthase-1 (mPGES-1) activity in response to both lipopolysaccharide-treated BV2 cell with IC(50) of 230 nM and recombinant human mPGES-1 protein with IC(50) of 249 nM in vitro. In superoxide dismutase 1(G93A) transgenic mouse model of ALS, AAD-2004 blocked free radical production, PGE(2) formation, and microglial activation in the spinal cords. As a consequence, AAD-2004 reduced autophagosome formation, axonopathy, and motor neuron degeneration, improving motor function and increasing life span. In these assays, AAD-2004 was superior to riluzole or ibuprofen. Gastric bleeding was not induced by AAD-2004 even at a dose 400-fold higher than that required to obtain maximal therapeutic efficacy in superoxide dismutase 1(G93A). Targeting both mPGES-1-mediated PGE(2) and free radicals may be a promising approach to reduce neurodegeneration in ALS and possibly other neurodegenerative diseases.

Nodakenin suppresses lipopolysaccharide-induced inflammatory responses in macrophage cells by inhibiting tumor necrosis factor receptor-associated factor 6 and nuclear factor-κB pathways and protects mice from lethal endotoxin shock.

Nodakenin, a coumarin isolated from the roots of Angelicae gigas, has been reported to possess neuroprotective, antiaggregatory, antibacterial, and memory-enhancing effects. In the present study, we investigated the anti-inflammatory effects of nodakenin by examining its in vitro inhibitory effects on inducible nitric-oxide synthase (iNOS), cyclooxygenase-2 (COX-2), and proinflammatory cytokines in lipopolysaccharide (LPS)-induced RAW 264.7 macrophages and mouse peritoneal macrophages and its in vivo effects on LPS-induced septic shock in mice. Our results indicate that nodakenin concentration-dependently inhibits iNOS and COX-2 at the protein, mRNA, and promoter binding levels, and these inhibitions cause attendant decreases in the production of nitric oxide (NO) and prostaglandin E2 (PGE2). Furthermore, we found that nodakenin inhibits the production and mRNA expression of tumor necrosis factor-α (TNF-α), interleukin (IL)-6, and IL-1ß induced by LPS. Molecular data revealed that nodakenin suppressed the transcriptional activity and translocation of nuclear factor-κB (NF-κB) by inhibiting inhibitory κB-α degradation and IκB kinase-α/ß phosphorylation. In addition, nodakenin was found to significantly inhibit the LPS-induced binding of transforming growth factor-ß-activated kinase 1 to tumor necrosis factor receptor-associated factor 6 (TRAF6) by reducing TRAF6 ubiquitination. Pretreatment with nodakenin reduced the serum levels of NO, PGE2, and proinflammatory cytokines and increased the survival rate of mice with LPS-induced endotoxemia. Taken together, our data suggest that nodakenin down-regulates the expression of the proinflammatory iNOS, COX-2, TNF-α, IL-6, and IL-1ß genes in macrophages by interfering with the activation of TRAF6, thus preventing NF-κB activation.

Hybrid Deep Learning Approach for Automatic Detection in Musculoskeletal Radiographs.

The practice of Deep Convolution neural networks in the field of medicine has congregated immense success and significance in present situations. Previously, researchers have developed numerous models for detecting abnormalities in musculoskeletal radiographs of upper extremities, but did not succeed in achieving respectable accuracy in the case of finger radiographs. A novel deep neural network-based hybrid architecture named ComDNet-512 is proposed in this paper to efficiently detect the bone abnormalities in the musculoskeletal radiograph of a patient. ComDNet-512 comprises a three-phase pipeline structure: compression, training of the dense neural network, and progressive resizing. The ComDNet-512 hybrid model is trained with finger radiographs samples to make a binary prediction, i.e., normal or abnormal bones. The proposed model showed phenomenon outcomes when cross-validated on the testing samples of arthritis patients and gives many superior results when compared with state-of-the-art practices. The model is able to achieve an area under the ROC curve (AUC) equal to 0.894 (sensitivity = 0.941 and specificity = 0.847). The Precision, Recall, F1 Score, and Kappa values, recorded as 0.86, 0.94, 0.89, and 0.78, respectively, are better than any of the previous models'. With an increasing appearance of enormous cases of musculoskeletal conditions in people, deep learning-based computational solutions can play a big role in performing automated detections in the future.