bluuebunny/crossref_metadata_2025 huggingface dataset:
Neural Network-Based Ridesharing Policy for Reducing Rider Transportation Cost
Authors: Obias Karl , Magsino Elmer R. , Sybingco Edwin
Published: August 2021

With the increasing demand for taxis during peak hours, adding more vehicles is not always the optimal solution since this will also contribute to the increasing traffic congestion, noise, and air pollution. In this study, we propose a neural network-based ridesharing policy that will utilize the available seats in an occupied taxi to accommodate other queueing passengers. The proposed policy allows a single taxi to transport more people at the same time and reduces the passenger taxi fare. The ridesharing policy is extensively verified by studying empirical mobility trace datasets to determine its capability of reducing transportation costs for riders. Test results show that the proposed ridesharing policy can reduce the cost by approximately 120% with the implementation of ridesharing.
Autonomous Vehicle: Introduction and Key-elements
Authors: Halin Hafiz , Khairunizam Wan , Ali Hasimah
Published: August 2021

The development of autonomous vehicles is undergoing extensive research because the autonomous system must ensure passenger safety. Consumers are concerned about vehicle safety, data privacy, system safety, and the autonomous vehicle's legal liability. Autonomous develop based on several key elements; perception, data processing, path planning, and control system. Comfort and a safe autonomous system can be achieved by creating a controller that can imitate human intelligence and decision-making ability. The proposed controller will be developed from an analysis of the human driving characteristic. The Allied Research Market forecast the autonomous vehicle industries can generate a lot of revenue in the future.
Fusion wind and solar generation prototype design with Neural Network
Authors: Al Asbahi M M Y M , Mansor M N , Manan M R , Aziz M A A , Kawi R Md , Mohd Faudzi F H
Published: August 2021

Wind and solar power are the most common renewable resources of energy and their usage for power generation is quickly growing all over the world. However, both wind and solar power are difficult to predict manually due to every time changes in weather condition; therefore, power output of wind and solar is associated with some uncertainty. A reliable wind-solar day ahead load prediction with neural network was proposed to support a small microgrids system. All the system performance measurement such as sensitivity, specificity and accuracy give higher than 90%.
Five-phase multilevel inverter with output voltage waveform optimized using soft-computing methods
Authors: Yong W V , Chew W T , Ong S L , Leong J H
Published: August 2021

In this paper, genetic algorithm (GA) and particle swarm optimization (PSO) are employed to optimize the switching angles for 5-phase 15-level asymmetrical cascaded H-bridge multilevel inverter (ACHBMLI). Both optimization algorithms are applied in selective harmonic minimization pulse width modulation (SHMPWM) based switching-angle calculation technique that aims to maintain the desired fundamental harmonic while minimize the undesired harmonics. The performance of GA- and PSO-SHMPWM are compared in term of switching angles, objective function, and cumulative distribution function. For fair comparison, both GA- and PSO-SHMPWM are implemented and analyzed in MATLAB with the same population size and total number of iterations. Furthermore, a PSIM simulation model of 5-phase 15-level ACHBMLI has also been developed to validate the effectiveness of GA- and PSO-SHMPWM.
FPGA-based Design of Multiple Models for Industry 4.0 Cyber Security
Authors: Al-Salman Husham Ibrihem Mahdi , Ehkan Phaklen , Al-Doori Muataz Hamed
Published: August 2021

Industry 4.0 computing infrastructure depends on traditional processing platforms, and all these platforms perform well and achieving acceptable throughput. Two significant problems confront with these platforms are the delay in data processing and security hazard for potential threats. A design coding has been written using VHDL based on behaviour temporal parallelism for Industry 4.0 cybersecurity model. The models are divided into three namely, single, dual and quad models. Every model has three main components include UART, FIFO and DES algorithm for data encryption and decryption. Encryption is achieved more than one block at a time and over the number of designs to find the most suitable application for data processing. A DES algorithm based efficient implementing the design of 16-round multiple parallel models for low-cost, scalable, and robust encryption solution of a maximum clock frequency of 227MHz and it is capable of encrypting or decrypting data blocks at a rate of throughput 58,288Mbps.
Drowning Alert System using RF Communication and GPRS/GSM
Authors: Monish P , Darshan R , Ponvalavan K , Bharathi Dr. M
Published: August 2021

Drowning is the third leading cause of unintentional injury death worldwide, accounting for seven percent of all injury-related deaths. There are an estimated 320,000 annual drowning deaths worldwide. Globally, the highest drowning rates are among children below four years, followed by children of five to nine years. The available solutions for the problem involve wristband with RF connections, drowning alert system using RFID tag and RFID reader, a video based drowning alert system, monitoring system using LASER and LDR, etc. The current idea in the proposed system here helps in knowing the status of the person who is in water by detecting the heartbeat. The system will continuously monitor the heart rate of the person using a heartbeat sensor when he or she is in the water and if there is a sudden increase or decrease in heart rate with reference to the standard range, the system will send a traumatic signal using an RF transmitter to the lifeguard station that will notify alarm alerts using a light bulb and buzzer at the end of the receiver. The distress signal can also be sent manually at the touch of a button. The system also has the ability to send SMS or call to the monitors and also provides live tracking using GPS and GSM technique. The requirements are met and manageable. The proposed system has many advantages over the existing system such as the inclusion of the buzzer, the light indication system, the transmission through RF, manual control, SMS and call feature and finally, live tracking. This will help in preventing many drowning deaths in the future.
A novel chaotic teaching learning based optimization algorithm and its application in optimization of extreme learning machine
Authors: Ma Yunpeng , Tang Haoheng , Wang Heqi , Wang Zhenying , Zhang Xinxin , Li Lipeng
Published: August 2021

Recent ten years, the teaching learning based optimization algorithm (TLBO) has been widely concerned and successfully applied to solve various constraints and non-constraints problems. However, its convergence accuracy and convergence speed should be further improved. Therefore, a novel chaotic teaching learning based optimization algorithm (called CTLBO) is proposed. Firstly, chaotic variables are applied to initialize population individuals for increasing the diversity of population. Secondly, a kind of self-adaptive acceleration coefficient is introduced into teaching phase to enhance the convergence speed and solution quality. Finally, two population updating mechanisms are proposed to balance the exploration and exploitation capabilities in the learning phase. One is neighbor elitist search mechanism, another is chaos optimization mechanism. The performance of CTLBO is compared with five state-of-the-art optimization algorithms by several CEC mathematical problems. The experiment results show that the CTLBO yields better convergence rate than other algorithms on most testing functions. Additionally, the proposed CTLBO is applied to optimize the model parameters of extreme learning machine(ELM) and the tuned ELM is adopted to establish the NOx emissions model. Experiment results reveal that the NOx emissions model has good accuracy and meets the engineering requirement.
User online purchase behavior prediction based on fusion model of CatBoost and Logit
Authors: Cao Wenchao , Wang Ke , Gan Hongcheng , Yang Mingcheng
Published: August 2021

Analyzing users’ consumption preferences and willingness based on massive online behavior data can help optimize user experience and improve product purchase rate. For the online platform user data, feature engineering methods were used to pre-process, visualize, analyze and construct features. Then, a prediction and evaluation model of users’ online purchase behavior was developed based on the CatBoost algorithm, and the important features affecting users’ purchase behavior were quantitatively analyzed using the Logit Model. Compared with Random Forest, Support Vector Machine and XGBoost, the prediction model has better performance with prediction accuracy of 98.38% and F1 score of 0.775. The results show that user basic information, user access data and user login data have impact on user online purchase behavior. Especially the number of visits with or without coupons, unfinished orders of annual classes, number of coupons received, repeated learning of courses, public number following, days of login, city and number of learning classes sections are important features affecting purchase behavior.
Unified Distillation: Supervising the Student to Make Fewer Mistakes
Authors: Chen Yuan , Rong Pan
Published: August 2021

Knowledge distillation has become a popular task in modern deep learning applications by performing knowledge transfer from a cumbersome neural network commonly called “teacher model” to a much smaller network called “student model”. In traditional knowledge distilling process, there are usually two objectives for training the student model, namely hard target and soft target. However, sometimes it’s hard to find a trade-off between them. We unify the two objectives into one making it easier to perform knowledge distillation, and propose a novel distilling method called “Unified Distillation” to supervise the student to make fewer mistakes. The model can correct the wrong predictions according to the hard target, and maintain the advantage of knowledge distillation. Although our method can be used in almost all fields suitable for knowledge distillation, we choose neural machine translation as a study object for its complexity. We conducted experiments on three neural machine translation tasks, using a finetuned language model BERT as the teacher, and a Transformer base model as the student. The experimental results indicate that our method is better than traditional knowledge distillation method.
Differential evolution algorithm based on PCA to determine the objective function to optimize the configuration of microgrid
Authors: Wang Yunhu , Xu Ruobing , Duan Qing , Zhang Guobao , Huang Yongming
Published: August 2021

Recently, microgrids are increasingly used in our lives. The purpose of this paper is to solve the uncertainties of multi-objective decision-making and the instability of algorithms in the optimal configuration of traditional microgrids. This paper proposed a differential evolution algorithm based on PCA to determine the objective function. Firstly, a multi-objective optimization model of the micro-grid was established on the basis of fully considering the economy, environmental protection and stability of the micro-grid. Next, the first principal component of the multi-objective was extracted based on PCA combining innovatively the Pareto optimal solution set, and the multi-objective optimization problem was transformed into a single-objective optimization problem. Finally, the experimental simulation result shows that the algorithm proposed in this paper can comprehensively consider the economy, environmental protection and stability of the microgrid on the basis of ensuring the efficiency and stability of the algorithm to realize the optimized configuration of the microgrid.
Sequential Mechanical Fault Diagnosis in High Voltage Circuit Breaker using Attention Mechanism
Authors: Zhang Baoyu , Wang Xinru , Wang Jin
Published: August 2021

To address the shortcomings of traditional shallow-based model in terms of feature extraction and generalization capacity, this paper provides a high-voltage circuit breaker mechanical condition online detection scheme by using attention mechanism to locally weight the sample correlation and combining convolutional neural network (CNN) and long short-term memory (LSTM) network. The network uses convolutional layers for feature transformation of the raw vibration data, combined with the local time-domain feature representation capability of the gated unit, to extract fault-sensitive features. The temporal deep learning model using the attention mechanism enables it to extract global features of long-time sequences while making important features obtain higher weight parameters. The resulting model improves the overall learning and recognition efficiency. The experiments on the vacuum circuit breaker show that the proposed model has better performance than the classical diagnostic method based on the support vector machine.
Collaborative exploration of multiple unmanned surface vessels in complex areas based on PPO algorithm
Authors: Wang Kuo , Peng Haowen , Zhao Mingyang , Guan Wei
Published: August 2021

The increasing congestion of maritime traffic leads to frequent maritime traffic accidents. Unmanned search and rescue equipment provides a guarantee for rapid rescue operations. In marine accidents, the waiting time is short and the rescue risk factor is high. Unmanned surface vessel (USV) can transport rescue equipment to the accident site in the first time and carry out searching and rescuing, which will greatly improve rescue efficiency. With the development of technology, USV can autonomously conduct area exploration to assist in the discovery of survivors. This paper proposes a complex area exploration of multiple unmanned surface vessels based on the Proximal Policy Optimization algorithm (PPO). The PPO algorithm is used to improve training efficiency, achieve 100% search coverage in complex water surface areas, and make searching and rescuing operations safe, simple and efficient. Experimental simulations show that the algorithm can realize 100% area exploration, have high search efficiency, and consumes less energy. This is a new intelligent ship application that can complete 100% exploration without human involvement.
Cartilaginous spheroid-assembly design considerations for endochondral ossification: towards robotic-driven biomanufacturing
Authors: Nilsson Hall Gabriella , Rutten Iene , Lammertyn Jeroen , Eberhardt Jens , Geris Liesbet , Luyten Frank P , Papantoniou Ioannis
Published: August 2021

Spheroids have become essential building blocks for biofabrication of functional tissues. Spheroid formats allow high cell-densities to be efficiently engineered into tissue structures closely resembling the native tissues. In this work, we explore the assembly capacity of cartilaginous spheroids (d ∼ 150 µm) in the context of endochondral bone formation. The fusion capacity of spheroids at various degrees of differentiation was investigated and showed decreased kinetics as well as remodeling capacity with increased spheroid maturity. Subsequently, design considerations regarding the dimensions of engineered spheroid-based cartilaginous mesotissues were explored for the corresponding time points, defining critical dimensions for these type of tissues as they progressively mature. Next, mesotissue assemblies were implanted subcutaneously in order to investigate the influence of spheroid fusion parameters on endochondral ossification. Moreover, as a step towards industrialization, we demonstrated a novel automated image-guided robotics process, based on targeting and registering single-spheroids, covering the range of spheroid and mesotissue dimensions investigated in this work. This work highlights a robust and automated high-precision biomanufacturing roadmap for producing spheroid-based implants for bone regeneration.
Improving the low-cycle fatigue properties of laser-welded Al–Zn–Mg–Cu alloy joints using double-sided ultrasonic impact treatment
Authors: Chen Furong , Liu Chenghao
Published: August 2021

To improve the low-cycle fatigue properties of Al–Zn–Mg–Cu 7075 aluminum alloy laser-welded joints, a post-weld treatment method for double-sided ultrasonic impact treatment (DSUIT) was used to treat the joints. The mechanism of different DSUITs on the microstructure and fatigue properties of welded joints was analyzed. The results showed that DSUIT reduced the welding defects and roughness of the joint surface, and the dendrite structure at the upper and lower surfaces of the joints was broken to form a plastic deformation layer (PDL) with an approximate thickness of 100 μm. Furthermore, the texture strength and grain size at the PDL were reduced. A beneficial residual compressive stress was introduced into the upper and lower surfaces of the joints after the DSUIT. When the number of cycles was 2 × 106, the maximum fatigue strength of the joints was 103.02 MPa after DSUIT, indicating an increase of 111.8% of the untreated joints of 48.62 MPa. Moreover, observance of the fatigue fracture of the joints revealed that the PDL produced by the DSUIT inhibited the initiation and propagation of cracks at the surface. Therefore, the DSUIT effectively improved the low-cycle fatigue properties of the joints.
Mapping forest disturbances across the Southwestern Amazon: tradeoffs between open-source, Landsat-based algorithms
Authors: Reygadas Yunuen , Spera Stephanie , Galati Valerie , Salisbury David S , Silva Sonaira , Novoa Sidney
Published: August 2021

Local and cross-continental road building, increased economic teleconnections, growing agricultural demands, logging and mining practices, and general development processes are putting pressure on even the least densely populated regions of the Amazon, where local, regional, and global demand for food, fuel and fiber are resulting in observable biophysical effects. It is essential, then, that stakeholders can both map and understand the effects of these forest disturbances on ecosystem services. Multiple remote sensing algorithms focused on detecting vegetation changes have been developed: the challenge now lies in understanding which algorithm best suits the user´s study area and research objective. Using Google Earth Engine, we compared the performance of three algorithms –Continuous Degradation Detection (CODED), Landsat-based detection of trends in disturbance and recovery (LandTrendr), and Multi-variate Time-series Disturbance Detection (MTDD)– to detect and characterize forest disturbances in the Southwestern Amazon (Ucayali, Peru and Acre, Brazil) during the 2000–2020 period. In general, the results of all of the algorithms agreed with the reference data: overall accuracies were 94% (± 0.6% LandTrendr), 95% (±0.6% MTDD), and 96% (± 0.6% CODED). Although the map products exhibit similar spatial patterns, they often differ on the specific disturbance extent. CODED works well in capturing disturbances associated with roads, MTDD excels best at capturing entire disturbance patches, and LandTrendr excels both in terms of user friendliness and range of output options. Through three case study regions, we highlight land-cover change dynamics that have occurred in this remote, transboundary region over the last two decades. We also describe the strengths and weaknesses of each algorithm and demonstrate that it would be incorrect to assume that any one algorithm is the most accurate. Our work, then, improves the capacity of the community to understand how well each algorithm is suited best to map various forest disturbances to promote sustainable decision making.
A sophisticated case of division of labour in the trimorphic stamens of theCassia fistula(Leguminosae) flower
Authors: Saab Gabriella da Silva , Mansano Vidal de Freitas , Nogueira Anselmo , Maia Isabele Carvalho , Bergamo Pedro Joaquim , Paulino Juliana Villela
Published: August 2021

Buzz-pollinated pollen flowers have pollen as the primary resource for pollinators and must deal with a conflict between the exploitation of pollen grains by bees and pollination success. It has been hypothesized that heterostemony allows division of labour between stamens as a solution to the pollen dilemma. To test the division of labour hypothesis, we chose Cassia fistula, which has a trimorphic androecium and analysed androecium development, pollen grain release mechanisms and visitor behaviour. We explored the reflectance of floral organs and carried out an exclusion experiment to test the attractiveness of each stamen morph to the bee species. Finally, we explored the structural, ultrastructural and functional variation between the pollen grains, including pollen viability across stamen morphs. The differences among the three stamen morphs, which is developed from two whorls of the stamen, are the first evidence of the division of labour in our study system. Large Bombus and Xylocopa bees actively and exclusively exploited the pollen grains from the central poricidal anthers generating pollen deposition on their bodies. The reflectance pattern of floral organs indicated a targeting of these large bees to the central anthers, corroborated by the anther manipulative experiment where only the exclusion of the anthers positioned in the flower centre, especially the intermediate stamens, reduced bee visits. Both results revealed a division of labour, in which the intermediate stamen morph was responsible for both floral attractiveness and pollen resources. Only the largest stamen morph produced germinable pollen grains, highlighting their role as pollinating stamens. The smallest stamen morph has a less clear function, likely representing an economy in pollen production for feeding function. Our findings suggest that the evolution of the trimorphic androecium is associated with division of labour in large pollen flowers and can represent a strong strategy for circumventing the pollen dilemma, optimizing the feeding function by reducing pollen grain investment from central anthers.
Precise estimation of residue relative solvent accessible area from Cα atom distance matrix using a deep learning method
Authors: Gao Jianzhao , Zheng Shuangjia , Yao Mengting , Wu Peikun
Published: August 2021

Motivation

The solvent accessible surface is an essential structural property measure related to the protein structure and protein function. Relative solvent accessible area (RSA) is a standard measure to describe the degree of residue exposure in the protein surface or inside of protein. However, this computation will fail when the residues information is missing.

Results

In this article, we proposed a novel method for estimation RSA using the Cα atom distance matrix with the deep learning method (EAGERER). The new method, EAGERER, achieves Pearson correlation coefficients of 0.921–0.928 on two independent test datasets. We empirically demonstrate that EAGERER can yield better Pearson correlation coefficients than existing RSA estimators, such as coordination number, half sphere exposure and SphereCon. To the best of our knowledge, EAGERER represents the first method to estimate the solvent accessible area using limited information with a deep learning model. It could be useful to the protein structure and protein function prediction.

Availabilityand implementation

The method is free available at https://github.com/cliffgao/EAGERER.

Supplementary information

Supplementary data are available at Bioinformatics online.
Rethinking the Rhetorical Epistemics of Gaslighting
Authors: Graves Clint G , Spencer Leland G
Published: August 2021

Gaslighting is defined as a dysfunctional communication dynamic in which one interlocutor attempts to destabilize another’s sense of reality. In this article, we advance a model of gaslighting based in an epistemic rhetoric perspective. Our model directs attention to the rhetorics used to justify competing knowledge claims, as opposed to philosophical models that tend to rely on objective truth-value. We probe the discursive manifestations of gaslighting in logocentric, ethotic, or pathemic terms. We then apply our model to explain sexist and racist gaslighting that derives power from normatively instantiated discourses of rape culture and White supremacy. Specifically, our analysis identifies the appeal structures used to legitimate such gaslighting in response to disclosures of sexual violence and testimony about racial injustice.
Critical oxygen delivery threshold during cardiopulmonary bypass in older cardiac surgery patients with increased frailty risk
Authors: Smoor Rosa M , van Dongen Eric P A , Verwijmeren Lisa , Schreurs Inge A A M , Vernooij Lisette M , van Klei Wilton A , Noordzij Peter G
Published: August 2021

OBJECTIVES

Older patients have a higher cardiac surgery-associated acute kidney injury (CSA-AKI) related mortality. Low oxygen delivery (DO2) during cardiopulmonary bypass (CPB) is a risk factor for CSA-AKI, but critical DO2 thresholds for older patients are unknown. This study investigated critical DO2 thresholds for CSA-AKI in patients ≥70 years undergoing on-pump cardiac surgery.

METHODS

Patients were enrolled from July 2015 until August 2017. CPB data from 432 patients were collected, and DO2 values were calculated per minute. The primary outcome was CSA-AKI. The association between DO2 and CSA-AKI was analysed with multivariable regression analysis. Multiple DO2 thresholds were analysed. The association between CSA-AKI and the area below the DO2 thresholds (DO2 deficit) was evaluated, as was the association between frailty and CSA-AKI.

RESULTS

CSA-AKI occurred in 63 (14.6%) patients. Mean and nadir (lowest) DO2 values were lower in patients with CSA-AKI (283 vs 312 ml/min/m2; P-value <0.001 and 238 vs 270 ml/min/m2; P-value <0.001, respectively). The adjusted relative risk for CSA-AKI was 1.006 [99% confidence interval (CI) 1.001–1.012] per ml/min/m2 nadir DO2 decrease. The critical DO2 threshold was 270 ml/min/m2 [adjusted relative risk 2.06 (99% CI 1.33–2.80)]. The DO2 deficit below 270 ml/min/m2 was associated with CSA-AKI [adjusted relative risk 2.84 (99% CI 1.87–3.81)]. No association between frailty and CSA-AKI was found (P = 0.82).

CONCLUSIONS

Low DO2 increased the risk for CSA-AKI in older patients who had cardiac surgery. A critical DO2 threshold of 270 ml/min/m2 was applicable for frail and non-frail patients. The efficacy of a DO2 >270 ml/min/m2 to reduce CSA-AKI in older patients needs further evaluation.
Cardinium inhibits Wolbachia in its mite host, Tyrophagus putrescentiae, and affects host fitness
Authors: Hubert Jan , Nesvorna Marta , Pekar Stano , Green Stefan J , Klimov Pavel B
Published: August 2021

Interactions among endosymbiotic bacteria inside their eukaryotic hosts are poorly understood, particularly in mites. The mite Tyrophagus putrescentiae is a common, medically important generalist species that has many intracellular and gut bacterial symbionts. In the experiments, we examined bacterial abundances and composition in mite populations obtained by controlled mixing of stock mite populations that differed in the presence/absence of the major intracellular bacteria Wolbachia and Cardinium. Changes in microbial communities were characterized using 16S ribosomal RNA high-throughput sequencing (pooled mite individuals) and quantitative PCR for key microbial taxa (individual mites). Mite fitness was estimated as a parameter of population growth. We detected that in mixed mite populations, Cardinium and Wolbachia can co-occur in the same mite individual. The presence of Cardinium was negatively correlated with the presence of Wolbachia and Bartonella, while the Bartonella and Wolbachia were positively correlated in individual level samples. Since mixed populations had lower abundances of Wolbachia, while the abundance of Cardinium did not change, we suggest that the presence of Cardinium inhibits the growth of Wolbachia. The mixed mite populations had lower population growth than parental populations. The possible effect of symbionts on the fitness of mixed population is discussed.
Parallel Independent Losses of G-Type Lysozyme Genes in Hairless Aquatic Mammals
Authors: Zhang Xiaoqing , Chi Hai , Li Gang , Irwin David M , Zhang Shuyi , Rossiter Stephen J , Liu Yang
Published: August 2021

Lysozyme enzymes provide classic examples of molecular adaptation and parallel evolution, however, nearly all insights to date come from chicken-type (c-type) lysozymes. Goose-type (g-type) lysozymes occur in diverse vertebrates, with multiple independent duplications reported. Most mammals possess two g-type lysozyme genes (Lyg1 and Lyg2), the result of an early duplication, although some lineages are known to have subsequently lost one copy. Here we examine g-type lysozyme evolution across >250 mammals and reveal widespread losses of either Lyg1 or Lyg2 in several divergent taxa across the mammal tree of life. At the same time, we report strong evidence of extensive losses of both gene copies in cetaceans and sirenians, with an additional putative case of parallel loss in the tarsier. To validate these findings, we inspected published short-read data and confirmed the presence of loss of function mutations. Despite these losses, comparisons of selection pressures between intact g- and c-type lysozyme genes showed stronger purifying selection in the former, indicative of conserved function. Although the reasons for the evolutionary loss of g-type lysozymes in fully aquatic mammals are not known, we suggest that this is likely to at least partially relate to their hairlessness. Indeed, although Lyg1 does not show tissue-specific expression, recent studies have linked Lyg2 expression to anagen hair follicle development and hair loss. Such a role for g-type lysozyme would explain why the Lyg2 gene became obsolete when these taxa lost their body hair.
Homologous Recombinational Repair of Double-Strand Breaks in Yeast Is Enhanced by MAT Heterozygosity Through yKU-Dependent and -Independent Mechanisms
Authors: Clikeman Jennifer A. , Khalsa Guru Jot , Barton Sandra L. , Nickoloff Jac A.
Published: August 2021

DNA double-strand breaks (DSBs) are repaired by homologous recombination (HR) and nonhomologous end-joining (NHEJ). NHEJ in yeast chromosomes has been observed only when HR is blocked, as in rad52 mutants or in the absence of a homologous repair template. We detected yKu70p-dependent imprecise NHEJ at a frequency of ∼0.1% in HR-competent Rad+ haploid cells. Interestingly, yku70 mutation increased DSB-induced HR between direct repeats by 1.3-fold in a haploid strain and by 1.5-fold in a MAT homozygous (a/a) diploid, but yku70 had no effect on HR in a MAT heterozygous (a/α) diploid. yku70 might increase HR because it eliminates the competing precise NHEJ (religation) pathway and/or because yKu70p interferes directly or indirectly with HR. Despite the yku70-dependent increase in a/a cells, HR remained 2-fold lower than in a/α cells. Cell survival was also lower in a/a cells and correlated with the reduction in HR. These results indicate that MAT heterozygosity enhances DSB-induced HR by yKu-dependent and -independent mechanisms, with the latter mechanism promoting cell survival. Surprisingly, yku70 strains survived a DSB slightly better than wild type. We propose that this reflects enhanced HR, not by elimination of precise NHEJ since this pathway produces viable products, but by elimination of yKu-dependent interference of HR.
The correlation of substitution effects across populations and generations in the presence of nonadditive functional gene action
Authors: Legarra Andres , Garcia-Baccino Carolina A. , Wientjes Yvonne C. J. , Vitezica Zulma G.
Published: August 2021

Allele substitution effects at quantitative trait loci (QTL) are part of the basis of quantitative genetics theory and applications such as association analysis and genomic prediction. In the presence of nonadditive functional gene action, substitution effects are not constant across populations. We develop an original approach to model the difference in substitution effects across populations as a first order Taylor series expansion from a “focal” population. This expansion involves the difference in allele frequencies and second-order statistical effects (additive by additive and dominance). The change in allele frequencies is a function of relationships (or genetic distances) across populations. As a result, it is possible to estimate the correlation of substitution effects across two populations using three elements: magnitudes of additive, dominance, and additive by additive variances; relationships (Nei’s minimum distances or Fst indexes); and assumed heterozygosities. Similarly, the theory applies as well to distinct generations in a population, in which case the distance across generations is a function of increase of inbreeding. Simulation results confirmed our derivations. Slight biases were observed, depending on the nonadditive mechanism and the reference allele. Our derivations are useful to understand and forecast the possibility of prediction across populations and the similarity of GWAS effects.
The quantitative genetics of the prevalence of infectious diseases: hidden genetic variation due to indirect genetic effects dominates heritable variation and response to selection
Authors: Bijma Piter , Hulst Andries D , de Jong Mart C M
Published: August 2021

Infectious diseases have profound effects on life, both in nature and agriculture. However, a quantitative genetic theory of the host population for the endemic prevalence of infectious diseases is almost entirely lacking. While several studies have demonstrated the relevance of transmission of infections for heritable variation and response to selection, current quantitative genetics ignores transmission. Thus, we lack concepts of breeding value and heritable variation for endemic prevalence, and poorly understand response of endemic prevalence to selection. Here, we integrate quantitative genetics and epidemiology, and propose a quantitative genetic theory for the basic reproduction number R0 and for the endemic prevalence of an infection. We first identify the genetic factors that determine the prevalence. Subsequently, we investigate the population-level consequences of individual genetic variation, for both R0 and the endemic prevalence. Next, we present expressions for the breeding value and heritable variation, for endemic prevalence and individual binary disease status, and show that these depend strongly on the prevalence. Results show that heritable variation for endemic prevalence is substantially greater than currently believed, and increases strongly when prevalence decreases, while heritability of disease status approaches zero. As a consequence, response of the endemic prevalence to selection for lower disease status accelerates considerably when prevalence decreases, in contrast to classical predictions. Finally, we show that most heritable variation for the endemic prevalence is hidden in indirect genetic effects, suggesting a key role for kin-group selection in the evolutionary history of current populations and for genetic improvement in animals and plants.
Early-Life Stress Induces Prodromal Features of Parkinsonism in Ageing Rats
Authors: Ren Chao , Wang Fen , He Kai-Jie , Zhang Yu-Ting , Li Ling-Xi , Zhang Jin-Bao , Chen Jing , Mao Cheng-Jie , Liu Chun-Feng
Published: August 2021

Early-life stress (ELS) can cause long-term effects on human health, ranging from adolescence to adulthood, and even to gerontic. Although clinical retrospective data suggest that ELS may be related to senile neurodegenerative diseases such as Parkinson’s disease (PD), there are few prospective investigations to explore its real contribution to PD. Here, we investigated the behavioral, histochemical, neuromorphological, and transcriptional changes induced by maternal separation (MS), an ELS model. Without neurotoxin, MS rats showed behavioral alterations in olfaction, locomotion, and gait characters after depression compared with control rats. Based on neuroimaging and histochemistry, although we found that the dopaminergic system in the striatum was impaired after MS, the decrease of striatal dopamine level was ~33%. Consistently, tyrosine hydroxylase immunostaining positive neurons of MS rats in the substantia nigra showed deficit by about 20% in cell counting. Furthermore, using transcriptome sequencing, we discovered many differentially expressed genes (DEGs) of MS rats in the striatum significantly enriched in the pathway of dopaminergic synapse, and the biological process of locomotion and neuromuscular process controlling balance. Encouragingly, some representative DEGs relating to PD were singled out. These results suggest that ELS-depression rats potentially mimic some key features of prodromal stage of PD during natural senescence. In conclusion, our findings provide some novel insights into the future pathogenesis and therapeutic studies for PD related to depression.
Canagliflozin Increases Intestinal Adenoma Burden in Female ApcMin/+ Mice
Authors: Korfhage Justin , Skinner Mary E , Basu Jookta , Greenson Joel K , Miller Richard A , Lombard David B
Published: August 2021

The diabetes drug canagliflozin extends life span in male mice. Since malignant neoplasms are the major cause of death in most mouse strains, this observation suggests that canagliflozin might exert anti-neoplastic effects in male mice. Here, we treated a mouse neoplasia model, the adenoma-prone ApcMin/+ strain, with canagliflozin, to test the effects of this drug on intestinal tumor burden. Surprisingly, canagliflozin increased the total area of intestine involved by adenomas, an effect most marked in the distal intestine and in female mice. Immunohistochemical analysis suggested that canagliflozin may not influence adenoma growth via direct SGLT1/2 inhibition in neoplastic cells. Our results are most consistent with a model where canagliflozin aggravates adenoma development by altering the anatomic distribution of intestinal glucose absorption, as evidenced by increases in postprandial GLP-1 levels driven by delayed glucose absorption. We hypothesize that canagliflozin exacerbates adenomatosis in the ApcMin/+ model via complex, cell-non-autonomous mechanisms, and that sex differences in GLP-1 responses may in part underlie sexually dimorphic effects of this drug on life span.
Trans-Ethnic Meta-Analysis of Interactions Between Genetics and Early-Life Socioeconomic Context on Memory Performance and Decline in Older Americans
Authors: Faul Jessica D , Kho Minjung , Zhao Wei , Rumfelt Kalee E , Yu Miao , Mitchell Colter , Smith Jennifer A
Published: August 2021

Background

Later-life cognitive function is influenced by genetics as well as early- and later-life socioeconomic context. However, few studies have examined the interaction between genetics and early childhood factors.

Methods

Using gene-based tests (interaction sequence kernel association test [iSKAT]/iSKAT optimal unified test), we examined whether common and/or rare exonic variants in 39 gene regions previously associated with cognitive performance, dementia, and related traits had an interaction with childhood socioeconomic context (parental education and financial strain) on memory performance or decline in European ancestry (EA, N = 10 468) and African ancestry (AA, N = 2 252) participants from the Health and Retirement Study.

Results

Of the 39 genes, 22 in EA and 19 in AA had nominally significant interactions with at least one childhood socioeconomic measure on memory performance and/or decline; however, all but one (father’s education by solute carrier family 24 member 4 [SLC24A4] in AA) were not significant after multiple testing correction (false discovery rate [FDR] < .05). In trans-ethnic meta-analysis, 2 genes interacted with childhood socioeconomic context (FDR < .05): mother’s education by membrane-spanning 4-domains A4A (MS4A4A) on memory performance, and father’s education by SLC24A4 on memory decline. Both interactions remained significant (p < .05) after adjusting for respondent’s own educational attainment, apolipoprotein-ε4 allele (APOE ε4) status, lifestyle factors, body mass index, and comorbidities. For both interactions in EA and AA, the genetic effect was stronger in participants with low parental education.

Conclusions

Examination of common and rare variants in genes discovered through genome-wide association studies shows that childhood context may interact with key gene regions to jointly impact later-life memory function and decline. Genetic effects may be more salient for those with lower childhood socioeconomic status.
Do Perceptions of Cognitive Changes Matter in Self-Management Behaviors Among Persons With Mild Cognitive Impairment?
Authors: Kim Hyejin , Sereika Susan M , Albert Steven M , Bender Catherine M , Lingler Jennifer H
Published: August 2021

Background and Objectives

This secondary analysis examined (a) the association between illness perceptions (perceived understanding and cause of mild cognitive impairment [MCI]) and self-management behaviors for cognitive health, and (b) whether sociodemographic and clinical factors moderate such relationships among persons with MCI.

Research Design and Methods

We conducted a cross-sectional study of 85 participants using baseline data from the Return of Amyloid Imaging Scan Results Study. The coherence and causality subscales of the Revised Illness Perceptions Questionnaires were used. Self-management behaviors (dietary changes, physical activity, mental activities, dietary supplements) were assessed using the Risk Evaluation and Education for ALzheimer’s disease health behavior measure. Sociodemographic and clinical information was extracted from patients’ medical records. We performed hierarchical linear regression and binary logistic regression.

Results

We found no main effects for illness perceptions and self-management of cognitive health. Interaction effects were detected, including (a) coherence and age on the total number of self-management behaviors (b = 0.01, p = .04) and on physical activity (p = .04, odds ratio [OR] = 1.02, 95% confidence interval [CI] = 1.00–1.03), (b) causality and age on dietary supplements (p = .03, OR = 1.31, 95% CI = 1.02–1.67), and (c) causality and education on mental activities (p = .02, OR = 0.44, 95% CI = 0.22–0.88).

Discussion and Implications

Findings suggest that age and education moderate the relationship between illness perceptions and self-management behaviors. Health care professionals should consider subjective perceptions about MCI in light of sociodemographic and clinical factors when discussing cognitive health self-management.
Once after a full moon: acute type A aortic dissection and lunar phases
Authors: Bjursten Henrik , Oudin Åström Daniel , Nozohoor Shahab , Ahmad Khalil , Tang Mariann , Bjurbom Markus , Hansson Emma C , Jeppsson Anders , Joost Holdflod Møller Christian , Jormalainen Miko , Juvonen Tatu , Mennander Ari , Olsen Peter S , Olsson Christian , Ahlsson Anders , Oudin Anna , Pan Emily , Raivio Peter , Wickbom Anders , Sjögren Johan , Geirsson Arnar , Gudbjartsson Tomas , Zindovic Igor
Published: August 2021

OBJECTIVES

Acute type A aortic dissection (ATAAD) is a rare but severe condition, routinely treated with emergent cardiac surgery. Many surgeons have the notion that patients with ATAAD tend to come in clusters, but no studies have examined these observations. This investigation was undertaken to study the potential association between the lunar cycle and the incidence of ATAAD.

METHODS

We collected information on 2995 patients who underwent ATAAD surgery at centres from the Nordic Consortium for Acute Type A Aortic Dissection collaboration. We cross-referenced the time of surgery with lunar phase using a case-crossover design with 2 different definitions of full moon (>99% illumination and the 7-day full moon period).

RESULTS

The period when the moon was illuminated the most (99% definition) did not show any significant increase in incidence for ATAAD surgery. However, when the full moon period was compared with all other moon phases, it yielded a relative risk of 1.08 [95% confidence interval (CI) 1.00–1.17, P = 0.057] and, compared to waxing moon, only the relative risk was 1.11 (95% CI 1.01–1.23, P = 0.027). The peak incidence came 4–6 days after the moon was fully illuminated.

CONCLUSIONS

This study found an overrepresentation of surgery for ATAAD during the full moon phase. The explanation for this is not known, but we speculate that sleep deprivation during full moon leads to a temporary increase in blood pressure, which in turn could trigger rupture of the aortic wall. While this finding is interesting, it needs to be corroborated and the clinical implications are debateable.
On the resolution requirements for accurately representing interactions between plant canopy structure and function in three-dimensional leaf-resolving models
Authors: Bailey Brian N , Kent Eric R
Published: August 2021

While functional–structural plant models (FSPMs) have been proposed as a tool for better analysing and predicting interactions between plant structure and function, it is still unclear as to what spatial resolution is required to adequately resolve such interactions. Shadows cast by neighbouring leaves in a plant canopy create extremely large spatial gradients in absorbed radiation at the sub-leaf scale, which are usually not fully resolved in ‘leaf-resolving’ plant models. This failure to resolve sharp radiative gradients can propagate to other dependent biophysical models, and result in dramatic overprediction of whole-plant and -canopy fluxes with errors significantly higher than that of a statistical ‘big leaf’ or turbid medium model. Under-resolving radiative gradients creates a diffusive effect in the probability distribution of absorbed radiation, and smears out the effect of canopy structure, effectively undermining the original goal of a leaf-resolving model. Errors in whole-canopy fluxes of photosynthesis increased approximately linearly with increasing LAI, projected area fraction G, and decreased logarithmically as the fraction of incoming diffuse radiation was increased. When only one discrete element per leaf was used, errors in whole-canopy net CO2 flux could be in excess of 100 %. Errors due to sub-leaf resolution decreased exponentially as the number of elements per leaf was increased. These results prompt closer consideration of the impact of sub-leaf resolution on model errors, which is likely to prompt an increase in resolution relative to current common practice.