Visible-thermal tiny pedestrian detection in UAV aerial images is crucial for online decision-making in urban security and disaster response. However, the extremely small scale and sparse distribution of pedestrians cause discriminative cues to be submerged by dominant low-frequency background and contextual redundancy during feature learning. Meanwhile, cross-modal spatial misalignment and spatially varying modality reliability hinder stable fine-grained correspondence, thereby degrading fusion quality. To address these issues, QA2FDet is proposed as a quality-aware adaptive alignment fusion network comprising three modules: spectrum-spatial decoupled enhancement module (SDE), cross-modal correspondence mining module (CCM), and prior-informed gated fusion (PGF). SDE leverages the discrete cosine transform to disentangle redundant low-frequency background information, while deep semantic gating propagates high signal-to-noise ratio details into shallow representations to enhance subtle cues of tiny pedestrians and suppress high-frequency noise. To establish fine-grained neighborhood correspondences under slight spatial offsets, thermal-guided local asymmetric cross-attention is designed in CCM. Finally, region-level quality and modality discrepancy are jointly modeled for adaptive cross-modal fusion in PGF. Extensive experiments on multiple UAV-based RGBT detection benchmarks demonstrate that QA2FDet achieves state-of-the-art performance and exhibits strong robustness in challenging aerial scenes.
Insects inhabit complex vibroscapes shaped by substrate-borne vibrations from multiple biotic and abiotic sources. One underappreciated topic is how vibrations function in predator-prey interactions. Tiny warty birch caterpillars (Falcaria bilineata) are known to produce complex vibratory signals to defend leaf-tip territories against conspecifics, raising the question of whether vibratory signalling and sensing also play roles in predator-prey interactions. We staged encounters between resident neonate caterpillars and three natural intruders: conspecifics, ladybird beetle larvae and adult ladybird beetles, while simultaneously recording behaviour and substrate-borne vibrations. Resident caterpillars showed three key responses - vibratory signalling, freezing and dropping - but these responses varied strongly with intruder identity and stage of encounter. Residents signalled vigorously toward conspecifics, with rates escalating as intruders approached their territories. In contrast, encounters with predators evoked predator-specific defensive strategies including freezing and dropping. Adult ladybird beetles, which caused high mortality (43%), elicited rapid escape responses and subsequent territory abandonment, while ladybird beetle larvae, which caused no mortality, triggered slower responses with initial signalling that ceased upon closer approach. Critically, vibrations generated by the 'footsteps' of each approaching intruder type produced distinct vibratory 'signatures', differing in amplitude, spectral and temporal characteristics. Resident caterpillars also initiated defensive responses before physical contact, often when intruders were still centimetres away. Together, these findings demonstrate that these miniature larvae, no larger than ∼1-2 mm, thrive in complex vibroscapes where vibrations not only function to advertise territory ownership against conspecifics but also provide essential early-warning cues enabling sophisticated threat assessment and context-appropriate defensive responses in predator-rich environments.
Microbial biodiversity is essential for the proper functioning and balance in the ecosystem, and they possess numerous biotechnological and industrial applications. Microbial biobanks serve as a major resource for conserving valuable microbial diversity, providing access for education, research, and industrial applications. The present comprehensive review article discusses the history, responsibilities, and applications of microbial biobanks, covering different regulatory ecosystem and their guidelines for the governance of biobanks. It specifically provides the current global status of MCCs and precisely sheds light on MCCs in India. This review critically highlights a stark disparity in the bio-resources management, showing dominance of Asia with substantial investment in bio-infrastructure. In India, a significant geographical disparity was observed with a heavy concentration of culture collections in states with established research infrastructure. Despite the immense commercial potential of microbes, very few MCCs are held by the private sector globally, leaving the burden of conservation on the public sector and raising concerns about long-term survival and financial instability. Furthermore, there is a critical need for emerging advanced preservation strategies, AI-assisted tools for biobanking operations, and stringent quality control procedures in biobanks meeting international standards. In addition, the findings underscore the necessity of the digitalization of culture collections to facilitate global data sharing and robust policies for access and benefit sharing of microbial resources. In conclusion, addressing these regional disparities, mitigating financial and infrastructure gaps, using emerging and advanced strategies, and digitalization of CCs are imperative for the sustainability and applicability of the microbial biobanks.
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With improving survival, periviable neonates (≤25 weeks' gestation) represent a dynamic, but under-studied population in neonatal care, with persistently high cardiopulmonary and vascular vulnerability. Immature cardiovascular structure and function as a consequence of immature myocardial architecture, altered calcium handling, relative adrenal insufficiency, and persistent fetal shunts contribute to complex and dynamic cardiovascular physiology in this population. This may be present clinically in the form of hypotension and low end-organ perfusion. Traditional paradigms of blood pressure-based definitions of hypotension are poorly validated in this population and do not accurately reflect systemic blood flow or end-organ perfusion. Emerging evidence supports a phenotype-based multi-parametric approach to cardiovascular assessment, to distinguish ongoing physiological changes such as ductal physiology, pulmonary hypertension, low systemic vascular resistance and primary myocardial dysfunction phenotypes. However, significant knowledge gaps remain, including lack of normative hemodynamic values, and limited evidence guiding pharmacologic therapies. This narrative review focuses on the cardiovascular challenges in the management of periviable neonates as they transition to extrauterine life, delineating cardiac phenotypes, describing modalities of cardiovascular assessment and identifying existing knowledge gaps. We propose a physiology-based approach to cardiovascular management strategies based on existing, albeit limited, evidence. IMPACT: Periviable neonates present unique hemodynamic challenges due to structural and functional cardiovascular system immaturity, which can be categorized into different hemodynamic phenotypes dictated by baseline cardiac function, lung compliance and directionality of intracardiac shunts, especially the patent ductus arteriosus. In the absence of established normative reference values for common modalities of cardiac assessment, optimal care should consist of early identification of cardiac phenotypes, continuous surveillance, physiology-based management strategies, and frequent reassessment to guide individualized treatment.
Understanding human evolution relies on biomolecular data from ancient skeletal tissues, yet warm climates often cause complete collagen loss, excluding many regions from the study. This research investigates the survival of non-collagenous proteins (NCPs) and low-molecular-weight proteins in archaeological bones deemed collagen-free by traditional metrics. Using a multi-method approach, we employed sandwich enzyme-linked immunosorbent assays for osteocalcin quantification, Qubit fluorometry for total protein, and liquid chromatography tandem mass spectrometry (LC-MS/MS) for characterization, complemented by Fourier transform infrared spectroscopy to assess the diagenetic state. Samples included collagen-depleted bones from Neolithic Lebanon and Palaeolithic France, with well-preserved controls from Neolithic Serbia and Paleolithic Russia. Results indicate that bone-associated NCPs, including osteocalcin, survive only if the insoluble collagen is preserved. Methodologically, tangential flow filtration outperformed centrifugal devices for protein recovery. EDTA demineralization with FASP was most effective for maximizing collagen recovery, while HCl demineralized protein precipitation best detected unique NCPs. Collagen was identified in the soluble supernatants of most collagen-depleted bones. Abundant collagen peptides were identified in a sample with a 0% collagen yield and a very low amide-to-phosphate ratio. These findings demonstrate that bones unsuitable for traditional dating can still retain measurable collagen, broadening the range of biomolecular analyses possible in warm and humid climates.
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Phage infection undergoes a series of physiological transitions, holding crucial information about phage replication dynamics and potential phage-derived antimicrobials. Although phage-induced cytological changes have been used to infer phage-hijacking mechanisms, current approaches are limited by the lack of comprehensive single-cell morphological analysis and insufficient resolution of temporal dynamics, particularly for phages displaying short latent periods, thereby hindering systematic characterization of morphological transitions throughout the infection cycle. Here, we characterized a newly identified coliphage with a genome of 53 kbp, Tiny, which exhibits an unusually long latent period, making it an ideal candidate for resolving temporal morphological transitions. Tiny exhibits both temperature- and host-dependent killing profiles against diverse Escherichia coli strains, including ATCC 25922, uropathogenic E. coli, and avian pathogenic E. coli. While its replication efficiency is temperature-dependent, showing enhanced productivity at lower temperatures, the duration of its adsorption and latent period is largely host-dependent. Depending on the bacterial strain, Tiny exhibits either a prolonged latent period in slow-adsorbing strains or a rapid one in fast-adsorbing strains, regardless of infection temperature, suggesting phage-host compatibility. Single-cell bacterial cytological profiling of Tiny-infected ATCC 25922 cells revealed that Tiny progressively induces distinct bacterial morphological transitions throughout its lytic cycle, suggesting sequential interference with host physiology as part of its replication cycle prior to cell lysis. This work establishes a broadly applicable framework for dissecting lytic phage biology with high temporal resolution and lays the foundation for future integrative omics studies aimed at understanding how phages sequentially modulate their bacterial hosts. Antibiotics trigger unique patterns of morphological changes in bacteria, and these compound-specific signatures provide a basis for determining mechanisms of action in antibiotic discovery. By the same concept, phage-induced morphological changes can reveal key insights into phage replication dynamics and guide the identification of phage-derived antimicrobials. However, the complexity of phage biology and the variability of phage-host interactions pose challenges in interpreting these phenotypic outcomes. Here, we employed a phage-host pair that exhibits an unusually prolonged latent duration as a model to establish a broadly applicable framework for dissecting lytic phage biology with high temporal resolution. Through single-cell bacterial morphological analyses, this approach captures dynamic infection processes inducing morphological transitions across the phage replication cycle. This work provides a phenotypic analysis pipeline to advance our understanding of phage-host interactions and lays the foundation for future integrative omics studies to elucidate how phages sequentially modulate their bacterial hosts.
The domesticated apple (Malus domestica) is widely considered an admixture primarily involving M. sieversii, M. sylvestris, and M. orientalis, with possible contributions from tiny-fruited species such as M. baccata. Although this origin theory is well supported by genomic studies, the relative contributions of each progenitor species to cultivar genomes remain unclear, and taxonomic classifications of some Malus species are conflicting. The availability of new genomic tools and resources enables probing of these topics. A panel of wild Malus accessions was genotyped using the Illumina Infinium 20K apple SNP array to generate a library of species-specific SNP and haploblock alleles. After excluding heavily admixed individuals and masking admixture haplotypes in individuals with small amounts of admixture, this library was used to clarify the ancestry of 436 wild accessions and estimate the genomic proportions attributable to M. sieversii, M. sylvestris, M. orientalis, and collectively, tiny-fruited Malus species in M. domestica cultivars. The results were broadly consistent with previous studies but revealed new ancestry details and enabled precise haplotype-level attributions. Previously unreported admixture and taxonomic irregularities were detected for some wild accessions. All M. domestica cultivars were found to be mixtures of the three primary progenitors, with some also showing contributions from tiny-fruited Malus species. The relative contributions varied among cultivars, with evidence that older cultivars and traditional cultivars originating in eastern Europe or Western Asia had less M. sylvestris ancestry than modern cultivars and traditional cultivars originating in Western Europe. This study provides new insights into apple ancestry and highlights the need for clarification in Malus taxonomy. The findings have implications for germplasm management, historical research, and genetic improvement of apple cultivars. The species-specific allele library developed here offers a valuable resource for routine admixture estimation of Malus accessions genotyped on the same set of SNPs.
In this paper, we tackle a core challenge for wearable human activity recognition (HAR), namely the recognition of daily-living and locomotion activities from inertial sensor windows: delivering reliable, interpretable uncertainty on tiny microcontrollers where latency, RAM, and energy are tightly constrained. Existing embedded approaches either calibrate softmax confidences, which are cheap but brittle under sensor placement or tempo shifts, or rely on generative or posterior-sampling schemes that exceed TinyML budgets. We propose hyperdimensional distance- and uncertainty-aware human activity recognition (HDUQ-HAR), an on-device hyperdimensional computing (HDC) framework that encodes each IMU window into a bipolar hypervector, classifies via prototype similarity, and derives three complementary, lightweight uncertainty signals from the same representation: (1) distance to the class prototype, (2) similarity gap to the runner-up, and (3) vote-dispersion capturing n-gram consensus. A label-conditional conformal layer converts these scores into set-valued predictions with finite-sample coverage guarantees and exposes a human-readable reason code indicating why uncertainty increased. Across UCI HAR, WISDM, PAMAP2, and OPPORTUNITY with subject-out splits and realistic shifts (orientation, gain, time-warp, missing axis, cross-placement), HDUQ-HAR achieves near-target coverage at [Formula: see text] with near-singleton sets on i.i.d. data (average size 1.18-1.25) and robust shift/OOD detection (AUROC 0.92-0.96), while running in 3-5 ms/window on Cortex-M4 with ∼6-9 KB RAM and ∼5-7 KB Flash. By unifying HDC geometry with label-conditional conformal prediction, our method shows that efficiency and reliability can co-exist in wearables, yielding small, calibrated sets that expand gracefully under shift and actionable explanations practitioners can trust.
Background/Objectives: The aim of this study is to test different convolutional neural network (CNN) and Transformer-based models to detect and classify vertical misfit at the abutment-prosthesis interface on panoramic radiographs, and to develop a hybrid deep learning model enhanced with attention mechanisms. Methods: A dataset consisting of a total of 566 images, manually classified as 249 'fit' and 317 'misfit' cases by two experts, was created. Images were resized to 224 × 224 and divided into training, validation, and test groups. The deep learning model yielding the most successful results was determined as the backbone; a hybrid model was developed by integrating three different attention modules (SE, CBAM, and ECA) into this structure. Model performance was evaluated using accuracy, precision, sensitivity, and F1 score metrics. Results: CNN-based models (RegNetY-800MF, ConvNeXt-Tiny, EfficientNetV2-S, ResNet50) performed better than Transformer-based models (DeiT, Swin-Tiny) in all metrics. The proposed hybrid model exhibited the highest success among all tested models with a 99.12% accuracy rate. This model reached a 100% precision value in the misfit group and yielded no false positive results. The F1 scores of the hybrid model were recorded as 99.01% for the fit group and 99.21% for the misfit group. Conclusions: The findings of this study demonstrate that attention-enhancing deep learning frameworks have the potential to significantly improve the diagnostic utility of routine panoramic radiographs. It shows that panoramic imaging, when supported by advanced artificial intelligence, can provide valuable diagnostic support in detecting vertical misfit. The developed model has the potential to become a reliable clinical decision support system.
Facial emotion recognition (FER) is one of the main fields of research in image processing and artificial intelligence with applications in human-machine interaction, behavior analysis, and intelligent vision. The RAF DB dataset, a widely used benchmark dataset for emotion recognition, is used for the training and testing of deep learning models. In real application conditions, the facial image may be subjected to visual noise. Large glasses that occlude the eye region of the face are one example of environmental or artificial noise which distorts the visual information. If frames covering a large area of an expressive part of the face, like the eyes, are recognized as noise by the emotion recognition network this may lead to noticeable performance degradation. Artificial occlusion noise, similar to large glasses, was applied to the eye region of the facial images from the RAF DB dataset. The impact of this type of structural noise on the performance of deep-learning-based facial emotion recognition models is analyzed. Three state-of-the-art architectures (ResNet-50, Vision Transformer (ViT-Base/16) and Swin Transformer Tiny (Swin T)) are tested to determine their robustness to the noisy application conditions. The objective of the study is to determine and suggest possible methods to counter the decrease in recognition performance caused by the occlusion noise. Experimental results indicate that, within our evaluation setup, transformer-based architectures, especially Swin-T, tend to be more robust to the simulated visual noise, which may be useful for the design of facial emotion recognition models intended for more complex conditions.
Allergic reactions have surged to unprecedented levels, impacting about 30% of people globally. Fungi are a significant source of allergens, responsible for approximately 6% of respiratory diseases in the general population. However, identifying the exact cause of respiratory allergies is not always possible. To investigate the capacity of Erysiphe palczewskii and Erysiphe convolvuli, two representatives of common powdery mildew (Erysiphaceae), to elicit inflammatory and asthmatic reactions in mouse models of acute and chronic asthma. After sensitizing and subsequently challenging mice intranasally with extracts of E. palczewskii and E. convolvuli, we examined the levels of pro-inflammatory cytokines (IL-4, IL-5, IL-13, TNF-α, and TGF-β measured by ELISA), specific IgE production (measured by ELISA), and histological changes in the lungs of the animals following hematoxylin-eosin staining. In mouse models, we demonstrated that E. palczewskii and E. convolvuli induced robust production of all studied cytokines, elevated levels of specific IgE, and histological lung changes characteristic of acute and chronic asthma. These data indicate that both species are strong candidates as new fungal aeroallergens, but their clinical significance requires confirmation in larger studies, including human studies. Asthma is a common lung disease that makes breathing difficult. Many people with asthma react to tiny particles in the air, including fungi (moulds), but the exact fungal species causing symptoms are often unknown.In this study, we looked at two plant fungi Erysiphe palczewskii and Erysiphe convolvuli. These fungi create a white “powdery mildew” on common plants and can release large numbers of spores into the air. We wanted to find out whether extracts from these fungi can trigger asthma‑like reactions.We used a mouse model of both short‑term (acute) and long‑term (chronic) asthma. Mice were exposed to fungal extracts and then examined for signs of allergy. We measured allergy‑related blood markers, levels of specific antibodies (IgE), and changes in lung tissue.The fungal extracts caused increases in allergy‑related substances in the blood, higher levels of IgE, and clear structural changes in the lungs that resemble asthma. These effects were generally weaker than those produced by a well‑known allergen (ovalbumin) but followed the same pattern.Our findings suggest that these two common plant fungi may act as previously unrecognized airborne allergens. Further work in people will be needed to confirm whether they contribute to asthma and other allergic diseases.
We investigate the influence of drop volume on partial wetting of sessile drops on a horizontal solid substrate for up to large Bond numbers, considering water on both polymethyl methacrylate (PMMA) and aluminum-coated substrates, as well as glycerol on PMMA. The horizontal orientation of the substrate, along with methods for creating sessile drops, facilitated the rotational symmetry of drops to perform controlled and reproducible experiments. In particular, we explore the manner in which the statistic macroscopic contact angle (MCA) depends on the sessile drop volume or related Bond numbers, whether the drop is injected via a syringe positioned above the substrate (DSA30 Krüss equipment) or from below the substrate through a tiny hole drilled in it. In both cases, experimental results exhibit that as the drop volume is increased spanning Bond numbers in the range [0.1-14], the contact line advances on the substrate and the MCA significantly decreases down to an asymptotic value.
Bioprospecting, the search for useful biological resources, drives scientific innovation but can be tarnished by ethical challenges (e.g., biopiracy) when prior informed consent and benefit sharing are not formally established. Despite the widespread use of course-based undergraduate research experiences (CUREs) with a foundation in characterizing biodiversity and, in many cases, direct bioprospecting, to our knowledge, there are no published CURE curricula that teach the ethics of bioprospecting. Here we developed and vetted the Bioprospecting Module as a flexible, case-based curricular resource that can be implemented before students commence discovery-based research. The resource is focused on learning and analysis situations that warrant prior informed consent and mutually agreed terms and is adaptable to other science, technology, engineering, and math (STEM) courses that incorporate ethics. The module was implemented across 13 collegiate institutions with undergraduate students enrolled in a Tiny Earth CURE at diverse course types and institutional contexts. Student learning was evaluated using iteratively refined pre-post knowledge-based instruments aligned to learning objectives. Student perception and attitudinal measures were also surveyed to complement the knowledge measures. Throughout classroom implementation, students showed significant pre-post learning gains with a large effect size. Post-module performance was comparable across student groups and institutional contexts after accounting for baseline knowledge. Furthermore, ceiling effects were observed with higher pre-scores but did not undermine overall gains. Students reported increased perceived competence in module learning objectives and positive engagement with the module. This adaptable module provides a scalable curricular resource for embedding ethical bioprospecting and responsible and ethical conduct of research in STEM courses, helping students anticipate and navigate consent, benefit sharing, and stewardship issues in research fueling the bioeconomy.
Body condition score (BCS) is key to assessing the health, productivity, and energy balance of dairy cows. However, traditional manual methods are time-intensive, dependent on evaluator experience, and prone to subjective biases, limiting large-scale, frequent monitoring. In smallholder settings, where regular BCS monitoring and nutrition advisory support are unavailable, rationing often focuses on intake targets, overlooking body-condition feedback, resulting in overconditioned cows, particularly during the periparturient period. This increases the risk of metabolic disorders like ketosis from excessive lipid mobilization. Here, we developed and validated a fully automated regression-based BCS system from side-view images. Given a single on-farm photograph, the system automatically detects and crops the bovine region of interest (ROI), preserves the aspect ratio, and applies resizing and padding to the target resolution. The cropped image is then fed into a deep image regression model, estimating the continuous BCS and creating an automated evaluation. We validated the system using 3,208 side-view images of 211 Holstein cows from a commercial herd, capturing a range of real-world scenes and lighting conditions. To ensure reliable ground truth, 3 trained assessors independently scored the cows, achieving an Intraclass Correlation Coefficient (ICC) of 0.92 after consensus aggregation. The method employs a 2-stage design: first, a You Only Look Once version 11 (YOLOv11n) model pretrained on the COCO data set detects and crops the image with a mean Average Precision (mAP) of 99.5%, minimizing background interference and standardizing inputs; second, a side-view image regression model predicts the BCS. To ensure unbiased evaluation, we performed a stratified 5-fold cow-level cross-validation, ensuring images of the same cow appeared exclusively in either the training or validation sets. Comparisons of multiple backbones identified ConvNeXt-Tiny (ConvNeXt-T) as the optimal model, achieving a Mean Absolute Error (MAE) of 0.41 and a Pearson Correlation Coefficient of 0.62. These results indicate that the model's prediction error falls within the typical range of inter-assessor variability. Explainable artificial intelligence (AI) techniques revealed the model focuses on key anatomical regions used by human raters, such as tailhead, hooks, pins, and ribs. This suggests the model learned biologically relevant features, not background artifacts or irrelevant signals. This system is noninvasive, cost-effective, and infrastructure-light. A single side-view image suffices for initial screening, enabling both herd-level monitoring and individual follow-up, facilitating early detection of negative energy balance (NEB) and timely nutritional intervention. To ensure reproducibility and support edge deployment, all inference scripts, model weights, and logs are archived. Extreme BCS cases are rare in large-scale operations, but future work will expand the data set, perform external validation across farms, and assess uncertainty thresholds to enhance robustness. In conclusion, this study introduces a proof-of-concept automated side-view BCS regression system with multi-expert consensus labeling, a deployable pipeline, and explainability-based validation, offering potential technical support for precision dairy farming and improving animal welfare.
Multiple primary lung cancer (MPLC) is classified into synchronous and metachronous types. It is clinically rare for a single patient to present with both types simultaneously. EGFR tyrosine kinase inhibitors (EGFR-TKIs) are the first-line treatment for EGFR-sensitive mutant lung cancer, but their therapeutic value in unresectable GGN-type MPLC requires further validation. A female with no smoking history or family history of tumors underwent examination in March 2020, which revealed multiple ground-glass nodules (GGNs) in the left upper lobe. After ruling out distant metastasis, she underwent left upper lobectomy. Postoperative pathology confirmed synchronous MPLC (sMPLC), and genetic testing identified an EGFR L858R mutation. No adjuvant therapy was administered, and regular follow-up was maintained. In April 2022, follow-up computed tomography (CT) scans detected new multiple tiny GGNs in the left lower lobe. Subsequent monitoring showed progressive nodule enlargement. In 2024, bronchoscopic biopsy was performed, and pathology confirmed minimally invasive adenocarcinoma (MIA), consistent with metachronous MPLC (mMPLC). Considering the patient's previous EGFR mutation and the unresectable nature of the lesions, EGFR-TKI targeted therapy was initiated. After treatment, the multiple GGNs in the left lower lobe gradually shrank and ultimately disappeared, achieving sustained complete remission (CR). Follow-up to date has shown no recurrence or significant adverse reactions. Patients with multiple GGNs may develop both synchronous and mMPLC. For unresectable GGN-type MPLC with a confirmed history of EGFR-sensitive mutations, EGFR-TKI targeted therapy demonstrates definitive efficacy and can achieve complete remission.