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OpenAI, Google, and Microsoft have recently developed popular large language models (LLMs) with incredible clinical applications. LLMs specific to neurosurgery, such as AtlasGPT, have also been recently released. However, the comparative neurosurgical diagnostic capabilities of these models are not well studied. The aim of this study was to evaluate and compare the ability of LLMs to diagnose neurosurgical pathologies. Clinical vignettes (n = 148) extracted from a common neurosurgery case-based review textbook were stratified by subspecialty. OpenAI's ChatGPT-3.5 and ChatGPT-4, Google's Gemini, Microsoft Copilot, and AtlasGPT were prompted to provide a diagnosis: "Provide a neurosurgical diagnosis given the following history…[vignette]." Imaging was inputted for capable LLMs, and all queries were run in May 2024. Diagnoses were compared with the textbook for accuracy and errors were categorized appropriately. ChatGPT-4 was the most accurate model (74% correct), followed by AtlasGPT (63% correct), ChatGPT-3.5 (53% correct), Microsoft Copilot (48% correct), and Gemini (36% correct). Chi-square comparisons demonstrated that ChatGPT-4 was more accurate in providing clinical diagnoses than its counterparts (p = 0.005). Across all vignettes and LLMs, most errors were due to an inability to attribute a key piece of information (generally imaging data) to the diagnostic process while otherwise using logical stepwise reasoning. ChatGPT-4 offered the most accurate diagnoses when given established clinical vignettes. Adding imaging processing capabilities and relevant data significantly increased the accuracy of LLM diagnoses. LLMs can offer accurate assessments of common neurosurgical conditions but necessitate detailed prompting from clinicians. Artificial intelligence has incredible clinical potential; however, practitioners must be cautious and think critically while using them for diagnostic purposes.

People in substance use disorder recovery represent a marginalized and underserved student population. Improving access to higher education for people in recovery can benefit the individual, institution, and society at large. This study explored the barriers to college applications and admissions that people in recovery face and the resources that they draw on to overcome these barriers. Semistructured interviews were completed with 17 undergraduate students in recovery at a large public university without a collegiate recovery program. Inductive thematic analysis was used to document students' experiences related to applications and admissions. Participants described barriers to higher education at multiple levels, including personal challenges (e.g., competing priorities), consequences of past use (e.g., a record of criminal legal system involvement), challenges in the social environment (e.g., substance use on campus), complex admission processes (e.g., regarding transfer credits), and stigma (i.e., anticipated, experienced, and internalized). Despite these challenges, students embodied an incredible amount of resolve. Participants were unafraid to ask for help and mobilized the resources available through their social networks. Having role models in recovery, support from faculty and staff, and flexible options for course scheduling were discussed as facilitators to higher education access. Findings highlight policies and practices that institutions of higher education can adopt to capitalize on the strengths of prospective students in recovery and promote equity and inclusivity for this underrepresented student population.

Parental care in animals consists of an incredible diversity of forms, providing powerful opportunities for studying the proximate basis of behavior. Three-spined stickleback fish, a classic model system in ecology, evolution, and behavior, are good subjects for studying the neurobiological basis of paternal care as male sticklebacks are solely responsible for providing care to their developing offspring. Here we review what is known about the neural, neuroendocrine, genetic, and molecular basis of paternal care in sticklebacks, highlighting the ways in which natural phenotypic variation within and among populations has been leveraged to improve our understanding of the proximate basis of behavioral variation. For example, diversity in care among populations, including the complete evolutionary loss of parental care in the so-called "white" sticklebacks, provides insight into the proximate mechanisms by which highly divergent behavior in whites diverged so rapidly and dramatically from the ancestral care-giving form. Moreover, comparisons within populations across stages of care reveal deeply conserved neural, genomic, and neuroendocrine mechanisms regulating paternal and maternal behavior across vertebrates. Moving forward, improved neuroscientific tools and resources for sticklebacks will enable functional manipulations and in vivo studies, opening up novel opportunities to address outstanding questions about the origin of behavioral diversity.

Urea electrolysis is promising for energy-saving hydrogen production and effective treatment of urea-polluted water. However, the activity and stability of Ni-based electrocatalysts for the anodic urea oxidation reaction (UOR) are limited by the lack of active NiOOH and strong intermediates adsorption. Although oxygen vacancies (Ov) benefit for the UOR, continuous generation and stabilization of Ov remain challenges. Herein, we propose an "amorphous oxygen mechanism (AOM)" for urea electrooxidation in amorphous nickel tungsten oxide (NiWOx) with tunable Ov concentrations. Systematic experimental and theoretical studies demonstrate that enriched Ov, not only facilitate the formation of active NiOOH species, but also significantly reduce the energy barrier of the rate-determining step. More importantly, the amorphous state allows more defects, which enables the in situ regeneration and sustainability of Ov during the UOR via a continuous oxygen escape in the amorphous catalyst. Notably, NiWOx with the highest Ov achieves an ultralow potential of 1.34 V at 10 mA cm-2 with incredible 400 h stability. Moreover, only 1.46 V is demanded for urea electrolysis with 100 mA cm-2 in an anion-exchange membrane electrolyzer. The long-term stability is also impressive. This work highlights the significant role of amorphous structure, providing valuable insights into catalyst design in electrocatalysis.

Cubic spinel high-entropy oxides are compositionally complex, multifunctional materials that continue to garner incredible research interest. They offer tunable magnetic properties, enhanced catalytic activity, and structural stability in oxygen evolution reactions. They have shown improved cyclability as anodes in Li-ion battery research when compared to their binary counterparts. However, due to the high number of elements in spinel high-entropy oxides, a systematic study that links the improved physical properties to the atomic and electronic structures of these materials is challenging. Atomic and electronic structures are heavily tied to the cation distribution in cubic spinel oxides. Yet very little research is focused on determining the cation distribution in cubic spinel high-entropy oxides. This work seeks to bridge this gap and demonstrates the power of resonant X-ray diffraction (ReXRD) in determining the cation distribution in the Co-free (Cr,Mn,Fe,Mg,Ni)3O4 composition. Results from ReXRD showed pronounced cation site selectivity in (Cr,Mn,Fe,Mg,Ni)3O4. Ni and Cr were found to prefer the octahedral site, while Mg was predominantly found in the tetrahedral site of the cubic spinel structure. X-ray absorption spectroscopy and neutron total scattering revealed Ni cation clustering and local lattice distortions that were averaged on long length scales.

Among the many diverse traits of insects, the most speciose and successful terrestrial animals, is an incredible range of lifespans. While some are quite long-lived, such as cicadas (years as larvae) or termite queens (decades as adults), many insects, such as mayflies (Insecta: Ephemeroptera), have exceedingly short adult lifespans that serve essentially just for mating and selecting oviposition sites, foregoing feeding to reproduce as quickly as possible. This behavior is correlated with mouthparts that are highly reduced or even absent, and the alimentary system is converted into an air-filled space, possibly non-functional for digestion. The order-wide phenomenon of the co-opted gut is found in only one other group, the twisted-wing parasites (Insecta: Strepsiptera). Here, we present micro-CT scans and volume measurements (body and alimentary air) that reveal a previously undocumented inflated alimentary system in non-feeding species from five additional insect orders: Plecoptera, Embioptera, Megaloptera, Lepidoptera, and Diptera. The association between reduction or complete loss of adult feeding and a large volume of air in the gut is statistically highly significant. The reduction of mouthparts in these taxa reflects non-feeding in adults, indicating that convergence of this trait with alimentary inflation probably has adaptive functions. We discuss several, nonexclusive ways in which an inflated alimentary system can be adaptive for adult insects.

Mobile manipulators show incredible promise as domestic service robots - interacting with a wide variety of objects using increasingly affordable hardware. But although perception, manipulation, and mobility have advanced, there remain fundamental challenges in making robots more useful. How can a robot proactively identify tasks that it can complete while supporting individual human preferences for how a home should be configured? We propose using foundation models to first detect what has changed and then select appropriate tasks for the service robot. Change affords action. Only those objects that have been interacted with need to be considered for tasking. Other objects, even if located in non-standard positions in the house, can be ignored. Open-vocabulary based object detection and neural radiance field models are used to identify changes corresponding to fixed phrases. Large language models then validate which tasks should be completed by the robot. Experiments are conducted on data collected by both mobile phone and Stretch 2 Mobile Manipulator, demonstrating general applicability to a wide range of applications in the home.

Leishmaniasis is a devastating neglected tropical disease (NTD) that has affected millions of vulnerable populations globally. Despite incredible pharmaceutical innovations, the existing anti-leishmanial therapy faces several pitfalls, such as drug resistance, toxicity, selectivity, cost concerns, etc. In our efforts to identify new chemical scaffolds, the present investigation scrutinizes the anti-leishmanial potency of the synthesized coumarin-oxime ether derivatives against Leishmania donovani parasites. The primary screening shortlisted six potential molecules (5a, 5d, 5i, 5j, 5k, and 5n) with IC50 values in the range of 4.97 to 28.72 µM. Among them, two potent hits (5j and 5k) demonstrated a promising cytocompatibility profile coupled with significant effectiveness in clearing the intracellular amastigote burden. Mechanistic insights elucidated that the potent hits (5j and 5k) orchestrated significant cell membrane disruption, escalated cellular ROS, alteration in mitochondrial membrane potential (ΔΨm), ATP depletion, lipid accumulation, and cell cycle disruption culminating in parasitic mortality. Furthermore, computational analysis revealed a dual-targeting strategy wherein the identified molecules (5j and 5k) inhibit the Sterol C-24 methyl transferase (SMT) and Sterol 14-α demethylase (SDM) protein of the ergosterol biosynthesis pathway in Leishmania parasites. Altogether, the present research work highlighted coumarin derivatives as a promising scaffold for developing new, safe, and effective chemotherapeutics to cure leishmaniasis.

The NIH Revitalization Act was passed by the United States Congress in 1993 and mandated that researchers include women and minorities in clinical research. However, the extent that researchers have adhered to the NIH Revitalization Act in randomized controlled studies of parent training programs for treatment of child disruptive behavior is unknown. This systematic review examined how minority inclusion was addressed in randomized controlled trials (RCTs) of parent management training for child disruptive behavior. This review focused on a variety of parent training programs such as The Incredible Years, Parent-Child Interaction Therapy, and Coping Power. The researchers identified 35 studies that met the inclusion criteria. When ethnic and racial minority data was analyzed, it was found that 44% of participants were White; 13.16% were African American; 18.41% were Latinx; 2.91% were Asian; and 5.85% were reported as "other." Ethnic and racial data was not reported for 15.96% of participants. Overall, racial and ethnic inclusion in RCTs for parent training programs has improved in recent years and there is proximal concordance between population make-up and representation of African Americans and (to a lesser degree) Latinxs in parenting intervention RCTs. However there remains a need for improved inclusion that more closely represents the racial and ethnic make-up of the general population for other groups (e.g., Asians and Indigenous populations).

Metal-organic frameworks (MOFs) are represented as the potential candidates of photo and electro-catalytic CO2 reduction due to their incredible surface area, phenotypic structures, varied porosity and defined morphologies. Nevertheless, experimental usage of pristine MOFs in the reduction of CO2 is difficult, as they are inherently low-conductive in electricity, unstable in their makeup, and experience weak metal-oxygen interaction. The core concepts of photo and electro-catalytic CO2 reduction are first described in this review to give the background to the understanding of MOF-based catalytic systems. It then critically discusses the structural properties of MOFs and its derivatives and identifies the key shortcomings which limit catalytic performance. To address these issues, the recent developments of MOF-based composite catalysts have been discussed, such as MOF@graphene, MOF@metal oxides, MOF@MXene, and MOF@layered double hydroxide (LDH). These composites exhibit better conductivity, greater stability, better metaloxygen interactions and better catalytic strength than pristine MOFs. Finally, the development of the area occurs currently is summarized, and the future prospects are given, with rational designing of effective, durable, and scalable MOF-based composite catalysts towards sustainable CO2 reduction.

As the usage of digital financial transactions continue to swell, it becomes all the more crucial to employ classifiers with machine learning techniques in order to process credit card fraud detection. While complex ensemble models can reach incredible levels of predictive accuracy, the black-box nature of these algorithms often leaves us at a loss, and there hasn't been much research on how interpretable these high-performing models are across different settings. To contribute to this research gap, this study assesses four supervised learning algorithms (Logistic Regression, Random Forest, XGBoost and LightGBM) based on their predictive performance and applicability with Explainable Artificial Intelligence (XAI) techniques. To optimize the generalizability of their findings, the models were extensively tested and compared against three disparate public credit card transaction datasets. The performance, as measured by different metrics such as accuracy, precision, recall, F1-score and ROC-AUC gives the best results to tree-based algorithms ensembles (especially XGBoost) with linear methods also providing decent improvement over baseline models. Data used as input for training consisted of the top-performing models, and then SHAP (SHapley Additive exPlanations) framework was applied to help identify leading feature importance and interpret complicated predictive output. This study provides a comprehensive outline linking predictive performance to explainability in every combination of models, yielding impactful results for developing effective, transparent and accountable financial security systems.

Mission control, we need a read! Michael Pohlen, MD, joins cohosts Lindsey Negrete, MD, and Amy Maduram, MD, to discuss his incredible journey into space medicine, including engagement with the team leading the first radiograph obtained in space, medical conditions occurring during weightlessness, and the commercial shift into space research.

Notwithstanding the notable development achieved in the asymmetric and photochemical construction of spirooxindoles comprising heterocyclic compounds, constructing spirooxindoles containing carbocyclic compounds has not been extensively explored. Drawing inspiration from the incredible structural landscape and pharmaceutical activities associated with spirooxindoles, the synthesis of spirooxindoles comprising carbocyclic compounds to provide unique reactivity and activity is highly desirable. This mini-review article aims to give a brief outline of the visible-light-induced photochemical approaches established over the last decades for accessing spirooxindoles, comprising different ring-size carbocyclic scaffolds. Beyond outlining the development achieved to date, we have emphasized the reaction mechanism and highlighted the limitations associated with the reactions, providing insights for future advancement.