bluuebunny/crossref_metadata_2025_split huggingface dataset:
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.
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.
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.
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

Abstract

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

Abstract

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).
Pharmacological Characterization of SDX-7320/Evexomostat: A Novel Methionine Aminopeptidase Type 2 Inhibitor with Anti-tumor and Anti-metastatic Activity
Authors: Cornelius Peter , Mayes Benjamin A. , Petersen John S. , Turnquist David J. , Dufour Pierre J. , Dannenberg Andrew J. , Shanahan James M. , Carver Bradley J. 
Published: March 2024

Abstract
Methionine aminopeptidase type 2 (METAP2) is a ubiquitous, evolutionarily conserved metalloprotease fundamental to protein biosynthesis which catalyzes removal of the N-terminal methionine residue from nascent polypeptides. METAP2 is an attractive target for cancer therapeutics based upon its over-expression in multiple human cancers, the importance of METAP2-specific substrates whose biological activity may be altered following METAP2 inhibition, and additionally, that METAP2 was identified as the target for the anti-angiogenic natural product, fumagillin. Irreversible inhibition of METAP2 using fumagillin analogues has established the anti-angiogenic and anti-tumor characteristics of these derivatives; however, their full clinical potential has not been realized due to a combination of poor drug-like properties and dose-limiting central nervous system (CNS) toxicity. This report describes the physicochemical and pharmacological characterization of SDX-7320 (evexomostat), a polymer–drug conjugate of the novel METAP2 inhibitor (METAP2i) SDX-7539. In vitro binding, enzyme, and cell-based assays demonstrated that SDX-7539 is a potent and selective METAP2 inhibitor. In utilizing a high molecular weight, water-soluble polymer to conjugate the novel fumagillol-derived, cathepsin-released, METAP2i SDX-7539, limitations observed with prior generation, small molecule fumagillol derivatives were ameliorated including reduced CNS exposure of the METAP2i, and prolonged half-life enabling convenient administration. Multiple xenograft and syngeneic cancer models were utilized to demonstrate the anti-tumor and anti-metastatic profile of SDX-7320. Unlike polymer–drug conjugates in general, reductions in small molecule–equivalent efficacious doses following polymer conjugation were observed. SDX-7320 has completed a phase I clinical safety study in patients with late-stage cancer and is currently being evaluated in multiple phase Ib/II clinical studies in patients with advanced solid tumors.
Safety and Feasibility of a “Fast‐Track” Monitoring Protocol for Patients Treated With Intravenous Thrombolytic Therapy
Authors: Fukuda Keiko A. , Shah Kavit , Kenmuir Cynthia , Barnagian Derrick , Nawash Baraa , Desai Shashvat , Rocha Marcelo , Starr Matthew , Roach Eileen , Henry Stephanie , Molyneaux Bradley J. , Jadhav Ashutosh P. 
Published: March 2024

Background
Our health care systems continue to face significant strain due to chronically taxed intensive care resources. A subgroup of patients following thrombolytic stroke may not require prolonged intensive monitoring, alleviating some burden. Here, we describe the safety, feasibility, and utility of a Fast‐Track Protocol (FTP) for early deescalation of high‐acuity monitoring.

Methods
We compared a prospective cohort of patients on the FTP at our stroke centers from April 2020 to February 2022 to a similar retrospective cohort. Those who presented with a National Institutes of Health Stroke Scale <10 and without large‐vessel occlusion or flow‐limiting stenosis, intravenous antihypertensive use, and any hemodynamic or respiratory concerns were eligible. Primary outcomes included early neurologic deterioration, defined as worsening of National Institutes of Health Stroke Scale score of ≥4 points at 24 hours, parenchymal hemorrhage, and symptomatic intracranial hemorrhage.

Results
Of 574 patients undergoing thrombolysis, 119 (21%) were eligible for the FTP. A total of 100 (88%) were included for analysis. The median±interquartile range hospitalization was 2.0±1.6 days. None of the 4 cases of early neurologic deterioration were due to hemorrhage. No symptomatic intracranial hemorrhages occurred, and no patients on the FTP were transferred back to the intensive care unit. Median±interquartile range 90‐day modified Rankin scale score was 1±1.

Conclusion
FTP is a safe and feasible strategy to triage intensive care unit patients and decrease unnecessary intensive care unit monitoring. This is important in a postpandemic era as intensive care unit resources continue to fluctuate. Future studies are needed to establish the optimal level of monitoring in patients following thrombolysis.
Drift Analysis with Fitness Levels for Elitist Evolutionary Algorithms
Authors: He Jun , Zhou Yuren 
Published: March 2024

Abstract
The fitness level method is a popular tool for analyzing the hitting time of elitist evolutionary algorithms. Its idea is to divide the search space into multiple fitness levels and estimate lower and upper bounds on the hitting time using transition probabilities between fitness levels. However, the lower bound generated by this method is often loose. An open question regarding the fitness level method is what are the tightest lower and upper time bounds that can be constructed based on transition probabilities between fitness levels. To answer this question, we combine drift analysis with fitness levels and define the tightest bound problem as a constrained multiobjective optimization problem subject to fitness levels. The tightest metric bounds by fitness levels are constructed and proven for the first time. Then linear bounds are derived from metric bounds and a framework is established that can be used to develop different fitness level methods for different types of linear bounds. The framework is generic and promising, as it can be used to draw tight time bounds on both fitness landscapes with and without shortcuts. This is demonstrated in the example of the (1+1) EA maximizing the TwoMax1 function.
Reconciling Roles of External Forcing and Internal Variability in Arctic Sea Ice Change on Different Time Scales
Authors: Shen Zili , Duan Anmin , Zhou Wen , Peng Yuzhuo , Li Jinxiao 
Published: March 2024

Abstract
Two large ensemble simulations are adopted to investigate the relative contribution of external forcing and internal variability to Arctic sea ice variability on different time scales since 1960 by correcting the response error of models to external forcing using observational datasets. Our study suggests that previous approaches might overestimate the real impact of internal variability on Arctic sea ice change especially on long time scales. Our results indicate that in both March and September, internal variability plays a dominant role on all time scales over the twentieth century, while the anthropogenic signal on sea ice change can be steadily and consistently detected on a time scale of more than 20 years after the 2000s. We also reveal that the dominant mode of internal variability in March shows consistency across different time scales. On the contrary, the pattern of internal variability in September is highly nonuniform over the Arctic and varies across different time scales, indicating that sea ice internal variability in September at different time scales is driven by different factors.
Projectized Community of Practice: A Case Study of Globally Distributed Information Technology Organization
Authors: Sharma Rajeev , Bagchi Soumendra Narain 
Published: March 2024

The execution of globally distributed information technology service projects (GDITP) by globally distributed teams, while inherently complex, offers the advantages of swift implementation and seamless service delivery to global clients. The numerous challenges including the intangibility of customer specifications, the iterative nature of information technology (IT) activities and coordination difficulties arising from diverse teams contribute to complexity in managing these projects. Moreover, the organizational complexity is compounded by competing power centres, turning project delivery into a politically contested process. In traditional projects such as construction or customized manufacturing, overcoming aforementioned challenges through strong top-down leadership is typical in many time-bound projects. However, in the realm of IT projects, which are inherently people-centric, enforcing a command-and-control environment is challenging. Communities-of-practice (CoP) offer an alternative structure that engages highly skilled employees in a collaborative community, navigating the challenges posed by IT projects. Our study explores this innovative approach, focusing on a product firm effectively leveraging CoPs to successfully execute global service projects. Delving into the functioning of CoPs, our research illustrates how they scale up using both formal and informal networks to meet diverse global customer requirements. Despite operating globally, CoPs exhibit emergent collective mindfulness, adapting tools, processes and products to the demands of the projects. The study also details how the organization manages complexity while adhering to product architecture and a uniform project framework.
Hypomethylation of the LncRNA H19 promoter accelerates osteogenic differentiation of vascular smooth muscle cells by activating the Erk1/2 pathways
Authors: Wang Taoxia , Cheng Meijuan , Jin Jingjing , Bai Yaling , Zhang Dongxue , Zhang Shenglei , Xu Jinsheng 
Published: March 2024

Objective Vascular calcification is a common chronic kidney disease complication. This study aimed to investigate the function of long non-coding RNA (LncRNA) H19 in vascular calcification to explore new therapeutic strategies. Methods We induced osteogenic differentiation and calcification of vascular smooth muscle cells (VSMCs) using β-glycerophosphate. Then, we detected the LncRNA H19 promoter methylation status and Erk1/2 pathways using methylation-specific polymerase chain reaction and western blotting, respectively. Results Compared with the control group, high phosphorus levels induced VSMC calcification, accompanied by increases in LncRNA H19 and the osteogenic marker Runx2 and reduction of the contractile phenotype marker SM22a. LncRNA H19 knockdown inhibited osteogenic differentiation and calcification of VSMCs. However, the suppressed role of VSMC calcification caused by shRNA H19 was partially reversed by simultaneous activation of the Erk1/2 pathways. Mechanically, we found that the methylation rate of CpG islands in the LncRNA H19 promoter region was significantly lower in the high-phosphorus group, and the hypomethylation state elevated LncRNA H19 levels, which in turn regulated phosphorylated Erk1/2 expression. Conclusions LncRNA H19 promoted osteogenic differentiation and calcification of VSMCs by regulating the Erk1/2 pathways. Additionally, hypomethylation of LncRNA H19 promoter CpG islands upregulated LncRNA H19 levels and subsequently activated Erk1/2 phosphorylation.
Efficacy and safety of caspofungin for the treatment of invasive pulmonary aspergillosis in patients with chronic obstructive pulmonary disease
Authors: Li Shaoqiang , Li Zhengtu , Wang Penglei , Zhan Yangqing , Xie Jiaxing , Ye Feng 
Published: March 2024

Objectives We assessed the efficacy of a 3-week primary or salvage caspofungin regimen in patients with chronic obstructive pulmonary disease (COPD) and concomitant proven or suspected invasive pulmonary aspergillosis (IPA). Methods Forty-four patients were treated with an initial loading caspofungin dose of 70 mg, followed by a daily dose of 50 mg for 20 days. The main efficacy endpoint was clinical effectiveness. Secondary endpoints included the clinical efficacy of caspofungin after 1 week, therapeutic efficacy based on the European Organization for Research and Treatment of Cancer and Mycoses Study Group Education and Research Consortium (EORTC/MSG) criteria, the sensitivity of different Aspergillus strains to caspofungin in vitro, and the safety of caspofungin. Results An assessment of 42 patients in the intention-to-treat group revealed efficacy rates of 33.33% within 1 week and 38.10% within 3 weeks. According to the EORTC/MSG criteria, the treatment success rate was 38.10%. The success rate of first-line treatment was 54.76%, whereas salvage treatment had a success rate of 45.24%. No adverse events were reported among the participants. Conclusions Caspofungin is effective and safe as an initial or salvage treatment for patients with IPA and COPD.
Drugs associated with a risk of supraventricular tachycardia: analysis using the OpenVigil database
Authors: Chen Weihong , Li Shaobin , Huang Dongqin , Su Yuchao 
Published: March 2024

Objective The OpenVigil database can be used to assess medications that may cause supraventricular tachycardia (SVT) and to produce a reference for their safe use in clinical settings. Methods We analyzed first-quarter data from 2004 to 2023, obtained by searching the OpenVigil database using the keyword “supraventricular tachycardia.” Trade names and generic names were obtained by querying the RxNav database, and the proportions were summarized. The proportionate reporting ratio (PRR), reporting odds ratio, and chi-square values were also summarized. We created Asahi diagrams and set the screening criteria to drug events ≥30, PRR >2, and chi-square >4. Outcomes were evaluated using the Side Effect Resource database, several scientific literature databases, and the Hangzhou Yiyao Rational Medication System. Results A total of 2435 distinct medications were found to induce SVT between the first quarter of 2004 and 2023, leading to 22,375 documented adverse events related to SVT. Further investigation revealed that salbutamol, paroxetine, formoterol, paclitaxel, venlafaxine, and theophylline were most likely to cause SVT. Conclusion We conducted signal mining of adverse drug events using the OpenVigil database and evaluated the six drugs most likely to cause SVT. The results of this research can serve as a drug safety reference in the clinic.