Quadcopter UAVs Extended States/Disturbance Observer-Based Nonlinear Robust Backstepping Control.

A trajectory tracking control for quadcopter unmanned aerial vehicle (UAV) based on a nonlinear robust backstepping algorithm and extended state/disturbance observer (ESDO) is presented in this paper. To obtain robust attitude stabilization and superior performance of three-dimension position tracking control, the construction of the proposed algorithm can be separated into three parts. First, a mathematical model of UAV negatively influenced by exogenous disturbances is established. Following, an extended state/disturbance observer using a general second-order model is designed to approximate undesirable influences of perturbations on the UAVs dynamics. Finally, a nonlinear robust controller is constructed by an integration of the nominal backstepping technique with ESDO to enhance the performance of attitude and position control mode. Robust stability of the closed-loop disturbed system is obtained and guaranteed through the Lyapunov theorem without precise knowledge of the upper bound condition of perturbations. Lastly, a numerical simulation is carried out and compared with other previous controllers to demonstrate the great advantage and effectiveness of the proposed control method.

Doppler ultrasound predicts successful discontinuation of biological DMARDs in rheumatoid arthritis patients in clinical remission.

OBJECTIVE: To assess the ability of ultrasound to predict successful tapering and successful discontinuation of biological DMARDs (bDMARDs) at the 2-year follow-up in RA patients in sustained remission. METHODS: Patients in sustained remission (DAS28-CRP ≤ 2.6) and with no radiographic progression the previous year tapered bDMARDs according to a standardized regime. A total of 119 of these patients were included in this ultrasound substudy. At baseline, clinical assessment, MRI, X-ray and ultrasound of 24 joints were performed. Ultrasound-detected synovitis was defined and scored 0-3 using the OMERACT scoring system at the joint level for both grey-scale and Doppler activity. Sum scores for each ultrasound modality were calculated for 24 joints at the patient level. The final state of treatment was assessed after 2 years. The predictive value of ultrasound measures for successful tapering and discontinuation at the 2-year follow-up was assessed via logistic regression analyses. RESULTS: Negative IgM-RF [odds ratio (OR) = 0.29, 95% CI: 0.10-0.85; P = 0.024] and lower Doppler sum score of 24 joints (OR = 0.44, 95% CI: 0.15, 0.87; P = 0.014) were independent predictors for successful discontinuation of bDMARDs at the 2-year follow-up. The predictive value of the Doppler sum score was independent of MRI findings. Previous numbers of bDMARDs were predictive of successful tapering (OR = 0.58, 95% CI: 0.35, 0.91; P = 0.018), whereas ultrasound was not. Clinical parameters were not predictive of successful tapering/discontinuation. CONCLUSION: Doppler sum score was an independent predictor for successful discontinuation of bDMARDs at the 2-year follow-up-the odds for achieving successful discontinuation decreased by 56% per one-unit increase in Doppler sum score. Ultrasound could not predict successful tapering.

Robust Position Control of an Over-actuated Underwater Vehicle under Model Uncertainties and Ocean Current Effects Using Dynamic Sliding Mode Surface and Optimal Allocation Control.

Underwater vehicles (UVs) are subjected to various environmental disturbances due to ocean currents, propulsion systems, and un-modeled disturbances. In practice, it is very challenging to design a control system to maintain UVs stayed at the desired static position permanently under these conditions. Therefore, in this study, a nonlinear dynamics and robust positioning control of the over-actuated autonomous underwater vehicle (AUV) under the effects of ocean current and model uncertainties are presented. First, a motion equation of the over-actuated AUV under the effects of ocean current disturbances is established, and a trajectory generation of the over-actuated AUV heading angle is constructed based on the line of sight (LOS) algorithm. Second, a dynamic positioning (DP) control system based on motion control and an allocation control is proposed. For this, motion control of the over-actuated AUV based on the dynamic sliding mode control (DSMC) theory is adopted to improve the system robustness under the effects of the ocean current and model uncertainties. In addition, the stability of the system is proved based on Lyapunov criteria. Then, using the generalized forces generated from the motion control module, two different methods for optimal allocation control module: the least square (LS) method and quadratic programming (QP) method are developed to distribute a proper thrust to each thruster of the over-actuated AUV. Simulation studies are conducted to examine the effectiveness and robustness of the proposed DP controller. The results show that the proposed DP controller using the QP algorithm provides higher stability with smaller steady-state error and stronger robustness.

Robust Fault Estimation Using the Intermediate Observer: Application to the Quadcopter.

In this paper, an actuator fault estimation technique is proposed for quadcopters under uncertainties. In previous studies, matching conditions were required for the observer design, but they were found to be complex for solving linear matrix inequalities (LMIs). To overcome these limitations, in this study, an improved intermediate estimator algorithm was applied to the quadcopter model, which can be used to estimate actuator faults and system states. The system stability was validated using Lyapunov theory. It was shown that system errors are uniformly ultimately bounded. To increase the accuracy of the proposed fault estimation algorithm, a magnitude order balance method was applied. Experiments were verified with four scenarios to show the effectiveness of the proposed algorithm. Two first scenarios were compared to show the effectiveness of the magnitude order balance method. The remaining scenarios were described to test the reliability of the presented method in the presence of multiple actuator faults. Different from previous studies on observer-based fault estimation, this proposal not only can estimate the fault magnitude of the roll, pitch, yaw, and thrust channel, but also can estimate the loss of control effectiveness of each actuator under uncertainties.

Study on Dynamic Behavior of Unmanned Surface Vehicle-Linked Unmanned Underwater Vehicle System for Underwater Exploration.

This paper focuses on motion analysis of a coupled unmanned surface vehicle (USV)-umbilical cable (UC)-unmanned underwater vehicle (UUV) system to investigate the interaction behavior between the vehicles and the UC in the ocean environment. For this, a new dynamic modeling method for investigating a multi-body dynamics system of this coupling system is employed. Firstly, the structure and hardware composition of the proposed system are presented. The USV and UUV are modeled as rigid-body vehicles, and the flexible UC is discretized using the catenary equation. In order to solve the nonlinear coupled dynamics of the vehicles and flexible UC, the fourth-order Runge-Kutta numerical method is implemented. In modeling the flexible UC dynamics, the shooting method is applied to solve a two-point boundary value problem of the catenary equation. The interaction between the UC and the USV-UUV system is investigated through numerical simulations in the time domain. Through the computer simulation, the behavior of the coupled USV-UC-UUV system is analyzed for three situations which can occur. In particular, variation of the UC forces and moments at the tow points and the configuration of the UC in the water are investigated.

A Computational Framework Based on Ensemble Deep Neural Networks for Essential Genes Identification.

Essential genes contain key information of genomes that could be the key to a comprehensive understanding of life and evolution. Because of their importance, studies of essential genes have been considered a crucial problem in computational biology. Computational methods for identifying essential genes have become increasingly popular to reduce the cost and time-consumption of traditional experiments. A few models have addressed this problem, but performance is still not satisfactory because of high dimensional features and the use of traditional machine learning algorithms. Thus, there is a need to create a novel model to improve the predictive performance of this problem from DNA sequence features. This study took advantage of a natural language processing (NLP) model in learning biological sequences by treating them as natural language words. To learn the NLP features, a supervised learning model was consequentially employed by an ensemble deep neural network. Our proposed method could identify essential genes with sensitivity, specificity, accuracy, Matthews correlation coefficient (MCC), and area under the receiver operating characteristic curve (AUC) values of 60.2%, 84.6%, 76.3%, 0.449, and 0.814, respectively. The overall performance outperformed the single models without ensemble, as well as the state-of-the-art predictors on the same benchmark dataset. This indicated the effectiveness of the proposed method in determining essential genes, in particular, and other sequencing problems, in general.

Dose tapering and discontinuation of biological therapy in rheumatoid arthritis patients in routine care - 2-year outcomes and predictors.

Objectives: A cohort of routine care RA patients in sustained remission had biological DMARD (bDMARDs) tapered according to a treatment guideline. We studied: the proportion of patients whose bDMARD could be successfully tapered or discontinued; unwanted consequences of tapering/discontinuation; and potential baseline predictors of successful tapering and discontinuation. Methods: One-hundred-and-forty-three patients (91% receiving TNF inhibitor and 9% a non-TNF inhibitor) with sustained disease activity score (DAS28-CRP)⩽2.6 and no radiographic progression the previous year were included. bDMARD was reduced to two-thirds of standard dose at baseline, half after 16 weeks, and discontinued after 32 weeks. Patients who flared (defined as either DAS28-CRP ⩾ 2.6 and ΔDAS28-CRP ⩾ 1.2 from baseline, or erosive progression on X-ray and/or MRI) stopped tapering and were escalated to the previous dose level. Results: One-hundred-and-forty-one patients completed 2-year follow-up. At 2 years, 87 patients (62%) had successfully tapered bDMARDs, with 26 (18%) receiving two-thirds of standard dose, 39 (28%) half dose and 22 (16%) having discontinued; and 54 patients (38%) were receiving full dose. ΔDAS28-CRP0-2yrs was 0.1((-0.2)-0.4) (median (interquartile range)) and mean ΔTotal-Sharp-Score0-2yrs was 0.01(1.15)(mean(s.d.)). Radiographic progression was observed in nine patients (7%). Successful tapering was independently predicted by: ⩽1 previous bDMARD, male gender, low baseline MRI combined inflammation score or combined damage score. Negative IgM-RF predicted successful discontinuation. Conclusion: By implementing a clinical guideline, 62% of RA patients in sustained remission in routine care were successfully tapered, including 16% successfully discontinued at 2 years. Radiographic progression was rare. Maximum one bDMARDs, male gender, and low baseline MRI combined inflammation and combined damage scores were independent predictors for successful tapering.

Self-Organizing Type-2 Fuzzy Double Loop Recurrent Neural Network for Uncertain Nonlinear System Control.

Nonlinear systems, such as robotic systems, play an increasingly important role in our modern daily life and have become more dominant in many industries; however, robotic control still faces various challenges due to diverse and unstructured work environments. This article proposes a double-loop recurrent neural network (DLRNN) with the support of a Type-2 fuzzy system and a self-organizing mechanism for improved performance in nonlinear dynamic robot control. The proposed network has a double-loop recurrent structure, which enables better dynamic mapping. In addition, the network combines a Type-2 fuzzy system with a double-loop recurrent structure to improve the ability to deal with uncertain environments. To achieve an efficient system response, a self-organizing mechanism is proposed to adaptively adjust the number of layers in a DLRNN. This work integrates the proposed network into a conventional sliding mode control (SMC) system to theoretically and empirically prove its stability. The proposed system is applied to a three-joint robot manipulator, leading to a comparative study that considers several existing control approaches. The experimental results confirm the superiority of the proposed system and its effectiveness and robustness in response to various external system disturbances.

Improving Laryngoscopy Image Analysis Through Integration of Global Information and Local Features in VoFoCD Dataset.

The diagnosis and treatment of vocal fold disorders heavily rely on the use of laryngoscopy. A comprehensive vocal fold diagnosis requires accurate identification of crucial anatomical structures and potential lesions during laryngoscopy observation. However, existing approaches have yet to explore the joint optimization of the decision-making process, including object detection and image classification tasks simultaneously. In this study, we provide a new dataset, VoFoCD, with 1724 laryngology images designed explicitly for object detection and image classification in laryngoscopy images. Images in the VoFoCD dataset are categorized into four classes and comprise six glottic object types. Moreover, we propose a novel Multitask Efficient trAnsformer network for Laryngoscopy (MEAL) to classify vocal fold images and detect glottic landmarks and lesions. To further facilitate interpretability for clinicians, MEAL provides attention maps to visualize important learned regions for explainable artificial intelligence results toward supporting clinical decision-making. We also analyze our model's effectiveness in simulated clinical scenarios where shaking of the laryngoscopy process occurs. The proposed model demonstrates outstanding performance on our VoFoCD dataset. The accuracy for image classification and mean average precision at an intersection over a union threshold of 0.5 (mAP50) for object detection are 0.951 and 0.874, respectively. Our MEAL method integrates global knowledge, encompassing general laryngoscopy image classification, into local features, which refer to distinct anatomical regions of the vocal fold, particularly abnormal regions, including benign and malignant lesions. Our contribution can effectively aid laryngologists in identifying benign or malignant lesions of vocal folds and classifying images in the laryngeal endoscopy process visually.

Inhibition of de novo ceramide biosynthesis by FTY720 protects rat retina from light-induced degeneration.

Light-induced retinal degeneration (LIRD) in albino rats causes apoptotic photoreceptor cell death. Ceramide is a second messenger for apoptosis. We tested whether increases in ceramide mediate photoreceptor apoptosis in LIRD and if inhibition of ceramide synthesis protects the retina. Sprague-Dawley rats were exposed to 2,700 lux white light for 6 h, and the retinal levels of ceramide and its intermediary metabolites were measured by GC-MS or electrospray ionization tandem mass spectrometry. Enzymes of the de novo biosynthetic and sphingomyelinase pathways of ceramide generation were assayed, and gene expression was measured. The dosage and temporal effect of the ceramide synthase inhibitor FTY720 on the LIRD retina were measured by histological and functional analyses. Retinal ceramide levels increased coincident with the increase of dihydroceramide at various time points after light stress. Light stress in retina induces ceramide generation predominantly through the de novo pathway, which was prevented by systemic administration of FTY720 (10 mg/kg) leading to the protection of retinal structure and function. The neuroprotection of FTY720 was independent of its immunosuppressive action. We conclude that ceramide increase by de novo biosynthesis mediates photoreceptor apoptosis in the LIRD model and that inhibition of ceramide production protects the retina against light stress.

Expression and localization of CERKL in the mammalian retina, its response to light-stress, and relationship with NeuroD1 gene.

Mutations in the Ceramide kinase like (CERKL) gene are associated with retinitis pigmentosa (RP26) and cone-rod dystrophy. CERKL is homologous to Ceramide kinase (CERK), and its function is still unknown. The purpose of this study was to test the expression and distribution of this gene and its protein in rat and in mouse tissues, in light-stressed rat retinas and in the retinas of NeuroD1 knock-out mice to understand the role of CERKL in the retina. Expression of Cerkl and Cerk mRNA was determined by quantitative RT-PCR. Expression of the protein was determined by Western blotting with anti-CERKL antibody. Localization of the protein was determined by using immunofluorescence microscopy. With qRT-PCR, we revealed that the relative mRNA expression of Cerkl was the highest in the retina among all the rat tissue tested; it was >10-fold higher than in the brain. On the other hand, Cerk has ubiquitous expression and its relative abundance is >2 fold of Cerkl in the retina. Cerkl was expressed minimally in the developing mouse eyes and reached a peak at retinal maturity at 2 months. Western blots of retinal tissues revealed two major CERKL protein bands: 59 kDa (C1) and 37 kDa (C2). However, only C2 CERKL was found in the rat retinal rod outer segment (ROS) at level of that was not changed in light vs. dark adaptation. In the light-stressed retina, expression of Cerkl mRNA increased significantly, which was reflected in only on C2 CERKL protein. The CERKL protein localized prominently to the ganglion cells, inner nuclear layers (INL), retinal pigment epithelial (RPE) cells, and photoreceptor inner segments in the retinal sections. Nuclear localization of CERKL was not affected in RPE, INL and the ganglion cell layers in the light-stressed retina; however, the perinuclear and outer segment locations appear to be altered. In the NeuroD1 knock-out mouse retina, the expression of Cerkl mRNA and protein decreased and that decrease also pertains to C2 CERKL. In conclusion, the retina had the highest level of Cerkl mRNA and protein expression, which reached its maximum in the adult retina; CERKL localized to ROS and RPE cells and the light-adaptation did not change the level of CERKL in ROS; light-stress induced Cerkl expression in the retina; and its expression decreased in NeuroD1 knock-out retina. Thus, CERKL may be important for the stress responses and protection of photoreceptor cells.

Application of cluster repeated mini-batch training method to classify electroencephalography for grab and lift tasks.

Modern deep neural network training is based on mini-batch stochastic gradient optimization. While using extensive mini-batches improves the computational parallelism, the small batch training proved that it delivers improved generalization performance and allows a significantly smaller memory, which might also improve machine throughput. However, mini-batch size and characteristics, a key factor for training deep neural networks, has not been sufficiently investigated in training correlated group features and looping with highly complex ones. In addition, the unsupervised learning method clusters the data into different groups with similar properties to make the training process more stable and faster. Then, the supervised learning algorithm was applied with the cluster repeated mini-batch training (CRMT) methods. The CRMT algorithm changed the random minibatch characteristics in the training step into training in order of clusters. Specifically, the self-organizing maps (SOM) were used to cluster the information into n groups based on the dataset's labels Then, neural network models (ANN) were trained with each cluster using the cluster repeated mini-batch training method. Experiments conducted on EEG datasets demonstrate the survey of the proposed method and optimize it. In addition, the results in our research outperform other state-of-the-art methods.

Encapsulation of Triphasia trifolia extracts by pH and thermal dual-sensitive chitosan hydrogels for controlled release.

Recent studies have developed varied delivery systems incorporating natural compounds to improve the limitations of plant extracts for clinical use while enabling their controlled release at treatment sites. For the first time, ethanolic limeberry extract (Triphasia trifolia) has been successfully encapsulated in thermo-sensitive chitosan hydrogels by a facile in situ loading. The extract-incorporated chitosan hydrogels have a pH value of nearly 7.00, gelation temperatures in the range of 37-38 °C, and exhibit an open-cell porous structure, thus allowing them to absorb and retain 756 % of their mass in water. The in vitro extract release from the hydrogels is driven by both temperature and pH, resulting in more than 70 % of the initial extract being released within the first 24 h. Although the release half-life of hydrogels at pH 7.4 is longer, their release capacity is higher than that at pH 6.5. Upon a 2 °C increase in temperature, the time to release 50 % initial extract is sharply reduced by 20-40 %. The release kinetics from the hydrogels mathematically demonstrated that diffusion is a prominent driving force over chitosan relaxation. Consequently, the developed hydrogels encapsulating the limeberry extract show their heat and pH sensitivity in controlled release for treating chronic wounds.

Anxiety and associated factors among Vietnamese students during COVID-19 pandemic: A cross-sectional study.

Mental health care for students in general, particularly anxiety, is a significant problem that needs more attention, especially during the coronavirus disease 2019 (COVID-19) pandemic. This study aimed to describe the prevalence of anxiety and examine the associated factors among students during the COVID-19 pandemic in Vietnam. A cross-sectional study was conducted from August to September 2021 among 5730 students. An online survey was used to collect sociodemographic information, and the generalized anxiety disorder questionnaire (GAD-7) was used to assess anxiety symptoms among Vietnamese students. Results showed that the prevalence of anxiety among study participants was 16.2% (95% confidence interval [CI]: 15.3%-17.2%). Factors related to anxiety among students were gender, type of housemate, COVID-19 exposure/infection status, vaccination status, health status, academic performance, and social relationships during the COVID-19 pandemic. A significant number of students experienced anxiety during COVID-19, and this rate was related to several factors. Psychological interventions are required to support students during and after the COVID-19 pandemic and other health crises. Further studies are required to confirm our findings.

Encryption and Decryption of Audio Signal and Image Secure Communications Using Chaotic System Synchronization Control by TSK Fuzzy Brain Emotional Learning Controllers.

In this article, a new idea of chaos synchronization and chaos-based secure communication is developed. First, the chaotic master system is used as a transmitter in chaos-based secure communication, then a drive signal is constructed, and the information message is encrypted into the drive signal to form a transmitted signal for secure communication. Second, in the receiver, a recurrent Takagi-Sugeno-Kang (TSK) fuzzy brain emotional learning cerebellar model articulation controller (RTFBECAC) is developed to control the slave system to follow the master system in the transmitter. Third, after descripting the chaotic signal, the embedded information message can be recovered. Besides, the stability problem is analyzed in detail based on the stability theory. Finally, two simulation examples, including audio signal and image, are introduced to illustrate the effectiveness and the advantages of the proposed method.

Fabrication of lateral flow immunoassay strip for rapid detection of acute hepatopancreatic necrosis disease.

Acute hepatopancreatic necrosis disease (AHPND) is a contagious disease for the shrimp cultivation, thus early detection of disease is an unmet need. This present study documented for the first time a simple lateral flow immunoassay (LFIA) strip using polyclonal antibodies was created for the rapid detection both of PirAvp and PirBvp protein simultaneously. LFIA method based on the principle of sandwich format. The label is the colloidal gold. The polyclonal antibody was conjugated with the colloidal gold acting as biorecognition element and coated onto the conjugate pad. The rabbit anti-Pirvp, anti-PirBvp antibodies, and goat anti-rabbit IgG antibody were separately sprayed onto a nitrocellulose membrane to form two test lines and one control line, respectively. The appearance of red bands at the control line and the test line indicated a positive result. A single coloured band at control area indicated a negative result. The limit of detection of LFIA was found to be 125 ng, which could be visually detected by naked eye within 15 min. There was no cross-reactivity observed with VPnon-AHPND. Furthermore, the sensitivity and specificity of LFIA were 94.0% and 98.0%, respectively. The developed test strip could be a game changer for early and in situ diagnosis of AHPND.

Flare during tapering of biological DMARDs in patients with rheumatoid arthritis in routine care: characteristics and predictors.

OBJECTIVE: To identify predictors of flare in a 2-year follow-up study of patients with rheumatoid arthritis (RA) in sustained clinical remission tapering towards withdrawal of biological disease-modifying anti-rheumatic drugs (bDMARDs). METHODS: Sustained clinical remission was defined as Disease Activity Score for 28 joints (DAS28)-C reactive protein (CRP) ≤2.6 without radiographic progression for >1 year. bDMARDs were tapered according to a mandatory clinical guideline to two-thirds of standard dose at baseline, half of dose at week 16 and discontinuation at week 32. Prospective assessments for 2 years included clinical evaluation, conventional radiography, ultrasound and MRI for signs of inflammation and bone changes. Flare was defined as DAS28-CRP ≥2.6 with ∆DAS28-CRP ≥1.2 from baseline. Baseline predictors of flare were assessed by logistic regression analyses. RESULTS: Of 142 included patients, 121 (85%) flared during follow-up of which 86% regained remission within 24 weeks after flare. Patients that flared were more often rheumatoid factor positive, had tried more bDMARDs and had higher baseline ultrasound synovitis sum scores than those not flaring. For patients on standard dose, predictors of flare within 16 weeks after reduction to two-thirds of standard dose were baseline MRI-osteitis (OR 1.16; 95% CI 1.03 to 1.33; p=0.014), gender (female) (OR 6.71; 95% CI 1.68 to 46.12; p=0.005) and disease duration (OR 1.06; 95% CI 1.01 to 1.11; p=0.020). Baseline predictors for flare within 2 years were ultrasound grey scale synovitis sum score (OR 1.19; 95% CI 1.02 to 1.44; p=0.020) and number of previous bDMARDs (OR 4.07; 95% CI 1.35 to 24.72; p=0.007). CONCLUSION: The majority of real-world patients with RA tapering bDMARDs flared during tapering, with the majority regaining remission after stepwise dose increase. Demographic and imaging parameters (MR-osteitis/ultrasound greyscale synovitis) were independent predictors of immediate flare and flare overall and may be of importance for clinical decision-making in patients eligible for tapering.

BMDD: a novel approach for IoT platform (broker-less and microservice architecture, decentralized identity, and dynamic transmission messages).

Undeniably, Internet of Things (IoT) devices are gradually getting better over time; and IoT-based systems play a significant role in our lives. The pervasiveness of the new essential service models is expanding, and includes self-driving cars, smart homes, smart cities, as well as promoting the development of some traditional fields such as agriculture, healthcare, and transportation; the development of IoT devices has not shown any sign of cooling down. On the one hand, several studies are coming up with many scenarios for IoT platforms, but some critical issues related to performance, speed, power consumption, availability, security, and scalability are not yet fully resolved. On the other hand, IoT devices are manufactured and developed by different organizations and individuals; hence, there is no unified standard (uniformity of IoT devices), i.e., sending and receiving messages among them and between them and the upper layer (e.g., edge devices). To address these issues, this paper proposes an IoT Platform called BMDD (Broker-less and Microservice architecture, Decentralized identity, and Dynamic transmission messages) that has a combination of two architectural models, including broker-less and microservices, with cutting-edge technologies such as decentralized identity and dynamic message transmission. The main contributions of this article are five-fold, including: (i) proposing broker-less and microservice for the IoT platform which can reduce single failure point of brokering architecture, easy to scale out and improve failover; (ii) providing a decentralized authentication mechanism which is suitable for IoT devices attribute (i.e., mobility, distributed); (iii) applying the Role-Based Access Control (RBAC) model for the authorization process; (iv) exploiting the gRPC protocol combined with the Kafka message queue enhances transmission rates, transmission reliability, and reduces power consumption in comparison with MQTT protocol; and (v) developing a dynamic message transmission mechanism that helps users communicate with any device, regardless of the manufacturer, since it provides very high homogeneity.

T1 pseudohyperintensity on fat-suppressed magnetic resonance imaging: a potential diagnostic pitfall.

Magnetic resonance imaging findings in 2 patients with misleading T1 hyperintensity seen only on fat-suppressed images are presented; one with a renal cell carcinoma that was misinterpreted as a hemorrhagic cyst and the other with an ovarian serous cystadenocarcinoma that was misinterpreted as a complicated endometrioma. The apparent T1 hyperintensity on fat-suppressed images in these cases was likely due to varying perception of image signal dependent on local contrast, an optical effect known as the checker-shadow illusion. T1 pseudohyperintensity should be considered when apparently high T1 signal intensity is seen only on fat-suppressed images; review of non-fat-suppressed images may help prevent an erroneous diagnoses of blood-containing lesions.

Decoders configurations based on Unet family and feature pyramid network for COVID-19 segmentation on CT images.

Coronavirus Disease 2019 (COVID-19) pandemic has been ferociously destroying global health and economics. According to World Health Organisation (WHO), until May 2021, more than one hundred million infected cases and 3.2 million deaths have been reported in over 200 countries. Unfortunately, the numbers are still on the rise. Therefore, scientists are making a significant effort in researching accurate, efficient diagnoses. Several studies advocating artificial intelligence proposed COVID diagnosis methods on lung images with high accuracy. Furthermore, some affected areas in the lung images can be detected accurately by segmentation methods. This work has considered state-of-the-art Convolutional Neural Network architectures, combined with the Unet family and Feature Pyramid Network (FPN) for COVID segmentation tasks on Computed Tomography (CT) scanner samples from the Italian Society of Medical and Interventional Radiology dataset. The experiments show that the decoder-based Unet family has reached the best (a mean Intersection Over Union (mIoU) of 0.9234, 0.9032 in dice score, and a recall of 0.9349) with a combination between SE ResNeXt and Unet++. The decoder with the Unet family obtained better COVID segmentation performance in comparison with Feature Pyramid Network. Furthermore, the proposed method outperforms recent segmentation state-of-the-art approaches such as the SegNet-based network, ADID-UNET, and A-SegNet + FTL. Therefore, it is expected to provide good segmentation visualizations of medical images.