bluuebunny/crossref_metadata_2025 huggingface dataset:
X-Detect: explainable adversarial patch detection for object detectors in retail
Authors: Hofman Omer , Giloni Amit , Hayun Yarin , Morikawa Ikuya , Shimizu Toshiya , Elovici Yuval , Shabtai Asaf
Published: June 2024

Object detection models, which are widely used in various domains (such as retail), have been shown to be vulnerable to adversarial attacks. Existing methods for detecting adversarial attacks on object detectors have had difficulty detecting new real-life attacks. We present X-Detect, a novel adversarial patch detector that can: (1) detect adversarial samples in real time, allowing the defender to take preventive action; (2) provide explanations for the alerts raised to support the defender’s decision-making process, and (3) handle unfamiliar threats in the form of new attacks. Given a new scene, X-Detect uses an ensemble of explainable-by-design detectors that utilize object extraction, scene manipulation, and feature transformation techniques to determine whether an alert needs to be raised. X-Detect was evaluated in both the physical and digital space using five different attack scenarios (including adaptive attacks) and the benchmark COCO dataset and our new Superstore dataset. The physical evaluation was performed using a smart shopping cart setup in real-world settings and included 17 adversarial patch attacks recorded in 1700 adversarial videos. The results showed that X-Detect outperforms the state-of-the-art methods in distinguishing between benign and adversarial scenes for all attack scenarios while maintaining a 0% FPR (no false alarms) and providing actionable explanations for the alerts raised. A demo is available.
Improving interpretability via regularization of neural activation sensitivity
Authors: Moshe Ofir , Fidel Gil , Bitton Ron , Shabtai Asaf
Published: June 2024

State-of-the-art deep neural networks (DNNs) are highly effective at tackling many real-world tasks. However, their widespread adoption in mission-critical contexts is limited due to two major weaknesses - their susceptibility to adversarial attacks and their opaqueness. The former raises concerns about DNNs’ security and generalization in real-world conditions, while the latter, opaqueness, directly impacts interpretability. The lack of interpretability diminishes user trust as it is challenging to have confidence in a model’s decision when its reasoning is not aligned with human perspectives. In this research, we (1) examine the effect of adversarial robustness on interpretability, and (2) present a novel approach for improving DNNs’ interpretability that is based on the regularization of neural activation sensitivity. We evaluate the interpretability of models trained using our method to that of standard models and models trained using state-of-the-art adversarial robustness techniques. Our results show that adversarially robust models are superior to standard models, and that models trained using our proposed method are even better than adversarially robust models in terms of interpretability.(Code provided in supplementary material.)
Lattice cohomology and subspace arrangements: the topological and analytic cases
Authors: Ágoston Tamás
Published: June 2024

In this paper we consider the (topological) lattice cohomology $$\mathbb {H}^*$$

H

∗

of a surface singularity with rational homology sphere link. In particular, we will be studying two sets of (topological) invariants related to it: the weight function $$\upchi $$
χ
that induces the cohomology and the topological subspace arrangement $$T(\ell ,I)$$

T
(
ℓ
,
I
)

at each lattice point $$\ell $$
ℓ
— the latter of which is the weaker of the two. We shall prove that the two are in fact equivalent by establishing an algorithm to compute $$\upchi $$
χ
from the subspace arrangement. Replacing the topological arrangements with the analytic, we get another formula — one that connects them with the analytic lattice cohomology introduced and studied in our earlier papers. In fact, this connection served as the original motivation for the definition of the latter. Aside from the historical interest, this parallel also provides us with tools to study more easily the connection between the two cohomologies.
Modeling NP-problems with families of extended graph-based reaction systems
Authors: Kreowski Hans-Jörg , Lye Aaron
Published: June 2024

In this paper, we continue the investigation of graph-based reaction systems. We extend the notion by input and output states as well as admitted context sequences to model explicitly input–output relations and decision problems on the inputs. Moreover, we combine extended graph-based reaction systems into families to cover infinite input–output relations and decision problems on infinite sets of graphs. This is used to model NP-problems on graphs and reductions between them as well as to prove their correctness.
Incidence of brain metastasis according to patient race and primary cancer origin: a systematic review
Authors: Gomez David , Feng Jeffrey J. , Cheok Stephanie , Shah Ishan , Dicharry Holly , Cote David J. , Briggs Robert G. , Guerra Gage A. , Peterson Racheal , Salhia Bodour , Neman Josh , Attenello Frank , Chow Frances , Musabelliu Erion K. , Zada Gabriel
Published: June 2024

Purpose

A systematic review was conducted to investigate differences in incidence and primary origin of synchronous brain metastasis (sBM) in varying racial groups with different primary cancers.

Methods

Adhering to PRISMA 2020 guidelines a search was conducted using PubMed and Ovid databases for publications from January 2000 to January 2023, with search terms including combinations of “brain metastasis,” “race,” “ethnicity,” and “incidence.” Three independent reviewers screened for inclusion criteria encompassing studies clearly reporting primary cancer sites, patient demographics including race, and synchronous BM (sBM) incidence.

Results

Of 806 articles, 10 studies comprised of mainly adult patients from the United States met final inclusion for data analysis. Higher sBM incidence proportions were observed in American Indian/Alaska native patients for primary breast (p < 0.001), colorectal (p = 0.015), and esophageal cancers (p = 0.024) as well as in Asian or Pacific islanders for primary stomach (p < 0.001), thyroid (p = 0.006), and lung/bronchus cancers (p < 0.001) yet higher proportions in White patients for malignant melanoma (p < 0.001). Compared to White patients, Black patients had higher sBM incidence likelihood in breast cancer (OR = 1.27, p = 0.01) but lower likelihood in renal (OR = 0.46, p < 0.001) and esophageal cancers (OR = 0.31, p = 0.005). American Indian/Alaska native patients had a higher sBM likelihood (OR = 3.78, p = 0.004) relative to White patients in esophageal cancer.

Conclusions

These findings reveal several comparative racial differences in sBM incidence arising from different primary cancer origins, underscoring a need for further research to explain these variations. Identifying the factors contributing to these disparities holds the potential to promote greater equity in oncological care according to cancer type.
A Single Image High-Perception Super-Resolution Reconstruction Method Based on Multi-layer Feature Fusion Model with Adaptive Compression and Parameter Tuning
Authors: Zhang Rui , Ren Wenyu , Pan Lihu , Bai Xiaolu , Li Ji
Published: June 2024

We propose a simple image high-perception super-resolution reconstruction method based on multi-layer feature fusion model with adaptive compression and parameter tuning. The aim is to further balance the high and low-frequency information of an image, enrich the detailed texture to improve perceptual quality, and improve the adaptive optimization and generalization of the model in the process of super-resolution reconstruction. First, an effective multi-layer fusion super-resolution (MFSR) basic model is constructed by the design of edge enhancement, refine layering, enhanced super-resolution generative adversarial network and other sub-models, and effective multi-layer fusion. This further enriches the image representation of features of different scales and depths and improves the feature representation of high and low-frequency information in a balanced way. Next, a total loss function of the generator is constructed with adaptive parameter tuning performance. The overall adaptability of the model is improved through adaptive weight distribution and fusion of content loss, perceptual loss, and adversarial loss, and improving the error while reducing the edge enhancement model. Finally, a fitness function with the evaluation perceptual function as the optimization strategy is constructed, and the model compression and adaptive tuning of MFSR are carried out based on the multi-mechanism fusion strategy. Consequently, the construction of the adaptive MFSR model is realized. Adaptive MFSR can maintain high peak signal to noise ratio and structural similarity on the test sets Set5, Set14, and BSD100, and achieve high-quality reconstructed images with low learned perceptual image patch similarity and perceptual index, while having good generalization capabilities.
The shutdown problem: an AI engineering puzzle for decision theorists
Authors: Thornley Elliott
Published: June 2024

I explain and motivate the shutdown problem: the problem of designing artificial agents that (1) shut down when a shutdown button is pressed, (2) don’t try to prevent or cause the pressing of the shutdown button, and (3) otherwise pursue goals competently. I prove three theorems that make the difficulty precise. These theorems suggest that agents satisfying some innocuous-seeming conditions will often try to prevent or cause the pressing of the shutdown button, even in cases where it’s costly to do so. I end by noting that these theorems can guide our search for solutions to the problem.
Powers, persistence, and the problem of temporary intrinsics
Authors: Allen Sophie R.
Published: June 2024

David Lewis uses the problem of temporary intrinsics to motivate a perdurantist account of persistence in which four-dimensional individuals consist of temporal parts. Other philosophers use his argument to conclude that apparently persisting individuals are collections of temporal stages. In this paper, I investigate whether this argument is as effective in an ontology in which properties are causal powers and thus how seriously the problem should be taken. I go back to first principles to examine the ways in which individuals can change within an ontology of powers and then consider whether any of these ways are compatible with Lewis’s problem. I conclude that if powers are intrinsic, they are not temporary; and if they are temporary, they are not fully intrinsic. However, the situation with respect to changes in which powers are manifesting is not so clear cut, and so I explore how different conceptions of manifestation affect whether the problem of temporary intrinsics applies and what the powers theorist may say about them.
A Bayesian multilevel model for populations of networks using exponential-family random graphs
Authors: Lehmann Brieuc , White Simon
Published: June 2024

The collection of data on populations of networks is becoming increasingly common, where each data point can be seen as a realisation of a network-valued random variable. Moreover, each data point may be accompanied by some additional covariate information and one may be interested in assessing the effect of these covariates on network structure within the population. A canonical example is that of brain networks: a typical neuroimaging study collects one or more brain scans across multiple individuals, each of which can be modelled as a network with nodes corresponding to distinct brain regions and edges corresponding to structural or functional connections between these regions. Most statistical network models, however, were originally proposed to describe a single underlying relational structure, although recent years have seen a drive to extend these models to populations of networks. Here, we describe a model for when the outcome of interest is a network-valued random variable whose distribution is given by an exponential random graph model. To perform inference, we implement an exchange-within-Gibbs MCMC algorithm that generates samples from the doubly-intractable posterior. To illustrate this approach, we use it to assess population-level variations in networks derived from fMRI scans, enabling the inference of age- and intelligence-related differences in the topological structure of the brain’s functional connectivity.
Grab to fill the gap: key factors influencing Reynoutria japonica germination and seedling establishment in the secondary distribution range
Authors: Kadlecová Martina , Vojík Martin , Vacula Jaroslav , Berchová Bímová Kateřina
Published: June 2024

Reynoutria japonica (Japanese knotweed) is a highly invasive female plant that primarily reproduces through vegetative regeneration during secondary distribution. Despite producing a substantial number of viable hybrid seeds, the occurrence of seedlings and young plants in the wild remains rare; the reasons for this rarity are unclear. Environmental intolerance and chromosomal anomalies during hybridisation can impede seedling development. This study evaluates the combined impact of these factors by examining substrate, temperature, and light effects on germination, as well as assessing seedling genome sizes and viability. Seeds were cultivated in both natural and controlled environments to manage external influences. Flow cytometry was employed to evaluate chromosomal arrangements. The final germination was notably high at 99%, and 68% of seedlings thrived under controlled conditions, emphasising hybrid seedling viability regardless of highly polyploid levels ranging from pentaploid to aneuploid (2C DNA genome size from 5.17 to 11.95 pg). Thus, it is evident that seeds can germinate and produce vital seedlings despite various chromosomal sets. Even fluctuating temperatures and type of substrate do not limit seed germination. However, these results were obtained under laboratory conditions, with seeds and seedlings receiving regular irrigation. A significant seedling mortality rate (99%) was observed in the field experiment, and the final germination was also low there (15%). Observations suggest that water stress might be the cause of this mortality. Further research on water stress is necessary because it could be the primary factor limiting the successful generative spread of the knotweeds.
BrainLossNet: a fast, accurate and robust method to estimate brain volume loss from longitudinal MRI
Authors: Opfer Roland , Krüger Julia , Buddenkotte Thomas , Spies Lothar , Behrendt Finn , Schippling Sven , Buchert Ralph
Published: June 2024

Purpose

MRI-derived brain volume loss (BVL) is widely used as neurodegeneration marker. SIENA is state-of-the-art for BVL measurement, but limited by long computation time. Here we propose “BrainLossNet”, a convolutional neural network (CNN)-based method for BVL-estimation.

Methods

BrainLossNet uses CNN-based non-linear registration of baseline(BL)/follow-up(FU) 3D-T1w-MRI pairs. BVL is computed by non-linear registration of brain parenchyma masks segmented in the BL/FU scans. The BVL estimate is corrected for image distortions using the apparent volume change of the total intracranial volume. BrainLossNet was trained on 1525 BL/FU pairs from 83 scanners. Agreement between BrainLossNet and SIENA was assessed in 225 BL/FU pairs from 94 MS patients acquired with a single scanner and 268 BL/FU pairs from 52 scanners acquired for various indications. Robustness to short-term variability of 3D-T1w-MRI was compared in 354 BL/FU pairs from a single healthy men acquired in the same session without repositioning with 116 scanners (Frequently-Traveling-Human-Phantom dataset, FTHP).

Results

Processing time of BrainLossNet was 2–3 min. The median [interquartile range] of the SIENA-BrainLossNet BVL difference was 0.10% [− 0.18%, 0.35%] in the MS dataset, 0.08% [− 0.14%, 0.28%] in the various indications dataset. The distribution of apparent BVL in the FTHP dataset was narrower with BrainLossNet (p = 0.036; 95th percentile: 0.20% vs 0.32%).

Conclusion

BrainLossNet on average provides the same BVL estimates as SIENA, but it is significantly more robust, probably due to its built-in distortion correction. Processing time of 2–3 min makes BrainLossNet suitable for clinical routine. This can pave the way for widespread clinical use of BVL estimation from intra-scanner BL/FU pairs.
Powder bed fusion factory productivity increases using discrete event simulation and genetic algorithm
Authors: Al-zqebah Ruba , Guertler Matthias , Clemon Lee
Published: June 2024

Powder bed fusion is importance is growing with uses across industries in both polymer and metallic components, particularly in mass individualization. However, due to the relatively slow mass deposition speed compared to conventional methods, scheduling and production planning play a crucial role in scaling up additive manufacturing productivity to higher volumes. This paper introduces a framework combining discrete event simulation and a genetic algorithm showing makespan improvement opportunities for multiple powder bed fusion factories varying workers, jobs and available equipment. The results show that bottlenecks move among workstations based on worker and capital equipment availability, which depend on the size of the facility indicating a resource-driven constraint for makespan. A makespan reduction of 78% is achieved in the simulation. This shows the trade-off of worker and capital equipment to achieve makespan improvements. The addition of personnel or equipment increases production with further gains achieved by scheduling optimization. Two levels of job demands are analyzed showing productivity gains of 45% makespan improvement when adding the first worker and additional savings with scheduling optimization using a genetic algorithm up to 11%. Most research on additive manufacturing production has focused on the quality of produced parts and printing technology rather than factory level management. This is the first application of this methodology to varying sizes of these potential factories. The method developed here will help decision-makers to determine the appropriate number of resources to meet their customer demand on time, additionally, finding the optimal route for jobs before starting the production process.
Influence of the carbon fiber plate curvature of advanced footwear technology on the running energetic cost and 3000-m performance
Authors: Ruiz-Alias Santiago A. , Pérez-Castilla Alejandro , Soto-Hermoso Víctor M. , García-Pinillos Felipe
Published: June 2024

This study aimed to explore the effect of two similar commercialized advanced footwear technology models differing mainly in their plate geometry (i.e., low and high curvature plate) on the running energetic cost in both non-fatigued and fatigued states. It also sought to determine their impacts on 3000-m performance. Twelve highly trained male athletes performed four 5-min trials (two advanced technology models, two trials). In each experimental session, athletes completed a 3000-m trial which was followed by a 5-min trial to test the running energetic cost when fatigued. Athletes exhibited a lower running energetic cost when wearing the low curvature plate in a non-fatigued state (P = 0.034; − 1.13 [− 1.92 to − 0.11] %), which persisted with better performance in the 3000-m trial (P = 0.017; − 0.42 [− 0.74 to − 0.09] %). However, in a fatigued state, non-significant differences were reported (P = 0.207). The low curvature plate seems to be more effective than the high curvature plate when running at low intensity in a fresh state as well as in a maximal high-intensity effort. However, non-significant differences between plate geometries were observed when fatigue was increased.
How to select candidates for an undergraduate degree in psychology? Combining high-school GPA and admission test score
Authors: Sorgente Angela , Pietrabissa Giada , Antonietti Alessandro , Bonanomi Andrea , Castelnuovo Gianluca , Lanz Margherita , Tagliabue Semira , Traficante Daniela
Published: June 2024

The current study investigated whether the grade point average (GPA) of high school courses and the score on the test for admission to a bachelor’s degree in Psychology—assessing logical reasoning, reading comprehension, and general knowledge—can predict student academic success (AS) and to what extent. Archival data from 5147 candidates (82.4% female) collected over six years (2010–2016) were used. For 2559 of these, indicators of AS (graduation marks, average weighted marks, completion of the degree on time, and dropout) were also available. A series of hierarchical regressions were performed, in which the high school GPA was included as the first predictor, and the scores of the three sections of the admission test were included as the second set of predictors. Findings indicated high school GPA as the best predictor of AS. However, admission test scores predicted a portion of AS that the high school GPA was not able to account for. This result indicates that the administration of the admission test, which evaluates logical reasoning, reading comprehension, and general knowledge, can add relevant information to the high school GPA, thus increasing the efficacy in the selection of candidates, who can have the best chance to earn a bachelor’s degree in psychology.
Study of caesium adsorption onto alluvial sediments from the Italian Po Plain
Authors: Giacobbo F. , Pezzoli F. , Cydzik I. , Da Ros M. , Dapiaggi M. , Giudici M.
Published: June 2024

The study investigates the adsorption processes of caesium onto alluvial sediments from the Po Plain (northern Italy). Understanding these adsorption processes is crucial for assessing the safety of low- and intermediate-level radioactive waste repositories, including the proposed Italian repository. Adsorption kinetics and equilibrium experiments on sandy samples were conducted with the aim of evaluating how even small differences in clay content and mineralogy can affect kinetics and equilibrium adsorption behaviour. The obtained data were compared with literature studies and confirmed the significant affinity of caesium for sandy sediments, even for a mud content of less than 5%. Kinetics analysis revealed that a pseudo-second-order model best described the process, suggesting two-site occupancy adsorption kinetics attributed to the presence of illite and characterised by various different sites for caesium adsorption. Samples with higher clay and micaceous minerals content, cation exchange capacity and specific surface area exhibit faster kinetics and higher affinity for caesium. The study shows a significant variation in partition coefficient values, ranging from 57 to 750 mg L−1. This finding emphasises the importance of sediment composition in caesium adsorption, which is crucial for developing accurate environmental protection and safety assessment models.
A strategy for out-of-roundness damage wheels identification in railway vehicles based on sparse autoencoders
Authors: Magalhães Jorge , Jorge Tomás , Silva Rúben , Guedes António , Ribeiro Diogo , Meixedo Andreia , Mosleh Araliya , Vale Cecília , Montenegro Pedro , Cury Alexandre
Published: June 2024

Wayside monitoring is a promising cost-effective alternative to predict damage in the rolling stock. The main goal of this work is to present an unsupervised methodology to identify out-of-roundness (OOR) damage wheels, such as wheel flats and polygonal wheels. This automatic damage identification algorithm is based on the vertical acceleration evaluated on the rails using a virtual wayside monitoring system and involves the application of a two-step procedure. The first step aims to define a confidence boundary by using (healthy) measurements evaluated on the rail constituting a baseline. The second step of the procedure involves classifying damage of predefined scenarios with different levels of severities. The proposed procedure is based on a machine learning methodology and includes the following stages: (1) data collection, (2) damage-sensitive feature extraction from the acquired responses using a neural network model, i.e., the sparse autoencoder (SAE), (3) data fusion based on the Mahalanobis distance, and (4) unsupervised feature classification by implementing outlier and cluster analysis. This procedure considers baseline responses at different speeds and rail irregularities to train the SAE model. Then, the trained SAE is capable to reconstruct test responses (not trained) allowing to compute the accumulative difference between original and reconstructed signals. The results prove the efficiency of the proposed approach in identifying the two most common types of OOR in railway wheels.
Aggregation operators of complex fuzzy Z-number sets and their applications in multi-criteria decision making
Authors: Köseoğlu Ali , Altun Fatma , Şahin Rıdvan
Published: June 2024

Fuzzy sets (FSs) are a flexible and powerful tool for reasoning about uncertain situations that cannot be adequately expressed by classical sets. However, these sets fall short in two areas. The first is the reliability of this tool. Z-numbers are an extension of fuzzy numbers that improve the representation of uncertainty by combining two important components: restriction and reliability. The second is the problems that need to be solved simultaneously. Complex fuzzy sets (CFSs) overcome this problem by adding a second dimension to fuzzy numbers and simultaneously adding connected elements to the solution. However, they are insufficient when it comes to problems involving these two areas. We cannot express real-life problems that need to be solved at the same time and require the reliability of the information given with any set approach given in the literature. Therefore, in this study, we propose the complex fuzzy Z-number set (CFZNS), a generalization of Z-numbers and CFS, which fills this gap. We provide the operational laws of CFZNS along with some properties. Additionally, we define two essential aggregation operators called complex fuzzy Z-number weighted averaging (CFZNWA) and complex fuzzy Z-number weighted geometric (CFZNWG) operators. Then, we present an illustrative example to demonstrate the proficiency and superiority of the proposed approach. Thus, we process multiple fuzzy expressions simultaneously and take into account the reliability of these fuzzy expressions in applications. Furthermore, we compare the results with the existing set operations to confirm the advantages and demonstrate the efficiency of the proposed approach. Considering the simultaneous expression of fuzzy statements, this study can serve as a foundation for new aggregation operators and decision-making problems and can be extended to many new applications such as pattern recognition and clustering.
Pose estimation algorithm based on point pair features using PointNet +  +
Authors: Chen Yifan , Li Zhenjian , Li Qingdang , Zhang Mingyue
Published: June 2024

This study proposes an innovative deep learning algorithm for pose estimation based on point clouds, aimed at addressing the challenges of pose estimation for objects affected by the environment. Previous research on using deep learning for pose estimation has primarily been conducted using RGB-D data. This paper introduces an algorithm that utilizes point cloud data for deep learning-based pose computation. The algorithm builds upon previous work by integrating PointNet +  + technology and the classical Point Pair Features algorithm, achieving accurate pose estimation for objects across different scene scales. Additionally, an adaptive parameter-density clustering method suitable for point clouds is introduced, effectively segmenting clusters in varying point cloud density environments. This resolves the complex issue of parameter determination for density clustering in different point cloud environments and enhances the robustness of clustering. Furthermore, the LineMod dataset is transformed into a point cloud dataset, and experiments are conducted on the transformed dataset to achieve promising results with our algorithm. Finally, experiments under both strong and weak lighting conditions demonstrate the algorithm's robustness.
An edge-assisted group authentication scheme for the narrowband internet of things
Authors: Zhao Guosheng , Chen Huan , Wang Jian
Published: June 2024

As an emerging wireless communication technology, narrowband Internet of Things is widely used in various scenarios, but it also brings new security problems, especially the group authentication between terminal devices and servers. In narrowband Internet of Things, terminal devices are often deployed in unattended environments, which are vulnerable to various attacks and controlled by adversaries, resulting in identity information leakage. Secondly, a large number of terminal devices send authentication request information to the server at the same time, which is easy to cause network signaling congestion, and the centralized calculation mode of the server will cause a large delay in the authentication process. To solve these problems, we propose an edge-assisted group authentication scheme for narrowband Internet of Things. Firstly, a lightweight encryption algorithm LCHAOSAES was used to encrypt the terminal device identity to prevent the terminal device identity information leakage. Then, the edge computing method was used to add an edge network layer to the NB-IoT system architecture to assist in completing the group authentication of massive terminal devices, and the edge assistance could realize decentralization and reduce the load of centralized processing of the server. The Burrows Abadi Needham logic verification, informal security analysis and performance analysis show that the proposed scheme is robust under malicious attacks such as replay attack, man-in-the-middle attack and insider attack, and has less signaling overhead and delay.
Protein–protein-interaction assay coupled with proteome profiler and immune activation flow cytometry assay validate immune activation in 3D oral cancer-immune co-culture model
Authors: Chakraborty Rajdeep , Khodlan Pallavi , Liu Fei
Published: June 2024

Immune evasion is one of the hallmarks of cancer progression. Immunotherapy failed to produce appreciable treatment response in aggressive oral cancer cases. It is imperative to understand the molecular biological aspects of cancer-immune cell interactions and the reason for the lower anti-tumour reactivity of immune cells. Given that patient biopsy samples are heterogeneous, we constructed cancer-immune cell co-culture models using various oral cancer cells. In this paper, we present a simplified data-based method pipeline to identify the protein-interaction partners of proliferation mechanism proteins. The protocol pipeline makes use of data generated from broad-scale discovery and co-immunoprecipitation-based proteomics approaches, immune protein marker flow cytometry, and proteome profiler assays. Co-analyses of these data highlight genes of interest and provide in-depth insights into the functions of these genes, reveal the immune activation profile, provide a real-time visualization of immune cells attacking cancer cells, and assess the viability of the different cancer-immune cell co-culture models. Overall, this approach carries potential to enable detailed insights into the molecular underpinnings of oral cancer research and may also be used in research on other types of cancers.
China’s role in sovereign debt restructuring
Authors: Nishizawa Toshiro
Published: June 2024

As the world’s largest bilateral lender to low- and middle-income countries (LMICs), China faces challenges dealing with some sovereign borrowers in debt distress under the Belt and Road Initiative (BRI). The current global sovereign debt landscape suggests that the fundamental challenge of sovereign debt restructuring in the developing world is not China, but rather how to equitably address unsustainable debt owed to multiple categories of creditors. Nonetheless, by offering early-stage debt relief, China can alleviate the burden on borrowers who disproportionately owe significant debt to China and prevent trapping itself in unpaid debts. China’s policy decision will determine whether it can avoid the same mistake that Western creditors made of eventually losing all financial claims on many debt-distressed, heavily indebted poor countries (HIPCs) by the mid-2000s. Furthermore, a considerate approach by China is key to achieving the dual goals of mitigating LMICs’ debt distress and vulnerabilities to climate change by applying a climate-centered approach toward sustainable socio-economic development. China can contribute to creating better global debt governance, which should go hand in hand with global environmental governance and have critical economic, political, and social implications.
An index for measuring degree of departure from symmetry for ordinal square contingency tables
Authors: Ando Shuji , Momozaki Tomotaka , Masusaki Yuta , Tomizawa Sadao
Published: June 2024

For the analysis of square contingency tables with the same row and column ordinal classifications, this study proposes an index for measuring the degree of departure from the symmetry model using new cumulative probabilities. The proposed index is constructed based on the Cressie and Read’s power divergence, or the weighted average of the Patil and Taillie’s diversity index. This study derives a plug-in estimator of the proposed index and an approximate confidence interval for the proposed index. The estimator of the proposed index is expected to reduce the bias more than the estimator of the existing index, even when the sample size is not large. The proposed index is identical to the existing index under the conditional symmetry model. Therefore, assuming the probability structure in which the conditional symmetry model holds, the performances of plug-in estimators of the proposed and existing indexes can be simply compared. Through numerical examples and real data analysis, the usefulness of the proposed index compared to the existing index is demonstrated.
Monkeypox: genetic, clinical, molecular, diagnostic, and therapeutic perspectives
Authors: Wambani Josephine , Were Tom , Okoth Patrick
Published: June 2024

Monkeypox is a DNA virus that belongs to the orthopox virus family with two distinct clades known as West African and Congo Basin. This virus was initially discovered in crab-eating Macaques in 1958 and in 1970s it spread among people in the Democratic Republic of the Congo. Initially, there were several outbreaks of the disease reported in Africa and other regions of the world. The simultaneous spread in 19 countries in 2022 prompted severe worries. The monkeypox virus is closely related to smallpox, which was responsible for the highest fatality rate in human history, and a preconceived thought combined with fear is enough to make us shiver. Furthermore, the virus is often mistaken for a sexually transmitted infection or the Varicella zoster virus. The emergence of outbreaks outside of Africa is indicative of the disease’s global footprint. Increased detection and monitoring of monkey pox cases as well as understanding the disease’s ever-changing epidemiology is critical. Furthermore, intensive research is yet to identify the exact small mammal reservoir for monkeypox virus. Identifying the exact reservoir may aid in the identification of previously unknown high-risk activities for getting orthopoxvirus infections. Finally, a better understanding of the potential/suspected monkeypox viral transmission pathways is required so that public health officials can develop and implement interventions to lower the risk of human infection. This review focuses on the genetic, clinical, molecular, diagnostic, and therapeutic perspectives of monkeypox.
Learnable differencing center for nighttime depth perception
Authors: Yan Zhiqiang , Zheng Yupeng , Fan Deng-Ping , Li Xiang , Li Jun , Yang Jian
Published: June 2024

Depth completion is the task of recovering dense depth map from sparse ones, usually with the help of color images. Existing image guided methods perform well on daytime depth perception self-driving benchmarks, but struggle in nighttime scenarios with poor visibility and complex illumination. To address these challenges, we propose a simple yet effective learnable differencing center network (LDCNet). The key idea is to use recurrent inter-convolution differencing (RICD) and illumination affinitive intra-convolution differencing (IAICD) to enhance the nighttime color images and reduce the negative effects of the varying illumination, respectively. RICD explicitly estimates global illumination by differencing two convolutions with different kernels, treating the small-kernel-convolution feature as the center of the large-kernel-convolution feature in a new perspective. IAICD softly alleviates the local relative light intensity by differencing a single convolution, where the center is dynamically aggregated based on neighboring pixels and the estimated illumination map in the RICD. On both nighttime depth completion and depth estimation tasks, extensive experiments demonstrate the effectiveness of our LDCNet, reaching the state of the art.
Assessment of Gravity Waves From Tropopause to Thermosphere and Ionosphere in High‐Resolution WACCM‐X Simulations
Authors: Liu H.‐L. , Lauritzen P. H. , Vitt F. , Goldhaber S.
Published: June 2024

A new version of NCAR Whole Atmosphere Community Climate Model with thermosphere/ionosphere extension (WACCM‐X) has been developed. The main feature of this version is the species‐dependent spectral element (SE) dynamical core solved on a cubed sphere grid, eliminating the polar singularity and enabling simulations at high‐resolutions. Molecular viscosity and diffusion in the horizontal direction are also included. The Conservative Semi‐Lagrangian Multi‐Tracer Transport Scheme (CSLAM) is employed for the species transport. An efficient regridding scheme based on the Earth System Modeling Framework is used to map fields between the physics mesh and geomagnetic grid. Simulations have been performed at coarse (∼200 km and 0.25 scale height) and high (∼25 km and 0.1 scale height) resolutions. Spatial distribution of the resolved gravity waves from the high‐resolution simulations compares well with available observations in the middle and upper atmosphere. Analysis of the scale dependence of the gravity wave energy density and momentum flux shows that, while larger scale waves are dominant energetically at most latitudes, smaller scale waves contribute significantly to the total momentum flux, especially at mid‐high latitudes. The waves in the thermosphere are shown to be strongly modulated by the large‐scale wind through Doppler shift and molecular damping, and they cause large neutral atmosphere and plasma perturbations.
Major Modes of Climate Variability Dominate Nonlinear Antarctic Ice‐Sheet Elevation Changes 2002–2020
Authors: King Matt A. , Christoffersen Poul
Published: June 2024

We explore the links between elevation variability of the Antarctic Ice Sheet (AIS) and large‐scale climate modes. Using multiple linear regression, we quantify the time‐cumulative effects of El Niño Southern Oscillation (ENSO) and the Southern Annular Mode (SAM) on gridded AIS elevations. Cumulative ENSO and SAM explain a median of 29% of the partial variance and up to 85% in some coastal areas. After spatial smoothing, these signals have high spatial correlation with those from GRACE gravimetry (r∼ = 0.65 each). Much of the signal is removed by a firn densification model but inter‐model differences exist especially for ENSO. At the lower parts of the Thwaites and Pine Island glaciers, near their grounding line, we find the Amundsen Sea Low (ASL) explains ∼90% of the observed elevation variability. There, modeled firn effects explain only a small fraction of the variability, suggesting significant height changes could be a response to climatological ice‐dynamics.
Maternal obesity and offspring cardiovascular remodelling — the effect of preconception and antenatal lifestyle interventions: a systematic review
Authors: Burden Samuel J. , Alshehri Rahaf , Lamata Pablo , Poston Lucilla , Taylor Paul D.
Published: June 2024

Background

Preconception or antenatal lifestyle interventions in women with obesity may prevent adverse cardiovascular outcomes in the child, including cardiac remodelling. We undertook a systematic review of the existing data to examine the impact of randomised controlled trials of lifestyle interventions in pregnant women with obesity on offspring cardiac remodelling and related parameters of cardiovascular health.

Methods

This review was registered with PROSPERO (CRD42023454762) and aligns with PRISMA guidelines. PubMed, Embase, and previous reviews were systematically searched. Follow-up studies from randomised trials of lifestyle interventions in pregnant women with obesity, which included offspring cardiac remodelling or related cardiovascular parameters as outcome measures, were included based on pre-defined inclusion criteria.

Results

Eight studies from five randomised controlled trials were included after screening 3252 articles. Interventions included antenatal exercise (n = 2), diet and physical activity (n = 2), and preconception diet and physical activity (n = 1). Children were <2-months to 3–7-years-old, with sample sizes ranging between n = 18–404. Reduced cardiac remodelling, with reduced interventricular septal wall thickness, was consistently reported. Some studies identified improved systolic and diastolic function and a reduced resting heart rate. Risk of bias analyses rated all studies as ‘fair’ (some risk of bias). A high loss-to-follow-up was a common limitation.

Conclusion

Although there is some evidence to suggest that lifestyle interventions in women with obesity may limit offspring cardiac remodelling, further high-quality longitudinal studies with larger sample sizes are required to confirm these observations and to determine whether these changes persist to adulthood.
Targeted therapy for capillary-venous malformations
Authors: Zerbib Lola , Ladraa Sophia , Fraissenon Antoine , Bayard Charles , Firpion Marina , Venot Quitterie , Protic Sanela , Hoguin Clément , Thomas Amandine , Fraitag Sylvie , Duong Jean-Paul , Kaltenbach Sophie , Balducci Estelle , Lefevre Coline , Villarese Patrick , Asnafi Vahid , Broissand Christine , Goudin Nicolas , Nemazanyy Ivan , Autret Gwennhael , Tavitian Bertrand , Legendre Christophe , Arzouk Nadia , Minard-Colin Veronique , Chopinet Caroline , Dussiot Michael , Adams Denise M. , Mirault Tristan , Guibaud Laurent , Isenring Paul , Canaud Guillaume
Published: June 2024

Sporadic venous malformations are genetic conditions primarily caused by somatic gain-of-function mutation of PIK3CA or TEK, an endothelial transmembrane receptor signaling through PIK3CA. Venous malformations are associated with pain, bleedings, thrombosis, pulmonary embolism, esthetic deformities and, in severe cases, life-threatening situations. No authorized medical treatment exists for patients with venous malformations. Here, we created a genetic mouse model of PIK3CA-related capillary venous malformations that replicates patient phenotypes. We showed that these malformations only partially signal through AKT proteins. We compared the efficacy of different drugs, including rapamycin, a mTORC1 inhibitor, miransertib, an AKT inhibitor and alpelisib, a PI3Kα inhibitor at improving the lesions seen in the mouse model. We demonstrated the effectiveness of alpelisib in preventing vascular malformations’ occurrence, improving the already established ones, and prolonging survival. Considering these findings, we were authorized to treat 25 patients with alpelisib, including 7 children displaying PIK3CA (n = 16) or TEK (n = 9)-related capillary venous malformations resistant to usual therapies including sirolimus, debulking surgical procedures or percutaneous sclerotherapies. We assessed the volume of vascular malformations using magnetic resonance imaging (MRI) for each patient. Alpelisib demonstrated improvement in all 25 patients. Vascular malformations previously considered intractable were reduced and clinical symptoms were attenuated. MRI showed a decrease of 33.4% and 27.8% in the median volume of PIK3CA and TEK malformations respectively, over 6 months on alpelisib. In conclusion, this study supports PI3Kα inhibition as a promising therapeutic strategy in patients with PIK3CA or TEK-related capillary venous malformations.
DNA of neutrophil extracellular traps promote NF-κB-dependent autoimmunity via cGAS/TLR9 in chronic obstructive pulmonary disease
Authors: Chen Jun , Wang Tao , Li Xiaoou , Gao Lijuan , Wang Ke , Cheng Mengxin , Zeng Zijian , Chen Lei , Shen Yongchun , Wen Fuqiang
Published: June 2024

Chronic obstructive pulmonary disease (COPD) is characterised by persistent airway inflammation even after cigarette smoking cessation. Neutrophil extracellular traps (NETs) have been implicated in COPD severity and acute airway inflammation induced by short-term cigarette smoke (CS). However, whether and how NETs contribute to sustained airway inflammation in COPD remain unclear. This study aimed to elucidate the immunoregulatory mechanism of NETs in COPD, employing human neutrophils, airway epithelial cells (AECs), dendritic cells (DCs), and a long-term CS-induced COPD mouse model, alongside cyclic guanosine monophosphate-adenosine monophosphate synthase and toll-like receptor 9 knockout mice (cGAS-−/−, TLR9−/−); Additionally, bronchoalveolar lavage fluid (BALF) of COPD patients was examined. Neutrophils from COPD patients released greater cigarette smoke extract (CSE)-induced NETs (CSE-NETs) due to mitochondrial respiratory chain dysfunction. These CSE-NETs, containing oxidatively-damaged DNA (NETs-DNA), promoted AECs proliferation, nuclear factor kappa B (NF-κB) activation, NF-κB-dependent cytokines and type-I interferons production, and DC maturation, which were ameliorated/reversed by silencing/inhibition of cGAS/TLR9. In the COPD mouse model, blocking NETs-DNA-sensing via cGAS−/− and TLR9−/− mice, inhibiting NETosis using mitoTEMPO, and degrading NETs-DNA with DNase-I, respectively, reduced NETs infiltrations, airway inflammation, NF-κB activation and NF-κB-dependent cytokines, but not type-I interferons due to IFN-α/β receptor degradation. Elevated NETs components (myeloperoxidase and neutrophil elastase activity) in BALF of COPD smokers correlated with disease severity and NF-κB-dependent cytokine levels, but not type-I interferon levels. In conclusion, NETs-DNA promotes NF-κB-dependent autoimmunity via cGAS/TLR9 in long-term CS exposure-induced COPD. Therefore, targeting NETs-DNA and cGAS/TLR9 emerges as a potential strategy to alleviate persistent airway inflammation in COPD.