Natural Patterns Research Articles

The paradigm of possibilities leading to the formation of autoimmune and infectious phenotypes of common variable immune deficiency

The term variable in the definition of CVID is associated with the heterogeneity of the genetic nature and clinical manifestation of this variant of PI. Deciphering of the mechanisms or identifying biomarkers of clinical heterogeneity may be important in the timely diagnosis and prognosis of the course of CVID. The purpose of the study was to identify distinctive features in the parameters of the innate and adaptive immune response of the patients with infectious and autoimmune manifestations of CVID in remission and clinical manifestation. Fifteen patients, 11 women and 4 men with an average age 39.7±11.7 years were examined, and they were divided into two subgroups depending on clinical verification: infectious phenotype (10 people) and autoimmune phenotype (5 people). In the absence of clinical signs of activation of autoimmune pathology or exacerbation of chronic infectious processes, peripheral blood monocytes became the application point of distinctive values. An increase in the number of TLR9-expressing monocytes has been shown in patients with autoimmune clinical verification of CVID. The differences in the parameters of the immune status of patients conducted during the period of clinical manifestation consisted of a decrease in the relative content and absolute number of T regulatory lymphocytes, and an increase in the number of monocytes containing TLR9, TLR2 and HLA-DR in patients with an autoimmune phenotype relative to the subgroup with infectious manifestation. The data obtained reflect the involvement of the immunoregulatory potential of the immune system in the clinical manifestation of primary immunodeficiency, even under the conditions of pathogenetic substitution therapy. The evidence of the stated position is a decrease in immunosuppression in autoimmune manifestation due to a decrease in the number of peripheral T-regulatory cells and a smaller proportion of monocyte cells belonging to the M2 suppressive category. Attention is also drawn to the increased potential of primary response to patterns of various nature in autoimmune manifestation due to an increase in the number of monocytes expressing Toll-like receptors of various specificity. The presented results can be proposed as a diagnostic and prognostic indicator of the difference in clinical phenotypes of CVID.

Multi-scale analysis of nutrient and environmental dynamics in Hongfeng Lake Southwest China

Traditional linear correlation analysis may not fully capture the true relationship between these variables. Therefore, multi-scale running correlation analysis, such as time-dependent intrinsic correlation (TDIC) and continuous wavelet transform based on Hilbert–Huang transform (HHT), provides valuable insights into local correlations and the evolving relationship between nutrients and environmental factors over time. In this study, we investigated seven environmental factors and four water quality nutrient indicators in deep lakes on the Yungui Plateau in southwestern China. The results revealed that there may be strong correlations between environmental factors and nutrient levels during certain periods, while opposite trends may emerge at other times. These variations in correlation could be attributed to uncertain physical processes, spatial heterogeneity, or the impact of different climatic factors on local hydrological processes. Wavelet analysis indicated that changes in environmental factors lag behind those in nutrient levels, particularly on a cycle of about 12 months. This suggests that changes in environmental factors align with natural patterns after the water body has been polluted. These conclusions underscore the complexity and dynamic nature of the relationship between environmental factors and nutrient levels in water bodies, highlighting the importance of employing advanced analysis techniques to capture this complexity.

The primacy of density‐mediated indirect effects in a community of wolves, elk, and aspen

AbstractThe removal or addition of a predator in an ecosystem can trigger a trophic cascade, whereby the predator indirectly influences plants and/or abiotic processes via direct effects on its herbivore prey. A trophic cascade can operate through a density‐mediated indirect effect (DMIE), where the predator reduces herbivore density via predation, and/or through a trait‐mediated indirect effect (TMIE), where the predator induces an herbivore trait response that modifies the herbivore's effect on plants. Manipulative experiments suggest that TMIEs are an equivalent or more important driver of trophic cascades than are DMIEs. Whether this applies generally in nature is uncertain because few studies have directly compared the magnitudes of TMIEs and DMIEs on natural unmanipulated field patterns. A TMIE is often invoked to explain the textbook trophic cascade involving wolves (Canis lupus), elk (Cervus canadensis), and aspen (Populus tremuloides) in northern Yellowstone National Park. This hypothesis posits that wolves indirectly increase recruitment of young aspen into the overstory primarily through reduced elk browsing in response to spatial variation in wolf predation risk rather than through reduced elk population density. To test this hypothesis, we compared the effects of spatiotemporal variation in wolf predation risk and temporal variation in elk population density on unmanipulated patterns of browsing and recruitment of young aspen across 113 aspen stands over a 21‐year period (1999–2019) in northern Yellowstone National Park. Only 2 of 10 indices of wolf predation risk had statistically meaningful effects on browsing and recruitment of young aspen, and these effects were 8–28 times weaker than the effect of elk density. To the extent that temporal variation in elk density was attributable to wolf predation, our results suggest that the wolf–elk–aspen trophic cascade was primarily density‐mediated rather than trait‐mediated. This aligns with the alternative hypothesis that wolves and other actively hunting predators with broad habitat domains cause DMIEs to dominate whenever prey, such as elk, also have a broad habitat domain. For at least this type of predator–prey community, our study suggests that risk‐induced trait responses can be abstracted or ignored while still achieving an accurate understanding of trophic cascades.

Sharing a Stage in the Arena of Agency

Abstract Exactly five hundred years ago today, Erasmus engaged with Martin Luther in a debate on free will that continues to inspire, even if it evokes a notion of freedom that is no longer ours in every respect. Emphasising the need for a common-sense reading of Holy Scripture that did not require making conjectures about the existence of hidden forms of divine justice inconsistent with our own, Erasmus confronted Luther with the fact that his “necessitating” notion of grace was not in any way representative of earlier theological positions acknowledging the notion of free will. Refusing to be drawn towards an interpretation of grace that denied human beings the ability to activate the full spectrum of their natural reaction patterns, Erasmus stayed clear of translating earlier theological positions on grace in terms of an overruling of human agency. His theology continued to inspire later generations to work on their moral development rather than to act out of fear for divine retribution. Despite his name as a sceptic and a rhetorician, Erasmus’ decidedly this-worldly interpretation of religious doctrine not only reveals his argumentative agility and deep theoretical insight; it also prompts a universalist approach in theology that consistently favours a predisposition towards the humane.

Physiological rotation patterns of the thoracolumbar spine across different ages: A detailed analysis using upright CT

BackgroundThe rotational motion of the spine plays a crucial role in daily activities. Understanding the mechanisms of spinal rotation is essential for evaluating normal spinal function, especially in standing positions due to the influence of gravity. However, previous studies on spinal rotation have been limited. Research QuestionWhat are the differences in thoracolumbar rotation during trunk rotation in a standing position among different age and gender groups? MethodsThis cross-sectional study involved 49 healthy volunteers without back pain, including 24 younger participants (13 males, 11 females) and 25 elderly participants (12 males, 13 females). Upright and trunk-rotated CT (right-rotated standing positions) scans were taken. Vertebral rotation was measured using the femoral head center as an axis. ResultsAnalysis of spinal alignment in the standing position revealed mild rotation from the lumbar to thoracic vertebrae. The lumbar spine exhibited left rotation at apex of L3 (L3: −1.3±3.8°, p=0.01), while the lower thoracic spine showed right rotation at apex of T8 (T8: 1.9±2.4°, p<0.001) and the upper thoracic spine showed left rotation at apex of T3 (T3: −2.6±2.9°, p<0.001). The lumbar spine showed minimal rotation during maximum trunk rotation, with significant rotation noted above T10 (16 % vs 84 %). The total thoracolumbar spinal rotation at T1 showed significant differences by gender and age (male vs. female: 23.9±° vs. 30.3±°, p=0.001; young vs. elderly: 29.2±° vs. 25.0±°, p=0.028; elderly male vs. elderly female: 19.2±° vs. 30.4±°, p<0.001). Younger participants did not show significant gender differences, while elderly females retained more rotation compared to males. SignificanceThis pioneering study provides the first detailed report on the range of spinal rotation in a physiological standing situation, highlighting significant differences by gender and age. These findings offer new insights into the natural patterns of spinal rotation and their potential implications for diagnosing and treating spinal disorders.

Complex transcriptional regulations of a hyperparasitic quadripartite system in giant viruses infecting protists

Hyperparasitism is a common pattern in nature that is not limited to cellular organisms. Giant viruses infecting protists can be hyperparasitized by smaller ones named virophages. In addition, both may carry episomal DNA molecules known as transpovirons in their particles. They all share transcriptional regulatory elements that dictate the expression of their genes within viral factories built by giant viruses in the host cytoplasm. This suggests the existence of interactions between their respective transcriptional networks. Here we investigated Acanthamoeba castellanii cells infected by a giant virus (megavirus chilensis), and coinfected with a virophage (zamilon vitis) and/or a transpoviron (megavirus vitis transpoviron). Infectious cycles were monitored through time-course RNA sequencing to decipher the transcriptional program of each partner and its impact on the gene expression of the others. We found highly diverse transcriptional responses. While the giant virus drastically reshaped the host cell transcriptome, the transpoviron had no effect on the gene expression of any of the players. In contrast, the virophage strongly modified the giant virus gene expression, albeit transiently, without altering the protein composition of mature viral particles. The virophage also induced the overexpression of transpoviron genes, likely through the indirect upregulation of giant virus-encoded transcription factors. Together, these analyses document the intricated transcriptionally regulated networks taking place in the infected cell.

Exploring How Education Can Leverage Artificial Intelligence for Social Good

Artificial intelligence (AI) is rapidly transforming society and industries, presenting both opportunities and ethical challenges. AI enables machines to perform tasks traditionally done by humans, such as natural language processing, pattern recognition, decision-making, and problem-solving (Brookings, 2023). In education, AI enhances teaching methodologies, student assessment, and administrative tasks through tools like intelligent tutoring systems, adaptive learning platforms, and educational chatbots. These tools offer customised learning experiences, immediate feedback, and data-driven insights. This research aims to investigate how AI can be leveraged within education to promote social good by identifying how familiar educators and students are with AI tools, identify how educators and students perceive the role of AI in education and what are the current applications of AI technologies in educational settings and how widely are they used. Finally, discuss the opportunities and ethical considerations of integrating AI in education. AI technologies can address critical social challenges such as inequality, accessibility, and personalised learning. According to Luckin et al. (2016), "AI can provide tailored educational experiences that adapt to individual learning needs, thus promoting equity in education." This exploratory research begins with an overview of AI's role and tools in education, followed by a discussion of the challenges, opportunities, and ethical considerations associated with AI integration. Understandings are drawn from educator’s response to a questionnaire and a focus group with first year and final-year third level students. This qualitative data, analysed using NVivo software, reveals key themes and significant findings on effectively utilising AI in education.

Positive selection, genetic recombination, and intra-host evolution in novel equine coronavirus genomes and other members of the Embecovirus subgenus.

There are several examples of coronaviruses in the Betacoronavirus subgenus Embecovirus that have jumped from an animal to the human host. Studying how evolutionary factors shape coronaviruses in non-human hosts may provide insight into the coronavirus host-switching potential. Equids, such as horses and donkeys, are susceptible to equine coronaviruses (ECoVs). With increased testing prevalence, several ECoV genome sequences have become available for molecular evolutionary analyses, especially those from the United States of America (USA). To date, no analyses have been performed to characterize evolution within coding regions of the ECoV genome. Here, we obtain and describe four new ECoV genome sequences from infected equines from across the USA presenting clinical symptoms of ECoV, and infer ECoV-specific and Embecovirus-wide patterns of molecular evolution. Within two of the four data sets analyzed, we find evidence of intra-host evolution within the nucleocapsid (N) gene, suggestive of quasispecies development. We also identify 12 putative genetic recombination events within the ECoV genome, 11 of which fall in ORF1ab. Finally, we infer and compare sites subject to positive selection on the ancestral branch of each major Embecovirus member clade. Specifically, for the two currently identified human coronavirus (HCoV) embecoviruses that have spilled from animals to humans (HCoV-OC43 and HCoV-HKU1), we find that there are 42 and 2 such sites, respectively, perhaps reflective of the more complex ancestral evolutionary history of HCoV-OC43, which involves several different animal hosts.IMPORTANCEThe Betacoronavirus subgenus Embecovirus contains coronaviruses that not only pose a health threat to animals and humans, but also have jumped from animal to human host. Equids, such as horses and donkeys are susceptible to equine coronavirus (ECoV) infections. No studies have systematically examined evolutionary patterns within ECoV genomes. Our study addresses this gap and provides insight into intra-host ECoV evolution from infected horses. Further, we identify and report natural selection pattern differences between two embecoviruses that have jumped from animals to humans [human coronavirus OC43 and HKU1 (HCoV-OC43 and HCoV-HKU1, respectively)], and hypothesize that the differences observed may be due to the different animal host(s) that each virus circulated in prior to its jump into humans. Finally, we contribute four novel, high-quality ECoV genomes to the scientific community.

The distribution of beneficial mutational effects between two sister yeast species poorly explains natural outcomes of vineyard adaptation.

Domesticated strains of Saccharomyces cerevisiae have adapted to resist copper and sulfite, two chemical stressors commonly used in winemaking. S. paradoxus has not adapted to these chemicals despite being consistently present in sympatry with S. cerevisiae in vineyards. This contrast could be driven by a number of factors including niche differences or differential access to resistance mutations between species. In this study, we used a comparative mutagenesis approach to test whether S. paradoxus is mutationally constrained with respect to acquiring greater copper and sulfite resistance. For both species, we assayed the rate, effect size, and pleiotropic costs of resistance mutations and sequenced a subset of 150 mutants. We found that the distributions of mutational effects displayed by the two species were similar and poorly explained the natural pattern. We also found that chromosome VIII aneuploidy and loss of function mutations in PMA1 confer copper resistance in both species, whereas loss of function mutations in REG1 were only a viable route to copper resistance in S. cerevisiae. We also observed a de novo duplication of the CUP1 gene in S. paradoxus but not in S. cerevisiae. For sulfite, loss of function mutations in RTS1 and KSP1 confer resistance in both species, but mutations in RTS1 have larger effects in S. paradoxus. Our results show that even when available mutations are largely similar, species can differ in the adaptive paths available to them. They also demonstrate that assays of the distribution of mutational effects may lack predictive insight concerning adaptive outcomes.

Machine Learning Approach for Arabic Handwritten Recognition

Text recognition is an important area of the pattern recognition field. Natural language processing (NLP) and pattern recognition have been utilized efficiently in script recognition. Much research has been conducted on handwritten script recognition. However, the research on the Arabic language for handwritten text recognition received little attention compared with other languages. Therefore, it is crucial to develop a new model that can recognize Arabic handwritten text. Most of the existing models used to acknowledge Arabic text are based on traditional machine learning techniques. Therefore, we implemented a new model using deep machine learning techniques by integrating two deep neural networks. In the new model, the architecture of the Residual Network (ResNet) model is used to extract features from raw images. Then, the Bidirectional Long Short-Term Memory (BiLSTM) and connectionist temporal classification (CTC) are used for sequence modeling. Our system improved the recognition rate of Arabic handwritten text compared to other models of a similar type with a character error rate of 13.2% and word error rate of 27.31%. In conclusion, the domain of Arabic handwritten recognition is advancing swiftly with the use of sophisticated deep learning methods.

Chemistry-driven translocation of glycosylated proteins in mice

Cell surface glycans form various “glycan patterns” consisting of different types of glycan molecules, thus enabling strong and selective cell-to-cell recognition. We previously conjugated different N-glycans to human serum albumin to construct glycoalbumins mimicking natural glycan patterns that could selectively recognize target cells or control excretion pathways in mice. Here, we develop an innovative glycoalbumin capable of undergoing transformation and remodeling of its glycan pattern in vivo, which induces its translocation from the initial target to a second one. Replacing α(2,3)-sialylated N-glycans on glycoalbumin with galactosylated glycans induces the translocation of the glycoalbumin from blood or tumors to the intestine in mice. Such “in vivo glycan pattern remodeling” strategy can be used as a drug delivery system to promote excretion of a drug or medical radionuclide from the tumor after treatment, thereby preventing prolonged exposure leading to adverse effects. Alternatively, this study provides a potential strategy for using a single glycoalbumin for the simultaneous treatment of multiple diseases in a patient.

Influence of Walking Modification Strategies on Frontal Plane Hip Joint Moment and Gait Naturalness in Healthy Adults

OBJECTIVE: To evaluate the effects of lateral trunk lean and step width modification on hip adduction moment in healthy participants and examine their influence on walking naturalness. METHODS: Ten healthy participants underwent gait analysis under 4 walking conditions: natural walking, large trunk lean, doubled step width, and combined modification with slight trunk lean and 1.5 times step width. Early stance and late stance hip adduction moment were analyzed and compared using repeated-measures analysis of variance. Subjective naturalness of gait was also recorded. RESULTS: We observed a significant effect of walking condition on the hip adduction moment during early stance (F3,36 = 6.665, P = .001) and gait naturalness (F3,36 = 30.519, P&lt;.001), but not hip adduction moment during late stance ( P = .733). Pairwise comparison indicated significantly reduced hip adduction moment during early stance with large trunk lean when compared to natural walking (36.7% less, P = .002, Cohen’s d = −1.41). Both combined and doubled step width modifications did not yield the expected hip adduction moment reduction ( P&gt;.296). The perceived naturalness of the doubled step width modification was most comparable to natural walking ( P = .529), while the trunk lean modification was significantly less natural than natural walking and the least natural modification overall (81.1% less, P&lt;.001, Cohen’s d = −4.34). CONCLUSION: Although trunk lean modification effectively reduces hip joint loading, it is perceived as the least natural pattern. Step width was more challenging to modify than trunk lean. JOSPT Open 2024;2(4):306-314. Epub 7 August 2024. doi:10.2519/josptopen.2024.0463

Analysis of Vibration Characteristics of Cryogenic Insulated Cylinders Based on ANSYS

Abstract To ensure the safety of vehicle mounted cryogenic insulated cylinders during transportation, a finite element model for analyzing the vibration characteristics of vertical cryogenic insulated cylinders considering the frame was established based on ANSYS. The vibration characteristics of vertical cryogenic insulated cylinders under no-load conditions were studied, and the natural frequency patterns of cylinders under different filling rates were compared. The results showed that the connection between the bottom head of the inner liner and the cylinder, the operating frame, and the root of the bottom support were the locations where resonance occurs with larger amplitude. As the filling rate increased, the natural frequencies of each order of the cylinder decreased, and the maximum vibration amplitude of the inner cylinder decreased.

Controlled airflow fluctuations for improved performance of mist cooling systems: Enhanced evaporation and thermal comfort

Rising global temperatures and resulting heat stress on people necessitate sustainable outdoor cooling solutions. Mist cooling offers promise, and this work proposes a novel approach: incorporating fluctuating airflows into misting systems. We hypothesize that mist-cooled fluctuating airflows could provide more efficient outdoor cooling for people, for two reasons: 1) Increased turbulence and mixing promoting higher water evaporation rates; 2) dynamic airflow leveraging transient thermal perception for improved comfort.Wind tunnel experiments and numerical simulations support this hypothesis, revealing that fluctuating flow, compared to constant flow with the same average velocity, enhances evaporation. Lower frequencies, higher amplitudes, and profiles with steeper gradients led to higher evaporation rates. These findings assume an idealized domain, excluding natural wind patterns. Furthermore, predictions confirmed that fluctuating flows consistently provide superior thermal comfort compared to constant flows. Fluctuating airflow with misting achieved equivalent comfort at 38% lower energy consumption compared to fluctuating flow without misting and 81% lower compared to constant flow with misting. The proposed technology has the potential to improve outdoor comfort and decrease resource consumption. It is scalable, not complex, and can be implemented into existing systems, though it may face challenges such as increased wear on fan components.

The Legal System of Natural Ecological Protection and Restoration in Newly Built Areas Based on the Multi-Dimensional Character-Istics of the Ecosystem

Natural environment protection compensation refers to the legal system that protects the natural ecological environment, protects the natural environment and makes the beneficiaries of the natural environment get compensation by some means, so as to adjust the interests of the relevant subjects of natural ecological environment protection. This paper discusses the ecosystem service function and its type division of newly built areas in Ganjiang, and the emergy evaluation of ecosystem service function of newly built areas in Ganjiang, establishes the regular scheduling and joint optimal scheduling models of natural ecosystem service value single reservoir, intro-duces the corresponding model solving methods, and applies the ant colony algorithm to the optimal schedule is a lesson. According to the ant colony algorithm, the best way to study the region is to determine these algorithms. Combined with the kernel density analysis method, the spatial scope, potential corridors and key recovery points of ecological corridors are identified, and the optimization mode of natural ecological security pattern of Shule River is constructed. The experimental results show that the optimized ant colony algorithm proves that joint scheduling plays a more prominent role in ecological environment protection, mainly in ecological support and ecological regulation. At the same time, it verifies the applicability of ant colony algorithm in joint scheduling, and improves the average protection efficiency of natural ecology to 20.9%.

Modeling aboveground carbon stock under the forest canopy influence

The dominance, size, and sociological position of tree species are essential components of natural forest patterns, providing critical information for understandinh forest dynamics. These patterns can be influenced by a variety of factors. This study aimed to evaluate the effectiveness of using spectral, hydrological, and geographical variables to estimate aboveground carbon stock (AGC) across four canopy strata, defined by diametric percentiles, in a Brazilian Atlantic Forest remnant. Our methodology, which employed machine learning techniques (Random Forest - RF + Genetic Algorithm - GA) and Multiple Linear Regression (MLR) to model AGC, proved to be highly efficient, as evidenced by our results. We observed a wide range of AGC values, from 0.37 to 467.71 MgC.ha-1, with an average of 77.4 MgC.ha-1. Trees in the 30th, 60th, and 90th percentiles contributed, respectively, 97.32%, 87.74%, and 52.02% of the total AGC. Spectral and hydrological variables combined with basal area explain AGC stock. Our findings demonstrate the robustness of machine learning techniques and MLR methods in obtaining accurate carbon estimates and generating an optimized dataset. Trees within the 30th percentile represent a smaller portion of the total AGC, and their removal does not interfere with the relationship between AGC and spectral variables.

Enhancing RNA-seq bias mitigation with the Gaussian self-benchmarking framework: towards unbiased sequencing data

BackgroundRNA sequencing is a vital technique for analyzing RNA behavior in cells, but it often suffers from various biases that distort the data. Traditional methods to address these biases are typically empirical and handle them individually, limiting their effectiveness. Our study introduces the Gaussian Self-Benchmarking (GSB) framework, a novel approach that leverages the natural distribution patterns of guanine (G) and cytosine (C) content in RNA to mitigate multiple biases simultaneously. This method is grounded in a theoretical model, organizing k-mers based on their GC content and applying a Gaussian model for alignment to ensure empirical sequencing data closely match their theoretical distribution.ResultsThe GSB framework demonstrated superior performance in mitigating sequencing biases compared to existing methods. Testing with synthetic RNA constructs and real human samples showed that the GSB approach not only addresses individual biases more effectively but also manages co-existing biases jointly. The framework’s reliance on accurately pre-determined parameters like mean and standard deviation of GC content distribution allows for a more precise representation of RNA samples. This results in improved accuracy and reliability of RNA sequencing data, enhancing our understanding of RNA behavior in health and disease.ConclusionsThe GSB framework presents a significant advancement in RNA sequencing analysis by providing a well-validated, multi-bias mitigation strategy. It functions independently from previously identified dataset flaws and sets a new standard for unbiased RNA sequencing results. This development enhances the reliability of RNA studies, broadening the potential for scientific breakthroughs in medicine and biology, particularly in genetic disease research and the development of targeted treatments.

Assessing hydrological changes in the Ganga River with the range of variability approach

ABSTRACT The study quantitatively assesses the hydrological alterations induced by the Luv Kush Barrage on the Ganga River, India, using the Range of Variability Approach (RVA) to analyze changes in flow conditions before and after the barrage construction. The Ganga River, a crucial water source for millions, has experienced significant hydrological changes due to human interventions, notably the construction of the Luv Kush Barrage. Employing indicators of hydrological alteration (IHAs), we compared pre-impact (1972–1994) and post-impact (2001–2014) flow data to ascertain the barrage’s effects on the river’s flow regime. Our findings reveal significant alterations in the magnitude of monthly streamflow, especially during the monsoon season, with notable decreases in July, August, and September. Alterations were observed in the annual extreme flow conditions, frequency, and duration of high and low pulses, indicating a comprehensive impact on the river’s natural flow patterns. These alterations affect the river’s ecological health, affecting aquatic habitats, fishery resources, and community livelihoods dependent on the river. The study underscores the necessity for integrated water resource management practices that consider the ecological and socio-economic implications of hydrological alterations.

Endoscopic surgical management of juvenile nasopharyngeal angiofibroma: Correlating tumour characteristics, risk of hemorrhage, and recurrence

ObjectivesJuvenile nasopharyngeal angiofibroma is an uncommon tumour primarily seen in young males. It is characterized by its vascular nature and aggressive growth pattern, presenting a significant surgical challenge. Endoscopic sinonasal surgery has become the treatment of choice, reducing both morbidity and blood loss, as well as recurrence rates. This study aims to present the clinical characteristics and surgical outcomes of patients with juvenile nasopharyngeal angiofibroma treated exclusively with endoscopic surgery at our centre. MethodsA retrospective cohort study was conducted on cases of juvenile nasopharyngeal angiofibroma treated with endoscopic surgery at a single centre between 2013 and 2023. ResultsA total of 21 patients were included in the study, with a mean age of 17.7 years. 57.14 % of the patients presented with an advanced stage (Stage ≥ IIc Radkowsky). The median volume was 79.2 cm3. Pre-surgical embolization was performed in all cases. 7 patients (33.3 %) required blood transfusion. The need for blood transfusion was associated with greater tumour volume (p = 0.0028), intracranial extension (p = 0.025), extension to the infratemporal fossa (p = 0.024), and orbital extension (p = 0.026). There were 2 cases (9.5 %) of tumour recurrence/persistence, both classified as stage IIIb. Tumour persistence/recurrence was more common in patients with intracranial extension (p = 0.045). ConclusionExclusive endoscopic resection of these tumours, regardless of volume and extension, does not increase the risk of blood loss or recurrence. Advanced-stage tumours, especially those with intracranial extension, present a higher risk of bleeding and tumour persistence. In these cases, the low morbidity of the endoscopic approach becomes more evident.

Paramps: Convolutional neural networks based on tensor decomposition for heart sound signal analysis and cardiovascular disease diagnosis

Currently, convolutional neural networks have demonstrated outstanding efficiency in heart sound detection and automatic diagnosis of cardiovascular diseases. However, due to the non-stationary nature and complex data patterns caused by environmental noise and stethoscope differences, traditional neural networks are limited in extracting discriminative features. This article proposes a convolutional neural network based on tensor decomposition to address this issue. This model uses a convolutional neural network with four parallel structures to extract audio features of heart sound signals and introduces a tensor network to use tensor decomposition to perform low-rank approximation on the convolutional kernel, compress model parameters, reduce redundancy, and improve performance. When processing feature data, the model divides large areas of features into locally unordered small areas to achieve feature compression and reorganization, ensuring that crucial information is preserved while compressing parameters. The model can accurately capture spatial structural information and critical features by refining the matrix product state layer. Experiments were conducted on the 2016 PhysioNet/CinC Challenge and the Yaseen heart sound public dataset, the experimental results show that the proposed method has an accuracy of 96.4% and 99.2% on two datasets, specificity of 99.1% and 99.8%, demonstrating its excellent generalization ability and diagnostic accuracy.