Recently, transformer-based methods have achieved significant progress in lightweight image super-resolution (SR). However, most of these approaches primarily aim to improve either inference speed or reconstruction quality, while overlooking memory consumption, thereby limiting their practicality on resource-constrained devices. In this paper, we propose a memory-efficient divide-and-conquer attention (MEDCA) for SR, which substantially reduces memory usage while achieving notable improvements in reconstruction performance and competitive inference speed. To address the high memory and space complexity of standard window-based self-attention (WSA), MEDCA adopts a divide-and-conquer strategy. Specifically, the input features are first split into multiple subspaces along the channel dimension. Each subspace is further partitioned into multiple windows, which are then evenly divided into two parts using distinct asymmetric strategies. Self-attention is independently applied to each part, and the outputs are aggregated to form the final representation. Compared with traditional WSA methods such as SwinIR, MEDCA reduces the space complexity within each subspace by half. Furthermore, we design multiple asymmetric partitioning strategies that allow the model to extract features from a broader spatial context, thereby enabling it to capture richer spatial information and enhance its representation capacity. Extensive experiments demonstrate that MEDCA significantly reduces memory consumption while outperforming existing lightweight state-of-the-art methods and maintaining competitive inference speed across multiple public benchmark datasets. In particular, compared with the state-of-the-art method HiT-SRF, MEDCA improves the average performance by 0.12dB across five public test sets, while maintaining comparable inference time and requiring only 29.7% of the memory used by HiT-SRF. The code and models are provided at https://github.com/hms-source/MEDCA.
This work introduces a divide-and-conquer (DC) quantum linear-response framework for scalable excited-state simulations, in which excitation energies and oscillator strengths are extracted from the poles of the frequency-dependent dynamical polarizability. This feature naturally enables a fragmentation-based formulation, while retaining the ability to describe nonlocal excitations beyond predefined localization regions. The method, termed DC-qUCCSD-LR, builds upon the established self-consistent quantum linear-response (qLR) theory combined with the variational unitary coupled-cluster ansatz with single and double excitations. For linear hydrogen chains, nH2, the DC-qUCCSD-LR method reproduces full configuration interaction (FCI) excitation energies while significantly reducing quantum resource requirements, achieving favorable scaling of O(n1.5) for gate counts and O(n2.2) for measurements with respect to the molecular size, n. These results demonstrate that the polarizability-based qUCC-LR framework and its DC extension provide an accurate and scalable foundation for quantum excited-state simulations.
We present an analytic gradient method for divide-and-conquer (DC)-based nonlocal excited-state calculations, enabling efficient geometry optimization of large molecular systems exhibiting delocalized or charge-transfer excitations. Transition density matrices are extracted from response densities near polarizability poles, and subsystem Z-vector equations are solved independently, reducing the dominant computational scaling from O(N3.55) in standard time-dependent Hartree-Fock/density functional theory to O(N1.60). Applications to push-pull polyenes and [9]cycloparaphenylene ([9]CPP) demonstrate that the DC-based gradients systematically converge toward standard results with increasing buffer size and accurately reproduce excited-state structural relaxation, including the pronounced quinoid distortion and large Stokes shift in [9]CPP. These results establish the present method as a practical and scalable approach for excited-state geometry optimization in systems beyond the feasible range of conventional excited-state techniques.
Predicting the Li-ion migration energy barrier in battery cathode materials via machine learning has attracted increasing attention. However, the capture of complex material features via the accurate definition of structural descriptors and cross-scale information integration for neural network modeling remains challenging. Benefiting from the reported divide-and-conquer strategy, we propose a convolutional neural network (CNN) model employing hybrid geometric-topological descriptors for the efficient prediction of Li-ion migration energy barriers, in which the geometric descriptors capture local Li-O polyhedra of initial and transition states, as well as the topological descriptors derived from persistent homology characterize structural connectivity and ring channels along the migration pathway. Compared with the recurrent neural network (RNN) and Fourier-feature network (FFN) models, the CNN model optimized via residual block structures and L2 regularization achieves a mean absolute error (MAE) of 0.0589 eV. The minimum Li-O distance during the Li-ion migration is identified as the most critical factor affecting the migration barrier, and the comparable importance scores of dmin, dstd, and H0 bars suggest a synergistic effect of multiple structural descriptors, highlighting the necessity of adopting hybrid geometric-topological descriptors. The present work provides an efficient and accurate approach for high-throughput screening of materials with rapid Li-ion diffusion.
Small-object detection in unmanned aerial vehicle (UAV) images poses significant challenges due to limited pixel representation, complex backgrounds, and insufficient feature discriminability. While one-stage detectors like YOLO offer a favorable speed-accuracy trade-off, their performance on small objects is often hampered by conflicts between semantic and spatial information during multi-scale feature fusion in existing networks. To address this, we propose LHA-YOLO, a lightweight and high-accuracy network based on YOLO11. The network is built upon two core innovations. The first is the Lightweight Feature Extraction Module (LFEM), which employs a parallel spatial-channel attention mechanism to extract discriminative cross-dimensional features efficiently and with low computational cost. The second is the Divide-and-Conquer Propagation Path (DCPP) strategy. This strategy decouples and separately optimizes the handling of semantic and spatial information within its bidirectional propagation paths. To achieve this, the top-down path utilizes the Channel Attention-guided Semantic Aggregation (CASA) module to enhance semantic consistency. In parallel, the bottom-up path employs the Spatial Attention-guided Detail Aggregation (SADA) module to preserve spatial fidelity. Extensive evaluation on the VisDrone and UAVDT datasets shows that LHA-YOLO strikes a favorable balance between performance and efficiency. On VisDrone, it improves mAP50 from 39.4% to 41.6% and mAP50-95 from 23.5% to 24.9% over YOLOv11s. On UAVDT, it raises mAP50 from 32.2% to 36.9% and mAP50-95 from 19.4% to 22.9%, while reducing GFLOPs from 21.3 to 18.8. These results confirm the efficacy of our design for real-time UAV applications.
Dermatology trainees are no strangers to standardized examinations that assess basic science and medical knowledge, from the Medical College Admission Test and the National Board of Medical Examiners Subject Examinations to the United States Medical Licensing Examination series. To become a board-certified dermatologist, one must complete the American Board of Dermatology (ABD) Certification Pathway-a staged evaluation beginning with a BASIC exam for first-year residents followed by 4 CORE exam modules and a final APPLIED exam following residency completion that assesses one's ability to apply therapeutic knowledge and clinical reasoning in scenarios relevant to the practice of general dermatology. For exam preparation, there are more resources available than you will have time to explore fully. This article features high-yield tips and study resources alongside test-day strategies to help you perform at your best.
Chimeric antigen receptor T (CAR-T) cell therapy, a groundbreaking technology in tumor immunotherapy, has demonstrated unprecedented potential in the field of autoimmune diseases in recent years. This article provides a systematic review of the developmental trajectory and core concepts of CAR-T-cell therapy in autoimmune diseases, emphasizing its conceptual evolution from traditional "killing" strategies to "precision immune remodeling". Leveraging multitarget approaches (e.g., CD19 and B-cell maturation antigen) and chimeric autoantigen receptor technology, it achieves efficient elimination of pathogenic B cells, plasma cells, and autoreactive T cells, along with profound remodeling of the immune microenvironment, thereby inducing long-term disease remission and restoring immune tolerance. Nevertheless, unresolved challenges still exist in monotherapy strategies, such as antigen escape, nontumor toxicity of emerging targets, limited in vivo persistence, high production costs, and immune reconstitution imbalance. Future research ought to concentrate on the development of multitarget/logic-gated chimeric antigen receptor constructs, the optimization of chimeric antigen receptor architecture and nonviral delivery systems, the validation of the long-term safety of universal CAR-T cells, the customization of personalized treatment regimens, and the exploration of mechanisms for modulating the immune microenvironment. This review emphasizes that CAR-T-cell therapy shows potential for initiating a new era of personalized, mechanism-driven treatment for autoimmune diseases, offering crucial insights for clinical translation.
Growing evidence has revealed that DEAD-box RNA helicase 5 (DDX5, also known as p68) and ubiquitin-conjugating enzyme E2T (UbE2T) are two emerging and highly promising cancer therapeutic targets. This article provides the first comprehensive review of the physical and functional relationship between these two cancer targets, further refining their therapeutic potentials in solid cancers. In addition, the consequences of simultaneously degrading DDX5 and UbE2T proteins by the small-molecule dual molecular glue degrader FL118 in difficult-to-treat advanced cancers are presented.Specifically, this article reviews: (1) the roles of DDX5 and UbE2T in diverse cancer DNA repair pathways; (2) the physical binding relationship and potential functional roles of DDX5 in topoisomerase regulation; (3) the involvement of DDX5 in EZH2- and NANOG-associated prostate cancer stem cell (PCSC)-driven neuroendocrine prostate cancer (NEPC), castration-resistant prostate cancer (CRPC), and metastatic CRPC (mCRPC); (4) the contributions of DDX5 and UbE2T to inflammatory and immune regulation within the tumor microenvironment (TME); (5) FL118 as a small-molecule dual molecular glue degrader selectively targeting both DDX5 and UbE2T; (6) the high efficacy of FL118 against multiple difficult-to-treat advanced and metastatic cancers, including advanced colorectal cancer (CRC), pancreatic ductal adenocarcinoma (PDAC), osteosarcoma, Ewing sarcoma, ovarian cancer, and glioma/glioblastoma; (7) the resistance of ABCG2-expressing cancer cells to common anticancer agents but not to FL118; (8) the favorable pharmacokinetic and toxicology profiles of FL118 in mice, rats, and dogs; (9) the distinct functions of DDX5 in normal tissues, cells, and organs versus cancer; and (10) FL118 as a drug platform enabling the development of novel analogs and derivatives.Based on this review, we conclude that DDX5 and UbE2T represent superior anticancer therapeutic targets, and that the high efficacy of FL118 against multiple difficult-to-treat cancers is attributable to its function as a bona fide small-molecule dual molecular glue degrader that physically targets and degrades both DDX5 and UbE2T. Strikingly, this activity is observed regardless of the expression status of ABC transporter proteins, ABCG2/BCRP, ABCB1/Pgp/MDR1, and/or ABCC1/MRP1 in cancer cells.
Placenta is a focal point of cellular interactions of maternal and fetal immune systems, fostering immune tolerance essential for a successful pregnancy. However, the mechanisms underlying immune dysregulation in disorders such as preeclampsia remain poorly understood. We constructed a multilayered atlas of the human placenta by performing single-cell RNA sequencing across distinct placental layers from both normal and severe preeclamptic pregnancies. Rather than focusing on specific regions of placenta, such as the villi and decidua, we explore placental architecture by collecting pair-matched tissues from individual pregnancies to suggest appropriate placental-wide immune atlas. To interpret intercellular communication in those unexplored placental layers, we designed two conceptual models: one based on immune interaction frequency (IIF) and another on immune tolerance influencers (IT). We applied machine learning classifiers to identify gene signatures associated with preeclampsia. We observed extensive admixture of semi-allogeneic fetal and maternal cells across all placental layers, regardless of disease status. This contradicts the IIF-based model, which is premised on that such intermixing frequency is a pathologic feature specific to preeclampsia. Instead, analysis under the IT framework revealed key molecular determinants of preeclampsia. Notably, classifier-prioritized genes associated with preeclampsia were enriched for ligands and receptors supporting a role for intercellular immune interactions. Among them, the ligand-receptor pair SPP1-CD44 between fetus and maternal immune cells emerged as peculiarly associated influencers of preeclampsia. Spatial image analysis confirmed co-localization of SPP1-CD44 expression within immune cell populations in preeclamptic placental tissue. Our study provides a comprehensive map of the human placenta and identifies disease-specific immune signaling pathways in preeclampsia using the divide and rule approach. The findings highlight SPP1-CD44 as a putative target of immune dysregulation, offering new insight into the cellular basis of maternal-fetal tolerance and its breakdown in pregnancy-related disorders. Not applicable. [Image: see text]
Background Laparoscopic cholecystectomy is the accepted standard treatment for symptomatic gallstone disease. However, severe inflammation, fibrosis, adhesions, or anatomical distortion can obscure critical landmarks and increase the risk of bile duct injury. In such cases, timely recognition of operative difficulty and appropriate modification of technique are essential to ensure patient safety. Methods This prospective case series was conducted at a tertiary care center, R L Jalappa Hospital & Research Center, Tamaka, Karnataka, India, in 2024 and included seven adult patients whose intraoperative findings met predefined criteria for difficult laparoscopic cholecystectomy. Demographic data, operative challenges, technical adaptations, postoperative outcomes, and 30-day follow-up findings were documented and analyzed descriptively. Results Operative difficulty was associated with a frozen Calot's triangle, dense omental or bowel adhesions, a mucocele with impacted stones, a short cystic duct, severe fibrosis, and vascular anomalies. Structured bailout strategies were employed based on intraoperative findings. These included fundus-first (dome-down) dissection, fenestrating subtotal cholecystectomy, selective partial cystic ductotomy for impacted stone retrieval, endoloop ligation of the cystic duct stump, and judicious drain placement. In one patient, transient postoperative jaundice resolved spontaneously without intervention. No bile duct injuries or major postoperative complications occurred. All patients recovered satisfactorily, and no delayed morbidity was identified at the 30-day follow-up. Conclusion Difficult laparoscopic cholecystectomy requires vigilance, thorough anatomical knowledge, and the readiness to adapt the operative plan. Familiarity with bailout techniques and individualized intraoperative decision-making can prevent major biliary injuries and enable the safe completion of surgery, even in challenging operative fields.
Elevated low-density lipoprotein cholesterol (LDL-C) represents a key causal factor in atherosclerotic cardiovascular disease. Robust evidence demonstrates that early, marked, and sustained reduction of LDL-C is associated with a significant decrease in the risk of major adverse cardiovascular events. However, a substantial proportion of high-risk patients fail to achieve guideline-recommended LDL-C targets with statin therapy alone, thus requiring additional lipid-lowering treatments. Proprotein convertase subtilisin/kexin type 9 (PCSK9) inhibitors, particularly the monoclonal antibodies evolocumab and alirocumab, have been shown to reduce LDL-C levels by ∼60% and to provide clear cardiovascular outcome benefits in secondary prevention. More recently, the VESALIUS-CV trial extended these findings to the setting of primary prevention in high cardiovascular risk patients without prior major cardiovascular events. In this trial, evolocumab achieved a significant and sustained reduction in LDL-C levels and significantly reduced the risk of a first cardiovascular event, including myocardial infarction and ischaemia-driven revascularization, while maintaining a safety profile comparable to placebo. Overall, the available evidence supports the role of evolocumab as an effective and safe strategy for cardiovascular risk reduction across the entire risk continuum, from secondary to primary prevention, underscoring the importance of a personalized therapeutic approach based on the patient's global cardiovascular risk profile.
This article investigates how Russia employs gender-based disinformation as a strategic tool to advance its foreign policy objectives. Although existing research on gender-based foreign influence and disinformation emphasizes its domestic social effects, it has not fully explored how states instrumentalize gendered narratives to pursue broader geopolitical goals. Using qualitative content analysis of 367 pro-Kremlin disinformation articles, we identify eight narratives that Russia employs to undermine the moral legitimacy of Western states and institutions, such as NATO and the EU, while simultaneously legitimizing its own actions. We show that Russia strategically deploys disinformation to resonate with conservative audiences. Our analysis highlights two core mechanisms: legitimacy sabotage and legitimation, both of which are rooted in the contestation of moral authority. By weaponizing gendered narratives, Russia effectively reconfigures legitimacy landscapes in its favour, illustrating the broader strategic logic of gender-based disinformation within contemporary hybrid warfare tactics. This research presents a novel framework for understanding how states use identity-based narratives in the context of great power competition.
How treatment shapes survival after breast-cancer recurrence in Latin America remains poorly described. We extended follow-up of the Latin American Cancer Research Network cohort to quantify post-recurrence overall survival, describe systemic-therapy pathways, and assess the robustness of survival estimates to incomplete follow-up. We conducted a cohort study of women enrolled with stage I-III breast cancer at 31 centres in Argentina, Brazil, Chile, Mexico, and Uruguay between 2011 and 2014. Vital status and systemic treatments were updated from medical records to July 1, 2025. The main outcome was overall survival after recurrence, defined from first recurrence to death from any cause or censoring. Kaplan-Meier curves and log-rank tests compared survival by immunohistochemistry-defined subtype. Adjusted hazard ratios (HRs) with 95% confidence intervals (CIs) were estimated with a Firth-penalised Cox model. Sensitivity analyses included best-case and worst-case censoring, inverse-probability-of-censoring weighting, and Fine-Grey competing-risk regression. Systemic-therapy sequences were reconstructed for up to six lines. Vital status was updated for 970 of 1191 women (81.4%), and 162 had a documented first recurrence. Median overall survival after recurrence was 24.0 months (IQR 9.6-45.6). Compared with triple-negative breast cancer, adjusted HRs were 0.64 (95% CI 0.20-1.77) for hormone receptor-negative/HER2-positive disease, 0.54 (0.26-1.14) for hormone receptor-positive/HER2-negative disease, and 0.93 (0.39-2.20) for hormone receptor-positive/HER2-positive disease. Chemotherapy was the first-line regimen in 83 of 162 patients (51%), endocrine monotherapy in 55 of 162 (34%), and trastuzumab-pertuzumab-taxane or cyclin-dependent kinase 4 and 6 inhibitor-based regimens in eight of 162 (5%). Overall, 87 of 162 patients (54%) initiated second-line therapy. Adjusted subtype HRs were imprecise and should be interpreted cautiously. Steep treatment-line attrition, limited uptake of contemporary targeted therapies, and incomplete follow-up in some health-system settings indicate modifiable regional gaps in metastatic breast-cancer care. ASCO Conquer Cancer & Pfizer Competitive Grant for Quality Improvement (contract award No. 87534309); Center for Global Health at the United States-National Cancer Institute at the National Institutes of Health (contract award No. HHSN2612010000871/NO2-PC-2010-00087); Fogarty International Center, NIH, HHS; and Susan G. Komen for the Cure; in Argentina, Instituto Nacional del Cáncer (Ministry of Health), Fundación Argentina de Nanotecnología, Agencia Nacional de Promoción Científica y Tecnológica, CONICET (Ministry of Science, Technology, and Productive Innovation); Brazil, Ministério da Saúde (Ministry of Health); Chile, Instituto de Salud Pública (Public Health Institute) and Ministerio de Salud (Ministry of Health); and Mexico, Consejo Estatal de Ciencia y Tecnología de Jalisco (COECYTJAL) and Universidad de Sonora (University of Sonora). La forma en que el tratamiento condiciona supervivencia tras recurrencia del cáncer de mama en América Latina sigue estando escasamente descrita. Extendimos el seguimiento de la cohorte de la Latin American Cancer Research Network para cuantificar la supervivencia global después de recurrencia, describir trayectorias de tratamiento sistémico y evaluar solidez de las estimaciones frente al seguimiento incompleto. Realizamos un estudio de cohorte de mujeres con cáncer de mama estadio I-III en 31 centros de Argentina, Brasil, Chile, México y Uruguay entre 2011 y 2014. Estado vital y tratamientos sistémicos se actualizaron hasta Julio 1, 2025. El desenlace principal fue supervivencia global después de recurrencia. Se comparó supervivencia según subtipo por inmunohistoquímica mediante curvas de Kaplan–Meier y pruebas de log-rank. Los cocientes de riesgos ajustados (HR) con intervalos de confianza (IC) del 95% se estimaron con un modelo de Cox penalizado de Firth. También se realizaron análisis de sensibilidad y se reconstruyeron secuencias de tratamiento sistémico hasta seis líneas. Se actualizó estado vital de 970/1191 mujeres (81.4%), y 162 tuvieron una primera recurrencia documentada. Supervivencia global después de recurrencia fue 24.0 meses (RIC 9.6–45.6). En comparación con cáncer de mama triple-negativo, HR ajustados fueron 0.64 (IC 95% 0.20–1.77) para receptores hormonales negativos y HER2-positivo, 0.54 (0.26–1.14) para receptores hormonales positivos y HER2-negativo, y 0.93 (0.39–2.20) para receptores hormonales positivos y HER2-positivo. Quimioterapia fue tratamiento de primera línea en 83/162 pacientes (51%), monoterapia endocrina en 55/162 (34%), y esquemas con trastuzumab-pertuzumab-taxano o inhibidores CDK4/6 en 8/162 (5%). 87/162 pacientes (54%) iniciaron segunda línea. HR ajustados fueron imprecisos y deben interpretarse con cautela. La marcada pérdida de pacientes entre líneas, limitada incorporación de terapias dirigidas contemporáneas y el seguimiento incompleto en algunos entornos del sistema de salud señalan brechas regionales modificables en atención de cáncer de mama metastásico. ASCO Conquer Cancer & Pfizer Competitive Grant for Quality Improvement (contrato No. 87534309); Center for Global Health del United States National Cancer Institute en los National Institutes of Health (contrato No. HHSN2612010000871/NO2-PC-2010-00087); Fogarty International Center, NIH, HHS; y Susan G. Komen for the Cure; en Argentina, Instituto Nacional del Cáncer, Fundación Argentina de Nanotecnología, Agencia Nacional de Promoción Científica y Tecnológica y CONICET; en Brasil, Ministério da Saúde; en Chile, Instituto de Salud Pública y Ministerio de Salud; y en México, Consejo Estatal de Ciencia y Tecnología de Jalisco (COECYTJAL) y Universidad de Sonora.
Shotgun sequencing of >400 genomes from the ossuary of the Royal Basilica of Székesfehérvár identified three additional skeletal remains carrying the Árpád Dynasty's Y chromosome haplogroup R-ARP, raising the total known R-ARP+ individuals from four to seven. Kinship and IBD analyses placed these individuals within the dynasty alongside Béla, Duke of Macsó (†1272)-a Rurikid prince and great-great-grandson of King Béla III- and a fetus from a tomb adjacent to Béla III. One of these is King Béla II "the Blind" (1131-1141); a second is a likely second-degree relative of St. Ladislaus (probably an uncle, identity unresolved); the third, buried outside the original Basilica walls, is related to the Árpáds only by Y-haplogroup. The authenticity of Béla, Duke of Macsó's remains was genetically confirmed. IBD also linked Árpáds to conquering Hungarians, Vikings, and the Aba, Báthory, and Corvinus families.
Conventional open-pit production scheduling models often neglect environmental costs, government royalties, and operational penalties, resulting in economically optimistic but practically unrealistic mine plans. This study develops a Mixed-Integer Non-Linear Programming (MINLP) model for long-term production scheduling at the Kal-e Kafi copper deposit in Iran. The model explicitly integrates environmental costs, government royalties, and penalty costs for deviations from target mill feed tonnage and grade directly into the objective function and block economic value calculation. Implemented on a scaled block model comprising 20,746 selective mining units and solved using the BARON 23.1.7 solver within a Divide and Conquer framework, the MINLP model achieves a net present value of $129.34 million, a stripping ratio of 0.64:1, a mine life of 14 years, and extractable ore tonnage of 14.24 million tonnes. Comparative analysis against the industry-standard Datamine NPV Scheduler reveals that although the commercial software reports a higher nominal NPV, it does so by externalizing environmental liabilities, royalty obligations, and operational penalties. The MINLP model demonstrates comparatively better operational efficiency through a lower stripping ratio, a longer mine life, and higher ore recovery. A comprehensive sensitivity analysis confirms model robustness, with the environmental cost elasticity near zero, indicating that internalizing environmental expenditures does not compromise economic competitiveness. These findings demonstrate that responsible mining produces field-realistic, operationally feasible, and economically defensible mine plans, bridging the gap between theoretical optimization and practical sustainability.
Three vertebrate lineages conquered powered fight in different epochs of the Phanerozoic: pterosaurs, dinosaurs (birds) and mammals (bats). The evolutionary mechanisms proposed for the demanding transition to powered flight have been contentious. For mammals, recent evidence suggests that the Darwinian hypothesis of a gliding transition is supported, based on aerodynamic and paleo-atmosphere reconstructions. However, it is surprising that the many (at least 12) confirmed, independent lineages of gliding mammals that existed since the Jurassic to the present never evolved powered flight. To explain this, Pennycuick in 2008 advanced the theoretical concept of the squirrel barrier: gliding mammals face a tradeoff between the cost of abandoning their agile arboreal lifestyle, and the potential gains of powered flight, thereby limiting the evolution of the higher-aspect-ratio wings required for achieving the latter. This concept was seldom discussed, used or tested. Here we examine critically the squirrel barrier concept and its implicit components, contrasting its predictions with available evidence, specifically from morphology, aerodynamics, and fossils. We found the concept highly relevant, and more complex than originally stated. A more nuanced approach to the probable conditions that must be met to transition from the locomotor style of ancestors to flying descendants reveals several intermediate such barriers, of which the squirrel barrier is one requiring profound evolutionary changes. Glide distance is a measure of glide efficiency, because the longer the glide the better the use of the large amount of energy invested in climbing up to the launch point in the canopy. Based on observational data and aerodynamics, we hypothesize that colugos (Dermoptera: Cynocephalidae) are able to maximize glide distance and glide ratio beyond average performance and thus might be beyond the squirrel barrier as proposed by Pennycuick, but still falling too short of reaching sustained powered flight, which constitute an additional, "hard" flight barrier.
Human Genome Project (HGP), Genome Wide Association Studies (GWAS) and The Cancer Genome Atlas (TCGA) are some of the remarkable research endeavors that generated massive amounts of information about Single Nucleotide Polymorphisms (SNPs) and other genetic variations, providing valuable insights for understanding the association of SNPs with diseases. It enables early diagnosis, prevention, and treatment planning for diseases. In this study, a novel approach is proposed for the identification of SNPs. This approach consists of two techniques: technique I introduces a modified matching strategy for chosen matching algorithms and technique II combines the Divide-and-Conquer technique with technique 1. Performance evaluation of the proposed techniques is performed using performance metrics such as Precision, Recall, F-measure, Execution Time, and Resource Utilization (including CPU Utilization and RAM Usage). The proposed techniques overcome most of the research gaps and shortcomings of the existing techniques.
Sepsis remains a major challenge in global clinical practice due to its persistently high mortality rate. Traditional antibiotics address symptoms rather than root causes, while the problem of drug resistance continues to escalate. Detoxification strategies such as monoclonal antibodies have shown limited efficacy in complex clinical settings, as their single-target specificity and narrow activity spectrum restrict their ability to neutralize diverse bacterial toxins. The therapeutic bottleneck lies in the absence of effective methods capable of simultaneously neutralizing Gram-negative bacterial endotoxins (such as LPS) and Gram-positive bacterial exotoxins (such as Hlα). To overcome this limitation, we drew inspiration from the natural "bait" mechanisms of living organisms to develop a biomimetic nanoscale detoxifier with broad-spectrum toxin adsorption and neutralization capacity. Specifically, chloroquine was used to inhibit autophagy lysosome formation in macrophages, promoting the release of exosomes enriched with toxin receptors CD14 and ADAM10. These exosomes (Exo) were then fused with artificial liposomes (Lip) possessing extensive membrane space to construct exosome membrane hybrid liposomes (Elip). Our results demonstrated that Elip effectively neutralized the hemolytic activity of Hlα and adsorbed LPS in vitro. In a subcutaneous HIα-induced local inflammation model, Elip completely prevented local skin and muscle tissue necrosis. In a systemic inflammation model induced by intravenous HIα, Elip significantly alleviated acute inflammatory damage in the lungs and liver, reducing key pro-inflammatory factor levels to near-normal ranges. In an LPS-induced shock model, all mice in the Elip treatment group survived. This study robustly confirmed that Elip successfully resolves the clinical challenge of simultaneously clearing endotoxins and exotoxins through a "synergistic detoxification" mechanism, offering a novel therapeutic strategy with significant translational potential that transcends traditional antibiotics for conquering sepsis.
This work performed a unique conjugation of metronidazole with berberine via thiazolidinedione to afford a novel structural skeleton of metronidazolyl vinylthiazolidionyl tetrahydroberberines (MVTs) with multitargeting antibacterial potential to conquer bacterial resistance. Most of the prepared MVTs exhibited an effective activity against most of the tested bacteria. Notably, butyl MVT 7b (MIC = 0.005 mM) demonstrated the strongest antibacterial activity against S. aureus 25923, being 3- and 69-fold more active than norfloxacin and berberine, respectively. Low hemolysis, cytotoxicity, and drug resistance, effective biofilm eradication, and efficient transport by human serum albumin suggested the favorable druggability of MVT 7b. Highly active MVT 7b could cause bacterial death through multitargeting effects, including disrupting the bacterial membrane, triggering oxidative stress, and intercalating into DNA without cleaving DNA. Moreover, MVT 7b was more potent than norfloxacin in vivo against S. aureus 25923. These medicinal chemobiological investigations demonstrated MVTs as potential multitargeting antibacterial candidates for alleviating bacterial resistance.
Humans often learn better when problems are broken down into parts, but this phenomenon has eluded explanation at the computational level. Here we study how differing training curricula help or hinder learning in a classic probabilistic cue combination task. Training curricula that 'divide and conquer' by presenting one cue at a time facilitate later performance on test trials involving multiple cues. This effect is captured by a hybrid learning framework that arbitrates between two different learning strategies: a marginal updating process, which assigns credit to each cue independent of every other, and a joint updating process, which distributes credit across cues on the basis of their joint presence. We use this theory to generate new 'skewed distribution' multi-cue curricula that should and should not successfully promote human learning. It makes accurate predictions, demonstrating that we can use computational insights of learning to accelerate human probabilistic learning.