Ridge B is one of the least studied areas in Antarctica but has been considered to be a potential location for the oldest ice on Earth. Among important parameters for calculating where very old ice may exist, geothermal heat flux (GHF) is critical but poorly understood. Here, GHF is determined by quantifying the transitions between dry and wet basal conditions using a radioglaciological method applied to airborne radio-echo sounding data. GHF is then constrained by a thermodynamic model matched to the transitions. The results show that GHF in Ridge B varies locally and ranges from 48.5 to 65.1 mW m−2, with an average value of 58.0 mW m−2, which is consistent with the current known GHF constrained by subglacial lakes and derived from Vostok ice core temperature measurements. Our work highlights the value of considering local GHF when locating the oldest ice in this potential region or other regions.

Background: Neurosurgical conditions impose a significant burden on the Canadian healthcare system. This study quantifies the economic impact and explores predictive models for postoperative length of stay. Methods: We analyzed data from the Canadian Institute for Health Information National Health Expenditure Trends database for 2015-2019, focusing on case volumes, healthcare costs, and lengths of stay (LOS) across age groups and conditions. Decision tree models were created to predict total LOS from patient age and average acute LOS. Results: There was a modest increase in case volumes from 6,220 ± 3,103 in 2015 to 6,492 ± 3,240 in 2018, with a slight decrease in 2019. The total estimated hospital costs ranged from 2.27 ± 0.38 million CAD in 2015 to 2.23 ± 0.44 million CAD in 2019. The highest costs were seen in the 18-59 age group, at 2.53 ± 0.43 million CAD. Decision tree models showed high accuracy for predicting LOS in cases like spinal injury (F1-score: 0.98) but were less accurate for interventions with trauma or complications (F1-scores from 0.66 to 0.97). Conclusions: The study delineates the financial demands of neurosurgery in Canada and suggests decision tree models as useful tools for predicting hospital stay, with variable accuracy depending on the case complexity.

Background: The formation of pial-pial collateral network aneurysms due to carotid occlusion is a rare neurological phenomenon. This case details a 69-year-old male who developed a pial-pial collateral network aneurysm secondary to left internal carotid artery occlusion, leading to intracranial hemorrhage. Methods: The patient presented with altered consciousness due to left temporal intracerebral hemorrhage, subdural hematoma, and intraventricular hemorrhage. Cerebral angiography revealed an occluded left internal carotid artery, with superficial temporal artery (STA) and superior orbital artery anastomosis, and extensive pial-pial collaterals from the posterior temporal artery. A 4 mm aneurysm arising from this collateral network was identified. Surgical intervention involved a left temporal craniectomy and excision of the aneurysm, prioritizing the preservation of the STA. N.B., Informed patient consent was obtained in this study. Results: Successful aneurysm removal and preservation of collateral pathways were confirmed by postoperative imaging. The patient exhibited rapid neurological improvement; by postoperative day (POD) one, the patient showed limited response to stimuli. He was extubated by POD4 and discharged on POD27, where he conversed well, was independently ambulatory, and needed minimal to no assistance in activities of daily living. Conclusions: This case highlights the need for careful preoperative planning and intraoperative precision, especially in preserving vital collateral vascular pathways.

Background: The integration of Artificial Intelligence (AI) in medical education is an area of growing importance. While AI models have been evaluated extensively in multiple-choice question formats, their proficiency in written exams remains to be explored. Methods: Four AI models—GPT-4 (OpenAI), Claude-2.1 (Anthropic), Gemini Pro (Google), and Perplexity 70B (Perplexity)—were tested using the Canadian Royal College Sample Neurosurgery Exam. The written exam covered diagnostic reasoning, knowledge of neurosurgical conditions, and understanding of radiographic imaging techniques. Results: GPT-4 and Perplexity 70B both achieved a score of 68.42%, followed by Claude-2.1 with 60.53%, and Gemini Pro with 57.89%. The models showed proficiency in answering questions that required factual knowledge, such as identifying pathogens in spinal epidural abscess. However, they struggled with more complex diagnostic reasoning tasks, particularly in explaining the pathophysiology behind a sudden rise in blood pressure during surgery and interpreting radiographic characteristics of intracranial abscesses on MRI. Conclusions: The findings indicate that while AI models like GPT-4 and Perplexity 70B are adept at handling factual neurosurgical questions, their performance in complex diagnostic reasoning in a written format is less consistent. This underscores the need for more advanced and specialized AI training, particularly in the nuances of medical diagnostics and decision-making.

Background: Chordomas are rare, malignant bone tumors that present significant challenges in management and treatment due to their complex anatomical locations and propensity for recurrence. Advancements in artificial intelligence (AI) and machine learning (ML) show promise in improving chordoma management. Methods: A comprehensive literature search was conducted following PRISMA guidelines across multiple databases, including MEDLINE, Cochrane, Embase, Scopus, and Web of Science. The search targeted articles related to AI and ML applications in clinical tasks associated with chordoma management. The selection process involved systematic screening, data extraction, and assessment of inter-rater variability. Results: The search yielded 1,006 records, with 18 included for analysis. Convolutional neural networks (CNNs) excelled in tumor volume estimation, with the state-of-the-art model achieving a Dice similarity score of 74.2%, sensitivity of 79.4%, and positive predictive value of 74.3%. Clustering algorithms were effective in prognostic evaluations. Bayesian models and logistic regression demonstrated robustness in diagnostics. Support vector machines (SVMs) were noted for their diagnostic precision. Conclusions: AI and ML algorithms, particularly CNNs, clustering algorithms, Bayesian models, logistic regression, and SVMs, show promise in improving chordoma management through enhanced imaging, diagnostics, and prognostics. Future research should focus on larger, externally validated datasets and explore underutilized techniques like multi-modal data integration.

Background: Sex is associated with differences in early outcomes with preterm males at greater risk for mortality and morbidity. The objective of this study was to examine preterm sex differences in neurodevelopmental outcomes and brain development from early-life to 8-years. Methods: A prospective cohort of preterm infants born 24-32 weeks gestation were followed to 8-years with standardized measures. MRI scans were performed after birth, term-equivalent age and 8-years. Associations between sex, risk factors, brain volumes, white matter fractional anisotropy (FA) and outcomes were assessed using generalized estimating equations. Results: Preterm males (N=83) and females (N=72) had similar risk factors, brain injury and pain exposure. Sex was a predictor of cognitive scores (P=0.02) and motor impairment (P=0.03), with males having lower cognitive scores and higher motor impairment over time. There was a sex effect for FA (P=0.04), with males having lower FA over time. There were significant sex-brain injury and sex-pain interactions for cognitive and motor outcomes. Conclusions: In this longitudinal study, preterm males had lower cognitive scores and greater motor impairment, which may relate to differences in white matter maturation. Effects of brain injury and pain on outcomes is moderated by sex, indicating a differential response to early-life adversity in preterm males and females.

Background: Intraoperative testing for awake craniotomies requires a multidisciplinary team which may not be available in low-resource settings. We explored the creation of an AI tool for automated testing. Methods: We developed a NodeJS application, EloquentAid (https://www.eloquentaid.com/), for language testing automation. The workflow was as follows: users select an image-based naming task and verbally identify the image in English. Then, the application transcribes the response using OpenAI’s Whisper transcription service. Finally, the application evaluates response correctness. Feedback is provided through auditory and color signals. To assess its reliability, we tested the EloquentAid versus a human rater using a 57-item test based on the Boston Naming Test. Participants were neurosurgery and neurology residents from the Philippines. Qualitative surveys were obtained post-test. Results: A total of 798 observations were recorded (N=14). Human-application agreement was 60.52%. Cohen’s kappa was 0.31 (fair agreement). There were no false positive identifications by EloquentAid. Noun-type was felt to affect human error (i.e. “knocker,” “yolk,” “trellis”). Accent and pronunciation were felt to affect EloquentAid errors. Conclusions: EloquentAid is a promising tool to facilitate intraoperative testing and brain mapping using AI for speech recognition and response evaluation. Preliminary data shows fair human-app agreements. Improvements in test items and pronunciation recognition may be made.

Echinococcosis poses a significant threat to public health. The Chinese government has implemented prevention and control measures to mitigate the impact of the disease. By analyzing data from the Chinese Center for Disease Control and Prevention and the State Council of the People’s Republic of China, we found that implementation of these measures has reduced the infection rate by nearly 50% between 2004 to 2022 (from 0.3975 to 0.1944 per 100,000 person-years). Nonetheless, some regions still bear a significant disease burden, and lack of detailed information limites further evaluation of the effects on both alveolar and cystic echinococcosis. Our analysis supports the continuing implementation of these measures and suggests that enhanced wildlife management, case-based strategies, and surveillance systems will facilitate disease control.

Competition among the two-plasmon decay (TPD) of backscattered light of stimulated Raman scattering (SRS), filamentation of the electron-plasma wave (EPW) and forward side SRS is investigated by two-dimensional particle-in-cell simulations. Our previous work [K. Q. Pan et al., Nucl. Fusion 58, 096035 (2018)] showed that in a plasma with the density near 1/10 of the critical density, the backscattered light would excite the TPD, which results in suppression of the backward SRS. However, this work further shows that when the laser intensity is so high ($>{10}^{16}$ W/cm2) that the backward SRS cannot be totally suppressed, filamentation of the EPW and forward side SRS will be excited. Then the TPD of the backscattered light only occurs in the early stage and is suppressed in the latter stage. Electron distribution functions further show that trapped-particle-modulation instability should be responsible for filamentation of the EPW. This research can promote the understanding of hot-electron generation and SRS saturation in inertial confinement fusion experiments.

A multiple-vehicles time-coordination guidance technique based on deep learning is suggested to address the cooperative guiding problem of hypersonic gliding vehicle entry phase. A dual-parameter bank angle profile is used in longitudinal guiding to meet the requirements of time coordination. A vehicle trajectory database is constructed along with a deep neural network (DNN) structure devised to fulfill the error criteria, and a trained network is used to replace the conventional prediction approach. Moreover, an extended Kalman filter is constructed to detect changes in aerodynamic parameters in real time, and the aerodynamic parameters are fed into a DNN. The lateral guiding employs a logic for reversing the sign of bank angle, which is based on the segmented heading angle error corridor. The final simulation results demonstrate that the built DNN is capable of addressing the cooperative guiding requirements. The algorithm is highly accurate in terms of guiding, has a fast response time, and does not need inter-munition communication, and it is capable of solving guidance orders that satisfy flight requirements even when aerodynamic parameter disruptions occur.

This study sought to investigate Chinese farmers’ attitude towards animal welfare by using the Theory of Planned Behaviour (TPB). According to the TPB, an individual's intention to behave in a certain way is determined by his/her attitude towards the behaviour (specific attitude — importance — and general attitudes), the perceived behavioural control (easiness), and the supposed opinion of the people who are important to him/her (subjective norms). A total of 253 questionnaires were used, which included the three main animal productions in China (swine, poultry and cattle). Chinese farmers have perceived the improvement of animal welfare as two abstracts: general attitudes (reward-seeking, and empathic farmer); and four specific categories of actions (favourable environment, animal health, humane treatment of animals and farmers’ well-being). Our analysis revealed that general and specific attitudes were the strongest predictors of farmers’ intentions to improve animal welfare in the questionnaire study. In fact, Chinese farmers considered it fairly important to improve the animal welfare measures considered in the survey. In contrast, the same animal welfare measures were considered difficult to improve by the farmers as indicated by the lack of association between the easiness of improving animal welfare and the intentions. In addition, veterinarians, agricultural advisers, and scientific experts were considered to be relatively influential subjective norms as regards the activities of the farmers. This is the first study to provide an insight into the underlying meanings and values of Chinese farmers’ views on improvements to animal welfare.

Nonalcoholic fatty liver disease (NAFLD) is a complex metabolic-inflammatory disease associated with poor outcomes and decreased quality of life. NAFLD is overrepresented in patients with psychiatric disorders like depression, bipolar disorder, and schizophrenia; however, a comprehensive review on NAFLD and psychiatric disorders remains to be delineated. This review endeavors to investigate the association of NAFLD with psychiatric disorders, including shared pathogenesis and future clinical derivatives. Extant literature suggests that patients with psychiatric disorders (in particular, mood disorders) are more susceptible to the development of NAFLD due to multiple reasons, including but not limited to hypothalamic–pituitary–adrenal axis dysregulation, metabolic syndrome, and chronic perceived stress. Moreover, the clinical manifestations of mood disorders (e.g., anhedonia, psychomotor retardation, lifestyle modification, etc.), and potentially long-term treatment with weight-gaining agents, differentially affect these patients, making them more prone to NAFLD. Considering the increased morbidity associated with both mood disorders and NAFLD, our review recommends regular screenings for NAFLD in select patients with mood disorders exhibiting signs of increased risk (i.e., obesity, metabolic syndrome, diabetes, or family history of NAFLD) for better diagnosis and holistic care of both potentially interrelated conditions.