bluuebunny/crossref_metadata_2025 huggingface dataset:
Surgical critical care: Is work–life expectancy increasing? An analysis of American Board of Surgery recertification rates across subspecialties
Authors: Brigmon Erika Paola , Ciaraglia Angelo , Scherer Elizabeth P , Schwartz Deborah L , Nicholson Susannah , Dent Daniel L
Published: April 2024

The practice of surgical critical care (SCC) has traditionally necessitated additional in-house, extended night and weekend clinical commitments, which can be viewed as less desirable for many surgeons. Therefore, the authors have observed that some SCC surgeons elect to transition their practice to focus solely on general surgery (GS) rather than continuing practicing both SCC and GS. We hypothesized that surgeons with a practice focused on SCC are more likely to make the transition to a GS practice than those who have certification in other subspecialties that are certified through the American Board of Surgery.
Digital quantum simulation of gravitational optomechanics with IBM quantum computers
Authors: Carmona Rufo Pablo Guillermo , Mazumdar Anupam , Bose Sougato , Sabín Carlos
Published: April 2024

We showcase the digital quantum simulation of the action of a Hamiltonian that governs the interaction between a quantum mechanical oscillator and an optical field, generating quantum entanglement between them via gravitational effects. This is achieved by making use of a boson-qubit mapping protocol and a digital gate decomposition that allow us to run the simulations in the quantum computers available in the IBM Quantum platform. We present the obtained results for the fidelity of the experiment in two different quantum computers, after applying error mitigation and post-selection techniques. The achieved results correspond to fidelities over 90%, which indicates that we were able to perform a faithful digital quantum simulation of the interaction and therefore of the generation of quantum entanglement by gravitational means in optomechanical systems.
Reliable Estimation of Causal Effects Using Predictive Models
Authors: Ali Mahdi Hadj , Biannic Yann Le , Wuillemin Pierre-Henri
Published: April 2024

In recent years, machine learning algorithms have been widely adopted across many fields due to their efficiency and versatility. However, the complexity of predictive models has led to a lack of interpretability in automatic decision-making. Recent works have improved general interpretability by estimating the contributions of input features to the predictions of a pre-trained model. Drawing on these improvements, practitioners seek to gain causal insights into the underlying data-generating mechanisms. To this end, works have attempted to integrate causal knowledge into interpretability, as non-causal techniques can lead to paradoxical explanations. In this paper, we argue that each question about a causal effect requires its own reasoning and that relying on an initial predictive model trained on an arbitrary set of variables may result in quantification problems when estimating all possible effects. As an alternative, we advocate for a query-driven methodology that addresses each causal question separately. Assuming that the causal structure relating the variables is known, we propose to employ the tools of causal inference to quantify a particular effect as a formula involving observable probabilities. We then derive conditions on the selection of variables to train a predictive model that is tailored for the causal question of interest. Finally, we identify suitable eXplainable AI (XAI) techniques to estimate causal effects from the model predictions. Furthermore, we introduce a novel method for estimating direct effects through intervention on causal mechanisms.
Launching and Positioning of Amul Camel Milk
Authors: Patel Naresh K.
Published: April 2024

Amul is known globally for its cooperative model and it enjoys a healthy market share in the organized milk market of the country. Post-independence Amul plays a vital role in providing food security to marginalized milk-producing farmers and serving urban customers. Amul is also known for continuous improvement and product development. Amul launched camel milk in the Indian market in selected cities of Gujarat in January 2019 and emerged as the first to launch camel milk.

Camel milk has many health-related benefits, particularly for diabetics, Autism, Food allergies, and other autoimmune diseases. Amul is also expecting to demand camel milk due to the increased health consciousness of the urban consumers’ segment. Amul camel milk got an excellent response initially. As per the MD of Amul, Mr. R. S. Shodhi, the major challenge is milk procurement. Being socially committed, Amul is closely working with camel breeders and also having social upliftment behind starting the camel milk. In the initial stage, they have the struggle to establish the entire supply chain of camel milk. And the primary issue is shelf life and awareness of camel milk.
Two-Phase Speckle Noise Removal in US Images: Speckle Reducing Improved Anisotropic Diffusion and Optimal Bayes Threshold
Authors: Shabana Sulthana S. L. , Sucharitha M.
Published: April 2024

Medial images are contaminated by multiplicative speckle noise, which dramatically reduces ultrasound images and has a detrimental impact on a variety of image interpretation tasks. Hence, to overcome this issue, this paper presented a Two-Phase Speckle Reduction approach with Improved Anisotropic Diffusion and Optimal Bayes Threshold termed TPSR-IADOT, which includes the phases like image enhancement and two-level decomposition processes. Initially, the speckle noise is subjected to an image enhancement process where the Speckle Reducing Improved Anisotropic Diffusion (SRAID) filtering process is carried out for the speckle removal process. Afterwards, two-level decomposition takes place which utilizes Discrete Wavelet Transform (DWT) to remove the residual noise. As the speckle noise is mostly present in the high-frequency band, Improved Bayes Threshold will be applied to the high- frequency subbands. Finally, to provide the best outcomes, an optimization algorithm termed Self Improved Pelican Optimization Algorithm (SI-POA) in this work via choosing the optimal threshold value. The efficiency of the proposed method has been validated on an ultrasound image database using Simulink in terms of PSNR, SSIM, SDME and MAPE. Accordingly, from the analysis, it is proved that the proposed TPSR-IADOT attains the PSNR of 40.074, whereas the POA is 38.572, COOT is 38.572, BES is 37.003, PRO is 30.419, WOA is 33.218, RFU-LA is 29.935 and SSI-COA is 39.256, for noise variance[Formula: see text]0.1.
On matrix-valued Riesz bases over LCA groups
Authors: Jyoti  , Vashisht Lalit Kumar
Published: April 2024

A Riesz basis for a separable Hilbert space [Formula: see text] is the image of an orthonormal basis under a bounded, linear and bijective operator acting on [Formula: see text]. Equivalently, it is an exact frame that shares the properties of a basis for [Formula: see text]. Let [Formula: see text] be a [Formula: see text]-compact and metrizable locally compact abelian group, and [Formula: see text] and [Formula: see text] be positive integers. It is illustrated that the image of a matrix-valued orthonormal basis under a bijective, bounded and linear operator acting on the matrix-valued signal space [Formula: see text] may not be a frame, hence not a basis of the space [Formula: see text]. We introduce a notion of matrix-valued Riesz basis in the space [Formula: see text], where the adjointability of a bounded linear operator in the definition of Riesz basis with respect to the matrix-valued inner product plays a crucial role. We establish the existence of matrix-valued Riesz bases of the space [Formula: see text]. Extending results for standard Riesz bases of separable Hilbert spaces, we give necessary and sufficient conditions, and a characterization of matrix-valued Riesz bases of the space [Formula: see text].
Texture classification network integrating adaptive wavelet transform
Authors: Yu Su-Xi , He Jing-Yuan , Wang Yi , Cai Yu-Jiao , Yang Jun , Lin Bo , Yang Wei-Bin , Ruan Jian
Published: April 2024

Graves’ disease is a common condition that is diagnosed clinically by determining the smoothness of the thyroid texture and its morphology in ultrasound images. Currently, the most widely used approach for the automated diagnosis of Graves’ disease utilizes Convolutional Neural Networks (CNNs) for both feature extraction and classification. However, these methods demonstrate limited efficacy in capturing texture features. Given the high capacity of wavelets in describing texture features, this research integrates learnable wavelet modules utilizing the Lifting Scheme into CNNs and incorporates a parallel wavelet branch into the ResNet18 model to enhance texture feature extraction. Our model can analyze texture features in spatial and frequency domains simultaneously, leading to optimized classification accuracy. We conducted experiments on collected ultrasound datasets and publicly available natural image texture datasets, our proposed network achieved 97.27% accuracy and 95.60% recall on ultrasound datasets, 60.765% accuracy on natural image texture datasets, surpassing the accuracy of ResNet and confirming the effectiveness of our approach.
Optimal Release Policy for Software with Epistemic Uncertainty
Authors: Liu Zhe , Wang Shihai , Liu Bin , Kang Rui
Published: April 2024

In this paper, finding the optimal release time of software is one of the challenging problems for software companies. Various optimal release policies have been proposed under the framework of probability theory, which is an axiomatic mathematical system dealing with aleatory uncertainties rather than epistemic uncertainties. Nevertheless, software faults embody lot of epistemic uncertainties that are more suitable to be modeled by uncertainty theory. Given this, a series of software belief reliability growth models under the framework of uncertainty theory were proposed, whose superiorities have been shown by several real data analyses. Up to now, the optimal software release policy based on software belief reliability growth models has not been studied comprehensively. As a consequence, in this paper, we discuss optimal release policy for software based on software belief reliability growth models. Meanwhile, a sensitivity analysis is conducted to investigate the effects of various model parameters on the optimal release time.
Periodic solutions for an “SVIQR” epidemic model in a seasonal environment with general incidence rate
Authors: El Hajji Miled
Published: April 2024

Seasonality is repetitive in the ecological, biological and human systems. Seasonal factors affect both pathogen survival in the environment and host behavior. In this study, we considered a five-dimensional system of ordinary differential equations modeling an epidemic in a seasonal environment with a general incidence rate. We started by studying the autonomous system by investigating the global stability of steady states. Later, we proved the existence, uniqueness, positivity and boundedness of a periodic orbit in a non-autonomous system. We demonstrate that the global dynamics are determined using the basic reproduction number [Formula: see text] which is defined by the spectral radius of a linear integral operator. We showed that if [Formula: see text], then the disease-free periodic solution is globally asymptotically stable and if [Formula: see text], then the trajectories converge to a limit cycle reflecting the persistence of the disease. Finally, we present a numerical investigation that support our results.
Deterministic and stochastic analysis of a coral reef ecosystem with grazed macroalgae
Authors: Xu Chaoqun , Chen Wenkai , Hu Jianghong
Published: April 2024

Based on the work of Mumby et al. [Thresholds and the resilience of Caribbean coral reefs, Nature 450 (2007) 98–101], this study is devoted to investigate the deterministic and stochastic features of a coral reef ecosystem in which macroalgae and coral compete to occupy algae turfs, and the macroalgae are grazed by parrotfish. By taking the grazing rate as the focused parameter, we completely analyze the global dynamics of the deterministic coral reef ecosystem, including bistable phenomenon, linear equilibria and degenerate attractor. It is found that for different grazing rates, the coral reef system may exhibit rich dynamics which are closely dependent on the initial values. Furthermore, we derive a stochastic coral reef model in the form of continuous-time Markov chain (CTMC), and estimate the extinction probabilities of macroalgae and coral by using the branching process theory. Analytical results reveal that the macroalgae or coral species will go to extinction in a positive probability, even if it can survive in the deterministic environment.
Dynamics of slow–fast modified Leslie–Gower model with weak Allee effect and fear effect on predator
Authors: Wu Yuhang , Ni Mingkang
Published: April 2024

In this paper, we focus on the dynamics of the modified Leslie–Gower model with a weak Allee effect and fear effect on predators. Assuming that the inherent growth rate of prey is much faster than that of predators, this becomes a singular perturbation problem. Based on the geometric singular perturbation theory, we prove the existence of canard cycles and relaxation oscillation and provide an asymptotic expression of the relaxation oscillation period. Furthermore, it demonstrates that, under certain conditions, the degenerate transcritical bifurcation point is a global attractor using geometric singular perturbation theory, the center manifold theorem and the blow-up method. Numerical simulations verify the theoretical results.
Stochastic dynamical behaviors of SOS/ERK signaling pathway perturbated by external noise
Authors: Jiang Zhiyuan , Su You-Hui , Yin Hongwei
Published: April 2024

The SOS/ERK cascades are key signaling pathways that regulate cellular processes ranging from cellular proliferation, differentiation and apoptosis to tumor formation. However, the properties of these signaling pathways are not well understood. More importantly, how stochastic perturbations of internal and external cellular environment affect these pathways remains unanswered. To answer these questions, we, in this paper, propose a stochastic model according to the biochemical reaction processes of the SOS/ERK pathways, and, respectively, research the dynamical behaviors of this model under the four kinds of noises: Gaussian noise, colored noise, Lévy noise and fraction Brown noise. Some important results are found that Gaussian and colored noises have less effect on the stability of the system when the strength of the noise is small; Lévy and fractional Brownian noises significantly change the trajectories of the system. Power spectrum analysis shows that Lévy noise induces a system with quasi-periodic trajectories. Our results not only provide an understanding of the SOS/ERK pathway, but also show generalized rules for stochastic dynamical systems.
Minimal wave speed for a reaction–diffusion tuberculosis model with the fast and slow progression
Authors: Shi Wanxia , Wang Jie
Published: April 2024

This work is concerned with full information about the traveling wave solutions for a diffusive tuberculosis model with fast and slow progression. After relaxing the rigid ordered restriction on the diffusion rates typically employed in the previous classical study, and overcoming some significant technical obstacles to verify the boundedness of the infected components, we first obtain the existence of super-critical traveling waves connecting disease-free equilibrium to endemic equilibrium. Then, on the basis of the uniform boundedness for sequences of super-critical traveling waves and further delicate analysis, the existence of critical traveling waves is thoroughly explored. Finally, the nonexistence of non-negative bounded traveling waves is discussed in two cases.
A fractional control model to study Monkeypox transport network-related transmission
Authors: Zhang Nan , Emmanuel Addai , Mezue Mary Nwaife , Rashid Saima , Akinnubi Abiola , Abdul-Hamid Zalia , Asamoah Joshua Kiddy K.
Published: April 2024

Effective disease control measures to manage the spread of Monkeypox (Mpox) virus are crucial, especially given the serious public health risks posed by the ongoing global epidemic in regions where the virus is both prevalent and not. This study introduces a precise model, based on the Caputo fractional derivative, which takes into account both human and non-human populations as well as public transportation, to delve into the transmission characteristics of Mpox outbreaks. By employing the fixed point theorem, we have precisely determined the solutions regarding existence and uniqueness. We have analyzed the stability of various equilibrium states within the model to assess Mpox’s transmission capabilities. Additionally, through detailed numerical simulations, we have gauged the impact of critical model parameters that contribute to enhancing Mpox prevention and management strategies. The insights gained from our research significantly enrich epidemiological understanding and lay the foundation for improved disease containment approaches.
Codimension-1,2 bifurcation analysis of glucose model with obesity effect
Authors: Rabo Mahmoud A. Abd , Mahmoud Gamal M. , Aly Shaban , Abed-Elhameed Tarek M.
Published: April 2024

This work focuses on studying high-codimensional bifurcations in the glucose model with obesity’s effect. We examine the related dynamical behaviors, taking into account the risk and adverse health effects associated with obesity’s impact on the glucose model. Through the application of the normal form method, we demonstrate that the model exhibits codimension-1 and codimension-2 bifurcations such as saddle node bifurcation and cusp bifurcation. Additionally, we introduce and prove a theorem that establishes the presence of Hopf bifurcation in the model. The computation of the first Lyapunov coefficient is achieved using center manifold theory. To support our theoretical analysis and showcase the model’s complex dynamical behaviors, including periodic curve families, we provide numerical simulations. These simulations contribute to understanding clinical observations regarding the effects of obesity on glucose levels, oscillations and disorders. Ultimately, this research can aid in the early control of blood glucose levels.
Deterministic vs. stochastic dynamics of a predator–prey system with disease in prey: Impact of fear and supplementary foods
Authors: Roy Subarna , Samanta Sudip , Nayak Himadri , Tiwari Pankaj Kumar
Published: April 2024

In this study, we delve into the dynamics of a generalist predator–prey system with prevalence of a disease in the prey population and the presence of predator-induced fear. In this intricate ecological web, the fear response triggered by predators influences the birth rate of susceptible prey population and also escalates intra-specific competition among them. In an effort to encapsulate a more realistic scenario, we extend our deterministic model to its stochastic counterpart by introducing environmental white noise that impacts the mortality rates of both prey and predator species. Comprehensive mathematical analyses are performed on both the deterministic and stochastic systems to elucidate their qualitative behaviors. Specifically, we derive conditions under which the stochastic system exhibits a unique global positive solution and explore the potential extinction of species within the ecosystem. To unravel the intricate dynamics numerically, we perform sensitivity of parameters, and construct one- and two-parameter bifurcation diagrams. Our investigations reveal that when the costs of fear surpass specific thresholds and disease incidence is high, both the susceptible and infected prey populations face extinction. Notably, the introduction of supplementary foods can lead to system’s destabilization, potentially pushing it towards a state where only predators can persist. Moreover, we observe that lower intensities of white noise have minimal impact on the system’s dynamics, while higher noise intensities introduce substantial fluctuations and may precipitate the extinction of species within the ecosystem. Furthermore, we elucidate the abundances of prey and predator species within the ecosystem through histogram plots.
Dynamics of an Echinococcosis transmission model
Authors: Peng Chun , Wang Kai , Wang Weiming
Published: April 2024

In this paper, we propose an Echinococcosis model with logistic growth. After giving the basic reproductive number [Formula: see text], we prove that [Formula: see text] can be used to govern the threshold dynamics of the model: if [Formula: see text], the disease will go to extinction, while if [Formula: see text], the disease will persist. Based on the data of Echinococcosis in Ürümqi, Xinjiang, China during 2006–2016, we estimate the parameters in the model and calculate that [Formula: see text] (95% CI [0.767, 4.327]). The results show that Echinococcosis is endemic in Ürümqi, China. In addition, we obtain that [Formula: see text]% and [Formula: see text]%, which indicates that the model has certain reliability and rationality. Furthermore, we carry out the sensitivity analysis of the model parameters to identify the key factors affecting the prevalence of Echinococcosis, and the effective control efforts are suggested focusing on reducing the proportion rate from sheep to dogs and increasing the recovery rate of dogs to curb the prevalence of Echinococcosis in Ürümqi, China.
West Barents Sheared Margin Composite Tectono-Sedimentary Element, Norwegian–Greenland Sea and Fram Strait
Authors: Faleide Jan Inge , Wong Po Wan , Hassaan Muhammad , Gabrielsen Roy Helge , Abdelmalak Mohamed Mansour , Planke Sverre , Tsikalas Filippos , Manton Ben , Myklebust Reidun
Published: April 2024

The West Barents Sheared Margin developed in response to break-up and initial opening of the NE Atlantic and links to the Arctic Eurasia Basin. It consists of three first-order segments: (1) a southern sheared margin segment bounding the deep SW Barents Sea basins; (2) a central rifted margin segment west of Bjørnøya; and (3) a northern initially sheared margin segment that was later exposed to oblique extension west of Svalbard. The geometry of the margin included releasing, as well as restraining, bends that inflicted substantial impact on the deformation along the sheared margin. In light of structural, stratigraphic and sedimentological investigations in recent years, the first-order three-fold subdivision of the Barents shear margin has been strengthened and expanded on. It has become even clearer that this subdivision reflects the margin geometry and the shifting far-field and local stress configurations. Prior to the final stages of rifting and break-up of the NE Atlantic in the Paleogene, the study area was part of a broad and extensive basin province comprising deep sedimentary basins. The line of break-up cut diagonally across the regional basin province so that different parts of it are now located in the mid-Norwegian margin, in the NE Greenland margin and in the SW Barents Sea. These areas share a long and complex history of multiple rift phases throughout Late Paleozoic and Mesozoic times, and information from these areas is integrated into the description of the West Barents Sheared Margin Composite Tectono-Sedimentary Element. The marginal basins in the SW Barents Sea are the least explored area on the Norwegian continental shelf and sporadic exploration activity has had limited success. A petroleum potential still exist that will be elaborated in renewed upcoming activity.
Sarıçay Dam: a case study on in-depth seismic evaluation, deformation modulus and key geotechnical parameters
Authors: Demirdogen Sarper , Yildirim Sefa
Published: April 2024

The Sarıçay Dam project in southwestern Türkiye integrates advanced geotechnical evaluation methods for building dams in areas with challenging geology and seismic activity. This study foregrounds an in-depth analysis of deformation modulus, the efficacy of consolidation grouting and shear strength parameters. It addresses critical issues such as encountering faults during construction and their mitigation, with the ‘observational approach’ being adopted to inform strategies and solutions and/or any modifications to these, including any design aspects. The appropriate selection of the deformation modulus and understanding its enhancement through consolidation grouting remain long-standing issues in geotechnical engineering. The comprehensive analyses of the deformation modulus revealed that certain empirical equations yield results comparable to those from
in situ
tests, although some may significantly overestimate; hence, correlating these tests with geophysical methods is crucial for accurate evaluation. Moreover, the deformation modulus results, derived from both rock pressuremeter and dilatometer tests on similarly characterized rock masses, displayed a notable comparability, despite the inherent scatter attributed to rock masses’ heterogeneity and anisotropy. The findings revealed that consolidation grouting, despite its minimal effect on increasing the deformation modulus (4–18%), plays a role in reinforcing the foundation by filling joints and fractures. The research experiences and outcomes on this project will contribute to the knowledge base for future dam projects.
Unbiased Semantic Representation Learning Based on Causal Disentanglement for Domain Generalization
Authors: Jin Xuanyu , Li Ni , Kong Wanzeng , Tang Jiajia , Yang Bing
Published: April 2024

Domain generalization primarily mitigates domain shift among multiple source domains, generalizing the trained model to an unseen target domain. However, the spurious correlation usually caused by context prior (e.g., background) makes it challenging to get rid of the domain shift. Therefore, it is critical to model the intrinsic causal mechanism. The existing domain generalization methods only attend to disentangle the semantic and context-related features by modeling the causation between input and labels, which totally ignores the unidentifiable but important confounders. In this article, a Causal Disentangled Intervention Model (CDIM) is proposed for the first time, to the best of our knowledge, to construct confounders via causal intervention. Specifically, a generative model is employed to disentangle the semantic and context-related features. The contextual information of each domain from generative model can be considered as a confounder layer, and the center of all context-related features is utilized for fine-grained hierarchical modeling of confounders. Then the semantic and confounding features from each layer are combined to train an unbiased classifier, which exhibits both transferability and robustness across an unknown distribution domain. CDIM is evaluated on three widely recognized benchmark datasets, namely, Digit-DG, PACS, and NICO, through extensive ablation studies. The experimental results clearly demonstrate that the proposed model achieves state-of-the-art performance.
The Role of the Bayes Factor in the Evaluation of Evidence
Authors: Aitken Colin , Taroni Franco , Bozza Silvia
Published: April 2024

The use of the Bayes factor as a metric for the assessment of the probative value of forensic scientific evidence is largely supported by recommended standards in different disciplines. The application of Bayesian networks enables the consideration of problems of increasing complexity. The lack of a widespread consensus concerning key aspects of evidence evaluation and interpretation, such as the adequacy of a probabilistic framework for handling uncertainty or the method by which conclusions regarding how the strength of the evidence should be reported to a court, has meant the role of the Bayes factor in the administration of criminal justice has come under increasing challenge in recent years. We review the many advantages the Bayes factor has as an approach to the evaluation and interpretation of evidence.
Hepatic lipid droplet-associated proteome changes distinguish dietary-induced fatty liver from glucose tolerance in male mice
Authors: Van Woerkom Andries , Harney Dylan J. , Nagarajan Shilpa R. , Hakeem-Sanni Mariam F. , Lin Jinfeng , Hooke Matthew , Pulpitel Tamara , Cooney Gregory J. , Larance Mark , Saunders Darren N. , Brandon Amanda E. , Hoy Andrew J.
Published: April 2024

This study identified a fatty liver lipid droplet proteome and one associated with glucose tolerance. Notably, glucose intolerance was linked with changes in the ratio of adipose triglyceride lipase to perilipin 5 that is indicative of dysregulation of neutral lipid homeostasis.
Chronic pancreatitis-associated metabolic bone diseases: epidemiology, mechanisms, and clinical advances
Authors: Wang Tianlin , Xiong Ke , He Yanli , Feng Binbin , Guo LinBin , Gu Jingliang , Zhang Mengrui , Wang Hong , Wu Xiaohao
Published: April 2024

Chronic pancreatitis (CP) is a progressive inflammatory disease with an increasing global prevalence. In recent years, a strong association between CP and metabolic bone diseases (MBDs), especially osteoporosis, has been identified, attracting significant attention in the research field. Epidemiological data suggest a rising trend in the incidence of MBDs among CP patients. Notably, recent studies have highlighted a profound interplay between CP and altered nutritional and immune profiles, offering insights into its linkage with MBDs. At the molecular level, CP introduces a series of biochemical disturbances that compromise bone homeostasis. One critical observation is the disrupted metabolism of vitamin D and vitamin K, both essential micronutrients for maintaining bone integrity, in CP patients. In this review, we provide physio-pathological perspectives on the development and mechanisms of CP-related MBDs. We also outline some of the latest therapeutic strategies for treating patients with CP-associated MBDs, including stem cell transplantation, monoclonal antibodies, and probiotic therapy. In summary, CP-associated MBDs represent a rising medical challenge, involving multiple tissues and organs, complex disease mechanisms, and diverse treatment approaches. More in-depth studies are required to understand the complex interplay between CP and MBDs to facilitate the development of more specific and effective therapeutic approaches.
Cultivable Fungi from Amazon River Dolphins Engaged in Wildlife Ecotourism in the Anavilhanas National Park, Brazil
Authors: Alves Marla Jalene , Fonseca Fernanda Rodrigues , Barroso Layssa do Carmo , Souza Érica Simplício de , Vidal Marcelo Derzi , Jackisch-Matsuura Ani Beatriz , Souza João Vicente Braga de , Siciliano Salvatore
Published: April 2024

Amazon River dolphins are an important flagship species in the Anavilhanas National Park, Brazil, where they interact with visitors. This study aimed to quantify and identify fungi isolated from dolphin skin and oral samples and their surrounding environment in this unique ecosystem. Samples were collected from three dolphins and water samples from Flutuante dos Botos and the Novo Airão city harbor. Fungi were isolated using culture media and identified through micromorphology assays and ITS region sequencing. Oral swab samples resulted in culture of Trichosporon montevideense and Exophiala dermatitidis. Skin samples from one dolphin revealed Toxicocladosporium irritans and Diaporthe lithocarpus. Water samples exhibited higher fungal counts and diversity, with Rhodotorula mucilaginosa, Exophiala dermatitidis, Penicillium citrinum, Fomitopsis meliae, and Nectria pseudotrichia identified at the collection site and Candida spencermartinsiae and Penicillium chermesinum at the city harbor. This study provides important insights into the fungal diversity associated with Amazon River dolphins and their environment, enhancing our understanding of the public health and ecological dynamics in the Anavilhanas National Park.
Modeling the Impact of Air Pollution and Meteorological Variables on COVID-19 Transmission in Western Cape, South Africa
Authors: Mutinda John Kamwele , Langat Amos Kipkorir
Published: April 2024

Understanding the factors that influence COVID-19 transmission is essential in assessing and mitigating the spread of the pandemic. This study focuses on modeling the impact of air pollution and meteorological parameters on the risk of COVID-19 transmission in Western Cape Province, South Africa. The data used in this study consist of air pollution parameters, meteorological variables, and COVID-19 incidence observed for 262 days from April 26, 2020, to January 12, 2021. Lagged data were prepared for modeling based on a 6-day incubation period for COVID-19 disease. Based on the overdispersion property of the incidence, negative binomial (NB) and generalised Poisson (GP) regression models were fitted. Stepwise regression was used to select the significant predictors in both models based on the Akaike information criterion (AIC). The residuals of both NB and GB regression models were autocorrelated. An autoregressive integrated moving average (ARIMA) model was fitted to the residuals of both models. ARIMA (7, 1, 5) was fitted to the residuals of the NB model while ARIMA (1, 1, 6) was fitted for the residuals of the GP model. NB + ARIMA (7, 1, 5) and GP + ARIMA (1, 1, 6) models were tested for performance using root mean square error (RSME). GP + ARIMA (1, 1, 6) was selected as the optimal model. The results from the optimal model suggest that minimum temperature, ambient relative humidity, ambient wind speed, PM2.5, and NO2 at various lags are positively associated with COVID-19 incidence while maximum relative humidity, minimum relative humidity, solar radiation, maximum temperature, NO, PM load, PM10, SO2, and NOX at various lags have a negative association with COVID-19 incidence. Ambient wind direction and temperature showed a nonsignificant association with COVID-19 at all lags. This study suggests that meteorological and pollution parameters play a vital independent role in the transmission of the SARS-CoV-2 virus.
Stress and Psychosocial Distress Scale with Blunted Oscillatory Dynamics Serving Abstract Reasoning
Authors: Schantell Mikki , Glesinger Ryan , Coutant Anna T. , Okelberry Hannah J. , John Jason A. , Dietz Sarah M. , Springer Seth D. , Arif Yasra , Wilson Tony W.
Published: April 2024

Background. Chronic stress is associated with a multitude of psychopathological disorders that share similar alterations in neural dynamics and symptomatology. Applying the National Institute of Mental Health’s Research Domain Criteria (RDoC) framework, we probed the stress-diathesis model by identifying how a transdiagnostic psychosocial distress index representing high-dimensional patterns of stress-related aberrations was coupled to the neural oscillatory dynamics serving abstract reasoning. Methods. The sample consisted of 69 adults (mean age=44.77 years, SD=13.66) who completed the NIH Toolbox Emotion Battery (NIHTB-EB) and a matrix reasoning task during magnetoencephalography (MEG). A transdiagnostic psychosocial distress index was computed using exploratory factor analysis with assessments from the NIHTB-EB. Whole-brain correlations were conducted using the resulting psychosocial distress index for each oscillatory response, and the resulting peak voxels were extracted for mediation analyses to assess the degree to which neural oscillatory activity mediates the interplay between perceived stress and psychosocial distress. Results. We found that elevated psychosocial distress was associated with blunted oscillatory alpha/beta and gamma responses in key cortical association regions. Further, we found that only alpha/beta activity in the right superior temporal sulcus partially mediated the relationship between perceived stress and psychosocial distress. Conclusions. The present study is among the first to couple perceived stress and psychosocial distress with alterations in oscillatory activity during a matrix reasoning task. These findings illuminate the relationship between perceived stress and neural alterations associated with psychopathology.
CFD Applications to Pressurized Thermal Shock‐Related Phenomena
Authors: Okagaki Yuria , Hibiki Takashi , Sibamoto Yasuteru
Published: April 2024

In pressurized water reactor accident scenarios, the injection of water from the emergency core cooling system (ECCS) (ECC injection) might induce a pressurized thermal shock (PTS), affecting the reactor pressure vessel (RPV) integrity. Therefore, PTS is a vital research issue in reactor safety, and its analysis is essential for evaluating the integrity of RPVs, which determines the reactor life. The PTS analysis comprises a coupled analysis between thermal–hydraulic and structural analyses. The thermal–hydraulic approach is particularly crucial, and reliable computational fluid dynamic (CFD) simulations should play a vital role in the future because predicting the temperature gradient of the RPV wall requires data on the transient temperature distribution of the downcomer (DC). Since one‐dimensional codes cannot predict the complex three‐dimensional flow features during ECC injection, PTS is one reactor safety issue where CFD simulation can benefit from complement evaluations with thermal–hydraulic system analysis codes. This study reviewed from the viewpoint of the turbulence models most affecting PTS analysis based on papers published since 2010 on single‐ and two‐phase flow CFD simulation for the experiment on PTS performed in the Rossendorf coolant mixing model (ROCOM), transient two‐phase flow (TOPFLOW), upper plenum test facility (UPTF), and large‐scale test facility (LSTF). The results revealed that in single‐phase flow CFD simulation, where knowledge and experience are sufficient, various turbulence models have been considered, and many analyses using large eddy simulation (LES) have been reported. For two‐phase flow analysis of air–water conditions, interface capturing/tracking methods were used in addition to two‐fluid models. The standard k–ε and shear stress transport (SST) k–ω models were still in the validated phase, and various turbulence models have yet to be fully validated. In the two‐phase flow analysis of steam–water conditions, many studies have used two‐fluid models and Reynolds‐averaged Navier‐Stoke (RANS), and NEPTUNE_CFD, in particular, has been reported to show excellent prediction performance based on years of accumulated validation.
Giant Retinal Astrocytoma: A Case Report of an Uncommon Presentation of Tuberous Sclerosis in a Young Female
Authors: Thakkar Keval , Raveena Fnu , Kumar Aakash , Mal Doongro , Kumar Dileep , Ahuja Neha , Mandhan Rahul , Baig Aqsa , Singh Manjeet , Shah Heeya , Sajjad Taha , Singh Mansi
Published: April 2024

Tuberous sclerosis (TS) is a rare multisystem autosomal dominant genetic disorder with characteristic pathognomonic genetic mutations involving the TSC (tuberous sclerosis complex) group of genes. Ocular signs are fairly common and include an achromic patch and retinal astrocytic hamartomas, which usually have a maximum size of between 0.5 and 5 mm. The incidence of tuberous sclerosis is estimated to be 1 in 5000−10,000 individuals, with both familial and sporadic cases reported. The diagnostic criteria for tuberous sclerosis include the presence of major and/or minor clinical features as well as genetic mutations. We present the case of a 15‐year‐old girl, presented with a history of seizures and blurred vision. Physical examination revealed angiofibroma on the face. Further evaluation, including contrast‐enhanced MRI of the brain and ophthalmological consultation, led to the diagnosis of tuberous sclerosis. Additional imaging studies confirmed the presence of subependymal giant cell astrocytoma, retinal astrocytoma, lymphangioleiomyomatosis in the lungs, and renal angiomyolipoma. This case highlights the importance of considering tuberous sclerosis in patients presenting with seizures and ocular symptoms. This case sheds light on early diagnosis and appropriate management which are crucial in preventing complications and improving patient outcomes.
Stability Results for a Class of Fractional Itô–Doob Stochastic Integral Equations
Authors: Kahouli Omar , Aloui Ali , Mchiri Lassaad , Ben Makhlouf Abdellatif
Published: April 2024

In this paper, we study the Hyers–Ulam stability of Hadamard fractional Itô–Doob stochastic integral equations by using the Banach fixed point method and some mathematical inequalities. Finally, we exhibit three theoretical examples to apply our theory.
The Effect of Spatial Scale on the Prediction of Tropical Forest Attributes from Image Texture
Authors: Gallardo-Cruz J. Alberto , Solórzano Jonathan V. , González Edgar J. , Meave Jorge A.
Published: April 2024

The availability of high-resolution satellite imagery has boosted the modelling of tropical forest attributes based on texture metrics derived from grey-level co-occurrence matrices (GLCMs). This procedure has shown that GLCM metrics are good predictors of vegetation attributes. Nonetheless, the procedure is also sensitive to the scale of analysis (image resolution and plot size). This study aimed to analyse the effect of spatial scale on the modelling of forest attributes, and to provide some ecological insight into such effect. Nineteen 32 × 32 m sampling plots were used to quantify forest structure (basal area: BA; mean height: H; standard deviation of height, HSD; density, D; and aboveground biomass, AGB). The 19 plots were subdivided into four 16 × 16 m, one of which was subdivided into four 8 × 8 m plots. To match this design, 12 GLCM metrics were calculated from a GeoEye-1 image (pixel size ≤ 2 m) using a 5-, 9-, and 21-pixel window from the R, NIR, NDVI, and EVI bands. For each of the windows, we modelled the five structural variables as linear combinations of the 12 metrics through linear models. The modelling potential ranged from high (R2 = 0.70) to low (0.11). H was the best-predicted attribute; this occurred at the smallest scale, with increasing scales producing lower R2 values. The second best-predicted attribute was HSD, which peaked at the intermediate scale. D and AGB displayed a similar pattern. BA was the only attribute best predicted at the largest scale. Thus, in predicting tropical forest attributes from GLCM-derived texture metrics, the spatial scale to be used should reflect the spatial scale at which ecological processes occur. Therefore, understanding how ecological processes express themselves in a remotely sensed image becomes a critical task.
Evaluating Exploitation of White Crappie and Black Crappie in Three Southeast Kansas Reservoirs
Authors: Guiot Danci L. , Lundgren Seth A. , Phelps Quinton , Kim Hae , Lynott Sean
Published: April 2024

White Crappie (Pomoxis annularis) and Black Crappie (P. nigromaculatus) were studied in three southeast Kansas reservoirs to assess exploitation and determine if current regulations were conducive for sustainable populations. Trap nets were used to sample crappie in Elk City Reservoir, Big Hill Reservoir, and Parsons City Lake. Crappie measuring 210 mm and greater received Floy FD-94 tags that served as entries for rewards to encourage tag reporting. Harvest and angler demographic information were collected when tags were reported. Postcard surveys and motion-sensed cameras were used in conjunction to estimate angler effort and reporting rate on Parsons City Lake. Annual exploitation rates (i.e., percent of fish harvested) determined by tag returns and corrected for nonreporting and tag loss were 28.3%, 21.8%, and 3.9% on Elk City Reservoir, Big Hill Reservoir, and Parsons City Lake, respectively. Overfishing was not occurring, so current statewide regulations were likely appropriate, and no changes may be necessary.
Warburg Effect Reshapes Tumor Immunogenicity
Authors: Enríquez José A. , Mittelbrunn María
Published: April 2024

Tumor cells rewire their metabolism to fulfill the demands of highly proliferative cells. This changes cellular metabolism to adapt to fuel and oxygen availability for energy production and to increase the synthesis capacity of building blocks for cell division and growth. In addition, the metabolic shift modulates the immunogenicity of the tumor cells. Recently, Mahmood and colleagues reported a connection between mitochondrial DNA mutations in cancer cells and their response to immunotherapy in a mouse model of melanoma.
Red Blood Cell Polyunsaturated Fatty Acids and Mortality Following Breast Cancer
Authors: Parada Humberto , Wu Tianying , Hoh Eunha , Rock Cheryl L. , Martinez Maria E.
Published: April 2024

Background:

Intake of polyunsaturated fatty acids (PUFA) may affect mortality following breast cancer; however, epidemiological studies have relied on the self-reported assessment of PUFA intake. Herein, we examined the associations between red blood cell (RBC) PUFAs and mortality.

Methods:

This nested case–control study included 1,104 women from the Women’s Healthy Eating and Living study, a multistate randomized controlled trial. Cases (n = 290) comprised women who died from 1995 to 2006. Matched controls (n = 814) comprised women who were alive at the end of follow-up. PUFAs were measured in baseline RBC samples and included four ω-3 and seven ω-6 PUFAs. We examined each PUFA individually and principal components factor analysis (PCFA)-derived scores in association with all-cause mortality (ACM) and breast cancer–specific mortality (BCM) using conditional logistic regression to estimate ORs and 95% confidence intervals (CIs).

Results:

In fully adjusted models, ACM ORs were elevated among women with PUFAs above the median  (>median) versus at the median or below (≤median) for α-linolenic acid (ALA; OR = 1.63, 95% CI, 1.18–2.24) and linolenic acid (LA; OR = 1.56, 95% CI, 1.16–2.09). BCM ORs were elevated for ALA (OR = 1.83, 95% CI, 1.27–2.63), LA (OR = 1.70, 95% CI, 1.23–2.37), and γ-linolenic acid (GLA; OR = 1.50, 95% CI, 1.04–2.16). PCFA Factor 1 (arachidonic acid–adrenic acid–docosapentaenoic acid) scores above the median (vs. ≤median) were associated with lower odds of ACM (OR = 0.71, 95% CI, 0.52–0.97) and BCM (OR = 0.69, 95% CI, 0.49–0.97). PCFA Factor 4 (ALA/GLA) scores above the median (vs. ≤median) were associated with increased odds of BCM (OR = 1.47, 95% CI, 1.04–2.09).

Conclusions:

RBC ALA, LA, and GLA may be prognostic indicators among breast cancer survivors.

Impact:

These results are important for understanding the associations between a biomarker of PUFA intake and mortality among BC survivors.