Search Results (1,540)

This study aims to investigate the structural, spectroscopic, and electronic properties of the synthetic missile fuel exo- and endo-tetrahydrodicyclopentadiene (THDCPD, JP-10) using density functional theory (DFT). It is to understand the dominance of the liquid exo-isomer (96%) of the jet fuel from the subtle differences between the isomers. The present DFT calculations reveal that the exo-isomer is 15.51 kJ/mol more stable than the endo-isomer, attributed to the flipping of the triangular ΔC8-C10-C9 ring in its norbornane skeleton. Calculated nuclear magnetic resonance (¹³C-NMR) and infrared (IR) spectra, validated by experimental data, reveal larger chemical shifts for junction carbons (C1/C2 and C3/C4) due to reduced electron shielding and show distinct vibrational patterns. Charge analysis indicates that all carbon atoms are negatively charged except for the C1/C2 carbons which are positively charged in both isomers. While overall IR spectra of the isomers appear similar, bands near 3000 cm⁻¹ correspond to distinctly different vibrational modes. The exo-isomer’s electronic structure features a more delocalized HOMO and a larger HOMO-LUMO gap (7.63 eV) than the endo-isomer (7.37 eV). All such differences contribute to the properties of exo-THDCPD and, therefore, why the exo-isomer dominates JP-10 fuel. Full article

Structured light encompasses a vast variety of light fields. It has unique properties such as non-uniform transverse intensity and a polarization pattern across their beam cross-sections. In this contribution, we discuss the photoexcitation of a single ionic target system driven by different sets of structured light modes. Specifically, we provide a compilation of transition amplitudes for various structured light modes interacting with atomic systems based on the first-order perturbation theory. To illustrate this, we will choose an electric quadrupole transition ($4s{}_{\mathrm{}}{}^2{S}_{1/2}_{\mathrm{}}^{\mathrm{}}\to 3d{}_{\mathrm{}}{}^2{D}_{5/2}_{\mathrm{}}^{\mathrm{}}$) in the target Ca⁺ ion driven by a structured light field. For this particular interaction, we examine how the beam parameters affect the population of magnetic sub-levels in the atomic excited state. Full article

Autoimmune pancreatitis (AIP) is a rare chronic pancreatitis subtype that often mimics pancreatic cancer due to the overlapping clinical and radiological features, posing significant diagnostic challenges. Similarly, distinguishing AIP from pancreatic neuroendocrine neoplasms (PanNENs), which present with nonspecific symptoms, adds complexity to clinical evaluations. We present the case of a 46-year-old male with recurrent acute idiopathic pancreatitis. Abdominal computed tomography (CT) revealed a 25 mm hypodense mass in the pancreatic tail with mild arterial contrast enhancement. Magnetic resonance imaging (MRI) showed the mass to be hypointense on T2-weighted sequences, with no diffusion restriction and an enhancement pattern akin to normal pancreatic tissue. The endoscopic ultrasound-guided fine needle biopsy (EUS-FNB) was inconclusive. Gallium-68 DOTATATE positron emission tomography–CT (Ga-68 DOTATATE PET-CT) showed an increased tracer uptake, leading to a distal pancreatectomy with a splenectomy. Histopathology demonstrated chronic sclerotic pancreatitis with inflammatory infiltrates. Elevated serum IgG4 levels confirmed the diagnosis of type 1 AIP Differentiating AIP from pancreatic malignancies, including PanNENs, is both critical and complex. This case highlights a misdiagnosis of PanNENs in a patient with focal AIP, where neuroendocrine hyperplasia and islet cell clusters within fibrotic areas mimicked PanNENs, even on Ga-68 PET-CT. The findings emphasize the potential for false positives with Ga-68 DOTATATE PET-CT and the importance of integrating clinical, radiological, and histological data for an accurate diagnosis. Full article

Diagnosing bipolar disorder (BD) and schizophrenia (SCH) presents significant challenges due to overlapping symptoms, reliance on subjective assessments, and the late-stage manifestation of many symptoms. Current methods using structural magnetic resonance imaging (sMRI) as input data often fail to provide the objectivity and sensitivity needed for early and accurate diagnosis. sMRI is well known to be capable of detecting anatomical changes, such as reduced gray matter volume in SCH or cortical thickness alterations in BD. However, advanced techniques are required to capture subtle neuroanatomical patterns critical for distinguishing these disorders in sMRI. Deep learning (DL) has emerged as a transformative tool in neuroimaging analysis, offering the ability to automatically extract intricate features from large datasets. Building on its success in other domains, including autism spectrum disorder and Alzheimer’s disease, DL models have demonstrated the potential to detect subtle structural changes in BD and SCH. Recent advancements suggest that DL can outperform traditional statistical methods, offering higher classification accuracy and enabling the differentiation of complex psychiatric disorders. In this context, this study introduces a novel deep learning framework for distinguishing BD and SCH using sMRI data. The model is specifically designed to address subtle neuroanatomical differences, offering three key contributions: (1) a tailored DL model that leverages explainability to extract features that boost psychiatric MRI analysis performance, (2) a comprehensive evaluation of the model’s performance in classifying BD and SCH using both spatial and morphological analysis together with classification metrics, and (3) detailed insights, which are derived from both quantitative (performance metrics) and qualitative analyses (visual observations), into key brain regions most relevant for differentiating these disorders. The results have achieved an accuracy of 78.84%, an area under the curve (AUC) of 83.35%, and a Matthews correlation coefficient (MCC) of 59.10% using the proposed framework. These metrics significantly outperform traditional machine learning models. Furthermore, the proposed method demonstrated superior precision and recall for both BD and SCH, with notable improvements in identifying subtle neuroanatomical patterns. Depending on the acquired result, it can be said that the proposed method enhances the application of DL in psychiatry, paving the way for more objective, non-invasive diagnostic tools with the potential to improve early detection and personalized treatment. Full article

Hypertrophic cardiomyopathy (HCM) is often characterized by augmented cardiac contractility, which frequently remains undetectable in its early stages. Emerging evidence suggests that hypercontractility is linked to mitochondrial defects that develop early in HCM progression. However, imaging markers for identifying these early alterations in myocardial function are lacking. We used cardiac magnetic resonance feature tracking (CMR-FT) to assess myocardial strain in a Mybpc3-knockin (KI) mouse model that mimicked human HCM. While homozygous (HOM) mice exhibited cardiac hypertrophy, heterozygous (HET) mice represented an early, asymptomatic stage of HCM. To explore mitochondrial contributions to hypercontractility, we evaluated mitochondrial integrity via scanning electron microscopy (SEM) and correlated these findings with strain abnormalities. Young HET female, but not male mice exhibited significant torsion abnormalities (p = 0.02), reduced left ventricular global longitudinal strain (LVGLS, p = 0.009), and impaired right ventricular global longitudinal strain (RVGLS, p = 0.035) compared to the controls. Strain abnormalities correlated strongly with mitochondrial morphological alterations, including changes in volume and area distribution (R > 0.7). Abnormal myocardial strain patterns, including torsion and GLS, serve as early markers of HCM and are closely associated with underlying mitochondrial dysfunction. The HET Mybpc3-KI HCM model provides important insights into the initial stages of HCM progression, highlighting strain abnormalities and sex-specific differences to enhance early diagnosis and therapeutic strategies. Full article

The heterogeneity of breast tumors might reflect biological complexity and provide prediction clues for the sensitivity of treatment. This study aimed to construct a model based on tumor heterogeneity in magnetic resonance imaging (MRI) for predicting the pathological complete response (pCR) to neoadjuvant chemotherapy (NAC). This retrospective study involved 217 patients with biopsy-confirmed invasive breast cancer who underwent MR before and after NAC. Patients were randomly divided into the training cohort and the validation cohort at a 1:1 ratio. MR images were processed by algorithms to quantify the heterogeneity of tumors. Models incorporating heterogeneity and clinical characteristics were constructed to predict pCR. The patterns of heterogeneity variation during NAC were classified into four categories abbreviated as the heterogeneity high-keep group (H_keep group), heterogeneity low-keep group (L_keep group), heterogeneity rising group, and decrease group. The average heterogeneity in patients achieving pCR was significantly lower than in those who did not (p = 0.029). Lower heterogeneity was independently associated with pCR (OR, 0.401 [95%CI: 0.21, 0.76]; p = 0.007). The model combining heterogeneity and clinical characteristics demonstrated improved specificity (True Negative Rate 0.857 vs. 0.698) and accuracy (Accuracy 0.828 vs. 0.753) compared to the clinical model. Survival outcomes were best for the L_keep group and worst for the rising group (Log-rank p = 0.031). Patients with increased heterogeneity exhibited a higher risk of recurrence approaching two years post-surgery, particularly within the non-pCR population. The quantified heterogeneity of breast cancer in MRI offers a non-invasive method for predicting pCR to NAC and evaluating the implementation of precision medicine. Full article

A pattern-reconfigurable, vertically polarized (VP), electrically small (ES), Huygens source antenna (HSA) is demonstrated. A custom-designed reconfigurable inverted-F structure is embedded in a hollowed-out cylindrical dielectric resonator (DR). It radiates VP electric dipole fields that excite the DR’s HEM_11δ mode, which in turn acts as an orthogonal magnetic dipole radiator. The HSA’s unidirectional properties are thus formed. It becomes low-profile and electrically small through a significant lowering of its operational frequency band by loading the DR’s top surface with a metallic disk. The entire 360° azimuth range is covered by each of the HSA’s four 90° reconfigurable states, emitting a unidirectional wide beam. A prototype was fabricated and tested. The measured results, which are in good agreement with their simulated values, demonstrate that the developed wideband Huygens source antenna, with its 0.085 λ_L low profile and its 0.20 λ_L × 0.20 λ_L compact transverse dimensions, hence, electrically small size with ka = 0.89, exhibits a wide 14.1% fractional impedance bandwidth and a 6.1 dBi peak realized gain in all four of its pattern-reconfigurable states. Full article

Energy issues, including energy generation, conversion, transmission and detection, are fundamental factors in all systems. In micro- and nanosystems, dealing with these energy issues requires novel nanostructures and precise technology. However, both concept and setup are not well established yet in the microsystems, especially for those at the nanometer scale. Here, we demonstrate electromagnetic nanocoils with 100 nm diameters based on uniform and periodic InGaN nanoring arrays grown on patterned GaN surfaces using nanoscale selective area epitaxy (NSAE). We observed stronger photoluminescence from the periodic InGaN nanoring arrays compared to the non-uniform InGaN nanorings, which indicates good crystal quality of the InGaN nanostructure with the NSAE. Based on this kind of nanostructure, electromagnetic induction from the nanorings is detected through the rebound movement of high-energy electron diffraction patterns that are influenced by a modulated external magnetic field. Our results clearly show the generation of an inductive current and internal magnetic field in the nanorings. We anticipate this kind of nanostructure to be a potential key element for energy conversion, transfer and detection in nanosystems. For example, it could be used to fabricate microtransformers and micro- and nanosensors for electromagnetic signals. Full article

Coal resource extraction and utilization are essential for sustainable development and economic growth. This study integrates a pseudo-triaxial mechanical loading system with low-field nuclear magnetic resonance (NMR) to enable the preliminary visualization of coal’s pore-fracture structure (PFS) under mechanical stress. Pseudo-triaxial and cyclic loading–unloading tests were combined with real-time NMR monitoring to model porosity recovery, pore size evolution, and energy dissipation, while also calculating the fractal dimensions of pores in relation to stress. The results show that during the compaction phase, primary pores are compressed with limited recovery after unloading. In the elastic phase, both adsorption and seepage pores transform significantly, with most recovering post-unloading. After yield stress, new fractures and pores form, and unloading enhances fracture connectivity. Seepage pore porosity shows a negative exponential relationship with axial strain before yielding, and a logarithmic relationship afterward. The fractal dimension of adsorption pores decreases during compaction and increases afterward, while the fractal dimension of seepage pores decreases before yielding and increases post-yielding. These findings provide new insights into the flow patterns of methane in coal seams. Full article

The identification of areas that are susceptible to damage due to earthquakes is of utmost importance in tectonically active regions like Northeast India. This may provide valuable inputs for seismic hazard analysis; however, it poses significant challenges. The present study emphasized the integration of Interferometric Synthetic Aperture Radar (InSAR) deformation rates with conventional geological and geophysical data to investigate earthquake damage susceptibility in the Barapani Shear Zone (BSZ) region of Northeast India. We used MintPy v1.5.1 (Miami INsar Timeseries software in PYthon) on the OpenSARLab platform to derive time series deformation using the Small Baseline Subset (SBAS) technique. We integrated geology, geomorphology, gravity, magnetic field, lineament density, slope, and historical earthquake records with InSAR deformation rates to derive earthquake damage susceptibility using the weighted overlay analysis technique. InSAR time series analysis revealed distinct patterns of ground deformation across the Barapani Shear Zone, with higher rates in the northern part and lower rates in the southern part. The deformation values ranged from 6 mm/yr to about 18 mm/yr in BSZ. Earthquake damage susceptibility mapping identified areas that are prone to damage in the event of earthquakes. The analysis indicated that about 46.4%, 51.2%, and 2.4% of the area were low, medium, and high-susceptibility zones for earthquake damage zone. The InSAR velocity rates were validated with Global Positioning System (GPS) velocity in the region, which indicated a good correlation (R² = 0.921; ANOVA p-value = 0.515). Additionally, a field survey in the region suggested evidence of intense deformation in the highly susceptible earthquake damage zone. This integrated approach enhances our scientific understanding of regional tectonic dynamics, mitigating earthquake risks and enhancing community resilience. Full article

In response to the growth of emerging resistance to conventional antifungal drugs, antifungal proteins (AFPs) of filamentous Ascomycetes origin have been discovered in recent years. Understanding the structure of AFPs is crucial for elucidating their antifungal mechanisms and developing new therapeutic agents. While nuclear magnetic resonance (NMR) has proven effective in determining the structures of small proteins, some AFP structures remain unresolved, necessitating the use of alternative prediction methods. Through bioinformatics analysis and heatmaps of amino acid sequence identity and similarity matrix, we categorized AFPs into three major classes and six subcategories, revealing structural and bioactivity differences. We employed AlphaFold (AF) to predict the 3D structures of six different AFPs, with predictions compared to NMR-derived structures. The results demonstrated a high degree of consistency between AF and NMR structures, with AF excelling in structural quality assessment and accurately capturing complex disulfide bond patterns. Both AF2 and AF3 models outperform the NMR model in overall structural quality and coherence, with AF3 showing the best performance. However, the limitations of AF should be considered, including its reduced accuracy in predicting multi-metal ion complexes, suboptimal performance in highly flexible or disordered regions, and its inability to account for multiple conformers, as it generates only a single dominant structure. Moreover, while AF3 accurately predicts all disulfide bond patterns, AF2 falls short in this regard. This study verifies the reliability of AF in the structural prediction of cysteine-rich AFPs while highlighting these constraints, offering important support for the rational design of new protein-based antifungal drugs. Full article

Deep learning has become indispensable for identifying hierarchical spatial features (SFs), which are crucial for linking neurological disorders to brain functionality, from functional Magnetic Resonance Imaging (fMRI). Unfortunately, existing methods are constrained by architectures that are either linear or nonlinear, limiting a comprehensive categorization of spatial features. To overcome this limitation, we introduce the Semi-Nonlinear Deep Efficient Reconstruction (SENDER) framework, a novel hybrid approach designed to simultaneously capture both linear and nonlinear spatial features, providing a holistic understanding of brain functionality. In our approach, linear SFs are formed by directly integrating multiple spatial features at shallow layers, whereas nonlinear SFs emerge from combining partial regions of these features, yielding complex patterns at deeper layers. We validated SENDER through extensive qualitative and quantitative evaluations with four state-of-the-art methods. Results demonstrate its superior performance, identifying five reproducible linear SFs and eight reproducible nonlinear SFs. Full article

The precise and deterministic integration of fluorescent emitters with photonic nanostructures is an important challenge in nanophotonics and key to the realization of hybrid photonic systems, supporting effects such as emission enhancement, directional emission, and strong coupling. Such integration typically requires the definition or immobilization of the emitters at defined positions with nanoscale precision. While various methods were already developed for creating localized emitters, in this work we present a new method for the deterministic fabrication of fluorescent nanostructures featuring well-defined optical transitions; it works with a minimal amount of steps and is scalable. Specifically, electron-beam lithography is used to directly pattern a mixture of the negative-tone electron-beam resist with the europium complex Eu(TTA)₃, which exhibits both electric and magnetic dipolar transitions. Crucially, the lithography process enables precise control over the shape and position of the resulting fluorescent structures with a feature size of approx. 100 $\mathrm{n}$$\mathrm{m}$. We demonstrate that the Eu(TTA)₃ remains fluorescent after exposure, confirming that the electron beam does not alter the structure the optical transitions. This work supports the experimental study of local density of optical states in nanophotonics. It also expands the knowledge base of fluorescent polymer materials, which can have applications in polymer-based photonic devices. Altogether, the presented fabrication method opens the door for the realization of hybrid nanophotonic systems incorporating fluorescent emitters for light-emitting dielectric metasurfaces. Full article

Background: The rising prevalence of metabolic-dysfunction-associated steatotic liver disease (MASLD) is a significant health challenge, and the consumption of ultra-processed foods (UPFs) could play a key role. Aim: The aim is assess the impact of UPF consumption changes on the development and progression of MASLD in adults. Design: This is a longitudinal study to assess how changes in UPF consumption affect liver fat and MASLD parameters over 6 months in 70 participants. Methods: Dietary intake was assessed using a validated food frequency questionnaire, and foods were classified according to the NOVA system. Participants were divided into three groups based on UPF consumption changes: maximum (T1), medium (T2), and minimum reduction (T3). Fatty liver parameters were assessed with magnetic resonance imaging and ultrasonography. Mediterranean diet (Med-diet) adherence and sociodemographic parameters were also recorded. The General Linear Model was used to determine relationships between UPF consumption, fatty liver disease parameters, and diet. Results: Participants in T1 experienced a 7.7% reduction in intrahepatic fat content (IFC) compared to 2.6% in T3. T1 showed increased Med-diet adherence and decreased meat and sweets consumption. The energy intake decreased by 605.3 kcal/day in T1, while T3 showed an increase of 209.5 kcal/day. Conclusions: Reducing UPF consumption leads to a decrease in IFC, associated with high Med-diet adherence and low calorie intake. Adopting these dietary patterns aligns with global sustainability goals and could further benefit MASLD patients by addressing environmental challenges alongside improving liver health. Full article

An improved method for identifying nighttime tweek signals in WHU VLF measurements was developed by redesigning the extraction process and validated through comparison with World-Wide Lightning Location Network (WWLLN) data. Using the enhanced method, 1,728,032 tweek signals were identified from four years (2018–2021) of VLF data, forming the most comprehensive tweek dataset for the mid–low latitude region in China. Statistical analysis reveals distinct nighttime variations in tweek occurrence rates, which increase from 18:00 LT to 20:00 LT, remain high until 04:00 LT, and gradually decrease towards sunrise. Seasonal differences in propagation distance are evident, ranging from ~2000 km in summer to ~4000 km in winter, corresponding to the seasonal shift of lightning activity. The cutoff frequency showed apparent daily and seasonal fluctuations, and the trends of daily variation are opposite between winter and summer. The annual variation in cutoff frequency presents a pattern different from previous cognition, with a minimum of 1.62 kHz in summer and a maximum of 1.68 kHz in winter, influenced by the magnetic cyclotron frequency at ionospheric reflection points. These findings improve the understanding of nighttime tweek characteristics and ionospheric dynamics in East Asia, offering valuable insights for ionospheric research and VLF communication systems. Full article