Microbe-disease associations prediction by graph regularized non-negative matrix factorization with L 2 , 1 $$ {L}_{2,1} $$ norm regularization terms.

Microbes are involved in a wide range of biological processes and are closely associated with disease. Inferring potential disease-associated microbes as the biomarkers or drug targets may help prevent, diagnose and treat complex human diseases. However, biological experiments are time-consuming and expensive. In this study, we introduced a new method called iPALM-GLMF, which modelled microbe-disease association prediction as a problem of non-negative matrix factorization with graph dual regularization terms and L 2 , 1 $$ {L}_{2,1} $$ norm regularization terms. The graph dual regularization terms were used to capture potential features in the microbe and disease space, and the L 2 , 1 $$ {L}_{2,1} $$ norm regularization terms were used to ensure the sparsity of the feature matrices obtained from the non-negative matrix factorization and to improve the interpretability. To solve the model, iPALM-GLMF used a non-negative double singular value decomposition to initialize the matrix factorization and adopted an inertial Proximal Alternating Linear Minimization iterative process to obtain the final matrix factorization results. As a result, iPALM-GLMF performed better than other existing methods in leave-one-out cross-validation and fivefold cross-validation. In addition, case studies of different diseases demonstrated that iPALM-GLMF could effectively predict potential microbial-disease associations. iPALM-GLMF is publicly available at https://github.com/LiangzheZhang/iPALM-GLMF.

Exploring the utility of a social norm approach in reducing younger drivers' engagement in hand-held phone use while driving: A qualitative study.

INTRODUCTION: Engagement in hand-held phone use while driving among young drivers is a prevalent concern in society, despite countermeasures to deter the behavior. The social norm approach has been effective in reducing negative behaviors in young adults (e.g., binge drinking, drink driving). However, whether this approach can reduce hand-held phone use while driving in this population has not been thoroughly investigated. METHOD: The qualitative study explored young drivers' attitudes and opinions on social norm messages designed to reduce hand-held phone use while driving. In addition, young drivers' opinions on current campaigns were explored to provide further insight into the effectiveness of these messages. Thirty young drivers were interviewed and shown six social norm messages. RESULTS: The data were analyzed using reflexive thematic analysis, resulting in five themes and one sub-theme: (1) Road safety messages with minimal impact on hand-held phone use while driving; (2) What constitutes an effective road safety message for hand-held phone use while driving; (3) Comparisons between social norm messages and road safety messages; (4) The potential benefits of combined social norms, (4a) Improving and optimizing the message; and (5) "It's kinda just numbers on a screen": Negative views on social norm messages. Results highlight the diverse opinions towards road safety campaigns and the need to increase exposure to these messages. Further, a combined social norm message was perceived as most effective in reducing engagement in hand-held phone use while driving. CONCLUSIONS: The current study provides preliminary evidence that the social norm approach may be effective in reducing hand-held phone use among young drivers. Further, this study highlights the need to maximize exposure to phone use while driving campaigns in this high-risk cohort. PRACTICAL APPLICATIONS: Results support the development of a social norm messaging intervention to reduce young drivers hand-held phone use while driving.

Impacts of ecosystem service message framing and dynamic social norms on public support for tropical forest restoration.

The effectiveness of strategic psychology-based marketing techniques for increasing public support for conservation is poorly understood. We assessed how such techniques affect support for tropical rainforest restoration with a controlled online experiment with 1166 nationally representative residents of the United Kingdom. We tested whether support increased when adding ecosystem service (ES) framings to typical nongovernmental organizations' (NGOs) biodiversity-focused messages that emphasize benefits to UK residents or people living near the tropical restoration site and a dynamic social norm nudge that emphasized increasing popularity of environmental restoration. We considered how respondents' psychological traits (nature connection, self-efficacy, psychological benefits of supporting charities, awareness of environmental degradation in the Global South, and climate change skepticism) influenced responses. Outcomes included respondents' reported advertisement sufficiency, sympathetic attitudes, behavioral support, and financial support. The study population typically found advertisements sufficient and exhibited sympathetic attitudes and financial, but not behavioral, support. Younger people exhibited greater conservation support than older respondents. Messages framed solely on biodiversity conservation were as effective as those highlighting additional ES benefits received by UK residents and people near the tropical restoration site. This suggests that framing around ESs, rather than nature's intrinsic value, may not strengthen public support for conservation. The dynamic social norm nudge had perverse effects. It reduced perceived social norms and most outcome variables. Alternative dynamic norm nudges warrant testing, but our results support research suggesting dynamic norm nudges can be ineffective when associated with activism, challenging their use by conservation NGOs. Psychological benefits of supporting charities and perceived self-efficacy increased support for advertisements, highlighting the benefits of including impact statements relating respondents' support to specific outcomes. Climate change skepticism decreased support, whereas nature connection and perceived static social norms increased it, highlighting the need to increase nature connection and pro-environmental social norms to elevate public support for conservation.

Impactos del encuadre de los mensajes sobre servicios ambientales y las normas sociales dinámicas sobre el apoyo público hacia la restauración de bosques tropicales Resumen Sabemos poco sobre la eficiencia de las técnicas de mercadotecnia basadas en la psicología estratégica para aumentar el apoyo público a la conservación. Evaluamos cómo afectan dichas técnicas al apoyo a la restauración de la selva tropical mediante un experimento controlado en línea con 1,166 residentes del Reino Unido representativos a nivel nacional. Comprobamos si el apoyo aumentaba al añadir marcos de servicios ambientales a los mensajes típicos de las organizaciones no gubernamentales (ONG) centrados en la biodiversidad, que hacen hincapié en los beneficios para los residentes del Reino Unido o las personas que viven cerca del lugar de restauración tropical y un empuje dinámico de normas sociales que hacía hincapié en la creciente popularidad de la restauración ecológica. Analizamos la influencia de los rasgos psicológicos de los encuestados (conexión con la naturaleza, autoeficacia, beneficios psicológicos de apoyar a organizaciones benéficas, experiencia de degradación ambiental en el Sur Global y escepticismo ante el cambio climático) sobre las respuestas. Los resultados fueron la suficiencia de los anuncios, las actitudes de simpatía, el apoyo conductual y el apoyo económico. En general, la población del estudio consideró que los anuncios eran suficientes y mostró actitudes de simpatía y apoyo económico, pero no conductuales. La población más joven mostró un mayor apoyo a la conservación que los encuestados de más edad. Los mensajes centrados únicamente en la conservación de la biodiversidad fueron tan eficaces como los que destacaban los beneficios adicionales de los servicios ambientales recibidos por los residentes del Reino Unido y las personas cercanas al lugar de restauración tropical. Esto sugiere que el encuadre en torno a los servicios ambientales, en lugar del valor intrínseco de la naturaleza, puede no reforzar el apoyo público a la conservación. El empuje dinámico de la norma social tuvo efectos perversos ya que redujo las normas sociales percibidas y la mayoría de las variables de resultado. Es necesario probar otros incentivos dinámicos, pero nuestros resultados corroboran las investigaciones que sugieren que los incentivos dinámicos pueden ser ineficaces cuando se asocian con el activismo, lo que cuestiona su uso por parte de las ONG de la conservación. Los beneficios psicológicos por apoyar a organizaciones benéficas y la autoeficacia percibida aumentaron el apoyo a los anuncios, lo que resalta las ventajas de incluir declaraciones de impacto que relacionen el apoyo de los encuestados con resultados específicos. El escepticismo ante el cambio climático redujo el apoyo, mientras que la conexión con la naturaleza y las normas sociales estáticas percibidas lo aumentaron, lo que destaca la necesidad de aumentar la conexión con la naturaleza y las normas sociales proambientales para elevar el apoyo público a la conservación.

Detecting context dependence in the expression of life history trade-offs.

Life history trade-offs are one of the central tenets of evolutionary demography. Trade-offs, depicting negative covariances between individuals' life history traits, can arise from genetic constraints, or from a finite amount of resources that each individual has to allocate in a zero-sum game between somatic and reproductive functions. While theory predicts that trade-offs are ubiquitous, empirical studies have often failed to detect such negative covariances in wild populations. One way to improve the detection of trade-offs is by accounting for the environmental context, as trade-off expression may depend on environmental conditions. However, current methodologies usually search for fixed covariances between traits, thereby ignoring their context dependence. Here, we present a hierarchical multivariate 'covariance reaction norm' model, adapted from Martin (2023), to help detect context dependence in the expression of life-history trade-offs using demographic data. The method allows continuous variation in the phenotypic correlation between traits. We validate the model on simulated data for both intraindividual and intergenerational trade-offs. We then apply it to empirical datasets of yellow-bellied marmots (Marmota flaviventer) and Soay sheep (Ovis aries) as a proof-of-concept showing that new insights can be gained by applying our methodology, such as detecting trade-offs only in specific environments. We discuss its potential for application to many of the existing long-term demographic datasets and how it could improve our understanding of trade-off expression in particular, and life history theory in general.

Development of Aczel-Alsina t-norm based linear Diophantine fuzzy aggregation operators and their applications in multi-criteria decision-making with unknown weight information.

Aczel-Alsina t-norm and t-conorm are intrinsically flexible and endow Aczel-Alsina aggregation operators with greater versatility and robustness in the aggregation process than operators rooted in other t-norms and t-conorm families. Moreover, the linear Diophantine fuzzy set (LD-FS) is one of the resilient extensions of the fuzzy sets (FSs), intuitionistic fuzzy sets (IFSs), Pythagorean fuzzy sets (PyFSs), and q-rung orthopair fuzzy sets (q-ROFSs), which has acquired prominence in decision analysis due to its exceptional efficacy in resolving ambiguous data. Keeping in view the advantages of both LD-FSs and Aczel-Alsina aggregation operators, this article aims to establish Aczel-Alsina operation rules for LD-FSs, such as Aczel-Alsina sum, Aczel-Alsina product, Aczel-Alsina scalar multiplication, and Aczel-Alsina exponentiation. Based on these operation rules, we expose the linear Diophantine fuzzy Aczel-Alsina weighted average (LDFAAWA) operator, and linear Diophantine fuzzy Aczel-Alsina weighted geometric (LDFAAWG) operator and scrutinize their distinctive characteristics and results. Additionally, based on these aggregation operators (AOs), a multi-criteria decision-making (MCDM) approach is designed and tested with a practical case study related to forecasting weather under an LD-FS setting. The developed model undergoes a comparative analysis with several prevailing approaches to demonstrate the superiority and accuracy of the proposed model. Besides, the influence of the parameter Λ on the ranking order is successfully highlighted.

To Speak Up or Not to Speak Up, Organisational and Individual Antecedents That Undergird This Behaviour in Resource Constrained Region.

AIM: To examine the organisational (i.e., perceived organisational support and psychologically safe environment) and individual (i.e., value, belief and norm) antecedents that strengthen healthcare workers' speaking-up behaviour in a developing economy. DESIGN: The study uses a cross-sectional design to gather the same data from healthcare workers within the Ashanti Region of Ghana. METHODS: The data collection happened between 15 June and 30 August 2023. A sample of 380 healthcare workers was selected from 20 facilities in the Ashanti Region of Ghana. A configurational approach, a fussy-set qualitative comparative analysis, was used to identify the configurations that caused high and low speaking-up behaviour among the study sample. RESULTS: The study results reveal that whereas four configurations generate high speaking-up behaviour, three configurations, by contrast, produce low speaking-up behaviour among healthcare workers. CONCLUSION: Results suggest that in so far as organisational support systems which take the form of a psychologically safe environment and perceived organisational support are vital in relaxing the hierarchical boundaries in a healthcare setting to improve healthcare workers' speaking-up behaviour, the individual value-based factors that take the form of values, beliefs and norms are indispensable as it provides the healthcare workers with the necessary inner drive to regard speaking-up behaviour on patient safety and care as a moral duty. IMPACT: Healthcare workers' speaking-up behaviour is better achieved when organisational support systems complement the individual norms, values and beliefs of the individual. REPORTING METHOD: Adhered to Strengthening Reporting of Observational Studies in Epidemiology guidelines. PATIENT OR PUBLIC CONTRIBUTION: No patient or public contribution.

Effects of experimental, network-based social circle norm feedback on studying behavior and alcohol consumption.

Misrepresentation of peer behavior has often been observed in college students and may lead to over-expression of alcohol consumption and under-expression of studying. While social norm feedback approaches have had mixed success in addressing these misrepresentations and altering behavior, they may have been too unspecific to be effective and did not directly assess individual perception accuracy. We thus investigated how specific, one-time feedback on the behavioral distribution of alcohol consumption or study time of a clearly defined, individually-adjusted social circle would affect the respective norm estimations and behavior of a class of Psychology students (n = 89 in January) across their first year of study. Students overestimated alcohol consumption and partially underestimated studying norms. While social circle feedback on alcohol consumption did not clearly affect both individual estimation accuracy and alcohol consumption, feedback on peers' studying time increased studying with no clear effect on estimation accuracy. This indicates that social circle norm feedback may be suitable to evoke behavioral effects. The correction of the detected inaccuracies did not appear to be a precondition for the feedback to be effective.

Radioactivity as a driver of bacterial community composition in naturally radioactive mineral springs in the French Massif Central.

Some natural environments on Earth are characterised by high levels of radiation, including naturally radioelement enriched mineral springs in the French Massif Central. Therefore, naturally radioactive mineral springs are interesting ecosystems for understanding how bacterial populations in these springs have adapted to high levels of natural and chronic radioactivity over the very long term. The aim of this study was to analyse the bacterial communities of sediments from five naturally radioactive mineral springs in the French Massif Central, sampled in autumn 2019 and spring 2020, and to observe whether radionuclides, compared to other physicochemical parameters, are drivers of the bacterial community structuring in these extreme environments. Physicochemical measurements showed that two springs, Dourioux and Montagne had high radioelement concentrations/activities (uranium, thorium and radon). Analysis of the structure of the bacterial communities, by next generation sequencing based on 16S rRNA gene sequencing, showed that the presence of radionuclides in Dourioux and Montagne, did not lead to a reduction in bacterial diversity and richness compared to the other springs. However, Dourioux and Montagne were characterised by specific bacterial populations, whose presence correlates with the radioelement concentrations/activities measured in these springs. This suggests that radioelements could partly explain the structuring of bacterial communities in these springs. In addition, several of these operational taxonomic units (OTUs) specific to Dourioux and Montagne, mainly affiliated to Proteobacteria, Firmicutes, Acidobacteria, Actinobacteria, and Bacteroidetes, could be involved in the biogeochemistry of radionuclides through different mechanisms (biosorption, biomineralisation, bioaccumulation, and bioreduction), which would allow the development of other bacterial species sensitive to these metals/radioelements. In particular, the co-occurrence of sulphate and/or iron-reducing bacteria, capable of bioreducing uranium, with fermentative bacteria, releasing sources of organic carbons, reflects associations of bacteria with complementary functions that allow them to grow in this peculiar environment and maintain a high diversity in these extreme environments. This study has provided a better understanding of the structuring of bacterial communities exposed to ionising radiation for thousands of years in naturally radioactive environments.

Social sanctions in response to injunctive norm violations.

Injunctive social norms are societal standards for how people are expected to behave. When individuals transgress these norms, they face social sanctions for their behavior. These sanctions can take many forms ranging from verbal or non-verbal reactions and from disapproval to ostracism. We review the stable characteristics and situational variables that affect a bystander's tendency to enact social sanctions against someone who violates an injunctive social norm. Stable characteristics include the bystander's extraversion, altruism, the belief that others can change their behavior, and their cultural background. Situational factors include the extent to which the violated norm implicates the bystander, the social hierarchies among the bystander and transgressor, the presence of additional bystanders, and (when applicable) the bystander's relationship to the victim of the norm violation. We also discuss the costs that a bystander can incur by attempting to enact social sanctions. We conclude with a discussion of the application of social sanctions to enforce pro-social social norms.

Exponential synchronization of quaternion-valued memristor-based Cohen-Grossberg neural networks with time-varying delays: norm method.

In this paper, the exponential synchronization of quaternion-valued memristor-based Cohen-Grossberg neural networks with time-varying delays is discussed. By using the differential inclusion theory and the set-valued map theory, the discontinuous quaternion-valued memristor-based Cohen-Grossberg neural networks are transformed into an uncertain system with interval parameters. A novel controller is designed to achieve the control goal. With some inequality techniques, several criteria of exponential synchronization for quaternion-valued memristor-based Cohen-Grossberg neural networks are given. Different from the existing results using decomposition techniques, a direct analytical approach is used to study the synchronization problem by introducing an improved one-norm method. Moreover, the activation function is less restricted and the Lyapunov analysis process is simpler. Finally, a numerical simulation is given to prove the validity of the main results.

A review of radiation doses and associated parameters in Western Australian mining operations (2020-23).

The Western Australian mining industry is a global supplier of critical minerals, including lithium and rare earths. The lithology of these minerals is associated with elevated concentrations of naturally occurring radionuclides (NORs). An increase in the number of mines producing the minerals has witnessed a commensurate increase in the number of workers potentially exposed to the radiation from NORs. The regulatory framework in Western Australia underwent significant change in March 2022. Mining operations whose workers are likely to receive doses greater than one mSvy-1 are referenced as relevant mines and are required to submit an annual report of worker doses to the mining regulator. This research provides an overview of the new legislative framework and updates the information in Ralph and Cattani (2022) to include data derived from annual radiation doses reported by relevant mines in the period spanning 2020-21 to 2022-23. In 2022-23, 38 mining operations were identified as relevant mines, an increase of sixteen from 2020-21. The mean effective dose (ED) reported in the three-year period was 1.0 mSv, and the maximum ED was 4.9 mSv. The collective effective dose of the mine worker population reached an historical maximum of 2,339 man.mSv in 2022-23. Inhalation of long-lived alpha emitting radionuclides in dust remains the most significant contributor to worker doses. Inhalation of radon-22, radon-220 and their short-lived progeny, once considered as a negligible contributor to worker doses, is the second most significant exposure pathway. A declining trend in the number of samples collected per worker is highlighted as requiring remediation to provide confidence in the reported dose estimates. The transition to the new legislative framework for radiation protection in mines has been supported by the publication of guidance materials which have been widely endorsed by the industry. .

Norms of structured random matrices.

For m , n ∈ N , let X = ( X ij ) i ≤ m , j ≤ n be a random matrix, A = ( a ij ) i ≤ m , j ≤ n a real deterministic matrix, and X A = ( a ij X ij ) i ≤ m , j ≤ n the corresponding structured random matrix. We study the expected operator norm of X A considered as a random operator between ℓ p n and ℓ q m for 1 ≤ p , q ≤ ∞ . We prove optimal bounds up to logarithmic terms when the underlying random matrix X has i.i.d. Gaussian entries, independent mean-zero bounded entries, or independent mean-zero ψ r ( r ∈ ( 0 , 2 ] ) entries. In certain cases, we determine the precise order of the expected norm up to constants. Our results are expressed through a sum of operator norms of Hadamard products A ∘ A and ( A ∘ A ) T .

Underdetermined Blind Source Separation of Audio Signals for Group Reared Pigs Based on Sparse Component Analysis.

In order to solve the problem of difficult separation of audio signals collected in pig environments, this study proposes an underdetermined blind source separation (UBSS) method based on sparsification theory. The audio signals obtained by mixing the audio signals of pigs in different states with different coefficients are taken as observation signals, and the mixing matrix is first estimated from the observation signals using the improved AP clustering method based on the "two-step method" of sparse component analysis (SCA), and then the audio signals of pigs are reconstructed by L1-paradigm separation. Five different types of pig audio are selected for experiments to explore the effects of duration and mixing matrix on the blind source separation algorithm by controlling the audio duration and mixing matrix, respectively. With three source signals and two observed signals, the reconstructed signal metrics corresponding to different durations and different mixing matrices perform well. The similarity coefficient is above 0.8, the average recovered signal-to-noise ratio is above 8 dB, and the normalized mean square error is below 0.02. The experimental results show that different audio durations and different mixing matrices have certain effects on the UBSS algorithm, so the recording duration and the spatial location of the recording device need to be considered in practical applications. Compared with the classical UBSS algorithm, the proposed algorithm outperforms the classical blind source separation algorithm in estimating the mixing matrix and separating the mixed audio, which improves the reconstruction quality.

A simplified adversarial architecture for cross-subject silent speech recognition using electromyography.

Objective. The decline in the performance of electromyography (EMG)-based silent speech recognition is widely attributed to disparities in speech patterns, articulation habits, and individual physiology among speakers. Feature alignment by learning a discriminative network that resolves domain offsets across speakers is an effective method to address this problem. The prevailing adversarial network with a branching discriminator specializing in domain discrimination renders insufficiently direct contribution to categorical predictions of the classifier.Approach. To this end, we propose a simplified discrepancy-based adversarial network with a streamlined end-to-end structure for EMG-based cross-subject silent speech recognition. Highly aligned features across subjects are obtained by introducing a Nuclear-norm Wasserstein discrepancy metric on the back end of the classification network, which could be utilized for both classification and domain discrimination. Given the low-level and implicitly noisy nature of myoelectric signals, we devise a cascaded adaptive rectification network as the front-end feature extraction network, adaptively reshaping the intermediate feature map with automatically learnable channel-wise thresholds. The resulting features effectively filter out domain-specific information between subjects while retaining domain-invariant features critical for cross-subject recognition.Main results. A series of sentence-level classification experiments with 100 Chinese sentences demonstrate the efficacy of our method, achieving an average accuracy of 89.46% tested on 40 new subjects by training with data from 60 subjects. Especially, our method achieves a remarkable 10.07% improvement compared to the state-of-the-art model when tested on 10 new subjects with 20 subjects employed for training, surpassing its result even with three times training subjects.Significance. Our study demonstrates an improved classification performance of the proposed adversarial architecture using cross-subject myoelectric signals, providing a promising prospect for EMG-based speech interactive application.

Low Carbon Sustainable Diet Choices-An Analysis of the Driving Factors behind Plant-Based Egg Purchasing Behavior.

In the face of escalating climate change, environmental pollution, and global crises, plant-based eggs are a viable solution for environmental conservation and health promotion. This study uses an integrated framework combining value-belief-norm (VBN) theory and the Theory of Planned Behavior (TPB) to explore the factors influencing Taiwanese consumers' inclination towards plant-based eggs. Through convenience sampling, 417 questionnaires were issued and 387 valid responses were received, indicating a 92.8% valid response rate. The analysis indicates that consumers' biospheric and altruistic values significantly enhance their awareness of outcomes, which, coupled with their sense of responsibility, strengthens personal norms. From the TPB perspective, perceived behavioral control is identified as a critical driver of the intention to purchase plant-based eggs, with attitudes and subjective norms playing significant roles. Subjective norms positively impact personal norms, thereby influencing consumers' behavioral intentions. The findings confirm the integrated model's theoretical and practical validity, and offer valuable insights for businesses. By developing adept marketing strategies that consider market dynamics, this study enhances the effectiveness and value of marketing efforts by boosting consumers' engagement with plant-based eggs. These insights contribute to advancing environmental sustainability goals.

Do Human Assertions Really Adhere Strictly to Norms? The Effect of Threatening Content in Information on Personalized Norm Perception.

Assertion is the use of declarative sentences to convey information, which necessitates meeting the "justified-belief norm" as a prerequisite. However, a significant amount of misinformation that did not meet these conditions was spread during COVID-19, leading to a reintroduction of the assertion norm. One possible hypothesis is that the threatening content of the misinformation influenced the perception of the norm. However, this remains unclear to researchers. Therefore, we conducted two experiments to investigate the effect of threatening content in information on individuals' perceptions of norms. In all the experiments, participants read backstories with and without threatening content, followed by answering assertion questions. It was observed that people do follow a looser assertion norm for information that contains threatening content. Additionally, further exploration revealed that threatening factors also lead individuals to more easily perceive the related content as truth and reduce the probability of being blamed. These two outcomes provide some explanation for the underlying mechanism of threatening factors' influence. The research results further refined the theory of assertion norms, offering a certain basis for information management.

Estimation of genotype by environmental interaction for litter traits by reaction norm model in Taiwan Landrace sows.

The negative effects of heat stress on swine reproduction have been well documented and the recent global warming trend caused by climate change is leading to more days with high temperatures every year. This has caused a reduction in litter trait performance of Landrace sows in Taiwan, a country extending across tropical and subtropical oceanic zones. Therefore, this study developed a modified model to determine which stages of pregnancy, before, early, middle, and late, had the largest impacts of heat stress on litter traits. A reaction norm model (RNM) was used to identify sows with high resilience to heat stress for litter traits followed by analysis of the modified model. Data from Landrace sows were collected from 2 farms in Taiwan between 2008 and 2021. A total of 11,059 records were collected for total number born (TNB), number born alive (NBA), and stillborn rate (STBR). The results showed that the heritabilities of TNB, NBA, and STBR were 0.170, 0.115, and 0.077, respectively. These results were similar between the conventional model and the modified model. In the modified model, the before and early stages of sow pregnancy were the significant periods for TNB and NBA (P < 0.05), while the early and middle stages were significant for STBR (P < 0.05). According to the RNM results, the heritability estimates for TNB, NBA, and STBR were 0.23 to 0.11, 0.18 to 0.08, and 0.10 to 0.04, respectively, showing a decrease from low temperature-humidity index (THI) to high THI. The minimum genetic correlations between the highest and the lowest THI for TNB, NBA, and STBR were 0.85, 0.64, and 0.80, respectively. The results of the RNM for breeding value showed re-ranking across THI values. In conclusion, similar results were obtained for heritability when the model was modified for heat stress estimation. Yet re-ranking of breeding values across THI could help farmers to select not only for improved litter trait performance but also for heat stress resilience of Landrace sows in Taiwan.

Heat stress caused by climate change is a challenge for the pig industry, especially in countries located in tropical and subtropical zones, such as Taiwan. It can adversely affect litter traits, leading to less pork production and higher economic losses to farms. Therefore, identifying sows with the potential to tolerate high heat with high humid condition is an important task for the pig industry. This article proposes a reaction norm model to determine the trend in breeding value across temperature and humidity index values and its implications for litter traits of sows. Our results indicate that litter traits can be used to select sows with high heat tolerance.

Genotype-by-gestational thermal environment interaction and its impact on the future performance of tropical composite beef cattle offspring.

With global warming, there are growing challenges for raising taurine and composite beef cattle populations in tropical regions, including elevated temperatures, limited forage availability, parasite infestation, and infectious diseases. These environmental factors can trigger specific physiological responses in the developing fetus, which may have long-term implications on its performance. Therefore, the main objective of this study was to assess the influence of naturally induced thermal stress during the gestation period on the subsequent performance of tropical composite beef cattle progeny. Furthermore, we aimed to investigate the impact of genotype-by-gestational thermal environment interaction (G×Eg) on traits under selection pressure in the breeding population. A total of 157,414 animals from 58 farms located in various Brazilian states were recorded for birth weight (BW), preweaning weight gain (PWG), yearling weight (YW), hip height (HH), scrotal circumference (SC), and days to first calving (DFC). We first applied a linear regression model to the BW data, which revealed that the last 40 d of gestation were suitable for calculating the mean temperature humidity index (THIg). Subsequent regression analyses revealed that for every 10-unit increase in THIg, detrimental effects of approximately 1.13% to 16.34% are expected for all traits evaluated. Genetic parameters were estimated through a reaction norm model using THIg as the environmental descriptor. The posterior means of heritability estimates (SD) were 0.35 (0.07), 0.25 (0.03), 0.31 (0.03), 0.37 (0.01), 0.29 (0.07), and 0.20 (0.09) for the direct effect of BW, PWG, YW, HH, SC, and DFC, respectively. These estimates varied along the range of THIg values, suggesting a variable response to selection depending on the thermal environment during gestation. Genetic correlation estimates between more divergent THIg values were low or negative for YW, PWG, and DFC, indicating that the best-performing individuals at low THIg values may not perform as well at high THIg values and vice versa. Overall, thermal stress during gestation impacts the future performance of beef cattle offspring. Our results indicate the need for developing effective breeding strategies that take into account G×Eg effects and the re-ranking of breeding animals along the THIg scale, particularly for traits such as DFC that are highly sensitive to thermal stress.

With global warming posing increasing challenges in tropical regions, this study aimed to assess the impact of thermal stress during gestation on the performance of composite beef cattle offspring. Environmental factors such as high temperatures, humidity, limited forage availability, and parasite infestation can elicit physiological responses in the developing fetus, affecting its long-term performance and welfare. Using the temperature humidity index (THIg) of the late gestation as a measure of thermal environment, a reaction norm model was applied to analyze the birth weight, preweaning weight gain, yearling weight, hip height, scrotal circumference, and days to first calving (DFC). Results revealed that increasing THIg values were associated with a detrimental effect in these traits. Genotype-by-environment interaction was found to significantly influence trait variability, with DFC showing the strongest effect. Negative genetic correlations were observed between divergent THIg values, suggesting that individuals performing well in mild thermal environments may not excel in high thermal stress conditions. The heritability estimates varied along the THIg scale, indicating that selection response may vary depending on the thermal environment during gestation. These findings emphasize the need for breeding strategies that account for genotype-by-environment effects and consider the impact of thermal stress on cattle performance.

Robust PCA with Lw,∗ and L2,1 Norms: A Novel Method for Low-Quality Retinal Image Enhancement.

Nonmydriatic retinal fundus images often suffer from quality issues and artifacts due to ocular or systemic comorbidities, leading to potential inaccuracies in clinical diagnoses. In recent times, deep learning methods have been widely employed to improve retinal image quality. However, these methods often require large datasets and lack robustness in clinical settings. Conversely, the inherent stability and adaptability of traditional unsupervised learning methods, coupled with their reduced reliance on extensive data, render them more suitable for real-world clinical applications, particularly in the limited data context of high noise levels or a significant presence of artifacts. However, existing unsupervised learning methods encounter challenges such as sensitivity to noise and outliers, reliance on assumptions like cluster shapes, and difficulties with scalability and interpretability, particularly when utilized for retinal image enhancement. To tackle these challenges, we propose a novel robust PCA (RPCA) method with low-rank sparse decomposition that also integrates affine transformations τi, weighted nuclear norm, and the L2,1 norms, aiming to overcome existing method limitations and to achieve image quality improvement unseen by these methods. We employ the weighted nuclear norm (Lw,∗) to assign weights to singular values to each retinal images and utilize the L2,1 norm to eliminate correlated samples and outliers in the retinal images. Moreover, τi is employed to enhance retinal image alignment, making the new method more robust to variations, outliers, noise, and image blurring. The Alternating Direction Method of Multipliers (ADMM) method is used to optimally determine parameters, including τi, by solving an optimization problem. Each parameter is addressed separately, harnessing the benefits of ADMM. Our method introduces a novel parameter update approach and significantly improves retinal image quality, detecting cataracts, and diabetic retinopathy. Simulation results confirm our method's superiority over existing state-of-the-art methods across various datasets.

Continuous Norming Approaches: A Systematic Review and Real Data Example.

Norming of psychological tests is decisive for test score interpretation. However, conventional norming based on subgroups results either in biases or require very large samples to gather precise norms. Continuous norming methods, namely inferential, semi-parametric, and (simplified) parametric norming, propose to solve those issues. This article provides a systematic review of continuous norming. The review includes 121 publications with overall 189 studies. The main findings indicate that most studies used simplified parametric norming, not all studies considered essential distributional assumptions, and the evidence comparing different norming methods is inconclusive. In a real data example, using the standardization sample of the Need for Cognition-KIDS scale, we compared the precision of conventional, semi-parametric, and parametric norms. A hierarchy in terms of precision emerged with conventional norms being least precise, followed by semi-parametric norms, and parametric norms being most precise. We discuss these findings by comparing our findings and methods to previous studies.