Variety Of Features Research Articles

Connectome-driven neural inventory of a complete visual system.

Vision provides animals with detailed information about their surroundings, conveying diverse features such as color, form, and movement across the visual scene. Computing these parallel spatial features requires a large and diverse network of neurons, such that in animals as distant as flies and humans, visual regions comprise half the brain's volume. These visual brain regions often reveal remarkable structure-function relationships, with neurons organized along spatial maps with shapes that directly relate to their roles in visual processing. To unravel the stunning diversity of a complex visual system, a careful mapping of the neural architecture matched to tools for targeted exploration of that circuitry is essential. Here, we report a new connectome of the right optic lobe from a male Drosophila central nervous system FIB-SEM volume and a comprehensive inventory of the fly's visual neurons. We developed a computational framework to quantify the anatomy of visual neurons, establishing a basis for interpreting how their shapes relate to spatial vision. By integrating this analysis with connectivity information, neurotransmitter identity, and expert curation, we classified the ~53,000 neurons into 727 types, about half of which are systematically described and named for the first time. Finally, we share an extensive collection of split-GAL4 lines matched to our neuron type catalog. Together, this comprehensive set of tools and data unlock new possibilities for systematic investigations of vision in Drosophila, a foundation for a deeper understanding of sensory processing.

EXPLORING CONTEMPORARY INDIAN SOCIETY: THEMES AND NARRATIVES IN ARVIND ADIGA'S “BETWEEN THE ASSASSINATIONS”

This research paper examines the collection of interconnected stories titled "Between the Assassinations" by Arvind Adiga, published in 2008. The paper explores the various themes and narratives presented in the novel, which provides a snapshot of life in the fictional town of Kittur on India's southwestern coast. The study delves into the captivating portrayal of contemporary India, focusing on themes such as caste discrimination, religious tensions, and the impact of urbanization on small towns. Through a comprehensive analysis of the novel, this research aims to shed light on the social, political, and cultural complexities depicted by Adiga, highlighting the struggles, dreams, and aspirations of diverse characters from different walks of life.

Book bans in political context: Evidence from US schools.

In the 2021-2022 school year, more books were banned in US school districts than in any previous year. Book banning and other forms of information censorship have serious implications for democratic processes, and censorship has become a central theme of partisan political rhetoric in the United States. However, there is little empirical work on the exact content, predictors of, and repercussions of this rise in book bans. Using a comprehensive dataset of 2,532 bans that occurred during the 2021-2022 school year from PEN America, combined with county-level administrative data, multiple book-level digital trace datasets, restricted-use book sales data, and a new crowd-sourced dataset of author demographic information, we find that (i) banned books are disproportionately written by people of color and feature characters of color, both fictional and historical, in children's books; (ii) right-leaning counties that have become less conservative over time are more likely to ban books than neighboring counties; and (iii) national and state levels of interest in books are largely unaffected after they are banned. Together, these results suggest that rather than serving primarily as a censorship tactic, book banning in this recent US context, targeted at low-interest children's books featuring diverse characters, is more similar to symbolic political action to galvanize shrinking voting blocs.

Development of Earth Gravity Models in A Sustainable Environment: An Overview of Nigeria’s Ecosystem

The evolution of Earth gravity models in Nigeria's ecological context is essential for advancing sustainable environmental stewardship. These models serve as crucial tools to understand the geological intricacies and environmental dynamics across Nigeria's diverse ecosystems. This article obtained data from previous existing data to emphasizes the significance of sustainable development, technological innovation, and the integration of sustainability principles into Earth gravity modeling efforts. The gravitational variations observed across Nigeria's ecosystems highlight the diverse geological features influencing gravitational anomalies. For instance, forests with dense vegetation exhibit gravitational variations ranging from -5.2 to -2.8 mGal, while coastal areas experiencing erosion and sea level rise show variations from -1.8 to 1.0 mGal. The article explores both advancements and challenges in developing Earth gravity models within Nigeria's unique environmental landscape, emphasizing the intersection of technology and sustainability. By leveraging advanced technologies and interdisciplinary methodologies, Earth gravity models contribute significantly to understanding the gravitational forces shaping the planet's surface and subsurface. Understanding the methodology behind Earth gravity models in a sustainable environment is crucial for effective environmental management and resource conservation, providing valuable tools for preserving Nigeria's diverse ecosystems for future generations. Through this integrated approach, Earth gravity models pave the way for enhanced environmental monitoring, land-use planning, and conservation strategies tailored to Nigeria's specific ecological challenges and opportunities.

Atypical Endometriosis: A Comprehensive Systematic Review of Pathological Patterns and Diagnostic Challenges.

Endometriosis is a benign condition affecting women of reproductive age. A potential association with ovarian cancer has been documented. Atypical endometriosis (AE) is characterized by deviations from the typical microscopic appearance of endometriosis, including cytologic and architectural atypia. AE has been recognized as a potential precursor to endometriosis-associated ovarian cancers (EAOC), particularly endometrioid and clear cell subtypes. AE presents challenges in diagnosis due to its diverse clinical and pathological features, often requiring careful histological evaluation for accurate identification. Architectural AE, defined by localized proliferation of crowded glands with atypical epithelium resembling endometrial neoplasia, and cytologic AE, characterized by nuclear atypia within the epithelial lining of endometriotic cysts, are key subtypes. Immunohistochemical and molecular studies have revealed aberrant expression of markers such as Ki67, COX-2, BAF250a, p53, estrogen receptor, progesterone receptor, and IMP-3. Long-term follow-up studies suggest relatively low recurrence and malignant transformation rates among patients with AE, but uncertainties persist regarding its exact malignancy potential and optimal management strategies. Integration of artificial intelligence and shared molecular aberrations between AE and EAOC may enhance diagnostic accuracy. Continuous interdisciplinary collaboration and ongoing research efforts are crucial for a deeper understanding of the relationship between endometriosis and carcinogenesis, ultimately improving patient care and surveillance.

A deep learning methodology based on adaptive multiscale CNN and enhanced highway LSTM for industrial process fault diagnosis

Intelligent fault diagnostic techniques are crucial for ensuring the long-term reliability of manufacturing. The process variables collected by sensors in real industrial systems typically exhibit diverse time scales and data features. To overcome the above issues, we propose a comprehensive model known as adaptive multiscale CNN and enhanced highway LSTM (ACEL). To begin with, multiscale features under variable operating conditions are automatically extracted by constructing the parallel convolutional module. Then, to selectively focus on the feature representation within the channels, efficient channel attention is introduced to downplay the channels with less important information. In addition, the bidirectional LSTM is designed to generate more fine-grained hybrid features based on contextual information and local features learned by the CNN. An enhanced highway configuration is designed to be used to bolster the global temporal dependencies. Finally, ACEL is applied to the Tennessee Eastman benchmark and CSTR simulation, and multiple statistical metrics show that the proposed model outperforms other advanced fault diagnosis methods.

Transcription profiling reveals tissue-specific metabolic pathways in the fat body and ovary of the diapausing mosquito Culex pipiens

The northern house mosquito, Culex pipiens, employs diapause as an essential survival strategy during winter, inducing important phenotypic changes such as enhanced stress tolerance, lipid accumulation, and extended longevity. During diapause, the cessation of reproductive development represents another distinctive phenotypic change, underlining the need for adjusted modulation of gene expressions within the ovary. Although considerable advancements in screening gene expression profiles in diapausing and non-diapausing mosquitoes, there remains a gap in tissue-specific transcriptomic profiling that could elucidate the complicated formation of diverse diapause features in Cx. pipiens. Here, we filled this gap by utilizing RNA sequencing, providing a detailed examination of gene expression patterns in the fat body and ovary during diapause compared to non-diapause conditions. Functional annotation of upregulated genes identified associations with carbohydrate metabolism, stress tolerance, immunity, and epigenetic regulation. The validation of candidate genes using quantitative real-time PCR verified the differentially expressed genes identified in diapausing mosquitoes. Our findings contribute novel insights into potential regulators during diapause in Cx. pipiens, thereby opening possible avenues for developing innovative vector control strategies.

Overview and evolutionary path of Estonian coastal lagoons

Coastal lagoons are highly productive and biologically diverse landscape features that sustain a range of valuable natural services to societies. Acting as an interface between land and sea, they evolve naturally, but are also vulnerable to various forms of human activity. Based on old cartographic material, LiDAR elevation data, Holocene sea level histories, and shoreline modelling, we study the development of coastal lagoons on the postglacially uplifting, tideless coast of the brackish water Baltic Sea. Ecological succession of habitats along the path from bays to semi-enclosed lagoons, coastal lakes or bogs is discussed utilizing community structure analysis of habitat building bottom vegetation. There are about 600 lagoons in Estonia, but they are typically small (bigger ones up to 6 km2) and mostly <1 m deep. Due to ongoing postglacial uplift (1.5−3.4 mm/a), lagoons are just a relatively short (50−500-years long) transitional phase in a long (up to ∼10 000-years) succession. Locating at an altitude of up to 20−30 m, some older, more resilient palaeolagoons can be nowadays distinguished as lakes or bogs, while smaller ones have blended into the surrounding landscape. The contemporary lagoons are ephemeral too, because the emerging terrain has already been flattened by erosion and sedimentation. After separation from the sea, the brackish water habitat is gradually replaced with freshwater habitat and both biodiversity and plants coverage usually increases. As organic deposits build up, low-growth shores are replaced with high-growth vegetation; reedbeds expand. Detached from their marine past, communities typical for lakes, fens, bogs or forests are finally formed. As a result of eutrophication, ongoing climate change and sea level rise, the balance between emergence of new lagoons and their disappearance due to distancing and swamping has shifted over the past 100 years.

Expression of CD44 is associated with aggressiveness in seminomas

BackgroundTesticular germ cell tumors (TGCTs) exhibit diverse biological and pathological features and are divided in two main types, seminomas and nonseminomatous germ cell tumors (NSGCTs). CD44 is a cell surface receptor, which is highly expressed in malignancies and is implicated in tumorigenesis affecting cell-matrix interactions and cell signaling.Methods and resultsHere, we examined the expression of CD44 in tumor cell lines and in patients’ material. We found that CD44 is over-expressed in TGCTs compared to normal tissues. Immunohistochemical staining in 71 tissue specimens demonstrated increased expression of CD44 in some patients, whereas CD44 was absent in normal tissue. In seminomas, a high percentage of tumor and stromal cells showed cytoplasmic and/or cell surface staining for CD44 as well as increased staining for CD44 in the tumor stroma was found in some cases. The increased expression of CD44 either in tumor cells or in stromal components was associated with tumor size, nodal metastasis, vascular/lymphatic invasion, and disease stage only in seminomas. The increased stromal expression of CD44 in TGCTs was positively associated with angiogenesis.ConclusionsCD44 may exhibit diverse biological functions in seminomas and NSGCTs. The expression of CD44 in tumor cells as well as in tumor stroma fosters an aggressive phenotype in seminomas and should be considered in disease treatment.

Estimating optimal split delivery vehicle routing problem solution values

This paper explores the application of linear regression models to estimate the optimal solution value (i.e., the sum of tour lengths) for the Split Delivery Vehicle Routing Problem (SDVRP). We present novel models that integrate topological features along with the mean and standard deviation of feasible solution values, achieving an impressive accuracy with an error margin of approximately 3%. To obtain random feasible solutions for the SDVRP quickly, we propose a modified Clarke & Wright algorithm with split delivery (MCWSD). Our results demonstrate the potential of extending our earlier work to more complex routing problems, highlighting the importance of incorporating diverse features to obtain accurate approximations.

High-level vision task-driven infrared and visible image fusion approach: Progressive Semantic Enhancement based Multi-scale Cross-modality Interactive Network

Infrared-visible image fusion aims to merge data for a more accurate scene representation. Current research focuses mainly on enhancing visual appeal rather than improving performance in high-level vision tasks. To address this gap, we propose the Semantic Enhanced Multi-scale Cross-modality Interactive Image Fusion Network (SeMIFusion). Initially, Multi-scale Cross-modality Feature Fusion (MCFF) module is devised to extract shallow and deep features across different modalities. During feature extraction, Texture Enhancer (TE) and Semantify Enhancer (SE) blocks capture diverse hierarchical features across multi-scale layers, seamlessly integrating into the Semantic Feature Integration (SFI) module for profound semantic information extraction. Furthermore, an Image Scene Reconstruction (ISR) module maintains original image details in fused features, ensuring image fidelity. Additionally, incorporating a visual preservation guiding mask prioritizes retaining visual quality during reconstruction, preventing degradation. Extensive experiments demonstrate our method’s superiority in preserving visual effects and texture details, especially in high-level vision tasks. The source code is available at https://github.com/yyzzttkkjj/SeMIFusion.

Identification and clinical validation of diverse cell-death patterns-associated prognostic features among low-grade gliomas

Low-grade glioma (LGG) is heterogeneous at biological and transcriptomic levels, and it is still controversial for the definition and typing of LGG. Therefore, there is an urgent need for specific and practical molecular signatures for accurate diagnosis, individualized therapy, and prognostic evaluation of LGG. Cell death is essential for maintaining homeostasis, developing and preventing hyperproliferative malignancies. Based on diverse programmed cell death (PCD) related genes and prognostic characteristics of LGG, this study constructed a model to explore the mechanism and treatment strategies for LGG cell metastasis and invasion. We screened 1161 genes associated with PCD and divided 512 LGG samples into C1 and C2 subtypes by consistent cluster analysis. We analyzed the two subtypes' differentially expressed genes (DEGs) and performed functional enrichment analysis. Using R packages such as ESTIMATE, CIBERSOTR, and MCPcounter, we assessed immune cell scores for both subtypes. Compared with C1, the C2 subtype has a poor prognosis and a higher immune score, and patients in the C2 subtype are more strongly associated with tumor progression. LASSO and COX regression analysis screened four characteristic genes (CLU, FHL3, GIMAP2, and HVCN1). Using data sets from different platforms to validate the four-gene feature, we found that the expression and prognostic correlation of the four-gene feature had a high degree of stability, showing stable predictive effects. Besides, we found downregulation of CLU, FHL3, and GIMAP2 significantly impairs the growth, migration, and invasive potential of LGG cells. Take together, the four-gene feature constructed based on PCD-related genes provides valuable information for further study of the pathogenesis and clinical treatment of LGG.

Studies on Morphological Effect of Some Pesticides on Fresh Water Fish, Labeo rohita in Sai River of Jaunpur District, Uttar Pradesh, India

Pesticides are one of the potentially harmful toxic chemicals introduced into the aquatic environment. Fish serve as important bio-indicators for aquatic life. Widely used pesticides formulation two investigate possible chemical biological and toxicological effects of pesticides that may enter into our eco-system. Effects of pesticides (herbicides insecticides, rodenticides and fungicides) to loss protein content of the liver gill and intestinal tissue of the fresh water fish, Labeorohita.The Recent study is aimed two assess the toxicological impacts of treated and untreated effluents on aquatic fresh water fish, Labeo rohita. The Most common edible fish having diverse characters includes difference in morphology, food and feeding etc. Symptoms were nearly identical on various host spp (Labeo rohita. with respect to behaviour of the infected fish. The toxicity of pesticides to an organism is usualy expressed in terms of the LD50 (lethal dose 50 percent) and LC50 (50 percent lethal concentration) observed the behavioural studies of exposed to Labeorohita. Therefore, the protection and prevention of aquatic ecosystem will be possible only with the appropriate and rationalized application of pesticides.

Imaging characteristics and diagnosis of primary middle mediastinal tumors

Objectives: To investigate the proportion of different histological types and CT enhanced imaging features of primary middle mediastinal lesions in order to improve the understanding of these tumors and the accuracy of preoperative diagnosis. Methods: Retrospective analysis was conducted on 84 patients with primary middle mediastinal lesions and clear histological classifications diagnosed and treated at the Cancer Hospital, Chinese Academy of Medical Sciences from January 2012 to December 2022. Clinical, imaging, and pathological data were collected and classified according to tumor histological classifications. CT imaging manifestations such as tumor location, size, morphology, edge, boundary, internal components, enhancement characteristics, and surrounding tissue invasion were evaluated and recorded. Results: The histological types of the primary middle mediastinal lesions from the 84 patients included mesenchymal tumors, anterior intestinal cysts, giant lymph node hyperplasia, substernal goiter, neuroendocrine carcinoma, lymphohematopoietic system tumors, and mesothelioma, accounting for 28.6%, 27.4%, 14.3%, 3.6%, 11.9%, 9.5%, and 4.8%, respectively. Mesenchymal tumors included peripheral nerve sheath tumors, vascular tumors, adipogenic tumors, solitary fibrous tumors, and synovial sarcoma, accounting for 54.2%, 20.8%, 12.5%, 8.3%, and 4.2%, respectively. The above tumors had diverse imaging manifestations and specific imaging features. Mature fat were found in 3 cases of liposarcoma; Calcification was observed in 2 cases of thyroid nodules and 7 cases of giant lymph node hyperplasia; Enhanced scanning showed significant enhancement in 2 cases of solitary fibrous tumors, 3 cases of thyroid nodules, and 11 cases of giant lymph node hyperplasia; Mediastinal large lymph nodes was observed in 6 cases of lymphoma and 3 cases of mesothelioma; High invasiveness was observed in 4 cases of mesothelioma and 9 cases of neuroendocrine carcinoma. Conclusion: Mediastinal tumors have low incidence rate and rich histological types, and their imaging manifestations are diverse. Preoperative differential diagnosis can be made according to their specific imaging characteristics.

Time Series Forecasting of Cardiovascular Mortality: Machine Learning Based on State Economic and Local Medical Data.

This study focuses on the complex interplay of healthcare, economic factors, and population dynamics, addressing a research gap in regional-level models that integrate diverse features within a temporal framework. Our primary objective is to develop an advanced temporal model for predicting cardiovascular mortality in Russian regions by integrating global and local healthcare features with economic and population dynamics. Utilizing a dataset from the Almazov Center's Department of Mortality Performance Monitoring, covering 94 regions and 752 records from January 1, 2015, to December 31, 2023, our analysis incorporates key parameters such as angioplasty procedures, population morbidity rates, Ischemic Heart Disease (IHD) and Cardiovascular Diseases (CVD) monitoring, and demographic data. Employing XGBoost and a regression model, our methodology ensures the model's robustness and generalizability.

Bidirectionally regulated synthetic ferrocene-based aminal-linked porous organic polymer artificial enzyme towards photothermal-amplified synergistic antibacterial and wound healing therapy

Development of hetero-atom rich porous organic polymers (POPs) in facile and easy accessible methods are of significant importance to broad application potentials and enrich diversity of such materials. Here, a bidirectional adjustment strategy, starting with amino and aldehyde monomers, concurrently, was used for the rational preparation of N-rich aminal-linked POPs with customized structure and function. Five novel ferrocene-based aminal-linked POPs, were facilely fabricated via the one-pot catalyst-free Schiff base polycondensation between the 1,1′-Ferrocene dicarboxaldehyde and different types of triazine-bearing monomers with diverse geometrical features and functions (Porphyrin, cationic viologen, Ferrocene, 1,4,7,10-tetraazacyclododecane, and benzyl). The concurrent introduction of Ferrocene (redox-active) and triazine-Porphyrin (photoactive) endowed remarkable mimicking peroxidase-like activity and excellent photothermal and photodynamic activity with Fc-TDPP, which could be used as a broad spectrum fungicide, exerting photothermal enhanced photodynamic and enzyme antibacterial therapy. Notably, in vitro and in vivo experiments confirmed Fc-TDPP could not only kill S. aureus and E. coli quickly and efficiently, but also significantly accelerate the repairing of open wounds with S. aureus-infection. The as-proposed strategy are available for the low-cost constructing N-rich POPs with specific structure and functions targeted for wide potential applications in various fields.

Firing feature-driven neural circuits with scalable memristive neurons for robotic obstacle avoidance

Neural circuits with specific structures and diverse neuronal firing features are the foundation for supporting intelligent tasks in biology and are regarded as the driver for catalyzing next-generation artificial intelligence. Emulating neural circuits in hardware underpins engineering highly efficient neuromorphic chips, however, implementing a firing features-driven functional neural circuit is still an open question. In this work, inspired by avoidance neural circuits of crickets, we construct a spiking feature-driven sensorimotor control neural circuit consisting of three memristive Hodgkin-Huxley neurons. The ascending neurons exhibit mixed tonic spiking and bursting features, which are used for encoding sensing input. Additionally, we innovatively introduce a selective communication scheme in biology to decode mixed firing features using two descending neurons. We proceed to integrate such a neural circuit with a robot for avoidance control and achieve lower latency than conventional platforms. These results provide a foundation for implementing real brain-like systems driven by firing features with memristive neurons and put constructing high-order intelligent machines on the agenda.

Detection of Road Crack Images Based on Multistage Feature Fusion and a Texture Awareness Method.

Structural health monitoring for roads is an important task that supports inspection of transportation infrastructure. This paper explores deep learning techniques for crack detection in road images and proposes an automatic pixel-level semantic road crack image segmentation method based on a Swin transformer. This method employs Swin-T as the backbone network to extract feature information from crack images at various levels and utilizes the texture unit to extract the texture and edge characteristic information of cracks. The refinement attention module (RAM) and panoramic feature module (PFM) then merge these diverse features, ultimately refining the segmentation results. This method is called FetNet. We collect four public real-world datasets and conduct extensive experiments, comparing FetNet with various deep-learning methods. FetNet achieves the highest precision of 90.4%, a recall of 85.3%, an F1 score of 87.9%, and a mean intersection over union of 78.6% on the Crack500 dataset. The experimental results show that the FetNet approach surpasses other advanced models in terms of crack segmentation accuracy and exhibits excellent generalizability for use in complex scenes.

Lightweight interactive feature inference network for single-image super-resolution

The emergence of convolutional neural network (CNN) and transformer has recently facilitated significant advances in image super-resolution (SR) tasks. However, these networks commonly construct complex structures, having huge model parameters and high computational costs, to boost reconstruction performance. In addition, they do not consider the structural prior well, which is not conducive to high-quality image reconstruction. In this work, we devise a lightweight interactive feature inference network (IFIN), complementing the strengths of CNN and Transformer, for effective image SR reconstruction. Specifically, the interactive feature aggregation module (IFAM), implemented by structure-aware attention block (SAAB), Swin Transformer block (SWTB), and enhanced spatial adaptive block (ESAB), serves as the network backbone, progressively extracts more dedicated features to facilitate the reconstruction of high-frequency details in the image. SAAB adaptively recalibrates local salient structural information, and SWTB effectively captures rich global information. Further, ESAB synergetically complements local and global priors to ensure the consistent fusion of diverse features, achieving high-quality reconstruction of images. Comprehensive experiments reveal that our proposed networks attain state-of-the-art reconstruction accuracy on benchmark datasets while maintaining low computational demands. Our code and results are available at: https://github.com/wwaannggllii/IFIN.

Strengthening The Character of Global Diversity Through Child-friendly School Programs by Utilizing 5S (Smile, Greetings, Greetings, Politeness, Courtesy) in the Regional Coordinator for Education, South Cilacap District

This study aims to analyze the strengthening of Global Diversity through the Child-friendly School Program utilizing the 5S culture (Smile, Greeting, Salutation, Politeness, Courtesy) at SD KorwilBiddik South Cilacap, and to describe the supporting and inhibiting factors of learning to strengthen Global Diversity through this program in the South CilacapBiddik Regional Council. Using a descriptive qualitative research approach, the study focuses on students, teachers, and elementary school principals in both public and private schools in the region, particularly on the implementation of child-friendly schools through the 5S culture to enhance the character of global diversity among students. The findings reveal that schools in the South CilacapBiddik Regional Council have effectively implemented child-friendly school policies and integrated the 5S program into daily school activities. The supporting factors include school policies, teachers, the school environment, parents, supportive subject matter, and adequate facilities and infrastructure. However, there are inhibiting factors such as inconsistencies among school residents in implementing the program, the diversity of student characteristics, and differing habits of students at school and at home. Despite these challenges, the 5S program has been well-implemented daily, fostering respectful and friendly behavior towards teachers and peers, thereby enhancing students' global diversity character.