Interfacial Robustness and Improved Kinetics of Single-Crystal Ni-Rich Co-Free Cathodes Enabled by Surface Crystal-Facet Modulation.

The elimination of Co from Ni-rich layered cathodes is critical to reduce the production cost and increase the energy density for sustainable development. Herein, a delicate strategy of crystal-facet modulation is designed and explored, which is achieved by simultaneous Al/W-doping into the precursors, while the surface role of the crystal-facet is intensively revealed. Unlike traditional studies on crystal structure growth along a certain direction, this work modulates the crystal-facet at the nanoscale based on the effect of W-doping dynamic migration with surface energy, successfully constructing the core-shell (003)/(104) facet surface. Compared to the (003) plane, the induced (104) facet at the surface can provide more space for Li+-activity, enabling lower interfacial polarization and higher Li+-transport rate. Additionally, bulk Al-doping is beneficial for enhancing the Li+-diffusion from the exterior surface to the interior lattice. With improved interfacial stability and restrained surface erosion, the product exhibits superior capacity retention and boosted rate performance under the elevated temperature.

Robustness of radiomics features on 0.35 T magnetic resonance imaging for magnetic resonance-guided radiotherapy.

Background and purpose: MR-guided radiotherapy adds the precision of magnetic resonance imaging (MRI) to the therapeutic benefits of a linear accelerator. Prior to each therapeutic session, an MRI generates a significant volume of imaging data ripe for analysis. Radiomics stands at the forefront of medical imaging and oncology research, dedicated to mining quantitative imaging attributes to forge predictive models. However, the robustness of these models is often challenged. Materials and methods: To assess the robustness of feature extraction, we conducted reproducibility studies using a 0.35 T MR-linac system, employing both a specialized phantom and patient-derived images, focusing on cases of pancreatic cancer. We extracted shape-based, first-order and textural features from patient-derived images and only first-order and textural features from phantom-derived images. The impact of the delay between simulation and first fraction images was also assessed with an equivalence test. Results: From 107 features evaluated, 58 (54 %) were considered as non-reproducible: 18 were uniformly inconsistent across both phantom and patient images, 9 were specific to phantom-based analysis, and 31 to patient-derived data. Conclusion: Our findings show that a significant proportion of radiomic features extracted from this dual dataset were unreliable. It is essential to discard these non-reproducible elements to refine and enhance radiomic model development, particularly for MR-guided radiotherapy in pancreatic cancer.

The role of hyetograph shape and designer subjectivity in the design of an urban drainage system.

Even though it has been established that a hyetograph's shape affects the results of hydrological simulations, common engineering practice does not always account for this fact. Instead, a single design storm is often considered sufficient for designing a urban drainage system. This study examines the impact that this design paradigm, combined with the uncertainty introduced by subjective choices made during the design process, has on the robustness of a designed system. To do so, we evaluated a set of individual designs created by engineering students using the same Chicago hyetograph as a design storm. We then created ensembles of hyetographs with the same precipitation volume and duration as the Chicago hyetograph and evaluated the designs' hydrological responses. The results showed that designs, which performed equally well for the initial design storm, triggered varying responses for the storms in the ensembles and, consequently, showed different levels of robustness, hinting at a need to adapt the current design approach.

A longitudinal study of the influence of air pollutants on children: a robust multivariate approach.

This paper aims to evaluate the statistical association between exposure to air pollution and forced expiratory volume in the first second (FEV1) in both asthmatic and non-asthmatic children and teenagers, in which the response variable FEV1 was repeatedly measured on a monthly basis, characterizing a longitudinal experiment. Due to the nature of the data, an robust linear mixed model (RLMM), combined with a robust principal component analysis (RPCA), is proposed to handle the multicollinearity among the covariates and the impact of extreme observations (high levels of air contaminants) on the estimates. The Huber and Tukey loss functions are considered to obtain robust estimators of the parameters in the linear mixed model (LMM). A finite sample size investigation is conducted under the scenario where the covariates follow linear time series models with and without additive outliers (AO). The impact of the time-correlation and the outliers on the estimates of the fixed effect parameters in the LMM is investigated. In the real data analysis, the robust model strategy evidenced that RPCA exhibits three principal component (PC), mainly related to relative humidity (Hmd), particulate matter with a diameter smaller than 10 µm (PM10) and particulate matter with a diameter smaller than 2.5 µm (PM2.5).

An Empirical Study on the Effect of Training Data Perturbations on Neural Network Robustness.

The vulnerability of modern neural networks to random noise and deliberate attacks has raised concerns about their robustness, particularly as they are increasingly utilized in safety- and security-critical applications. Although recent research efforts were made to enhance robustness through retraining with adversarial examples or employing data augmentation techniques, a comprehensive investigation into the effects of training data perturbations on model robustness remains lacking. This paper presents the first extensive empirical study investigating the influence of data perturbations during model retraining. The experimental analysis focuses on both random and adversarial robustness, following established practices in the field of robustness analysis. Various types of perturbations in different aspects of the dataset are explored, including input, label, and sampling distribution. Single-factor and multi-factor experiments are conducted to assess individual perturbations and their combinations. The findings provide insights into constructing high-quality training datasets for optimizing robustness and recommend the appropriate degree of training set perturbations that balance robustness and correctness, and contribute to understanding model robustness in deep learning and offer practical guidance for enhancing model performance through perturbed retraining, promoting the development of more reliable and trustworthy deep learning systems for safety-critical applications.

Orchestrated Movement Sequences and Shape-Memory-like Effects in Pine Cones.

Hygroscopic seed-scale movement is responsible for the weather-adaptive opening and closing of pine cones and for facilitating seed dispersal under favorable environmental conditions. Although this phenomenon has long been investigated, many involved processes are still not fully understood. To gain a deeper mechanical and structural understanding of the cone and its functional units, namely the individual seed scales, we have investigated their desiccation- and wetting-induced movement processes in a series of analyses and manipulative experiments. We found, for example, that the abaxial scale surface is responsible for the evaporation of water from the closed cone and subsequent cone opening. Furthermore, we tested the capability of dry and deformed scales to restore their original shape and biomechanical properties by wetting. These results shed new light on the orchestration of scale movement in cones and the involved forces and provide information about the functional robustness and resilience of cones, leading to a better understanding of the mechanisms behind hygroscopic pine cone opening, the respective ecological framework, and, possibly, to the development of smart biomimetic actuators.

ROM-based stochastic optimization for a continuous manufacturing process.

This paper proposes a model-based optimization method for the production of automotive seals in an extrusion process. The high production throughput, coupled with quality constraints and the inherent uncertainty of the process, encourages the search for operating conditions that minimize nonconformities. The main uncertainties arise from the process variability and from the raw material itself. The proposed method, which is based on Bayesian optimization, takes these factors into account and obtains a robust set of process parameters. Due to the high computational cost and complexity of performing detailed simulations, a reduced order model is used to address the optimization. The proposal has been evaluated in a virtual environment, where it has been verified that it is able to minimize the impact of process uncertainties. In particular, it would significantly improve the quality of the product without incurring additional costs, achieving a 50% tighter dimensional tolerance compared to a solution obtained by a deterministic optimization algorithm.

Eight challenges in developing theory of intelligence.

A good theory of mathematical beauty is more practical than any current observation, as new predictions about physical reality can be self-consistently verified. This belief applies to the current status of understanding deep neural networks including large language models and even the biological intelligence. Toy models provide a metaphor of physical reality, allowing mathematically formulating the reality (i.e., the so-called theory), which can be updated as more conjectures are justified or refuted. One does not need to present all details in a model, but rather, more abstract models are constructed, as complex systems such as the brains or deep networks have many sloppy dimensions but much less stiff dimensions that strongly impact macroscopic observables. This type of bottom-up mechanistic modeling is still promising in the modern era of understanding the natural or artificial intelligence. Here, we shed light on eight challenges in developing theory of intelligence following this theoretical paradigm. Theses challenges are representation learning, generalization, adversarial robustness, continual learning, causal learning, internal model of the brain, next-token prediction, and the mechanics of subjective experience.

Dosimetric Study and Robustness Analysis of Base Note Intensive Locked Field Radiotherapy for Left Breast Cancer.

Background: The locked vision plan can make the left breast cancer heart and lung organs dose. Objective: The aim of the present study was to compare the dosimetric differences between field-locked and field-split plans in intensity-modulated radiotherapy for left-sided breast cancer, to explore the effect of field-locking on the low-dose region, and to evaluate its robustness to the radiotherapy target, in order to provide a reference for the selection of clinical radiotherapy protocols. Methods: A total of 30 patients were selected after radical left breast cancer surgery, and 7-field locked-field and split-field plans were developed to compare the dose difference (∆D) between the target area and each organ at risk, and to introduce offsets of 3, 5 and 7 mm in six directions and recalculate the perturbed dose distributions, and to compare the ∆D between the original and the perturbed plans according to the robustness of the plans. Results: The results revealed that the D98%, D95% and Dmean values of the planning target volume (PTV) of the two plans differed little and were not statistically different. The locked field plan provided better protection for the left lung, right lung, heart, right breast and left anterior descending coronary artery. For PTV∆D98%, PTV∆D95%, PTV∆Dmean, the ∆D was higher for the Locked Fields plan, and for LungL∆5, LungL∆20 and Heart∆mean, the ∆D was higher for the original plan. Discussion: It was concluded that the field-locking plan could reduce the low-dose area of the affected lung and provide improved protection to the remaining critical organs, and the field-locking plan was more robust in protecting critical organs. Meanwhile, the field-locking plan showed higher sensitivity to positional deviation for target PTV.

Stability Indicating UPLC Method Development and Validation for the Quantitative Estimation of Pazopanib in Pure form and Marketed Pharmaceutical Dosage form.

An analytical, accurate, precise, specific, efficient and simple Ultra-Performance Liquid Chromatography method has been developed and validated for the determination of Pazopanib in bulk and was applied on marketed Pharmaceutical Dosage form. The mobile phase used for the chromatographic runs consisted of 0.1% OPA Buffer and Acetonitrile in the ratio of 30:70% v/v. The separation was achieved on a BHEL UPLC column using isocratic mode. Pazopanib Drug peak were well separated and were detected by a PDA detector at 256 nm. The developed method was linear at the concentration range 6-14 µg/ml for Pazopanib. The method has been validated according to ICH guidelines with respect to system suitability, specificity, precision, accuracy and robustness. The LOD and LOQ for the Pazopanib were found to be 0.5853 µg/ml and 1.7738µg/ml respectively. The developed method is simple, precise, specific, accurate and rapid, making it suitable for estimation of Pazopanib in bulk and marketed pharmaceutical dosage form dosage form.

Polycomb group proteins confer robustness to aposematic coloration in the milkweed bug, Oncopeltus fasciatus.

Aposematic coloration offers an opportunity to explore the molecular mechanisms underlying canalization. In this study, the role of epigenetic regulation underlying robustness was explored in the aposematic coloration of the milkweed bug, Oncopeltus fasciatus. Polycomb (Pc) and Enhancer of zeste (E(z)), which encode components of the Polycomb repressive complex 1 (PRC1) and PRC2, respectively, and jing, which encodes a component of the PRC2.2 subcomplex, were knocked down in the fourth instar of O. fasciatus. Knockdown of these genes led to alterations in scutellar morphology and melanization. In particular, when Pc was knocked down, the adults developed a highly melanized abdomen, head and forewings at all temperatures examined. In contrast, the E(z) and jing knockdown led to increased plasticity of the dorsal forewing melanization across different temperatures. Moreover, jing knockdown adults exhibited increased plasticity in the dorsal melanization of the head and the thorax. These observations demonstrate that histone modifiers may play a key role during the process of canalization to confer robustness in the aposematic coloration.

Robustness quantification of a mutant library screen revealed key genetic markers in yeast.

BACKGROUND: Microbial robustness is crucial for developing cell factories that maintain consistent performance in a challenging environment such as large-scale bioreactors. Although tools exist to assess and understand robustness at a phenotypic level, the underlying metabolic and genetic mechanisms are not well defined, which limits our ability to engineer more strains with robust functions. RESULTS: This study encompassed four steps. (I) Fitness and robustness were analyzed from a published dataset of yeast mutants grown in multiple environments. (II) Genes and metabolic processes affecting robustness or fitness were identified, and 14 of these genes were deleted in Saccharomyces cerevisiae CEN.PK113-7D. (III) The mutants bearing gene deletions were cultivated in three perturbation spaces mimicking typical industrial processes. (IV) Fitness and robustness were determined for each mutant in each perturbation space. We report that robustness varied according to the perturbation space. We identified genes associated with increased robustness such as MET28, linked to sulfur metabolism; as well as genes associated with decreased robustness, including TIR3 and WWM1, both involved in stress response and apoptosis. CONCLUSION: The present study demonstrates how phenomics datasets can be analyzed to reveal the relationship between phenotypic response and associated genes. Specifically, robustness analysis makes it possible to study the influence of single genes and metabolic processes on stable microbial performance in different perturbation spaces. Ultimately, this information can be used to enhance robustness in targeted strains.

Robustness and evolvability: Revisited, redefined and applied.

Building on and extending existing definitions of robustness and evolvability, we propose and utilize new formal definitions, with matching measures, of robustness and evolvability of systems with genotypes and corresponding phenotypes. We explain and show how these measures are more general and more representative of the concepts they stand for, than the commonly used/referenced measures originally proposed by Wagner. Further, a versatile digital modeling approach (BNK) is proposed that is inspired by NK systems. However, unlike NK systems, BNK incorporates a genotype and a phenotype, in addition to fitness. We develop and apply an Evolutionary Algorithm to a BNK-modeled system to find different types of perfect oscillators. We then map the resulting oscillating systems to possible genetic circuit realizations. Continuing with the synthetic biology theme, we also investigate the effect of noise in DNA synthesis on the predicted functionality of a DNA-based biosensor (i.e., its robustness), and we carry out a theoretical assessment of the evolvability of different types of ribozymes, undergoing directed evolution.

Benchmarking robustness of deep neural networks in semantic segmentation of fluorescence microscopy images.

BACKGROUND: Fluorescence microscopy (FM) is an important and widely adopted biological imaging technique. Segmentation is often the first step in quantitative analysis of FM images. Deep neural networks (DNNs) have become the state-of-the-art tools for image segmentation. However, their performance on natural images may collapse under certain image corruptions or adversarial attacks. This poses real risks to their deployment in real-world applications. Although the robustness of DNN models in segmenting natural images has been studied extensively, their robustness in segmenting FM images remains poorly understood RESULTS: To address this deficiency, we have developed an assay that benchmarks robustness of DNN segmentation models using datasets of realistic synthetic 2D FM images with precisely controlled corruptions or adversarial attacks. Using this assay, we have benchmarked robustness of ten representative models such as DeepLab and Vision Transformer. We find that models with good robustness on natural images may perform poorly on FM images. We also find new robustness properties of DNN models and new connections between their corruption robustness and adversarial robustness. To further assess the robustness of the selected models, we have also benchmarked them on real microscopy images of different modalities without using simulated degradation. The results are consistent with those obtained on the realistic synthetic images, confirming the fidelity and reliability of our image synthesis method as well as the effectiveness of our assay. CONCLUSIONS: Based on comprehensive benchmarking experiments, we have found distinct robustness properties of deep neural networks in semantic segmentation of FM images. Based on the findings, we have made specific recommendations on selection and design of robust models for FM image segmentation.

Pyramid-Shaped Superhydrophobic Surfaces for Underwater Drag Reduction.

Superhydrophobic surfaces hold immense potential in underwater drag reduction. However, as the Reynolds number increases, the drag reduction rate decreases, and it may even lead to a drag increase. The reason lies in the collapse of the air mattress. To address this issue, this paper develops a pyramid-shaped robust superhydrophobic surface with wedged microgrooves, which exhibits a high gas fraction when immersed underwater and good ability to achieve complete spreading and recovery of the air mattress through air replenishment in the case of collapse of the air mattress. Pressure drop tests in a water tunnel confirm that with continuous air injection, the drag reduction reaches 64.8% in laminar flow conditions, substantially greater than 38.4% in the case without air injection, and can achieve 50.8% drag reduction in turbulent flow. This result highlights the potential applications of superhydrophobic surfaces with air mattress recovery for drag reduction.

Standardizing Protein Corona Characterization in Nanomedicine: A Multicenter Study to Enhance Reproducibility and Data Homogeneity.

We recently revealed significant variability in protein corona characterization across various proteomics facilities, indicating that data sets are not comparable between independent studies. This heterogeneity mainly arises from differences in sample preparation protocols, mass spectrometry workflows, and raw data processing. To address this issue, we developed standardized protocols and unified sample preparation workflows, distributing uniform protein corona digests to several top-performing proteomics centers from our previous study. We also examined the influence of using similar mass spectrometry instruments on data homogeneity and standardized database search parameters and data processing workflows. Our findings reveal a remarkable stepwise improvement in protein corona data uniformity, increasing overlaps in protein identification from 11% to 40% across facilities using similar instruments and through a uniform database search. We identify the key parameters behind data heterogeneity and provide recommendations for designing experiments. Our findings should significantly advance the robustness of protein corona analysis for diagnostic and therapeutics applications.

The multiverse of data preprocessing and analysis in graph-based fMRI: A systematic literature review of analytical choices fed into a decision support tool for informed analysis.

The large number of different analytical choices used by researchers is partly responsible for the challenge of replication in neuroimaging studies. For an exhaustive robustness analysis, knowledge of the full space of analytical options is essential. We conducted a systematic literature review to identify the analytical decisions in functional neuroimaging data preprocessing and analysis in the emerging field of cognitive network neuroscience. We found 61 different steps, with 17 of them having debatable parameter choices. Scrubbing, global signal regression, and spatial smoothing are among the controversial steps. There is no standardized order in which different steps are applied, and the parameter settings within several steps vary widely across studies. By aggregating the pipelines across studies, we propose three taxonomic levels to categorize analytical choices: 1) inclusion or exclusion of specific steps, 2) parameter tuning within steps, and 3) distinct sequencing of steps. We have developed a decision support application with high educational value called METEOR to facilitate access to the data in order to design well-informed robustness (multiverse) analysis.

Reinforcing thermostability and pH robustness of exo-inulinase facilitated by ReverseTag/ReverseCatcher tagging system.

Enhancing protein stability is pivotal in the field of protein engineering. Protein self-cyclization using peptide a tagging system has emerged as an effective strategy for augmenting the thermostability of target proteins. In this study, we utilized a novel peptide tagging system, ReverseTag/ReverseCatcher, which leverages intramolecular ester bond formation. Initially, we employed GFP as a model to validate the feasibility of cyclization mediated by ReverseTag/ReverseCatcher in improving the protein thermostability. Cyclized GFP (cGFP) retained 30 % of its relative fluorescence after a 30-min incubation at 100 °C, while both GFP and linear GFP (lGFP) completely lost their fluorescence within 5 min. Additionally, we applied this method to exo-inulinase (EXINU), resulting in a variant named cyclized EXINU (cEXINU). The T50 and t1/2 values of cEXINU exhibited significant enhancements of 10 °C and 10 min, respectively, compared to EXINU. Furthermore, post-cyclization, EXINU demonstrated a broad operational pH range from 5 to 10 with sustained catalytic activity, and cEXINU maintained a half-life of 960 min at pH 5 and 9. Molecular dynamics simulations were conducted to elucidate the mechanisms underlying the enhanced thermostability and pH robustness of EXINU following cyclization. This study highlights that cyclization substanitially enhances the stability of both highly stable protein GFP and low-stable protein EXINU, mediated by the ReverseTag/ReverseCatcher tagging system. The ReverseTag/ReverseCatcher tagging system proves to be a potent conjugation method, with potential applications in improving thermostability, pH robustness, and other areas of protein engineering.

Comparative Analysis of Non-Rigid Registration Techniques for Liver Surface Registration.

Non-rigid surface-based soft tissue registration is crucial for surgical navigation systems, but its adoption still faces several challenges due to the large number of degrees of freedom and the continuously varying and complex surface structures present in the intra-operative data. By employing non-rigid registration, surgeons can integrate the pre-operative images into the intra-operative guidance environment, providing real-time visualization of the patient's complex pre- and intra-operative anatomy in a common coordinate system to improve navigation accuracy. However, many of the existing registration methods, including those for liver applications, are inaccessible to the broader community. To address this limitation, we present a comparative analysis of several open-source, non-rigid surface-based liver registration algorithms, with the overall goal of contrasting their strength and weaknesses and identifying an optimal solution. We compared the robustness of three optimization-based and one data-driven nonrigid registration algorithms in response to a reduced visibility ratio (reduced partial views of the surface) and to an increasing deformation level (mean displacement), reported as the root mean square error (RMSE) between the pre-and intra-operative liver surface meshed following registration. Our results indicate that the Gaussian Mixture Model - Finite Element Model (GMM-FEM) method consistently yields a lower post-registration error than the other three tested methods in the presence of both reduced visibility ratio and increased intra-operative surface displacement, therefore offering a potentially promising solution for pre- to intra-operative nonrigid liver surface registration.