bluuebunny/crossref_metadata_2025 huggingface dataset:
Efficient k-Clique Listing: An Edge-Oriented Branching Strategy
Authors: Wang Kaixin , Yu Kaiqiang , Long Cheng
Published: March 2024

k-clique listing is a vital graph mining operator with diverse applications in various networks. The state-of-the-art algorithms all adopt a branch-and-bound (BB) framework with a vertex-oriented branching strategy (called VBBkC), which forms a sub-branch by expanding a partial k-clique with a vertex. These algorithms have the time complexity of O(k · m · (δ/2)k-2 ), where m is the number of edges in the graph and δ is the degeneracy of the graph. In this paper, we propose a BB framework with a new edge-oriented branching (called EBBkC), which forms a sub-branch by expanding a partial k-clique with two vertices that connect each other (which correspond to an edge ). We explore various edge orderings for EBBkC such that it achieves a time complexity of O( m · δ + k · m · (τ/2)k-2 ), where τ is an integer related to the maximum truss number of the graph and we have τ < δ. The time complexity of EBBkC is better than that of VBBkC algorithms for k>3 since both O(m · δ) and O(k · m · (τ/2)k-2 ) are bounded by O(k · m · (δ/2)k-2 ). Furthermore, we develop specialized algorithms for sub-branches on dense graphs so that we can early-terminate them and apply the specialized algorithms. We conduct extensive experiments on 19 real graphs, and the results show that our newly developed EBBkC based algorithms with the early termination technique consistently and largely outperform the state-of-the-art (VBBkC based) algorithms.
Relative Keys: Putting Feature Explanation into Context
Authors: An Shuai , Cao Yang
Published: March 2024

Formal feature explanations strictly maintain perfect conformity but are intractable to compute, while heuristic methods are much faster but can lead to problematic explanations due to lack of conformity guarantees. We propose relative keys that have the best of both worlds. Relative keys associate feature explanations with a set of instances as context, and warrant perfect conformity over the context as formal explanations do, whilst being orders of magnitudes faster and working for complex blackbox models. Based on it, we develop CCE, a prototype that computes explanations with provably bounded conformity and succinctness, without accessing the models. We show that computing the most succinct relative keys is NP-complete and develop various algorithms for it under the batch and online models. Using 9 real-life datasets and 7 state-of-the-art explanation methods, we demonstrate that CCE explains cases where existing methods cannot, and provides more succinct explanations with perfect conformity for cases they can; moreover, it is 2 orders of magnitude faster.
One Seed, Two Birds: A Unified Learned Structure for Exact and Approximate Counting
Authors: Li Yingze , Wang Hongzhi , Liu Xianglong
Published: March 2024

The modern database has many precise and approximate counting requirements. Nevertheless, a solitary multidimensional index or cardinality estimator is insufficient to cater to the escalating demands across all counting scenarios. Such approaches are constrained either by query selectivity or by the compromise between query accuracy and efficiency.

We propose CardIndex, a unified learned structure to solve the above problems. CardIndex serves as a versatile solution that not only functions as a multidimensional learned index for accurate counting but also doubles as an adaptive cardinality estimator, catering to varying counting scenarios with diverse requirements for precision and efficiency. Rigorous experimentation has showcased its superiority. Compared to the state-of-the-art (SOTA) autoregressive data-driven cardinality estimation baselines, our structure achieves training and updating times that are two orders of magnitude faster. Additionally, our CPU-based query estimation latency surpasses GPU-based baselines by two to three times. Notably, the estimation accuracy of low-selectivity queries is up to 314 times better than the current SOTA estimator. In terms of indexing tasks, the construction speed of our structure is two orders of magnitude faster than RSMI and 1.9 times faster than R-tree. Furthermore, it exhibits a point query processing speed that is 3%-17% times faster than RSMI and 1.07 to 2.75 times faster than R-tree and KDB-tree. Range queries under specific loads are 20% times faster than the SOTA indexes.
DTT: An Example-Driven Tabular Transformer for Joinability by Leveraging Large Language Models
Authors: Dargahi Nobari Arash , Rafiei Davood
Published: March 2024

Many organizations rely on data from government and third-party sources, and those sources rarely follow the same data formatting. This introduces challenges in integrating data from multiple sources or aligning external sources with internal databases. Commercial database systems do not offer adequate support for integrating data from heterogeneous sources, and manual integration is both time-consuming and inefficient. State-of-the-art data integration approaches that rely on similarity functions and textual transformations often fail to handle challenging cases where multiple mappings are required, or the mappings go beyond simple textual transformations.

In this paper, we study the potentials of deep neural models for transforming tables for joinability. In particular, we cast the problem as a prediction task and develop a framework that leverages large deep-learning language models to transform tabular data from a source formatting to a desired target representation. Our framework can efficiently learn the patterns for mapping a source formatting into an expected target using just a few examples, which can then be used for tasks such as table joining, filling in missing values, and error detection. Compared to state-of-the-art mapping and joining approaches, our framework delivers noticeably more accurate and scalable performance on both real-world and synthetic datasets. Our experimental evaluation also shows that the performance of the proposed framework using our fine-tuned model is at par or better than large language models such as GPT-3, despite the significant difference in size, and that using large language models within our framework improves their performance.
Towards Buffer Management with Tiered Main Memory
Authors: Hao Xiangpeng , Zhou Xinjing , Yu Xiangyao , Stonebraker Michael
Published: March 2024

The scaling of per-GB DRAM cost has slowed down in recent years. Recent research has suggested that adding remote memory to a system can further reduce the overall memory cost while maintaining good performance. Remote memory (i.e., tiered memory), connected to host servers via high-speed interconnect protocols such as RDMA and CXL, is expected to deliver 100x (less than 1µs) lower latency than SSD and be more cost-effective than local DRAM through pooling or adopting cheaper memory technologies.

Tiered memory opens up a large number of potential use cases within database systems. But previous work has only explored limited ways of using tiered memory. Our study provides a systematic study for DBMS to build tiered memory buffer management with respect to a wide range of hardware performance characteristics. Specifically, we study five different indexing designs that leverage remote memory in different ways and evaluate them through a wide range of metrics including performance, tiered-memory latency sensitivity, and cost-effectiveness. In addition, we propose a new memory provisioning strategy that allocates an optimal amount of local and remote memory for a given workload. Our evaluations show that while some designs achieve higher performance than others, no design can win in all measured dimensions.
PACE: Poisoning Attacks on Learned Cardinality Estimation
Authors: Zhang Jintao , Zhang Chao , Li Guoliang , Chai Chengliang
Published: March 2024

Cardinality estimation (CE) plays a crucial role in database optimizer. We have witnessed the emergence of numerous learned CE models recently which can outperform traditional methods such as histograms and samplings. However, learned models also bring many security risks. For example, a query-driven learned CE model learns a query-to-cardinality mapping based on the historical workload. Such a learned model could be attacked by poisoning queries, which are crafted by malicious attackers and woven into the historical workload, leading to performance degradation of CE.

In this paper, we explore the potential security risks in learned CE and study a new problem of poisoning attacks on learned CE in a black-box setting. There are three challenges. First, the interior details of the CE model are hidden in the black-box setting, making it difficult to attack the model. Second, the attacked CE model's parameters will be updated with the poisoning queries, i.e., a variable varying with the optimization variable, so the problem cannot be modeled as a univariate optimization problem and thus is hard to solve by an efficient algorithm. Third, to make an imperceptible attack, it requires to generate poisoning queries that follow a similar distribution to historical workload. We propose a poisoning attack system, PACE, to address these challenges. To tackle the first challenge, we propose a method of speculating and training a surrogate model, which transforms the black-box attack into a near-white-box attack. To address the second challenge, we model the poisoning problem as a bivariate optimization problem, and design an effective and efficient algorithm to solve it. To overcome the third challenge, we propose an adversarial approach to train a poisoning query generator alongside an anomaly detector, ensuring that the poisoning queries follow similar distribution to historical workload. Experiments show that PACE reduces the accuracy of the learned CE models by 178×, leading to a 10× decrease in the end-to-end performance of the target database.
View-based Explanations for Graph Neural Networks
Authors: Chen Tingyang , Qiu Dazhuo , Wu Yinghui , Khan Arijit , Ke Xiangyu , Gao Yunjun
Published: March 2024

Generating explanations for graph neural networks (GNNs) has been studied to understand their behaviors in analytical tasks such as graph classification. Existing approaches aim to understand the overall results of GNNs rather than providing explanations for specific class labels of interest, and may return explanation structures that are hard to access, nor directly queryable. We propose GVEX, a novel paradigm that generates Graph Views for GNN EXplanation. (1) We design a two-tier explanation structure called explanation views. An explanation view consists of a set of graph patterns and a set of induced explanation subgraphs. Given a database G of multiple graphs and a specific class label l assigned by a GNN-based classifier M, it concisely describes the fraction of G that best explains why l is assigned by M. (2) We propose quality measures and formulate an optimization problem to compute optimal explanation views for GNN explanation. We show that the problem is Σ2P-hard. (3) We present two algorithms. The first one follows an explain-and-summarize strategy that first generates high-quality explanation subgraphs which best explain GNNs in terms of feature influence maximization, and then performs a summarization step to generate patterns. We show that this strategy provides an approximation ratio of 1/2. Our second algorithm performs a single-pass to an input node stream in batches to incrementally maintain explanation views, having an anytime quality guarantee of 1/4-approximation. Using real-world benchmark data, we experimentally demonstrate the effectiveness, efficiency, and scalability of GVEX. Through case studies, we showcase the practical applications of GVEX.
Discovering Functional Dependencies through Hitting Set Enumeration
Authors: Bleifuß Tobias , Papenbrock Thorsten , Bläsius Thomas , Schirneck Martin , Naumann Felix
Published: March 2024

Functional dependencies (FDs) are among the most important integrity constraints in databases. They serve to normalize datasets and thus resolve redundancies, they contribute to query optimization, and they are frequently used to guide data cleaning efforts. Because the FDs of a particular dataset are usually unknown, automatic profiling algorithms are needed to discover them. These algorithms have made considerable advances in the past few years, but they still require a significant amount of time and memory to process datasets of practically relevant sizes.

We present FDHits, a novel FD discovery algorithm that finds all valid, minimal FDs in a given relational dataset. FDHits is based on several discovery optimizations that include a hybrid validation approach, effective hitting set enumeration techniques, one-pass candidate validations, and parallelization. Our experiments show that FDHits, even without parallel execution, has a median speedup of 8.1 compared to state-of-the-art FD discovery algorithms while using significantly less memory. This allows the discovery of all FDs even on datasets that could not be processed by the current state-of-the-art.
ASM: Harmonizing Autoregressive Model, Sampling, and Multi-dimensional Statistics Merging for Cardinality Estimation
Authors: Kim Kyoungmin , Lee Sangoh , Kim Injung , Han Wook-Shin
Published: March 2024

Recent efforts in learned cardinality estimation (CE) have substantially improved estimation accuracy and query plans inside query optimizers. However, achieving decent efficiency, scalability, and the support of a wide range of queries at the same time, has remained questionable. Rather than falling back to traditional approaches to trade off one criterion with another, we present a new learned approach that achieves all these. Our method, called ASM, harmonizes autoregressive models for per-table statistics estimation, sampling for merging these statistics for join queries, and multi-dimensional statistics merging that extends the sampling for estimating thousands of sub-queries, without assuming independence between join keys. Extensive experiments show that ASM significantly improves query plans under a similar or smaller overhead than the previous learned methods and supports a wider range of queries.
PLAQUE: Automated Predicate Learning at Query Time
Authors: Lin Yiming , Mehrotra Sharad
Published: March 2024

Predicate pushing down is a key optimization used to speed up query processing. Much of the existing practice is restricted to pushing predicates explicitly listed in the query. In this paper, we consider the challenge of learning predicates during query execution which are then exploited to accelerate execution. Prior related approaches with a similar goal are restricted (e.g., learn only from only join columns or from specific data statistics). We significantly expand the realm of predicates that can be learned from different query operators (aggregations, joins, grouping, etc.) and develop a system, entitled PLAQUE, that learns such predicates during query execution. Comprehensive evaluations on both synthetic and real datasets demonstrate that the learned predicate approach adopted by PLAQUE can significantly accelerate query execution by up to 33x, and this improvement increases to up to 100x when User-Defined Functions (UDFs) are utilized in queries.
Machine Unlearning in Learned Databases: An Experimental Analysis
Authors: Kurmanji Meghdad , Triantafillou Eleni , Triantafillou Peter
Published: March 2024

Machine learning models based on neural networks (NNs) are enjoying ever-increasing attention in the Database (DB) community, both in research and practice. However, an important issue has been largely overlooked, namely the challenge of dealing with the inherent, highly dynamic nature of DBs, where data updates are fundamental, highly-frequent operations (unlike, for instance, in ML classification tasks). Although some recent research has addressed the issues of maintaining updated NN models in the presence of new data insertions, the effects of data deletions (a.k.a., "machine unlearning") remain a blind spot. With this work, for the first time to our knowledge, we pose and answer the following key questions: What is the effect of unlearning algorithms on NN-based DB models? How do these effects translate to effects on key downstream DB tasks, such as cardinality/selectivity estimation (SE), approximate query processing (AQP), data generation (DG), and upstream tasks like data classification (DC)? What metrics should we use to assess the impact and efficacy of unlearning algorithms in learned DBs? Is the problem of (and solutions for) machine unlearning in DBs different from that of machine learning in DBs in the face of data insertions? Is the problem of (and solutions for) machine unlearning for DBs different from unlearning in the ML literature? what are the overhead and efficiency of unlearning algorithms (versus the naive solution of retraining from scratch)? What is the sensitivity of unlearning on batching delete operations (in order to reduce model updating overheads)? If we have a suitable unlearning algorithm (forgetting old knowledge), can we combine it with an algorithm handling data insertions (new knowledge) en route to solving the general adaptability/updatability requirement in learned DBs in the face of both data inserts and deletes? We answer these questions using a comprehensive set of experiments, various unlearning algorithms, a variety of downstream DB tasks (such as SE, AQP, and DG), and an upstream task (DC), each with different NNs, and using a variety of metrics (model-internal, and downstream-task specific) on a variety of real datasets, making this also a first key step towards a benchmark for learned DB unlearning.
Wred: Workload Reduction for Scalable Index Tuning
Authors: Brucato Matteo , Siddiqui Tarique , Wu Wentao , Narasayya Vivek , Chaudhuri Surajit
Published: March 2024

Modern database systems offer index-tuning advisors that automatically identify a set of indexes to improve workload performance. Advisors leverage the optimizer's what-if API to optimize a query for a hypothetical index configuration. Because what-if calls constitute a major bottleneck of index tuning, existing techniques, such as workload compression, help reduce the number of what-if calls to speed up tuning. Unfortunately, even with small workloads and few what-if calls, tuning can still take hours due to the complexity of the queries (e.g., the number of joins, filters, group-by and order-by clauses), which increases their optimization time. This paper introduces workload reduction, a new complementary technique aimed at expediting index tuning by decreasing individual what-if call time without significantly affecting the quality of index tuning. We present an efficient workload reduction algorithm, called Wred, which rewrites each query in the original workload to eliminate column and table expressions unlikely to benefit from indexes, thereby accelerating what-if calls. We study its complexity and ability to maintain high index quality. We perform an extensive evaluation over industry benchmarks and real-world customer workloads, which shows that Wred results in a 3x median speedup in tuning efficiency over an industrial-strength state-of-the-art index advisor, with only a 3.7% median loss in improvement---where improvement is the total workload cost as estimated by the query optimizer---and results in up to 24.7x speedup with 1.8% improvement loss. Furthermore, combining Wred and Isum (a state-of-the-art workload compression technique for index tuning) results in higher speedups than either of the two techniques alone, with 10.5x median speedup and 5% median improvement loss.
SeRF: Segment Graph for Range-Filtering Approximate Nearest Neighbor Search
Authors: Zuo Chaoji , Qiao Miao , Zhou Wenchao , Li Feifei , Deng Dong
Published: March 2024

Effective vector representation models, e.g., word2vec and node2vec, embed real-world objects such as images and documents in high dimensional vector space. In the meanwhile, the objects are often associated with attributes such as timestamps and prices. Many scenarios need to jointly query the vector representations of the objects together with their attributes. These queries can be formalized as range-filtering approximate nearest neighbor search (ANNS) queries. Specifically, given a collection of data vectors, each associated with an attribute value whose domain has a total order. The range-filtering ANNS consists of a query range and a query vector. It finds the approximate nearest neighbors of the query vector among all the data vectors whose attribute values fall in the query range. Existing approaches suffer from a rapidly degrading query performance when the query range width shifts. The query performance can be optimized by a solution that builds an ANNS index for every possible query range; however, the index time and index size become prohibitive -- the number of query ranges is quadratic to the number n of data vectors. To overcome these challenges, for the query range contains all attribute values smaller than a user-provided threshold, we design a structure called the segment graph whose index time and size are the same as a single ANNS index, yet can losslessly compress the n ANNS indexes, reducing the indexing cost by a factor of Ω(n). To handle general range queries, we propose a 2D segment graph with average-case index size O(n log n) to compress n segment graphs, breaking the quadratic barrier. Extensive experiments conducted on real-world datasets show that our proposed structures outperformed existing methods significantly; our index also exhibits superior scalability.
SA2E-AD: A Stacked Attention Autoencoder for Anomaly Detection in Multivariate Time Series
Authors: Li Mengyao , Li Zhiyong , Yang Zhibang , Zhou Xu , Li Yifan , Wu Ziyan , Kong Lingzhao , Nai Ke
Published: March 2024

Anomaly detection for multivariate time series is an essential task in the modern industrial field. Although several methods have been developed for anomaly detection, they usually fail to effectively exploit the metrical-temporal correlation and the other dependencies among multiple variables. To address this problem, we propose a stacked attention autoencoder for anomaly detection in multivariate time series (SA2E-AD); it focuses on fully utilizing the metrical and temporal relationships among multivariate time series. We design a multiattention block, alternately containing the temporal attention and metrical attention components in a hierarchical structure to better reconstruct normal time series, which is helpful in distinguishing the anomalies from the normal time series. Meanwhile, a two-stage training strategy is designed to further separate the anomalies from the normal data. Experiments on three publicly available datasets show that SA2E-AD outperforms the advanced baseline methods in detection performance and demonstrate the effectiveness of each part of the process in our method.
A Language-independent Quantitative Analysis Method on Conformance between Legal Contract and Smart Contract
Authors: Zhao Can , Wang Yibing , Wang Dejun , Sun Guangyan , Meng Bo
Published: March 2024

The analysis on conformance between legal contracts and smart contracts is necessary and precondition for constructing the secure blockchain. Currently, the analysis method is the language-dependent single qualitative analysis. There does not exist quantitative metrics and quantitative analysis methods. Hence, the quantitative metric and language-independent quantitative analysis method are proposed to analyze the conformance between legal contract and smart contract. First, the definitions and metrics of partial conformance and complete conformance are proposed, and then rewrite logic and language-independent symbol execution are used to construct a language-independent quantitative method; then, the executable formal semantic for legal contract description language Business Process Model and Notation2.0 (BPMN2.0) is presented. Finally, the conformance of the five main methods for mapping BPMN2.0 legal contracts to Solidity smart contracts is analyzed. The results show that three methods have partial conformance, one method has complete conformance, and method has neither complete conformance nor partial conformance.
Automatically Inspecting Thousands of Static Bug Warnings with Large Language Model: How Far Are We?
Authors: Wen Cheng , Cai Yuandao , Zhang Bin , Su Jie , Xu Zhiwu , Liu Dugang , Qin Shengchao , Ming Zhong , Cong Tian
Published: March 2024

Static analysis tools for capturing bugs and vulnerabilities in software programs are widely employed in practice, as they have the unique advantages of high coverage and independence from the execution environment. However, existing tools for analyzing large codebases often produce a great deal of false warnings over genuine bug reports. As a result, developers are required to manually inspect and confirm each warning, a challenging, time-consuming, and automation-essential task.

This article advocates a fast, general, and easily extensible approach called
Llm4sa
that automatically inspects a sheer volume of static warnings by harnessing (some of) the powers of Large Language Models (LLMs). Our key insight is that LLMs have advanced program understanding capabilities, enabling them to effectively act as human experts in conducting manual inspections on bug warnings with their relevant code snippets. In this spirit, we propose a static analysis to effectively extract the relevant code snippets via program dependence traversal guided by the bug warning reports themselves. Then, by formulating customized questions that are enriched with domain knowledge and representative cases to query LLMs,
Llm4sa
can remove a great deal of false warnings and facilitate bug discovery significantly. Our experiments demonstrate that
Llm4sa
is practical in automatically inspecting thousands of static warnings from Juliet benchmark programs and 11 real-world C/C++ projects, showcasing a high precision (81.13%) and a recall rate (94.64%) for a total of 9,547 bug warnings. Our research introduces new opportunities and methodologies for using the LLMs to reduce human labor costs, improve the precision of static analyzers, and ensure software trustworthiness
Identifying Authorship in Malicious Binaries: Features, Challenges & Datasets
Authors: Gray Jason , Sgandurra Daniele , Cavallaro Lorenzo , Blasco Alis Jorge
Published: March 2024

Attributing a piece of malware to its creator typically requires threat intelligence. Binary attribution increases the level of difficulty as it mostly relies upon the ability to disassemble binaries to obtain authorship-related features. We perform a systematic analysis of works in the area of malware authorship attribution. We identify key findings and some shortcomings of current approaches and explore the open research challenges. To mitigate the lack of ground-truth datasets in this domain, we publish alongside this survey the largest and most diverse meta-information dataset of 17,513 malware labeled to 275 threat actor groups.
Explainable finite mixture of mixtures of bounded asymmetric generalized Gaussian and Uniform distributions learning for energy demand management
Authors: Al-Bazzaz Hussein , Azam Muhammad , Amayri Manar , Bouguila Nizar
Published: March 2024

We introduce a mixture of mixtures of bounded asymmetric generalized Gaussian and uniform distributions. Based on this framework, we propose model-based classification and model-based clustering algorithms. We develop an objective function for the minimum message length (MML) model selection criterion to discover the optimal number of clusters for the unsupervised approach of our proposed model. Given the crucial attention received by Explainable AI (XAI) in recent years, we introduce a method to interpret the predictions obtained from the proposed model in both learning settings by defining their boundaries in terms of the crucial features. Integrating Explainability within our proposed algorithm increases the credibility of the algorithm’s predictions since it would be explainable to the user’s perspective through simple If-Then statements using a small binary decision tree. In this paper, the proposed algorithm proves its reliability and superiority to several state-of-the-art machine learning algorithms within the following real-world applications: fault detection and diagnosis (FDD) in chillers, occupancy estimation and categorization of residential energy consumers.
Temporal Scene Montage for Self-Supervised Video Scene Boundary Detection
Authors: Tan Jiawei , Yang Pingan , Chen Lu , Wang Hongxing
Published: March 2024

Once a video sequence is organized as basic shot units, it is of great interest to temporally link shots into semantic-compact scene segments to facilitate long video understanding. However, it still challenges existing video scene boundary detection methods to handle various visual semantics and complex shot relations in video scenes. We proposed a novel self-supervised learning method, Video Scene Montage for Boundary Detection (VSMBD), to extract rich shot semantics and learn shot relations using unlabeled videos. More specifically, we present Video Scene Montage (VSM) to synthesize reliable pseudo scene boundaries, which learns task-related semantic relations between shots in a self-supervised manner. To lay a solid foundation for modeling semantic relations between shots, we decouple visual semantics of shots into foreground and background. Instead of costly learning from scratch as in most previous self-supervised learning methods, we build our model upon large-scale pre-trained visual encoders to extract the foreground and background features. Experimental results demonstrate VSMBD trains a model with strong capability in capturing shot relations, surpassing previous methods by significant margins. The code is available at https://github.com/mini-mind/VSMBD.
Guest Column: Structure in Communication Complexity and Constant-Cost Complexity Classes
Authors: Hatami Hamed , Hatami Pooya
Published: March 2024

Several theorems and conjectures in communication complexity state or speculate that the complexity of a matrix in a given communication model is controlled by a related analytic or algebraic matrix parameter, e.g., rank, sign-rank, discrepancy, etc. The forward direction is typically easy as the structural implications of small complexity often imply a bound on some matrix parameter. The challenge lies in establishing the reverse direction, which requires understanding the structure of Boolean matrices for which a given matrix parameter is small or large. We will discuss several research directions that align with this overarching theme.
Downregulation of protein phosphatase 2Aα in asthmatic airway smooth muscle
Authors: Reza Mohammad Irshad , Kumar Ashish , Pabelick Christina M. , Britt Rodney D. , Prakash Y. S. , Sathish Venkatachalem
Published: March 2024

This novel study deciphered the expression and function of protein phosphatase 2Aα (PP2Aα) in airway smooth muscle (ASM) during asthma and/or inflammation. We showed robust expression of PP2Aα in human ASM while its downregulation in asthmatic ASM. Similarly, we demonstrated reduced PP2Aα expression in ASM exposed to proinflammatory cytokines. PP2Aα activation inhibited bronchoconstriction of isolated mouse bronchi. In addition, we unveiled that PP2Aα activation inhibits the intracellular calcium release and myosin light chain phosphorylation in human ASM.
Chemical, Physical, and Biological Corneal Decellularization Methods: A Review of Literature
Authors: Procházková Alexandra , Poláchová Martina , Dítě Jakub , Netuková Magdaléna , Studený Pavel
Published: March 2024

The cornea is one of the most commonly transplanted tissues worldwide. It is used to restore vision when severe visual impairment or blindness occurs in patients with corneal diseases or after trauma. Due to the global shortage of healthy donor corneas, decellularized corneal tissue has significant potential as an alternative to corneal transplantation. It preserves the native and biological ultrastructure of the cornea and, therefore, represents the most promising scaffold. This article discusses different methods of corneal decellularization based on the current literature. We searched PubMed.gov for articles from January 2009 to December 2023 using the following keywords: corneal decellularization, decellularization methods, and corneal transplantation. Although several methods of decellularization of corneal tissue have been reported, a universal standardised protocol of corneal decellularization has not yet been introduced. In general, a combination of decellularization methods has been used for efficient decellularization while preserving the optimal properties of the corneal tissue.
The Rhetoric and Reality of Choice and Autonomy When Older People Are Discharged from Community Hospital at the End‐of‐Life in England: A Constructivist Grounded Theory Study
Authors: Mckean Emily , Butler Claire , Wilson Patricia
Published: March 2024

There has been an increasing acknowledgement in the UK of the importance of recognising frailty as a condition that leaves older people vulnerable to dramatic, sudden changes in health triggered by seemingly small events. In policy, the approach to managing frailty is often an emphasis on staying well with limited consideration to frailty as an end‐of‐life phase. Meanwhile, discharge from hospital continues to be complex. Overstretched acute hospitals are juxtaposed with community and social services that struggle to keep up with the demand of those being discharged and the labelling of older people as “bed blockers” at the centre of delayed discharges. This paper reports a study underpinned by constructivist grounded theory methodology, with the aim of exploring the experiences and perceptions of stakeholders. Semistructured interviews were conducted with 57 participants including patients, their informal carer(s), community hospital staff, community health professionals, and care home managers. The core category of “the drive to discharge conveyor belt” was derived from data analysis. A significant finding of this study was that of the carer, their burden, and their intrinsic role in facilitating discharge, filling in gaps in services, coordinating services, and enabling the patient to stay at home, with little consideration of their choices or autonomy. The “drive to discharge” impacts older people, their informal carers, and health professional. This study suggests how they may be supported, through an ethical lens.
State of Charge and State of Health Coestimation for Lithium‐Ion Capacitor Based on Multi‐innovation Filters
Authors: Min Fanqi , Fu Shiyi , Jiang Wenping , Zhang Liheng , Dang Guoju , Luo Ying , Yan Liqin , Xie Jingying , Lv Taolin , Gao Yunzhi
Published: March 2024

The lithium‐ion capacitor (LIC) is a new type of hybrid energy storage device, which combines the advantages of lithium‐ion battery and electric double layer capacitor. To achieve efficient and reliable application of LIC in practical scenarios, accurate model and state estimation method are needed. In this work, the electrical behavior of LIC is studied, which is then described by the Thevenin model. A multi‐innovation filter‐ (MIF‐) based coestimation method is proposed, in which the multi‐innovation linear Kalman filter (MI‐LKF) is used for model parameter identification, the multi‐innovation cubature Kalman filter (MI‐CKF) is used for state of charge estimation, and the multi‐innovation extended Kalman filter (MI‐EKF) is used for state of health estimation. Compared to traditional methods, this method can significantly improve estimation accuracy by only expanding the innovation used to update the state from a single moment to multiple moments. The experimental results indicate that the estimation errors of SOC and SOH can be constrained within ±0.5%. In addition, the proposed method has good robustness and can achieve high‐precision state estimation even in the face of noise interference, uncertain initial values of the algorithm, and uncertain starting operating points.
Cultural Echoes in Modern Design: Assessing Young Consumers’ Perceptions of Traditional Bamboo Weaving Patterns
Authors: Gao Muxing , Cao Xiaoxiao , Qian Lihuai
Published: March 2024

This study, employing convenience sampling of art specialists, investigates the emotional resonance of young consumers with Chinese bamboo patterns, underscoring their significance in the realm of contemporary design. Through the lens of Kansei engineering, we delve into the intricate world of bamboo weaving patterns, scrutinizing their perceptual evaluations and user preferences among 104 art university students aged 18–23. Our analysis not only identifies pivotal factors for innovative redesign but also underscores the pivotal role of these traditional patterns in enhancing the emotional and aesthetic appeal of products for the younger generation. The findings illuminate a notable appreciation among this demographic for the ecofriendly and culturally rich bamboo patterns, positioning them as essential elements in modern lifestyle products. The study advances the discourse on integrating cultural heritage into product innovation, advocating for designs that resonate with the aesthetic sensibilities and environmental values of today’s youth. This aligns with the critical insights into the various categories of patterns and their diverse applications in contemporary design, addressing the cognitive gap between designers and consumers and highlighting the importance of cultural motifs in product development.
A Collaborative Control Method of Transmission Amplitude and Phase for Transmitarray Antenna Design
Authors: Ding Dawei , Chen Junfeng , Li Guang , Hong Hailin
Published: March 2024

In this paper, a collaborative control method of transmission amplitude and phase for transmitarray antenna (TA) design is proposed. In this proposed method, one of the most popular hypersurface fitting models, Gaussian stochastic process (GP) model, is utilized to construct an accurate surrogate model. Following this implementation, a mapping relationship between structural parameters of TA unit cell and its transmission amplitude and phase is established. The most advantage of this method is its applicability for general TA designs because it is much difficult to control the amplitude and phase of unit cell independently through adjusting separate structural parameters. To verify the high efficiency of the proposed method, three TA antennas with different scanning angles are designed to obtain high sidelobe suppression level. Measured results show that the proposed collaborative control method of amplitude and phase is much promising for high sidelobe suppression level in TA designs.
A Simultaneously Transmit and Receive Antenna Terminal for In-Band Full-Duplex Applications
Authors: Meenal  , Patnaik Amalendu
Published: March 2024

In this paper, a low-profile co-linearly polarized simultaneously transmit-receive antenna (STRA) terminal is presented for 5G and upcoming technologies. The proposed STRA terminal comprises of an identical pair of spatially rotated half-circular microstrip antennas for transmission (Tx) and reception (Rx). The challenge of self-interference (SI) is addressed by employing passive SI cancellation techniques such as spatial diversity, field confinement, and surface perturbations. This has been accomplished by using fence-strip structure and V-shaped slots in the ground plane. The STRA was designed, fabricated, and tested for full-duplex operation in a sub-6 GHz band lying from 5.73 to 5.88 GHz, providing high interport isolation ranging from 35.5 dB to a maximum of 46.5 dB. The proposed STRA was implemented using Rogers 3003™ substrate. The measurements yield radiation characteristics with high gain of 5.45-6.0 dBi and 5-6.5 dBi for Tx and Rx antennas, respectively. The measured results are in good conformance with the simulated design. This antenna finds its potential usage in V2X and private 5G networks and can be extended for full-duplex MIMO implementations.
Comparison of Goal Achievement during an Early, Intensive Nutrition Intervention Delivered to People with Upper Gastrointestinal Cancer by Telephone Compared with Mobile Application
Authors: Furness Kate , Huggins Catherine E. , Hanna Lauren , Croagh Daniel , Sarkies Mitchell , Haines Terry P.
Published: March 2024

Objective. This study is aimed at exploring whether the mode of nutrition intervention delivery affected participant goal achievement in a three-arm randomised controlled trial of early and intensive nutrition intervention delivered to upper gastrointestinal cancer patients. Methods. Newly diagnosed upper gastrointestinal cancer patients were recruited from four tertiary hospitals in Melbourne, Australia. Participants in the intervention groups received a regular nutrition intervention for 18 weeks from an experienced dietitian via telephone or mobile application (app) using behaviour change techniques to assist in goal achievement. Univariate and multiple regression models using STATA determined goal achievement, dose, and frequency of contact between groups. A p value <0.05 was considered statistically significant. Results. The telephone group (n=38) had 1.99 times greater frequency of contact with the research dietitian (95% CI: 1.67 to 2.36, p<0.001) and 2.37 times higher frequency of goal achievement (95% CI: 1.1 to 5.11, p=0.03) compared with the mobile app group (n=36). The higher dose (RR 0.03) of intervention and more behaviour change techniques employed in the telephone group compared with the mobile app group increased participant goal achievement (95% CI: 0.01 to 0.04, p<0.001). Discussion. Telephone nutrition intervention delivery led to a higher frequency of goal achievement compared to the mobile app intervention. There was also a higher number of behaviour change techniques employed which may have facilitated the greater goal achievement. Mobile app-based delivery may have poorer acceptance in this population with high levels of withdrawal. Practice Implications. We need to ensure that specifically designed technologies for our target populations are fit for purpose, efficacious, and acceptable to both patients and healthcare providers. This trial is registered with ACTRN12617000152325.
Causal Relationship between Sex Hormone-Binding Globulin and Risk of Neuroblastoma: A Bidirectional Two-Sample Mendelian Randomization Study
Authors: Wu Han , Ding Miao , Zhu Jiabei , Mao Siwei , Tang Xiaochen , Fang Sijia , Liu Li , Pan Qiuhui , Yue Chaoyan
Published: March 2024

Background:

The causal relationship between sex hormone-binding globulin (SHBG) and neuroblastoma remains unknown. This study aimed to explore the causality between SHBG and the risk of neuroblastoma using bidirectional two-sample Mendelian randomization (MR) study.

Methods:

Instrumental variables associated with SHBG were obtained from the genome-wide association study (GWAS) of European containing 214,989 females and 185,221 males from the UK Biobank. Summary-level data for neuroblastoma were derived from the IEU OpenGWAS project with 1,627 patients and 3,254 controls. The inverse-variance weighted (IVW) method served as the primary analytic tool.

Results:

The IVW method revealed a significant positive causal relationship between male SHBG and the risk of neuroblastoma [OR, 2.169; 95% confidence interval (CI), 1.207–3.897; P = 0.010]. Conversely, female SHBG showed no significant causal link with neuroblastoma (IVW OR, 1.004; 95% CI, 0.542–1.860; P = 0.990). No significant reverse causality was detected. Sensitivity analyses validated these findings.

Conclusions:

Elevated SHBG levels in males, but not in females, can causally increase the risk of neuroblastoma. This gender-specific effect indicates a potential differential role of SHBG in the etiology of neuroblastoma. Further research is needed to elucidate the underlying mechanisms of this gender disparity. Monitoring SHBG levels, especially in males, could be pivotal in neuroblastoma risk assessment and management.

Impact:

This study highlights a novel gender-specific aspect in the risk of neuroblastoma, emphasizing the potential role of male SHBG levels in neuroblastoma incidence, and sets the stage for targeted preventative strategies and further investigation into gender-based biological mechanisms.
A Phase I Trial of Nab-Paclitaxel/Bevacizumab (AB160) Nano-Immunoconjugate Therapy for Gynecologic Malignancies
Authors: Kalogera Eleftheria , Nevala Wendy K. , Finnes Heidi D. , Suman Vera J. , Schimke Jill M. , Strand Carrie A. , Kottschade Lisa A. , Kudgus Rachel A. , Buhrow Sarah A. , Becher Laura R. , Geng Liyi , Glaser Gretchen E. , Grudem Megan E. , Jatoi Aminah , Klampe Carolyn M. , Kumar Amanika , Langstraat Carrie L. , McWilliams Robert R. , Wahner Hendrickson Andrea E. , Weroha S. John , Yan Yiyi , Reid Joel M. , Markovic Svetomir N. , Block Matthew S.
Published: March 2024

Purpose:

AB160 is a 160-nm nano-immunoconjugate consisting of nab-paclitaxel (ABX) nanoparticles noncovalently coated with bevacizumab (BEV) for targeted delivery into tissues expressing high levels of VEGF. Preclinical data showed that AB160 resulted in greater tumor targeting and tumor inhibition compared with sequential treatment with ABX then BEV. Given individual drug activity, we investigated the safety and toxicity of AB160 in patients with gynecologic cancers.

Patients and Methods:

A 3+3 phase I trial was conducted with three potential dose levels in patients with previously treated endometrial, cervical, and platinum-resistant ovarian cancer to ascertain the recommended phase II dose (RP2D). AB160 was administered intravenously on days 1, 8, and 15 of a 28-day cycle (ABX 75–175 mg/m2, BEV 30–70 mg/m2). Pharmacokinetic analyses were performed.

Results:

No dose-limiting toxicities (DLT) were seen among the three dose levels tested. Grade 3/4 toxicities included neutropenia, thromboembolic events, and leukopenia. DL2 (ABX 150 mg/m2, BEV 60 mg/m2) was chosen as the RP2D. Seven of the 19 patients with measurable disease (36.8%) had confirmed partial responses (95% confidence interval, 16.3%–61.6%). Pharmacokinetic analyses demonstrated that AB160 allowed 50% higher paclitaxel dosing and that paclitaxel clearance mirrored that of therapeutic antibodies.

Conclusions:

The safety profile and clinical activity of AB160 supports further clinical testing in patients with gynecologic cancers; the RP2D is DL2 (ABX 150 mg/m2, BEV 60 mg/m2).