130553 research outputs found
Sort by
Primary neurolymphomatosis presenting as paraparesis and diplopia in a young man
No full textA man in his mid-20s presented with a 5-month history of bilateral lower limb weakness and wasting along with bladder symptoms and erectile dysfunction. Additionally, he had binocular diplopia and progressive drooping of both eyelids. His clinical presentation was suggestive of bilateral third cranial nerve involvement with lumbosacral polyradiculopathy. Initial treatment with steroids for suspected idiopathic lumbosacral polyradiculopathy syndrome proved ineffective, as his symptoms progressively worsened without other systemic manifestations. We diagnosed a rare and treatable neurological condition, primary neurolymphomatosis, presenting as lumbosacral polyradiculopathy with bilateral third cranial nerve involvement. A definitive diagnosis was established only after nerve root biopsy, highlighting the crucial role of biopsy in confirming the diagnosis. Our case underscores the importance of early consideration for an invasive nerve root biopsy, enabling prompt treatment and an improved prognosis
Electrochemical CO<sub>2</sub> reduction in acidic media: a perspective
No full textThe electrochemical CO2 reduction reaction (eCO2RR) is a promising approach for converting CO2 to useful chemicals and, hence, achieving carbon neutrality. Though high selectivity and activity of products have been achieved recently, all are reported in neutral or alkaline electrolytes. Although these electrolyte media give high selectivity and activity, they face the major challenge of low CO2 utilization because of carbonate formation, which lowers the overall efficiency of the process. Conducting the eCO2RR in acidic media can help overcome the issue of carbonate formation and hence can increase the CO2 utilization efficiency. However, there are many challenges associated with acidic eCO2RR. Two major concerns are the highly competitive hydrogen evolution reaction in acidic media and salt precipitation issues. This Perspective focuses on the fundamentals of acidic eCO2RR, recent catalyst development strategies, and relevant problems that need to be addressed in the future. In the end, we provide a future outlook that will give an idea about the problems to focus on in the future in the field of acidic eCO2RR
A deep learning approach for objective evaluation of microscopic neuro-drilling craniotomy skills
No full textBackground:
Minimally invasive microscopic and endoscopic neurosurgery demands precise use of high-speed micro-drilling tools to prevent potential complications. Present-day neuro-drilling training methods include cadaveric specimens and surgical simulators. However, skills assessment is mostly manual, and there is a pressing need for automation and personalized feedback for trainee surgeons. The lack of well-annotated datasets limits deep learning (DL)-based automation.
Methods:
The study poses microscopic neuro-drilling skill evaluation as a rank estimation problem. It presents a geometric-order-learning based framework to effectively train transformer-based DL models in ultra-low-data settings. The study demonstrates that the proposed framework enhances feature separability in embedding space. Furthermore, it suggests a framework for automatic detection of the drilling regions. Additionally, the study contributes a comprehensive dataset of 435 images encompassing the micro-drilling on various specimens, including deceased-sheep heads and scapulae. The enhancement in the performance and practical utility of the proposed system is illustrated using various qualitative and quantitative methods.
Results:
The proposed model exhibits a mean squared error (MSE) of 0.77 and
accuracy of 95.17%. Utilization of the proposed framework results in an average improvement of 24.71%, in ± 1 accuracy, across five state-of-the-art (SOTA) transformer-based architectures. Additionally, significant enhancement is observed in feature separability in the embedding space for both Convolution Neural Network (CNN) and Transformer-based architectures. Furthermore, the proposed model outperforms the independent expert evaluator by 12.96% in MSE and 8.47% in ± 1 accuracy.
Conclusion:
This study introduces the first-ever well-annotated unbiased microscopic neuro-drilling effectiveness dataset and automated skill evaluation system, which surpasses the performance of an independent expert evaluator. It can be used as an unbiased automated evaluation tool for neurosurgical training worldwide
Understanding the Mechanical Properties of Pituitary Adenomas for Optimized Surgery
No full textPituitary adenoma (PA) is a common brain tumor located in a small cavity at the cranial base. It disrupts hormonal balance and compresses the optic nerves, leading to abnormal body growth, sexual dysfunction, vision loss, and mortality if untreated. Its surgical resection is highly challenging due to its small size, heterogeneous structure, deep location, and indistinct interface with surrounding nerves, arteries, and brain tissues. Mechanical properties of tumor tissues play a crucial role in their microstructure, growth, and progression. However, data on the mechanical properties of PA tissues is scarce. This study aims to provide detailed mechanical properties of various PA tissues and demonstrate the differences in stiffness between tumors and brain tissues. The viscoelastic properties and collagen content of postoperative PA tissues (n = 40) and normal human brain white matter (n = 7) were analyzed using in vitro nanoindentation and histological staining, respectively. Tumor consistency was also assessed preoperatively via magnetic resonance images (MRIs) and intraoperatively through surgeon feedback. PA tissues exhibited a considerable variation in viscoelastic properties; however, their average stiffness was significantly higher than normal brain white matter (p < 0.05). Tumors with firm consistency showed higher collagen content (29.8% ± 21.2%) than the soft (9.1% ± 8.1%) and medium (12.9% ± 9.7%) consistency tumors, however the correlation with mechanical properties was not strong (r = 0.40, p = 0.01). Strong correlations between preoperative predictions, intraoperative observations, and postoperative measurements emphasize the clinical relevance of these findings. These results underscore the potential of mechanical biomarkers to enhance surgical strategies, improve outcomes, and support applications in diagnosis, development of elastography and elastic image fusion algorithms, as well as in robot-assisted interventions
Grand challenges in industrial and systems engineering
No full textContemporary society faces a growing set of complex issues representing significant socioeconomic, health and well-being, environmental, and sustainability challenges. The discipline of industrial and systems engineering (ISE) can play an important role in addressing these issues. This paper identifies and discusses eight grand challenges for ISE. These grand challenges are (1) Artificial Intelligence (AI) For Business and Personal Use: Decision-Making and System Design and Operations, (2) Cybersecurity and Resilience, (3) Sustainability: Environment, Energy and Infrastructure, (4) Health Issues, (5) Social Issues, (6) Logistics and Supply Chain, (7) System Integration and Operations: Humans, Automation, and AI, and (8) Industrial and Systems Engineering Education. The discussed grand challenges were derived by accomplished ISE professionals who are the authors of this paper. The implications of the ISE grand challenges for education, training, research, and implementation of ISE principles and methodologies for the benefit of global society are discussed
Synthesis of synergistic catalysts: integrating defects, SMSI, and plasmonic effects for enhanced photocatalytic CO<sub>2</sub> reduction
No full textThis study explores how the strategic material design introduced synergetic coupling of strong metal–support interaction (SMSI) between copper (Cu) nanoparticles and titanium dioxide (TiO2) loaded on dendritic fibrous nanosilica (DFNS), defects within TiO2, and localized surface plasmon resonance (LSPR) of Cu. Mechanistic insights were gained using in situ high-energy radiation fluorescence detection X-ray absorption near edge structure (HERFD-XANES) spectroscopy, electron microscopy, and finite-difference time-domain (FDTD) simulations. The introduction of copper nanoparticles onto the TiO2 surface induces a change in the electronic structure and surface chemistry of TiO2, due to the electronic interactions between Cu sites and TiO2 at the interface, inducing SMSI. This resulted in enhancing light absorption, efficient charge transfer, reducing electron–hole recombination and enhancing the overall catalytic efficiency. The activation energy for CO2 reduction was significantly reduced in light as compared to dark. Control experiments revealed a dominant role of photoexcited hot carriers, alongside photothermal effects, in driving CO2 reduction, supported by super-linear light intensity dependence and reduced activation energies. The unique interplay of O-vacancy defects, electron–hole separation in TiO2 and LSPR effects in Cu led to the excellent performance of the DFNS/TiO2–Cu10 catalyst. The catalyst outperformed the reported photocatalytic systems with a CO production rate of ∼3600 mmol gCu−1 h−1 (360 mmol gcat−1 h−1) with nearly 100% selectivity. A reaction mechanism was proposed based on the intermediates observed using the in situ diffuse reflectance infrared Fourier transform spectroscopy (DRIFTS) and co-related to the electron transfer pathways to different reactants using HERFD-XANES. The study concluded that the synergistic coupling of Cu LSPR, charge carrier separation via SMSI at the Cu–TiO2 interface, and O-vacancy defects stabilized by SMSI enhance the photocatalytic CO2 reduction performance of this hybrid system
Mass balance of lake terminating Gepang Gath glacier (western Himalaya, India) and the role of glacier–lake interactions
No full textThe mass balance of lake-terminating glaciers responds to annual atmospheric variations, while calving-induced ice loss at the front is driven by local ice–water interactions. The current glaciological studies underestimate glacier response by neglecting the significant annual ice loss at the terminus through calving processes. This study integrates field measurements with remote sensing data to investigate the glaciological characteristics and proglacial lake evolution of the Gepang Gath glacier in the Chandra basin, Western Himalaya, India. Long-term observations reveal a continuous expansion of the proglacial lake from 0.21 ± 0.06 km2 (1962) to 1.21 ± 0.05 km2 (2023), along with terminus retreat of ∼2.76 km, attributed to calving at the ice–water interface. The glacier’s surface exhibits complex debris cover, with thicknesses up to 35 cm, creating significant spatial variations in surface mass balance. In-situ, glaciological measurements reveal a highly negative glacier-wide mass balance of −0.90 ± 0.30 m w.e. a−1 between the years 2014 and 2023. The geodetic estimates also reveal a negative mass balance of −0.61 ± 0.1 m w.e. a−1 over the past decade (2013–2023). The frontal area change (0.42 km2) and geodetic mass balance show a total volumetric ice loss of −21.77 × 106 m3 w.e. during the same period. Overall, the yearly frontal ice loss exacerbates the mass loss by 17–22%. These findings suggest that the presence of proglacial lakes plays a significant role in intensifying ice mass loss from Himalayan glaciers, strongly regulating their overall evolution
Role of phenology as discriminant in vegetation type mapping
No full textVegetation-type maps are crucial for landscape conservation and management, and provide essential data for decision-making. Due to cost and time constraints, traditional field-based methods have limitations in terms of scale and frequency in updates. The increasing availability of satellite sensors that offer high-temporal-frequency multispectral imagery has spurred the development of more efficient and accurate vegetation mapping techniques. Here, the potential of Sentinel-2 time series consisting of multiseason images was assessed to enhance the accuracy of the vegetation-type map, compared to a single image of the leaf-off season, using the Random Forest algorithm in the Similipal Biosphere Reserve, Odisha. Time-series imagery is pivotal for assessing vegetation phenology, offering comprehensive means to track temporal vegetation dynamics and discern seasonal patterns. The Biosphere Reserve is predominantly covered by tropical sal-mixed moist deciduous forest, followed by tropical sal-mixed dry deciduous and semievergreen forests. The classified output using Sentinel-2 time-series attained an overall accuracy of 97.1% with a kappa value of 0.96, whereas the classification based on a single image achieved 86.6% accuracy and a kappa value of 0.84. The overall accuracy of the output map suggests that leveraging phenological patterns extracted from multitemporal images, compared to a single image from the dry season, could enhance model performance by 9.7%
Even-Denominator Fractional Quantum Hall States in the Zeroth Landau Level of the Monolayer-like Band of ABA Trilayer Graphene
No full textEven-denominator fractional quantum Hall states (FQHSs) at half-filling are particularly intriguing due to their predicted non-Abelian excitations with non-trivial braiding statistics. Conventional theory suggests that such states primarily emerge in the first excited Landau level, a notion supported by existing experimental evidence. In this research article, we present an unexpected discovery of plausibly non-Abelian even-denominator FQHSs in the zeroth Landau level of Bernal-stacked trilayer graphene. Specifically, we observe robust FQHSs at filling factors ν = 5/2 and ν = 7/2, accompanied by their theoretically predicted Levin-Halperin daughter states at ν = 9/17 and ν = 7/13, respectively. Additionally, further away from these states, the standard Jain sequence of composite fermions (CFs) is detected. The even-denominator FQHSs and their corresponding daughter states strengthen with increasing magnetic fields, while the CF states weaken simultaneously. Interestingly, these even-denominator (and their daughter) FQHSs only appear at a finite displacement field, precisely when two Landau levels - originating from a monolayer-like band of trilayer graphene with distinct isospin indices - cross each other. We propose that the system’s lack of inversion symmetry leads to additional isospin interactions, enhancing Landau level mixing between these intersecting states and softening the short-range component of Coulomb repulsion, thereby stabilizing the even-denominator FQHSs. Our study challenges the current theoretical framework of even-denominator fractional quantum Hall states and expands the range of systems where they can be explored. It positions multilayer graphene as a promising platform for hosting Majorana excitations, potentially advancing fault-tolerant topological quantum computing
Modeling of Magnetic Local Time Asymmetry in Storm‐Time Low‐Latitude Geomagnetic Field Disturbances Due To Partial Ring Current
No full textThe intensity of storm-time disturbance in the ground magnetic field varies significantly at different longitudes due to the magnetic local time (MLT) dependent contributions from different magnetospheric and ionospheric currents. Local geomagnetic field disturbances at low-to-mid latitudes often deviate considerably from the global depression represented by symmetric geomagnetic storm indices (such as Dst/SymH/SMR). In this study, we quantitatively investigated the geomagnetic horizontal field depressions (ΔH) at different local time sectors, compared to the longitudinally averaged SuperMAG Ring current (SMR), at eleven low-latitude stations during a large number (665) of geomagnetic storms that occurred from 1996 to 2024. The relative disturbances (i.e., ΔH-SMR) exhibit significant asymmetry with respect to MLT, which further varies with storm evolution, intensity, and phase. The MLT asymmetry of ΔH grows rapidly in the early main phase and then grows gradually with storm intensity. Further, the MLT sector of weakest/strongest ΔH depression shifts from post-dawn/post-dusk to pre-dawn/pre-dusk periods as storm intensity increases. Finally, an empirical model is derived that can quantitatively represent the MLT variations in the low-latitude ΔH disturbances during geomagnetic storms. This model is very useful in estimating the low-latitude geomagnetic field disturbances at different longitudes/MLT sectors from the global SMR index and can have significant applications in space weather studies