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Protein phosphatase 2A (PP2A), one of the most abundant protein phosphatases, has divergent functions in multiple types of cells. Its inactivation has been closely associated with leukemia diseases. However, the physiological function of PP2A for hematopoiesis has been poorly understood in organisms. Drosophila hematopoiesis parallels the vertebrate counterpart in developmental and functional features but involves a much simpler hematopoietic system. Here, utilizing the Drosophila major larval hematopoietic organ lymph gland, we studied the function of PP2A for hematopoiesis in vivo. By knocking down the expression of Pp2A‐29B that encodes the scaffold subunit of the PP2A holoenzyme complex, we found that PP2A silencing in the differentiating hemocytes resulted in their excessive proliferation. Furthermore, this PP2A inhibition downregulated the expression of Smoothened (Smo), a crucial component in the Hedgehog pathway, and smo overexpression was able to rescue the phenotypes of PP2A depletion, indicating that Smo functions as a downstream effector of PP2A to restrict the hemocyte proliferation. PDGF/VEGF‐receptor (Pvr) overexpression also restored the Smo expression and lymph gland morphology of PP2A silencing, suggesting a PP2A‐Pvr‐Smo axis to regulate lymph gland growth and hemocyte proliferation. Moreover, inhibiting PP2A activity in the blood progenitor cells promoted their differentiation, but which was independent with Smo. Together, our data suggested that PP2A plays a dual role in the Drosophila lymph gland by preserving the progenitor population and restraining the hemocyte proliferation, to properly regulate the hematopoietic process. [ABSTRACT FROM AUTHOR]

This article explores the role of a long non-coding RNA called BLACAT1 in the progression of non-small cell lung cancer (NSCLC). The study found that BLACAT1 expression was significantly increased in lung cancer tissues compared to healthy tissues, and that high expression of BLACAT1 promoted the growth and progression of lung cancer cells. Additionally, BLACAT1 was found to activate the Sonic Hedgehog signaling pathway, which is involved in cancer progression. The findings suggest that BLACAT1 may be a potential target for the treatment of NSCLC. It is important to note that this article has been retracted. [Extracted from the article]

Introduction: Oral cancer poses a significant burden on public health in India, with higher incidence and mortality rates. Despite advancements in treatment modalities, prognosis remains poor due to factors such as localized recurrence and lymph node metastasis, potentially influenced by cancer stem cells. Among signaling pathways implicated in CSC regulation, the Hedgehog pathway plays a crucial role in oral squamous cell carcinoma (OSCC). Material & methods: 97 OSCC patients' tissue samples were collected and subjected to RNA isolation, cDNA synthesis and quantitative real-time PCR to analyze PTCH1 and SMO expression. Protein expression was assessed through immunohistochemistry. Clinicopathological parameters were correlated with gene and protein expression. Statistical analysis included Pearson chi-square tests, co-relation co-efficient tests, Kaplan–Meier survival analysis and ROC curve analysis. Results: PTCH1 expression correlated with lymphatic permeation (p = 0.002) and tumor stage (p = 0.002), while SMO expression correlated with lymph node status (p = 0.034) and tumor stage (p = 0.021). PTCH1 gene expression correlated with lymph node status (p = 0.024). High PTCH1 gene expression was associated with shorter survival in tongue cancer patients. ROC curve analysis indicated diagnostic potential for PTCH1 and SMO gene and cytoplasmic SMO expression in distinguishing malignant tissues from adjacent normal tissues. Conclusion: PTCH1 and SMO play a crucial role in oral cancer progression, correlating with tumor stages and metastatic potential. Despite not directly influencing overall survival, PTCH1 expression at specific anatomical sites hints at its prognostic implications. PTCH1 and SMO exhibit diagnostic potential, suggesting their utility as molecular markers in oral cancer management and therapeutic strategies. [ABSTRACT FROM AUTHOR]

In several applications, the accuracy and robust performance of the control method for the speed of permanent magnet synchronous motors (PMSMs) is critical. Model uncertainties, caused by inaccurate model identification, decrease the accuracy of PMSM control. To solve this problem, this paper presents a super robust control structure for the speed control of PMSMs. In the proposed method, the model uncertainties with Lipschitz condition together with disturbances are considered during the PMSM modeling, and their effects are handled using a robust state feedback control. To be more specific, the Lyapunov stability proof is performed in such a way that the model uncertainty effects are eliminated. Before that, the Lyapunov stability criteria have been selected in such a way that the Hꝏ conditions are considered and guaranteed. This issue helps to eliminate the effects of the disturbances. In addition, this paper considers another option to make the whole control structure robust against sudden load changes. To solve this problem, a fuzzy adaptive sliding mode observer (FASMO) is presented to determine the load torque and use it in the control signal generation. In this observer, the switched gain of the sliding mode observer (SMO) is adapted using a fuzzy system to eliminate the chattering phenomena and increase the estimation accuracy. In fact, the proposed method is called super robust because it resists model uncertainties, disturbances, and sudden load changes during three stages by robust state feedback control, Hꝏ criterion, and load estimator, respectively. The performance of the proposed approach is validated through a set of laboratory tests, and its superiority is shown compared to other methods. [ABSTRACT FROM AUTHOR]

Simple Summary: Honeybees (Apis mellifera) rely on their olfaction for various daily activities and tasks. Smoothened (Smo) serves as a critical element in the Hedgehog pathway and is likely associated with the modulation of the olfactory capabilities of bees. This study involved the identification, localization, and functional validation of Smo. The findings establish a foundational understanding of the role of Smo in influencing the olfactory abilities of bees. Smoothened (Smo) is a critical component regulating the Hedgehog signaling pathway. However, whether Smo is associated with the modulation of olfactory recognition capabilities of bees remains unclear. In this study, we amplified Smo from Apis mellifera. The coding sequence of Smo was 2952 bp long, encoded 983 amino acids. Smo was most highly expressed in the antennae. Cyclopamine (200 μg/mL) significantly reduced but purmorphamine (800 μg/mL) significantly increased Smo expression (p < 0.05). OR152 and OR2 expression in the cyclopamine group significantly decreased, whereas OR152 expression in the purmorphamine group significantly increased (p < 0.05). A significant decrease in the relative values of electroantennography was observed in the cyclopamine group exposed to neral. Behavioral tests indicated a significant decrease in the attractive rates of neral, VUAA1, linalool, and methyl heptenone in the cyclopamine group. Conversely, the selection rates of linalool and methyl heptenone in the purmorphamine group significantly increased. Our findings indicate that Smo may play a role in modulating olfactory receptors in bees. [ABSTRACT FROM AUTHOR]

In PMSM motor drive systems, control accuracy and resistance have always been the most important challenges for researchers. This paper addresses these two challenges and presents a control structure that makes the system performance robust against model uncertainties of system disturbances as well as sudden load changes. In other words, in this paper, an improved MPC controller has been presented, in which model uncertainties and possible disturbances on the system are considered during design. In addition, in order to apply the physical limitations of the electric motor during the design of this improved controller, the input limitations of the system have also been considered. Therefore, the desired MPC controller is a resistant and constrained MPC, it is resistant to uncertainties and disturbances. In addition, in order to further strengthen the control loop against sudden load changes, a sliding estimator has also been designed to estimate the load torque. This estimator itself is also resistant to the uncertainties of the model, which makes the entire control structure have a very robust performance. The performance of the method presented in this article has been compared with a conventional sliding method during a series of practical tests. The results showed that the proposed method has a higher accuracy of 3 rpm and a shorter convergence time of 2 seconds.

The relevance of the research topic is due to the current situation on the territory of Ukraine, which is characterized by extraordinary tension and confrontation between Russia and the countries of the collective West. The purpose of the study is to determine the Ukrainian factor in international policy and the position of the People’s Republic of China. This work is based on clerical, statistical and journalistic sources. This study is based on the theory of political realism, suggesting constant competition of states defending their national interests. In the process of research the author widely uses institutional, analytical and problem-chronological methods. The main conclusions of the study are the establishment of the main causes of the emergence of the Ukrainian crises, starting from the first years of independence of the state, the identification of Ukraine as an important outpost of the USA and its allies in the region of Eastern Europe, identifying the impact of the Ukrainian crisis of 2022 on international relations in general and specific countries, discovering the implementation of the process of transition from a unipolar world order led by the USA to a multipolar, identifying the reasons for non-interference in the conflict on the territory of Ukraine, which include the assessment of the northern Black Sea Region by the People’s Republic of China as a region meeting strictly economic interests, characterization of the special military operation as an aspect that forced the process of changing the geopolitical situation.

Background. A comparative analysis of the personal characteristics, coping behaviour, and resilience of students who left the line of contact and students living on the territory bordering zone of special military operation is the theoretical basis for the justification of the programme for psychological support for refugees from the combat zone, as well as the population living on the line of fight contact. Objectives. The study aims to compare the personal characteristics of students who left the line of fight contact and students living on the territory bordering the SMO zone Study Participants. First-year students of the Pedagogical Institute of the Belgorod State National Research University (n = 81) aged 18 to 20 years took part in the study. Of these, 40 students left the line of contact and 41 students were living on the territory bordering the SMO zone. Methods. To study the coping strategy of behaviour, we used the test “Overcoming difficult life situations” (by V. Janke and G. Erdmann in the adaptation of N.E. Vodopyanova). R. Kettell's 16-factor personality questionnaire was used to determine personal characteristics. The resilience of the subjects was studied using the resilience test (by S. Muddy in the adaptation of D.A. Leontiev and E.I. Rasskazova). Results. It is proved that students living on the territory bordering on the zone of SMO have a higher level of resilience. Their involvement in the ongoing life events, the desire to actively act, acquire new skills and experience, increase their self-confidence and contribute to the formation of pleasure from their own activities, in contrast to the students who left the fight zone and are more likely to show self-doubt, distrust of the world, anxiety, irritability, a sense of helplessness and unproductive coping strategies. Conclusions. The study identifies distinctive features in the personal qualities, coping behaviour and resilience of students who left the fightline and students living on the territory bordering the SMO zone. Students who left the combat zone are less resilient, more often use unproductive coping strategies, are more intellectual, anxious, irritable and tense, and more often show leadership potential, compared to the students who live in the territory bordering the combat contact line. The results obtained were taken into account when developing the “Resilient Personality” training; they form the basis of the programme for psychological support for refugees from the combat zone, as well as the population living in the territory bordering the SMO zone.

The purpose of this study was to investigate the effects of BOC on glioblastoma cells and its underlying mechanisms. In vitro, BOC-knockdown was performed in glioma cell lines. CCK-8 and Transwell were used to assess the impact of BOC on the viability, invasion, and migration of gliobma cells. RNA-seq technology was employed to analyze the differential gene expression between BOC-knockdown glioma cells and the control group, and qRT-PCR was used to validate the expression of downstream differential genes. SMO-overexpression was performed to investigate the effects of SMO on glioma cells. A BOC-knockdown mouse subcutaneous tumor model was to verify the effects of BOC on mouse tumors. Tissue microarray technology was used to detect the expression of BOC and SMO in samples of normal human brain tissue and glioma tissue. In vitro, BOC-knockdown inhibited the viability, invasion, and migration of glioma cells, as well as downregulated the expression of downstream differential genes SMO, EGFR, HRAS, and MRAS. Conversely, SMO-overexpression upregulated the viability, invasion, and migration abilities of BOC-knockdown cells. In vivo, BOC-knockdown suppressed tumor growth in mice and downregulated the expression of downstream differential genes SMO, EGFR, HRAS, and MRAS. Tissue microarray results showed that both BOC and SMO were highly expressed in glioma tissues. BOC is aberrantly overexpressed in glioma patients and promotes glioma development. Mechanistically, BOC activates the Hedgehog (Hh) and RAS signaling pathways by upregulating the expression of SMO, EGFR, HRAS, and MRAS, thereby facilitating the Proliferation, invasion and migration of glioma cells.

Agriculture serves as the predominant driver of a country's economy, constituting the largest share of the nation's manpower. Most farmers are facing a problem in choosing the most appropriate crop that can yield better based on the environmental conditions and make profits for them. As a consequence of this, there will be a notable decline in their overall productivity. Precision agriculture has effectively resolved the issues encountered by farmers. Today's farmers may benefit from what's known as precision agriculture. This method takes into account local climate, soil type, and past crop yields to determine which varieties will provide the best results. The explainable artificial intelligence (XAI) technique is used with radial basis functions neural network and spider monkey optimization to classify suitable crops based on the underlying soil and environmental conditions. The XAI technology would provide assets in better transparency of the prediction model on deciding the most suitable crops for their farms, taking into account a variety of geographical and operational criteria. The proposed model is assessed using standard metrics like precision, recall, accuracy, and F1‐score. In contrast to other cutting‐edge approaches discussed in this study, the model has shown fair performance with approximately 12% better accuracy than the other models considered in the current study. Similarly, precision has improvised by 10%, recall by 11%, and F1‐score by 10%. [ABSTRACT FROM AUTHOR]

The advent of newer and better technologies has made Human Activity Recognition (HAR) highly essential in our daily lives. HAR is a classification problem where the activity of humans is classified by analyzing the data collected from various sources like sensors, cameras etc. for a period of time. In this work, we have proposed a model for activity recognition which will provide a substructure for the assisted living environment. We used a genetic search based feature selection for the management of the voluminous data generated from various embedded sensors such as accelerometer, gyroscope, etc. We evaluated the proposed model on a sensor-based dataset - Human Activities and Postural Transitions Recognition (HAPT) which is publically available. The proposed model yields an accuracy of 97.04% and is better as compared to the other existing classification algorithms on the basis of several considered evaluation metrics. In this paper, we have also presented a cloud based edge computing architecture for the deployment of the proposed model which will ensure faster and uninterrupted assisted living environment. [ABSTRACT FROM AUTHOR]

In several applications, the accuracy and robust performance of the control method for the speed of permanent magnet synchronous motors (PMSMs) is critical. Model uncertainties, caused by inaccurate model identification, decrease the accuracy of PMSM control. To solve this problem, this paper presents a super robust control structure for the speed control of PMSMs. In the proposed method, the model uncertainties with Lipschitz condition together with disturbances are considered during the PMSM modeling, and their effects are handled using a robust state feedback control. To be more specific, the Lyapunov stability proof is performed in such a way that the model uncertainty effects are eliminated. Before that, the Lyapunov stability criteria have been selected in such a way that the Hꝏ conditions are considered and guaranteed. This issue helps to eliminate the effects of the disturbances. In addition, this paper considers another option to make the whole control structure robust against sudden load changes. To solve this problem, a fuzzy adaptive sliding mode observer (FASMO) is presented to determine the load torque and use it in the control signal generation. In this observer, the switched gain of the sliding mode observer (SMO) is adapted using a fuzzy system to eliminate the chattering phenomena and increase the estimation accuracy. In fact, the proposed method is called super robust because it resists model uncertainties, disturbances, and sudden load changes during three stages by robust state feedback control, Hꝏ criterion, and load estimator, respectively. The performance of the proposed approach is validated through a set of laboratory tests, and its superiority is shown compared to other methods.

Smoothened (Smo) is a critical component regulating the Hedgehog signaling pathway. However, whether Smo is associated with the modulation of olfactory recognition capabilities of bees remains unclear. In this study, we amplified Smo from Apis mellifera. The coding sequence of Smo was 2952 bp long, encoded 983 amino acids. Smo was most highly expressed in the antennae. Cyclopamine (200 μg/mL) significantly reduced but purmorphamine (800 μg/mL) significantly increased Smo expression (p < 0.05). OR152 and OR2 expression in the cyclopamine group significantly decreased, whereas OR152 expression in the purmorphamine group significantly increased (p < 0.05). A significant decrease in the relative values of electroantennography was observed in the cyclopamine group exposed to neral. Behavioral tests indicated a significant decrease in the attractive rates of neral, VUAA1, linalool, and methyl heptenone in the cyclopamine group. Conversely, the selection rates of linalool and methyl heptenone in the purmorphamine group significantly increased. Our findings indicate that Smo may play a role in modulating olfactory receptors in bees.

Multiple cancers have been associated with MYB-related protein B (MYBL2), its involvement in clear cell renal cell carcinoma (ccRCC) has yet to be demonstrated. Our study revealed a significant upregulation of MYBL2 in ccRCC tissues, correlating with clinicopathological features and patient prognosis. Increased MYBL2 expression promoted cell proliferation and suppressed apoptosis. RNA-seq analysis unveiled a reduction in smoothened (SMO) expression upon MYBL2 silencing. However, luciferase and chromatin immunoprecipitation (ChIP) assays demonstrated MYBL2's positive regulation of SMO expression by directly targeting the SMO promoter. Reintroduction of SMO expression in MYBL2-knocked down cells partially restored cell proliferation and mitigated apoptosis inhibition. Overall, these results indicate that MYBL2 facilitates ccRCC progression by enhancing SMO expression, suggesting its potential as an intriguing drug target for ccRCC therapy.

Abstract Background Congenital tibial hemimelia (CTH [MIM: 275220]) is a rare congenital limb deficiency that manifests as a shortened, curved, dysplastic or absent tibia with polydactyly. In previous studies, mutations of a distant sonic hedgehog (SHH) cis-regulator (ZRS) and a Shh repressor (GLI3) were identified. Case presentation Here, we admitted a 20-month-old boy who manifested with right tibial deformity, varus foot, ankle dislocation, and ipsilateral preaxial polydactyly. After genetic sequencing and data analysis, the results revealed a 443 A > G mutation in the father and a 536 C > T mutation in the mother in exon 2 of the Smoothed (SMO) gene at 7q32.1, with the coexistence of both mutant alleles in the proband/patient. Conclusions Our report suggests that even though not previously reported, SMO mutations may be associated with limb anomalies such as tibial hemimelia via Hh signaling in humans and has implications for genetic counseling.

Ameloblastomas are benign tumors associated with recurrence and morbidity. Mutations in the V-Raf murine sarcoma viral oncogene homolog B1 (BRAF) and smoothened (SMO) genes have been identified in ameloblastomas. The mutation rate of BRAF in different regions and mutation sites of SMO remains controversial. This study assesses the relationship between these mutations and clinicopathological features of ameloblastoma in Chinese patients. Ameloblastomas were surgically removed from 30 patients and BRAF gene polymorphisms were detected using DNA sequencing analysis. We also examined the relationship between BRAF or SMO mutations and clinicopathological parameters. BRAF V600E mutation was detected in 18 of 30 ameloblastomas. No significant correlation was found between BRAF V600E mutation and age, sex, location, histologic type, capsular invasion, or tumor recurrence. Five cases carried an SMO T364N mutation in exon 5, whereas six carried an SMO S590 T mutation in exon 10. The SMO mutation rate in patients aged ≤ 20 years was significantly higher than that in patients aged > 20 years. SMO T364N and S590 T mutations were closely related to age and capsular invasion. These findings indicate that BRAF V600E, SMO T364N, and SMO S590 T mutations play important roles in the pathogenesis of ameloblastoma. Key highlights The BRAF V600E mutation was highly prevalent in Chinese patients with ameloblastoma. SMO T364N and S590 T mutations are closely related to age and capsular invasion. The BRAF V600E, SMO T364N, and SMO S590 T mutations play important roles in the pathogenesis of ameloblastoma.

When the PMSM motor rotates at high speed, the motor winding current contains a lot of harmonics, and the harmonic current will increase the heat of the motor, thereby reducing the operating efficiency and service life of the motor. In view of the above problems, in the EV motor control, a rotor speed observer based on model reference adaptation was designed. This method was taken the stator current equation containing rotor position parameters as adjustable model, and the stator current equation of the motor itself as adjustable model. The reference model was designed the adaptive rate based on the Popov superstability theory; at the same time, the feed-forward compensation based on the sliding mode observer was designed to suppress harmonics. The simulation and experimental results have shown that the method can effectively reduce the current harmonics, speed error and torque ripple content of the EV motor compared with the MRAS speed sensorless control.

Due to the advantages of simple structure, small size, and high power density, permanent magnet synchronous motors (PMSM) have attracted the research interest of many scholars both domestically and abroad. However, traditional PMSM equipped with sensors, encoders, and other devices tend to have high equipment costs and rely heavily on the accuracy of the sensors for control effectiveness. Therefore, sensorless control has become a hot trend in the PMSM control field. In response to the chattering problem in sliding mode algorithms, this study first optimized the sliding mode reaching rate of a sensorless control system and applied it to construct a sliding mode observer and speed controller. Next, the improved sliding mode reaching rate-based sensorless control system was modeled and simulated in Matlab/Simulink, and its control performance was compared and analyzed with that of the traditional sliding mode reaching rate and replicated sliding mode reaching rate. Finally, comparative experiments were conducted on a test bench, and the results showed that, under the action of the improved sliding mode reaching rate, the chattering range of the output speed of the motor was +2%~+5%, which optimized the output speed of the PMSM and achieved the purpose of weakening the chattering. [ABSTRACT FROM AUTHOR]

Background: Congenital tibial hemimelia (CTH [MIM: 275220]) is a rare congenital limb deficiency that manifests as a shortened, curved, dysplastic or absent tibia with polydactyly. In previous studies, mutations of a distant sonic hedgehog (SHH) cis-regulator (ZRS) and a Shh repressor (GLI3) were identified. Case presentation: Here, we admitted a 20-month-old boy who manifested with right tibial deformity, varus foot, ankle dislocation, and ipsilateral preaxial polydactyly. After genetic sequencing and data analysis, the results revealed a 443 A > G mutation in the father and a 536 C > T mutation in the mother in exon 2 of the Smoothed (SMO) gene at 7q32.1, with the coexistence of both mutant alleles in the proband/patient. Conclusions: Our report suggests that even though not previously reported, SMO mutations may be associated with limb anomalies such as tibial hemimelia via Hh signaling in humans and has implications for genetic counseling. [ABSTRACT FROM AUTHOR]

Sequential minimal optimization (SMO) method is an algorithm for solving optimization problems arising from the training process of support vector machines (SVM). The SMO algorithm is mainly used to solve the optimization problem of the objective function of SVM, and it can have high accuracy. However, its optimization accuracy can be improved. Fractional order calculus is an extension of integer order calculus, which can more accurately describe the actual system and get more accurate results. In this paper, the fractional order sequential minimal optimization (FOSMO) method is proposed based on the SMO method and fractional order calculus for classification. Firstly, an objective function is expressed by a fractional order function using the FOSMO method. The representation and meaning of fractional order terms in the objective function are studied. Then the fractional derivative of Lagrange multipliers is obtained according to fractional order calculus. Lastly, the objective function is optimized based on fractional order Lagrange multipliers, and then some experiments are carried out on the linear and nonlinear classification cases. Some experiments are carried out on two-classification and multi-classification situations, and experimental results show that the FOSMO method can obtain better accuracy than the normal SMO method. [ABSTRACT FROM AUTHOR]

The article examines the political processes in Slovakia in 2022. It is noted that the past year has demonstrated the fragility and weakness of the ruling coalition, which, following a lengthy debate, was forced to resign. The author notes that the central reason for the collapse of the Cabinet was the personal hostility of the country’s leading politicians – I. Matovič and R. Sulik. However, they demonstrated a keen desire to delay the collapse of the coalition, aggravating the internal political situation in the country as much as possible, since they realized the impossibility of participating in the formation of the next government following the results of early elections. The paper notes significant electoral changes in Slovakia since 2020: there has been a total decline in the popularity of the parties that formed the Cabinet, and an increase in the authority of the forces in opposition and promising the return of stable times. Meanwhile, early elections scheduled for September 30, 2023 do not yet guarantee the opposition a victory. The Slovak political tradition and the experience of neighboring Central European countries point to the possibility of liberal politicians uniting in various political alliances to prevent their ideological opponents from gaining power

The human population is growing at an extremely rapid rate, the demand of food supplies for the survival and sustainability of life is a gleaming challenge. Each living being in the planet gets bestowed with the healthy food to remain active and healthy. Agriculture is a domain which is extremely important as it provides the fundamental resources for survival in terms of supplying food and thus the economy of the entire world is highly dependent on agricultural production. The agricultural production is often affected by various environmental and geographical factors which are difficult to avoid being part of nature. Thus, it requires proactive mitigation plans to reduce any detrimental effect caused by the imbalance of these factors. Precision agriculture is an approach that incorporates information technology in agriculture management, the needs of crops and farming fields are fulfilled to optimized crop health and resultant crop production. The proposed study involves an ambient intelligence-based implementation using machine learning to classify diseases in tomato plants based on the images of its leaf dataset. To analytically evaluate the performance of the framework, a publicly available plant-village dataset is used which is transformed to appropriate form using one-hot encoding technique to meet the needs of the machine learning algorithm. The transformed data is dimensionally reduced by Principal Component Analysis (PCA) technique and further the optimal parameters are selected using Spider Monkey Optimization (SMO) approach. The most relevant features as selected using the Hybrid PCA-SMO technique fed into a Deep Neural Networks (DNN) model to classify the tomato diseases. The optimal performance of the DNN model after implementing dimensionality reduction by Hybrid PCA-SMO technique reached at 99% accuracy was achieved in training and 94% accuracy was achieved after testing the model for 20 epochs. The proposed model is evaluated based on accuracy and loss rate metrics; it justifies the superiority of the approach.

Back pain is an issue affecting millions of people throughout the world. Research on back pain root cause detection is immense. The lumbar spine is a lower back region of the backbone that could also be responsible for back pain. Research on issues related to lumbar spine disease (LSD) is limited. The lumbar spine is critical for the human body as it supports the weight of the body. Many diseases can affect the lumbar spine adversely. This research is directed towards the detection of LSDs using optimized feature extraction and selection phases. Furthermore, the linearity-based model is used for feature selection, selecting only the best possible features to reduce the missed classification degree. The flow of the proposed research work is divided into phases. In the first phase, data collection is performed. Data collection in the proposed work includes both real-time and benchmark magnetic resonance imaging (MRI) datasets. The MRI dataset collected from the hospital is validated by medical experts. After data collection, pre-processing is applied. This step removes the noise from the collected dataset. The pre-processing mechanism is performed using histogram equalization, median filtering, validation, and normalization. After the pre-processing phase, background subtraction and region of interest (ROI) detection are performed using the region-cut mechanism. Optimal feature extraction is achieved using a differential spider monkey optimization (SMO), and feature selection is performed using a linearity-based convolutional neural network (CNN) model. In the end, ensemble-based classification is used for disease prediction. The validation of the result is conducted through classification accuracy, specificity, sensitivity, and F-Score. The high classification accuracy of 96% is achieved with multi support vector machine (MSVM), 94% with random forest (RF), 93.5% with a decision tree (DT), and 91% with the Naïve Bayes (NB) approach, proving the validity of the proposed approach.

Featured Application: The proposed solution belongs to the group of fault-tolerant control algorithms that are used in industrial drives. Systems of this type are particularly applicable in industries where an increased level of safety is required, such as electric vehicle drive systems, including electric and hybrid aircraft drive systems. This paper presents a novel speed sensor fault detection, classification, and compensation mechanism in a permanent magnet synchronous motor (PMSM) drive system. Application is based on state variable observers and shallow neural networks (NN). Classical fault detection mechanism based on state variable observers has been extended with neural networks. This enables improved detection efficiency and increases immunity to false alarms. In addition, the use of neural networks allowed for the classification of the failure type. Three types of failures are considered in the paper: signal loss, scaling error, and signal interference. The detection efficiency of the proposed solution is about 97%. On the other hand, the classification of the worst type of failure—signal loss—was achieved at the level of 100%. Other considered failure types are classified at the level of 80–90%. In addition, tests were carried out for two types of observers—model reference adaptive system and sliding mode observer—to compare the results. The work presents experimental results carried out for various operating conditions of the drive system. The failure classification times in the experimental tests were achieved at a level of less than 30 ms. [ABSTRACT FROM AUTHOR]

The article examines the experience of the Czech Republic in overcoming the migration crisis in 2022, which was caused by a special military operation in Ukraine. Despite the traditional ignorance of the problems of refugees from the Middle East and Africa, authorities, supported by the local population and non-profit organizations, were ready to provide large-scale assistance to Ukrainian refugees. The paper studies the main steps taken by P. Fiala’s Cabinet to support internally displaced persons. It is shown that measures were promptly taken to simplify the entry of Ukrainians into the labor market of the Czechia, access for children to educational institutions was opened, and temporary housing was provided. Amendments to the Czech legislation in the relevant areas of law and the weakening of the flow of refugees, already in the summer of 2022, allowed the Czechia to relatively easily go through the ordeal of the migration crisis, without using the country's full potential. At the same time, the study shows that the initial enthusiasm of the Czechs disappeared, and discussions began to arise about the formation of a Ukrainian minority in the Czechia and endowing it with political subjectivity. In turn, public opinion polls clearly showed that ordinary citizens were against the long stay of Ukrainians in the country.

Simple Summary: In the first sections of this review, we provide a comprehensive description of the Hedgehog signalling pathway in mammals and the main general models of pathway activation. Subsequently, the review focuses on the oncogenic role played by this pathway in rhabdomyosarcoma and the inhibitors developed to date, as well as the clinical trials available in sarcomas. Finally, we provide a discussion and critical review of the results obtained in the clinical setting and their strong dependency on the type of tumour. In some cases, strong discrepancies between encouraging preclinical data and clinical trial results are clearly evident. Aberrant activation of the Hedgehog (Hh) signalling pathway is known to play an oncogenic role in a wide range of cancers; in the particular case of rhabdomyosarcoma, this pathway has been demonstrated to be an important player for both oncogenesis and cancer progression. In this review, after a brief description of the pathway and the characteristics of its molecular components, we describe, in detail, the main activation mechanisms that have been found in cancer, including ligand-dependent, ligand-independent and non-canonical activation. In this context, the most studied inhibitors, i.e., SMO inhibitors, have shown encouraging results for the treatment of basal cell carcinoma and medulloblastoma, both tumour types often associated with mutations that lead to the activation of the pathway. Conversely, SMO inhibitors have not fulfilled expectations in tumours—among them sarcomas—mostly associated with ligand-dependent Hh pathway activation. Despite the controversy existing regarding the results obtained with SMO inhibitors in these types of tumours, several compounds have been (or are currently being) evaluated in sarcoma patients. Finally, we discuss some of the reasons that could explain why, in some cases, encouraging preclinical data turned into disappointing results in the clinical setting. [ABSTRACT FROM AUTHOR]

Hedgehog (Hh) signaling pathway plays a pivotal role in embryonic development. Hh binding to Patched1 (PTCH1) derepresses Smoothened (SMO), thereby activating the downstream signal transduction. Covalent SMO modification by cholesterol in its cysteine‐rich domain (CRD) is essential for SMO function. SMO cholesterylation is a calcium‐accelerated autoprocessing reaction, and STIM1‐ORAI1‐mediated store‐operated calcium entry promotes cholesterylation and activation of endosome‐localized SMO. However, it is unknown whether the Hh‐PTCH1 interplay regulates the activity of the endoplasmic reticulum (ER)‐localized SMO. Here, we found that PTCH1 inhibited the COPII‐dependent export of SMO from the ER, whereas Hh promoted this process. The RRxWxR amino acid motif in the cytosolic tail of SMO was essential for COPII recognition, ciliary localization, and signal transduction activity. Hh and PTCH1 regulated cholesterol modification of the ER‐localized SMO, and SMO cholesterylation accelerated its exit from ER. The GRAMD1/ASTER sterol transport proteins facilitated cholesterol transfer to ER from PM, resulting in increased SMO cholesterylation and enhanced Hh signaling. Collectively, we reveal a regulatory role of GRAMD‐mediated cholesterol transport in ER‐resident SMO maturation and Hh signaling. Synopsis: Cholesterol modification of Smoothened (SMO) is essential for Hedgehog signaling pathway activation. Here, cholesterol transport proteins GRAMD1s/ASTERs are shown to promote SMO cholesterylation in the endoplasmic reticulum (ER), thus accelerating its ER exit. SMO export from the ER is increased by Hedgehog and decreased by Patched1.The RRxWxR motif in the cytosolic tail of SMO is essential for its COPII recognition, ER exit, and ciliary localization.SMO cholesterylation promotes its export from the ER.GRAMD1/ASTER proteins transfer cholesterol from plasma membrane to ER and promote SMO cholesterylation. [ABSTRACT FROM AUTHOR]

This research proposes a more advanced way to address Combined Economic Emission Dispatch (CEED) concerns. Economic Load Dispatch (ELD) and Economic Emission Dispatch (EED) have been implemented to reduce generating unit fuel costs and emissions. When both economics and emission targets are taken into account, the dispatch of an aggregate cost-effective emission challenge emerges. This research affords a mathematical modeling-based analytical technique for solving economic, emission, and collaborative economic and emission dispatch problems with only one goal. This study takes into account both the fuel cost target and the environmental impact of emissions. This bi-intention CEED problem is converted to a solitary goal function using a price penalty factor technique. In this case, a metaheuristic and an environment-inspired, intelligent Spider Monkey Optimization technique (SMO) are used to address the CEED dilemma. By following the generator’s scheduling process, the SMO method is used to regulate the output from the power generation system in terms of pollution and fuel cost. The Fission-Fusion social (FFS) structure of spider monkeys promotes them to utilize a global optimization method known as SMO during foraging behaviour. The emphasis is mostly on lowering the cost of generation and pollution in order to improve the efficiency of the power system and handle dispatch problems with constraints. The economic dispatch has been remedied, and the improved result demonstrates that the system’s performance is stable and flexible in real time. Finally, the system’s output demonstrates that the system has improved in resolving CEED difficulties. When compared to earlier investigations, the proposed model’s findings have improved. As the generating units, wind and solar are used to explore the CEED crisis in the IEEE 30 bus system. [ABSTRACT FROM AUTHOR]

Background and Purpose: Liver fibrosis is a serious cause of morbidity and mortality worldwide characterized by accumulation of extracellular matrix produced by hepatic stellate cells (HSCs). The protein kinase CK2 is a pro‐survival kinase overexpressed in human tumours. However, the biological role of CK2 in liver fibrosis is largely unknown. We aimed to investigate the mechanism by which CK2 promotes liver fibrosis. Experimental Approach: In vitro, LX‐2 cells were stimulated with transforming growth factor‐β (TGF‐β). HSCs were also isolated for research. In vivo, the adeno‐associated virus AAV‐sh‐csnk2a1 was used to knockdown CK2α specifically in HSCs, and CX‐4945 was used to pharmacologically inhibit the enzymatic activity of CK2 in murine models of fibrosis induced by carbon tetrachloride (CCl4) and a 3,5‐diethoxycarbonyl‐1,4‐dihydrocollidine (DDC) diet. Histological and biochemical analyses were performed to study the involvement of CK2 in regulation of fibrogenic and fibrolytic factors as well as activation properties of HSCs. Key Results: HSC‐specific genetic invalidation of CK2α or pharmacological inhibition of CK2 protected mice treated with CCl4 or fed a DDC diet against liver fibrosis and HSC accumulation. Mechanistically, CK2α, which bound to Smoothened (SMO), was a positive regulator of the Hedgehog signal transduction pathway. CK2 prevented ubiquitination and proteasomal degradation of SMO, which was abolished by knockdown of CK2α or pharmacological inhibition of CK2. Conclusions and Implications: CK2 activation is critical to sustain the activated and fibrogenic phenotype of HSCs via SMO stabilization. Therefore, inactivation of CK2 by CX‐4945 may be of therapeutic interest for liver fibrotic diseases. [ABSTRACT FROM AUTHOR]

Applications for the Internet of Things (IoT) have been employed in a broad range of industries, including smart homes, healthcare, smart energy, and Industrial 4.0. IoT offers many advantages, such as ease and efficiency, but it also adds a lot of new risks. The problem is often made worse by the potential number of linked IoT devices and the ad hoc nature of such networks. IoT management now faces substantial difficulties related to safety and confidentiality. Current research has shown that deep learning algorithms are quite effective and have numerous benefits over previous approaches for doing security assessments of IoT devices. An integrated deep learning approach is created in this work to identify files containing malware and pirated software through the Internet of Things network The Hybrid Dual-Channel Convolution Neural Network (DCCNN) with Spider Monkey Optimization (SMO) is called DCCNN-SMO advocated leveraging stolen reference code to detect software piracy. For the purpose of examining software piracy, the dataset was gathered via Google Code Jam (GCJ). To research the suggested strategy, collected 100 input data for internet users from GCJ. In addition, the DCCNN-SMO is utilized to visualize colored images to identify harmful intrusions in IoT networks. From the Leopard Mobile database, malware samples were taken for testing. The actual results demonstrate that the recommended method for calculating the cyber security risks in IoT performs categorization more over current approaches. The proposed DCCNN-SMO + SVM approach provides better resultant value of 98.55% whereas the other existing approaches like GIST + SVM is 86.1%, CLGM + SVM is 92.06% and DNN + SVM is 97.46%, LBP + SVM is 78.05%.

The [Formula: see text]-insensitive-Least Squares Support Vector Regression ([Formula: see text]-LSSVR) optimization problem with [Formula: see text]-insensitive quadratic loss function is a non-smooth, piecewise quadratic complex problem. Therefore, the optimal solution of [Formula: see text]-LSSVR takes longer than Classic LSSVR. Additionally, due to its unbounded influence function, [Formula: see text]-LSSVR, like classical LSSVR, shows poor generalization ability in the presence of outliers. To overcome the above-mentioned drawbacks, this paper presents an [Formula: see text]-insensitive Weighted Least Squares Support Vector Regression ([Formula: see text]-WLSSVR) with equality constraints to increase robustness against outliers. To reduce the computational complexity and time, the optimization problem of [Formula: see text]-WLSSVR is solved by the Sequential Minimal Optimization (SMO) algorithm. Extensive simulations have shown that the presented [Formula: see text]-WLSSVR model combined with the SMO algorithm is more effective and efficient than LSSVR and its known sparse variants.

Least squares twin support vector classification for K-class (LST-KSVC) [1] is an efficient multiclass classifier that incorporates least squares strategy into twin support vector classification for K-class (Twin-KSVC) [2]. Because of its excellent classification performance, LST-KSVC has been applied in many fields. However, the LST-KSVC has some drawbacks: (1) It only implements empirical risk minimization (ERM), which reduces its generalization performance. (2) It is sensitive to noise and outliers. (3) The inverse matrices need to be calculated, which is impossible for many large-scale engineering problems. (4) For the nonlinear case, the LST-KSVC needs to reconstruct primal problems using the approximate kernel-generated surface (AKGS) and does not directly use kernel tricks as in the support vector machine (SVM) [3]. To address these shortcomings, an improved LST-KSVC based on energy model, which is called ELST-KSVC, is proposed in this paper. First, a regularization term is introduced into LST-KSVC to implement structural risk minimization (SRM). Second, energy parameters are introduced into LST-KSVC to reduce the effect of noise and outliers. Third, the dual problems are reconstructed to avoid inverse matrices. Furthermore, the sequential minimal optimization (SMO) algorithm is used to efficiently train subclassifiers. Finally, ELST-KSVC can directly use kernel tricks for nonlinear cases. Experimental results show that the ELST-KSVC has better generalization performance and higher learning speed. [ABSTRACT FROM AUTHOR]

Background: The emergence of immune checkpoint inhibitors (ICIs) is one of the most promising breakthroughs for the treatment of multiple cancer types, but responses vary. Growing evidence points to a link between developmental signaling pathway-related genes and antitumor immunity, but the association between the genomic alterations in these genes and the response to ICIs still needs to be elucidated. Methods: Clinical data and sequencing data from published studies and our cohort were collected to analyze the association of the mutation status of SMO with the efficacy of ICI therapy in the non-small cell lung cancer (NSCLC) cohort and the pan-cancer cohort. Furthermore, the correlation between SMO mutation and immunotherapeutic biomarkers such as immune cell infiltration, immune-related genes, and underlying signaling pathways was analyzed. Three SMO mutant plasmids were transfected into cells to explore the SMO mutation status in the context of its expression and cell growth. Result: In the NSCLC discovery cohort, the median progression-free survival in the SMO mutant (SMO_MUT) was longer than that in the wild type (SMO_WT) (23.0 vs. 3.8 months, adjusted p = 0.041). This finding was further confirmed in the NSCLC validation cohort (8.7 vs. 5.1 months, adjusted p = 0.013). In the pancancer cohort (n = 1,347), a significant overall survival advantage was observed in patients with SMO mutations [not reached (NR) vs. 18 months, adjusted p = 0.024]. In the subgroup analysis, the survival advantage of SMO_MUT against SMO_WT was prominent and consistent across genders, ages, treatment types, cancer types, and the tumor mutation burden (TMB) status (all pinteraction > 0.05). In an in vitro experiment, we found that both the mutant and wild-type plasmids can promote the expression of SMO, but the mutant plasmid had lower SMO mRNA and protein levels than the wild type. In CCK-8 experiments, we found that SMO_MUT plasmids can improve the growth of Calu-1 and PC-9 cells, but this capability varied between different mutations and cells. Upon further exploration, the SMO mutation status was found to be related to a higher TMB, more neoantigen load, more DNA damage repair (DDR) mutations, higher microsatellite instability (MSI) score, and higher CD8+ T-cell infiltration. Conclusions: The SMO mutation status is an independent prognostic factor that can be used to predict better clinical outcomes of ICI treatment across multiple cancer types. [ABSTRACT FROM AUTHOR]

Basal cell carcinoma (BCC) is one of the most common neoplasms in the population. A good prognosis and mainly non-aggressive development have made it underdiagnosed and excluded from the statistics. Due to the availability of efficient surgical therapy, BCC is sometimes overlooked in the search for novel therapies. Most clinicians are unaware of its complicated pathogenesis or the availability of effective targeted therapy based on Hedgehog inhibitors (HHI) used in advanced or metastatic cases. Nevertheless, the concomitance and esthetic burden of this neoplasm are severe. As with other cancers, its pathogenesis is multifactorial and complicated with a network of dependencies. Although the tumour microenvironment (TME), genetic aberrations, and risk factors seem crucial in all skin cancers, in BCC they all have become accessible as therapeutic or prevention targets. The results of this review indicate that a central role in the development of BCC is played by the Hedgehog (Hh) signalling pathway. Two signalling molecules have been identified as the main culprits, namely Patched homologue 1 (PTCH1) and, less often, Smoothened homologue (SMO). Considering effective immunotherapy for other neoplastic growths being introduced, implementing immunotherapy in advanced BCC is pivotal and beneficial. Up to now, the US Food and Drug Administration (FDA) has approved two inhibitors of SMO for the treatment of advanced BCC. Sonidegib and vismodegib are registered based on their efficacy in clinical trials. However, despite this success, limitations might occur during the therapy, as some patients show resistance to these molecules. This review aims to summarize novel options of targeted therapies in BCC and debate the mechanisms and clinical implications of tumor resistance. [ABSTRACT FROM AUTHOR]

Efficacy of any rotor position and speed estimation scheme in real-life hardware environment is largely depended on the effect of non-ideal physical attributes on that estimation scheme. Hybrid estimation schemes are used for speed and position estimation of PM synchronous motor for standstill to rated speed operation. The existing hybrid estimation schemes are developed without considering the effect of non-ideal physical attributes. As a result, their performance in real-life hardware environment cannot be assured, specially during low-speed operation as during this operating zone the effect on non-ideal physical attributes is more detrimental. In this work, a hybrid estimation scheme is developed by combining a "quadrature-phase lock-loop-based sliding mode observer" and "pulsating high-frequency signal injection-based technique" for sensorless vector control PM synchronous motor considering the effect of non-ideal physical attributes. Performance of proposed drive is evaluated by simulation and experimental study. [ABSTRACT FROM AUTHOR]

In this study, direct torque control (DTC) and field oriented control (FOC) simulation study of a speed sensorless permanent magnet synchronous motor (PMSM) is carried out. The speed signal required for the control methods was obtained with a sliding mode observer (SMO). As a result of the study, current, speed (measured and estimated) and torque signals of PMSM were obtained. Total harmonic distortion (THD) and discrete wavelet transform (DWT) analysis of the obtained signals were performed to evaluate the performance of DTC and FOC. In addition, DTC and FOC comparisons were made using the root mean square error (RMSE) criterion for the measured and estimated speed signals. In this study, it was determined that the DTC and FOC methods are superior and deficient compared to each other. [ABSTRACT FROM AUTHOR]

Sequential minimal optimization (SMO) method is an algorithm for solving optimization problems arising from the training process of support vector machines (SVM). The SMO algorithm is mainly used to solve the optimization problem of the objective function of SVM, and it can have high accuracy. However, its optimization accuracy can be improved. Fractional order calculus is an extension of integer order calculus, which can more accurately describe the actual system and get more accurate results. In this paper, the fractional order sequential minimal optimization (FOSMO) method is proposed based on the SMO method and fractional order calculus for classification. Firstly, an objective function is expressed by a fractional order function using the FOSMO method. The representation and meaning of fractional order terms in the objective function are studied. Then the fractional derivative of Lagrange multipliers is obtained according to fractional order calculus. Lastly, the objective function is optimized based on fractional order Lagrange multipliers, and then some experiments are carried out on the linear and nonlinear classification cases. Some experiments are carried out on two-classification and multi-classification situations, and experimental results show that the FOSMO method can obtain better accuracy than the normal SMO method.

Abstract Introduction The left-sided position of the mouth in amphioxus larvae has fascinated researchers for a long time. Despite the fundamental importance of mouth development in the amphioxus, the molecular regulation of its development is almost unknown. In our previous study, we showed that Hh mutation in the amphioxus leads to no mouth opening, indicating a requirement of Hh signaling for amphioxus mouth formation. Nevertheless, since the Hh mutant also exhibits defects in early left-right (LR) patterning, it remains currently unknown whether the loss of mouth opening is affected directly by Hh deficiency or a secondary effect of its influence on LR establishment. Results We demonstrated that knockout of the Smo gene, another key component of the Hh signaling pathway, in the amphioxus resulted in the absence of mouth opening, but caused no effects on LR asymmetry development. Upregulation of Hh signaling led to a dramatic increase in mouth size. The inability of Smo mutation to affect LR development is due to Smo’s high maternal expression in amphioxus eggs and cleavage-stage embryos. In Smo mutants, Pou4 and Pax2/5/8 expression at the primordial oral site is not altered before mouth opening. Conclusions Based on these results and our previous study, we conclude that Hh signal is necessary for amphioxus mouth formation and that the Hh-mediated regulation of mouth development is specific to the mouth. Our data suggest that Hh signaling regulates mouth formation in the amphioxus in a similar way as that in vertebrates, indicating the conserved role of Hh signaling in mouth formation.

Although 5-methylcytosine (m5C) has been identified as a novel and abundant mRNA modification and associated with energy metabolism, its regulation function in adipose tissue and skeletal muscle is still limited. This study aimed at investigating the effect of mRNA m5C on adipogenesis and myogenesis using Jinhua pigs (J), Yorkshire pigs (Y) and their hybrids Yorkshire–Jinhua pigs (YJ). We found that Y grow faster than J and YJ, while fatness-related characteristics observed in Y were lower than those of J and YJ. Besides, total mRNA m5C levels and expression rates of NSUN2 were higher both in backfat layer (BL) and longissimus dorsi muscle (LDM) of Y compared to J and YJ, suggesting that higher mRNA m5C levels positively correlate with lower fat and higher muscle mass. RNA bisulfite sequencing profiling of m5C revealed tissue-specific and dynamic features in pigs. Functionally, hyper-methylated m5C-containing genes were enriched in pathways linked to impaired adipogenesis and enhanced myogenesis. In in vitro, m5C inhibited lipid accumulation and promoted myogenic differentiation. Furthermore, YBX2 and SMO were identified as m5C targets. Mechanistically, YBX2 and SMO mRNAs with m5C modification were recognized and exported into the cytoplasm from the nucleus by ALYREF, thus leading to increased YBX2 and SMO protein expression and thereby inhibiting adipogenesis and promoting myogenesis, respectively. Our work uncovered the critical role of mRNA m5C in regulating adipogenesis and myogenesis via ALYREF-m5C-YBX2 and ALYREF-m5C-SMO manners, providing a potential therapeutic target in the prevention and treatment of obesity, skeletal muscle dysfunction and metabolic disorder diseases. [ABSTRACT FROM AUTHOR]

Aims: Sonic hedgehog subtype medulloblastoma is featured with overactivation of hedgehog pathway and can be targeted by SMO‐specific inhibitors. However, the resistance is frequently developed leading to treatment failure of SMO inhibitors. W535L mutation of SMO (SMOW535L) is thought to be an oncogenic driver for Sonic hedgehog subtype MB and confer resistance to SMO inhibitors. The regulation network of SMOW535L remains to be explored in comparison with wild‐type SMO (SMOWT). Methods: In this study, we profiled transcriptomes, methylomes, and interactomes of MB cells expression SMOWT or SMOW535L in the treatment of DMSO or SMO inhibitor, respectively. Results: Analysis of transcriptomic data indicated that SMO inhibitor disrupted processes of endocytosis and cilium organization in MB cells with SMOWT, which are necessary for SMO activation. In MB cells with SMOW535L, however, SMO inhibitor did not affect the two processes‐related genes, implying resistance of SMOW535L toward SMO inhibitor. Moreover, we noticed that SMO inhibitor significantly inhibited metabolism‐related pathways. Our metabolic analysis indicated that nicotinate and nicotinamide metabolism, glycerolipid metabolism, beta‐alanine metabolism, and synthesis and degradation of ketone bodies might be involved in SMOW535L function maintenance. Interactomic analysis revealed casein kinase II (CK2) as an important SMO‐associated protein. Finally, we linked CK2 and AKT together and found combination of inhibitors targeting CK2 and AKT showed synergetic effects to inhibit the growth of MB cells with SMO constitutive activation mutation. Conclusions: Taken together, our work described SMO‐related transcriptomes, metabolomes, and interactomes under different SMO status and treatment conditions, identifying CK2 and AKT as therapeutic targets for SHH‐subtype MB cells with SMO inhibitor resistance. [ABSTRACT FROM AUTHOR]

This paper presents a new grid integration control scheme that employs spider monkey optimization technique for maximum power point tracking and Lattice Levenberg Marquardt Recursive estimation with a hysteresis current controller for controlling voltage source inverter. This control scheme is applied to a PV system integrated to a three phase grid to achieve effective grid synchronization. To verify the efficacy of the proposed control scheme, simulations were performed. From the simulation results it is observed that the proposed controller provides excellent control performance such as reducing THD of the grid current to 1.75%.

Hedgehog (Hh) signaling pathway is an evolutionarily conserved signal transduction pathway that plays an important regulatory role during embryonic development, cell proliferation, and differentiation of vertebrates, and it is often inhibited in adult tissues. Recent evidence has shown that Hh signaling also plays a key role in rheumatic diseases, as alterations in their number or function have been identified in rheumatoid arthritis, osteoarthritis, ankylosing spondylitis, systemic sclerosis, and Sjogren’s Syndrome. As a result, emerging studies have focused on the blockade of this pathogenic axis as a promising therapeutic target in several autoimmune disorders; nevertheless, a greater understanding of its contribution still requires further investigation. This review aims to elucidate the most recent studies and literature data on the pathogenetic role of Hh signaling in rheumatic diseases.

Basal cell carcinoma (BCC) is the most common malignant tumor of the skin. Despite the indolent nature, metastatic BCC can occur, albeit rarely. Metastasis to the bone are very rare. From its approval, mBCC patients are treated with vismodegib, a selective hedgehog pathway inhibitor. Unfortunately, in recent period, it was demonstrated an emergence of drug resistance, due to Smoothened (SMO) mutation. To date, several groups are studying the effectiveness of immunotherapy in BCC. Clinical trials with Immune Checkpoint Inhibitors are ongoing. We report the rare case of a man with multiple bony metastasis, with a resistance to vismodegib, and we evaluated all manuscripts in literature reporting bone metastasis. Moreover, we review all the manuscripts in literature reporting bone metastasis, and we summarize the main therapeutic strategies, and the further perspectives.

This paper presents a novel speed sensor fault detection, classification, and compensation mechanism in a permanent magnet synchronous motor (PMSM) drive system. Application is based on state variable observers and shallow neural networks (NN). Classical fault detection mechanism based on state variable observers has been extended with neural networks. This enables improved detection efficiency and increases immunity to false alarms. In addition, the use of neural networks allowed for the classification of the failure type. Three types of failures are considered in the paper: signal loss, scaling error, and signal interference. The detection efficiency of the proposed solution is about 97%. On the other hand, the classification of the worst type of failure—signal loss—was achieved at the level of 100%. Other considered failure types are classified at the level of 80–90%. In addition, tests were carried out for two types of observers—model reference adaptive system and sliding mode observer—to compare the results. The work presents experimental results carried out for various operating conditions of the drive system. The failure classification times in the experimental tests were achieved at a level of less than 30 ms.

Simple Summary: Meningiomas are the most frequent intracranial tumors and comprise a heterogeneous spectrum of diseases, ranging from small, asymptomatic tumors that do not need treatment to large, symptomatic ones causing seizures or neurological deficits that require surgery and/or radiotherapy. Systemic therapy is reserved for progressive or recurrent meningiomas when surgery and/or radiotherapy options have been exhausted, with only modest activity in terms of disease control and survival. Novel molecular alterations are correlated with grading, location, and prognosis of meningiomas. Moreover, some of these driver alterations regulate meningioma growth and progression and may be targeted by specific drugs that are under investigation in clinical trials. Lastly, the microenvironment surrounding meningiomas may also contribute to regulating tumor growth: in particular, PD-L1 and/or M2 macrophage expression may represent a target for immunotherapy. Meningiomas are common intracranial tumors that can be treated successfully in most cases with surgical resection and/or adjuvant radiotherapy. However, approximately 20% of patients show an aggressive clinical course with tumor recurrence or progressive disease, resulting in significant morbidity and increased mortality. Despite several studies that have investigated different cytotoxic agents in aggressive meningiomas in the past several years, limited evidence of efficacy and clinical benefit has been reported thus far. Novel molecular alterations have been linked to a particular clinicopathological phenotype and have been correlated with grading, location, and prognosis of meningiomas. In this regard, SMO, AKT, and PIK3CA mutations are typical of anterior skull base meningiomas, whereas KLF4 mutations are specific for secretory histology, and BAP1 alterations are common in progressive rhabdoid meningiomas. Alterations in TERT, DMD, and BAP1 correlate with poor outcomes. Moreover, some actionable mutations, including SMO, AKT1, and PIK3CA, regulate meningioma growth and are under investigation in clinical trials. PD-L1 and/or M2 macrophage expression in the microenvironment provides evidence for the investigation of immunotherapy in progressive meningiomas. [ABSTRACT FROM AUTHOR]

Hedgehog (Hh) signaling is evolutionarily conserved and plays an instructional role in embryonic morphogenesis, organogenesis in various animals, and the central nervous system organization. Multiple feedback mechanisms dynamically regulate this pathway in a spatiotemporal and context-dependent manner to confer differential patterns in cell fate determination. Hh signaling is complex due to canonical and non-canonical mechanisms coordinating cell–cell communication. In addition, studies have demonstrated a regulatory framework of Hh signaling and shown that cholesterol is vital for Hh ligand biogenesis, signal generation, and transduction from the cell surface to intracellular space. Studies have shown the importance of a specific cholesterol pool, termed accessible cholesterol, which serves as a second messenger, conveying signals between smoothened (Smo) and patched 1 (Ptch1) across the plasma and ciliary membranes. Remarkably, recent high-resolution structural and molecular studies shed new light on the interplay between Hh signaling and cholesterol in membrane biology. These studies elucidated novel mechanistic insight into the release and dispersal of cholesterol-anchored Hh and the basis of Hh recognition by Ptch1. Additionally, the putative model of Smo activation by cholesterol binding and/or modification and Ptch1 antagonization of Smo has been explicated. However, the coupling mechanism of Hh signaling and cholesterol offered a new regulatory principle in cell biology: how effector molecules of the Hh signal network react to and remodel cholesterol accessibility in the membrane and selectively activate Hh signaling proteins thereof. Recognizing the biological importance of cholesterol in Hh signaling activation and transduction opens the door for translational research to develop novel therapeutic strategies. This review looks in-depth at canonical and non-canonical Hh signaling and the distinct proposed model of cholesterol-mediated regulation of Hh signaling components, facilitating a more sophisticated understanding of the Hh signal network and cholesterol biology. [ABSTRACT FROM AUTHOR]

Abstract Background Cancer is one of the major causes of death worldwide. To treat cancer, the use of anticancer peptides (ACPs) has attracted increased attention in recent years. ACPs are a unique group of small molecules that can target and kill cancer cells fast and directly. However, identifying ACPs by wet-lab experiments is time-consuming and labor-intensive. Therefore, it is significant to develop computational tools for ACPs prediction. Though some ACP prediction tools have been developed recently, their performances are not well enough and most of them do not offer a function to distinguish ACPs from antimicrobial peptides (AMPs). Considering the fact that a growing number of studies have shown that some AMPs exhibit anticancer function, this work tries to build a model for distinguishing AMPs from ACPs in addition to a model that predicts ACPs from whole peptides. Results This study chooses amino acid composition, N5C5, k-space, position-specific scoring matrix (PSSM) as features, and analyzes them by machine learning methods, including support vector machine (SVM) and sequential minimal optimization (SMO) to build a model (model 2) for distinguishing ACPs from whole peptides. Another model (model 1) that distinguishes ACPs from AMPs is also developed. Comparing to previous models, models developed in this research show better performance (accuracy: 85.5% for model 1 and 95.2% for model 2). Conclusions This work utilizes a new feature, PSSM, which contributes to better performance than other features. In addition to SVM, SMO is used in this research for optimizing SVM and the SMO-optimized models show better performance than non-optimized models. Last but not least, this work provides two different functions, including distinguishing ACPs from AMPs and distinguishing ACPs from all peptides. The second SMO-optimized model, which utilizes PSSM as a feature, performs better than all other existing tools.

The purpose of this study was to investigate the effects of BOC on glioblastoma cells and its underlying mechanisms. In vitro , BOC-knockdown was performed in glioma cell lines. CCK-8 and Transwell were used to assess the impact of BOC on the viability, invasion, and migration of gliobma cells. RNA-seq technology was employed to analyze the differential gene expression between BOC-knockdown glioma cells and the control group, and qRT-PCR was used to validate the expression of downstream differential genes. SMO-overexpression was performed to investigate the effects of SMO on glioma cells. A BOC-knockdown mouse subcutaneous tumor model was to verify the effects of BOC on mouse tumors. Tissue microarray technology was used to detect the expression of BOC and SMO in samples of normal human brain tissue and glioma tissue. In vitro , BOC-knockdown inhibited the viability, invasion, and migration of glioma cells, as well as downregulated the expression of downstream differential genes SMO, EGFR, HRAS, and MRAS. Conversely, SMO-overexpression upregulated the viability, invasion, and migration abilities of BOC-knockdown cells. In vivo , BOC-knockdown suppressed tumor growth in mice and downregulated the expression of downstream differential genes SMO, EGFR, HRAS, and MRAS. Tissue microarray results showed that both BOC and SMO were highly expressed in glioma tissues. BOC is aberrantly overexpressed in glioma patients and promotes glioma development. Mechanistically, BOC activates the Hedgehog (Hh) and RAS signaling pathways by upregulating the expression of SMO, EGFR, HRAS, and MRAS, thereby facilitating the Proliferation, invasion and migration of glioma cells. • BOC is highly expressed in glioblastoma tissue. • BOC promotes the development of glioblastoma. • BOC targets SMO to regulate the Hedgehog pathway. [ABSTRACT FROM AUTHOR]

For the problem of multiple sensor faults in the automated guided forklift (AGF), an equivalent model with system faults and disturbances is established. To detect multiple sensor faults in the AGF, a sliding mode observer (SMO) is proposed. It introduces a fault estimation algorithm that is designed by the fault residual and the residual is only sensitive to sensor faults which means that the SMO is robust to unknown input disturbances. On the other side, it can also judge the faulty sensor according to the feature vector of different sensors. To judge the type of sensor faults accurately, a mathematical model of sensor fault characteristics is established and it can provide a foundation for choosing appropriate fault-tolerant output compensation measures. Then an active fault-tolerant control method based on state feedback is proposed. It can restore the control system to normal and maintain the stability of the control system. Finally, experiments are given to verify the effectiveness of the proposed fault-tolerant control strategy.

Smoothened (Smo) inhibition by Patched (Ptch) is central to Hedgehog (Hh) signaling. Ptch, a proton driven antiporter, is required for Smo inhibition via an unknown mechanism. Hh ligand binding to Ptch reverses this inhibition and activated Smo initiates the Hh response. To determine whether Ptch inhibits Smo strictly in the same cell or also mediates non-cell-autonomous Smo inhibition, we generated genetically mosaic neuralized embryoid bodies (nEBs) from mouse embryonic stem cells (mESCs). These experiments utilized novel mESC lines in which Ptch1, Ptch2, Smo, Shh and 7dhcr were inactivated via gene editing in multiple combinations, allowing us to measure non-cell autonomous interactions between cells with differing Ptch1/2 status. In several independent assays, the Hh response was repressed by Ptch1/2 in nearby cells. When 7dhcr was targeted, cells displayed elevated non-cell autonomous inhibition. These findings support a model in which Ptch1/2 mediate secretion of a Smo-inhibitory cholesterol precursor.