Browse Articles

Research on multi-scale feature detection of open-pit mine road cracks

Machinability and tribological optimization of origami-inspired Almond Shell–PMMA via RSM, ML, and TOPSIS

An integrated approach combining Response Surface Methodology (RSM), Machine Learning (ML-SVM) and TOPSIS optimization method is applied in this study to analyse the tribological behaviour of 3D printed patterns of almond shell-PMMA (polymethyl methacrylate) origami inspired composites and machinability. The process of taking advantage of fold-based geometrical patterns (such as Miura-ori or triangular tessellation) to enhance load distribution and energy absorption in 3D printed specimen is called origami-inspired. These patterns promote a certain degree of structural rigidity and cause weakened materials to deform under applied stress in a controlled manner in general to improve the mechanical strength and wear-resistance. Besides tribological performance factors such as wear rate and friction coefficient, the factors to be evaluated on the machinability properties of cutting force, surface roughness and material removal rate include spindle speed (3000−9000 rpm), feed rate (0.05–0.15 mm/rev), and depth of cut (0.2–0.6 mm). Although the machine learning algorithms were able to make predictive models concerning wear performance and machinability, RSM was addressed to plan the experiments and conclusion of the parameters interaction. TOPSIS method identified the parameters combination that will serve the best by balancing between tribological efficiency and machinability. The novelty aspect of the current work is the inclusion of agricultural waste (10 percent almond shells) to the polymer matrices and and the use of a hybrid optimization strategy on the ways to optimize its functional properties with respect to being used in wear and machining applications. Notable findings indicate that the most influential variable affecting machinability and tribological results is the feed rate; in the best case scenario there is achievement of surface roughness of 1.2 10−15 m and wear rate aside at 1.5 in 10−4 mm 3/Nm. The proposed model was able to give a workable and industrially friendly composite method with great efficiency and sustainability in terms of optimization performance and predictability (R 2  > 0.95).

Pathfinding quantum simulations of neutrinoless double-β decay

Abstract We present results from co-designed quantum simulations of the neutrinoless double- β decay of a simple nucleus in 1+1D quantum chromodynamics using IonQ’s Forte-generation trapped-ion quantum computers. Electrons, neutrinos, and up and down quarks are distributed across two lattice sites and mapped to 32 qubits, with an additional 4 qubits used for flag-based error mitigation. A four-fermion interaction is used to implement weak interactions, and lepton-number violation is induced by a neutrino Majorana mass. Quantum circuits that prepare the initial nucleus and time evolve with the Hamiltonian containing the strong and weak interactions are executed on IonQ Forte Enterprise. Enabled by tuned model parameters, lepton-number violation is observed in real time, providing a clear signal of neutrinoless double- β decay. This was made possible by co-designing the simulation to maximally utilize the all-to-all connectivity and native gate-set available on IonQ’s quantum computers. Quantum circuit compilation techniques and co-designed error-mitigation methods, informed from executing benchmarking circuits with up to 2,356 two-qubit gates, enabled observables to be extracted with high precision. We discuss the potential of future quantum simulations to provide yocto-second resolution of the reaction pathways in these, and other, nuclear processes.

Identification of a circulating immunological signature as a liquid biopsy approach for the diagnosis of endometriosis

Study on salient object segmentation based on depth information guidance and SAM low-rank adaptation fine-tuning

Accurate segmentation of salient objects is crucial for various computer vision applications including image editing, autonomous driving, and object detection. While research on using depth information (RGB-D) in saliency detection is gaining significant attention, its broad application is limited by dependencies on depth sensors and the challenge of effectively integrating RGB and depth information. To address these issues, we propose an innovative method for salient object segmentation that integrates the Segment Anything Model (SAM), depth information, and cross-modal attention mechanisms. Our approach leverages SAM for robust feature extraction and combines it with a pre-trained depth estimation network to capture geometric information. By dynamically fusing features from RGB and depth modalities through a cross-modal attention mechanism, our method enhances the ability to handle diverse scenes. Additionally, we achieve computational efficiency without compromising precision by employing lightweight LoRA fine-tuning and freezing pre-trained weights. The use of a UNet decoder refines the segmentation output, ensuring the preservation of target boundary details in high-resolution outputs. Experiments conducted on five challenging benchmark datasets validate the effectiveness of our proposed method. Results show significant improvements over existing methods across key evaluation metrics, including MaxF, MAE, and S-measure. Particularly in tasks involving complex backgrounds, small targets, and multiple salient object segmentation, our method demonstrates superior performance and robustness. The significance of this work lies in advancing the application of depth-guided RGB in salient object segmentation while offering new insights into overcoming depth sensor dependency. Furthermore, it opens up novel pathways for the effective fusion of cross-modal information, thereby contributing to the broader development and diversification of related technologies and their applications.

High-speed blind structured illumination microscopy via unsupervised algorithm unrolling

Abstract Blind structured illumination microscopy (blind-SIM) is a valuable tool for achieving super-resolution without the need for known illumination patterns. However, in its current formulation the algorithm requires many iterations to converge, leading to long inference times and limited use for real-time or video-rate imaging. We present unrolled blind-SIM (UBSIM), an algorithm which integrates a learnable neural network inside the unrolled iterations of the blind-SIM algorithm. UBSIM delivers a reconstruction speed two to three orders of magnitude faster than that of current iterative blind-SIM methods, while achieving similar resolution and image quality. Furthermore, we demonstrate that UBSIM can be trained in an unsupervised manner that reduces hallucinations and produces superior generalization capability when compared to benchmark super-resolution networks. We test UBSIM experimentally on live cells and present video-rate super-resolution imaging up to 50 Hz. Using our method, we observe dynamic remodeling of the endoplasmic reticulum with high spatiotemporal resolution.

High isolation MIMO antenna based on metasurface for linear-circular polarization conversion and decoupling

Single-cell RNA-seq reveals the piperlongumine is a potential drug for ischemic stroke

Ischemic stroke is a cerebrovascular disease that can cause long-term neurological impairment, dementia, or death. It is the third most common cause of disability and the second leading cause of death worldwide. The aim of this study was to explore the underlying molecular mechanisms and potentially effective therapeutic drugs for ischemic stroke. Single-cell seq data (GSE174574) were downloaded from the Gene Expression Omnibus (GEO) database, and dimensionality reduction clustering was performed after quality control. Eighteen cell clusters were identified, which were annotated into 9 cell types according to specific marker genes. In addition, Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Set Enrichment Analysis (GSEA) enrichment analysis showed that apoptosis was significantly increased after ischemic brain injury, while p53 signaling pathway, TNF-a signaling pathway and mitogen-activated protein kinase (MAPK) signaling pathway may play an important role in ischemic stroke. Furthermore, Connectivity Map (CMap) analysis and molecular docking suggested that piperlongumine might be an effective drug for the treatment of ischemic stroke by binding to the proteins encoded by Actb and Cflar. Finally, in vitro and in vivo experiments conformed the effectiveness of piperlongumine. This study provided new ideas for the treatment of ischemic stroke.

Atomically tweaking spin-crossover cooperativity to augment molecular memory density

Construction and analysis of a packaging design preference model using eye-tracking degree of preference

Lipopolysaccharide induced mouse depression model can better simulate changes in peripheral blood FFAs in alMDD

Numerous studies have highlighted a strong association between free fatty acids (FFAs) and major depressive disorder (MDD), however, the underlying mechanisms remain poorly understood. Here, we demonstrate that peripheral blood FFAs levels are significantly reduced in adolescent patients with MDD (alMDD), as quantified by gas chromatography–mass spectrometry (GC-MS), providing critical insights for preclinical depression research. Both chronic social defeat stress (CSDS) and lipopolysaccharide (LPS) models induced depression-like behaviors and triggered varying degrees of variant expressions in peripheral blood FFAs in mice. Specifically, the LPS-induced model generally exhibited superior fidelity in recapitulating the items of FFAs profile of MDD patients. Notably, hexanoic acid (C6:0), nonanoic acid (C9:0) and nonadecylic acid (C19:0) levels in CSDS mice showed closer alignment with those in alMDD individuals, whereas, cis-9-palmitoleic acid (C16:1), and myristic acid (C14:0) levels in LPS mice more accurately simulated the FFAs alterations observed in alMDD. Collectively, this study identifies the LPS-induced depression-like model as a more reliable proxy for investigating FFAs dysregulation in alMDD.

The insect Toll pathway activates antibacterial immunity against the citrus Huanglongbing pathogen

Evaluating gait system vulnerabilities through PPO and GAN-generated adversarial attacks

Abstract This study delves into the vulnerabilities of deep learning-based gait recognition systems against adversarial attacks, a critical issue considering the increasing reliance on these technologies in high-security environments. We highlight a major issue concerning the susceptibility of these systems to adversarial interventions that compromise their reliability. The importance of this issue stems from the critical role of gait recognition in applications where security and accuracy are paramount. Our approach introduces an advanced methodology that integrates Proximal Policy Optimization (PPO) with Generative Adversarial Networks (GANs) to create and deploy adversarial attacks in the form of targeted adversarial patches. These patches are designed to deceive gait recognition algorithms without detection by human oversight, exploiting the models’ weaknesses to induce misclassification. This methodology not only leverages the strengths of GANs to produce deceptive examples but also innovatively utilizes PPO to ascertain their optimal placements, thereby maximizing the disruption on gait recognition systems. We assess the impact of these attacks using the CASIA Gait Database: Dataset B and the OU-ISIR Treadmill Dataset B - Clothes variation-, covering both real-world and controlled environments. Our results demonstrate a significant decline in recognition accuracy post-attack, underscoring the effectiveness of our adversarial approach. These findings underscore critical security flaws and actively inform the broader discussion aimed at boosting the robustness of gait recognition systems. The impact of our research extends significantly, providing crucial insights that aid in the creation of more secure, attack-resistant biometric recognition systems, thereby enhancing the resilience of gait recognition technologies against the backdrop of advancing cyber threats.

Cryoablation for fibroadenoma with liquid nitrogen based system: A retrospective analysis of prospectively collected data

Background Fibroadenoma is the most common benign breast lesion found on core needle biopsies. Surgical excision is the standard of care for these lesions. This retrospective study aims to evaluate the safety and efficacy of liquid nitrogen-based cryoablation in treating multiple fibroadenomas, including large lesions. Methods A liquid nitrogen-based cryoablation system was used to treat histologically confirmed benign fibroadenomas under ultrasound guidance at Premier Med Healthcare, Training, and Research Institute between 2017 and 2022. The number and times of freeze-thaw-freeze treatment cycles and the number of cryoprobe relocations were determined according to the location and size of the fibroadenomas. Sequential cryoprobe relocation was performed in case of large or multiple fibroadenomas treated in one session. Patients underwent ultrasound examination follow-up visits for up to 12 months post-cryoablation. Data were analyzed descriptively. Changes in lesion size were evaluated using the Wilcoxon signed-rank test. A p-value < 0.05 was considered statistically significant. Results 78 women with a mean age of 34.2 ± 9.8 were included. The number of lesions per patient ranged from 1 to 4, with 60% having one lesion, 25% two, 13% three, and 3% four. Lesions were evenly distributed between the left (48.4%) and right (51.6%) breasts, with the upper outer quadrant being the most common location (28%). Lesion size, diameter of the largest dimension, ranged from 7 to 80 mm (mean 25 ± 10.9). The mean procedure time was 13 ± 10.4 minutes with 1−5 relocations per cryoprobe. In 76% of cases, a single freeze-thaw-freeze cycle was sufficient. Multiple cryoprobe relocations were used for larger or multiple lesions to ensure full coverage. The median volume reduction was 80.6% (IQR: 56.6–92.6) at 6 ± 1.5 months and 92.9% (IQR: 73.6−100) at 12 ± 1.5 months. The reduction observed at 12 months (mean follow-up of 16.3 ± 10 months) was statistically significant (p < 0.0001). One minor adverse event occurred (1/123 = 0.81% [95%CI: 0.02%−4.45%]) that resolved with conservative treatment. Conclusion Cryoablation with a liquid nitrogen-based system proved safe and effective, with 92.9% volume reduction at one year post-cryoablation for fibroadenoma. Sequential cryoprobe relocations preserve safety and efficacy, allowing the flexibility necessary for complete ablation of large or multifocal lesions. With the inclusion of a large patient cohort, our study further reinforces the clinical value of cryoablation and brings the technique one step closer to integration into routine practice.

Earthquakes act as a capacitor for terrestrial organic carbon

Functional and evolutionary diversification of luciferase genes in Metridia lucens Boeck 1865

Abstract Bioluminescent organisms have developed extraordinary adaptations to produce light using a luciferin-luciferase reaction, fulfilling various ecological functions such as predator evasion, prey attraction, and intraspecies communication. Although the earliest record in the marine environment dates back some 540 million years, the evolutionary origins of this phenomenon remain largely unknown in most species. In Metridinidae copepods, light production capability is facilitated by a luciferase gene duplication. This study focuses on characterizing the luciferase genes of Metridia lucens , a copepod widely distributed throughout global oceans, excluding the high Arctic. Despite being the first species described in this genus, the genomic sequences of its luciferase genes remained unknown prior to this investigation. Here, using an integrated approach combining molecular cloning and high-throughput sequencing, we isolated and characterized M. lucens luciferase genes. Our results revealed an unexpectedly high genetic diversity both within and between specimens, consistent with the presence of an extended gene family in this copepod’s genome. We identified three distinct luciferase gene lineages, each represented by several copies, expanding our knowledge of marine bioluminescence evolution.

The influence of aspect markers and tense on the action-sentence compatibility effect in Mandarin action sentence comprehension

The Action-sentence Compatibility Effect (ACE) suggests that action sentences comprehension is based on embodied mental simulation. In studies of English, aspect markers and tense has been shown to influence ACE; however, their effects remain unclear in Mandarin. This study utilized Mandarin action sentences with single objects and employed the classic sentence sensibility judgment paradigm to investigate the ACE for concrete and abstract action sentences under different aspect marker (Experiment 1 and 2) or future tense (Experiment 3) conditions across three experiments. The results showed that ACE occurred for both concrete and abstract action sentences in the progressive aspect, perfective aspect, and future tense. Action simulation during sentence comprehension was unaffected by the type of aspect markers and future tense or the concreteness/abstractness of the sentences. These findings suggest that the comprehension of Mandarin action sentences relies on the mental simulation of verbs. Moreover, aspect markers and future tense in Mandarin do not influence the mental simulation process, and Mandarin action sentences comprehension may be centered around the mental simulation of the verb.

Broadband and high-resolution snapshot spectroscopy with high-index transition metal dichalcogenides

Downregulation of the deubiquitinating enzyme USP10 correlates with neuronal apoptosis in HTLV-1-associated myelopathy

Biological pretreatment and fermentation of Panicum antidotale biomass for pectinase production by Bacillus vallismortis

Halophytic plants are renewable source of lignocellulosic biomass, however, are underexplored for their utilization as a source of fermentation raw material. Similar to glycophytes, biomass from halophytes contains lignin that is removed to provide access to the fermentable carbohydrates. This study was designed to investigate biomass of a halophytic plant, Panicum antidotale, for pectinase production from a halotolerant strain of Bacillus vallismortis MH 10. Laccase from a fungus, Trametes pubescens MB 89 was employed as a pretreatment agent to remove lignin. The pectin content of P. antidotale biomass was also determined and compared with the pectin extracted from orange peels (OP). The changes in the P. antidotale biomass and pectin were investigated using Fourier Transform Infrared spectroscopy (FTIR) and Scanning Electron microscope (SEM). The strain MH 10 produced 17.39 IU mL -1 pectinase in the medium containing P. antidotale biomass supplemented with fungal laccase. The data showed that P. antidotale biomass has a meagre quantity of pectin (2.8%) compared to OP (14.9%). Yet the strain MH 10 produced 315 IU g -1 pectinase by fermenting pectin from P. antidotale but only 76.32 IU g -1 pectinase was obtained by using OP as substrate which showed preference of this strain towards halophytic substrate. The analysis further revealed that the strain effectively utilized 94% pectin content of P. antidotale biomass. The FTIR spectra corresponded to the changes in pectin spectrum indicating pectin consumption from P. antidotale biomass. The SEM images confirmed the laccase-mediated porosity and fragility in the biomass. Hence, this study provides a novel utilization of biomass from halophytes as a chemical feedstock.