bluuebunny/crossref_metadata_2025 huggingface dataset:
Improved resolution of avian influenza virus using Oxford Nanopore R10 sequencing chemistry
Authors: Ratcliff Jeremy D. , Merritt Brian , Gooden Hannah , Siegers Jurre Y. , Srikanth Abhinaya , Yann Sokhoun , Kol Sonita , Sin Sarath , Tok Songha , Karlsson Erik A. , Thielen Peter M.
Published: November 2024

Highly pathogenic avian influenza viruses continue to pose global risks to One Health, including agriculture, public, and animal health. Rapid and accurate genomic surveillance is critical for monitoring viral mutations, tracing transmission, and guiding interventions in near real-time. Oxford Nanopore sequencing holds promise for real-time influenza genotyping, but data quality from R9 chemistry has limited its adoption due to challenges resolving low-complexity regions such as the biologically critical hemagglutinin cleavage site, a homopolymer of basic amino acids that distinguish highly pathogenic strains. In this study, human and avian influenza isolates (
n
= 45) from Cambodia were sequenced using both R9.4.1 and R10.4.1 flow cells and chemistries to evaluate performance between approaches. Overall, R10.4.1 yielded increased data output with higher average quality compared to R9.4.1, producing improved consensus sequences using a reference-based bioinformatics approach. R10.4.1 had significantly lower minor population insertion and deletion frequencies, driven by improved performance in low sequence complexity regions prone to insertion and deletion errors, such as homopolymers. Within the hemagglutinin cleavage site, R10.4.1 resolved the correct motif in 90% of genomes compared to only 60% with R9.4.1. Further examination showed reduced frameshift mutations in consensus sequences generated with R10.4.1 that could result in incorrectly classified virulence with automated pipelines. Improved consensus genome quality from nanopore sequencing approaches, especially across biologically important low-complexity regions, is critical to reduce subjective hand-curation and will improve local and global genomic surveillance responses.

IMPORTANCE

This study demonstrates significant advancement in the field of influenza virus genomic surveillance by showcasing the superior accuracy and data quality of the Oxford Nanopore R10 sequencing chemistry compared to the older R9 chemistry. Improved resolution, including in the critical hemagglutinin multi-basic cleavage site, enables more reliable monitoring and tracking of viral mutations. This accelerates the ability to respond quickly to outbreaks, potentially improving impacts on public health, agriculture, and the economy by enabling more accurate and timely interventions.
Identification of atypical pediatric diabetes mellitus cases using electronic medical records
Authors: Astudillo Marcela F , Winter William E , Billings Liana K , Kreienkamp Raymond , Balasubramanyam Ashok , Redondo Maria J , Tosur Mustafa
Published: November 2024

Introduction

There are no established methods to identify children with atypical diabetes for further study. We aimed to develop strategies to systematically ascertain cases of atypical pediatric diabetes using electronic medical records (EMR).

Research design and methods

We tested two strategies in a large pediatric hospital in the USA. Strategy 1: we designed a questionnaire to rule out typical diabetes and applied it to the EMR of 100 youth with diabetes. Strategy 2: we built three electronic queries to generate reports of three atypical pediatric diabetes phenotypes: unknown type, type 2 diabetes (T2D) diagnosed <10 years old and autoantibody-negative type 1 diabetes (AbNegT1D).

Results

Strategy 1 identified six cases (6%) of atypical diabetes (mean diagnosis age=11±2.6 years, 16.6% men, 33% non-Hispanic white (NHW) and 66.6% Hispanic). Strategy 2: unknown diabetes type: n=68 (1%) out of 6676 patients with diabetes; mean diagnosis age=12.6±3.3 years, 32.8% men, 23.8% NHW, 47.6% Hispanic, 25.4% African American (AA), 3.2% other. T2D <10 years old: n=64 (6.6%) out of 1142 patients with T2D; mean diagnosis age=8.6±1.6 years, 20.3% men, 4.7% NHW, 65.6% Hispanic, 28.1% AA, 1.6% other. AbNegT1D: n=38 (5.6%) out of 680 patients with new onset T1D; mean diagnosis age=11.3±3.8 years; 57.9% men, 50% NHW, 19.4% Hispanic, 22.3% AA, 8.3% other.

Conclusions

In sum, we identified 1%–6.6% of atypical diabetes cases in a pediatric diabetes population with high racial and ethnic diversity using systematic review of the EMR. Better identification of these cases using unbiased approaches may advance precision diabetes.
Development of a nomogram for predicting pancreatic portal hypertension in patients with acute pancreatitis: a retrospective study
Authors: Zhang Yingjie , Su Qiaoyu , Li Yunling , Zhan Xianchen , Wang XiangPing , Zhang Linhui , Luo Hui , Kang Xiaoyu , Lv Yong , Liang Shuhui , Ren Gui , Pan Yanglin
Published: November 2024

Introduction

Pancreatic portal hypertension (PPH) is a rare complication of acute pancreatitis (AP) that can lead to severe gastrointestinal bleeding. The risk factors associated with PPH, as well as the overall prognosis, warrant further investigation. This study aims to develop and validate a nomogram to predict PPH in patients with AP.

Methods

Consecutive patients with AP from 2015 to 2023 were retrospectively included in the study. Demographic data, clinical manifestations within the first week of AP onset, and initial contrast-enhanced CT findings were used to develop the predictive model. Univariate and multivariate Cox regression analyses were performed to identify risk factors for PPH. Based on the results of the multivariate analysis, a nomogram was developed. The patients were randomly divided into training and validation sets at a 7:3 ratio. The accuracy and discriminative power of the predictive model were assessed using the area under the curve (AUC) from the receiver operating characteristic curve and the calibration curve.

Results

Of the 1473 patients with AP, 107 (7.3%) developed PPH within 6 months (range: 2–22 months) during follow-up. Multivariate regression analysis showed that body mass index (BMI) (HR, 1.10; 95% CI 1.04 to 1.16; p=0.001), moderately severe grade (HR, 9.36; 95% CI 4.58 to 19.13; p<0.001), severe grade (HR, 12.95; 95% CI 6.22 to 26.94; p<0.001), diabetes (HR, 2.26; 95% CI 1.47 to 3.47; p<0.001), acute fluid accumulation (HR, 2.13; 95% CI 1.31 to 3.47; p=0.002), and necrosis (HR, 3.64; 95% CI 2.30 to 5.78; p<0.001) were independent risk factors for PPH. A nomogram for predicting PPH was developed, with the predictive curves showing an AUC of 0.859 at 6 months and 0.846 at 9 months. In the validation set, the AUC at both time points was 0.812.

Conclusion

In summary, we identified BMI, moderately severe or severe AP, diabetes, acute fluid accumulation, and necrosis as risk factors for AP-related PPH. Using the largest cohort of patients with AP to date, we developed a highly accurate nomogram with strong discriminative ability for predicting PPH. Future studies with larger sample sizes are necessary to confirm our findings and conduct external validation.
Structures, processes and outcomes between first referral and referral hospitals in low-income and middle-income countries: a secondary preplanned analysis of the FALCON and ChEETAh randomised trials
Authors:   NIHR Global Health Research Unit on Global Surgery, Kamarajah Sivesh Kathir , Alexander Philip
Published: November 2024

First referral hospitals, often known as district hospitals, are neglected in the discourse on universal health coverage in low-income and middle-income countries (LMICs). However, these hospitals are important for delivering safe surgery for 313 million people. This study aims to understand the structures, processes and outcomes of patients undergoing surgery in these centres in LMICs. This is a preplanned secondary analysis using data from two high-quality randomised controlled trials undergoing major abdominal surgery across six LMICs. Type of hospital was the main explanatory variable, defined according to the WHO taxonomy as first referral (ie, district or rural) and referral (ie, secondary or tertiary). Of the included 15 657 patients across 80 hospitals from 6 countries, 3562 patients underwent surgery in first referral and 12 149 patients underwent surgery in referral centres. First referral centres have lower full-time surgeons (median: 1 vs 20, p<0.001) and medically trained anaesthetists (28.6% vs 87.1%, p<0.001) compared with referral centres. Patients undergoing surgery in first referral centres were more likely to have lower rates of American Society of Anaesthesiologist (ASA) grades III–V (8.1% vs 22.7%, p<0.001), but higher rates of emergency procedures (65.1% vs 56.6%, p<0.001). In first referral centres, there was a significantly higher use of WHO surgical safety checklist (99.4% vs 93.3%, p<0.001) compared with referral centres. In adjusted analyses, there were no differences in 30-day mortality (OR 1.09, 95% CI 0.73 to 1.62) and surgical site infection (OR 1.30, 95% CI 0.89 to 1.90) between first referral and referral centres. Postoperative mortality and surgical site infection remain similar between first referral and referral centres in LMICs. There may be a clear need to upscale surgical volume safely in first referral centres to meet global surgical needs. High-quality research is needed to drive safe expansion of surgical workforce and strengthen referral pathways within these surgical health systems in LMICs.
Combining quality improvement and critical care training: Evaluating an ICU CPR training programme quality improvement initiative at the National Hospital in Tanzania
Authors: Strelzer Samantha , Julius Joseph , Anicet Niyo , Byabato Odillo , Chiwanga Faraja , Hassan Saria , Kayandabila Festo , Laizer Agness , Majuta Trustworthy , Murray Brittany , Said Tatu , Ndile Samson
Published: November 2024

Background

The United Republic of Tanzania has had a 41.4% mortality rate in the intensive care unit. In Tanzania, the Ministry of Health and Social Welfare has implemented quality improvement (QI), yet the Tanzanian health sector continues to face resource constraints, unsustainable projects and gaps in knowledge and skills, contributing to unacceptably high mortality rates for Tanzanian patients. This research aims to determine if a Critical Care Training Program incorporating QI concepts can improve technical competence and self-efficacy of providers in a critical care setting in Tanzania.

Methods

A 2-day Critical Care Training Program was developed for providers. It included the following modules: vital signs directed therapy (VSDT), cardiopulmonary and brain resuscitation (CPBR), blood glucose monitoring, introduction to critical care concepts and the QI concept of change management. For analysis, data were collected from pretests and post-tests and reported in REDCap. Descriptive statistics and paired t-tests were performed (alpha=0.05).

Results

A total of 77 nurses and three providers attended the training. The overall score changes among participants for CPBR and VSDT were significant (p<0.001). Six out of 10 questions in CPBR demonstrated significant improved change (p<0.001). All questions in the VSDT training showed significant improvement (p<0.001). Based on hospital guidelines, 63 (95.5%) passed the CPBR evaluation and 62 (95.2%) passed VSDT.

Conclusion

A pre/post analysis demonstrated improvement in knowledge, skills and increased confidence towards emergencies. This study suggests a Critical Care Training Program significantly improves the knowledge among providers and that QI impacts culture of change. This research exemplifies a systematic approach to strengthening capacity of critical care delivery in limited resource settings, with implications for further innovation in other low- and middle-income countries.
The Local Metric Dimension and Distance-Edge-Monitoring Number of Graph
Authors: Yang Chenxu , Ji Zhen , Li Wen , Liang Yan
Published: November 2024

Let [Formula: see text] be a graph and [Formula: see text]. The vertex [Formula: see text] is said to resolve a pair [Formula: see text] and [Formula: see text] if and only if [Formula: see text]. A set [Formula: see text] is defined as a resolving set of [Formula: see text] if for all [Formula: see text], the pair [Formula: see text] is resolved by some [Formula: see text]. The minimum cardinality of a resolve set of [Formula: see text] is defined as [Formula: see text]. A set [Formula: see text] is a local resolve set of [Formula: see text] if for all [Formula: see text] such that [Formula: see text], the pair [Formula: see text] is resolved by some [Formula: see text]. The minimum cardinality of a local resolve set of [Formula: see text] is defined as [Formula: see text]. An edge [Formula: see text] is said to be monitored by [Formula: see text] if [Formula: see text] or [Formula: see text]. A set [Formula: see text] is a distance-edge-monitoring (DEM) set if for all [Formula: see text], [Formula: see text] is monitored by some [Formula: see text]. The minimum cardinality of a [Formula: see text] set of [Formula: see text] is defined as [Formula: see text].

In this paper, we obtained that [Formula: see text] for all non trivial graphs with order [Formula: see text], and the exact value of [Formula: see text] and [Formula: see text] for [Formula: see text], [Formula: see text]. Also, we obtained that if [Formula: see text] then [Formula: see text]. With respect to the relation between the defined graph invariants, it was proved a bound for [Formula: see text] for [Formula: see text] and the exact values of [Formula: see text] for [Formula: see text], where [Formula: see text] (resp, [Formula: see text])is the maximum value of [Formula: see text] (resp, [Formula: see text]) over all graphs [Formula: see text] with order [Formula: see text]. Finally, we proved that for [Formula: see text], there exists a graph with order [Formula: see text] such that [Formula: see text] and [Formula: see text].
Interference Wave Suppression Method of Distributed Optical Fiber Sensors in High-Resolution Seismic Observation
Authors: Kun Yu
Published: November 2024

Since distributed optical fiber sensors will generate strong interference waves during seismic wave acquisition, in order to obtain high-resolution seismic waves, a distributed optical fiber sensor interference wave suppression method in high-resolution seismic observation is proposed. By analyzing the generation process and characteristics of interference waves in distributed optical fiber sensors in high-resolution seismic observation, it is clear that the interference waves are cable waves and spring waves. The recursive cyclic translation adaptive threshold sheet transformation method is established. The improved particle swarm optimization algorithm based on multi-stage and multi-model is used to optimize the adaptive threshold and improve the interference wave suppression effect. The optimized recursive cyclic translation adaptive threshold sheet transformation method is used to decompose the seismic wave collected by the distributed optical fiber sensor, suppress the interference wave part, and obtain the effective seismic wave after interference wave suppression. The experimental results show that for single seismic wave and multi-channel seismic wave, this method can not only ensure the coherence of effective seismic wave, but also suppress interference wave more thoroughly. Under different noise levels, the correlation coefficient and amplitude attenuation of this method are high, and it has better interference wave suppression effect.
Ellipticity of cosmic voids in the case of one-parameter models of dark energy
Authors: Ebrahimi Esmaeil
Published: November 2024

In this paper, we considered four observationally constrained, one-parameter dark energy models to see their plausible impacts on the tidal ellipticity and related statistics of cosmic voids. Our main question is to see if different models of dark energy could be distinguished through ellipticity of cosmic voids. In this way, we employed an analytic method on the base of local tidal tensor. In this method, the center of a void is located at local minimum of the linear density field. At first, we obtained the effect of the one-parameter DE models on the distribution function of the cosmic voids and simultaneously compared the results with the standard cosmology ([Formula: see text]CDM). We found that the distribution function of these models is different with respect to that of [Formula: see text]CDM. In addition, we obtained the mean ellipticity of cosmic voids, [Formula: see text], against redshift and found that in all models the [Formula: see text] as well as [Formula: see text] has a decaying trend. Besides, we considered the mean ellipticity evolution versus the smoothing scale of voids ([Formula: see text]). We found that the differences between dark energy models are significantly discernible via the ellipticity evolution versus the void scale ([Formula: see text]). We saw that the studied models result in different ellipticity evolutions (versus [Formula: see text]) up to [Formula: see text] smaller with respect to that of the [Formula: see text]CDM.
Research on University Network Data Anomaly Detection and Security Protection Algorithm Based on Edge Computing
Authors: Mo JinPing , Zhang Zhigang
Published: November 2024

The significance of shielding university network data has improved with the rapid digitalization of the educational process. Distributed security and elevated perimeter defense threats, which place the bigger data center in danger, are the challenges of edge security. This research focuses on creating edge computing (EC) based anomaly detection and security protection that is specially designed for university networks. In this study, we suggested a novel social spider optimization enhanced long-short-term memory (SSO-ELSTM) for attack detection of university network data and security. In the suggested approach for attack detection, we have employed the CTU dataset. The data were preprocessed using Z-score normalization. Then the preprocessed data were used to extract the features for principle component analysis (PCA) using dimensional reduction. The proposed method is implemented using Python software. We compare the suggested method with other existing methods. The outcomes demonstrate that the suggested approach outperforms the other method in terms of accuracy, precision, recall, F1 score and error rate. This study presents a framework for using edge computing and sophisticated anomaly detection methods to enhance university network security; the outcome highlights practicality and efficiency of edge-based solutions for shielding sensitive university network information.
Optimal Dynamic Parameterized Subset Sampling
Authors: Gan Junhao , Umboh Seeun William , Wang Hanzhi , Wirth Anthony , Zhang Zhuo
Published: November 2024

In this paper, we study the Dynamic Parameterized Subset Sampling (DPSS) problem in the Word RAM model. In DPSS, the input is a set,
S
, of
n
items, where each item,
x
, has a non-negative integer weight, w(x). Given a pair of query parameters, (α, β), each of which is a non-negative rational number, a parameterized subset sampling query on
S
seeks to return a subset T ⊆ S such that each item x∈ S is selected in
T
, independently, with probability p_x(α, β) which is the minimum between 1 and w(x) / (α \cdot W + β), where
W
is the total weight of the items in
S
. More specifically, the DPSS problem is defined in a dynamic setting, where the item set,
S
, can be updated with insertions of new items or deletions of existing items. Our first main result is an optimal algorithm for solving the DPSS problem, which achieves O(n) pre-processing time, O(1+μ_S(α,β)) expected time for each query parameterized by (α, β), given on-the-fly, and O(1) time for each update; here, μ_S(α,β) is the expected size of the query result. At all times, the worst-case space consumption of our algorithm is linear in the current number of items in
S
. Our second main contribution is a hardness result for the DPSS problem when the item weights are O(1)-word float numbers, rather than integers. Specifically, we reduce Integer Sorting to the deletion-only DPSS problem with float item weights. Our reduction shows that an optimal algorithm for deletion-only DPSS with float item weights (achieving all the same bounds as aforementioned) implies an algorithm for sorting
N
integers in O(N) expected time. The latter remains an important open problem. Moreover, a deletion-only DPSS algorithm which supports float item weights, with complexities worse, by at most a factor of o(√łog łog N), than the optimal counterparts, would already improve the current-best integer sorting algorithm [FOCS 2002]. Last but not least, a key technical ingredient for our first main result is a set of exact and efficient algorithms for generating Bernoulli (of certain forms) and Truncated Geometric random variates in O(1) expected time with O(n) worst-case space in the Word RAM model. Generating Bernoulli and geometric random variates efficiently is of great importance not only to sampling problems but also to encryption in cybersecurity. We believe that our new algorithms may be of independent interests for related research.
Optimal (Multiway) Spatial Joins
Authors: Wang Ru , Tao Yufei
Published: November 2024

In a
spatial join
, we are given a constant number k ≥ 2 of sets - denoted as R1, R2, ..., Rk - containing axis-parallel rectangles in a 2D space. The objective is to report all k-tuples (r1, r2, ..., rk ) ∈ R1 × R2 × ... × Rk where the rectangles r1, r2, ..., rk have a non-empty intersection, i.e., r1 ∩ r2 ∩ ... ∩ rk ≠ ∅. The problem holds significant importance in spatial databases and has been extensively studied in the database community. In this paper, we show how to settle the problem in O(n logn + OUT) time - regardless of the constant k - where n = Ík i=1 |Ri | and OUT is the result size (i.e., the total number of k-tuples reported). The runtime is asymptotically optimal in the class of comparison-based algorithms, to which our solution belongs. Previously, the state of the art was an algorithm with running time O(n log
2k-1
n + OUT).
k-Clustering with Comparison and Distance Oracles
Authors: Galhotra Sainyam , Raychaudhury Rahul , Sintos Stavros
Published: November 2024

In this paper, we address clustering problems in scenarios where accurate direct access to the full dataset is impractical or impossible. Instead, we leverage oracle-based methods, which are particularly valuable in real-world applications where the data may be noisy, restricted due to privacy concerns or sheer volume. We utilize two oracles, the quadruplet and the distance oracle. The quadruplet oracle is a weaker oracle that only approximately compares the distances of two pairs of vertices. In practice, these oracles can be implemented using crowdsourcing or training classifiers or other predictive models. On the other hand, the distance oracle returns exactly the distance of two vertices, so it is a stronger and more expensive oracle to implement. We consider two noise models for the quadruplet oracle. In the adversarial noise model, if two pairs have similar distances, the response is chosen by an adversary. In the probabilistic noise model, the pair with the smaller distance is returned with a constant probability. We consider a set V of n vertices in a metric space that supports the quadruplet and the distance oracle. For each of the k-center, k-median, and k-means clustering problem on V, we design constant approximation algorithms that perform roughly O(nk) calls to the quadruplet oracle and O(k^2) calls to the distance oracle in both noise models. When the dataset has low intrinsic dimension, we significantly improve the approximation factors of our algorithms by performing a few additional calls to the distance oracle. We also show that for k-median and k-means clustering there is no hope to return any sublinear approximation using only the quadruplet oracle. Finally, we give constant approximation algorithms for estimating the clustering cost induced by any set of k vertices, performing roughly O(nk) calls to the quadruplet oracle and O(k^2) calls to the distance oracle.
Computing A Well-Representative Summary of Conjunctive Query Results
Authors: Agarwal Pankaj K. , Esmailpour Aryan , Hu Xiao , Sintos Stavros , Yang Jun
Published: November 2024

Data summarization is a powerful approach to deal with large-scale data analytics, which has wide applications in web search, recommendation systems, approximate query processing, etc. It computes a small, compact summary that preserves vital properties of the original data. In this paper, we study the data summarization problem of conjunctive query results, i.e., computing a k-size subset of a conjunctive query output, for any given k>0, that optimizes a certain objective. More specifically, we are interested in two commonly studied objectives: cohesion, which measures the maximum distance between a tuple in the query result tuples and its closest tuple in the summary (k-center clustering); and diversity, which measures the pairwise distances between the summary items. A simple approach that computes the entire query output and then applies existing algorithms on top of these materialized tuples suffers from high computational complexity because the query output can be large, e.g., for a relational database of N tuples, the number of result tuples can be N
O(1).
We propose O(1)-approximation algorithms that compute well-representative summaries of size k in time O(N*k
O(1)
), or even O(N+ k
O(1)
) in some cases, without computing all result tuples. We also propose the first efficient (2+\eps)-approximation algorithm for the k-center clustering problem over relational data. Our main idea is to formulate a few oracles that enable us to access specific query result tuples with certain properties, to show how these oracles can be implemented efficiently, and to compute desired summaries with few invocations of these oracles.
Computing Range Consistent Answers to Aggregation Queries via Rewriting
Authors: Amezian El Khalfioui Aziz , Wijsen Jef
Published: November 2024

We consider the problem of answering conjunctive queries with aggregation on database instances that may violate primary key constraints. In SQL, these queries follow the SELECT-FROM-WHERE-GROUP BY format, where the WHERE-clause involves a conjunction of equalities, and the SELECT-clause can incorporate aggregate operators like MAX, MIN, SUM, AVG, or COUNT. Repairs of a database instance are defined as inclusion-maximal subsets that satisfy all primary keys. For a given query, our primary objective is to identify repairs that yield the lowest aggregated value among all possible repairs. We particularly investigate queries for which this lowest aggregated value can be determined through a rewriting in first-order logic with aggregate operators.
Output-sensitive Conjunctive Query Evaluation
Authors: Deep Shaleen , Zhao Hangdong , Fan Austen Z. , Koutris Paraschos
Published: November 2024

Join evaluation is one of the most fundamental operations performed by database systems and arguably the most well-studied problem in the Database community. A staggering number of join algorithms have been developed, and commercial database engines use finely tuned join heuristics that take into account many factors including the selectivity of predicates, memory, IO, etc. However, most of the results have catered to either full join queries or non-full join queries but with degree constraints (such as PK-FK relationships) that makes join evaluation easier. Further, most of the algorithms are also not output-sensitive. In this paper, we present a novel, output-sensitive algorithm for the evaluation of acyclic Conjunctive Queries (CQs) that contain arbitrary free variables. Our result is based on a novel generalization of the Yannakakis algorithm and shows that it is possible to improve the running time guarantee of Yannakakis algorithm by a polynomial factor. Importantly, our algorithmic improvement does not depend on the use of fast matrix multiplication, as a recently proposed algorithm does. The application of our algorithm recovers known prior results and improves on known state-of-the-art results for common queries such as paths and stars. The upper bound is complemented with a matching lower bound for star queries, a restricted subclass of acyclic CQs, and a family of cyclic CQs conditioned on two variants of the
k
-clique conjecture.
A Comparative Evaluation of Deep Learning Techniques for Smart Contract Vulnerability Classification
Authors: Rossini Martina , Ferretti Stefano
Published: November 2024

Smart contracts are self-executing digital contracts that run on a blockchain network. They enable the automation and decentralization of various operations and have become increasingly popular in recent years. However, smart contracts are susceptible to vulnerabilities, and their deployment without proper security testing can result in severe consequences, such as financial losses and reputational damage. In this paper, we explore the use of deep learning techniques, particularly Convolutional Neural Networks (CNNs), for detecting and classifying vulnerabilities in smart contracts deployed on the Ethereum main net. We compare different kinds of neural architectures, i.e., a baseline LSTM, multiple 1D CNNs working on the smart contracts’ bytecode, a Vision Transformer (Swin v2 Tiny), and various 2D CNNs that work on RGB images obtained from the bytecode (i.e., ResNet-50, ResNeXt-50, Inception v3, and EfficientNetv2 Small). We provide an in-depth analysis of these techniques to classify a dataset of smart contracts we have collected. Our study shows that the use of deep neural networks can represent a promising technique to automatically assess smart contracts’ correctness and classify potential vulnerabilities. According to our experiments, the ResNet 1D CNN working directly on the smart contract bytecode offers the best results in terms of classification capabilities. Moreover, due to the unbalanced sizes of the different classes, the classification resulted in more effectiveness for the unchecked calls and reentrancy vulnerability classes, while still providing good results for others.
More than Skin Deep: Imaging of Dermatologic Disease in the Head and Neck
Authors: Overfield Cameron J. , Rhyner Patricia A. , Hall Matthew R. , Bhatt Alok A.
Published: November 2024

Radiology plays an integral role in guiding management of dermatologic disease and allows evaluation deep to the skin, determination of subcutaneous extent and spread, pretreatment detection of metastatic nodal disease, and posttreatment detection of persistence, recurrence, or unexpected complications.
Ankle and Foot Injuries in the Emergency Department: Checklist-based Approach to Radiographs
Authors: Pal Devpriyo , Raniga Sameer Bhimjibhai , Mittal Alok Kumar , Ahuja Ankita , Panwar Jyoti , Kumaravel Manickam
Published: November 2024

A thorough understanding of the important anatomic landmarks and imaging features of the injury patterns of ankle and foot injuries aids radiologists in improving diagnostic accuracy in the emergency department.
Label-Free Electrochemical Sensor Sensitivity Enhancement Using Field Effect Transistors: Fluoride Ions for Fuel Cell Degradation Analysis
Authors: Sharma Manobin , Bunn Jonathan , Shimp Drew , Dong Dongmei
Published: November 2024

In the context of proton exchange membrane fuel cells, precise monitoring of fluoride ions in the effluent water is crucial. Associated with the fluoride ion accumulation, membrane degradation and reduction of fuel cell efficiency arise. This study focuses on developing extended gate field-effect transistors (EGFET) sensors for real-time monitoring of fluoride ion concentrations. High sensitivity and specificity in detecting the concentration of fluoride ions using both N-type (TN0702) and P-type (LP0701) metal-oxide-semiconductor field-effect transistors (MOSFET) were achieved. The transfer characteristics (Id vs Vgs) owere thoroughly examined across a range of fluoride concentrations from parts per million (ppm) to parts per billion (ppb). N-type MOSFET-based sensors showed a distinct decrease in drain current (Id) with increasing fluoride concentration. With fluoride concentrations changed, the P-type MOSFET exhibited a notable shift in the threshold voltage (Vgs). A robust linear relationship was observed on calibration curves with R2 values exceeding 0.90 between the natural logarithm of fluoride concentration (lnC) and gate-source voltage (Vgs). This work demonstrates that MOSFET can significantly enhance the sensitivity of electrochemical sensors without complex labeling processes. The improvement has a future in the miniaturization of cost-effective sensor systems for fuel cell applications, facilitating real-time performance and degradation monitoring.
Tree‐Based Pipeline Optimization‐Based Automated‐Machine Learning Model for Performance Prediction of Materials and Structures: Case Studies and UI Design
Authors: Liang Shixue , Fei Zhengyu , Wu Junning , Lin Xing
Published: November 2024

Machine learning (ML) methods have become increasingly prominent for predicting material and structural performance in civil engineering. However, these methods often require repetitive iterations and optimizations by professionals to obtain an optimal model, which are time‐consuming and challenging for nonexpert users. In this paper, we propose an automated ML (Auto‐ML) model using the tree‐based pipeline optimization tool (TPOT) to address these limitations and streamline the performance prediction process. TPOT leverages genetic programming to optimize various ML models, including DT, RF, GBDT, LightGBM, and XGBoost, and to search possible models that fits a particular dataset, which cuts the most tedious parts of ML. To demonstrate the effectiveness of TPOT‐based Auto‐ML, two case studies are presented by using TPOT‐based Auto‐ML algorithms to construct prediction models for compressive strength of recycled micropowder mortar, and punching shear bearing capacity/failure mode of RC slab‐column joints. To explain the “black box” of Auto‐ML, Shapley Additive Explanation (SHAP) is introduced to interpret the best predictive models and rank the importance of influencing factors, providing a basis for material and structural design. Finally, a user interface (UI) for engineering applications is developed which enables end‐to‐end automation from data preprocessing to predictive results presentation.
RTO‐TV: Routed Tree Optimization and Trust‐Value‐Based Security Scheme to Prevent Black Hole Attack in MANET
Authors: Vadhana Kumari S. , Anslam Sibi S. , Selvan Shano Maria , Nadar Kannan Pauliah
Published: November 2024

The security and privacy of personal data have become a significant concern because of the vulnerability of wireless channels and the theft of sensitive information. Several security measures have been implemented in recent years to address this problem. Nevertheless, most protocols currently employed are insufficiently secure for mobile ad hoc networks (MANETs). This study introduces a routed tree optimization and trust value (RTO‐TV) technique to protect data transmission against black hole attacks in MANETs. This technique introduces a modified group optimization approach that enhances energy consumption by achieving efficient load‐balanced clustering, and presents an approach called RTO for selecting relay nodes (cluster heads [CH]). Additionally, a security method based on TVs has been suggested as a means of safeguarding against black hole attacks. The proposed RTO‐TV protocol was successfully implemented using an network simulator 2 (NS2) simulation tool. The comparative findings demonstrate that the proposed RTO‐TV method outperforms existing techniques in terms of delay, packet delivery ratio (PDR), energy consumption, detection rate, and throughput in the presence of a black hole attack.
Comparison of Internal and Marginal Adaptation of Endocrowns at Different Depths Fabricated by the Digital and Conventional Impression Techniques
Authors: Ardekani Fatemeh Razavi , Neshandar Asli Hamid , Musapoor Naghme , Falahchai Mehran
Published: November 2024

Introduction: Sufficient information is not available regarding the comparative accuracy of digital and conventional impression techniques at different depths for the fabrication of endocrown restorations. Thus, this study aimed to compare the marginal (M) and internal (I) adaptation of endocrowns at different depths fabricated by the digital and conventional impression techniques.

Materials and Methods: In this in vitro study, two endodontically treated molar teeth with 2‐ and 4‐mm cavity depths were used for the fabrication of endocrowns. Conventional and digital impressions were made from each tooth 17 times. A total of 68 zirconia endocrowns were fabricated and seated, and their I and M adaptation was assessed by the silicone replica technique. Data were statistically analyzed.

Results: The M and I gaps at 2‐ and 4‐mm cavity depths were significantly smaller in the digital, than the conventional, impression technique (p  < 0.05). Increasing the cavity depth significantly increased the M and I gaps only in the digital technique (p  < 0.05). The largest gap in all groups was noted in the pulpal (P) region (p  < 0.05). The smallest gap was found in the M and cervical (C) regions in the conventional groups with 2‐ and 4‐mm cavity depths, and the digital group with 4 mm cavity depth, and in the M region in the digital group with 2 mm cavity depth (p  < 0.05).

Conclusion: Unlike the conventional impression technique, the M and I adaptation decreased by an increase in cavity depth in the digital technique; nonetheless, the digital impression technique still showed higher M and I adaptation than the conventional technique.
The Efficacy of Sample Storage Envelopes in Collecting Scales From the Skin and Nails for Mycology Laboratory Diagnosis of Superficial Fungal Infection
Authors: Matthapan Lalita , Bunyaratavej Sumanas , Leeyaphan Charussri , Kobkurkul Pantaree , Hutachoke Thrit , Wongdama Supisara , Prasertsook Suthasanee , Pongkittilar Bawonpak , Panyawong Chatisa , Prasong Waranyoo , Plengpanich Akkarapong
Published: November 2024

Background: Fungal cultures are unavailable in many hospitals. The development of an effective sample storage solution for timely transportation would improve management of patients with superficial fungal skin and nail infections.

Objectives: This study aimed to evaluate the efficacy of sample storage envelopes to preserve skin and nail samples for timely microscopic examination and culture for superficial fungal infections.

Methods: Patients aged 18 years and above with suspected superficial fungal infections were enrolled. The samples were divided into four envelopes. The baseline 20% potassium hydroxide (KOH) examination and fungal culture served as reference points. The stored samples were reexamined on Days 3, 7, 14, and 28.

Results: The study included 90 patients with suspected superficial fungal infections (45 skin and 45 nail lesions). Reference KOH examinations showed branching septate hyphae in 36 (80.0%) for skin and 35 (77.8%) for nail infections. Reference fungal cultures were positive for the growth of dermatophyte and nondermatophyte molds in 34 (75.6%) and 28 (62.2%) in skin and nail infections, respectively. Sample storage envelopes maintained 100% sensitivity and specificity for up to 28 days with KOH examination for both skin and nail samples. On Day 28, the fungal culture sensitivity was 70.6% for the skin and 64.3% for the nail samples, with specificities of 100.0% and 88.2%, respectively.

Conclusions: Sample storage envelopes effectively maintained diagnostic accuracy for up to 28 days with KOH examination for both skin and nail samples. Given the high specificity even on Day 28 for fungal culture, transferring samples within 28 days remains a reliable practice.
Estimation and Prediction Under Different Schemes for a Flexible Symmetric Distribution With Applications
Authors: Chesneau Christophe , Pakyari Reza , Kohansal Akram , Bakouch Hassan S.
Published: November 2024

This paper introduces a new probability distribution called the mixture symmetric gamma (MSG) distribution, which is defined as a mixture of two symmetric gamma distributions. Its statistical properties and applications are explored. We first examine its mathematical properties, including the possible shapes of the corresponding probability density function, as well as the moments, and the moment‐generating function. We then look at parameter estimation using various frequentist and Bayesian methods, such as moment estimation, maximum likelihood method, least‐squares method, and Bayesian approaches. In addition, the prediction of future observations under the MSG model is extensively covered, considering both frequentist and Bayesian perspectives, including median prediction, best unbiased prediction, and Bayesian prediction. A comprehensive simulation study is conducted to evaluate the performance of the proposed estimation and prediction techniques. Finally, the practical applicability of the MSG model is demonstrated through the analysis of four real‐world datasets. It is shown to outperform several well‐known competing models in terms of goodness‐of‐fit. The results highlight the inherent simplicity, efficiency, robustness, and intuitive interpretability of the MSG distribution, making it a compelling choice for modeling data with a symmetric pattern, with potential applications in diverse domains.
Figure S8 from Functional RNAi Screening Identifies G2/M and Kinetochore Components as Modulators of TNFα/NF-κB Prosurvival Signaling in Head and Neck Squamous Cell Carcinoma
Authors: Morgan Ethan L. , Saleh Anthony D. , Cornelius Shaleeka , Carlson Sophie G. , Toni Tiffany , Cheng Hui , Jeon Jun , Viswanathan Ramya , Yang Xinping , Silvin Christopher , Clavijo Paul E. , Sowers Anastasia L. , Mitchell James B. , Ormanoglu Pinar , Lal Nag Madhu , Martin Scott E. , Chen Zhong , Van Waes Carter
Published: November 2024

Figure S8 and figure legend
Figure S6 from Functional RNAi Screening Identifies G2/M and Kinetochore Components as Modulators of TNFα/NF-κB Prosurvival Signaling in Head and Neck Squamous Cell Carcinoma
Authors: Morgan Ethan L. , Saleh Anthony D. , Cornelius Shaleeka , Carlson Sophie G. , Toni Tiffany , Cheng Hui , Jeon Jun , Viswanathan Ramya , Yang Xinping , Silvin Christopher , Clavijo Paul E. , Sowers Anastasia L. , Mitchell James B. , Ormanoglu Pinar , Lal Nag Madhu , Martin Scott E. , Chen Zhong , Van Waes Carter
Published: November 2024

Figure S6 and figure legend
Figure 4 from Functional RNAi Screening Identifies G2/M and Kinetochore Components as Modulators of TNFα/NF-κB Prosurvival Signaling in Head and Neck Squamous Cell Carcinoma
Authors: Morgan Ethan L. , Saleh Anthony D. , Cornelius Shaleeka , Carlson Sophie G. , Toni Tiffany , Cheng Hui , Jeon Jun , Viswanathan Ramya , Yang Xinping , Silvin Christopher , Clavijo Paul E. , Sowers Anastasia L. , Mitchell James B. , Ormanoglu Pinar , Lal Nag Madhu , Martin Scott E. , Chen Zhong , Van Waes Carter
Published: November 2024

TTK inhibition reduces NF-κB RELA nuclear localization and activity in HNSCC cells. A, Representative immunofluorescence images of RELA localization in UMSCC1 and UMSCC47 cells. Cells were transfected with TTK siRNA or control siRNA for 72 hours, with TNFα (20 ng/mL) added for 30 minutes. DAPI was used as a nuclear counterstain. B, Quantification of percentage nuclear RELA from A. Data represent the percentage nuclear localization of RELA from 15 cells and were analyzed using ImageJ as previously (12). Bars represent means ± SD. C, UMSCC1 and UMSCC47 cells were treated with 20 nmol/L BAY1217389 (B389) for 24 hours. Cells were then analyzed for the percentage of phosphorylated histone H3 (Ser10), a marker of mitosis. D, NF-κB reporter activity after treatment with increasing doses of B389 in UMSCC1κB cells. Cells were treated with increasing doses of B389 or vehicle control for 24 hours, with TNFα (20 ng/mL) added for the final 16 hours. E, Representative immunofluorescence images of RELA localization in UMSCC1 and UMSCC47 cells. Cells were treated with B389 (20  nmol/L) or vehicle control for 6 hours, with TNFα (20 ng/mL) added for 30 minutes. DAPI was used as a nuclear counterstain. F, Quantification of percentage nuclear RELA from E. Data represent the percentage nuclear localization of RELA from 15 cells and were analyzed using ImageJ as previously (12). All experiments are representative of at least three biological replicates. NS, not significant; ***, P < 0.001.
Supplementary Table S2 from Tumoral Interferon Beta Induces an Immune-Stimulatory Phenotype in Tumor-Associated Macrophages in Melanoma Brain Metastases
Authors: Gellert Julia , Agardy Dennis A. , Kumar Swaminathan , Kourtesakis Alexandros , Boschert Tamara , Jähne Kristine , Breckwoldt Michael O. , Bunse Lukas , Wick Wolfgang , Davies Michael A. , Platten Michael , Bunse Theresa
Published: November 2024

Supplementary Table S2 lists antibodies and dyes used.
Supplementary Table S1 from Tumoral Interferon Beta Induces an Immune-Stimulatory Phenotype in Tumor-Associated Macrophages in Melanoma Brain Metastases
Authors: Gellert Julia , Agardy Dennis A. , Kumar Swaminathan , Kourtesakis Alexandros , Boschert Tamara , Jähne Kristine , Breckwoldt Michael O. , Bunse Lukas , Wick Wolfgang , Davies Michael A. , Platten Michael , Bunse Theresa
Published: November 2024

Supplementary Table S1 lists primers used.
Data from Functional RNAi Screening Identifies G2/M and Kinetochore Components as Modulators of TNFα/NF-κB Prosurvival Signaling in Head and Neck Squamous Cell Carcinoma
Authors: Morgan Ethan L. , Saleh Anthony D. , Cornelius Shaleeka , Carlson Sophie G. , Toni Tiffany , Cheng Hui , Jeon Jun , Viswanathan Ramya , Yang Xinping , Silvin Christopher , Clavijo Paul E. , Sowers Anastasia L. , Mitchell James B. , Ormanoglu Pinar , Lal Nag Madhu , Martin Scott E. , Chen Zhong , Van Waes Carter
Published: November 2024

Abstract
Immune and radiation resistance of cancer cells to cytotoxicity mediated by TNFα is promoted by the transcription factor NF-κB in several cancers, including head and neck squamous cell carcinoma (HNSCC). Genomic alterations that converge on the TNFα/NF-κB signal axis were found in ∼40% of HNSCCs by The Cancer Genome Atlas. However, identification of therapeutic targets that contribute to aberrant TNFα/NF-κB activation and resistance has been challenging. Here, we conducted a functional RNAi screen to identify regulators of TNFα-induced NF-κB activation and cell viability, using parallel NF-κB β−lactamase reporter and cell viability assays in a HNSCC cell line which harbors expression and genomic alterations typically found in human papillomavirus–negative HNSCC. Besides multiple components of canonical TNFα/NF-κB signaling, we identified components of the WNT, NOTCH, and TGFβ pathways that we previously showed contribute to noncanonical activation of NF-κB. Unexpectedly, we also observed that multiple G2/M cell-cycle kinases [Aurora kinase A, polo-like kinase 1, WEE1, and threonine tyrosine kinase (TTK)], and structural kinetochore/microtubule components (NDC80 and NUF2), modulate TNFα-induced NF-κB activation and cell viability. Several of these targets inhibit TNF-induced nuclear translocation of RELA, consistent with prior reports linking NF-κB activation to G2/M kinases or microtubule assembly. Further investigation of an understudied mitotic kinase, TTK/monopolar spindle 1, shows that its inhibition or depletion attenuates TNFα-induced RELA nuclear translocation, promoting cell death, DNA damage, polyploidy, and mitotic catastrophe, leading to radiosensitization. Together, our RNAi screening identifies a critical linkage between the G2/M cell-cycle checkpoint/kinetochore components and NF-κB activity, as well as targets that can sensitize HNSCC cells to TNFα or radiation.
Significance:
Here, RNAi library screening reveals that multiple G2/M and kinetochore components, including TTK/monopolar spindle 1, modulate TNFα-induced NF-κB activation, cell survival, and genotoxicity, underscoring their potential importance as therapeutic targets in HNSCC.