bluuebunny/crossref_metadata_2025 huggingface dataset:
Identification of Typical and Anomalous Patterns in Electricity Consumption
Authors: Fidalgo José Nuno , Macedo Pedro
Published: March 2022

Nontechnical losses in electricity distribution networks are often associated with a countries’ socioeconomic situation. Although the amount of global losses is usually known, the separation between technical and commercial (nontechnical) losses will remain one of the main challenges for DSO until smart grids become fully implemented and operational. The most common origins of commercial losses are energy theft and deliberate or accidental failures of energy measuring equipment. In any case, the consequences can be regarded as consumption anomalies. The work described in this paper aims to answer a request from a DSO, for the development of tools to detect consumption anomalies at end-customer facilities (HV, MV and LV), invoking two types of assessment. The first consists of the identification of typical patterns in the set of consumption profiles of a given group or zone and the detection of atypical consumers (outliers) within it. The second assessment involves the exploration of the load diagram evolution of each specific consumer to detect changes in the consumption pattern that could represent situations of probable irregularities. After a representative period, typically 12 months, these assessments are repeated, and the results are compared to the initial ones. The eventual changes in the typical classes or consumption scales are used to build a classifier indicating the risk of anomaly.
Feature Mining: A Novel Training Strategy for Convolutional Neural Network
Authors: Xie Tianshu , Deng Jiali , Cheng Xuan , Liu Minghui , Wang Xiaomin , Liu Ming
Published: March 2022

In this paper, we propose a novel training strategy named Feature Mining for convolutional neural networks (CNNs) that aims to strengthen the network’s learning of the local features. Through experiments, we found that different parts of the feature contain different semantics, while the network will inevitably lose a large amount of local information during feedforward propagation. In order to enhance the learning of the local features, Feature Mining divides the complete feature into two complementary parts and reuses this divided feature to make the network capture different local information; we call the two steps Feature Segmentation and Feature Reusing. Feature Mining is a parameter-free method with a plug-and-play nature and can be applied to any CNN model. Extensive experiments demonstrated the wide applicability, versatility, and compatibility of our method.
Improving Image Quality Assessment Based on the Combination of the Power Spectrum of Fingerprint Images and Prewitt Filter
Authors: Shen Ting-Wei , Li Ching-Chuan , Lin Wan-Fu , Tseng Yu-Hao , Wu Wen-Fang , Wu Sean , Tseng Zong-Liang , Hsu Mao-Hsiu
Published: March 2022

The assessment of fingerprint image quality is critical for most fingerprint applications. It has an impact on the performance and compatibility of fingerprint recognition, authentication, and built-in cryptosystems. This paper developed an improved fingerprint image quality assessment derived from the image power spectrum approach and combined it with the Prewitt filter and an improved weighting method. The conventional image power spectrum approach and our proposed approach were implemented for accuracy and reliability tests using good, faulty, and blurred fingerprint images. The experimental results showed the proposed algorithm accurately identified the sharpness of fingerprint images and improved the average difference in FIQMs to 61% between three different levels of blurred fingerprints compared with that achieved by a conventional algorithm.
On the Black-Box Challenge for Fraud Detection Using Machine Learning (I): Linear Models and Informative Feature Selection
Authors: Chaquet-Ulldemolins Jacobo , Gimeno-Blanes Francisco-Javier , Moral-Rubio Santiago , Muñoz-Romero Sergio , Rojo-Álvarez José-Luis
Published: March 2022

Artificial intelligence (AI) is rapidly shaping the global financial market and its services due to the great competence that it has shown for analysis and modeling in many disciplines. What is especially remarkable is the potential that these techniques could offer to the challenging reality of credit fraud detection (CFD); but it is not easy, even for financial institutions, to keep in strict compliance with non-discriminatory and data protection regulations while extracting all the potential that these powerful new tools can provide to them. This reality effectively restricts nearly all possible AI applications to simple and easy to trace neural networks, preventing more advanced and modern techniques from being applied. The aim of this work was to create a reliable, unbiased, and interpretable methodology to automatically evaluate CFD risk. Therefore, we propose a novel methodology to address the mentioned complexity when applying machine learning (ML) to the CFD problem that uses state-of-the-art algorithms capable of quantifying the information of the variables and their relationships. This approach offers a new form of interpretability to cope with this multifaceted situation. Applied first is a recent published feature selection technique, the informative variable identifier (IVI), which is capable of distinguishing among informative, redundant, and noisy variables. Second, a set of innovative recurrent filters defined in this work are applied, which aim to minimize the training-data bias, namely, the recurrent feature filter (RFF) and the maximally-informative feature filter (MIFF). Finally, the output is classified by using compelling ML techniques, such as gradient boosting, support vector machine, linear discriminant analysis, and linear regression. These defined models were applied both to a synthetic database, for better descriptive modeling and fine tuning, and then to a real database. Our results confirm that our proposal yields valuable interpretability by identifying the informative features’ weights that link original variables with final objectives. Informative features were living beyond one’s means, lack or absence of a transaction trail, and unexpected overdrafts, which are consistent with other published works. Furthermore, we obtained 76% accuracy in CFD, which represents an improvement of more than 4% in the real databases compared to other published works. We conclude that with the use of the presented methodology, we do not only reduce dimensionality, but also improve the accuracy, and trace relationships among input and output features, bringing transparency to the ML reasoning process. The results obtained here were used as a starting point for the companion paper which reports on our extending the interpretability to nonlinear ML architectures.
Detection of Chinese Deceptive Reviews Based on Pre-Trained Language Model
Authors: Weng Chia-Hsien , Lin Kuan-Cheng , Ying Jia-Ching
Published: March 2022

The advancement of the Internet has changed people’s ways of expressing and sharing their views with the world. Moreover, user-generated content has become a primary guide for customer purchasing decisions. Therefore, motivated by commercial interest, some sellers have started manipulating Internet ratings by writing false positive reviews to encourage the sale of their goods and writing false negative reviews to discredit competitors. These reviews are generally referred to as deceptive reviews. Deceptive reviews mislead customers in purchasing goods that are inconsistent with online information and thus obstruct fair competition among businesses. To protect the right of consumers and sellers, an effective method is required to automate the detection of misleading reviews. Previously developed methods of translating text into feature vectors usually fail to interpret polysemous words, which leads to such functions being obstructed. By using dynamic feature vectors, the present study developed several misleading review-detection models for the Chinese language. The developed models were then compared with the standard detection-efficiency models. The deceptive reviews collected from various online forums in Taiwan by previous studies were used to test the models. The results showed that the models proposed in this study can achieve 0.92 in terms of precision, 0.91 in terms of recall, and 0.91 in terms of F1-score. The improvement rate of our proposal is higher than 20%. Accordingly, we prove that our proposal demonstrated improved performance in detecting misleading reviews, and the models based on dynamic feature vectors were capable of more accurately capturing semantic terms than the conventional models based on the static feature vectors, thereby enhancing effectiveness.
Genetic Algorithm versus Discrete Particle Swarm Optimization Algorithm for Energy-Efficient Moving Object Coverage Using Mobile Sensors
Authors: Chen Hao-Wei , Liang Chiu-Kuo
Published: March 2022

This paper addresses the challenge of moving objects in a mobile wireless sensor network, considering the deployment of a limited number of mobile wireless sensor nodes within a predetermined area to provide coverage for moving objects traveling on a predetermined trajectory. Because of the insufficient number and limited sensing range of mobile wireless sensors, the entire object’s trajectory cannot be covered by all deployed sensors. To address this problem and provide complete coverage, sensors must move from one point of the trajectory to another. The frequent movement quickly depletes the sensors’ batteries. Therefore, solving the moving object coverage problem requires an optimized movement repertoire where (1) the total moving distance is minimized and (2) the remaining energy is also as balanced as possible for mobile sensing. Herein, we used a genetic algorithm (GA) and a discrete particle swarm optimization algorithm (DPSO) to manage the complexity of the problem, compute feasible and quasi-optimal trajectories for mobile sensors, and determine the demand for movement among nodes. Simulations revealed that the GA produced trajectories significantly superior to those produced by the DPSO in terms of total traveled distance and balance of residual energy.
Design of Novel Cooling Systems Based on Metal Plates with Channels of Shapes Inspired by Nature
Authors: Guil-Pedrosa José Félix , García-Gutiérrez Luis Miguel , Cano-Pleite Eduardo , Soria-Verdugo Antonio
Published: March 2022

The effect of the channel shape of aluminum plates on cooling capacity was evaluated by studying different configurations. Common shapes of the channel, such as square and fork shapes, were compared with novel configurations inspired by shapes found in nature, specifically the shape of the outline of flowers, inspired these new configurations, consisting of channels with crateriform, salverform, and cruciform shapes. The aim of the study is to evaluate the effect of the channel shape on the cooling capacity of the metal plate. To that end, all the configurations were analyzed from a geometrical point of view, determining the minimum distance of each point across the plate to the channel. A finite difference method was implemented to study both transient and steady state heat dissipation across the plates for each configuration. Even though the effect of the channel shape on the average temperature of the plate is slight, the maximum temperature, the size and location of hot spots, and the temperature homogeneity of the plate are strongly affected by the shape of the channel through which the cooling fluid is circulated. A reduction in the maximum temperature of the plate during transient cooling of around 2 °C for the crateriform and salverform channels and approximately 4.5 °C for the cruciform channel can be attained, compared to the standard configurations. The steady state heat dissipation analysis concluded that the crateriform and salverform configurations reduced the maximum variation in temperature of the common configurations by roughly 15%, whereas a reduction of approximately 28% could be reached by the cruciform configuration. Regarding the homogeneity of temperature across the plate, a reduction up to 34.5% of the index of uniform temperature can be attained using the novel configurations during the steady state refrigeration of the plate. The cruciform channel is the optimal configuration for both transient and steady state cooling processes, reducing the size and temperature of hot spots and improving the temperature homogeneity of the plate, a result already anticipated by the geometrical analysis. In fact, the main conclusions attained from the cooling study are in good agreement with the results of the geometrical analysis. Therefore, the geometrical analysis was found to be a simple and reliable method to design the shape of channels of a cooling system.
The Impact of Waxy Wheat Flour, Inulin and Guar Gum on Post-Prandial Glycaemic and Satiety Indices, Sensory Attributes and Shelf Life of Tandoori and Pita Breads
Authors: Galali Yaseen , Rees Gail , Kuri Victor
Published: March 2022

The purpose of this study was to develop two different functional breads (Pita and Tandoori) supplemented with novel functional ingredients: waxy wheat flour (15%), inulin (8%) and guar gum (2%) independently and in combination. The breads were submitted to sensory characteristics, shelf life, glycaemic and satiety indices assessments. Both guar gum and inulin independently and in combination significantly (p < 0.05) decreased post-prandial blood glucose and glycemic response of Pita (GI of guar gum bread was 55%, inulin 57%) and Tandoori (GI of guar gum bread was 57% and inulin bread was 60%) compared to the control breads (GI 100%). Moreover, the results of the area under the curve of satiety showed that the addition of functional ingredients increased satiety levels as follows: for Pita, control was 355, a combination of all ingredients was 418, inulin was 451 and guar gum was 452; for Tandoori, control was 329, a combination of all ingredients was 420, inulin was 381 and guar gum was 390. The results showed that all sensory characteristics were improved, and breads were acceptable (all obtained more than five points) when the highest proportions of ingredients were added. Similarly, the shelf life of supplemented Pita and Tandoori breads was improved with the addition of ingredients. Therefore, the functional ingredients such as inulin and guar gum can be used independently and in combination to reduce GI and increase satiety of Pita and Tandoori bread with acceptable quality and shelf life.
URANS Analysis of a Launch Vehicle Aero-Acoustic Environment
Authors: Escartí-Guillem Mara S. , García-Raffi Luis M. , Hoyas Sergio
Published: March 2022

Predicting and mitigating acoustic levels become critical because of the harsh acoustic environment during space vehicle lift-off. This paper aimed to study the aero-acoustic environment during a rocket lift-off. The sound propagation within a launch event was studied using dedicated computational fluid dynamics (CFD). The resolution of all the phenomena that occur is unfeasible. We discuss the turbulence simplification and propose a feasible simulation through an unsteady Reynolds-averaged Navier–Stokes (URANS) model. The results were validated with experimental data showing a good correlation near the fairing surface and an improvable accuracy in the far field. To assess noise generation, the main shock waves were identified, and the evolution of the generated sound pressure was assessed. Moreover, vertical directivity was revealed by data analysis of the pressure field surrounding the fairing.
The Role of Auditory and Visual Components in Reading Training: No Additional Effect of Synchronized Visual Cue in a Rhythm-Based Intervention for Dyslexia
Authors: Cancer Alice , De Salvatore Marinella , Granocchio Elisa , Andreoli Luca , Antonietti Alessandro , Sarti Daniela
Published: March 2022

Based on the transfer effects of music training on the phonological and reading abilities of children with dyslexia, a computerized rhythmic intervention—the Rhythmic Reading Training (RRT)—was developed, in which reading exercises are combined with a rhythmic synchronization task. This rehabilitation program was previously tested in multiple controlled clinical trials, which confirmed its effectiveness in improving the reading skills of children and adolescents with dyslexia. In order to assess the specific contribution of the visual component of the training, namely, the presence of a visual cue supporting rhythmic synchronization, a controlled experimental study was conducted. Fifty-eight students with dyslexia aged 8 to 13 years were assigned to three conditions: (a) RRT auditory and visual condition, in which a visual cue was synchronized with the rhythmic stimulation; (b) RRT auditory-only condition, in which the visual cue was excluded; (c) no intervention. Comparisons of the participants’ performance before, after, and 3 months after the end of the intervention period revealed the significant immediate and long-term effect of both RRT conditions on reading, rapid naming, phonological, rhythmic, and attentional abilities. No significant differences were found between visual and auditory conditions, therefore showing no additional contribution of the visual component to the improvements induced by the RRT. Clinical Trial ID: NCT04995991.
Error-Correction Coding Using Polynomial Residue Number System
Authors: Kalmykov Igor Anatolyevich , Pashintsev Vladimir Petrovich , Tyncherov Kamil Talyatovich , Olenev Aleksandr Anatolyevich , Chistousov Nikita Konstantinovich
Published: March 2022

There has been a tendency to use the theory of finite Galois fields, or GF(2n), in cryptographic ciphers (AES, Kuznyechik) and digital signal processing (DSP) systems. It is advisable to use modular codes of the polynomial residue number system (PRNS). Modular codes of PRNS are arithmetic codes in which addition, subtraction and multiplication operations are performed in parallel on the bases of the code, which are irreducible polynomials. In this case, the operands are small-bit residues. However, the independence of calculations on the bases of the code and the lack of data exchange between the residues can serve as the basis for constructing codes of PRNS capable of detecting and correcting errors that occur during calculations. The article will consider the principles of constructing redundant codes of the polynomial residue number system. The results of the study of codes of PRNS with minimal redundancy are presented. It is shown that these codes are only able to detect an error in the code combination of PRNS. It is proposed to use two control bases, the use of which allows us to correct an error in any residue of the code combination, in order to increase the error-correction abilities of the code of the polynomial residue number system. Therefore, the development of an algorithm for detecting and correcting errors in the code of the polynomial residue number system, which allows for performing this procedure based on modular operations that are effectively implemented in codes of PRNS, is an urgent task.
Reproducibility of Fruit Spirit Distillation Processes
Authors: Heller Daniel , Einfalt Daniel
Published: March 2022

Fruit spirit distillations processes are based on physical principles of heat and mass transfer. These principles are decisive for the separation of desired and undesired aroma compounds, which affect the quality of the distilled product. It is mandatory to control heat and mass transfer parameters to be able to perform fruit spirit distillation processes in a reproducible manner and to achieve equal products with similar volatile compound compositions repeatedly. Up to now, only limited information is available on the magnitude of reproducibility errors since fruit spirit distillation columns are typically not equipped with a suitable control or monitoring technique. We upgraded a batch distillation column with digitized instrumentation and a control technique to be able to control crucial parameters such as thermal energy inputs and reflux rates. This study aimed to identify whether control over two distillation parameters has the potential to enable us to perform distillation processes repeatedly. This study analyzed the magnitude of reproducibility errors for (i) six monitored distillation process parameters and (ii) 13 quantified volatile compounds in the product between duplicated distillation runs performed with equal setups. A total of eight different distillations were performed in duplicate (n = 16), while the six distillation parameters were monitored and logged every ten seconds. The produced distillates were equally subsampled into 20 fractions and each fraction analyzed for 13 volatile compound concentrations. Based on a dataset of 28,600 monitored duplicate distillation process data points, this study showed that process parameters can indeed be replicated with a median relative standard deviation (RSD) of <0.1% to 7% when two crucial process parameters are controlled. The comparison of 1540 volatile compound concentrations in the product fractions showed a reproducibility error with an average median RSD of 9 ± 8%. This illustrated that by gaining control over thermal energy input and reflux rates, the reproducibility of fruit spirit distillation processes and their associated products can largely be met. It is advisable to equip distillation columns with a suitable control technique to be able to reproduce the performance of fruit spirit distillations.
Recent Developments and Current Applications of Hydrogels in Osteoarthritis
Authors: Zhao Tianhao , Wei Zhanqi , Zhu Wei , Weng Xisheng
Published: March 2022

Osteoarthritis (OA) is a common degenerative joint disease that causes disability if left untreated. The treatment of OA currently requires a proper delivery system that avoids the loss of therapeutic ingredients. Hydrogels are widely used in tissue engineering as a platform for carrying drugs and stem cells, and the anatomical environment of the limited joint cavity is suitable for hydrogel therapy. This review begins with a brief introduction to OA and hydrogels and illustrates the effects, including the analgesic effects, of hydrogel viscosupplementation on OA. Then, considering recent studies of hydrogels and OA, three main aspects, including drug delivery systems, mesenchymal stem cell entrapment, and cartilage regeneration, are described. Hydrogel delivery improves drug retention in the joint cavity, making it possible to deliver some drugs that are not suitable for traditional injection; hydrogels with characteristics similar to those of the extracellular matrix facilitate cell loading, proliferation, and migration; hydrogels can promote bone regeneration, depending on their own biochemical properties or on loaded proregenerative factors. These applications are interlinked and are often researched together.
Investigating the Processing Potential of Ethiopian Agricultural Residue Enset/Ensete ventricosum for Biobutanol Production
Authors: Seid Nebyat , Griesheimer Pia , Neumann Anke
Published: March 2022

The Enset plant is a potential food source for about 20 million Ethiopians. A massive amount of residual byproduct is discarded from traditional Ethiopian Enset food processing. This study shows a compositional analysis of Enset biomass and its use for biobutanol production. The Enset biomass was pretreated with 2% (w/v) NaOH or 2% (v/v) H2SO4 and subjected to enzymatic hydrolysis. The enzymatic hydrolysates were then fermented anaerobically by C. saccharoperbutylacetonicum DSM 14923. The majority of Enset biomass waste samples contained 36–67% cellulose, 16–20% hemicelluloses, and less than 6.8% lignin. In all alkali-pretreated Enset biomass samples, the enzyme converted 80–90% of the biomass to glucose within 24 h, while it took 60 h to convert 48–80% of the acid-pretreated Enset biomass. In addition, the alkali pretreatment method released more glucose than the acid pretreatment in all Enset biomass samples. After 72 h of ABE fermentation, 2.8 g/L acetone, 9.9 g/L butanol, and 1.6 g/L ethanol were produced from mixed Enset waste hydrolysate pretreated with alkali, achieving an ABE yield of 0.32 g/g and productivity of 0.2 g × L−1 × h−1, showing the first value of butanol produced from Enset biomass in the literature.
Effects of Jump-Rope-Specific Footwear Selection on Lower Extremity Biomechanics
Authors: Yu Hai-Bin , Li Jing , Zhang Rui , Hao Wei-Ya , Lin Jian-Zhi , Tai Wei-Hsun
Published: March 2022

Footwear is among the most important equipment in sports to decrease injuries and enhance performance during exercise. In this study, we investigated differences in lower extremity plantar pressure and muscle activations during jump rope activities. Ten participants performed jump rope under two landing conditions with different footwear. A force platform (AMTI, 1000 Hz), a Novel Pedar-X system (Nove, 100 Hz), and a wireless electromyography (EMG) system (Noraxon, 1500 Hz) were used to measure biomechanical parameters during the jump rope exercise. Vertical ground reaction forces (vGRF), plantar pressure, and lower extremity muscle activations were analyzed. One-leg landing resulted in a significantly greater vGRF and shorter fly time than two-leg landing (p < 0.05). A significantly higher peak pressure and lesser toe (LT) area pressure was shown with the jumping shoe (all p < 0.05), but lower plantar pressure resulted in the middle foot area (p < 0.05). The EMG results of tibialis anterior (TA) were significantly greater with one-leg landing (all p < 0.05) during the pre- and background activity (BGA) phases. The results suggest that plantar pressure distribution should be considered when deciding on footwear during jump rope exercises, but care should be taken with regards to recovery after repeated collisions and fatigue. The jumping shoe provides benefits in terms of decreased plantar pressure sustained during jump rope exercises.
High Proportions of Radiation-Resistant Strains in Culturable Bacteria from the Taklimakan Desert
Authors: Liu Yang , Chen Tuo , Li Juan , Wu Minghui , Liu Guangxiu , Zhang Wei , Zhang Binglin , Zhang Songlin , Zhang Gaosen
Published: March 2022

The Taklimakan Desert located in China is the second-largest shifting sand desert in the world and is known for its harsh conditions. Types of γ-rays or UV radiation-resistant bacterial strains have been isolated from this desert. However, there is no information regarding the proportions of the radiation-resistant strains in the total culturable microbes. We isolated 352 bacterial strains from nine sites across the Taklimakan Desert from north to south. They belong to Actinobacteria, Firmicutes, Proteobacteria, and Bacteroidetes. The phylum Actinobacteria was the most predominant in abundance and Firmicutes had the highest species richness. Bacteroidetes had the lowest abundance and was found in four sites only, while the other three phyla were found in every site but with different distribution profiles. After irradiating with 1000 J/m2 and 6000 J/m2 UV-C, the strains with survival rates higher than 10% occupied 72.3% and 36.9% of all culturable bacteria, respectively. The members from Proteobacteria had the highest proportions, with survival rates higher than 10%. After radiation with 10 kGy γ-rays, Kocuria sp. TKL1057 and Planococcus sp. TKL1152 showed higher radiation-resistant capabilities than Deinococcus radiodurans R1. Besides obtaining several radiation-resistant extremophiles, this study measured the proportions of the radiation-resistant strains in the total culturable microbes for the first time. This study may help to better understand the origin of radioresistance, especially by quantitatively comparing proportions of radiation-resistant extremophiles from different environments in the future.
Significance of Lipid Fatty Acid Composition for Resistance to Winter Conditions in Asplenium scolopendrium
Authors: Voronkov Alexander , Ivanova Tatiana
Published: March 2022

Ferns are one of the oldest land plants. Among them, there are species that, during the course of evolution, have adapted to living in temperate climates and under winter conditions. Asplenium scolopendrium is one such species whose fronds are able to tolerate low subzero temperatures in winter. It is known that the resistance of ferns to freezing is associated with their prevention of desiccation via unique properties of the xylem and effective photoprotective mechanisms. In this work, the composition of A. scolopendrium lipid fatty acids (FAs) at different times of the year was studied by gas–liquid chromatography with mass spectrometry to determine their role in the resistance of this species to low temperatures. During the growing season, the polyunsaturated FA content increased significantly. This led to increases in the unsaturation and double-bond indices by winter. In addition, after emergence from snow, medium-chain FAs were found in the fronds. Thus, it can be speculated that the FA composition plays an important role in the adaptation of A. scolopendrium to growing conditions and preparation for successful wintering.
RETRACTED: Kim et al. Advantage of Species Diversification to Facilitate Sustainable Development of Aquaculture Sector. Biology 2022, 11, 368
Authors: Kim Dae-Young , Shinde Surendra , Kadam Avinash , Saratale Rijuta , Saratale Ganesh , Kumar Manu , Syed Asad , Bahkali Ali , Ghodake Gajanan
Published: March 2022

Biology retracts the article “Advantage of Species Diversification to Facilitate Sustainable Development of Aquaculture Sector” cited above [...]
Conformational Rearrangements in the Redox Cycling of NADPH-Cytochrome P450 Reductase from Sorghum bicolor Explored with FRET and Pressure-Perturbation Spectroscopy
Authors: Zhang Bixia , Kang ChulHee , Davydov Dmitri R.
Published: March 2022

NADPH-cytochrome P450 reductase (CPR) from Sorghum bicolor (SbCPR) serves as an electron donor for cytochrome P450 essential for monolignol and lignin production in this biofuel crop. The CPR enzymes undergo an ample conformational transition between the closed and open states in their functioning. This transition is triggered by electron transfer between the FAD and FMN and provides access of the partner protein to the electron-donating FMN domain. To characterize the electron transfer mechanisms in the monolignol biosynthetic pathway better, we explore the conformational transitions in SbCPR with rapid scanning stop-flow and pressure-perturbation spectroscopy. We used FRET between a pair of donor and acceptor probes incorporated into the FAD and FMN domains of SbCPR, respectively, to characterize the equilibrium between the open and closed states and explore its modulation in connection with the redox state of the enzyme. We demonstrate that, although the closed conformation always predominates in the conformational landscape, the population of open state increases by order of magnitude upon the formation of the disemiquinone state. Our results are consistent with several open conformation sub-states differing in the volume change (ΔV0) of the opening transition. While the ΔV0 characteristic of the oxidized enzyme is as large as −88 mL/mol, the interaction of the enzyme with the nucleotide cofactor and the formation of the double-semiquinone state of CPR decrease this value to −34 and −18 mL/mol, respectively. This observation suggests that the interdomain electron transfer in CPR increases protein hydration, while promoting more open conformation. In addition to elucidating the functional choreography of plant CPRs, our study demonstrates the high exploratory potential of a combination of the pressure-perturbation approach with the FRET-based monitoring of protein conformational transitions.
Alterations in Proteostasis System Components in Peripheral Blood Mononuclear Cells in Parkinson Disease: Focusing on the HSP70 and p62 Levels
Authors: Vavilova Julia D. , Boyko Anna A. , Troyanova Natalya I. , Ponomareva Natalya V. , Fokin Vitaly F. , Fedotova Ekaterina Y. , Streltsova Maria A. , Kust Sofya A. , Grechikhina Maria V. , Shustova Olga A. , Azhikina Tatyana L. , Kovalenko Elena I. , Sapozhnikov Alexander M.
Published: March 2022

Parkinson disease (PD) is attributed to a proteostasis disorder mediated by α-synuclein accumulating in a specific brain region. PD manifestation is often related to extraneuronal alterations, some of which could be used as diagnostic or prognostic PD biomarkers. In this work, we studied the shifts in the expression of proteostasis-associated chaperones of the HSP70 family and autophagy-dependent p62 protein values in the peripheral blood mononuclear cells (PBMC) of mild to moderate PD patients. Although we did not detect any changes in the intracellular HSP70 protein pool in PD patients compared to non-PD controls, an increase in the transcriptional activity of the stress-associated HSPA1A/B and HSPA6 genes was observed in these cells. Basal p62 content was found to be increased in PD patients’ PBMC, similarly to the p62 level in substantia nigra neural cells in PD. Moreover, the spontaneous apoptosis level was increased among PBMC and positively correlated with the p62 intracellular level in the PD group. A combined HSPA6- and p62-based analysis among 26 PD patients and 36 age-matched non-PD controls pointed out the diagnostic significance of these markers, with intermediate sensitivity and high specificity of this combination when observing patients diagnosed with PD.
Co-Application of Tetramycin and Chitosan in Controlling Leaf Spot Disease of Kiwifruit and Enhancing Its Resistance, Photosynthesis, Quality and Amino Acids
Authors: Zhang Cheng , Li Haitao , Wu Xiaomao , Su Yue , Long Youhua
Published: March 2022

Leaf spot disease caused by Lasiodiplodia theobromae and Alternaria tenuissima is a seriously fungal disease in kiwifruit production. In this study, the co-application of tetramycin and chitosan against leaf spot disease in kiwifruit and its effects on the disease resistance, photosynthesis, quality and amino acids of kiwifruit were investigated. The results show that tetramycin exhibited an excellent antifungal activity against L. theobromae and A. tenuissima with EC50 values of 2.37 and 0.16 mg kg−1. In the field, the foliar co-application of tetramycin and chitosan could effectively control leaf spot disease with control efficacy of 89.44% by spraying 0.3% tetramycin aqueous solutions (AS) 5000 time liquid + chitosan 100 time liquid, which was significantly (ANOVA, p < 0.01) higher than 79.80% of 0.3% tetramycin AS 5000 time liquid and 56.61% of chitosan 100 time liquid. Simultaneously, the co-application of tetramycin and chitosan was more effective than tetramycin or chitosan alone in enhancing the disease resistance and photosynthesis of kiwifruit leaves, as well as improving the quality and amino acids of kiwifruit fruits. This work highlights that chitosan is a practicable, cost-effective and eco-friendly adjuvant of tetramycin for controlling leaf spot disease of kiwifruit, enhancing resistance and photosynthesis of leaves and improving quality and amino acids of fruits.
Modulation of Macrophage Response by Copper and Magnesium Ions in Combination with Low Concentrations of Dexamethasone
Authors: Díez-Tercero Leire , Delgado Luis M. , Perez Roman A.
Published: March 2022

Macrophages have been deemed crucial for correct tissue regeneration, which is a complex process with multiple overlapping phases, including inflammation. Previous studies have suggested that divalent ions are promising cues that can induce an anti-inflammatory response, since they are stable cues that can be released from biomaterials. However, their immunomodulatory potential is limited in a pro-inflammatory environment. Therefore, we investigated whether copper and magnesium ions combined with low concentrations of the anti-inflammatory drug, dexamethasone (dex), could have a synergistic effect in macrophage, with or without pro-inflammatory stimulus, in terms of morphology, metabolic activity and gene expression. Our results showed that the combination of copper and dex strongly decreased the expression of pro-inflammatory markers, while the combination with magnesium upregulated the expression of IL-10. Moreover, in the presence of a pro-inflammatory stimulus, the combination of copper and dex induced a strong TNF-α response, suggesting an impairment of the anti-inflammatory actions of dex. The combination of magnesium and dex in the presence of a pro-inflammatory stimulus did not promote any improvement in comparison to dex alone. The results obtained in this study could be relevant for tissue engineering applications and in the design of platforms with a dual release of divalent ions and small molecules.
The Impact of SARS-CoV-2 Infection on Youth Mental Health: A Narrative Review
Authors: Brasso Claudio , Bellino Silvio , Blua Cecilia , Bozzatello Paola , Rocca Paola
Published: March 2022

Background: COVID-19 pandemic has affected the physical health, psychological wellbeing, and mental health of the whole population. Young people are among those most at risk of developing mental health symptoms or disorders related to the pandemic. Purpose: the present narrative review is aimed at providing an updated overview of the current literature concerning the psychological impact of the SARS-CoV-2 infection but also of the COVID-19 outbreak, environmental restriction, and social distancing on mental health outcomes among the youth population aged between 15 and 25 years. Methods: in December 2021, an electronic search on this topic was performed on PubMed. Relevant publications from January 2020 until December 2021 were included. Findings: 53 cross-sectional studies, 26 longitudinal studies, 4 ecological studies, 1 qualitative study, and 1 systematic review were included. We found many methodological limitations in the studies included, especially poor choice of study samples and short follow-ups. Little literature was in support of a strong relationship between SARS-CoV-2 infection and consequences on youth mental health. On the contrary, many studies showed how extraordinary measures to limit the spread of the virus have impacted young people in terms of onset of new mental disorders and symptoms, suicidality, and access to emergency psychiatric services. Depressive and anxiety symptoms and disorders show the greatest increase in incidence, especially in girls and young women. Conclusions: it seems important to pay attention to the mental health of young people in relation to the consequences of the COVID-19 pandemic. However, studies with more robust methodologies and longer follow-ups are needed to establish precise indications for targeted interventions in this context.
‘You’re Not Alone for China’: The First Song in Times of COVID-19 to Keep the Faith in a World Crying in Silence
Authors: Giménez-Llort Lydia
Published: March 2022

Collective mourning is an expression of societal maturity, cohesion, and respect. The world is in grief, but in early January 2020, before nobody could even imagine that SARS-CoV-2 would turn into the COVID-19 pandemic, a music video version of a pop ballad about love and isolation was spread across a Chinese social network. The song ‘You Are Not Alone’ was adapted as a cover by young foreigners living in China to express their support to bereaved families and frontline workers and encourage the people of China, their second home. At that time, the rest of the world looked to distant China but could hardly expect to face the same adversity months later. The authors reported that the music video was a spontaneous artistic expression copying such traumatic events and the mourning process. The present work analyses how the music was blended with lyrics and images describing the outbreak in Wuhan to reach their goal. The original song and this shortened version for China were compared regarding musical and lyric structures and main characteristics. Additionally, an analysis of the two videos was done regarding cinemetric variables and non-verbal communication that emphasized the power of songs to express deep sorrow and sympathy but also to give hope. Psychological first aid, the five stages of the mourning process by Kübler-Ross, the dual-process model by Stroebe and Schut, and Taylor’s tend-to-befriend provided a better understanding of the translation from interpersonal to societal mourning. Finally, other memorable songs that society spontaneously chose to be performed alone or together to cope with sudden and dramatic situations, mitigate physical distancing, and alleviate human suffering are discussed. Music, lyrics, and artistic performance are playing a key role in building social and emotional ties during this pandemic, hampering individual and social pain and sorrow despite cultural barriers.
The Association of the Gut Microbiota with Clinical Features in Schizophrenia
Authors: Nocera Annamarie , Nasrallah Henry
Published: March 2022

The connection between gut microbiota and schizophrenia has become a fertile area of research. The relationship is bidirectional and quite complex, but is likely to lead to practical clinical applications. For example, commensal microbiota have been shown to produce inflammatory metabolites that can cross the blood–brain barrier—a possible neurobiological precursor of psychosis. Antipsychotics that treat these individuals have been shown to alter gut microbiota. On the other hand, life style in schizophrenia, such as diet and decreased exercise, can be disruptive to the normal microbiome diversity. In the present paper, we conduct a review of PubMed literature focusing on the relationship of gut microbiota with clinical symptoms of schizophrenia, which, to our knowledge, has not yet been reviewed. Numerous clinical characteristics were identified correlating to gut microbial changes, such as violence, negative symptoms, treatment resistance, and global functioning. The most consistently demonstrated correlations to gut microbial changes across studies were for the overall symptom severity and negative symptom severity. Although numerous studies found changes in these domains, there is much variability between the bacteria that change in abundance between studies, likely due to the regional and methodological differences between studies. The current literature shows promising correlations between gut microbiota profiles and several clinical features of schizophrenia, but initial studies require replication.
BIM for CREM: Exploring the Benefit of Building Information Modelling for Facility Management in Corporate Real Estate Management
Authors: Benn Maximilian , Stoy Christian
Published: March 2022

The implementation of BIM in FM has been of steadily growing interest for academic research. Yet the benefits of BIM for the FM in CREM have, to the present day, been explored to a limited extent. As research on BIM for FM in CREM remains narrowly investigated, this study follows an exploratory approach to formulate implications for further research directions. Therefore, a four-stage procedure was adopted: (1) identification and definition of BIM uses for FM in CREM; (2) validation of the BIM uses for FM in CREM and the expert survey questionnaire; (3) assessment of each BIM use’s benefit by experts; and (4) analysis of the correlations between BIM uses’ benefit assessments and the respondents’ industries, the resulting associations, and the prioritisation for the development of BIM uses for FM in CREM. Based on that methodology, it was shown that the BIM use for FM in CREM with the highest priority for development is 46 Facilities and Equipment: Repair and Renewal. The BIM use with the lowest priority is 21 Visualisation. As a result, four implications on the development of BIM uses for FM in CREM were formulated regarding case studies, information requirements, and technical requirements, as well as process and personnel requirements.
The Evolution of Immune Checkpoint Inhibitors in Advanced Urothelial Carcinoma
Authors: Houssiau Hélène , Seront Emmanuel
Published: March 2022

Urothelial carcinoma is an aggressive cancer and development of metastases remains a challenge for clinicians. Immune checkpoint inhibitors (ICIs) are significantly improving the outcomes of patients with metastatic urothelial cancer (mUC). These agents were first used in monotherapy after failure of platinum-based chemotherapy, but different strategies explored the optimal use of ICIs in a first-line metastatic setting. The “maintenance” strategy consists of the introduction of ICIs in patients who experienced benefit from first-line chemotherapy in a metastatic setting. This allows an earlier use of ICIs, without waiting for disease progression. We review the optimal management of mUC in the era of ICIs, based on the key clinical messages arising from the pivotal trials.
Simulating the Dynamic Intra-Tumor Heterogeneity and Therapeutic Responses
Authors: Liu Yongjing , Feng Cong , Zhou Yincong , Shao Xiaotian , Chen Ming
Published: March 2022

A tumor is a complex tissue comprised of heterogeneous cell subpopulations which exhibit substantial diversity at morphological, genetic and epigenetic levels. Under the selective pressure of cancer therapies, a minor treatment-resistant subpopulation could survive and repopulate. Therefore, the intra-tumor heterogeneity is recognized as a major obstacle to effective treatment. In this paper, we propose a stochastic clonal expansion model to simulate the dynamic evolution of tumor subpopulations and the therapeutic effect at different times during tumor progression. The model is incorporated in the CES webserver, for the convenience of simulation according to initial user input. Based on this model, we investigate the influence of various factors on tumor progression and treatment consequences and present conclusions drawn from observations, highlighting the importance of treatment timing. The model provides an intuitive illustration to deepen the understanding of temporal intra-tumor heterogeneity dynamics and treatment responses, thus helping the improvement of personalized diagnostic and therapeutic strategies.
A Weakly Supervised Deep Learning Method for Guiding Ovarian Cancer Treatment and Identifying an Effective Biomarker
Authors: Wang Ching-Wei , Lee Yu-Ching , Chang Cheng-Chang , Lin Yi-Jia , Liou Yi-An , Hsu Po-Chao , Chang Chun-Chieh , Sai Aung-Kyaw-Oo , Wang Chih-Hung , Chao Tai-Kuang
Published: March 2022

Ovarian cancer is a common malignant gynecological disease. Molecular target therapy, i.e., antiangiogenesis with bevacizumab, was found to be effective in some patients of epithelial ovarian cancer (EOC). Although careful patient selection is essential, there are currently no biomarkers available for routine therapeutic usage. To the authors’ best knowledge, this is the first automated precision oncology framework to effectively identify and select EOC and peritoneal serous papillary carcinoma (PSPC) patients with positive therapeutic effect. From March 2013 to January 2021, we have a database, containing four kinds of immunohistochemical tissue samples, including AIM2, c3, C5 and NLRP3, from patients diagnosed with EOC and PSPC and treated with bevacizumab in a hospital-based retrospective study. We developed a hybrid deep learning framework and weakly supervised deep learning models for each potential biomarker, and the experimental results show that the proposed model in combination with AIM2 achieves high accuracy 0.92, recall 0.97, F-measure 0.93 and AUC 0.97 for the first experiment (66% training and 34%testing) and high accuracy 0.86 ± 0.07, precision 0.9 ± 0.07, recall 0.85 ± 0.06, F-measure 0.87 ± 0.06 and AUC 0.91 ± 0.05 for the second experiment using five-fold cross validation, respectively. Both Kaplan-Meier PFS analysis and Cox proportional hazards model analysis further confirmed that the proposed AIM2-DL model is able to distinguish patients gaining positive therapeutic effects with low cancer recurrence from patients with disease progression after treatment (p < 0.005).