Subtle Relationships Research Articles

O cuidado em comunidades de pesca: reflexões sobre e para o currículo da Educação Infantil

ABSTRACT In Brazil, since the first decade of this century, there has been a concern as to outlining the meaning of care as a principle of the curriculum and the early childhood education policy. Based on references from philosophy and historical and sociological analyses, care is outlined as relationships and actions that have the other in mind and become constitutive of being, implying the constitution of each person. Between 2021 and 2022, we carried out post-doctoral research that sought to deepen the meanings of care to reflect on the early childhood education curriculum. The idea of the project was to consider care from the perspective of traditional populations and social movements. For this, we carried out an ethnography with a fishing community on the north coast of the state of Alagoas, Brazil. The study refutes notions that traditional people offer precarious care, as considered by the colonizer. In fact, it is just the opposite. Traditional peoples develop community care, implying greater freedom for children to move around community, and greater autonomy in relation to the demands of life and to act according to their curiosities. Subtle friendly relationships that are expressed in silence, economy of words and attention to gestures are at the basis of care. We consider that early childhood education curricula need to incorporate the different forms and meanings of caring for babies and children, and not subordinate social identities.

Associations of self‐reported psychological stress with memory and cerebrospinal fluid biomarkers of Alzheimer’s disease in middle‐aged adults

AbstractBackgroundSelf‐reported psychological stress is associated with an increased risk of cognitive decline and dementia. However, it remains unclear whether stress is related to key biomarkers of Alzheimer’s disease (AD). This cross‐sectional study examined the relationship of psychological stress with memory and cerebrospinal fluid (CSF) biomarkers of AD in middle‐aged adults without dementia.MethodThe Healthy Brain Project (HBP) is a prospective web‐based study of adults aged between 40 and 70 years. A total of 2208 participants (76% female) completed the Depression, Anxiety, and Stress Scale (DASS) and Paired Associates Learning subtest of the CANTAB unsupervised on the HBP web platform. Additionally, a sub‐group of participants (n = 67; 41% APOE e4 carriers) attended an in‐person assessment where CSF samples were obtained to measure levels of amyloid‐beta (Aβ42), total tau (t‐tau), phosphorylated tau 181 (p‐tau), and neurofilament light chain (NFL). Separate linear regression analyses investigated associations of the DASS‐Stress score with memory performance and AD biomarkers. All analyses were adjusted for age, sex, education, and race.ResultIn the web‐based cohort, higher levels of self‐reported psychological stress were significantly associated with poorer memory (unstandardised β = ‐0.014, standard error [SE] = 0.004, p <.001). However, there were no significant associations between psychological stress and CSF levels of Aβ42, t‐tau, p‐tau, and NFL (all p > .05), with small effect sizes (Cohen’s f2 ≤ 0.05).ConclusionHigher levels of self‐reported psychological stress were associated with poorer memory in a large middle‐aged cohort but were not associated with AD biomarkers in a subset of participants with available CSF. One potential explanation is that non‐AD pathologies may underlie the relationship between stress and poorer memory; for example, poor coping strategies (e.g., smoking, overeating, and physical activity) might increase the risk of vascular cognitive impairment. However, it is also likely that the small sample size of the in‐clinic cohort was insufficient to detect any subtle relationships between stress and AD pathologies. Further research is needed to determine the associations between stress and the development of the clinical and biological manifestations of AD.

Predicting clay compressibility for foundation design with high reliability and safety: A geotechnical engineering perspective using artificial neural network and five metaheuristic algorithms

This study aims at predicting clay compressibility (clay compressibility) for foundation design with unprecedented reliability and safety. To achieve this goal, we set forth several scientific objectives to enhance the accuracy of clay compressibility predictions and ensure the robustness of our models. Firstly, we explored an updated dataset comprising diverse clay types, focusing on the intricate relationship between clay compressibility and essential soil properties. By incorporating particle size distribution, the ratio between liquid limit (wLL) and plasticity index (PI), and the ratio between void ratio (eLL) and wLL, our objective was to gain a comprehensive understanding of clay behavior, facilitating more accurate predictions. Secondly, we employed Artificial Neural Networks (ANNs) as our primary modeling tool. ANNs, with their capacity to discern complex patterns within data, formed the foundation of our predictive model. Our scientific objective was to harness the power of ANNs to capture subtle relationships among input variables, thereby ensuring a robust foundation for clay compressibility predictions. To further refine our predictions, our third objective was to optimize the ANN model. We utilized five distinct metaheuristic algorithms—Bird Swarm Algorithm (BSA), Dragonfly Optimization (DO), Elephant Herding Optimization (EHO), Jaya Algorithm (JA), and Whale Optimization Algorithm (WOA). Our aim was to fine-tune the ANN model's parameters using these algorithms, capitalizing on their strengths to achieve superior predictive performance. Additionally, our study aimed to pioneer the development and evaluation of hybrid models for forecasting clay compressibility. By integrating the ANN model with the metaheuristic algorithms (BSA-ANN, DO-ANN, EHO-ANN, JA-ANN, and WOA-ANN), our objective was to create hybrid frameworks that synergistically combined the strengths of both approaches. We rigorously evaluated these hybrid models, ensuring their efficacy and reliability in practical engineering applications. Crucially, we validated our models through practical engineering scenarios. For this purpose, we prepared 30 new soil samples mirroring real-world conditions. Our objective was to apply the developed models to these samples, affirming their effectiveness and reliability in actual foundation design applications. In pursuit of identifying the most accurate and reliable predictive model, our final objective focused on the BSA-ANN model. Remarkably, this model demonstrated an exceptional accuracy of 99.8%. Our objective was to establish the BSA-ANN model as the preferred choice for practical engineering applications, ensuring high reliability and safety in foundation design.

Improving Phishing Website Detection with Machine Learning: Revealing Hidden Patterns for Better Accuracy

Phishing attacks remain a significant threat to internet users globally, leading to substantial financial losses and compromising personal information. This research study investigates various machine learning models for detecting phishing websites, with a primary focus on achieving high accuracy. After an extensive analysis, the Random Forest Classifier emerged as the most suitable choice for this task. Our methodology leveraged machine learning techniques to uncover subtle patterns and relationships in the data, going beyond traditional URL and content-based restrictions. By incorporating diverse website features, including URL and derived attributes, Page source code-based features, HTML JavaScript-based features, and Domain-based features, we achieved impressive results. The proposed approach effectively classified the majority of websites, demonstrating the efficiency of machine learning in addressing the phishing website detection challenge with an accuracy of over 98%, recall exceeding 98%, and a false positive rate of less than 4%. This research offers valuable insights to the field of cyber security, providing internet users with improved protection against phishing attempts.

The Influence of Entrepreneurial Bricolage on Opportunity Recognition for New Ventures Based on Artificial Intelligence

This study delves into the subtle relationships between entrepreneurial bricolage, the use of artificial intelligence (AI), entrepreneurial knowledge, and the identification of opportunities in the field of entrepreneurship. It fills information gaps about the impact of these elements on opportunity recognition while examining the mediating role of entrepreneurial expertise. The major goal of this study was to investigate how entrepreneurial bricolage and AI adoption influence opportunity recognition, with a particular emphasis on the mediating role of entrepreneurial knowledge. Furthermore, the study hoped to contribute empirical data that could improve our knowledge of how these aspects interact. A structured questionnaire was provided to 420 entrepreneurs as part of a quantitative study methodology to collect data. For data analysis, SPSS was used, which included descriptive statistics, correlation analysis, regression analysis, and mediation analysis. According to the findings, entrepreneurial knowledge serves as a bridge between entrepreneurial bricolage and opportunity recognition, as well as between AI utilization and opportunity recognition. As a result, the study presents empirical evidence demonstrating the importance of entrepreneurial bricolage, AI adoption, and entrepreneurial knowledge in improving opportunity recognition. These findings highlight the importance of these aspects and their interdependence in the context of entrepreneurship. This work has practical consequences for businesses, politicians, and researchers alike by shining light on the optimal allocation of resources, the adoption of AI, and the cultivation of knowledge to better opportunity recognition. Furthermore, it adds empirical data on the role of entrepreneurial knowledge as a mediator in the theoretical understanding of entrepreneurship and innovation.

The Role of Technological Innovation on the Effect of International Strategic Alliances on Corporate Competitiveness in Jordanian International Business Administration: Moderating and Mediating Analysis

Within the context of Jordanian international business management, the current study explores the subtle relationships between global strategic alliances (ISA), corporate competitiveness (CC), and technological innovation (TI). Partial Least Squares Structural Equation Modeling (PLS-SEM) is used to assess the interaction between these variables as well as the mediation and moderation effects. Utilizing a sample size of 350 individuals who occupy professional, management, and decision-making roles in Jordanian businesses engaged in international commerce, the research article shows notable and significant positive associations. The results of the path analysis show a strong relationship between global strategic alliances (ISA) and both corporate competitiveness (CC) and technological innovation (TI). This shows that ISA is essential in fostering innovation inside organizations and developing competitive strategies. Additionally, this study looks at the connection between corporate competitiveness (CC) and technical innovation (TI), finding a very strong one. This emphasizes how important technological advancement is in enhancing a company's ability to compete. The model's mediating and moderating effects are the focus of the study's most important findings. The results provide solid support for the mediating role, indicating that the correlations between ISA, TI, and CC are interrelated and have an impact on one another. Furthermore, the findings of this study demonstrate a noteworthy moderation effect, suggesting the existence of external factors that exert an influence on the associations between these variables. This research provides valuable perspectives on the intricacies of global strategic partnerships, technical advancements, and corporate competitiveness within the realm of international business administration in Jordan. Through the elucidation of the complex interconnections and fundamental mechanisms, this research endeavour enhances our understanding of the influence of these factors on the competitive strategies and innovation endeavours of organisations.

New sources of biological data supporting environmental history of a tropical forest of south-eastern Brazil

The Tijuca Forest results from a reforestation project executed in the second half of the nineteenth century, with native and exotic species planted in the mountains of Rio de Janeiro, Brazil. Aiming at a theoretical reflection, the use of integrated methodologies from different areas of knowledge was evaluated to expand the research of the environmental history of a tropical forest in south-eastern Brazil, using everything from the search for historical documents to the counting of tree growth rings. It was possible to find some of the trees from the initial reforestation efforts, to circumscribe the locations of the first plantations, hitherto unknown, as well as the original intentions of tree selection: a reflection of the silvicultural model of the period that incorporated the tropical identity of the forest without giving up the market demand. The project area indicates that the planted forest would cover approximately 330 ha, prioritizing restoration by micro basins. In the 33 years analysed, about 155,000 trees were planted (110,000 with success) from a repertoire of at least 107 species, prioritizing the native ones. The quality of the wood, mainly for the naval segment, was prioritized, but other purposes were also met. The model and selection criteria for planting areas and species characterize a strategic plan that foresaw the recovery of water sources simultaneously with the generation of wealth in the medium and long term, diversifying the use of the soil and inaugurating a cycle of silvicultural exploitation in the country. This study re-signifies the understanding of the Tijuca Forest ecology, not only in space but mainly in time, revealing the subtle relationships between tropical nature and society in formation.

Improved GNNs for Log D7.4 Prediction by Transferring Knowledge from Low-Fidelity Data

The n-octanol/buffer solution distribution coefficient at pH = 7.4 (log D7.4) is an indicator of lipophilicity, and it influences a wide variety of absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties and druggability of compounds. In log D7.4 prediction, graph neural networks (GNNs) can uncover subtle structure-property relationships (SPRs) by automatically extracting features from molecular graphs that facilitate the learning of SPRs, but their performances are often limited by the small size of available datasets. Herein, we present a transfer learning strategy called pretraining on computational data and then fine-tuning on experimental data (PCFE) to fully exploit the predictive potential of GNNs. PCFE works by pretraining a GNN model on 1.71 million computational log D data (low-fidelity data) and then fine-tuning it on 19,155 experimental log D7.4 data (high-fidelity data). The experiments for three GNN architectures (graph convolutional network (GCN), graph attention network (GAT), and Attentive FP) demonstrated the effectiveness of PCFE in improving GNNs for log D7.4 predictions. Moreover, the optimal PCFE-trained GNN model (cx-Attentive FP, Rtest2 = 0.909) outperformed four excellent descriptor-based models (random forest (RF), gradient boosting (GB), support vector machine (SVM), and extreme gradient boosting (XGBoost)). The robustness of the cx-Attentive FP model was also confirmed by evaluating the models with different training data sizes and dataset splitting strategies. Therefore, we developed a webserver and defined the applicability domain for this model. The webserver (http://tools.scbdd.com/chemlogd/) provides free log D7.4 prediction services. In addition, the important descriptors for log D7.4 were detected by the Shapley additive explanations (SHAP) method, and the most relevant substructures of log D7.4 were identified by the attention mechanism. Finally, the matched molecular pair analysis (MMPA) was performed to summarize the contributions of common chemical substituents to log D7.4, including a variety of hydrocarbon groups, halogen groups, heteroatoms, and polar groups. In conclusion, we believe that the cx-Attentive FP model can serve as a reliable tool to predict log D7.4 and hope that pretraining on low-fidelity data can help GNNs make accurate predictions of other endpoints in drug discovery.

Age and skill in chess: Accuracy and speed‐accuracy performance in reasoning and knowledge

AbstractIndividual differences in cognitive performance depend on age, skill, and type of task. Nonetheless, whether performance is measured with accuracy (ACC) or with the trade‐off between responding speed and accuracy (SAT) could render subtle different relationships. Age and skill might associate more strongly with SAT performance in reasoning tasks, whereas they might relate more similarly with either ACC or SAT in knowledge tasks. These expectations were evaluated here with data from the cognitively taxing domain of chess (n = 259). Age was associated more strongly with the SAT than with the ACC measures in reasoning tasks. In contrast, skill related more robustly with the ACC than with the SAT measures in knowledge tasks. The main findings suggest that the associations of age and skill with performance vary depending on the type of task, but also on whether considering accuracy or speed‐accuracy measures of cognitive performance.

Graph Enhanced Fuzzy Clustering for Categorical Data Using a Bayesian Dissimilarity Measure

Categorical data are widely available in many real-world applications, and to discover valuable patterns in such data by clustering is of great importance. However, the lack of a decent quantitative relationship among categorical values makes traditional clustering approaches, which are usually developed for numerical data, perform poorly on categorical datasets. To solve this problem and boost the performance of clustering for categorical data, we propose a novel fuzzy clustering model in this article. At first, by approximating the maximum a posteriori (MAP) estimation of a discrete distribution of data partition, a new fuzzy clustering objective function is designed for categorical data. The Bayesian dissimilarity measure is formulated in this objective to tackle the subtle relationships between categorical values efficiently. Then, to further enhance the performance of clustering, a novel Kullback–Leibler divergence-based graph regularization is integrated into the clustering objective to exploit the prior knowledge on datasets, for example, the information about correlations of data points. The proposed model is solved by the alternative optimization and the experimental results on the synthetic and real-world datasets show that it outperforms the classical and relevant state-of-the-art algorithms. We also present the parameter analysis of our approach, and conduct a comprehensive study on the effectiveness of the Bayesian dissimilarity measure and the KL divergence-based graph regularization.

Interaction Between Memory Load and Experimental Design on Brain Connectivity and Network Topology.

The conventional approach to investigating functional connectivity in the block-designed study usually concatenates task blocks or employs residuals of task activation. While providing many insights into brain functions, the block design adds more manipulation in functional network analysis that may reduce the purity of the blood oxygenation level-dependent signal. Recent studies utilized one single long run for task trials of the same condition, the so-called continuous design, to investigate functional connectivity based on task functional magnetic resonance imaging. Continuous brain activities associated with the single-task condition can be directly utilized for task-related functional connectivity assessment, which has been examined for working memory, sensory, motor, and semantic task experiments in previous research. But it remains unclear how the block and continuous design influence the assessment of task-related functional connectivity networks. This study aimed to disentangle the separable effects of block/continuous design and working memory load on task-related functional connectivity networks, by using repeated-measures analysis of variance. Across 50 young healthy adults, behavioral results of accuracy and reaction time showed a significant main effect of design as well as interaction between design and load. Imaging results revealed that the cingulo-opercular, fronto-parietal, and default model networks were associated with not only task activation, but significant main effects of design and load as well as their interaction on intra- and inter-network functional connectivity and global network topology. Moreover, a significant behavior-brain association was identified for the continuous design. This work has extended the evidence that continuous design can be used to study task-related functional connectivity and subtle brain-behavioral relationships.

Correlating local chemical and structural order using Geographic Information Systems-based spatial statistics

Analysis of nanoscale short-range chemical and/or structural order via (scanning) transmission electron microscopy (S/TEM) imaging is fundamentally limited by projection of the three dimensional sample, which averages informational along the beam direction. Extracting statistically significant spatial correlations between the structure and chemistry determined from these two-dimensional datasets thus remains challenging. Here, we apply methods commonly used in Geographic Information Systems (GIS) to determine the spatial correlation between measures of local chemistry and structure from atomic-resolution STEM imaging of a compositionally complex relaxor, Pb(Mg1/3Nb2/3)O3 (PMN). The approach is used to determine the type of ordering present and to quantify the spatial variation of chemical order, oxygen octahedral distortions, and oxygen octahedral tilts. The extent of autocorrelation and inter-feature correlation among these short-range ordered regions are then evaluated through a spatial covariance analysis, showing correlation as a function of distance. The results demonstrate that integrating GIS tools for analyzing microscopy datasets can serve to unravel subtle relationships among chemical and structural features in complex materials that can be hidden when ignoring their spatial distributions.

Early career software developers and work preferences in software engineering

AbstractContext: The software engineering researchers and practitioners echoed the need for investigations to better understand the engineers developing software and services. In light of current studies, there are significant associations between the personalities of software engineers and their work preferences. However, limited studies are using psychometric measurements in software engineering.Objective: We aim to evaluate attitudes of early‐stage software engineers and investigate link between their personalities and work preferences.Method: We collected extensive psychometric data from 303 graduate‐level students in Computer Science programs at four Pakistani and one Swedish university using Five‐Factor Model. The statistical analysis investigated associations between various personality traits and work preferences.Results: The data support the existence of two clusters of software engineers, one of which is more highly rated across the board. Numerous correlations exist between personality qualities and the preferred types of employment for software developers. For instance, those who exhibit greater levels of emotional stability, agreeableness, extroversion, and conscientiousness like working on technical activities on a set timetable. Similar relationships between personalities and occupational choices are also evident in the earlier studies. More neuroticism is reported in female respondents than in male respondents. Higher intelligence was demonstrated by those who worked on the “entire development process” and “technical components of the project.”Conclusion: When assigning project tasks to software engineers, managers might use the statistically significant relationships that emerged from the analysis of personality attributes. It would be beneficial to construct effective teams by taking personality factors like extraversion and agreeableness into consideration. The study techniques and analytical tools we use may identify subtle relationships and reflect distinctions across various groups and populations, making them valuable resources for both future academic research and industrial practice.

Systematic analysis of the impact of slurry coating on manufacture of Li-ion battery electrodes via explainable machine learning

The manufacturing process strongly affects the electrochemical properties and performance of lithium-ion batteries. In particular, the flow of electrode slurry during the coating process is key to the final electrode properties and hence the characteristics of lithium-ion cells, however it is given little consideration. In this paper the effect of slurry structure is studied through the physical and rheological properties and their impact on the final electrode characteristics, for a graphite anode. As quantifying the impact of the large number of interconnected control variables on the electrode is a challenging task via traditional trial-and-error approaches, an explainable machine learning methodology as well as a systematic statistical analysis method is proposed for comprehensive assessments. The analysis is based upon an experimental dataset in lab-scale involving 9 main factors and 6 interest variables which cover practical range of variables through various combinations. While the predictability of response variables is evaluated via linear and nonlinear models, complementary techniques are utilised for variables importance, contribution, and first and second order effects to increase the model transparency. While coating gap is identified as the most influential factor for all considered responses, other subtle relationships are also extracted, highlighting that dimensionless numbers can serve as strong predictors for models. The impact of slurry viscosity and surface tension on electrode thickness, coat weight and porosity are also extracted, demonstrating their importance for electrode quality. These variables have been rarely considered in previous works, as the relationships are difficult to extract by trial and error due to interdependencies. Here we demonstrate how model-based analysis can overcome these difficulties and pave the way towards an optimised electrode manufacturing process of next generation Lithium-ion batteries.

The LINK 2022 Journal: a collection on practice-led research

In recent years there has been a growing interest in the relationship between research and Design practice. Since the validation of practice-oriented research in universities in the 90s, an increasing number of Design students can submit a creative work along with a written exegesis for assessment purposes. The creative practice encompasses making an artefact with a technique or process according to the academic protocols of rigour that is common to areas of Communication Design and familiar to other fields of knowledge such as nursing, business and science. For the practitioner, it involve­s the conception of ideas and the search for new methods conceived through art and design outcomes, performances, or any other form of an artefact combined with exegesis writing. The consolidation of academic programmes that incorporate practice in research is growing, and they operate in the dimensions where tacit practice and critical thinking are developed simultaneously. The emergency of this form of research focuses on the subtle relationships between creative methods that arise from reflection and action on both the design studio practices and the practitioner's written accounts. The Dossier LINK Journal: Practice-led research in Design aims to extend the understanding of this form of knowing in action and assesses the production of epistemologies that impact the discipline and the broader area of artistic research. It builds upon the editors' selection of the best presentations from the conference LINK 2021 Practice-led research in Art, Communication and Design, where authors were invited to expand their submissions in the form of an academic article. These manuscripts advance historical, contextual and philosophical approaches to demonstrate the generation of new knowledge that can only be obtained through practice. They provide a snapshot of the state-of-the-art practice-led research in the academic domain of the PhD's degree. This collection is a result of an international partnership between the LINK Practice-led Research Group from the Auckland University of Technology - AUT, in Aotearoa, New Zealand, the Journal GEMInIS from the Federal University of São Carlos – UFSCar and Anhembi Morumbi University. For this volume, LINK Journal selected ten articles that offer an updated panorama on how practice-led research has been incorporated into Academia to challenge the assumptions about new ways to investigate and communicate new knowledge through artistic practice. The bilingual edition reinforces LINK's commitment to producing and distributing knowledge about practice-led research worldwide via open-access databases, entirely free of coasts for authors. This dossier has as thematic co-editors Dr Marcos Mortensen Steagall (AUT) and Professor João Massarolo (UFSCar), Professor Sergio Nesteriuk Gallo (Anhembi Morumbi University) and Fiona Grieve (AUT). We hope you enjoy the reading.

Online Power Management for Multi-Cores: A Reinforcement Learning Based Approach

Power and energy is the first-class design constraint for multi-core processors and is a limiting factor for future-generation supercomputers. While modern processor design provides a wide range of mechanisms for power and energy optimization, it remains unclear how software can make the best use of them. This article presents a novel approach for runtime power optimization on modern multi-core systems. Our policy combines power capping and uncore frequency scaling to match the hardware power profile to the dynamically changing program behavior at runtime. We achieve this by employing reinforcement learning (RL) to automatically explore the energy-performance optimization space from training programs, learning the subtle relationships between the hardware power profile, the program characteristics, power consumption and program running times. Our RL framework then uses the learned knowledge to adapt the chip's power budget and uncore frequency to match the changing program phases for any new, previously unseen program. We evaluate our approach on two computing clusters by applying our techniques to 11 parallel programs that were not seen by our RL framework at the training stage. Experimental results show that our approach can reduce the system-level energy consumption by 12 percent, on average, with less than 3 percent of slowdown on the application performance. By lowering the uncore frequency to leave more energy budget to allow the processor cores to run at a higher frequency, our approach can reduce the energy consumption by up to 17 percent while improving the application performance by 5 percent for specific workloads.

Intestinal Microbial Diversity of Free-Range and Captive Yak in Qinghai Province.

Background: The gut microbiome is a large and complex organic assemblage with subtle and close relationships with the host. This symbiotic mechanism is important for the health and adaptability of the host to the environment. Compared with other ruminants, there are few studies on yak intestinal microbes. The study of the gut microbiota of the yak will help us better understand the correlation between the microbiota and the environmental adaptability of the host. In this study, we adapted 16S rDNA sequencing technology to investigate the diversity and composition of the intestinal microbial community in free-range yaks and captive yaks living on the Qinghai–Tibet Plateau (QTP). Results: Sequencing results showed that the intestinal microbial community diversity was significantly different between free-range yaks and captive yaks. Firmicutes and Bacteroidetes were the dominant bacteria in both free-range and captive yaks. However, there were differences between the microbes of the two analyzed feeding styles in different classification levels. Compared with the captive type, free-range yaks had a higher abundance of Ruminococcaceae, Eubacteriaceae, Desulfovibrionaceae, Elusimicrobium, and Oscillibacter, while the abundance of Succinivibrionaceae, Clostridiales, Lachnospiraceae, Prevotellaceae, Roseburia, and Barnesiella was relatively low. The feeding method may be the key factor for the formation of intestinal flora differences in yaks, while altitude did not significantly affect Qinghai yak. Conclusions: In this study, we used 16S rDNA sequencing technology to investigate the composition of intestinal flora in free-range and captive yaks living on the QTP. The exploration of dietary factors can provide a theoretical basis for scientifically and rationally breeding yaks and provides a new direction for the development of prebiotics and microecological agents.

Quality assessment of anatomical MRI images from generative adversarial networks: Human assessment and image quality metrics

BackgroundGenerative Adversarial Networks (GANs) can synthesize brain images from image or noise input. So far, the gold standard for assessing the quality of the generated images has been human expert ratings. However, due to limitations of human assessment in terms of cost, scalability, and the limited sensitivity of the human eye to more subtle statistical relationships, a more automated approach towards evaluating GANs is required. New methodWe investigated to what extent visual quality can be assessed using image quality metrics and we used group analysis and spatial independent components analysis to verify that the GAN reproduces multivariate statistical relationships found in real data. Reference human data was obtained by recruiting neuroimaging experts to assess real Magnetic Resonance (MR) images and images generated by a GAN. Image quality was manipulated by exporting images at different stages of GAN training. ResultsExperts were sensitive to changes in image quality as evidenced by ratings and reaction times, and the generated images reproduced group effects (age, gender) and spatial correlations moderately well. We also surveyed a number of image quality metrics. Overall, Fréchet Inception Distance (FID), Maximum Mean Discrepancy (MMD) and Naturalness Image Quality Evaluator (NIQE) showed sensitivity to image quality and good correspondence with the human data, especially for lower-quality images (i.e., images from early stages of GAN training). However, only a Deep Quality Assessment (QA) model trained on human ratings was able to reproduce the subtle differences between higher-quality images. ConclusionsWe recommend a combination of group analyses, spatial correlation analyses, and both distortion metrics (FID, MMD, NIQE) and perceptual models (Deep QA) for a comprehensive evaluation and comparison of brain images produced by GANs.

Identifying critical factors in systems with interrelated components: A method considering heterogeneous influence and strength attenuation

Identifying the key factors of interconnected systems and their relationships is a typical task in system analysis. However, most previous methods consider less about the nuanced effects of positive and negative influences on the accuracy of identification of critical factors in systems. The initial importance and strength attenuation effects of factors are often omitted in previous studies. To solve these problems, a novel method considering heterogeneous influence and strength attenuation (HISA) effects is developed in this paper. The effectiveness of the proposed method is validated by applying it to design of smart home system. Comparisons between the proposed method and previous approaches are conducted. The results show that the proposed method can make full use of the limited information and discern subtle relationships between factors to obtain more accurate and reasonable results.

Is Retention Time and/or Structure Matching a Solution to the Challenge of Providing Quantitative Data When Screening for Extractables and Leachables by GC/MS?

Leachables can potentially and adversely affect patient safety. Thus, drug products and medical devices are chromatographically screened for organic leachables (and extractables), establishing these compounds' identity and quantity. Accurate quantitation of extractables and leachables is challenging given compound-to-compound variation in response factors. One proposed means for managing variation and improving quantitation accuracy is the use of retention time (RT) and structure to match analytes with their most relevant quantitation surrogate. Although the scientific basis for relationships between RT and structure versus response is unclear, the use of matching was investigated using databases of response factors (RFs) or relative response factors (RRFs), RTs, and structures for extractables/leachables. Gas chromatography with mass spectrometry (MS) detection was investigated as response variation in this technique is less than with other screening methods such as liquid chromatography with MS detection. The overall RF variation across RT and structure makes it difficult to establish whether RT and response or structure and response can be correlated. Rigorous statistical analysis of the data concludes that there are no discernible relationships between these quantities; however, casual visual examination suggests that subtle relationships might exist. The effect that RT or structure matching could have on quantitation accuracy was considered, presuming that the visual trends were real. Under this presumption, it was estimated that RT matching could at most improve quantitation accuracy by 25%, and that structure matching could improve accuracy by at most 50%. However, these improvements do not address the response variation that is independent of RT or structure, and thus it is concluded that RT or structure matching are not viable solutions to RF variation. Rather, it is recommended that databases of authentic RRFs be aggressively populated to provide accurate quantitation. Compounds for which authentic RRFs cannot be secured are most effectively quantified using the median RRF.