Distinct Architectures Research Articles

Immunogenomics and spatial proteomic mapping highlight distinct neuro-immune architectures in melanoma vs. non-melanoma-derived brain metastasis.

Brain metastases (BrMs) are a devastating complication of solid tumours. A better understanding of BrMs biology is needed to address their challenging clinical management. Immunogenomic and digital spatial analyses were applied to interrogate the peripheral blood and tumour specimens derived from 53 unique patients with BrMs originating from different solid tumours. At craniotomy time, patients with melanoma-derived brain metastasis (MBM) displayed in the periphery lower neutrophil-lymphocyte ratio (NLR) compared to non-melanoma-derived brain metastasis (non-MBM). Regardless of the primary tumour source, higher NLR was associated with reduced overall survival (OS). Tumour MicroEnviroment genomic evaluations revealed higher expression of genes identifying NK, CD8 and B cells in MBM vs. non-MBM. Moreover, MBM patients with longer OS displayed increased CD8+ cell infiltration. Spatial proteomic analysis further highlighted enriched infiltration of CD8+ cells, antigen-presenting cells, T-cell agonists and B cells in MBM. Conversely, increased expression of genes and proteins associated with neurodevelopment, cell-cell adhesion and neutrophil infiltration were observed in non-MBM. These findings reveal an increased immunogenicity of MBM vs non-MBM and highlight the presence of a unique neuro-immune interplays in MBM vs non-MBM, suggesting that a balance between neuro-immune architectures might be associated with diverging clinical outcome of patients with BrMs.

Investigating quantitative approach for microalgal biomass using deep convolutional neural networks and image recognition

The effective monitoring of microalgae cultivation is crucial for optimizing their energy utilization efficiency. In this paper, a quantitative analysis method, using microalgae images based on two convolutional neural networks, EfficientNet (EFF) and residual network (RES), is proposed. Suspension samples prepared from two types of dried microalgae powders, Rhodophyta (RH) and Spirulina (SP), were used to mimic real microalgae cultivation settings. The method’s prediction accuracy of the algae concentration ranges from 0.94 to 0.99. RH, with a distinctively pronounced red-green-blue value shift, achieves a higher prediction accuracy than SP. The prediction results of the two algorithms were significantly superior to those of a linear regression. Additionally, RES outperforms EFF in terms of its generalization ability and robustness, which is attributable to its distinct residual block architecture. The RES provides a viable approach for the image-based quantitative analysis.

Construction of plum-branch-like CoSe2@N-Doped carbon/carbon fiber with dual protective mechanisms for enhanced lithium storage

Cobalt selenide (CoSe2) emerges as a highly promising anode material for lithium-ion batteries (LIBs) owing to its high theoretical capacity and cost-effectiveness. Despite these merits, its practical utilization faces challenges stemming from substantial volume fluctuations and limited electronic conductivity. To tackle these issues, a plum-branch-like structure of CoSe2@N-doped carbon that embedded in one-dimensional N-doped carbon fibers (CoSe2@NC/CFs), is successfully synthesized through an in-situ confinement method. Well-defined CoSe2@NC nanoparticles, featuring diameters between 20∼30 ​nm, are uniformly dispersed on both the inner and outer surfaces of the carbon fibers. The distinctive architecture of CoSe2@NC/CFs ensures an increased number of active sites, elevated electronic conductivity, alleviated volume expansion, and accelerated reaction kinetics. Consequently, the CoSe2@NC/CFs exhibits remarkable cycling performance and exceptional rate capability. Operating at a current density of 1000 ​mA ​g−1, the CoSe2@NC/CFs anode sustains a capacity of 664 ​mA ​h ​g−1 with no obvious capacity decay over 500 cycles. Even at a high current density of 5000 ​mA ​g−1, it maintains a capacity of 445 ​mA ​h g−1 with a mere 0.02 ​% capacity decay per cycle. This study introduces a novel approach to anode material design, showcasing significant advancements in lithium-ion battery technology.

F-actin architecture determines the conversion of chemical energy into mechanical work

Mechanical work serves as the foundation for dynamic cellular processes, ranging from cell division to migration. A fundamental driver of cellular mechanical work is the actin cytoskeleton, composed of filamentous actin (F-actin) and myosin motors, where force generation relies on adenosine triphosphate (ATP) hydrolysis. F-actin architectures, whether bundled by crosslinkers or branched via nucleators, have emerged as pivotal regulators of myosin II force generation. However, it remains unclear how distinct F-actin architectures impact the conversion of chemical energy to mechanical work. Here, we employ in vitro reconstitution of distinct F-actin architectures with purified components to investigate their influence on myosin ATP hydrolysis (consumption). We find that F-actin bundles composed of mixed polarity F-actin hinder network contraction compared to non-crosslinked network and dramatically decelerate ATP consumption rates. Conversely, linear-nucleated networks allow network contraction despite reducing ATP consumption rates. Surprisingly, branched-nucleated networks facilitate high ATP consumption without significant network contraction, suggesting that the branched network dissipates energy without performing work. This study establishes a link between F-actin architecture and myosin energy consumption, elucidating the energetic principles underlying F-actin structure formation and the performance of mechanical work.

Encasing Few-Layer MoS2 within 2D Ordered Cubic Graphitic Cages to Smooth Trapping-Conversion of Lithium Polysulfides for Dendrite-Free Lithium-Sulfur Batteries.

The industrialization of lithium-sulfur (Li-S) batteries faces challenges due to the shuttling effect of lithium polysulfides (LiPSs) and the growth of lithium dendrites. To address these issues, a simple and scalable method is proposed to synthesize 2D membranes comprising a single layer of cubic graphitic cages encased with few-layer, curved MoS2. The distinctive 2D architecture is achieved by confining the epitaxial growth of MoS2 within the open cages of a 2D-ordered mesoporous graphitic framework (MGF), resulting in MoS2@MGF heterostructures with abundant sulfur vacancies. The experimental and theoretical studies establish that these MoS2@MGF membranes can act as a multifunctional interlayer in Li-S batteries to boost their comprehensive performance. The inclusion of the MoS2@MGF interlayer facilitates the trapping and conversion kinetics of LiPSs, preventing their shuttling effect, while simultaneously promoting uniform lithium deposition to inhibit dendrite growth. As a result, Li-S batteries with the MoS2@MGF interlayer exhibit high electrochemical performance even under high sulfur loading and lean electrolyte conditions. This work highlights the potential of designing advanced MoS2-encased heterostructures as interlayers, offering a viable solution to the current limitations plaguing Li-S batteries.

Robust and multifunctional MXene/rGO composite aerogels toward highly efficient solar-driven interfacial evaporation and wastewater treatment

Given the ever-growing global water resource crisis, exploiting multifunctional materials featuring with high interfacial solar steam generation efficiency and good purification capability to achieving seawater desalination and wastewater treatment simultaneously are highly desired, yet it still remains a huge challenge to date. Herein, robust and multifunctional MXene/rGO (MRGA) and polydopamine & chitosan @ MRGA (CS&PDA@MRGA, CPM) three-dimensional composite aerogels with distinct interconnected cellular architecture were developed via a facile ice template-assisted chemical reduction self-assembly technology and subsequent sequential deposition modification. Due to the favorable graphene oxide-assisted assembly behavior, the resultant aerogels displayed a desirable mechanical robustness along with an excellent lightweight property. Benefiting from the strong electrostatic interaction deriving from aerogel surface moieties and dye molecules, MRGA-12 exhibited a prominent adsorption capability towards various dyes and achieved a superb adsorption capacity of up to 396.05 mg/g for malachite green (MG). Moreover, by virtue of the synergistic effect between the intentionally regulated low reduction degree of rGO and the integration of PDA and CS, CPM-12 achieved a dramatically reduced evaporation enthalpy of water from 2256 to 1617.18 J/g. Accordingly, this distinct feature resulted in an extraordinary solar-driven interfacial evaporation performance with a remarkable evaporation rate of 1.86 kg/m2/h and a high evaporation efficiency of 83.28 % under 1 sun illumination. Therefore, together with the terrific oil/water separation capability, these novel MXene-based aerogels hold a great potential for high-performance solar-driven interfacial evaporation and wastewater treatment.

Topological Insights into Nanostar Dendrimers by Computing the Augmented Zagreb Index.

The field of nanobiotechnology uses precise nanofabrication techniques to advance our understanding and control of biological systems. Due to their remarkable properties, dendrimers, which are hyperbranched macromolecular structures with distinct and well-defined architectures, have emerged as pivotal entities within this field. They are gaining increasing attention for their potential to catalyze a paradigm shift in medical therapeutics, biotechnological applications, and advanced material sciences. This paper focuses on a novel analytical expression and determines the precise value of the augmented Zagreb index, a topological descriptor, for eight classes of nanostar dendrimers. The Zagreb index is a topological invariant to predict molecular behaviour and reactivity. In this paper, we have explored its application in characterizing the branching of nanostar dendrimers through computational modelling and mathematical rigor. Our research has measured the augmented Zagreb index for nanostar dendrimers, which fall into eight distinct classes. The results better explain the relationship between the dendrimers' topology and chemical properties. This correlation has implications for their structural stability and reactivity, potentially leading to new applications. Developing the augmented Zagreb index for nanostar dendrimers is a significant breakthrough in nanobiotechnology. Based on the correlation between the calculated topological index and the corresponding molecular attributes, our analytical approach has opened up new possibilities for designing and synthesizing dendrimers tailored to specific functions in medical and material science applications. This precise topological quantification could significantly enhance the utility and functionalization of dendrimers in cutting-edge nanotechnological applications.

An early-onset specific polygenic risk score optimizes age-based risk estimate and stratification of prostate cancer: population-based cohort study

BackgroundEarly-onset prostate cancer (EOPC, ≤ 55 years) has a unique clinical entity harboring high genetic risk, but the majority of EOPC patients still substantial opportunity to be early-detected thus suffering an unfavorable prognosis. A refined understanding of age-based polygenic risk score (PRS) for prostate cancer (PCa) would be essential for personalized risk stratification.MethodsWe included 167,517 male participants [4882 cases including 205 EOPC and 4677 late-onset PCa (LOPC)] from UK Biobank. A General-, an EOPC- and an LOPC-PRS were derived from age-specific genome-wide association studies. Weighted Cox proportional hazard models were applied to estimate the risk of PCa associated with PRSs. The discriminatory capability of PRSs were validated using time-dependent receiver operating characteristic (ROC) curves with additional 4238 males from PLCO and TCGA. Phenome-wide association studies underlying Mendelian Randomization were conducted to discover EOPC linking phenotypes.ResultsThe 269-PRS calculated via well-established risk variants was more strongly associated with risk of EOPC [hazard ratio (HR) = 2.35, 95% confidence interval (CI) 1.99–2.78] than LOPC (HR = 1.95, 95% CI 1.89–2.01; I2 = 79%). EOPC-PRS was dramatically related to EOPC risk (HR = 4.70, 95% CI 3.98–5.54) but not to LOPC (HR = 0.98, 95% CI 0.96–1.01), while LOPC-PRS had similar risk estimates for EOPC and LOPC (I2 = 0%). Particularly, EOPC-PRS performed optimal discriminatory capability for EOPC (area under the ROC = 0.613). Among the phenomic factors to PCa deposited in the platform of ProAP (Prostate cancerAge-basedPheWAS; https://mulongdu.shinyapps.io/proap), EOPC was preferentially associated with PCa family history while LOPC was prone to environmental and lifestyles exposures.ConclusionsThis study comprehensively profiled the distinct genetic and phenotypic architecture of EOPC. The EOPC-PRS may optimize risk estimate of PCa in young males, particularly those without family history, thus providing guidance for precision population stratification.

Tuning interfacial viscoelasticity of catechol-functionalized alginate by pH-responsive covalent network for stabilizing high internal phase pickering emulsions

Polysaccharide-based soft particles have wide applications in preparing high phase Pickering emulsions (HIPPEs) because of their adjustable interfacial properties, which can advance the stability of HIPPEs by strengthening their interfacial viscoelasticity. Nevertheless, there is limited research on the interfacial viscoelasticity of soft particles regarding the stability of HIPPEs. We synthesized Alg-CA soft particles by the Ugi reaction, featuring catechol functionality and amphiphilic alginate. These particles can self-assemble under pH stimulation, resulting in a network structure that impacts the interfacial viscoelasticity of HIPPEs. We investigated the relationship between self-assembled microstructure and interfacial viscoelasticity using transmission electron microscopy (TEM) and rheometry. The study indicates that the Alg-CA solution produced a compact network structure at a pH level of 3, leading to the formation of highly stable HIPPEs (with a viscosity of approximately 1000 Pa·s). Additionally, by utilizing emulsion templates, 3D network aerogels with pH-sensitive, consistent microstructures may be developed, which presents a promising approach for designing and producing aerogels with distinct microporous architectures.

Holistic Processing of Sawdust to Enable Sustainable Hybrid Li-Ion Capacitors

Activated carbon has long been recognized as a promising electrode material for energy storage devices. The extraordinarily high specific area makes it challenging to replace in supercapacitors since electrical double-layer capacitors need such surfaces but also porous networks to enable electrolyte penetration. As a raw material for synthesizing activated carbon, sawdust offers key benefits, such as its renewability, abundance, favorable physical attributes for energy storage, and a more environmentally friendly synthesis process compared to mined alternative sources. In this work, electrochemical characterization is carried out which highlights the critical role of pelletization in enhancing the capacitive performance of sawdust-derived activated carbon, in addition to the implicit handling and logistical benefits. Subsequently, a Li-ion capacitor is assembled with an organic solvent-based electrolyte, sawdust-derived activated carbon serving as the positive electrode, and an Al-based foil negative electrode, potentially combining high energy and power density materials into a hybrid device. Despite commendable electrochemical performance and the use of a sustainable waste-derived positive electrode with a commoditized negative electrode, challenges remain regarding the ability to mitigate the role of surface functional groups that are stabilized by bio-carbon thermal treatments. Nevertheless, this distinctive architecture holds promise as an alternative high-power energy storage technology for a future filled with renewable energy, electric vehicles, and portable electronic devices.

Designing MnO2@amorphous carbon core-shell structure as a cathode to enhance rate performance and cycling stability of aqueous zinc-ion batteries

Manganese dioxide has received significant attention as a prospective cathode material in aqueous zinc-ion batteries (ZIBs) owing to its elevated working voltage and commendable energy density. Nevertheless, challenges such as structural instability, manganese dissolution, and poor electrical conductivity remain significant barriers to the practical application of research. Here, the MnO2 nanorods encapsulated in the amorphous carbon (MnO2@C) with core-shell structure are prepared by a special hard template method, and investigated as cathode materials for ZIBs. The amorphous carbon, characterized by its structural defects and disorder, manifests heightened active site density, thereby establishing an efficient conduit for charge transport. Concurrently, the nano-dimensional interstitial spaces augment the active sites available for zinc insertion reactions, thereby effectively curtailing the ion diffusion path and, consequently, engendering heightened electrochemical performance. Moreover, the distinctive core-shell architecture of MnO2@C not only furnishes substantial spatial accommodation but also serves to alleviate volume alterations throughout charge and discharge events. Consequently, MnO2@C demonstrates outstanding electrochemical performance, with reversible capacities of 210 mAh·g−1 and 50 mAh·g−1 at 0.1 A·g−1 and 1.6 A·g−1, respectively. Furthermore, even after 600 cycles at 0.8 A·g−1, it maintains exceptional cycling performance, reaching 102 mAh·g−1, endowing MnO2@C cathodes with robust competitiveness in aqueous ZIBs.

Pipelined and Wave Pipelined Approach Based Comparative Analysis for 16x16 Vedic Multiplier

Objectives: This work objective is to construct an FPGA-based 16x16 Vedic multiplier and assess the performance of the multiplier using three distinct architectures: pipeline, wave pipeline, and modified wave pipeline in terms of delay and clock skew. Methods: The 16 × 16 Vedic multiplier was constructed and designed through four numbers of an 8x8 Vedic multiplier. For the 16x16 Vedic multiplier, the 3-stage pipeline and wave pipeline techniques are applied, and the delay performances of the Vedic multiplier are compared. Delay optimization: In the wave pipeline Vedic multiplier architecture, the delay is decreased by inserting the known delay on the longest path delay between the multiplier and adder. Clock skew optimization: The clock skew issue of the wave pipeline Vedic multiplier architecture is minimized by adjusting the setup time violation of the clock signal that is connected to the input and output registers. Findings: The delay performance of the Vedic multiplier was evaluated by the synthesis tools Xilinx 12.1, Xilinx ISE 14.2, and Altera, and based on the synthesis report, the Xilinx synthesis tool offers 73.71% delay performance for the pipeline approach and 53.39% for the wave pipeline approach compared to the Altera tool. Further delay is reduced by the proposed modified wave pipeline approach, which saves 2.122 ns of delay compared to the wave pipeline architecture. The clock skew performance was analyzed using the Time Quest timing analyzer tool, and it was minimized to 0.035 from 0.048 compared to the wave pipeline approach. Novelty: In this work, the modified wave pipeline approach has been applied to the existing Vedic multiplier architecture, and it offers less delay as well as less clock skew compared to the existing method. Hence, the performance of the Vedic multiplier with a modified wave pipelined approach was evaluated through a 3-tap FIR filter by applying a vibroarthrography signal. Keywords: Pipeline, Wave Pipeline, Vedic Multiplier, Clock skew, Set up violation, Altera quartex- II Time quest timing analyzer tool

NiCo2O4@SnS2 nanosheets on carbon cloth as efficient bi-functional material for high performance supercapacitor and sensor applications

Bi-functional materials provide an opportunity for the development of high-performance devices. Up till now, bi-functional performance of NiCo2O4@SnS2 nanosheets is rarely investigated. In this work, NiCo2O4@SnS2 nanosheets were synthesized on carbon cloth by utilizing a simple hydrothermal technique. The developed electrode (NiCo2O4@SnS2/CC) was investigated for the detection of L-Cysteine and supercapacitors applications. As a non-enzymatic sensor, the electrode proved to be highly sensitive for the detection of L-cysteine. The electrode exhibits a reproducible sensitivity of 4645.82 μA mM−1 cm−2 in a wide linear range from 0.5 to 5 mM with a low limit of detection (0.005 μM). Moreover, the electrode shows an excellent selectivity and long-time stability. The high specific surface area, enhanced kinetics, good synergy and distinct architecture of NiCo2O4@SnS2 nanosheets produce a large number of active sites with substantial energy storage potential. As a supercapacitor, the electrode exhibits improve capacitance of 655.7 F g−1 at a current density of 2 A g−1 as compare to NiCo2O4/CC (560 F g−1). Moreover, the electrode achieves 95.3% of its preliminary capacitance after 10 000 cycles at 2 A g−1. Our results show that NiCo2O4@SnS2/CC nanosheets possess binary features could be attractive electrode material for the development of non-enzymatic biosensors as well as supercapacitors.

Bioinspired photonic crystal structures from Papilio Ulysses butterfly wings for versatile laser applications

Photonic crystals found within the wing of a Papilio Ulysses butterfly have demonstrated their efficacy in facilitating laser processes, particularly as a scattering material for imaging and illumination applications. This study delves into the investigation of the photonic crystal's band gap within the butterfly's wing, aiming to realize blue band-edge lasing using a newly developed gain medium comprising diphenylaminofluorene-based chromophores featuring a donor-acceptor-donor (D-A-D) motif. The versatility of the fabricated sample is highlighted, as it can function as both a single-mode laser and a random laser, with the operational mode determined by adjusting the excitation power and the direction of the pump source. Specifically, it exhibits a threshold of 2.5 μJ for random laser operation and 7.6 μJ for single-mode laser operation. The unique architectural elements found in the butterfly's wing, including the ridges on the wing scale and the photonic crystal within the scales, play distinct roles in enabling random laser and single-mode laser actions, respectively. This distinctive butterfly wing architecture holds promise for inspiring the development of future photoelectronic circuits requiring a hybrid design that incorporates both single-mode and comb-mode laser functionalities.

Fully automated deep learning system for osteoporosis screening using chest computed tomography images.

Osteoporosis, a disease stemming from bone metabolism irregularities, affects approximately 200 million people worldwide. Timely detection of osteoporosis is pivotal in grappling with this public health challenge. Deep learning (DL), emerging as a promising methodology in the field of medical imaging, holds considerable potential for the assessment of bone mineral density (BMD). This study aimed to propose an automated DL framework for BMD assessment that integrates localization, segmentation, and ternary classification using various dominant convolutional neural networks (CNNs). In this retrospective study, a cohort of 2,274 patients underwent chest computed tomography (CT) was enrolled from January 2022 to June 2023 for the development of the integrated DL system. The study unfolded in 2 phases. Initially, 1,025 patients were selected based on specific criteria to develop an automated segmentation model, utilizing 2 VB-Net networks. Subsequently, a distinct cohort of 902 patients was employed for the development and testing of classification models for BMD assessment. Then, 3 distinct DL network architectures, specifically DenseNet, ResNet-18, and ResNet-50, were applied to formulate the 3-classification BMD assessment model. The performance of both phases was evaluated using an independent test set consisting of 347 individuals. Segmentation performance was evaluated using the Dice similarity coefficient; classification performance was appraised using the receiver operating characteristic (ROC) curve. Furthermore, metrics such as the area under the curve (AUC), accuracy, and precision were meticulously calculated. In the first stage, the automatic segmentation model demonstrated excellent segmentation performance, with mean Dice surpassing 0.93 in the independent test set. In the second stage, both the DenseNet and ResNet-18 demonstrated excellent diagnostic performance in detecting bone status. For osteoporosis, and osteopenia, the AUCs were as follows: DenseNet achieved 0.94 [95% confidence interval (CI): 0.91-0.97], and 0.91 (95% CI: 0.87-0.94), respectively; ResNet-18 attained 0.96 (95% CI: 0.92-0.98), and 0.91 (95% CI: 0.87-0.94), respectively. However, the ResNet-50 model exhibited suboptimal diagnostic performance for osteopenia, with an AUC value of only 0.76 (95% CI: 0.69-0.80). Alterations in tube voltage had a more pronounced impact on the performance of the DenseNet. In the independent test set with tube voltage at 100 kVp images, the accuracy and precision of DenseNet decreased on average by approximately 14.29% and 18.82%, respectively, whereas the accuracy and precision of ResNet-18 decreased by about 8.33% and 7.14%, respectively. The state-of-the-art DL framework model offers an effective and efficient approach for opportunistic osteoporosis screening using chest CT, without incurring additional costs or radiation exposure.

Fine-tuned combination of cell and electrode designs unlocks month-long stable low temperature Cu-based CO2 electrolysis

The urgency of achieving green chemical production through Cu-based CO2 electroreduction necessitates a rapid transition towards technical maturity and commercialization in the pursuit of addressing the global imperative of decarbonization. Surprisingly, limited emphasis has been placed on exploration of readily scalable cell and electrode designs, which are pivotal in ushering in the era of stable and selective CO2 electrolyzers, showcasing the innovative potential within this area. Herein, we report a breakthrough in achieving month-long stability in the production of C2H4, representing an unprecedented milestone in low-temperature CO2 to C2+ electrolysis. Initial investigations involved the evaluation of five distinct cell architectures for Cu-based CO2 electrolyzers, guided by considerations of cell potentials, scalability with current technology, and CO2 crossover. An innovative multilayer Gas Diffusion Electrode (GDE), featuring an anion exchange ionomer and metal oxide layer, is introduced for CEM-based zero-gap cells, enabling C2H4 formation despite acidic surroundings. However, selectivity towards C2H4 proved suboptimal for extended stability testing. Conversely, the tailored multilayer GDE for one-gap cell architecture achieves a commendable 54 % faradaic efficiency (FE) towards C2+ products at 300 mA/cm2. Remarkably, chronopotentiometric tests demonstrate 720 h of stability (FEC2H4 > 20 %) at 100 mA/cm2. At higher current densities (300 mA/cm2), stability is reduced to 75 h, with detailed analyses revealing distinct degradation mechanisms. At 100 mA/cm2, salt formation predominates, while at 300 mA/cm2, catalyst layer restructuring degrades catalytic activity towards C2H4. Our research underscores the potential for stable, high C2+ selectivity through innovative electrode design and scalable cell architectures, advancing sustainable CO2 utilization.

A comprehensive deep learning geometric shape optimization framework with field prediction surrogate and reinforcement learning

The optimization of aerodynamic components' geometric shapes demands a novel technical approach for adaptive and efficient exploration and decision-making within the design space. In this study, we introduce an innovative shape optimization framework that leverages deep reinforcement learning with neural network surrogate models. The field prediction surrogate, realized by two distinct U-net architectures, can efficiently generate holistic field solutions based on the transformed mesh coordinates. Subsequently, an inference engine dynamically calculates the key metric of the flow fields, serving as the objective function for the subsequent geometry-aware Deep Q network (DQN)-based optimization. The framework's efficacy is validated using a rocket nozzle as an illustrative example. During surrogate validation, under both friction and frictionless conditions, the l1 errors of the entire flow field of both the U-net vision transformer (ViT) and U-net convolutional neural network (CNN) architectures are less than 0.4%. The proposed U-net ViT consistently outperforms U-net CNN, and the superiority is particularly evident in complex flow areas, outlet sections, and vacuum thrust prediction. Following training, the DQN model is employed to explore the design variable space. The B-spline defining profile successfully is optimized to a final expanding segment shape with improved thrust. Under frictionless conditions, it closely approaches the theoretical optimum. In the practical condition considering friction, the optimized shape gains a 2.96% thrust improvement. The results demonstrate that the proposed framework, especially when coupled with U-net ViT, exhibits enhanced accuracy and adaptability for shape optimization tasks.

The mechanical influence of densification on epithelial architecture.

Epithelial tissues are the most abundant tissue type in animals, lining body cavities and generating compartment barriers. The function of a monolayered epithelial tissue-whether protective, secretory, absorptive, or filtrative-relies on the side-by-side arrangement of its component cells. The mechanical parameters that determine the shape of epithelial cells in the apical-basal plane are not well-understood. Epithelial tissue architecture in culture is intimately connected to cell density, and cultured layers transition between architectures as they proliferate. This prompted us to ask to what extent epithelial architecture emerges from two mechanical considerations: A) the constraints of densification and B) cell-cell adhesion, a hallmark feature of epithelial cells. To address these questions, we developed a novel polyline cell-based computational model and used it to make theoretical predictions about epithelial architecture upon changes to density and cell-cell adhesion. We tested these predictions using cultured cell experiments. Our results show that the appearance of extended lateral cell-cell borders in culture arises as a consequence of crowding-independent of cell-cell adhesion. However, cadherin-mediated cell-cell adhesion is associated with a novel architectural transition. Our results suggest that this transition represents the initial appearance of a distinctive epithelial architecture. Together our work reveals the distinct mechanical roles of densification and adhesion to epithelial layer formation and provides a novel theoretical framework to understand the less well-studied apical-basal plane of epithelial tissues.

Employing deep learning and transfer learning for accurate brain tumor detection

Artificial intelligence-powered deep learning methods are being used to diagnose brain tumors with high accuracy, owing to their ability to process large amounts of data. Magnetic resonance imaging stands as the gold standard for brain tumor diagnosis using machine vision, surpassing computed tomography, ultrasound, and X-ray imaging in its effectiveness. Despite this, brain tumor diagnosis remains a challenging endeavour due to the intricate structure of the brain. This study delves into the potential of deep transfer learning architectures to elevate the accuracy of brain tumor diagnosis. Transfer learning is a machine learning technique that allows us to repurpose pre-trained models on new tasks. This can be particularly useful for medical imaging tasks, where labelled data is often scarce. Four distinct transfer learning architectures were assessed in this study: ResNet152, VGG19, DenseNet169, and MobileNetv3. The models were trained and validated on a dataset from benchmark database: Kaggle. Five-fold cross validation was adopted for training and testing. To enhance the balance of the dataset and improve the performance of the models, image enhancement techniques were applied to the data for the four categories: pituitary, normal, meningioma, and glioma. MobileNetv3 achieved the highest accuracy of 99.75%, significantly outperforming other existing methods. This demonstrates the potential of deep transfer learning architectures to revolutionize the field of brain tumor diagnosis.

Expediting Contrastive Language-Image Pretraining via Self-Distilled Encoders

Recent advances in vision language pretraining (VLP) have been largely attributed to the large-scale data collected from the web. However, uncurated dataset contains weakly correlated image-text pairs, causing data inefficiency. To address the issue, knowledge distillation have been explored at the expense of extra image and text momentum encoders to generate teaching signals for misaligned image-text pairs. In this paper, our goal is to resolve the misalignment problem with an efficient distillation framework. To this end, we propose ECLIPSE: Expediting Contrastive Language-Image Pretraining with Self-distilled Encoders. ECLIPSE features a distinctive distillation architecture wherein a shared text encoder is utilized between an online image encoder and a momentum image encoder. This strategic design choice enables the distillation to operate within a unified projected space of text embedding, resulting in better performance. Based on the unified text embedding space, ECLIPSE compensates for the additional computational cost of the momentum image encoder by expediting the online image encoder. Through our extensive experiments, we validate that there is a sweet spot between expedition and distillation where the partial view from the expedited online image encoder interacts complementarily with the momentum teacher. As a result, ECLIPSE outperforms its counterparts while achieving substantial acceleration in inference speed.