Shedding light on biochemical changes in single neuron‐like pheochromocytoma cells following exposure to synchrotron sourced terahertz radiation using synchrotron source Fourier transform infrared microspectroscopy

Synchrotron sourced Fourier transform infrared (SS FTIR) microspectroscopy was employed to investigate the biological effects on the neuron‐like pheochromocytoma (PC 12) cells after exposure to synchrotron sourced terahertz (SS THz) radiation. Over 10 min of exposure, the PC 12 cells received a total energy of 600 J m 2 , with a total incident power density of ∼1.0 W m −2 (0.10 mW cm −2 ) at the beam extraction port (BEP) of the THz beamline at the Australian Synchrotron. To investigate the metabolic response of PC 12 cells after synchrotron THz radiation exposure, we utilized the FTIR microscope at the Infrared Microspectroscopy IRM beamline, which offers high photon flux and diffraction‐limited spatial resolution enabling the detection of functional group variations in biological molecules at a single‐cell level. Principal component analysis (PCA) based on the SS FTIR spectral data revealed a distinct separation of SS THz‐exposed and control (non‐exposed) cells. According to the PCA loadings, the key changes in the exposed cells involved lipid and protein compositions as indicated by the stretching vibrations of CH 2 /CH 3 groups and amide I/II bands, respectively. An increase in lipids, such as cholesterol, or notable changes in their compositions and in some protein secondary structures were observed in the SS THz‐exposed cells. The PCA analysis further suggests that PC 12 cells might maintain cell membrane stability after SS THz irradiation through higher volumes of cholesterol and cell morphology via regulation of the synthesis of cytoskeleton proteins such as actin‐related proteins. The outcome of this study re‐emphasized the exceptional SS FTIR capability to perform single‐cell analysis directly, providing (i) unique biological information on cell variability within the population as well as between different groups, and (ii) evidence of molecular changes in the exposed cells that could lead to a deeper understanding of the effect of THz exposure at a single‐cell level.

Response of pheochromocytoma neuronal cells to varying intensity of continuous wave terahertz radiation

The effects of varying intensities of Australian Synchrotron source terahertz (THz) radiation on pheochromocytoma (PC 12) neuronal cells were investigated. PC 12 cells were exposed to THz radiation at beam incident power intensities of 0.25 W m −2 (low intensity, LI), 0.5 W m −2 (medium intensity, MI) and 1 W m −2 (high intensity, HI) for 10 min. After exposure, the morphological and physiological status of the cells was evaluated using scanning electron microscopy (SEM) and confocal laser scanning microscopy. SEM imaging revealed that, after exposure to LI THz radiation, the cells exhibited membrane protrusions (blebs) measuring 70–120 nm in diameter. In contrast, cells exposed to HI THz radiation demonstrated increased uptake of FITC–dextran and nanospheres. Analysis of single‐cell populations counterstained with 4′,6‐diamidino‐2‐phenylindole (DAPI) showed a decrease in the proportion of DAPI‐positive cells, with approximately 90, 80 and 50% remaining positive after exposure to LI, MI and HI THz radiation, respectively. However, only a slight increase in the proportion of dead cells was observed at varying THz intensities. Proteomic analysis of the cell changes following exposure to LI and HI THz irradiation indicated that THz radiation activated the CaN complex and upregulated genes involved in ribosome biogenesis and DNA damage repair.

A high‐resolution data set of fatty acid‐binding protein structures. I. Dynamics of FABP4 and ligand binding

Fatty acid‐binding proteins (FABPs) are involved in the uptake and intracellular trafficking of fatty acids for metabolic and gene‐regulatory purposes. FABPs are known to associate with membranes and also enter the nucleus. Using NMR and a human FABP4 (hFABP4) preparation completely free of endogenous ligands, we studied the influence of fatty acids and inhibitors on the conformational flexibility and bicelle/membrane association of this isoform. Binding of fatty acids and ligands rigidifies hFABP4, particularly at the portal region where ligands enter the binding site. Depending on the nature of the ligand, hFABP4 stays associated with bicelles via the portal region or segregates into solution, a prerequisite for nuclear import using a nonclassical nuclear localization signal. These results indicate that different ligands can lead to different biological outcomes. One of the major determinants for FABP4 segregation is Phe58, which in X‐ray crystal structures adopts different conformations as a function of ligand volume. It is possible that other FABP isoforms use a similar mechanism for ligand‐dependent membrane detachment and activation of nuclear import.

Modeling Puerto Rico Grid’s Sequential Failures for Hurricanes Using Electric Grid Resilience & Assessment System (EGRASS) Tool and Dynamic Contingency Analysis Tool (DCAT)

A dynamic sliding window encoding for secured DNA data storage compliant with biological and indexing constraints

In this paper, we propose a novel dynamic sliding window encoding for storing encrypted data in a DNA form taking into account biological constraints (G-C content and homopolymers) and prohibited nucleotide motifs used for data indexing. Its originality is twofold. First, it stands on a new dynamic encoding (DE) which takes advantage of variable length DNA codewords to encode binary data and to avoid homopolymers longer than N bases. Second, DE is combined with a sliding window strategy to detect whether a prohibited motif is about to appear. In this case, our proposal will add a non-coding base to the sequence to prevent the encoding of this motif. Unlike existing schemes, its scaling for high values of N and of prohibited motifs is extremely simple. It is furthermore independent of the cryptosystem. We provide the theoretical information rate of our proposal for a given number of prohibited motifs and a maximum homopolymer length. In general, it offers much higher performances, particularly in terms of information rate, than existing schemes while controlling G-C content with an extremely high probability. Moreover, experiments conducted with a recent DNA storage chain simulator show that our proposal does not require more copies than other encoding strategies for the correct reading of DNA sequences.

Marine Object Detection Using LiDAR on an Unmanned Surface Vehicle.

Marine object detection plays a crucial role in various applications such as collision avoidance and autonomous navigation in maritime environments. While most existing datasets focus on 2D object detection, this research introduces a novel 3D object detection approach that relies exclusively on LiDAR (Light Detection And Ranging) data, specifically tailored for small Unmanned Surface Vehicles (USVs), where energy efficiency and computational constraints are key challenges. This study contributes a new point cloud dataset collected from a 2-meter autonomous USV and augmented through a hardware-in-the-loop simulation environment. The PointPillars network, chosen for its efficiency in processing LiDAR data, was trained and evaluated in this maritime context. A comparative analysis was also conducted between the proposed LiDAR-only method and a multimodal (LiDAR-camera) approach. The core innovation of this work is a step for late fusion strategy, where object detection is performed independently across sensors before integration. This results in a significantly less resource-intensive solution compared to early fusion methods. Consequently, the LiDAR-only approach highly suitable for deployment on compact, low-power autonomous surface drones, marking a step forward in practical and scalable marine perception systems.

Should Automated Vehicles Stop at Yellow Lights? A Study of the Dilemma Zone and Rear-End Collisions

The advent of automated vehicles (AVs) has ushered in a new era of transportation, promising increased safety and efficiency. However, ensuring the robust performance of these vehicles in critical scenarios, particularly in intersection areas, remains a paramount concern. A key objective of this research is the identification and detection of critical behaviors within intersection areas. Through a combination of parameters distribution, hypothesis, and scenario-based simulations, the study aims to establish the impact behavior differences between AVs and conventional vehicles (CVs) at the yellow light onset have on accident occurrences. AVs are expected to follow traffic rules more strictly than human drivers do. However, this study shows that abrupt deceleration at yellow lights, while reducing the number of red lights running to almost zero, might increase rear-end collisions by up to 48% and increase crash severity, based on observed AV deceleration rates at intersections. A new behavior for AVs is proposed that can prevent 90% of accidents while avoiding red light running.

BeliefTrack: A New Framework for Improving SORT-Like Tracking Algorithms with Multi-Feature Association and Confidence Management

DeepSORT [1], a widely recognized Kalman filter-based tracking-by-detection (KFTBD) algorithm, has inspired various derivative versions, named SORT-like algorithms in this paper. Building on our previous work and SORT-like algorithms, this paper introduces an innovative framework that integrates a multi-feature association based on belief theory, and several enhanced components based on confidence, such as the Kalman filter introducing associations confidence, which aims to improve tracking performance in various complex environments. The proposed method is designed to be generic and can be integrated into a SORT-like tracker for improvement. The performance and compatibility of the new framework, namely BeliefTrack, have been evaluated and validated by 1) applying it to several datasets containing various and complex environments, 2) comparing it to DeepSORT as the baseline and several variants versions from StrongSORT [2], and 3) adapting it to different detectors. In all cases, our BeliefTrack demonstrates improved results, sometimes significantly.

Synergetic integration of multi-temporal remote sensing mosaic and conventional soil map for mapping organic carbon content in Chernozems

The spatial distribution of soil organic carbon (SOC) is assessed using digital soil mapping (DSM) methods, where remote sensing data serve as a key factor. Although soil maps provide valuable information, they are not frequently employed as predictors in soil modelling. This study explored and compared the utilisation of a multi-temporal mosaic of Sentinel-2 (S2) data, in combination with a conventional soil map, for SOC mapping in Chernozem soils of the forest-steppe zone. We implemented two algorithms to estimate SOC content in topsoil (0-30 cm) under several scenarios. As initial predictive covariates, we used a temporal mosaic of S2 bare soil spectra and a large-scale soil type map. We first implemented either partial least square regression (PLSR) or random forest (RF) to predict SOC from S2-derived bare soil spectral responses. Using solely the bare soil temporal mosaic spectra, PLSR proved more effective than RF to predict SOC. The PLSR-derived map of SOC predictions was then combined with the soil type map using RF. This latter combination led to the best SOC prediction performance and least uncertainty. Our findings indicate that integrating a soil map into a remote-sensing-based DSM prediction of SOC yields benefits in SOC mapping compared to using solely remote sensing or soil map data.

Industrially Fabricated Single-Electron Quantum Dots in Si/Si—Ge Heterostructures

This letter reports the compatibility of heterostructure-based spin qubit devices with industrial CMOS technology. It features Si/Si-Ge quantum dot devices fabricated using Infineon’s 200mm production line within a restricted thermal budget. The devices exhibit state-of-the-art charge sensing, charge noise and valley splitting characteristics, showing that industrial fabrication is not harming the heterostructure quality. These measured parameters are all correlated to spin qubit coherence and qubit gate fidelity. We describe the single electron device layout, design and its fabrication process using electron beam lithography. The incorporated standard 90nm back-end of line flow for gate-layer independent contacting and wiring can be scaled up to multiple wiring layers for scalable quantum computing architectures. In addition, we present millikelvin characterization results. Our work exemplifies the potential of industrial fabrication methods to harness the inherent CMOS-compatibility of the Si/Si-Ge material system, despite being restricted to a reduced thermal budget. It paves the way for advanced quantum processor architectures with high yield and device quality.