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‘Big data’ is massive amounts of information that can work wonders. It has become a topic of special interest for the past two decades because of a great potential that is hidden in it. Various public and private sector industries generate, store, and analyze big data with an aim to improve the services they provide. In the healthcare industry, various sources for big data include hospital records, medical records of patients, results of medical examinations, and devices that are a part of internet of things. Biomedical research also generates a significant portion of big data relevant to public healthcare. This data requires proper management and analysis in order to derive meaningful information. Otherwise, seeking solution by analyzing big data quickly becomes comparable to finding a needle in the haystack. There are various challenges associated with each step of handling big data which can only be surpassed by using high-end computing solutions for big data analysis. That is why, to provide relevant solutions for improving public health, healthcare providers are required to be fully equipped with appropriate infrastructure to systematically generate and analyze big data. An efficient management, analysis, and interpretation of big data can change the game by opening new avenues for modern healthcare. That is exactly why various industries, including the healthcare industry, are taking vigorous steps to convert this potential into better services and financial advantages. With a strong integration of biomedical and healthcare data, modern healthcare organizations can possibly revolutionize the medical therapies and personalized medicine.

We present a detailed study of structural, magnetic and thermodynamic properties of a series of Heusler alloys Fe 2-x Mn x CrAl (x = 0, 0.25, 0.5, 0.75 and 1). Structural investigation of this series is carried out using high resolution synchrotron X-ray diffraction. Results suggest that with increasing Mn concentration, the L 2 1 structure of Fe 2 CrAl is destabilized. The DC magnetization results show a decrement in paramagnetic (PM) to ferromagnetic (FM) phase transition temperature ( T C ) with increasing Mn concentration. From the systematic analysis of magnetic memory effect, heat capacity, time dependent magnetization, and DC field dependent AC susceptibility studies it is observed that, Fe 2 CrAl exhibits cluster glass(CG)-like transition approximately at 3.9 K ( T f2 ). The alloys, Fe 1.75 Mn 0.25 CrAl and Fe 1.5 Mn 0.5 CrAl exhibit double CG-like transitions near T f1 ~ 22 K, T f2 ~ 4.2 K and T f1 ~ 30.4 K, T f2 ~ 9.5 K respectively, however, in Fe 1.25 Mn 0.75 CrAl, a single CG-like transition is noted at T f2 ~ 11.5 K below T C . Interestingly, FeMnCrAl shows the absence of long ranged magnetic ordering and this alloy undergoes three CG-like transitions at ~22 K ( T f * ), 16.6 K ( T f1 ) and 11 K ( T f2 ). At high temperatures, a detailed analysis of temperature response of inverse DC susceptibility clearly reveals the observation of Griffiths phase (GP) above 300 K ( T *) in Fe 2 CrAl and this phase persists with Mn concentration with a decrement in T *.

Industrial evolution and agricultural expansion, explained by continuing population growth, have rendered enormous problems for the world throughout the past few decades, primarily because of waste generation. To reduce environmental impact and dependence on fossil fuels, scientists have explored replacing synthetic polymers with environmentally friendly and sustainable alternatives in many emergent applications. In this regard, microbial biopolymers have gained special attention. Many biopolymers originating from various strains of bacteria, fungi, and algae have been reported and their possible applications have increased rapidly. This review focuses on the wide range of microbial biopolymers, their characteristics, and factors influencing their production. The present study also describes the environmental applications of microbial biopolymers. The use of these biopolymers is very attractive as a value-added and sustainable approach to wastewater treatment. By acting as adsorbents, coagulants, and flocculants as well as filters in membrane processes, microbial biopolymers shine as promising solutions beyond conventional methods. They can be integrated into various stages of the treatment process, further enhancing the efficiency of wastewater treatment methods. Microbial biopolymer applications in bioremediation and soil stabilization are also reviewed. Several studies have demonstrated the strong potential of biopolymers in soil improvement due to their ability to minimize permeability, eliminate heavy metals, stabilize soil, and limit erosion. Challenges related to scaling up and the downstream processing of microbial biopolymers, as well as its future perspectives in environmental applications, are also discussed.

We examine the impact of changes in ozone (O3), particulate matter (PM2.5), temperature, and humidity on the health of vegetation in dense urban environments, using a very high-resolution, ground-based Visible and Near-Infrared (VNIR, 0.4–1.0 μm with a spectral resolution of 0.75 nm) hyperspectral camera deployed by the Urban Observatory (UO) in New York City. Images were captured at 15 min intervals from 08h00 to 18h00 for 30 days between 3 May and 6 June 2016 with each image containing a mix of dense built structures, sky, and vegetation. Vegetation pixels were identified using unsupervised k-means clustering of the pixel spectra and the time dependence of the reflection spectrum of a patch of vegetation at roughly 1 km from the sensor that was measured across the study period. To avoid illumination and atmospheric variability, we introduce a method that measures the ratio of vegetation pixel spectra to the spectrum of a nearby building surface at each time step relative to that ratio at a fixed time. This “Compound Ratio” exploits the (assumed) static nature of the building reflectance to isolate the variability of vegetation reflectance. Two approaches are used to quantify the health of vegetation at each time step: (a) a solar-induced fluorescence indicator (SIFi) calculated as the simple ratio of the amplitude of the Compound Ratio at 0.75 μm and 0.9 μm, and (b) Principal Component Analysis (PCA) decomposition designed to capture more global spectral features. The time dependence of these vegetation health indicators is compared to that of O3, PM2.5, temperature, and humidity values from a distributed and publicly available in situ air quality sensor network. Assuming a linear relationship between vegetation health indicators and air quality indicators, we find that changes in both SIF indicator values and PC amplitudes show a strong correlation (r2 value of 40% and 47%, respectively) with changes in air quality, especially in comparison with nearby buildings used as controls (r2 value of 1% and 4%, respectively, and with all molecular correlations consistent with zero to within 3σ uncertainty). Using the SIF indicator, O3 and temperature exhibit a positive correlation with changes in photosynthetic rate in vegetation, while PM2.5 and humidity exhibit a negative correlation. We estimate full covariant uncertainties on the coefficients using a Markov Chain Monte Carlo (MCMC) approach and demonstrate that these correlations remain statistically significant even when controlling for the effects of diurnal sun-sensor geometry and temperature variability. This work highlights the importance of quantifying the effects of various air quality parameters on vegetation health in urban environments in order to uncover the complexity, covariance, and interdependence of the numerous factors involved.

Using side-facing observations of the New York City (NYC) skyline, we identify lighting technologies via spectral signatures measured with Visible and Near Infrared (VNIR) hyperspectral imaging. The instrument is a scanning, single slit spectrograph with 872 spectral channels from 0.4–1.0 μ m. With a single scan, we are able to clearly match the detected spectral signatures of 13 templates of known lighting types. However, many of the observed lighting spectra do not match those that have been measured in the laboratory. We identify unknown spectra by segmenting our observations and using Template-Activated Partition (TAP) clustering with a variety of underlying unsupervised clustering methods to generate the first empirically-determined spectral catalog of roughly 40 urban lighting types. We show that, given our vantage point, we are able to determine lighting technology use for both interior and exterior lighting. Finally, we find that the total brightness of our scene shows strong peaks at the 570 nm Na - II , 595 nm Na - II and 818 nm Na - I lines that are common in high pressure sodium lamps, which dominate our observations.

Medical imaging systems such as computed tomography (CT) and magnetic resonance imaging (MRI) are vital tools in clinical diagnosis and treatment planning. However, these modalities are inherently susceptible to Gaussian noise introduced during image acquisition, leading to degraded image quality and impaired visualization of critical anatomical structures. Effective denoising is therefore essential to enhance diagnostic accuracy while preserving fine details such as tissue textures and structural boundaries. This study proposes a robust and efficient denoising framework specifically designed for CT and MRI images corrupted by Gaussian noise. The method integrates a cluster-wise principal component analysis (PCA) thresholding approach guided by the Marchenko–Pastur (MP) law from random matrix theory and a non-local means algorithm. Noise level estimation is achieved globally by analysing the statistical distribution of eigenvalues from noisy image patch matrices and leveraging the MP law to accurately determine the Gaussian noise variance. An adaptive clustering technique is employed to group similar patches based on underlying features such as textures and edges and enables localized denoising operations tailored to heterogeneous image regions. Within each cluster denoising is performed in two stages where initially hard thresholding based on the MP law is applied to the singular values in the SVD domain to obtain a low-rank approximation that preserves essential image content while removing noise-dominated components. Residual noise in the low-rank matrix is then further suppressed through a coefficient-wise linear minimum mean square error LMMSE estimator in the PCA transform domain. Finally, a non-local means algorithm refines the denoised image by computing weighted averages of pixel intensities and prioritizing neighbourhood similarity over spatial proximity to effectively preserve edges and textures while reducing Gaussian noise. Experimental evaluations on CT and MRI datasets demonstrate that the proposed method achieves superior denoising performance while maintaining high structural similarity and perceptual quality compared to existing state-of-the-art approaches. The method demonstrates adaptability noise reduction capability and preservation of anatomical detail that make it well suited for precision critical medical imaging applications.

This topical Collection presents a series of studies that examine molecular mechanisms, diagnostic approaches, and therapeutic strategies relevant to oral cancer. The included investigations explore topics ranging from metastasis-associated pathways and immune evasion to emerging detection technologies such as deep learning-based impedance spectroscopy and volatile organic compound analysis. Together, these studies reflect the growing convergence of molecular oncology, digital diagnostics, and translational clinical research in shaping the future of oral cancer management. This Collection represents an ongoing effort to consolidate current knowledge and guide future research in oral squamous cell carcinoma.

Replacing inefficient lighting with energy-efficient alternatives is a proven way to reduce urban energy use, yet evaluating such policies remains challenging. For example, in 2013, New York City (NYC) initiated a program to replace 250,000 high-pressure sodium (HPS) streetlights with light-emitting diodes (LEDs) by 2017, but no subsequent evaluation was published. Here, we employ ground-based hyperspectral imaging (HSI; 0.4–1.0 microns, ∼850 bands) observations from the “Urban Observatory” (UO), obtained in 2013 and 2018, to quantitatively characterize this technological transition. Following co-registration, artifact removal, and source identification, we classified individual light source technologies using both a maximum correlation approach with spectral templates of known lighting types and a one-dimensional Convolutional Neural Network (1D-CNN) trained on 1321 manually labeled spectra, achieving an average precision of ∼92% for the 2013 data and ∼94% for the 2018 data across technology classes. Scene-level mixture modeling indicates a reduction in the HPS-to-LED brightness ratio from 1.15 (2013) to 0.27 (2018), demonstrating the capability of longitudinal HSI for evaluating urban lighting policy outcomes.

Noise pollution is one of the topmost quality of life issues for urban residents in the United States. Continued exposure to high levels of noise has proven effects on health, including acute effects such as sleep disruption, and long-term effects such as hypertension, heart disease, and hearing loss. To investigate and ultimately aid in the mitigation of urban noise, a network of 55 sensor nodes has been deployed across New York City for over two years, collecting sound pressure level (SPL) and audio data. This network has cumulatively amassed over 75 years of calibrated, high-resolution SPL measurements and 35 years of audio data. In addition, high frequency telemetry data have been collected that provides an indication of a sensors’ health. These telemetry data were analyzed over an 18-month period across 31 of the sensors. It has been used to develop a prototype model for pre-failure detection which has the ability to identify sensors in a prefail state 69.1% of the time. The entire network infrastructure is outlined, including the operation of the sensors, followed by an analysis of its data yield and the development of the fault detection approach and the future system integration plans for this.

Oral submucous fibrosis (OSF) is a chronic, progressive, and fibrotic condition of the oral mucosa that carries an elevated risk of malignant transformation. We aimed to identify and validate novel genes associated with the regulation of epithelial-to-mesenchymal transition (EMT) in OSF. Genes regulating EMT were identified through differential gene expression analysis, using a LogFC threshold of -1 and + 1 and a padj value < 0.05, based on data from GEO datasets and the TCGA-HNSC datasets. The curated EMT genes were correlated with functional cancer states and subjected to clustering to identify candidate genes. Integration of bioinformatics and proteomics led to the discovery of the EMT genes MMP9 , SPARC , and ITGA5 as novel candidates. Comprehensive pathway and immunohistochemical analyses confirmed their roles in regulating EMT in OSF, oral squamous cell carcinoma (OSCC), and OSF-associated squamous cell carcinoma (OSFSCC). The significant roles of MMP9 , SPARC , and ITGA5 in fibrosis and malignancy suggest a novel mechanism in which fibrosis-associated type 2 EMT undergoes transition to type 3 EMT, driving OSF towards malignancy.