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PAC1 receptor agonist maxadilan alleviates corneal inflammation in a murine model of LPS-induced ocular inflammation
Ocular inflammation is a leading cause of visual impairment and blindness worldwide. Systemic bacterial infections can spread hematogenously, resulting in endogenous endophthalmitis. These infections present significant diagnostic and therapeutic challenges due to their systemic origin and involvement of multiple ocular structures such as the retina and cornea. Pituitary adenylate cyclase-activating polypeptide (PACAP), acting through its selective receptor PAC1, exerts potent anti-inflammatory, anti-edema, and regenerative effects in various ocular conditions. This study aimed to assess the protective role of maxadilan, a selective PAC1 receptor agonist, in reducing corneal inflammation in a mouse model of lipopolysaccharide (LPS) -induced ocular inflammation. Mice received systemic LPS injections followed by intravitreal administration of maxadilan. Animals were monitored in vivo using optical coherence tomography (OCT) and tonometry. Post-mortem analyses included evaluation of cytokine expression profiles and routine corneal histological assessment. OCT revealed significantly reduced corneal edema and improved tissue structure with maxadilan administration. Additionally, maxadilan lowered intraocular pressure and reduced inflammatory cell infiltration into the aqueous humor. Cytokine profiling showed decreased expression of critical pro-inflammatory mediators such as GM-CSF, IL-6, MIP-1a, TNF-a, and TREM-1 in the maxadilan-treated group. Histological analyses confirmed that maxadilan preserved normal corneal architecture and effectively prevented pathological keratinization compared to untreated controls. These findings suggest that PAC1 receptor activation has therapeutic potential for controlling inflammation and maintaining corneal health in ocular inflammation.
Direct Synthesis of Polar Polyolefin Elastomers Enabled by Neutral Nickel‐Catalyzed High Temperature Solution Copolymerization
ABSTRACT Copolymerization of olefin and polar monomer to produce the desired polar polyolefin materials is a longstanding challenge, which becomes much more difficult when carried out at industrially preferred high temperatures due to both catalyst deactivation and rapid chain transfer reactions. Here, we report for the first time the direct synthesis of high‐molecular‐weight polar polyolefin elastomers (P‐POEs) from high‐temperature solution copolymerization of ethylene and biomass‐derived polar monomer at above 130°C. The key lies in the discovery of highly thermostable α‐ketocarboxamide nickel catalysts, which are not only neutral catalysts that do not require excess alkyl aluminum as an activator/scavenge/protecting agent but also in situ generate desired branches (36.8–70.6/1000C) through chain walking reaction that circumvents the need of α‐olefins. Molecular weights of P‐POEs with ester incorporations of 0.6–8.7 mol% produced at 130°C–170°C, which is also the preferred temperature for the synthesis of commercially non‐polar polyolefin elastomer (POE), reach 59–276 kDa that are 2–9 times higher than those obtained previously at high temperatures. Both high‐molecular‐weights and polar functional characteristics enable P‐POE as attractive materials, endowing them with desired properties, including tensile strength, strain recovery, melt flowability, hydrophilicity, dyeability, adhesion, and compatibilization properties.
Flexible electrolyte-gated oxide transistors for synaptic memory and neuromorphic computing
Flexible neuromorphic hardware integrating learning, memory, and reliable information processing is crucial for next-generation wearable electronics and artificial intelligent systems. Here, we developed flexible electrolyte-gated oxide transistors (EGOTs) for neuromorphic computing and memory applications. The optimized devices demonstrate key synaptic functionalities under electrical stimulation. The cognitive processes, such as repetitive learning, forgetting, and memory efficiency via a gate-voltage-controlled phenomenological weighting model, were also emulated. In addition, a convolutional neural network trained using EGOT characteristics achieves high classification accuracy on the Fashion-MNIST dataset for both raw and noise-perturbed inputs. These results highlight the promise of flexible devices for neuromorphic computing, wearable intelligence, and bioinspired artificial intelligence hardware.
Assessment of waterlogging tolerance in tea genotypes through morpho-physiological and biochemical profiling
Tea is one of the major economic crops in Bangladesh, however, its productivity is severely constrained by waterlogging stress arising from erratic climatic changes. A significant knowledge gap persists regarding the morpho-physiological and biochemical determinants of waterlogging tolerance and post-stress recovery in tea, hindering the development of tolerant genotypes. Therefore, the present study aimed to investigate the integrated morpho-physiological and biochemical responses of tea genotypes under waterlogging stress to identify tolerant genotype(s) based on stress tolerance indices (STIs). Eight pre-screened tea genotypes—P/LAL/08/23, P/LAL/08/62, P/LAL/09/116, P/AFN/11/35, P/AFN/11/46, P/OTI/31, P/AFN/13/90, P/AFN/11/31, with two control clones, BT2 (popular variety) and TV9 (waterlogging-tolerant variety), were evaluated using 23 morpho-physiological and biochemical parameters maintaining a two-factorial completely randomized design experiment. One set of tea genotypes was exposed to a 14-day waterlogging phase, followed by a 14-day recovery phase (total 28 days of stress), while another set of plants was maintained under control (non-stress) conditions, for the same period, to calculate STIs. Root fresh weight (RFW), leaf number (NL), net photosynthesis ( Pn ), transpiration rate ( E ), stomatal conductance ( g s ), relative leaf water content (RWC), and absolute growth rate (AGR) were significantly decreased under waterlogging condition compared to the control. Leaf chlorophyll a (CHA), chlorophyll b (CHB), and total carotenoids (CRTN), were reduced under stress, whereas proline content (of leaf: PRLF and root: PRRT), total antioxidant activity (of leaf: TACL and root: TACR), and lipid peroxidation (of leaf: LOPL and root: LPOR) increased as a part of adaptive responses. The traits such as RFW, NL, Pn , E , g s , RWC, AGR, CHA, CHB, CRTN, and LPOL, were detected as most influential at both phases of stress conditions from principal component analysis. Ultimately, the P/AFN/13/90 was identified as the most waterlogging-tolerant genotype, exhibiting higher STIs for maximum traits during both the waterlogging and recovery phases.
Hybridized Local and Charge‐Transfer Emitter With a Dual‐Hindered Indoline Donor for Fast and Efficient Red Plastic Scintillators
ABSTRACT Flexible organic red/NIR scintillators that convert ionizing radiation into long‐wavelength photons are intrinsically compatible with silicon photomultiplier (SiPM), offering strong potential for medical imaging and non‐destructive testing. However, their practical development is limited by inadequate radiation absorption, inefficient exciton utilization, and sluggish radioluminescence response. Herein, we report a fast and efficient red organic scintillator based on a hybridized local and charge‐transfer (HLCT) emitter featuring an ultra‐strong dual‐hindered indoline donor. The chair‐shaped indoline donor, incorporating a nonconjugated sterically hindered ring, effectively red‐shifts the radioluminescence to match SiPM sensitivity while affording a large Stokes shift and aggregation‐induced emission characteristics. The introduction of an additional bulky tert ‐butyl group further suppresses nonradiative decay and, unexpectedly, accelerates reverse intersystem crossing from high‐lying excited states, enabling intense radioluminescence with an ultrafast decay lifetime of 3.94 ns—among the fastest reported for red scintillators. The resulting scintillator, c ID‐Bt, achieves efficient triplet exciton harvesting and delivers a high light yield of 24231 photons MeV − 1 (1.8 times the commercial benchmark BC‐430), an ultralow detection limit of 168 nGy s − 1 and a resolution of 16.8 lp mm − 1 . Furthermore, c ID‐Bt–encapsulated plastic scintillators and scintillating fibers demonstrate potential for radiation detection and x‐ray imaging, establishing a viable strategy for next‐generation organic red/NIR scintillators.
A highly sensitive diamond NV magnetometer using Ramsey interferometry with a short sensor-to-sample distance
In this study, we developed a diamond quantum magnetometer based on Ramsey interferometry with a short sensor-to-sample distance. Conventional biomagnetic sensors with ensemble nitrogen-vacancy centers using continuous-wave optically detected magnetic resonance and Ramsey methods typically rely on watt-level lasers to achieve high sensitivity, resulting in thermal issues. In contrast, by employing the light-trapping diamond waveguide technique in a high-pressure and high-temperature diamond sample treated with electron beam irradiation, we obtained a high optical power conversion efficiency of 9.5%, enabling us to simultaneously achieve a high sensitivity of 2.93(7) pT/Hz in the 100–400 Hz frequency range and a minimal temperature increase of only approximately 13 K at a low laser power of 210 mW. Using a dry phantom designed to mimic magnetoencephalography signals, we measured a weak magnetic field of 77.7(2) pT without signal averaging at a sensor-to-sample distance of 2.5 mm. This short distance measurement prevents severe spatial signal attenuation, yielding a high signal-to-noise ratio. The development here is crucial for practical biomagnetic applications based on Ramsey interferometry.
Automated concrete crack detection enhanced by deep learning and generative adversarial networks
Cracks on the surface of concrete structures are an obvious indication of their condition. However, traditional visual inspection methods are significantly influenced by human factors. They are accurate for larger cracks. However, they introduce subjective judgment when detecting smaller cracks. They also pose safety risks. Automated detection methods face challenges in achieving lightweight implementation due to limited annotated data and difficulties in complex environments. This paper proposes a method for detecting and measuring concrete cracks. The method uses a generative adversarial network (GAN) to augment data and a U-Net segmentation network. Conditional GAN (cGAN) is proposed to generate high-fidelity crack images using training-set data only, avoiding data leakage and effectively solving insufficient sample issues. A U-Net segmentation network that has been enhanced performs crack segmentation. This network incorporates channel attention modules and dilated residual blocks. A hybrid loss function that combines the Dice coefficient and binary cross-entropy resolves class imbalance. Fractal theory quantifies the geometric features of the detected cracks. The crack segmentation model that was trained on cGAN-augmented datasets achieved an average precision (AP) of 82.03% and an F1 score of 83.24%, as demonstrated by experimental results. The fractal dimension–based crack measurement method achieves an accuracy of ≤4% error for complex crack networks (fractal dimension >1.6). The proposed method provides a reproducible, automated, high-precision solution for concrete crack detection and quantification, with verified stability and engineering applicability.
Analyte-class-dependent electrostatic–transport coupling in graphene FET biosensors
The transfer curve of a graphene field-effect transistor (GFET) biosensor encodes analyte information through multiple figures of merit, yet how electrostatic gating and carrier transport couple at the graphene–electrolyte interface remains unresolved. Here, stability-filtered polynomial analysis of three reduced graphene oxide GFET datasets (pH, Cl−, and HEV ORF2) is consistent with analyte-dependent coupling patterns across these three representative systems: ionic sensing—particularly pH—exhibits a stable, reproducible VCNP·gm,h interaction consistent with co-varying surface-potential shifts and mobility changes, whereas protein detection (ORF2) is characterized by transconductance–curvature cross terms consistent with Debye-screened scattering. Chloride sensing occupies an intermediate regime with weaker but reproducible VCNP-linked contributions within a curvature-dominant pattern. These explicit second-order coupling terms suggest that ionic-sensing behavior may be associated with electrostatic–transport coupling, which is potentially tunable through quantum-capacitance/double-layer engineering, while protein sensing appears to respond primarily through scattering channels accessible via functionalization design.
Academic and psychological determinants of mental stress among medical students in Dhaka: A multinomial logistic regression approach
As stress among medical students is a global health concern due to comprehensive academic engagements, this study aimed to assess the educational and psychological factors related to mental stress among medical students in Dhaka city. A descriptive type of cross-sectional study was conducted in two medical colleges in Dhaka city among 380 medical students from 3 rd year to 5 th year, using a pre-tested, self-administered, and structured questionnaire. The data obtained were analyzed using IBM SPSS Statistics (version 23) and Stata (version 14.2) employing the chi-square association test for bivariate analysis and subsequent multinomial logistic regression for the significant variables. Among 380 respondents, 68.7% were female, with 51.8% of respondents from Shaheed Suhrawardy Medical College. The mean stress score of medical students was 21.64 (SD = 6.64), with 67.9% and 22.6% as moderately and highly stressed, respectively. In bivariate analysis, 29 academic factors and 09 psychological factors were significantly associated with stress. In multinomial logistic regression analysis, three models were conducted, containing variables related to academic stress in model 1, variables related to psychological stress in model 2, and all stress variables in model 3. From model 1, academic performance, study and exam score anxiety, fear of future uncertainty and new living, administration satisfaction, and lack of entertainment facilities are significantly associated with stress. Additionally, the factors significantly associated with stress are time management, social life perception, and feeling lonely for model 2 and all stress-related variables, including study and exam score worry, difficulty in understanding context, fear of new living, administration and lecture satisfaction, teaching strategy satisfaction, time management, parental support in study, and feeling loneliness for model 3. Proper interventions for stressors of mental stress, further assessment, and rehabilitation for high and moderate-stressed respondents could be a prime concern to lessen the burden of stress among medical students.
Multiwavelength plasmonic optoelectronic memristor for color image classification via physical reservoir
Optoelectronic memristors have tremendous potential for constructing highly efficient neuromorphic visual systems because of their integration of sensing, storage, and computing. Here, a multiwavelength-modulated plasmonic optoelectronic memristor is developed using Au/WO3 nanocomposite. Benefitting from the size-dependent localized surface plasmon resonance absorption property, the distinction of the R (680 nm), G (550 nm), and B (450 nm) light response can be utilized in a single device. Based on this, multiwavelength synaptic plasticity is mimicked, including excitatory postsynaptic current, paired-pulse facilitation, and spike-number-dependent plasticity. Furthermore, 4-bit reservoirs with RGB inputs are implemented for constructing an RGB reservoir computing system. Consequently, considerable accuracy (98.3%) for classification of color images is implanted via the RGB input mode, demonstrating the advantage of color information for complicated neuromorphic tasks. This work demonstrates the potential of the plasmonic optoelectronic memristors for high-performance neuromorphic vision systems.
Adverse effects of removable orthodontic aligners: A systematic review with single-arm meta-analysis
Background Removable orthodontic aligners are widely used due to their aesthetic appeal, removability, and perceived comfort. However, evidence regarding their potential adverse effects remains limited. Objective To identify and synthesize the adverse effects associated with removable orthodontic aligner therapy. Methods This systematic review with single-arm meta-analysis evaluated adverse effects related to removable orthodontic aligner therapy. Seven electronic databases (Cochrane, Embase, LILACS, Livivo, MEDLINE, Scopus, and Web of Science) and gray literature sources (ProQuest Dissertations and Google Scholar) were searched without language or date restrictions. Two calibrated reviewers independently selected studies. Risk of bias was assessed using RoB 2 for randomized trials and ROBINS-I for non-randomized studies. Due to clinical and methodological heterogeneity, most outcomes were synthesized narratively; meta-analyses were conducted only for pain/discomfort, apical root resorption, and plaque. Results Thirty-four studies met the inclusion criteria, including 10 randomized controlled trials, 6 non-randomized clinical studies, and 18 cohort studies. Randomized trials were predominantly at low risk of bias, whereas most non-randomized and cohort studies were at moderate to high risk. Pain and apical root resorption were the most frequently reported adverse effects. Pain peaked within 24 hours, decreased by day 3, and was minimal by 1 week. Apical root resorption was generally small, with a mean linear loss of −0.33 mm (95% CI −0.55 to −0.11) and a volumetric loss of −4.37 mm³ (95% CI −5.51 to −3.24). Findings on plaque at 3 months were inconclusive. Other reported outcomes included periodontal changes, enamel demineralization, white spot lesions, speech alterations, halitosis, open gingival embrasures, temporomandibular symptoms, and awake bruxism. Conclusion Short-term pain and minor apical root resorption appear to be the most frequently reported adverse effects of removable orthodontic aligners, although the certainty of evidence varies across outcomes. Speech alterations, discomfort, and white spot lesions have also been reported.
Machine-learning-identified two-dimensional van der Waals multiferroics for four-state nonvolatile memory
Two-dimensional (2D) van der Waals (vdW) multiferroics offer an attractive platform for four-state nonvolatile memory by combining switchable ferroelectric (FE) polarization and magnetization within a single material system. However, their development is hindered by the scarcity of synthesizable candidates and the lack of nondestructive readout schemes. Here, we develop a synthesizability-aware machine-learning workflow that combines positive-unlabeled bagging ensembles with transfer learning and integrate it with first-principles calculations to explore the 2D vdW ABC2X6 family, yielding a set of high-confidence multiferroic candidates together with interpretable chemical design rules. Among them, AuCrP2S6 monolayer emerges as a representative system with a ferromagnetic ground state, a sizable out-of-plane polarization of 7.46 pC/m, and a moderate FE switching barrier of ∼130 meV/f.u. Moreover, the nonlinear optical response mediated by the bulk photovoltaic effect in AuCrP2S6 provides a dual-channel probe of the ferroic orders, in which the polarization direction governs the photocurrent sign while the magnetic order selects the spin channel via robust exchange splitting. This intrinsic coupling enables the nondestructive readout of four logic states within a single atomic layer, thereby providing an optoelectronic readout scheme for next-generation multistate memory.
Cancer-associated fibroblast subtype signature gene predicts survival and immunotherapy response in sarcoma
Background Sarcomas show heterogeneous responses to immune-checkpoint blockade (ICB), and cancer-associated fibroblasts (CAFs) are considered to shape the tumor immune microenvironment, yet CAF programs that predict ICB outcomes in sarcoma remain unclear. Methods We investigated the interaction between different cell types in the sarcoma tumor microenvironment and their effects on immune-checkpoint blockade response, with a focus on identifying signature genes and molecular mechanisms that distinguish tumor-promoting from tumor-suppressive CAFs at the single-cell level. We analyzed single-cell data from two different sarcoma cohorts, transcriptome profiles of 206 sarcoma patients recruited from The Cancer Genome Atlas, and predicted immune-checkpoint blockade (ICB) response data inferred using the TIDE algorithm from 64 TCGA sarcoma patients. Results We found 134 stem-like CAF-related signature genes in the recurrent group and eight signature genes in the metastasis group, defining three CAF subtypes (myofibroblastic CAF, antigen-presenting CAF, and inflammatory CAF). SIG134 and SIG8 were associated with TIDE-inferred ICB response in subtype-specific analyses: SIG134 in STLMS and ULMS, and SIG8 in MFS, STLMS, and ULMS. In addition, the MDK-NCL ligand-receptor signal transduction pathway was linked to the infCAF subtype and myoCAFs in metastatic sarcoma. Furthermore, SIG4 ( MDK , SDC2 , LRP1 , and NCL ) was highly expressed in inflammatory CAFs. Conclusions Single-cell-derived CAF signatures may reflect sarcoma subtype-specific stromal programs associated with predicted ICB response and clinical outcome. SIG4 is proposed as a candidate prognostic signature that warrants further validation.
Investigation of the microscopic mechanisms for InGaN quantum well performance optimization in UVA-LEDs
InGaN multiple quantum wells (MQWs) operating in the ultraviolet A spectral band face significant challenges in achieving high quantum efficiency due to the pronounced quantum-confined Stark effect (QCSE) and the difficulty of incorporating In at low In compositions. This work employs a dual strategy of optimizing ultra-thin QW structures and incorporating GaN interlayers to enhance the quantum efficiency of InGaN/GaN heterostructures. The synergistic interplay between the quantum confinement effect and interfacial strain effectively reduces the density of non-radiative recombination centers and mitigates the detrimental impact of the QCSE. Combining electron energy loss spectroscopy with first-principles calculations, we have characterized the local charge reconstruction at the heterointerfaces. This characterization unequivocally confirms the regulatory role of QW interfaces with different thicknesses in modulating the built-in electric field. Our findings provide a novel research paradigm for a more in-depth exploration of the mechanisms governing interface and defect behavior in semiconductor heterostructures.
Chronic CBD treatment differentially modulates neurobehavioral outcomes and endocannabinoid signaling in an aged HIV-1 Tat transgenic mouse model
As the population of older individuals living with HIV continues to expand, identifying non-euphoric therapeutic interventions for HIV-associated complications is essential. While cannabidiol (CBD) has demonstrated neuroprotective potential, its effects following prolonged exposure in the context of an aging biological environment remain poorly understood. This study utilized aged (15–18 months) male and female HIV-1 Tat transgenic mice to evaluate the impact of chronic CBD (3 mg/kg, s.c., for 12 weeks). A behavioral battery was employed to assess object recognition memory, anxiety-like behavior, spontaneous nociception, and locomotor activity. Subsequently, liquid chromatography-tandem mass spectrometry and Western blotting were used to map endocannabinoid ligands (AEA, 2-AG, AA), metabolic enzymes (FAAH, MAGL), and receptors (CB 1 R, CB 2 R, GPR55) across the prefrontal cortex, amygdala, brainstem, and spinal cord. Results indicated that chronic CBD improved recognition memory specifically in female Tat(+) mice. While CBD treatment did not affect spinal cord-related tail flick sensitivity it universally increased supraspinal hot plate sensitivity. Chronic CBD treatment also elevated baseline body temperature and modulated body mass without impairing general locomotion. Furthermore, chronic CBD restructured the eCB signaling landscape across all examined CNS regions in a highly sex- and genotype-dependent manner. Notably, cannabinoid receptor and GPR55 expression exhibited distinct regulatory shifts governed by significant three-way interactions. These findings demonstrate that chronic CBD intervention interacts with the eCB system of aged mice in a region-specific manner. These results underscore the critical importance of considering age, sex, and treatment duration when evaluating cannabinoid-based therapies for the management of neuroHIV.
All optical broadband tunable quantum frequency shifter
A frequency shifter of the photon is a key component for frequency-multiplexed high-capacity quantum communications and frequency-encoded quantum computation. Existed methods for shifting the frequency of a photon based on electro-optical or acousto-optical effect, however, suffer the limited frequency shift up to a few hundreds of GHz; furthermore, high-quality micro-wave electronics are required. The frequency of a photon can also be shifted with the frequency difference equal to the frequency of pump laser by using an all optical-wave-mixing approach, which is usually about tens of THz. So, there is a big frequency shifting gap between these methods. Here, we propose a new scheme of a quantum frequency shifter based on an all-optical-wave-mixing process, which can theoretically achieve a frequency shift ranging from GHz to a few THz, therefore bridging the gap. As a principle of poof, by using two pump beams in a three-wave mixing cascading process, a heralded single photon is frequency-shifted more than ±400 GHz, and the shift can be tuned continuously over broadband by changing the frequency difference between two pump lasers. In addition, high coincidence to accidence ratio between the shifted photons and the heralded photon indicates the preserve of quantum properties. The present quantum frequency shifter is in analog to an electro-optical based shifter, but with much broader tuning ability. Our all-optical quantum frequency shifter will become a fundamental building block for high-speed quantum communication networks and frequency-domain photonic quantum computation.
HPV vaccination intention and its association with vaccine hesitancy and health beliefs among unvaccinated Chinese medical college students: A cross-sectional study
Background Human papillomavirus (HPV) infection poses a significant public health challenge, and HPV vaccination uptake among Chinese college students remains suboptimal. Therefore, it is essential to conduct ongoing, in-depth investigations into the psychological and cognitive mechanisms of HPV vaccination intention. This study aims to explore the associations among HPV vaccination intention, vaccine hesitancy, and health beliefs among Chinese medical college students. Methods A cross-sectional survey was conducted from June 16 to July 16, 2024, to assess sociodemographic characteristics, vaccine hesitancy, and health belief model (HBM) among college students at Guangdong Medical University. Descriptive statistics, univariate analysis, and binary logistic regression were performed to identify factors associated with HPV vaccination intentions among unvaccinated students. Results Among 3,584 unvaccinated participants, 73.16% expressed willingness to receive the HPV vaccine. The key significant associations identified included gender (male) (OR: 0.26; 95% CI: 0.22 ~ 0.31; p < 0.001), parental occupation (employees in enterprises, commercial, and service industries (including self-employed)) (OR: 1.43; 95% CI: 1.18 ~ 1.75; p < 0.001), and information sources (hospitals or doctors) (OR: 1.37; 95% CI: 1.15 ~ 1.64; p = 0.001). Vaccination intention was also directly associated with the perceived necessity (OR: 0.91; 95% CI: 0.88 ~ 0.94; p < 0.001) and importance (OR: 1.07; 95% CI: 1.03 ~ 1.10; p < 0.001) of vaccine hesitancy, as well as the perceived benefits of behavior for the HBM (OR: 1.21; 95% CI: 1.12 ~ 1.32; p < 0.001). Conclusions In this sample, Chinese medical college students demonstrated a relatively high willingness to receive HPV vaccination. Willingness was positively associated with perceived necessity, importance, and benefits of vaccination. Integrating structured clinician-delivered counseling into curricula, using parental occupation data for targeted subsidies, and embedding classroom sessions comparing personal HPV risk with vaccine safety data may improve uptake. Government evaluation of gender-neutral vaccination policies and provision of male-inclusive campus services are also recommended.
Demonstration of exchange biased de Gennes spin valves
De Gennes spin valves, consisting of a thin superconductor sandwiched between two ferromagnetic insulating layers, have been shown to exhibit nonvolatile bistable switching behavior, making them attractive devices for a variety of cryogenic electronic applications. In this work, we introduce an additional control knob by interfacing one of the ferromagnetic insulator layers with an antiferromagnet, thereby invoking the phenomenon of exchange bias. We demonstrate that such exchange biased de Gennes spin valves show field cooling direction-dependent variation in Tc, and exhibit more robust nonvolatile bistability due to antiferromagnet-induced modification of the magnetic switching behavior, including a substantial coercivity enhancement, significantly reducing the sensitivity of their switching behavior to small magnetic field fluctuations.
A feature-efficient dual-task machine learning framework for predicting bone mineral density and osteoporosis stratification in resource-constrained environments
Osteoporosis is a chronic skeletal disorder characterized by progressive bone mineral density (BMD) loss and structural deterioration, significantly increasing fracture risk. Despite its high prevalence, early detection remains challenging due to its asymptomatic progression and the limitations of conventional diagnostic techniques, such as Dual-Energy X-ray Absorptiometry (DXA). While DXA remains the clinical benchmark for BMD assessment, its high cost, limited accessibility, and inability to directly detect vertebral fractures necessitate the development of alternative, cost-effective, and widely deployable diagnostic methodologies. A dataset of 159 patient records was collected from NORI and CDA Hospital, incorporating 17 input features spanning demographics, genetic/blood type, clinical history and lab tests parameters. To bridge this gap, we developed a practical machine learning model tailored for clinics with limited resources. Instead of relying on expensive imaging, our framework uses only basic, highly accessible clinical markers—specifically ABO blood groups, serum calcium, and potassium levels. Because these tests are inexpensive and easily processed in standard laboratories, our approach removes the financial and technical hurdles of advanced diagnostics, making early screening possible in remote or underfunded healthcare settings. Data preprocessing involved rigorous feature selection, standardization, hyper-parameters tuning, clinically relevant features derivation and biomarker combinations. For classification, ensemble voting classifier was trained on key biomarkers— Weight, Potassium, Calcium and Total Vitamin D—achieving an accuracy of 90% and an AU-ROC score of 0.93 in predicting osteoporosis severity. In parallel, extreme gradient boosting Regressor trained on Age, Weight, ABO Group and Total Vitamin D demonstrated an R 2 of 0.536 for lumbar spine BMD estimation. The proposed framework demonstrates the viability of leveraging machine learning for non-invasive osteoporosis screening and fracture risk assessment, offering a radiation-free and clinically accessible complementary pre-screening tool.
Consistent description of room-temperature electron spin relaxation in GaAs/AlGaAs (001) quantum wells
Spin relaxation of two-dimensional electrons in GaAs/AlGaAs (001) quantum wells (QWs) has been widely studied and is generally governed by the D'yakonov–Perel' (DP) mechanism at room temperature, yet the reported electron spin relaxation times and the Dresselhaus spin–orbit coupling constant, γD, remain quantitatively inconsistent. Here, we present a unified quantitative interpretation of room-temperature DP spin relaxation using γD=8.8 eVÅ3, identical to the value for bulk GaAs. We show that the previously reported more-than-twofold variation in the quantization-energy dependence of the spin relaxation time originates primarily from the electron-density dependence of electron–electron scattering. Our results resolve a long-standing ambiguity in room-temperature electron spin relaxation in (001) QWs and provide a reliable basis for quantitative modeling of semiconductor spintronic devices.