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HLA-II peptide-binding diversity shapes humoral immune responses and susceptibility to infections
Abstract Infectious diseases represent a leading cause of mortality worldwide and have shaped the evolution of the human immune system. Human leukocyte antigen (HLA) class II molecules are key to the humoral immune response by presenting peptides to helper T cells. The peptide-binding range varies greatly among HLA-II variants, but the role of this variation in humoral immunity and infection susceptibility has remained unexplored. Here, we integrate data on HLA-II immunopeptidomics and antibody responses in ~1500 individuals and demonstrate that a broad peptide-binding repertoire of HLA-II is linked to antibody production against specific pathogen-associated proteins and more favorable outcomes in several common infections. In the UK Biobank, individuals carrying such HLA-II molecules had a reduced risk of severe infections and up to a 45% lower incidence of diseases caused by common pathogens. These findings suggest that the peptide diversity presented by HLA-II variants shapes humoral immunity and can serve as a risk factor for infection susceptibility.
Study on the effect of an inducer on the cavitation characteristics of low-specific-speed onboard cooling pumps
Abstract In this study, numerical simulations using the SST k-ω turbulence model and the Zwart-Gerber-Belamri cavitation model are coupled with experimental validation to systematically analyze the effect of an inducer on the internal flow field and cavitation performance of a pump under various operating conditions. The optimal matching angle between the inducer and the impeller is also investigated. The results indicate that following the pressurization and preswirl induced by the inducer, the areas of the low-pressure and low-velocity zones at the impeller inlet are significantly reduced, while the size and intensity of the vortices near the outlet region within the impeller also decrease. Under rated and high-flow-rate operating conditions, the inducer enhances the cavitation resistance of the centrifugal pump and mitigates the formation and growth of cavitation bubbles inside the impeller. However, with increasing impeller speed, when the speed reached 12,500 r/min, severe cavitation occurred within the inducer, thereby diminishing its cavitation suppression effect and intensifying the cavitation phenomenon within the impeller. The matching angle between the inducer and the impeller not only affects pump performance but also significantly influences its cavitation resistance. This angle θ is formed by fixing the impeller and rotating the inducer circumferentially, which denotes the circumferential offset angle of the inducer blade outlet edges before and after rotation. When the angle θ = 120°, the inducer exhibits the most pronounced cavitation suppression effect within the impeller. These findings offer valuable engineering guidance for the study of cavitation resistance in low-specific-speed onboard cooling pumps operating at high speeds.
Single-nucleus analysis of human white adipose tissue reveals adipocyte subsets with distinct metabolic profiles
Transcriptomic profiling of HBV transgenic mouse livers reveals ZBP1-associated necroptotic signaling
Regime shifts of AMOC-sea surface temperature relationship
Abstract The Atlantic Meridional Overturning Circulation (AMOC) plays critical roles in regulating climate, and subpolar North Atlantic sea-surface temperature (SST) patterns are widely used to infer changes in its strength. Yet the stationarity of their relationship remains unclear. Here, using Community Earth System Model simulations spanning various climate states and a multi-model ensemble, we show that this relationship is state-dependent. We identify three distinct regimes: strong AMOC with the typical dipole fingerprint; intermediate AMOC with amplified subpolar SST anomalies; and weak AMOC with muted North Atlantic signals. These regimes arise primarily from changes in atmospheric radiative processes, while ocean processes contribute indirectly through air–sea interactions. The year of peak AMOC–SST sensitivity emerges as a predictor of the transition into the weak-AMOC regime and associated decay of the North Atlantic warming hole, offering a physically-based constraint on model uncertainties in climate projections. Our findings also imply that SST-based AMOC indicators must account for state dependence.
Prognostic value of the lactate-to-albumin ratio for short- and long-term mortality in sepsis
Abstract The lactate-to-albumin ratio (LAR) integrates acute metabolic stress with the systemic response reflected by serum albumin, but its association with longer-term outcomes in sepsis remains incompletely characterized. We analyzed 4,916 adults meeting Sepsis-3 criteria in the MIMIC-IV database. Baseline LAR was calculated from the first lactate and albumin measurements obtained within 24 h of intensive care unit admission. Associations with in-hospital, 28-day, 90-day, and 365-day mortality were evaluated using multivariable Cox models, restricted cubic splines, and time-dependent receiver operating characteristic analysis. Hospital non-survivors had a higher median LAR than survivors (0.974 vs. 0.564; P < 0.001). Each one-unit increase in LAR was associated with higher mortality after adjustment for age, sex, body mass index, SOFA score, and APACHE III score (adjusted hazard ratios, 1.149–1.172; all P < 0.001). Time-dependent areas under the curve were 0.653 (95% CI, 0.631–0.675) at 28 days, 0.684 (0.660–0.707) at 90 days, and 0.665 (0.635–0.694) at 365 days. Baseline LAR is an accessible marker of risk that may complement, but should not replace, established clinical assessment. External validation and analyses incorporating serial measurements and treatment timing are required before clinical thresholds are adopted.
The inherent capacity of neurons to learn order relations and support abstract reasoning
Abstract Brains extract relations between objects and concepts and integrate them into cognitive maps for decision-making. But it remains unclear how they achieve that. Here we present a rigorous theory showing that single neurons can already learn to extract ranks of items in a linear order with a simple local rule for synaptic plasticity. The resulting model explains human brain data on the emergence of cognitive maps from linear orders, accounts for the terminal item effect in transitive inference, and enables rapid reconfiguration of internal representations when new evidence appears. We also present a theoretical explanation for the surprising fact that 2D projections of neural representations of linear orders in the brain are curved rather than linear. Since the model requires only local synaptic plasticity in shallow networks, it is suited for relational learning and fast inference on low-energy edge devices. We demonstrate this on the neuromorphic chip Loihi 2.
Measurement properties of the Wolf Motor Function Test for assessing upper limb function in Spanish adults with stroke
Identification of methylation-sensitive human transcription factors using meSMiLE-seq
Abstract Transcription factors (TFs) are key players in eukaryotic gene regulation, but the DNA binding specificity of many TFs remains unknown. Here, we assay 284 mostly uncharacterized putative human TFs using selective microfluidics-based ligand enrichment followed by sequencing (SMiLE-seq), revealing 74 new DNA binding motifs. To investigate whether TFs lacking detectable motifs preferably bind epigenetically modified DNA, we develop methylation-sensitive SMiLE-seq (meSMiLE-seq), a microfluidic assay that simultaneously probes binding to methylated and unmethylated DNA. Using meSMiLE-seq, we assay 114 TFs and identify DNA-binding models for 48 proteins, including known methylation-sensitive binding modes for POU5F1 and RFX5. 11 TFs prefer methylated DNA or display alternative methylation-dependent motifs (e.g. PRDM13), while 13 show aversion to methylated sequences (e.g. USF3). Finally, we identify ZHX2 as a putative Z-DNA binder. Altogether, our study significantly expands the human TF codebook, while providing a versatile platform to quantitatively assay the impact of DNA modifications on TF binding.
Evaluation of Moringa oleifera extract and metal-organic frameworks (MOFs) as therapeutic agents against chronic Toxoplasma gondii infection in mice
Abstract Toxoplasmosis, a zoonotic infection caused by the protozoan Toxoplasma gondii , remains a significant global health concern, with seroprevalence exceeding 60% in specific regions. Given the limitations of current therapies for chronic stages, this study investigated the therapeutic efficacy of Moringa oleifera (MO) extract and MO-loaded copper-based metal-organic frameworks (MO@L-AA-Cu) in a murine model. Sixty-five female Swiss albino mice were utilized. Following the establishment of a negative control group (n=5), the remaining mice were orally inoculated with 20 cysts of the T. gondii ME49 strain to induce chronic infection. Treatment efficacy was evaluated via brain cyst quantification, histopathological examination of the brain, eyes, and spleen, and molecular analysis using RT-PCR. All treatment groups exhibited significant reductions in parasitic load compared to the positive control. The highest reduction was observed in group G3e (73.61%), followed by G3c (61.64%), G3d (56.91%), G3a (54.64%), and G3b (44.33%). Histopathological findings demonstrated a marked attenuation of inflammatory infiltrates, fibrosis, and structural tissue damage in groups receiving MO and Spiramycin encapsulated within Cu-MOFs. RT-PCR results corroborated these findings, identifying the lowest parasite DNA levels in the G3e cohort. The integration of MO extract and Spiramycin into L-AA-Cu MOFs significantly enhances their antiparasitic and anti-inflammatory properties. These findings suggest that MO-loaded MOFs represent a promising nanotherapeutic platform for the treatment of chronic toxoplasmosis, offering superior efficacy over conventional formulations.
Bypassing the imaging thickness limit with a translationally-invariant nonlocal thin film
Low-carbohydrate Mediterranean style diet improves pain and body composition in women with lipedema: role of redox balance
Hydration-dependent exciton-phonon coupling in a metal-organic framework photocatalyst
Abstract Metal-organic frameworks (MOFs) have potential for light-harvesting applications owing to their tunable optoelectronic properties via building block selection. The presence of bound solvent in MOFs is known to impact gas adsorption properties, but little is known about their effect on optical properties. Here, we investigate how the presence of bound water can modulate the optical properties of a reported MOF photocatalyst, SU-101. Using the GW approximation and the ab initio Bethe-Salpeter equation (BSE) and treating exciton-phonon coupling with a finite difference method, we show that exciton-phonon interactions in SU-101 are strong and highly sensitive to the presence of bound water molecules. Specifically, we find that the presence of bound water is associated with a stronger localization of excitons due to phonons. Guided by our calculations, we synthesize SU-101 and study this MOF under different water loadings. We measure photoluminescence (PL) emission and UV-Vis absorption spectra of the MOFs, the latter in good agreement with our computational predictions. By considering the interplay of water loading, phonons, and excitons, our calculations rationalize measured UV-Vis absorption and PL spectra. Our work also highlights the pivotal role of exciton-phonon interactions and their sensitivity to hydration on the optoelectronic properties of MOFs with large pores and flexible organic linkers.
Finite element investigation of TPMS-inspired microplate structures for mechanical stability enhancement in sports equipment
Atomically thin heterojunction-based optoelectronic synaptic devices operable from −190 °C to 450 °C
Air pollution implies unequal threats to adverse birth outcomes across maternal educational levels in Madrid, Spain
A family of ultrathin non-van der waals kamiokite-type oxides for two-dimensional electronics
A novel hybrid transformer-based framework (H-ConvNeXt–Swin) to classify brain tumors using MRI
Abstract This paper introduces a hybrid deep learning model combining ConvNeXt and Swin Transformer for classifying brain tumors from MRI scans. The ConvNeXt backbone is employed to obtain detailed local spatial features, whereas the Swin Transformer identifies hierarchical long-range dependencies, facilitating complementary feature representation. The proposed model is evaluated on a combined public MRI dataset of 7,023 images distributed across four categories: glioma, meningioma, pituitary, and no tumor. Experimental results demonstrate that the proposed hybrid architecture outperforms several state-of-the-art convolutional and transformer-based models, achieving an accuracy of 95.37% with competitive precision and F-score. Additionally, qualitative explainability assessment using attention-based visualization techniques offers insight into the model’s decision-making by highlighting diagnostically significant regions. Furthermore, we evaluate the proposed H-ConvNeXt-Swin model on the unified dataset and also report source-stratified performance on each of the three constituent datasets: Figshare, SARTAJ, and Br35H. Future work will focus on validating the proposed framework on multi-center clinical datasets and extending it to more complex tasks such as tumor localization and segmentation.