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Relational primitives can facilitate rapid inductive generalization beyond individual mental-state reasoning. Computational models of social cognition should incorporate social relationships as core primitives, enabling richer explanations for how minimal social observations lead to flexible, efficient, and culturally adaptive social predictions.
Performance testing is a critical tool for achieving genetic progress, yet its long-term effectiveness in primitive breeds under conservation programs is poorly documented. The Konik Polski horse breeding program aims to improve utility while preserving primitive characteristics. This retrospective study analyzed a 25-year longitudinal dataset (n = 1,608) from the official Field Performance Test to quantify genetic trends in gait efficiency, using nonparametric methods to assess the effects of test type, year, and age on stride length and speed at the walk and trot. To estimate heritability, a single-trait sire model and a linear mixed model were fitted. A significant positive trend was observed for walk stride length, which increased by approximately 10 cm under saddle and 19 cm in harness over the study period (p < 0.001). In contrast, trot stride length showed high variability but no consistent trend, whereas trot speed declined. A strong negative correlation between trot stride length and trot time (ρ =  - 0.51) confirmed that longer strides were associated with faster movement, which is a desirable trait. The analysis revealed moderate heritability for walk stride length (h 2 = 0.277). Genealogical line accounted for a negligible proportion of the total phenotypic variance for most traits, and age at testing did not significantly influence performance. Walk- and trot-related performance traits were not highly heritable in Konik Polski horses. Overall, the results indicate that the level of preparation is the most critical factor.
Three-dimensional graphene metamaterials based on triply periodic minimal surfaces (TPMS) can achieve exceptional mechanical properties; however, the P-type Schwarz Primitive surface remains largely underexplored. For the first time, we quantified how unit cell size (30 Å, 45 Å, 60 Å), architectural dimensionality (0D, 1D, 2D), and tensile strain rate govern the uniaxial strength and fracture strain of pure carbon P-type multidimensional structures. Reactive molecular dynamics simulations reveal that both strength and fracture strain increase significantly as the unit cell size decreases. Furthermore, these structures exhibit distinct mechanical responses depending on their dimensionality, demonstrating a pronounced size effect. Additionally, strain rate plays a critical role: moderate strain rates facilitate atomic rearrangement and the formation of truss-like load-bearing networks, thereby enhancing load-bearing capacity. These findings establish clear structure-property relationships for scalable, ultralight carbon nano-architectures. All atomistic tensile tests were performed with LAMMPS using the AIREBO potential to describe C-C interactions. P-type Schwarz Primitive surfaces were generated with an in-house protocol that hexagonalizes a Delaunay triangulation without Voronoi tessellation. After energy minimization and 50 ps NVT equilibration at 300 K with a Nosé-Hoover thermostat, uniaxial tension was applied in strain-control mode while the lateral cell faces remained traction-free. Stress-strain curves and atomic fracture sequences were analyzed with OVITO.
The broad range of commentaries addressed central debates in the origins, nature, and development of relationship knowledge. These debates mirror those that have played out in other domains: do infants possess innate, abstract primitives for representing relationships? Or is their understanding primarily the result of learning? Should we consider infants' behavior in experiments a reflection of abstract cognitive concepts or as reflexes or embodied knowledge? The commentaries also raise questions about other possible cognitive primitives as well as the evolutionary roots of such knowledge. What is the result of statistical learning, and what are inductive biases? Ultimately, these commentaries examine how innate representations of relationships may facilitate the resolution of other learning challenges, including the acquisition of cultural norms, institutional roles, group dynamics, and moral principles. Ultimately, I argue that advancing our understanding will require integrative approaches drawing on developmental, comparative, computational, and cultural research. This exchange clarifies many pressing empirical puzzles, setting the stage for a research program that I hope others across disciplines and theoretical standpoints will join in.
The tripartite relationship model is unlikely to reflect how relationships are represented in the mind, given considerable difficulty in wresting clear conceptual distinctions from it. Instead, it offers a descriptive framework for organizing findings. Here, we present examples of conceptual difficulties of the model and elaborate on the functional importance of third-party inference based on our agent-based modeling work.
The persistent primitive hypoglossal artery (PPHA) is a rare and anatomically complex cerebrovascular variant. Although typically asymptomatic, its presence is associated with various cerebrovascular pathologies, including intracranial aneurysms, carotid artery stenosis, acute large vessel occlusion, moyamoya disease, brain arteriovenous malformations, and other vascular anomalies. When such lesions involve the PPHA, therapeutic intervention may be necessitated. Surgical management is particularly challenging due to the artery's deep anatomical location and intricate surrounding vasculature. Consequently, endovascular therapy (EVT) has emerged as a preferable alternative to open surgery, offering a favorable safety profile and reduced technical complexity. Despite this, a substantial need remains in the literature regarding systematic evaluations of the PPHA's clinical significance in vascular pathology and the efficacy of EVT. This review aims to address this need through a comprehensive narrative synthesis of available literature and clinical experience in managing these complex cases. This review found that when EVT is required, the PPHA can serve as an access route. However, given that it often provides the sole blood supply to the posterior circulation-particularly in the context of bilateral hypoplastic vertebral arteries-the vessel must be meticulously preserved during interventions for associated conditions.
Human motion provides a valuable source of information for robotic skill acquisition, and Learning from Demonstration (LfD) has been widely adopted as an intuitive paradigm for enabling robots to learn tasks from human demonstrations. However, the lack of an explicit representation of transferable motion knowledge significantly limits the adaptability of LfD when tasks involve varying spatial constraints or environmental configurations. To address this challenge, this paper proposes a motion representation framework based on two fundamental properties of motion and introduces a novel Motion Knowledge Transferring Gaussian Mixture Model (MKT-GMM) for trajectory generalization across related tasks. In the proposed framework, demonstration trajectories from a source task are first collected through kinesthetic teaching and encoded using a Gaussian Mixture Model (GMM), where each Gaussian component represents a local motion primitive. Transferable motion knowledge is captured by jointly preserving the statistical characteristics of individual motion primitives and the geometric relationships between adjacent primitives. For a target task in which only task constraints are specified, the learned motion knowledge is transferred by adapting the GMM parameters through affine transformations combined with constraint-error minimization, enabling feasible trajectories to be generated without additional demonstrations or model retraining. The final motions are reconstructed using Gaussian Mixture Regression (GMR), ensuring smooth and consistent trajectory generation. To further improve the robustness of trajectory transfer, a pseudo via-point mechanism is introduced to automatically generate intermediate constraints when explicit via-points are unavailable. Experiments conducted on a robotic manipulation platform, including handwriting motion learning and pick-and-place tasks under varying task configurations, demonstrate that the proposed method effectively captures transferable motion knowledge and achieves reliable trajectory generalization for previously unseen tasks.
The emergence of membrane boundaries represents a decisive transition in the origin of life, yet the molecular nature of the earliest abiotic membranes remains uncertain. Existing models based on simple fatty acids, while experimentally tractable, often lack the environmental robustness required under fluctuating prebiotic conditions. Furthermore, the absence of clear pathways linking primitive amphiphiles to later phospholipid systems highlights the need for chemically continuous intermediate frameworks. Here, we explore borate-bridged amphiphile-carbohydrate conjugates as plausible intermediates between simple prebiotic surfactants and modern lipid bilayers. These conjugates arise from low-molecular-weight polyols-including glycerol, butane-1,2,3,4-tetraol, pentane-1,2,3,4,5-pentaol, and hexane-1,2,3,4,5,6-hexitol-reacting with long-chain alkyl ethers and borate species under alkaline conditions, enabling reversible coupling to ribose and other vicinal diol-containing sugars. This chemistry integrates three essential properties for early compartmentalization: hydrolytically robust ether-linked hydrophobic domains, multivalent and highly hydrated headgroups, and environmentally responsive borate coordination. Comparative physicochemical analysis suggests that single-tail alkylglycerol derivatives preferentially form micelles and interfacial films, while di- and tri-tail tetritol and pentitol conjugates favor lamellar assemblies and vesicle formation across realistic prebiotic pH and salinity ranges. Hexitol-based systems, particularly those bearing three hydrophobic chains, may act as membrane-stabilizing components that enhance rigidity and reduce permeability under extreme conditions. We propose that heterogeneous mixtures dominated by two-tail polyol diethers, supplemented by tri-tail stabilizers and surface-active alkylglycerols, could provide mechanically robust, pH-tunable, and sugar-decorated abiotic membranes. Such borate-mediated amphiphiles offer a chemically coherent framework linking carbohydrate stabilization, ether lipid persistence, and dynamic self-assembly, potentially representing a transitional stage in the evolutionary pathway from primitive amphiphilic films to biologically encoded membranes.
Diamonds crystallise from fluids/melts circulating in the Earth's mantle. Analysis of these fluids is possible if they remain entrapped in the diamond during growth, but such fluid inclusions are rarely observed in gem-quality stones. We investigated thin films surrounding mineral inclusions, previously described as silicic fluid rims containing Si2O(OH)6 and Si(OH)4, in gem-quality lithospheric diamonds from Siberia. Using micro-Raman spectroscopy and Laser-Ablation Inductively-Coupled-Plasma-Mass-Spectrometry (LA-ICPMS) depth-profiling, we obtained compositional data from silicic fluid rims surrounding both silicate and non-silicate inclusions. Slow LA-ICPMS depth-profiling at the diamond-inclusion interface enabled differentiating the respective diamond/fluid-rim/mineral-inclusion contributions, allowing detection of Sr, Nb, Ba, La, Ce, Nd, and Th from the fluid rim. Here, we compare silicic fluid rims and other mantle-derived fluids/melts based on trace element ratios relative to the primitive mantle. Their (La/Nb)N_(Ba/Nb)N and (Nd/Nb)N_(Th/Nb)N systematics align with primitive mantle-like high-density fluids and group 2 kimberlites, suggesting an origin from kimberlite-like melts.
Multistate mechanisms underlie many of the complex functions observed in natural proteins. The ability to rationally design multistate proteins would have transformative implications for many areas of biotechnology, yet lies beyond the capabilities of existing deep learning frameworks for protein design. To address this gap, we introduce SwitchCraft, a versatile and programmatic framework for designing state-switching proteins based on backpropagation through compositional design constraints parameterized by structure prediction models. In silico evaluations demonstrate success on a wide range of state-switching functional primitives, from allosteric regulation of motifs to discrimination of bound ligand identities. Using these primitives, we demonstrate an in silico strategy for de novo design of fluorescent biosensors to arbitrary small molecule analytes. These results position SwitchCraft at the inception of a powerful paradigm for higher-order functional protein design. Code is available at https://github.com/bjing2016/switchcraft.
Existing 4D Gaussian Splatting methods typically rely on per-Gaussian deformation from a canonical space to target frames, which overlooks the strong redundancy among spatially and temporally adjacent Gaussian primitives and leads to suboptimal efficiency. To address this limitation, we propose ADC-GS++, an anchor-driven compact Gaussian splatting frame work for efficient and high-quality dynamic scene reconstruction. Specifically, ADC-GS++ organizes Gaussian primitives into an anchor-based canonical representation, enabling attribute sharing across local regions. To efficiently model dynamic scenes, we introduce a static-dynamic decomposition mechanism and further employ a coarse-to-fine deformation strategy driven by dynamic anchors at multiple granularities. In addition, a unified rate-distortion optimization is adopted to achieve a balanced trade-off between storage efficiency and reconstruction fidelity. Furthermore, a temporal significance-based anchor refinement strategy is employed to dynamically grow and prune anchors, allowing robust adaptation to complex and large-scale motions. Extensive experiments on multiple real-world dynamic scene datasets demonstrate that ADC-GS++ significantly improves rendering speed over deformation-based approaches by 300% 700%, while maintaining competitive rendering quality. More over, ADC-GS++ achieves a more favorable rate-distortion trade off, resulting in substantially reduced storage consumption across different bitrate settings.
DICER1 is a key RNase III endoribonuclease involved in microRNA biogenesis and is implicated in both constitutional DICER1-related tumor predisposition and sporadic thyroid tumorigenesis. In thyroid pathology, DICER1 alterations have been reported across a broad spectrum of lesions, but their reported frequency and clinicopathologic significance vary substantially across published series. We performed a systematic review of thyroid-focused studies published between 2020 and 2025. Studies were included in the quantitative synthesis only if they provided an interpretable lesion-level numerator and denominator for thyroid lesions harbouring DICER1 alterations. Additional thyroid-focused studies that refined the cytomorphologic, histologic, and lesion-spectrum interpretation of DICER1-associated thyroid disease, but lacked a suitable denominator for prevalence meta-analysis, were retained for qualitative clinicopathologic synthesis. Random-effects meta-analysis of logit-transformed proportions was used for the quantitative component. Seven studies met criteria for quantitative synthesis, comprising 16,831 thyroid lesions, of which 317 were reported as DICER1-altered. Study-level proportions ranged from 1.4% in a large adult consecutive molecular-testing cohort to 22.0% in a pediatric follicular-patterned tumor cohort. The pooled proportion of thyroid lesions harbouring DICER1 alterations was 5.76% (95% CI, 2.49%-12.78%), with substantial heterogeneity (I² = 96.94%). Exploratory stratification showed lower pooled proportions in predominantly adult cohorts and higher pooled proportions in pediatric or young-enriched cohorts. Qualitative synthesis showed that DICER1 alterations occur across a wide thyroid lesion spectrum, including multinodular and follicular nodular disease, follicular adenoma, NIFTP, follicular thyroid carcinoma, follicular-patterned papillary thyroid carcinoma, and rare higher-grade or primitive malignant tumors such as thyroblastoma. Cytologically and histologically, these lesions frequently show follicular-patterned architecture, often with macrofollicular or mixed follicular growth and relatively bland nuclear features.Seven studies met criteria for quantitative synthesis, comprising 16,831 thyroid lesions, of which 317 were reported as DICER1-altered. Study-level proportions ranged from 1.4% in a large adult consecutive molecular-testing cohort to 22.0% in a pediatric follicular-patterned tumor cohort. The pooled proportion of thyroid lesions harbouring DICER1 alterations was 5.76% (95% CI, 2.49%-12.78%), with substantial heterogeneity (I² = 96.94%). Exploratory stratification showed lower pooled proportions in predominantly adult cohorts and higher pooled proportions in pediatric or young-enriched cohorts. Qualitative synthesis showed that DICER1 alterations occur across a wide thyroid lesion spectrum, including multinodular and follicular nodular disease, follicular adenoma, NIFTP, follicular thyroid carcinoma, follicular-patterned papillary thyroid carcinoma, and rare higher-grade or primitive malignant tumors such as thyroblastoma. Cytologically and histologically, these lesions frequently show follicular-patterned architecture, often with macrofollicular or mixed follicular growth and relatively bland nuclear features. DICER1 alterations are uncommon in large unselected adult thyroid cohorts but enriched in pediatric, young-adult, and follicular-patterned settings. They should not be interpreted as isolated lesion-specific markers. Rather, their diagnostic significance lies in the integration of molecular findings with lesion type, cytomorphology, histology, patient age, and clinical context, including the selective recognition of cases in which constitutional DICER1-related tumor predisposition should be considered.
Excessive plantar pressure has been recognized as a key risk factor for diabetic foot ulceration. To address this problem, a partition-infilled functional insole based on triply periodic minimal surface (TPMS) lattice structures was proposed and systematically investigated. First, the mechanical responses of three representative TPMS structures, namely Gyroid, Diamond and Primitive, were characterized by compression experiments and finite element (FE) analysis. Subsequently, a partition infilling strategy was designed according to distribution of the plantar pressure. Under additive manufacturing constraint of minimal thickness of 0.2 mm, different TPMS lattices were assigned to specific plantar regions. Gait experiments and FE results demonstrated that, compared to a uniformly Gyroid-infilled insole and a single-lattice gradient-infilled insole, the partition-infilled insole achieved significant reductions in both peak and mean pressures. A Primitive lattice in the heel region exhibited superior pressure-relief performance, whereas Gyroid and Diamond lattices in the forefoot and midfoot balanced cushioning with overall structural stability. Owing to the mathematical definitions of different TPMS structures, smooth topological transitions between lattices were enabled, further improving comfort and manufacture ability. An integrated design-manufacturing framework for personalized diabetic insoles was established. The potential of TPMS partition infilling for redistributing plantar loads and reducing ulceration risk was verified, providing theoretical and experimental support for subsequent clinical applications and large-scale additive manufacturing.
Human induced pluripotent stem cells (hiPSCs)-derived kidney organoids can resemble early stages of human kidney development, morphology and architecture. However, one of the main limitations of the organoids is the reduced vascularization, which limits differentiation and maturation. To increase the oxygen and nutrient supply, multiple vascularization strategies were proposed in literature, including organ-on-chip, hydrogels with angiogenetic cues, and co-culture with endothelial cells. In this work, we developed a three-dimensional (3D) printed chip by extruding sacrificial pluronic, in a fully automated and cost-effective way. By dissolving the pluronic, two circular cross-sectional channels, together with three separated central gel compartments, were created. Human umbilical vein endothelial cells (HUVECs) were seeded in the coated 3D printed chip, and after seven days kidney organoids were added in the central gel compartments, embedded in a partially digested decellularized extracellular matrix (ddECM) hydrogel, and co-cultured for five days under perfusion. At the end of the co-culture, capillary-like structures were formed towards the organoids both in the outer and central parts, colocalizing with LTL and PODXL positive stained areas. We were able to develop primitive capillary-like structures throughout the organoids, using an ad-hoc designed 3D printed chip. Our strategy provides new possibilities to investigate further organoid maturation, drug testing and disease modeling.
Composed Image Retrieval (CIR) represents a challenging retrieval task that targets locating specific images through multimodal inputs. Despite recent progress in CIR techniques, prior approaches often overlook cases where images appear visually alike yet differ in attributes, potentially undermining both multimodal feature fusion and similarity modeling. To mitigate this limitation, we design a unified representation of cross-modal features based on attribute prototypes. Nevertheless, the task is far from straightforward, owing to three core issues: (1) entanglement in attribute-level semantics, (2) inconsistency across modalities, and (3) supervised signal missing. To tackle the above obstacles, we introduce a COMposed image retrieval network guided By attrIbute-based NEighbor Relations (COMBINER). Specifically, we first design an Adaptive Semantic Disentanglement module, which is capable of disentangling attribute features based on multimodal primitive features. Secondly, we propose a Unified Prototype-based Composition module, which can construct cross-modal unified prototypes (CUP) and facilitate multimodal feature composition. Finally, we introduce a Dual Relations Modeling module, which can mine pairwise and neighbor relations based on attribute similarity. Compared to traditional neighbor relations modeling CIR methods, COMBINER represents the first study addressing the phenomenon of visually similar but attribute-unrelated samples. It achieves a more accurate understanding of the semantic relations among samples by employing an attribute prototype-based similarity metric. Comprehensive experiments conducted on three benchmark datasets confirm the effectiveness of our proposed COMBINER. The implementation of our method will be accessed at https://github.com/Lee-zixu/COMBINER.
3D Gaussian Splatting has emerged as a promising technique for real-time novel view synthesis, achieving rendering quality comparable to neural radiance fields while enabling significantly faster inference. Despite its growing adoption in various applications including autonomous driving, robotics, and augmented reality, the adversarial robustness of 3D Gaussian Splatting remains largely unexplored. This paper presents a comprehensive empirical analysis of 3D Gaussian Splatting robustness against multi-view inconsistency attacks, which inject imperceptible perturbations into training images to disrupt the reconstruction process. We propose an adversarial attack framework that maximizes view-dependent inconsistencies while preserving visual imperceptibility through semantic-aware perturbation constraints. Our extensive experiments on the Mip-NeRF 360 dataset reveal that 3D Gaussian Splatting exhibits strong robustness to multi-view photometric inconsistency attacks, with quality degradation limited to less than one percent across standard metrics including Peak Signal-to-Noise Ratio, Structural Similarity Index, and Learned Perceptual Image Patch Similarity. We identify three key factors contributing to this robustness: multi-view averaging during optimization, the explicit Gaussian primitive representation, and the gradient-based optimization dynamics that naturally suppress view-dependent artifacts. These findings provide important insights for deploying 3D Gaussian Splatting in security-sensitive applications and suggest directions for developing more effective adversarial attack strategies.
Background. Preoperative risk stratification of pancreatic neuroendocrine tumors (PNETs) is constrained by the unavailability of histologic grade before resection. We hypothesized that a panel of biologically informed CT-radiomic signatures, combined with patient-level Δ-radiomics referenced to the contralateral pancreas, would support preoperative discrimination of progression and grade in a two-center pilot cohort. Methods. Forty-four patients with histologically confirmed PNET who underwent contrast-enhanced preoperative CT and surgical resection at two academic centers were analyzed. Lesion and contralateral non-tumor-bearing pancreatic parenchyma regions of interest were revised in 3D Slicer by a board-certified pancreatic surgeon and verified intraoperatively against surgical pathology. PyRadiomics v3.0 features were extracted with IBSI-concordant settings. Parametric ComBat batch correction was applied across the two centers (biological-covariate balance verified beforehand), and Δ-radiomic features (lesion combat-pancreas combat) were computed for the 106 intensity/texture primitives. We constructed a panel of biology-informed hybrid signatures partitioned into a preoperative lesion-only family (Family A; seven signatures) and a preoperative Δ-radiomic family (Family B; three signatures). Candidate features were filtered through correlation clustering, baseline-adjusted likelihood-ratio testing with Benjamini-Hochberg FDR control, and 100-bootstrap stability selection. Three predictor blocks were compared per target with three classifiers each (Logistic Regression, Random Forest, Gradient Boosting): M0 (five-variable clinical baseline), MA (M0 + Family A), and MB (M0 + Family B). Discrimination was reported as AUC with bootstrap 95% CI; calibration was assessed using the Brier score and TRIPOD-recommended calibration intercept and slope; and cross-center generalization was evaluated with leave-one-center-out (LOCO) cross-validation. Univariable Cox regression with bootstrap and permutation inference was used for progression-free survival (PFS). Results. The cohort had 16 progression events and eight deaths (median follow-up was 38 months, IQR 14-59). Prespecified clinical-radiomic and Δ-radiomic signatures were associated with progression-free survival, including B2 = ΔBusyness × Ki-67 (HR 0.38, 95% CI 0.19-0.76, p = 0.006). For progression prediction, the Δ-radiomic model achieved the strongest discrimination, with a nested cross-validation AUC of 0.85 and leave-one-center-out AUC of 0.87. For higher-grade disease, radiomic models also demonstrated high discrimination, with AUCs up to 0.93. Conclusions. Radiomics-derived shape and texture features, especially when combined with clinical markers, may noninvasively identify aggressive PNET phenotypes and support preoperative risk stratification. Prospective validation in larger multicenter cohorts is warranted.
Visual Simultaneous Localization and Mapping (vSLAM) is fundamental to enabling robotic mobile manipulation-i.e., the seamless integration of navigation, perception, and dexterous interaction with objects in unstructured environments. Yet current vSLAM research largely lacks a principled, task-oriented framework for map classification, resulting in suboptimal map representations that hinder robustness and efficiency in dynamic indoor settings. To bridge this gap, we propose a purpose-driven taxonomy of vSLAM maps specifically designed for mobile manipulation tasks. This taxonomy comprises four complementary categories: geometric 3D maps, semantic maps, object-level maps, and hybrid maps-each distinguished by its representational granularity, functional scope, and suitability for downstream manipulation primitives. We provide a systematic comparative analysis of their construction pipelines, underlying technical assumptions, and real-world deployment contexts, evaluating them rigorously across three critical dimensions: environmental adaptability, pose estimation accuracy, and real-time computational feasibility. Finally, we synthesize key limitations in existing approaches and identify concrete, high-impact directions for future work-including tight coupling between mapping semantics and manipulation affordances, and scalable learning-based map fusion.
Extrarenal Wilms tumors (ERWTs) (i.e., nephroblastoma) are exceptionally rare tumors that have only been reported approximately 100 times in the literature. These tumors necessitate histology (rather than imaging) for proper identification, often resulting in a postoperative diagnosis. At 20 weeks of gestation, a female fetus was diagnosed with a subcutaneous lumbosacral mass by prenatal ultrasound (US). Days after birth, the mass was resected and pathologically determined to be an ERWT. Specifically, the excised mass had a triphasic histologic pattern, including blastemal, stromal, and primitive epithelial components. Centrally, the lesion demonstrated cystic and pseudopapillary architectural features, while peripherally, the lesion was more solid with the morphologic appearance of Wilms tumor (WT). This case is unique due to (1) the unusual lumbosacral location, (2) the presence of normal bilateral kidneys on US, and (3) the detection of the mass on in utero imaging studies. Including our report, ERWT has been reported 16 times in the (para)spinal/vertebral region and range from T9 to the coccyx. Most patients initially receive surgery (gross total resection, if feasible) followed by chemotherapy (vincristine and dactinomycin) and radiation depending on final pathology and additional case considerations. Due to the rarity of ERWTs, there is no standardized treatment. Complete excision with adjuvant chemotherapy (vincristine and dactinomycin) is most often suggested as a best/most appropriate approach to treatment, with the addition of radiotherapy for recurrence and/or metastasis. This case report and literature review highlights the necessity of considering ERWT as a potential diagnosis when faced with a patient who has a lumbosacral paraspinal/spinal mass, even if other clinicoradiographic features typical of WT are not identified. We also call to light the need for a standardized treatment regimen for ERWT.
The creation of life from chemical systems remains poorly understood. Previous studies have discussed the possibility that fatty acids played a crucial role in the emergence of early primitive cells. This work discusses the idea that a droplet containing the specific combination (C16-/C18═) of a saturated fatty acid with 16 carbons (C16-) and a monounsaturated fatty acid with 18 carbons (C18═) exhibits the best migration characteristics, including Lévy flights, sustained velocity, and a large active region, among many single types or mixtures of fatty acids. Common phospholipids in many modern cells also have this specific C16-/C18═ combination as their hydrophobic tails. Hence, a match is achieved between best migration and modern cells despite vast possibilities on both sides. Lévy flights are an optimal searching strategy for scarce resources; optimal searching is critical for survival among competitors. Droplets with other fatty acids have either random or ballistic motions that are less optimal for searching. This match thus points to the possibility that the best migration of the C16-/C18═ combination may potentially be the fundamental mechanism for the evolution of a chemical into a living system.