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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 It is important to note that this article has been retracted.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]

Meningioma is the most common type of primary brain tumor. Surgical resection followed by surveillance is the first‐line treatment for the majority of symptomatic meningiomas; however, recent advances in molecular sequencing, DNA methylation, proteomics, and single‐cell sequencing provide insights into further characterizing this heterogeneous group of tumors with a wide range of prognoses. A subset of these tumors are highly aggressive and cause severe morbidity and mortality. Therefore, identifying those individuals with a poor prognosis and intervening are critical. This review aims to help readers interpret the molecular profiling of meningiomas to identify patients with worse prognoses and guide the management and strategy for surveillance. In light of recent advances in the molecular profiling of meningiomas, this review provides practical guidance for incorporating genomic, methylomic, and proteomic information about meningiomas into management decisions. [ABSTRACT FROM AUTHOR]

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]

The Internet of things (IoT) is an embedded network of networked computing devices found in everyday objects, allowing them to send and receive data and makes our lives comfortable. Therefore, IoT is a very important technology and at the same time security has become a challenge, due to several issues, including poor interoperability, security flaws, privacy concerns, and lack of industry standards. Cyber-attacks on the IoT may impact security and privacy. To address the IoT-related security issue, nowadays, intrusion detection system (IDS) and machine learning technique are commonly used. In addition, to speed up the detection process, optimal features selection techniques are incorporated into the IDS. This paper proposes an efficient IoT IDS based on a hybrid feature selection and supervised machine learning-based technique. To examine the robustness of the proposed IDS, experiments are carried out on two datasets, i.e. NSL-KDD and UNSW-NB15. The proposed IDS achieves a detection accuracy rate of 99.3% on the NSL-KDD dataset, and 99.4% on UNSW-NB15 dataset. The proposed IDS was also compared to the deep learning-based IDS and found out that the proposed IDS achieves better performance in term of accuracy and learning runtime. [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 third most prevalent malignancy to cause mortality is hepatocellular carcinoma (HCC). The Hedgehog (Hh) signaling pathway is activated by binding to the transmembrane receptor Patched-1 (PTCH-1), which depresses the transmembrane G protein-coupled receptor Smoothened (SMO). This study was performed to examine the preventative and therapeutic effects of cannabidiol in adult rats exposed to diethyl nitrosamine (DENA)-induced HCC. A total of 50 male rats were divided into five groups of 10 rats each. Group I was the control group. Group II received intraperitoneal (IP) injections of DENA for 14 weeks. Group III included rats that received cannabidiol (CBD) orally (3–30 mg/kg) for 2 weeks and DENA injections for 14 weeks. Group IV rats received oral CBD for 2 weeks before 14 weeks of DENA injections. Group V included rats that received CBD orally for 2 weeks after their last injection of DENA. Measurements were made for alanine aminotransferase (ALT), aspartate aminotransferase (AST), gamma glutamyl transferase (GGT), superoxide dismutase (SOD), catalase (CAT), malondialdehyde (MDA), and alpha fetoprotein (AFP). Following total RNA extraction, Smo, Hhip, Ptch-1, and Gli-1 expressions were measured using quantitative real-time polymerase chain reaction (qRT–PCR). A histopathological analysis of liver tissues was performed. The liver enzymes, oxidant–antioxidant state, morphological, and molecular parameters of the adult male rat model of DENA-induced HCC showed a beneficial improvement after CBD administration. In conclusion, by focusing on the Hh signaling system, administration of CBD showed a beneficial improvement in the liver enzymes, oxidant–antioxidant status, morphological, and molecular parameters in the DENA-induced HCC in adult male rats. [ABSTRACT FROM AUTHOR]

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.

Controlling continuous data, such as Electroencephalographic (EEG) data, is challenging. The EEG data is recorded during the Concealed Information Test (CIT), performed with a 16-channel electrode. Some of the meta-heuristic algorithms that are already in use need to be improved so that they can handle the balance between the exploration and exploitation stages, deal with the problem of local optimums, and take continuous data. The Spider Monkey Optimization (SMO) algorithm is utilized as a candidate rule miner known as the SM-Candidate Rule Miner (SM-CRM) for categorizing EEG data into two groups, like guilty or innocent. These problems are solved by making an exhaustive optimal rule set that balances the SM-CRM's sensitivity, accuracy, and specificity. These rules are applied to the entire EEG dataset, and ten cross-folder validations are performed to get the individual confusion matrix. The SM-CRM method was better than other well-known algorithms regarding average sensitivity, specificity, and classification accuracy. It achieved a decent mean rule length and mean rule set size. Compared to meta-heuristic algorithms like the genetic and bat, the proposed framework achieved a maximum accuracy of 97.66%. [ABSTRACT FROM AUTHOR]

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.

In today’s scenario manufacturing industries are highly complex and prone to failure. That’s why redundancy allocation problem (RAP) and time dependent availability analysis plays a major role for the successful life cycle of a manufacturing industry. RAP is a Np-hard problem which is very difficult to solve by traditional methods. Therefore in this paper, RAP for the Manufacturing system is solved by Spider monkey optimization. SMO is recent meta-heuristic technique. Till now it is not used to solve RAP. Further results are compared with the Particle swarm optimization algorithm and comparison validates the better performance of SMO in this problem. As mentioned above, for avoiding the complete breakdown of the manufacturing system time- dependent availability is analyzed in this study. Firstly failure and repair data is collected from the manufacturing system then with the help of this information transition diagram is developed. Further equations are developed from transition diagram by using Markov birth death process then equations are solved with the use of Runge-Kutta method. This methodology is implemented in MATLAB. [ABSTRACT FROM AUTHOR]

This paper proposes an optimised Medium Access Control (MAC) protocol to minimise the number of channel access collisions and to improve the bandwidth efficiency of IoT-based Wireless Body Area Networks (WBANs). The frame structure of the proposed MAC protocol consists of three different phases, namely Contention Phase (CP), Transmission Phase (TP), and Inactive Phase (IP). During CP, the proposed protocol prioritises the life-sensitive data. Based on allocated priority, the communicating sensor nodes (SNs) compete for channel access. During TP, the successful SNs transmit their data. We use TDMA mechanism for data transmission and Spider Monkey Optimisation (SMO) technique to improve the bandwidth efficiency of the system by selecting the best possible TDMA time slot according to the data packet size of the SNs. During IP, the SNs go into sleep mode to save energy. We simulate the scenario on MATLAB simulator. The simulation results show that the proposed SMO-MAC improves the throughput by 22.23%, 14.00%, and 17.62%, residual energy by 60.76%, 81.17%, and 124.34%, communication reliability by 70.32%, 52.60%, and 52.60%, and data packet transmission delay by 80.74%, 70.10%, and 77.40% as compared to DRT-MAC, PSO-MAC, and GA-MAC, respectively. [ABSTRACT FROM AUTHOR]

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]

Practical information can be drawn from rainfall for making long-term water resources management plans, taking flood mitigation measures, and even establishing proper irrigation systems. Given that a large amount of data with high resolution is required for physical modeling, this study proposes a new standalone sequential minimal optimization (SMO) regression model and develops its ensembles using Dagging (DA), random committee (RC), and additive regression (AR) models (i.e., DA-SMO, RC-SMO, and AR-SMO) for rainfall prediction. First, 30-year monthly data derived from the year 1988 to 2018 including evaporation, maximum and minimum temperatures, maximum and minimum relative humidity rates, sunshine hours, and wind speed as input and rainfall as the output were acquired from a synoptic station in Kermanshah, Iran. Next, based on the Pearson correlation coefficient (r-value) between input and output variables, different input scenarios were formed. Then, the dataset was separated into three subsets: development (1988–2008), calibration (2009–2010), and validation (2011–2018). Finally, the performance of the developed algorithms was validated using different visual (scatterplot and boxplot) and quantitative (percentage of BIAS, root mean square error, Nash–Sutcliffe efficiency, and mean absolute error) metrics. The results revealed that minimum relative humidity had the greatest effect on rainfall prediction. The most effective input scenario featured all the input variables except for wind speed. Our findings indicated that the DA-SMO ensemble algorithm outperformed other algorithms. [ABSTRACT FROM AUTHOR]

Studies in the past decade have shown that lipid droplets stored in liver cells under starvation are encapsulated by autophagosomes and fused to lysosomes via the endocytic system. Autophagy responds to a variety of environmental factors inside and outside the cell, so it has a complex signal regulation network. To this end, we first explored the role of Hedgehog (Hh) in autophagy and lipid metabolism. Treatment of normal mouse liver cells with SAG and GDC-0449 revealed elevated phosphorylation of AMP-activated protein kinase (AMPK) and increased lipidation of LC3. SAG, and GDC-0449 were agonist and antagonist of Smoothened (Smo) in canonical Hh pathway, respectively, but they played a consistent role in the regulation of autophagy in hepatocytes. Moreover, SAG and GDC-0449 did not affect the expression of glioma-associated oncogene (Gli1) and patched 1, suggesting the absence of canonical Hh signaling in hepatocytes. We further knocked down the Smo and found that the effects of SAG and GDC-0449 disappeared, indicating that the non-canonical Smo pathway was involved in the regulation of autophagy in hepatocytes. In addition, SAG and GDC-0449 promoted lipid degradation and inhibited lipid production signals. Knockdown of Smo slowed down the rate of lipid degradation rather than Sufu or Gli1, indicating that Hh signaling regulated the lipid metabolism via Smo. In summary, activates AMPK via Smo to promote autophagy and lipid degradation. [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]

In this research, we proposed a protocol for information transmission in a communication system. The protocol consists of three parts: Publisher, Broker, and Subscriber. The main problem in the transmission is the reliability of the broker. It is very difficult to get the status of the broker whether it is active or not. Secondly, we also worked on the part of security. Here by employing a Watchdog timer to determine whether the broker is active or not. To make the system more secure AES algorithm is incorporated into it. To validate the suggested protocol hardware setup is built and performance is tested in various conditions. Performance tests show that our proposed system outperforms the current system when compared to the existing system. The suggested system architecture's temporal and spatial complexity are also presented in this research. [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%.