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Article
Individual Disturbance and Attraction Repulsion Strategy Enhanced Seagull Optimization for Engineering Design
by , , , and
Mathematics 2022, 10(2), 276; https://doi.org/10.3390/math10020276 (registering DOI) - 16 Jan 2022
Abstract
The seagull optimization algorithm (SOA) is a novel swarm intelligence algorithm proposed in recent years. The algorithm has some defects in the search process. To overcome the problem of poor convergence accuracy and easy to fall into local optimality of seagull optimization algorithm, [...] Read more.
The seagull optimization algorithm (SOA) is a novel swarm intelligence algorithm proposed in recent years. The algorithm has some defects in the search process. To overcome the problem of poor convergence accuracy and easy to fall into local optimality of seagull optimization algorithm, this paper proposed a new variant SOA based on individual disturbance (ID) and attraction-repulsion (AR) strategy, called IDARSOA, which employed ID to enhance the ability to jump out of local optimum and adopted AR to increase the diversity of population and make the exploration of solution space more efficient. The effectiveness of the IDARSOA has been verified using representative comprehensive benchmark functions and six practical engineering optimization problems. The experimental results show that the proposed IDARSOA has the advantages of better convergence accuracy and a strong optimization ability than the original SOA. Full article
(This article belongs to the Special Issue Evolutionary Computation 2022)
Review
Performance Efficiency of Conventional Treatment Plants and Constructed Wetlands towards Reduction of Antibiotic Resistance
by and
Antibiotics 2022, 11(1), 114; https://doi.org/10.3390/antibiotics11010114 (registering DOI) - 16 Jan 2022
Abstract
Domestic and industrial wastewater discharges harbor rich bacterial communities, including both pathogenic and commensal organisms that are antibiotic-resistant (AR). AR pathogens pose a potential threat to human and animal health. In wastewater treatment plants (WWTP), bacteria encounter environments suitable for horizontal gene transfer, [...] Read more.
Domestic and industrial wastewater discharges harbor rich bacterial communities, including both pathogenic and commensal organisms that are antibiotic-resistant (AR). AR pathogens pose a potential threat to human and animal health. In wastewater treatment plants (WWTP), bacteria encounter environments suitable for horizontal gene transfer, providing an opportunity for bacterial cells to acquire new antibiotic-resistant genes. With many entry points to environmental components, especially water and soil, WWTPs are considered a critical control point for antibiotic resistance. The primary and secondary units of conventional WWTPs are not designed for the reduction of resistant microbes. Constructed wetlands (CWs) are viable wastewater treatment options with the potential for mitigating AR bacteria, their genes, pathogens, and general pollutants. Encouraging performance for the removal of AR (2–4 logs) has highlighted the applicability of CW on fields. Their low cost of construction, operation and maintenance makes them well suited for applications across the globe, especially in developing and low-income countries. The present review highlights a better understanding of the performance efficiency of conventional treatment plants and CWs for the elimination/reduction of AR from wastewater. They are viable alternatives that can be used for secondary/tertiary treatment or effluent polishing in combination with WWTP or in a decentralized manner. Full article
(This article belongs to the Special Issue The Distribution of Antibiotic Resistance in Terrestrial Ecosystems)
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Review
Obesity and Male Reproduction: Do Sirtuins Play a Role?
by , , , , , , , and
Int. J. Mol. Sci. 2022, 23(2), 973; https://doi.org/10.3390/ijms23020973 (registering DOI) - 16 Jan 2022
Abstract
Obesity is a major current public health problem of global significance. A progressive sperm quality decline, and a decline in male fertility, have been reported in recent decades. Several studies have reported a strict relationship between obesity and male reproductive dysfunction. Among the [...] Read more.
Obesity is a major current public health problem of global significance. A progressive sperm quality decline, and a decline in male fertility, have been reported in recent decades. Several studies have reported a strict relationship between obesity and male reproductive dysfunction. Among the many mechanisms by which obesity impairs male gonadal function, sirtuins (SIRTs) have an emerging role. SIRTs are highly conserved nicotinamide adenine dinucleotide (NAD+)-dependent deacetylases that play a role in gene regulation, metabolism, aging, and cancer. SIRTs regulate the energy balance, the lipid balance, glucose metabolism, and adipogenesis, but current evidence also indicates a role for SIRTs in male reproduction. However, the majority of the studies have been conducted in animal models and very few have been conducted with humans. This review shows that SIRTs play an important role among the molecular mechanisms by which obesity interferes with male fertility. This highlights the need to deepen this relationship. It will be of particular interest to evaluate whether synthetic and/or natural compounds capable of modifying the activity of SIRTs may also be useful for the treatment of obesity and its effects on gonadal function. Although few studies have explored the role of SIRT activators in obesity-induced male infertility, some molecules, such as resveratrol, appear to be effective in modulating SIRT activity, as well as counteracting the negative effects of obesity on male fertility. The search for strategies to improve male reproductive function in overweight/obese patients is a challenge and understanding the role of SIRTs and their activators may open new interesting scenarios in the coming years. Full article
(This article belongs to the Special Issue Diet and Metabolism: Molecular Mechanisms of Health and Disease)
Article
Hybrid Direction of Arrival Precoding for Multiple Unmanned Aerial Vehicles Aided Non-Orthogonal Multiple Access in 6G Networks
by
Appl. Sci. 2022, 12(2), 895; https://doi.org/10.3390/app12020895 (registering DOI) - 16 Jan 2022
Abstract
Unmanned aerial vehicles (UAV) have attracted increasing attention in acting as a relay for effectively improving the coverage and data rate of wireless systems, and according to this vision, they will be integrated in the future sixth generation (6G) cellular network. Non-orthogonal multiple [...] Read more.
Unmanned aerial vehicles (UAV) have attracted increasing attention in acting as a relay for effectively improving the coverage and data rate of wireless systems, and according to this vision, they will be integrated in the future sixth generation (6G) cellular network. Non-orthogonal multiple access (NOMA) and mmWave band are planned to support ubiquitous connectivity towards a massive number of users in the 6G and Internet of Things (IOT) contexts. Unfortunately, the wireless terrestrial link between the end-users and the base station (BS) can suffer severe blockage conditions. Instead, UAV relaying can establish a line-of-sight (LoS) connection with high probability due to its flying height. The present paper focuses on a multi-UAV network which supports an uplink (UL) NOMA cellular system. In particular, by operating in the mmWave band, hybrid beamforming architecture is adopted. The MUltiple SIgnal Classification (MUSIC) spectral estimation method is considered at the hybrid beamforming to detect the different direction of arrival (DoA) of each UAV. We newly design the sum-rate maximization problem of the UAV-aided NOMA 6G network specifically for the uplink mmWave transmission. Numerical results point out the better behavior obtained by the use of UAV relays and the MUSIC DoA estimation in the Hybrid mmWave beamforming in terms of achievable sum-rate in comparison to UL NOMA connections without the help of a UAV network. Full article
(This article belongs to the Special Issue Unmanned Aerial Vehicles)
Article
Synthesis of Porous BPPO-Based Anion Exchange Membranes for Acid Recovery Via Diffusion Dialysis
by , , and
Membranes 2022, 12(1), 95; https://doi.org/10.3390/membranes12010095 (registering DOI) - 16 Jan 2022
Abstract
Diffusion dialysis (DD) is an anion exchange membrane-based functional separation process used for acid recovery. TMA (trimethylamine) and BPPO (brominated poly (2,6-dimethyl-1,4-phenylene oxide) were utilized in this manuscript to formulate AEMs (anion exchange membranes) for DD (diffusion dialysis) using the phase-inversion technique. FTIR [...] Read more.
Diffusion dialysis (DD) is an anion exchange membrane-based functional separation process used for acid recovery. TMA (trimethylamine) and BPPO (brominated poly (2,6-dimethyl-1,4-phenylene oxide) were utilized in this manuscript to formulate AEMs (anion exchange membranes) for DD (diffusion dialysis) using the phase-inversion technique. FTIR (Fourier transfer infrared) analysis, proton NMR spectroscopy, morphology, IEC (ion exchange capacity), LER (linear expansion ratio), CR (fixed group concentration), WR (water uptake/adsorption), water contact angle, chemical, and thermal stability, were all used to evaluate the prepared membranes. The effect of TMA content within the membrane matrix on acid recovery was also briefly discussed. It was reported that porous AEMs have a WR of 149.6% to 233.8%, IEC (ion exchange capacity) of 0.71 to 1.43 mmol/g, CR (fixed group concentration) that ranged from 0.0046 mol/L to 0.0056 mol/L, LER of 3.88% to 9.23%, and a water contact angle of 33.10° to 78.58°. The UH (acid dialysis coefficients) for designed porous membranes were found to be 0.0043 to 0.012 m/h, with separation factors (S) ranging from 13.14 to 32.87 at the temperature of 25 °C. These observations are comparable to those found in the DF-120B commercial membrane with UH of 0.004 m/h and S of 24.3 m/h at the same temperature (25 °C). This porous membranes proposed in this paper are excellent choices for acid recovery through the diffusion dialysis process. Full article
(This article belongs to the Special Issue Electrodialysis Membranes)
Article
Novel Motor-Kinetic-Energy-Based Power Pulsation Buffer Concept for Single-Phase-Input Electrolytic-Capacitor-Less Motor-Integrated Inverter System
by , , , and
Electronics 2022, 11(2), 280; https://doi.org/10.3390/electronics11020280 (registering DOI) - 16 Jan 2022
Abstract
The motor integration of singe-phase-supplied Variable-Speed Drive (VSDs) is prevented by the significant volume, short lifetime, and operating temperature limit of the electrolytic capacitors required to buffer the pulsating power grid. The DC-link energy storage requirement is eliminated by using the kinetic energy [...] Read more.
The motor integration of singe-phase-supplied Variable-Speed Drive (VSDs) is prevented by the significant volume, short lifetime, and operating temperature limit of the electrolytic capacitors required to buffer the pulsating power grid. The DC-link energy storage requirement is eliminated by using the kinetic energy of the motor as a buffer. The proposed concept is called the Motor-Integrated Power Pulsation Buffer (MPPB), and a control technique and structure are detailed that meet the requirements for nominal and faulted operation with a simple reconfiguration of existing controller blocks. A 7.5 kW, motor-integrated hardware demonstrator validated the proposed MPPB concept and loss models for a scroll compressor drive used in auxiliary railway applications. The MPPB drive with a front-end CISPR 11/Class A EMI filter, PFC rectifier stage, and output-side inverter stage achieved a power density of 0.91 kW/L1 (15 W in3). The grid-to-motor-shaft efficiency exceeded 90% for all loads over 5 kW or 66% of nominal load, with a worst-case loss penalty over a conventional system of only 17%. Full article
(This article belongs to the Special Issue 10th Anniversary of Electronics: Recent Advances in Power Electronics)
Case Report
How Many Times Can One Go Back to the Drawing Board before the Accurate Diagnosis and Surgical Treatment of Glucagonoma?
by , , , , , , , , , , , , and
Diagnostics 2022, 12(1), 216; https://doi.org/10.3390/diagnostics12010216 (registering DOI) - 16 Jan 2022
Abstract
Glucagonomas are neuroendocrine tumors (NETs) that arise from the alpha cells of the pancreatic islets. They are typically slow-growing tumors associated with abnormal glucagon secretion, resulting in one or more non-specific clinical features, such as necrolytic migratory erythema (NME), diabetes, diarrhea, deep vein [...] Read more.
Glucagonomas are neuroendocrine tumors (NETs) that arise from the alpha cells of the pancreatic islets. They are typically slow-growing tumors associated with abnormal glucagon secretion, resulting in one or more non-specific clinical features, such as necrolytic migratory erythema (NME), diabetes, diarrhea, deep vein thrombosis, weight loss, and depression. Here, we report the case of a 44-year-old male with a history of diabetes mellitus, presenting with a pruritic and painful disseminated cutaneous eruption of erythematous plaques, with scales and peripheral pustules, misdiagnosed as disseminated pustular psoriasis and treated for 2 years with oral retinoid and glucocorticoids. During this period, the patient complained of weight loss of 32 kg and diarrhea and developed deep vein thrombosis. These symptoms, together with an inadequate response to therapy of the skin lesions, led to the reassessment of the initial diagnosis. Laboratory tests confirmed elevated plasma glucagon levels (>1000 pg/mL) and computed tomography (CT) scans revealed a 35/44 mm tumor in the pancreatic tail. Due to considerable disease complications and the COVID-19 pandemic, the surgical removal of the tumor was delayed for nearly 2 years. During this time, somatostatin analogue therapy efficiently controlled the glucagonoma syndrome and likely prevented tumor progression. As in other functional pancreatic NETs, the early clinical recognition of hormonal hypersecretion syndrome and the multidisciplinary approach are the keys for best patient management. Full article
(This article belongs to the Special Issue Abdominal Surgical Diseases: Diagnosis, Treatment and Management)
Article
Predictive Role of the D-Dimer Level in Acute Kidney Injury in Living Donor Liver Transplantation: A Retrospective Observational Cohort Study
by , , , and
J. Clin. Med. 2022, 11(2), 450; https://doi.org/10.3390/jcm11020450 (registering DOI) - 16 Jan 2022
Abstract
This study aimed to determine the association between serum D-dimer levels and the risk of acute kidney injury (AKI) in patients undergoing living donor liver transplantation (LDLT). Clinical data of 675 patients undergoing LDLT were retrospectively analyzed. The exclusion criteria included a history [...] Read more.
This study aimed to determine the association between serum D-dimer levels and the risk of acute kidney injury (AKI) in patients undergoing living donor liver transplantation (LDLT). Clinical data of 675 patients undergoing LDLT were retrospectively analyzed. The exclusion criteria included a history of kidney dysfunction, emergency cases, and missing data. The final study population of 617 patients was divided into the normal and high D-dimer groups (cutoff: 0.5 mg/L). After LDLT, 145 patients (23.5%) developed AKI. A high D-dimer level (>0.5 mg/L) was an independent predictor of postoperative development of AKI in the multivariate analysis when combined with diabetes mellitus [DM], platelet count, and hourly urine output. AKI was significantly higher in the high D-dimer group than in the normal D-dimer group (odds ratio [OR], 2.792; 95% confidence interval [CI], 1.227–6.353). Patients with a high D-dimer exhibited a higher incidence of early allograft dysfunction, longer intensive care unit stay, and a higher mortality rate. These results could improve the risk stratification of postoperative AKI development by encouraging the determination of preoperative D-dimer levels in patients undergoing LDLT. Full article
(This article belongs to the Special Issue Recent Advance in Liver Transplantation)
Article
The Effects of Permanent Magnet Segmentations on Electromagnetic Performance in Ironless Brushless DC Motors
by , , , and
Energies 2022, 15(2), 621; https://doi.org/10.3390/en15020621 (registering DOI) - 16 Jan 2022
Abstract
This paper investigates the electromagnetic torque by considering back electromagnetic force (back-EMF) trapezoidal degrees of ironless brushless DC (BLDC) motors through the two-dimensional finite element method (2-D FEM). First, the change percentages of the electromagnetic torque with back-EMF trapezoidal degrees, relative to those [...] Read more.
This paper investigates the electromagnetic torque by considering back electromagnetic force (back-EMF) trapezoidal degrees of ironless brushless DC (BLDC) motors through the two-dimensional finite element method (2-D FEM). First, the change percentages of the electromagnetic torque with back-EMF trapezoidal degrees, relative to those of PMs without segments, are investigated on the premise of the same back-EMF amplitude. It is found that both PM symmetrically and asymmetrically segmented types influence back-EMF trapezoidal degrees. Second, the corresponding electromagnetic torque, relative to that of PMs without segments, is studied in detail. The results show that the electromagnetic torque can be improved or deteriorated depending on whether the back-EMF trapezoidal degree is lower or higher than that of PMs without segments. Additionally, the electromagnetic torque can easily be improved by increasing the number of PMs’ symmetrical segments. In addition, the electromagnetic torque in PMs with asymmetrical segments is always higher than that of PMs without segments. Finally, two ironless PM BLDC motors with PMs symmetrically segmented into three segments and without segments are manufactured and tested. The experimental results show good agreement with those of the 2-D FEM method. This approach provides significant guidelines to electromagnetic torque improvement without much increase in manufacturing costs and process complexity. Full article
(This article belongs to the Special Issue Advanced Electrical Machine Design and Optimization)
Article
Manipulation of Skyrmion Motion Dynamics for Logical Device Application Mediated by Inhomogeneous Magnetic Anisotropy
by , , , , , , and
Nanomaterials 2022, 12(2), 278; https://doi.org/10.3390/nano12020278 (registering DOI) - 16 Jan 2022
Abstract
Magnetic skyrmions are promising potential information carriers for future spintronic devices owing to their nanoscale size, non-volatility and high mobility. In this work, we demonstrate the controlled manipulation of skyrmion motion and its implementation in a new concept of racetrack logical device by [...] Read more.
Magnetic skyrmions are promising potential information carriers for future spintronic devices owing to their nanoscale size, non-volatility and high mobility. In this work, we demonstrate the controlled manipulation of skyrmion motion and its implementation in a new concept of racetrack logical device by introducing an inhomogeneous perpendicular magnetic anisotropy (PMA) via micromagnetic simulation. Here, the inhomogeneous PMA can be introduced by a capping nano-island that serves as a tunable potential barriers/well which can effectively modulate the size and shape of isolated skyrmion. Using the inhomogeneous PMA in skyrmion-based racetrack enables the manipulation of skyrmion motion behaviors, for instance, blocking, trapping or allowing passing the injected skyrmion. In addition, the skyrmion trapping operation can be further exploited in developing special designed racetrack devices with logic AND and NOT, wherein a set of logic AND operations can be realized via skyrmion–skyrmion repulsion between two skyrmions. These results indicate an effective method for tailoring the skyrmion structures and motion behaviors by using inhomogeneous PMA, which further provide a new pathway to all-electric skyrmion-based memory and logic devices. Full article
(This article belongs to the Special Issue Nano-Magnets and Nano-Magnetisms)
Article
Understanding Dilated Mathematical Relationship between Image Features and the Convolutional Neural Network’s Learnt Parameters
by , , and
Entropy 2022, 24(1), 132; https://doi.org/10.3390/e24010132 (registering DOI) - 16 Jan 2022
Abstract
Deep learning, in general, was built on input data transformation and presentation, model training with parameter tuning, and recognition of new observations using the trained model. However, this came with a high computation cost due to the extensive input database and the length [...] Read more.
Deep learning, in general, was built on input data transformation and presentation, model training with parameter tuning, and recognition of new observations using the trained model. However, this came with a high computation cost due to the extensive input database and the length of time required in training. Despite the model learning its parameters from the transformed input data, no direct research has been conducted to investigate the mathematical relationship between the transformed information (i.e., features, excitation) and the model’s learnt parameters (i.e., weights). This research aims to explore a mathematical relationship between the input excitations and the weights of a trained convolutional neural network. The objective is to investigate three aspects of this assumed feature-weight relationship: (1) the mathematical relationship between the training input images’ features and the model’s learnt parameters, (2) the mathematical relationship between the images’ features of a separate test dataset and a trained model’s learnt parameters, and (3) the mathematical relationship between the difference of training and testing images’ features and the model’s learnt parameters with a separate test dataset. The paper empirically demonstrated the existence of this mathematical relationship between the test image features and the model’s learnt weights by the ANOVA analysis. Full article
(This article belongs to the Topic Machine and Deep Learning)
Article
Rapid-Erection Backstepping Tracking Control for Electrohydraulic Lifting Mechanisms of Launcher Systems
by , , and
Appl. Sci. 2022, 12(2), 893; https://doi.org/10.3390/app12020893 (registering DOI) - 16 Jan 2022
Abstract
Uncertainties and disturbances widely exist in electrohydraulic lifting mechanisms of launcher systems, which may worsen the rapid-erection tracking accuracy and even make the system unstable. To deal with the issue, an asymptotic tracking control framework is developed for electrohydraulic lifting mechanisms of launcher [...] Read more.
Uncertainties and disturbances widely exist in electrohydraulic lifting mechanisms of launcher systems, which may worsen the rapid-erection tracking accuracy and even make the system unstable. To deal with the issue, an asymptotic tracking control framework is developed for electrohydraulic lifting mechanisms of launcher systems. Firstly, the dynamic equations and state-space forms of the electrohydraulic lifting mechanism are modeled. Based on the system model, a nonlinear rapid-erection robust controller is constructed to achieve the improvement of the system control performance, in which a nonlinear feedback term is employed to remove the effects of uncertainties and disturbances on tracking performance. Compared to the existing results, the asymptotic tracking stability of the closed-loop system can be assured based on the Lyapunov theory analysis. In the end, the simulation example of an actual electrohydraulic lifting mechanism of the launcher system is done to validate the effectiveness with the proposed controller. Full article
(This article belongs to the Special Issue Recent Advances in Flow Control)
Review
Molecular Mechanisms Leading from Periodontal Disease to Cancer
by , , , , , , , and
Int. J. Mol. Sci. 2022, 23(2), 970; https://doi.org/10.3390/ijms23020970 (registering DOI) - 16 Jan 2022
Abstract
Periodontitis is prevalent in half of the adult population and raises critical health concerns as it has been recently associated with an increased risk of cancer. While information about the topic remains somewhat scarce, a deeper understanding of the underlying mechanistic pathways promoting [...] Read more.
Periodontitis is prevalent in half of the adult population and raises critical health concerns as it has been recently associated with an increased risk of cancer. While information about the topic remains somewhat scarce, a deeper understanding of the underlying mechanistic pathways promoting neoplasia in periodontitis patients is of fundamental importance. This manuscript presents the literature as well as a panel of tables and figures on the molecular mechanisms of Porphyromonas gingivalis and Fusobacterium nucleatum, two main oral pathogens in periodontitis pathology, involved in instigating tumorigenesis. We also present evidence for potential links between the RANKL–RANK signaling axis as well as circulating cytokines/leukocytes and carcinogenesis. Due to the nonconclusive data associating periodontitis and cancer reported in the case and cohort studies, we examine clinical trials relevant to the topic and summarize their outcome. Full article
(This article belongs to the Special Issue Molecular Mechanisms of Periodontal Disease 2.0)
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Article
Potential of AOD Retrieval Using Atmospheric Emitted Radiance Interferometer (AERI)
by , and
Remote Sens. 2022, 14(2), 407; https://doi.org/10.3390/rs14020407 (registering DOI) - 16 Jan 2022
Abstract
Aerosols in the atmosphere play an essential role in the radiative transfer process due to their scattering, absorption, and emission. Moreover, they interrupt the retrieval of atmospheric properties from ground-based and satellite remote sensing. Thus, accurate aerosol information needs to be obtained. Herein, [...] Read more.
Aerosols in the atmosphere play an essential role in the radiative transfer process due to their scattering, absorption, and emission. Moreover, they interrupt the retrieval of atmospheric properties from ground-based and satellite remote sensing. Thus, accurate aerosol information needs to be obtained. Herein, we developed an optimal-estimation-based aerosol optical depth (AOD) retrieval algorithm using the hyperspectral infrared downwelling emitted radiance of the Atmospheric Emitted Radiance Interferometer (AERI). The proposed algorithm is based on the phenomena that the thermal infrared radiance measured by a ground-based remote sensor is sensitive to the thermodynamic profile and degree of the turbid aerosol in the atmosphere. To assess the performance of algorithm, AERI observations, measured throughout the day on 21 October 2010 at Anmyeon, South Korea, were used. The derived thermodynamic profiles and AODs were compared with those of the European center for a reanalysis of medium-range weather forecasts version 5 and global atmosphere watch precision-filter radiometer (GAW-PFR), respectively. The radiances simulated with aerosol information were more suitable for the AERI-observed radiance than those without aerosol (i.e., clear sky). The temporal variation trend of the retrieved AOD matched that of GAW-PFR well, although small discrepancies were present at high aerosol concentrations. This provides a potential possibility for the retrieval of nighttime AOD. Full article
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Article
TLCrys: Transfer Learning Based Method for Protein Crystallization Prediction
by , , , and
Int. J. Mol. Sci. 2022, 23(2), 972; https://doi.org/10.3390/ijms23020972 (registering DOI) - 16 Jan 2022
Abstract
X-ray diffraction technique is one of the most common methods of ascertaining protein structures, yet only 2–10% of proteins can produce diffraction-quality crystals. Several computational methods have been proposed so far to predict protein crystallization. Nevertheless, the current state-of-the-art computational methods are limited [...] Read more.
X-ray diffraction technique is one of the most common methods of ascertaining protein structures, yet only 2–10% of proteins can produce diffraction-quality crystals. Several computational methods have been proposed so far to predict protein crystallization. Nevertheless, the current state-of-the-art computational methods are limited by the scarcity of experimental data. Thus, the prediction accuracy of existing models hasn’t reached the ideal level. To address the problems above, we propose a novel transfer-learning-based framework for protein crystallization prediction, named TLCrys. The framework proceeds in two steps: pre-training and fine-tuning. The pre-training step adopts attention mechanism to extract both global and local information of the protein sequences. The representation learned from the pre-training step is regarded as knowledge to be transferred and fine-tuned to enhance the performance of crystalization prediction. During pre-training, TLCrys adopts a multi-task learning method, which not only improves the learning ability of protein encoding, but also enhances the robustness and generalization of protein representation. The multi-head self-attention layer guarantees that different levels of the protein representation can be extracted by the fine-tuned step. During transfer learning, the fine-tuning strategy used by TLCrys improves the task-specialized learning ability of the network. Our method outperforms all previous predictors significantly in five crystallization stages of prediction. Furthermore, the proposed methodology can be well generalized to other protein sequence classification tasks. Full article
(This article belongs to the Collection Feature Papers in Molecular Informatics)
Article
An In-Orbit Stereo Navigation Camera Self-Calibration Method for Planetary Rovers with Multiple Constraints
by , , , , and
Remote Sens. 2022, 14(2), 402; https://doi.org/10.3390/rs14020402 (registering DOI) - 16 Jan 2022
Abstract
In order to complete the high-precision calibration of the planetary rover navigation camera using limited initial data in-orbit, we proposed a joint adjustment model with additional multiple constraints. Specifically, a base model was first established based on the bundle adjustment model, second-order radial [...] Read more.
In order to complete the high-precision calibration of the planetary rover navigation camera using limited initial data in-orbit, we proposed a joint adjustment model with additional multiple constraints. Specifically, a base model was first established based on the bundle adjustment model, second-order radial and tangential distortion parameters. Then, combining the constraints of collinearity, coplanarity, known distance and relative pose invariance, a joint adjustment model was constructed to realize the in orbit self-calibration of the navigation camera. Given the problem of directionality in line extraction of the solar panel due to large differences in the gradient amplitude, an adaptive brightness-weighted line extraction method was proposed. Lastly, the Levenberg-Marquardt algorithm for nonlinear least squares was used to obtain the optimal results. To verify the proposed method, field experiments and in-orbit experiments were carried out. The results suggested that the proposed method was more accurate than the self-calibration bundle adjustment method, CAHVOR method (a camera model used in machine vision for three-dimensional measurements), and vanishing points method. The average error for the flag of China and the optical solar reflector was only 1 mm and 0.7 mm, respectively. In addition, the proposed method has been implemented in China’s deep space exploration missions. Full article
(This article belongs to the Section Satellite Missions for Earth and Planetary Exploration)
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Article
Age and Gender Impact on Password Hygiene
by , , and
Appl. Sci. 2022, 12(2), 894; https://doi.org/10.3390/app12020894 (registering DOI) - 16 Jan 2022
Abstract
Password hygiene plays an essential part in securing systems protected with single-factor authentication. A significant fraction of security incidents happen due to weak or reused passwords. The reasons behind differences in security vulnerable behaviour between various user groups remains an active research topic. [...] Read more.
Password hygiene plays an essential part in securing systems protected with single-factor authentication. A significant fraction of security incidents happen due to weak or reused passwords. The reasons behind differences in security vulnerable behaviour between various user groups remains an active research topic. The paper aims to identify the impact of age and gender on password strength using a large password dataset. We recovered previously hashed passwords of 102,120 users from a leaked customer database of a car-sharing company. Although the measured effect size was small, males significantly had stronger passwords than females for all age groups. Males aged 26–45 were also significantly different from all other groups, and password complexity decreased with age for both genders equally. Overall, very weak password hygiene was observed, 72% of users based their password on a word or used a simple sequence of digits, and passwords of over 39% of users were found in word lists of previous leaks. Full article
(This article belongs to the Special Issue State-of-the-Art of Cybersecurity)
Article
GC- and UHPLC-MS Profiles as a Tool to Valorize the Red Alga Asparagopsis armata
by , , , , and
Appl. Sci. 2022, 12(2), 892; https://doi.org/10.3390/app12020892 (registering DOI) - 16 Jan 2022
Abstract
Asparagopsis armata Harvey is a red alga native from the southern hemisphere and then introduced in the Mediterranean Sea and the Atlantic Ocean, including the Azores Archipelago, where it is considered an invasive alga. Some studies show that the extracts exhibit antimicrobial and [...] Read more.
Asparagopsis armata Harvey is a red alga native from the southern hemisphere and then introduced in the Mediterranean Sea and the Atlantic Ocean, including the Azores Archipelago, where it is considered an invasive alga. Some studies show that the extracts exhibit antimicrobial and antifouling activities, and it is incorporated in some commercialized cosmetic products. (e.g., Ysaline®). However, knowledge of this species chemical composition is scarce. The GC-MS and UHPLC-MS profiles of both the nonpolar and polar extracts were established to contribute to this problem solution. According to the results, A. armata is rich in a great structural variety of halogenated lipophilic and aromatic compounds, some of them identified here for the first time. In the lipophilic extract, 25 compounds are identified, being the halogenated compounds and fatty acids, the two major compound families, corresponding to 54.8% and 35.7% of identified compounds (224 and 147 mg/100 g of dry algae, respectively). The 1,4-dibromobuten-1-ol and the palmitic acid are the two most abundant identified compounds (155 and 83.4 mg/100 g of dry algae, respectively). The polar extract demonstrated the richness of this species in brominated phenolics, from which the cinnamic acid derivatives are predominant. The results obtained herein open new perspectives for valuing the A. armata as a source of halogenated compounds and fatty acids, consequently improving its biotechnological and economic potential. Promoting this seaweed and the consequent increase in its demand will contribute to biodiversity conservation and ecosystem sustainability. Full article
(This article belongs to the Special Issue Advances on Applications of Bioactive Natural Compounds)
Article
Dynamic Finite Element Modelling and Vibration Analysis of Prestressed Layered Bending–Torsion Coupled Beams
by and
Appl. Mech. 2022, 3(1), 103-120; https://doi.org/10.3390/applmech3010007 (registering DOI) - 16 Jan 2022
Abstract
Free vibration analysis of prestressed, homogenous, Fiber-Metal Laminated (FML) and composite beams subjected to axial force and end moment is revisited. Finite Element Method (FEM) and frequency-dependent Dynamic Finite Element (DFE) models are developed and presented. The frequency results are compared with those [...] Read more.
Free vibration analysis of prestressed, homogenous, Fiber-Metal Laminated (FML) and composite beams subjected to axial force and end moment is revisited. Finite Element Method (FEM) and frequency-dependent Dynamic Finite Element (DFE) models are developed and presented. The frequency results are compared with those obtained from the conventional FEM (ANSYS, Canonsburg, PA, USA) as well as the Homogenization Method (HM). Unlike the FEM, the application of the DFE formulation leads to a nonlinear eigenvalue problem, which is solved to determine the system’s natural frequencies and modes. The governing differential equations of coupled flexural–torsional vibrations, resulting from the end moment, are developed using Euler–Bernoulli bending and St. Venant torsion beam theories and assuming linear harmonic motion and linearly elastic materials. Illustrative examples of prestressed layered, FML, and unidirectional composite beam configurations, exhibiting geometric bending-torsion coupling, are studied. The presented DFE and FEM results show excellent agreement with the homogenization method and ANSYS modeling results, with the DFE’s rates of convergence surpassing all. An investigation is also carried out to examine the effects of various combined axial loads and end moments on the stiffness and fundamental frequencies of the structure. An illustrative example, demonstrating the application of the presented methods to the buckling analysis of layered beams is also presented. Full article
(This article belongs to the Special Issue Mechanical Design Technologies for Beam, Plate and Shell Structures)
Article
Entropy of Artificial Intelligence
by and
Universe 2022, 8(1), 53; https://doi.org/10.3390/universe8010053 (registering DOI) - 16 Jan 2022
Abstract
We describe a model of artificial intelligence systems based on the dimension of the probability space of the input set available for recognition. In this scenario, we can understand a subset, which means that we can decide whether an object is an element [...] Read more.
We describe a model of artificial intelligence systems based on the dimension of the probability space of the input set available for recognition. In this scenario, we can understand a subset, which means that we can decide whether an object is an element of a given subset or not in an efficient way. In the machine learning (ML) process we define appropriate features, in this way shrinking the defining bit-length of classified sets during the learning process. This can also be described in the language of entropy: while natural processes tend to increase the disorder, that is, increase the entropy, learning creates order, and we expect that it decreases a properly defined entropy. Full article
(This article belongs to the Section Mathematical Physics)
Article
Multidimensional Prayer Inventory: Psychometric Properties and Clinical Applications
by , , and
Religions 2022, 13(1), 79; https://doi.org/10.3390/rel13010079 (registering DOI) - 16 Jan 2022
Abstract
Prayer is one of the most important aspects of religious/spiritual life. The psychological literature has identified various types of prayer and a few methods for measuring it. The Multidimensional Prayer Inventory (MPI) has received much attention from researchers since it allows for the [...] Read more.
Prayer is one of the most important aspects of religious/spiritual life. The psychological literature has identified various types of prayer and a few methods for measuring it. The Multidimensional Prayer Inventory (MPI) has received much attention from researchers since it allows for the capture of the most universal forms of prayer, characteristic of the Judeo-Christian tradition: Adoration, Confession, Thanksgiving, Supplication, and Reception. The aim of this article was to examine psychometric properties and clinical applications of the Polish MPI. In four studies, we established the internal structure of the MPI using Principal Component Analysis (PCA, study 1) and Confirmatory Factor Analysis (CFA, study 2), examined its validity and reliability in relation to religiousness (study 3), and analysed its clinical application (study 4). The Polish MPI has been confirmed as a reliable and valid measure of five types of prayer for use in research settings. Full article
(This article belongs to the Special Issue Prayer: A Psychological Perspective)
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Article
Plasma Proteomics in Healthy Subjects with Differences in Tissue Glucocorticoid Sensitivity Identifies a Novel Proteomic Signature
by , , , , , , , and
Biomedicines 2022, 10(1), 184; https://doi.org/10.3390/biomedicines10010184 (registering DOI) - 16 Jan 2022
Abstract
: Significant inter-individual variation in terms of susceptibility to several stress-related disorders, such as myocardial infarction and Alzheimer’s disease, and therapeutic response has been observed among healthy subjects. The molecular features responsible for this phenomenon have not been fully elucidated. Proteomics, in association [...] Read more.
: Significant inter-individual variation in terms of susceptibility to several stress-related disorders, such as myocardial infarction and Alzheimer’s disease, and therapeutic response has been observed among healthy subjects. The molecular features responsible for this phenomenon have not been fully elucidated. Proteomics, in association with bioinformatics analysis, offer a comprehensive description of molecular phenotypes with clear links to human disease pathophysiology. The aim of this study was to conduct a comparative plasma proteomics analysis of glucocorticoid resistant and glucocorticoid sensitive healthy subjects and provide clues of the underlying physiological differences. For this purpose, 101 healthy volunteers were given a very low dose (0.25 mg) of dexamethasone at midnight, and were stratified into the 10% most glucocorticoid sensitive (S) (n = 11) and 10% most glucocorticoid resistant (R) (n = 11) according to the 08:00 h serum cortisol concentrations determined the following morning. One month following the very-low dose dexamethasone suppression test, DNA and plasma samples were collected from the 22 selected individuals. Sequencing analysis did not reveal any genetic defects in the human glucocorticoid receptor (NR3C1) gene. To investigate the proteomic profile of plasma samples, we used Liquid Chromatography–Mass Spectrometry (LC-MS/MS) and found 110 up-regulated and 66 down-regulated proteins in the S compared to the R group. The majority of the up-regulated proteins in the S group were implicated in platelet activation. To predict response to cortisol prior to administration, a random forest classifier was developed by using the proteomics data in order to distinguish S from R individuals. Apolipoprotein A4 (APOA4) and gelsolin (GSN) were the most important variables in the classification, and warrant further investigation. Our results indicate that a proteomics signature may differentiate the S from the R healthy subjects, and may be useful in clinical practice. In addition, it may provide clues of the underlying molecular mechanisms of the chronic stress-related diseases, including myocardial infarction and Alzheimer’s disease. Full article
(This article belongs to the Section Molecular and Translational Medicine)
Article
Enhanced Recognition of Amputated Wrist and Hand Movements by Deep Learning Method Using Multimodal Fusion of Electromyography and Electroencephalography
by , , , , and
Sensors 2022, 22(2), 680; https://doi.org/10.3390/s22020680 (registering DOI) - 16 Jan 2022
Abstract
Motion classification can be performed using biometric signals recorded by electroencephalography (EEG) or electromyography (EMG) with noninvasive surface electrodes for the control of prosthetic arms. However, current single-modal EEG and EMG based motion classification techniques are limited owing to the complexity and noise [...] Read more.
Motion classification can be performed using biometric signals recorded by electroencephalography (EEG) or electromyography (EMG) with noninvasive surface electrodes for the control of prosthetic arms. However, current single-modal EEG and EMG based motion classification techniques are limited owing to the complexity and noise of EEG signals, and the electrode placement bias, and low-resolution of EMG signals. We herein propose a novel system of two-dimensional (2D) input image feature multimodal fusion based on an EEG/EMG-signal transfer learning (TL) paradigm for detection of hand movements in transforearm amputees. A feature extraction method in the frequency domain of the EEG and EMG signals was adopted to establish a 2D image. The input images were used for training on a model based on the convolutional neural network algorithm and TL, which requires 2D images as input data. For the purpose of data acquisition, five transforearm amputees and nine healthy controls were recruited. Compared with the conventional single-modal EEG signal trained models, the proposed multimodal fusion method significantly improved classification accuracy in both the control and patient groups. When the two signals were combined and used in the pretrained model for EEG TL, the classification accuracy increased by 4.18–4.35% in the control group, and by 2.51–3.00% in the patient group. Full article
(This article belongs to the Section Intelligent Sensors)
Article
Serpinin in the Skin
by , , , , , , , , and
Biomedicines 2022, 10(1), 183; https://doi.org/10.3390/biomedicines10010183 (registering DOI) - 16 Jan 2022
Abstract
The serpinins are relatively novel peptides generated by proteolytic processing of chromogranin A and they are comprised of free serpinin, serpinin-RRG and pGlu-serpinin. In this study, the presence and source of these peptides were studied in the skin. By Western blot analysis, a [...] Read more.
The serpinins are relatively novel peptides generated by proteolytic processing of chromogranin A and they are comprised of free serpinin, serpinin-RRG and pGlu-serpinin. In this study, the presence and source of these peptides were studied in the skin. By Western blot analysis, a 40 kDa and a 50 kDa protein containing the sequence of serpinin were detected in the trigeminal ganglion and dorsal root ganglia in rats but none in the skin. RP-HPLC followed by EIA revealed that the three serpinins are present in similar, moderate amounts in rat dorsal root ganglia, whereas in the rat skin, free serpinin represents the predominant molecular form. There were abundant serpinin-positive cells in rat dorsal root ganglia and colocalization with substance P was evident. However, much more widespread distribution of the serpinins was found in dorsal root ganglia when compared with substance P. In the skin, serpinin immunoreactivity was found in sensory nerves and showed colocalization with substance P; as well, some was present in autonomic nerves. Thus, although not exclusively, there is evidence that serpinin is a constituent of the sensory innervation of the skin. The serpinins are biologically highly active and might therefore be of functional significance in the skin. Full article
(This article belongs to the Special Issue Neuropeptides in Biomedicines)
Article
Endophytic Colletotrichum Species from Aquatic Plants in Southwest China
by , , , and
J. Fungi 2022, 8(1), 87; https://doi.org/10.3390/jof8010087 (registering DOI) - 16 Jan 2022
Abstract
Colletotrichum species are plant pathogens, saprobes, and endophytes in many economically important hosts. Many studies have investigated the diversity and pathogenicity of Colletotrichum species in common ornamentals, fruits, and vegetables. However, Colletotrichum species occurring in aquatic plants are not well known. During the [...] Read more.
Colletotrichum species are plant pathogens, saprobes, and endophytes in many economically important hosts. Many studies have investigated the diversity and pathogenicity of Colletotrichum species in common ornamentals, fruits, and vegetables. However, Colletotrichum species occurring in aquatic plants are not well known. During the investigation of the diversity of endophytic fungi in aquatic plants in southwest China, 66 Colletotrichum isolates were obtained from aquatic plants there, and 26 of them were selected for sequencing and analyses of actin (ACT), chitin synthase (CHS-1), glyceraldehyde-3-phosphate dehydrogenase (GAPDH), the internal transcribed spacer (ITS) region, and β-tubulin (TUB2) genomic regions. Based on morphological characterization and multi-locus phylogenetic analyses, 13 Colletotrichum species were recognized, namely, C. baiyuense sp. nov., C. casaense sp. nov., C. demersi sp. nov., C. dianense sp. nov., C. fructicola, C. garzense sp. nov., C. jiangxiense, C. karstii, C. philoxeroidis sp. nov., C. spicati sp. nov., C. tengchongense sp. nov., C. vulgaris sp. nov., C. wuxuhaiense sp. nov. Two species complexes, the C. boninense species complex and C. gloeosporioides species complex, were found to be associated with aquatic plants. Pathogenicity tests revealed a broad diversity in pathogenicity and aggressiveness among the eight new Colletotrichum species. Full article
(This article belongs to the Special Issue Fungal Biodiversity and Ecology 2.0)

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