bluuebunny/crossref_metadata_2025 huggingface dataset:
Persistence of sewage-associated genetic markers in advanced and conventional treated recycled water: implications for microbial source tracking in surface waters
Authors: Lobos Aldo E. , Brandt Amanda M. , Gallard-Góngora Javier F. , Korde Ruchi , Brodrick Eleanor , Harwood Valerie J.
Published: June 2024

Sewage contamination of environmental waters is increasingly assessed by measuring DNA from sewage-associated microorganisms in microbial source tracking (MST) approaches. However, DNA can persist through wastewater treatment and reach surface waters when treated sewage/recycled water is discharged, which may falsely indicate pollution from untreated sewage. Recycled water discharged from an advanced wastewater treatment (AWT) facility into a Florida stream elevated the sewage-associated HF183 marker 1,000-fold, with a minimal increase in cultured
Escherichia coli
. The persistence of sewage-associated microorganisms was compared by qPCR in untreated sewage and recycled water from conventional wastewater treatment (CWT) and AWT facilities.
E. coli
(EC23S857) and sewage-associated markers HF183, H8, and viral crAssphage CPQ_056 were always detected in untreated sewage (6.5–8.7 log
10
GC/100 mL). Multivariate analysis found a significantly greater reduction of microbial variables via AWT vs CWT. Bacterial markers decayed ~4–5 log
10
through CWT, but CPQ_056 was ~100-fold more persistent. In AWT facilities, the log
10
reduction of all variables was ~5. In recycled water, bacterial marker concentrations were significantly correlated (
P
≤ 0.0136; tau ≥ 0.44); however, CPQ_056 was not correlated with any marker, suggesting varying drivers of decay. Concentrations of cultured
E. coli
carrying the H8 marker (EcH8) in untreated sewage were 5.24–6.02 log
10
CFU/100 mL, while no
E. coli
was isolated from recycled water. HF183 and culturable EcH8 were also correlated in contaminated surface waters (odds ratio
β
1
= 1.701). Culturable EcH8 has a strong potential to differentiate positive MST marker signals arising from treated (e.g., recycled water) and untreated sewage discharged into environmental waters.

IMPORTANCE

Genes in sewage-associated microorganisms are widely accepted indicators of sewage pollution in environmental waters. However, DNA persists through wastewater treatment and can reach surface waters when recycled water is discharged, potentially causing false-positive indications of sewage contamination. Previous studies have found that bacterial and viral sewage-associated genes persist through wastewater treatment; however, these studies did not compare different facilities or identify a solution to distinguish sewage from recycled water. In this study, we demonstrated the persistence of bacterial marker genes and the greater persistence of a viral marker gene (CPQ_056 of crAssphage) through varying wastewater treatment facilities. We also aim to provide a tool to confirm sewage contamination in surface waters with recycled water inputs. This work showed that the level of wastewater treatment affects the removal of microorganisms, particularly viruses, and expands our ability to identify sewage in surface waters.
Metagenome-assembled genomes of an acid-tolerant nitrifying bacterial community isolated from a bioreactor used in ammonium scrubbers at animal-rearing facilities
Authors: Manning Viola A. , Moore Phillip A. , Trippe Kristin M.
Published: June 2024

We report 12 metagenome-assembled genomes (MAGS) of a bioreactor community of acid-tolerant nitrifying bacteria. The MAGS include autotrophs in the
Nitrospira
genus and heterotrophs in the Xanthomonadales, Ktedonobacterales, Cytophagales, Burkholderiales, and Hyphomicrobiales. These taxonomic and genomic data provide insights into the core community members required for nitrification at low pH.
Pediatric urinary tract infections caused by poultry-associated
Escherichia coli
Authors: Aziz Maliha , Davis Gregg S. , Park Daniel E. , Idris Azza H. , Sariya Sanjeev , Wang Yashan , Zerbonne Sarah , Nordstrom Lora , Weaver Brett , Statham Sally , Johnson Timothy J. , Campos Joseph , Castro-Nallar Eduardo , Crandall Keith A. , Wu Zhenke , Liu Cindy M. , DeBiasi Roberta L. , Price Lance B.
Published: June 2024

Escherichia coli
is the leading cause of urinary tract infections (UTIs) in children and adults. The gastrointestinal tract is the primary reservoir of uropathogenic
E. coli
, which can be acquired from a variety of environmental exposures, including retail meat. In the current study, we used a novel statistical-genomic approach to estimate the proportion of pediatric UTIs caused by foodborne zoonotic
E. coli
strains.
E. coli
urine isolates were collected from DC residents aged 2 months to 17 years from the Children’s National Medical Center Laboratory, 2013–2014. During the same period,
E. coli
isolates were collected from retail poultry products purchased from 15 sites throughout DC. A total of 52 urine and 56 poultry isolates underwent whole-genome sequencing, core genome phylogenetic analysis, and host-origin prediction by a Bayesian latent class model that incorporated data on the presence of mobile genetic elements (MGEs) among
E. coli
isolates from multiple vertebrate hosts. A total of 56 multilocus sequence types were identified among the isolates. Five sequence types—ST10, ST38, ST69, ST117, and ST131—were observed among both urine and poultry isolates. Using the Bayesian latent class model, we estimated that 19% (10/52) of the clinical
E. coli
isolates in our population were foodborne zoonotic strains. These data suggest that a substantial portion of pediatric UTIs in the Washington DC region may be caused by
E. coli
strains originating in food animals and likely transmitted via contaminated poultry meat.

IMPORTANCE

Escherichia coli
UTIs are a heavy public health burden and can have long-term negative health consequences for pediatric patients.
E. coli
has an extremely broad host range, including humans, chickens, turkeys, pigs, and cattle.
E. coli
derived from food animals is a frequent contaminant of retail meat products, but little is known about the risk these strains pose to pediatric populations. Quantifying the proportion of pediatric UTIs caused by food-animal-derived
E. coli
, characterizing the highest-risk strains, and identifying their primary reservoir species could inform novel intervention strategies to reduce UTI burden in this vulnerable population. Our results suggest that retail poultry meat may be an important vehicle for pediatric exposure to zoonotic
E. coli
strains capable of causing UTIs. Vaccinating poultry against the highest-risk strains could potentially reduce poultry colonization, poultry meat contamination, and downstream pediatric infections.
The nasal microbiota is a potential diagnostic biomarker for sepsis in critical care units
Authors: Tan XiLan , Liu Haiyue , Qiu Wen , Li Zewen , Ge Shuang , Luo Yuemei , Zeng Nianyi , Chen Manjun , Zhou Qiqi , Cai Shumin , Long Jun , Cen Zhongran , Su Jin , Zhou Hongwei , He Xiaolong
Published: June 2024

This study aimed to characterize the composition of intestinal and nasal microbiota in septic patients and identify potential microbial biomarkers for diagnosis. A total of 157 subjects, including 89 with sepsis, were enrolled from the affiliated hospital. Nasal swabs and fecal specimens were collected from septic and non-septic patients in the intensive care unit (ICU) and Department of Respiratory and Critical Care Medicine. DNA was extracted, and the V4 region of the 16S rRNA gene was amplified and sequenced using Illumina technology. Bioinformatics analysis, statistical processing, and machine learning techniques were employed to differentiate between septic and non-septic patients. The nasal microbiota of septic patients exhibited significantly lower community richness (
P
= 0.002) and distinct compositions (
P
= 0.001) compared to non-septic patients.
Corynebacterium
,
Staphylococcus
,
Acinetobacter
, and
Pseudomonas
were identified as enriched genera in the nasal microbiota of septic patients. The constructed machine learning model achieved an area under the curve (AUC) of 89.08, indicating its efficacy in differentiating septic and non-septic patients. Importantly, model validation demonstrated the effectiveness of the nasal microecological diagnosis prediction model with an AUC of 84.79, while the gut microecological diagnosis prediction model had poor predictive performance (AUC = 49.24). The nasal microbiota of ICU patients effectively distinguishes sepsis from non-septic cases and outperforms the gut microbiota. These findings have implications for the development of diagnostic strategies and advancements in critical care medicine.

IMPORTANCE

The important clinical significance of this study is that it compared the intestinal and nasal microbiota of sepsis with non-sepsis patients and determined that the nasal microbiota is more effective than the intestinal microbiota in distinguishing patients with sepsis from those without sepsis, based on the difference in the lines of nasal specimens collected.
Seasonal human coronaviruses OC43, 229E, and NL63 induce cell surface modulation of entry receptors and display host cell-specific viral replication kinetics
Authors: Siragam Vinayakumar , Maltseva Mariam , Castonguay Nicolas , Galipeau Yannick , Srinivasan Mrudhula Madapuji , Soto Justino Hernandez , Dankar Samar , Langlois Marc-André
Published: June 2024

The emergence of the COVID-19 pandemic prompted an increased interest in seasonal human coronaviruses. OC43, 229E, NL63, and HKU1 are endemic seasonal coronaviruses that cause the common cold and are associated with generally mild respiratory symptoms. In this study, we identified cell lines that exhibited cytopathic effects (CPE) upon infection by three of these coronaviruses and characterized their viral replication kinetics and the effect of infection on host surface receptor expression. We found that NL63 produced CPE in LLC-MK2 cells, while OC43 produced CPE in MRC-5, HCT-8, and WI-38 cell lines, while 229E produced CPE in MRC-5 and WI-38 by day 3 post-infection. We observed a sharp increase in nucleocapsid and spike viral RNA (vRNA) from day 3 to day 5 post-infection for all viruses; however, the abundance and the proportion of vRNA copies measured in the supernatants and cell lysates of infected cells varied considerably depending on the virus-host cell pair. Importantly, we observed modulation of coronavirus entry and attachment receptors upon infection. Infection with 229E and OC43 led to a downregulation of CD13 and GD3, respectively. In contrast, infection with NL63 and OC43 leads to an increase in ACE2 expression. Attempts to block entry of NL63 using either soluble ACE2 or anti-ACE2 monoclonal antibodies demonstrated the potential of these strategies to greatly reduce infection. Overall, our results enable a better understanding of seasonal coronaviruses infection kinetics in permissive cell lines and reveal entry receptor modulation that may have implications in facilitating co-infections with multiple coronaviruses in humans.

IMPORTANCE

Seasonal human coronavirus is an important cause of the common cold associated with generally mild upper respiratory tract infections that can result in respiratory complications for some individuals. There are no vaccines available for these viruses, with only limited antiviral therapeutic options to treat the most severe cases. A better understanding of how these viruses interact with host cells is essential to identify new strategies to prevent infection-related complications. By analyzing viral replication kinetics in different permissive cell lines, we find that cell-dependent host factors influence how viral genes are expressed and virus particles released. We also analyzed entry receptor expression on infected cells and found that these can be up- or down-modulated depending on the infecting coronavirus. Our findings raise concerns over the possibility of infection enhancement upon co-infection by some coronaviruses, which may facilitate genetic recombination and the emergence of new variants and strains.
QED-vacuum response and Cherenkov radiated energy in non-linear and Lorentz-symmetry violating scenarios
Authors: Gaete Patricio , Helayël-Neto José Abdalla
Published: June 2024

We explore the physical effects of non-linear electrodynamic coupled to parameters that indicate a violation of Lorentz-symmetry breaking (LSV). To do this, we consider a general non-linear photonic Lagrangian that is coupled to the Carroll–Field–Jackiw’s model (CFJ). Our investigation reveals how the constitutive properties of the vacuum and wave propagation are affected by the interference of the parameters LSV with the specific non-linear electrodynamic model under consideration. We also analyze the refractive indices for this new medium characterized by the coupling between non-linearities and the operators that carry the LSV message. Our results show that the QED-vacuum responds with birefringence and a dispersive propagation of waves. After that, we study the electromagnetic radiation produced by a moving charged particle interacting with this new medium. Our analysis shows that the emitted radiation is similar (resembles) to that of the Cherenkov effect for certain intensities of background magnetic fields. Finally, we compute the static potential profile within the framework of the gauge-invariant, but path-dependent, variables formalism. We find a logarithmic correction to the usual static Coulomb potential stemming from the LSV parameter, and there are also corrections due to the non-linearity. However, the logarithm behavior disappears whenever the LSV parameter is switched off.
Analyzing Russia’s propaganda tactics on Twitter using mixed methods network analysis and natural language processing: a case study of the 2022 invasion of Ukraine
Authors: Alieva Iuliia , Kloo Ian , Carley Kathleen M.
Published: June 2024

This paper examines Russia’s propaganda discourse on Twitter during the 2022 invasion of Ukraine. The study employs network analysis, natural language processing (NLP) techniques, and qualitative analysis to identify key communities and narratives associated with the prevalent and damaging narrative of “fascism/Nazism” in discussions related to the invasion. The paper implements a methodological pipeline to identify the main topics, and influential actors, as well as to examine the most impactful messages in spreading this disinformation narrative. Overall, this research contributes to the understanding of propaganda dissemination on social media platforms and provides insights into the narratives and communities involved in spreading disinformation during the invasion.
Light management of solar cells by implementation of nano/microstructures
Authors: Zhang Xiyue , Chen Bitao , Wang Zherui , He Jian , Zhan Xinghua , Chen Fei , Long Fei
Published: June 2024

Research on the improvement of the photoelectric conversion efficiency of solar cells is always the focus. In this paper, an efficient anti-reflection micro/nanostructure is proposed to improve the conversion efficiency of the solar cell. Graded effective refractive index theory is used to achieve the anti-reflection effect while the simulation model is established by FDTD. A specific periodic nanostructure is obtained, which can achieve a good anti-reflection effect. According to the simulation model, the reflectivity of the solar cell is reduced by 0.85% and the transmittance is increased by 0.85% in the band range of 200 nm to 1000 nm. Specifically, high anti-reflection phenomena are obtained in the band range of ultraviolet and blue light, in which the reflectivity is reduced by 1.56% and the transmittance is increased by 1.55%. Based on the simulation results, the array nanostructure is produced by etching the self-assembled polystyrene (PS) microspheres. Finally, the required structure is formed on the silicon wafer by nanoimprinting and etching technology. The reflectivity of 2.8% is obtained on silicon, which can potentially increase the opto-electrical performance of the solar cell.
Solitonic structures and chaotic behavior in the geophysical Korteweg–de Vries equation: A μ-symmetry and g′-expansion approach
Authors: Kopçasız Bahadır , Yaşar Emrullah
Published: June 2024

This study discusses the [Formula: see text] and Lie symmetries, [Formula: see text]-conservation laws, analytical solutions, chaotic phenomena, and sensitivity analysis of the geophysical Korteweg–de Vries equation (GKdVE). The GKdVE describes the propagation of long waves in geophysical systems like oceans, taking into account the influence of the Coriolis force due to Earth’s rotation. We aim to understand the behavior of waves better in geophysical settings and their potential applications across fields like oceanography, meteorology, and climate science. By using the similarity variables, the GKdVE is transformed into a reduced ordinary differential equation (RODE). We employ the ([Formula: see text])-expansion procedure in one of the RODEs to obtain soliton solutions. Thanks to the ([Formula: see text])-expansion procedure, we discover six wave solutions. Through the implementation of the variational problem strategy, we derive both the Lagrangian and the [Formula: see text]-conservation law ([Formula: see text]-CL). Additionally, we revisit the planar dynamical system associated with the equation of interest, conducting a sensitive inspection to assess its sensitivity. Moreover, the introduction of a perturbed term reveals chaotic and quasi-periodic behaviors across a range of parameter values. Furthermore, we provide visual demonstrations of these properties through figures depicting the exact solutions.
Dynamics of ternary nanofluid through radiated sensor surface: Numerical investigation
Authors: Ullah Basharat , Afzal Umair , Khan Umar , Muhammad Taseer
Published: June 2024

Application: The impact of flow, heat transfer, and magneto hydrodynamics on sensor surfaces between two parallel compressing plates with porous walls has been examined in this study. This study focuses on understanding unsteady compressed flow in two dimensions, utilizing Aluminum oxide, copper oxide, and titanium dioxide with base fluid polymers as the base fluid. Nanofluids, known as nanometer suspensions in traditional nanoscale fluid transfer, are explored for their potential application in improving lubricative and cooling properties. Purpose and methodology: This study aims to investigate the behavior of a tri-hybrid nanofluid (Aluminum oxide, copper oxide, and titanium dioxide with base fluid polymers) in terms of flow dynamics, heat transfer, and magneto hydrodynamics. Energy and momentum equations, considering magneto hydrodynamic forms and heat transfer, are analyzed. The study employs numerical methods, including similarity transforms and a shooting approach, to solve the governing equations. Core findings: Several parameters, including permeable parameter, magnetic parameter, squeeze flow index parameter, volume fraction by nanoparticles, and radiation parameter, are investigated for their effects on temperature profile and velocity profile. The study illustrates these effects graphically and discusses the influence of these parameters on different components of velocity and temperature fields. Additionally, the impact of the radiation parameter ([Formula: see text] on temperature fields is examined for both positive. Future work: Future research may focus on further optimizing the tri-hybrid nanofluid composition for specific applications, exploring additional parameters that may affect flow behavior, heat transfer, and entropy generation. Additionally, experimental validation of the numerical findings and the development of more advanced numerical techniques for solving complex fluid dynamics problems could be the areas of interest for future work.
Traveling wave solutions of Fordy–Gibbons equation
Authors: Cevikel Adem C.
Published: June 2024

The Fordy–Gibbons equation is a nonlinear differential equation. Physically, the motion of a damped oscillator with a more complex potential than in basic harmonic motion is described by the Fordy–Gibbons equation. For the equation under consideration, numerous novel families of precise analytical solutions are being successfully found. The soliton solutions are represented as rational and exponential functions. To further illustrate the potential and physical behavior of the equation, the findings are also stated visually. Three approaches are suggested in this paper for solving the Fordy–Gibbons equation. These solutions are new solutions.
Soret and Dofour effects on unsteady MHD convection flow over an infinite vertical porous plate
Authors: Gayathri M. , Babu B. Hari , Krishna M. Veera
Published: June 2024

In this paper, the computational examination is carried out on the heat generation, Soret and Dufour’s influence on the unsteady MHD convection flow of an incompressible viscous fluid with chemical reaction. It is due to the exponentially accelerated vertical porous plate embedded in a permeable medium with ramped wall temperature together with surface concentration and also with thermal radiation impacts. The basic governing set of the equations of the fluid dynamics to the flow is converted into nondimensional form by inserting suitable nondimensional parameters and variables. In addition, the resultant equations are solved computationally with the efficient Crank–Nicolsons implicit finite difference methodology. The influences for several imperative substantial parameters for the model on the velocity, temperature and concentration for the fluids, the skin friction coefficient, Nusselt and Sherwood number for together thermal situations have been explored with the help of graphical profiles and tabular forms. It is found that, the increasing quantities of the Dufour, temperature generation and thermal radiation parameters, the fluid temperature as well as velocity enhances. Similarly, it is noted that an escalating Soret parameter causes the fluid’s velocity and concentration whereas the chemical reaction parameter notifies reversal outputs.
Theoretical screening of corrole-based covalent organic framework metal single-atom catalysts for ORR and OER
Authors: Qu Yingjie , Li Bin , Chen Hangping , Liang Jin-Xia , Wang Haiyan , Zhu Chun
Published: June 2024

Corrole-based covalent organic frameworks (COFs) are newly developing porous crystalline materials in the catalysis field due to their thermal stability, and ease of being functionalized by high-valent metal at the corrole center. In this work, based on the monolayer TPAPC-COF (m-TPAPC-COF), a series of stable metal corrole-based 2D monolayer SACs M1@TPAPC-COFs (M = Fe, Co, Ni, Cu, Ru, Rh, Os, Ir, Pt, Au) have been obtained by screening using density functional theory (DFT) calculations. As O2 can be adsorbed and activated on M1@TPAPC-COFs (M = Fe, Co, Ru, Rh, Os, Ir), we predicted two types of non-noble metal corrole-based COFs SACs, one is a bifunctional SAC Fe1@TPAPC-COF with high electrocatalytic activity for ORR/OER and the other is a Co1@TPAPC-COF with high electrocatalytic activity for OER, respectively. This work demonstrates that corrole-based COF is a promising 2D material for constructing high-performance SACs for the ORR and OER.
Synthesis and characterization of porphyrin-MWCNT nanohybrid and its utilization as an antimicrobial agent against S. aureus and E. coli
Authors: Tasleem Mohammad , Chaudhary Pankaj Kumar , Prasad Ramasare , Sankar Muniappan
Published: June 2024

In the past two decades, the overuse of antibiotic drugs has increased resistance against various available medications in the market. Therefore, the rise in multi-drug resistant microorganisms poses a formidable global health challenge. So, to cope with these situations, we have synthesized a nanohybrid composed of MWCNT and porphyrin, which could be a suitable alternative. Herein, porphyrin is utilized as a visible light photosensitizer to produce reactive oxygen species, which can cause toxicity for bacteria and, ultimately cell death. We have evaluated the effectiveness of MWCNT-porphyrin nanohybrid against the biofilm of Escherichia coli and Staphylococcus aureus pathogens. The Microdilution method shows a concentration of 62.5 [Formula: see text]g/mL as a MIC50 value, which reduces the population of both organisms by around 50%. This nanohybrid can be used as an efficient substitute for conventional antibiotics to treat local infections.
Detecting Process Duration Drift Using Gamma Mixture Models in a Left-Truncated and Right-Censored Environment
Authors: Yang Lingkai , McClean Sally , Donnelly Mark , Khan Kashaf , Burke Kevin
Published: June 2024

Within the realm of business context, process duration signifies time spent by customers between successive activities. This temporal perspective offers important insight to customer behavior, highlighting potential bottlenecks, and influencing business management decisions. The distribution of these process duration often changes over time due to factors such as seasonality, emerging legislation, changes to supply chains, and customer demand. Referred to as concept drift, these variations pose challenges for robust process modeling, understanding, and refinement. Subsequently, gamma mixture models are widely employed to model durations. These source data can, however, become left-truncated and right-censored within any specific observation window thereby necessitating a (well-known) modification to the likelihood function. The approach reported in this article leveraged this adapted likelihood across a series of observation windows, applying the likelihood ratio test to identify duration changes/concept drift. Due to its flexibility in modelling any duration distribution, the gamma mixture model was used with Nelder–Mead optimized likelihood for the left-truncated and right-censored data. The number of gamma components was determined by the Bayesian information criterion. The proposed framework underwent validation through simulated exponential samples, leading to recommendations for its practical application. Subsequently, we applied the methodology to three real-life event logs exhibiting diverse characteristics. Experimental results showcase the effectiveness of our approach in terms of data fitting, as compared to Kaplan–Meier curves, and in detecting instances of drift. This comprehensive validation underscores the practical utility and reliability of our framework for dynamic business scenarios.
Attribute Diversity Aware Community Detection on Attributed Graphs Using Three-View Graph Attention Neural Networks
Authors: Zhang Yang , Yu Ting , Chi Shengqiang , Wang Zhen , Gao Yue , Zhang Ji , Zhou Tianshu
Published: June 2024

Community detection is a fundamental yet important task for characterizing and understanding the structure of attributed graphs. Existing methods mainly focus on the structural tightness and attribute similarity among nodes in a community. However, grouping numerous semantically homogeneous nodes will result in information cocoons and thus reduce the robustness of community structure and the efficiency of node collaboration in real-world applications, such as recommendation systems and collaboration networks. Since nodes with closer connections tend to be more similar, finding communities with dense structures and diverse attributes poses great challenges to mining latent relationships between the graph structure and attribute distribution. To our best knowledge, very little research has been conducted to address this challenge. In this article, we propose a novel three-view graph attention neural networks (TvGANN) model to formally address the attribute diversity aware community detection problem. TvGANN reveals correlations between the graph structure and attributes distribution from the perspective of node organization, attribute co-occurrence, and the node-attribute interaction. It effectively captures structural features and attributes distribution by feeding a structural network and an attribute co-occurrence network into graph attention modules through the encoder–decoder framework. It also learns heterogeneous information by feeding a network into a meta-node attention module. Then, it fuzes the three modules and clusters the embedding representations through a Student's
t
-distribution approach, which iteratively refines the clustering results. The experiments show that our method not only improves the quality in dense community detection but also performs efficiently for attributed graphs.
Neuron Sensitivity-Guided Test Case Selection
Authors: Huang Dong , Bu Qingwen , Fu Yichao , Qing Yuhao , Xie Xiaofei , Chen Junjie , Cui Heming
Published: June 2024

Deep neural networks (DNNs) have been widely deployed in software to address various tasks (e.g., autonomous driving, medical diagnosis). However, they can also produce incorrect behaviors that result in financial losses and even threaten human safety. To reveal and repair incorrect behaviors in DNNs, developers often collect rich, unlabeled datasets from the natural world and label them to test DNN models. However, properly labeling a large number of datasets is a highly expensive and time-consuming task.

To address the above-mentioned problem, we propose neuron sensitivity-guided test case selection (NSS), which can reduce the labeling time by selecting valuable test cases from unlabeled datasets. NSS leverages the information of the internal neuron induced by the test cases to select valuable test cases, which have high confidence in causing the model to behave incorrectly. We evaluated NSS with four widely used datasets and four well-designed DNN models compared to the state-of-the-art (SOTA) baseline methods. The results show that NSS performs well in assessing the probability of failure triggering in test cases and in the improvement capabilities of the model. Specifically, compared to the baseline approaches, NSS achieves a higher fault detection rate (e.g., when selecting 5% of the test cases from the unlabeled dataset in the MNIST and LeNet1 experiment, NSS can obtain an 81.8% fault detection rate, which is a 20% increase compared with SOTA baseline strategies).
Revealing the Unseen: AI Chain on LLMs for Predicting Implicit Dataflows to Generate Dataflow Graphs in Dynamically Typed Code
Authors: Huang Qing , Luo Zhiwen , Xing Zhenchang , Zeng Jinshan , Chen Jieshan , Xu Xiwei , Chen Yong
Published: June 2024

Dataflow graphs (DFGs) capture definitions (defs) and uses across program blocks, which is a fundamental program representation for program analysis, testing and maintenance. However, dynamically typed programming languages like Python present implicit dataflow issues that make it challenging to determine def-use flow information at compile time. Static analysis methods like Soot and WALA are inadequate for handling these issues, and manually enumerating comprehensive heuristic rules is impractical. Large pre-trained language models (LLMs) offer a potential solution, as they have powerful language understanding and pattern matching abilities, allowing them to predict implicit dataflow by analyzing code context and relationships between variables, functions, and statements in code. We propose leveraging LLMs’ in-context learning ability to learn implicit rules and patterns from code representation and contextual information to solve implicit dataflow problems. To further enhance the accuracy of LLMs, we design a five-step chain of thought (CoT) and break it down into an Artificial Intelligence (AI) chain, with each step corresponding to a separate AI unit to generate accurate DFGs for Python code. Our approach’s performance is thoroughly assessed, demonstrating the effectiveness of each AI unit in the AI Chain. Compared to static analysis, our method achieves 82% higher def coverage and 58% higher use coverage in DFG generation on implicit dataflow. We also prove the indispensability of each unit in the AI Chain. Overall, our approach offers a promising direction for building software engineering tools by utilizing foundation models, eliminating significant engineering and maintenance effort, but focusing on identifying problems for AI to solve.
The enduring metabolic improvement of combining dual amylin and calcitonin receptor agonist and semaglutide treatments in a rat model of obesity and diabetes
Authors: Larsen Anna Thorsø , Mohamed Khaled Elhady , Melander Simone Anna , Karsdal Morten Asser , Henriksen Kim
Published: June 2024

These studies describe the benefit of combining dual amylin and calcitonin receptor agonists (DACRA) with semaglutide for long-term treatment of obesity and type 2 diabetes. Combination treatment induced sustained weight loss and improved glucose control. A DACRA-driven reduction in a serological biomarker of cardiac fibrosis indicated a reduced risk of complications. These results highlight DACRAs as a promising candidate for combination treatment of obesity and type 2 diabetes and related long-term complications.
Advances in Bacterial Lysate Immunotherapy for Infectious Diseases and Cancer
Authors: Rahman Md. Mijanur , Grice I. Darren , Ulett Glen C. , Wei Ming Q.
Published: June 2024

Antigenic cell fragments, pathogen‐associated molecular patterns, and other immunostimulants in bacterial lysates or extracts may induce local and systemic immune responses in specific and nonspecific paradigms. Based on current knowledge, this review aimed to determine whether bacterial lysate has comparable functions in infectious diseases and cancer treatment. In infectious diseases, including respiratory and urinary tract infections, immune system activation by bacterial lysate can identify and combat pathogens. Commercially available bacterial lysates, including OM‐85, Ismigen, Lantigen B, and LW 50020, were effective in children and adults in treating respiratory tract infections, chronic obstructive pulmonary disease, rhinitis, and rhinosinusitis with varying degrees of success. Moreover, OM‐89, Uromune, Urovac, Urivac, and ExPEC4V showed therapeutic benefits in controlling urinary tract infections in adults, especially women. Bacterial lysate‐based therapeutics are safe, well‐tolerated, and have few side effects, making them a good alternative for infectious disease management. Furthermore, a nonspecific immunomodulation by bacterial lysates may stimulate innate immunity, benefiting cancer treatment. “Coley’s vaccine” has been used to treat sarcomas, carcinomas, lymphomas, melanomas, and myelomas with varying outcomes. Later, several similar bacterial lysate‐based therapeutics have been developed to treat cancers, including bladder cancer, non‐small cell lung cancer, and myeloma; among them, BCG for in situ bladder cancer is well‐known. Proinflammatory cytokines, including IL‐1, IL‐6, IL‐12, and TNF‐α, may activate bacterial antigen‐specific adaptive responses that could restore tumor antigen recognition and response by tumor‐specific type 1 helper cells and cytotoxic T cells; therefore, bacterial lysates are worth investigating as a vaccination adjuvants or add‐on therapies for several cancers.
Phenolic Screening and Mixture Design Optimization for In Vitro Assessment of Antioxidant and Antimicrobial Activities of Honey, Propolis, and Bee Pollen
Authors: Aboulghazi Abderrazak , Fadil Mouhcine , Touzani Soumaya , Hibaoui Lahbib , Hano Christophe , Lyoussi Badiaa
Published: June 2024

The current study used a design of experiments to evaluate the potential synergistic effects of three Moroccan bee products (honey (H), propolis (P), and bee pollen (BP)) on the free radical inhibition and antibacterial activity against clinical strains of Staphylococcus aureus and Escherichia coli. The phytochemical contents of these three bee products were first evaluated using HPLC‐DAD with 20 identified compounds (9 in both H and P extracts and 13 in BP extract). The P extract had the highest phytochemical content, with high levels of flavanone pinocembrin, flavanol catechin, lignan pinoresinol, and simple phenolics (p‐coumaric and gallic acids). Then, the optimized mixtures were determined using an augmented simplex‐centroid design. The optimized formulations (H33%:P43%:BP24%) and (H21%:P47%:BP32%) presented the optimal total phenolic content and DPPH‐IC50 with 226.88 mgGAE/g and 10.64 µg/mL, respectively, whereas the formulations (H26%:P52%:BP22%) and (H35%:P40%:BP25%) showed the optimal antimicrobial activity against S. aureus (MICS.aureus equal to 4.34 µg/mL) and E. coli (MICE.coli equal to 5.70 µg/mL), respectively. The predicted responses from these mixture proportions were also experimentally validated. Compared to the single free radical activity and antibacterial effect of each isolated bee product, these optimized formulations demonstrated an increased biological activity, and the determination of the fractional inhibitory concentrations revealed a synergistic effect between these products. This study emphasizes the interest in optimized bee product mixtures for practical applications beyond the pharmaceutical and food industries. Their potential can be extended to nutraceuticals, cosmeceuticals, animal health, environmental sustainability, and advanced biomedical research, offering holistic solutions for diverse challenges across various sectors. Exploring these applications further can unlock new avenues for innovation and sustainable development.
Tissue-Free Liquid Biopsies Combining Genomic and Methylation Signals for Minimal Residual Disease Detection in Patients with Early Colorectal Cancer from the UK TRACC Part B Study
Authors: Slater Susanna , Bryant Annette , Aresu Maria , Begum Ruwaida , Chen Hsiang-Chi , Peckitt Clare , Lazaro-Alcausi Retchel , Carter Paul , Anandappa Gayathri , Khakoo Shelize , Melcher Lucinda , Potter Vanessa , Marti Francisca M. , Huang Joesph , Branagan Graham , George Nicol , Abulafi Muti , Duff Sarah , Raja Ashraf , Gupta Ashish , West Nicholas , Bucheit Leslie , Rich Thereasa , Chau Ian , Cunningham David , Starling Naureen ,   for the TRACC Part B trial investigators
Published: June 2024

Purpose:

The absence of postoperative circulating tumor DNA (ctDNA) identifies patients with resected colorectal cancer (CRC) with low recurrence risk for adjuvant chemotherapy (ACT) de-escalation. Our study presents the largest resected CRC cohort to date with tissue-free minimal residual disease (MRD) detection.

Experimental Design:

TRACC (tracking mutations in cell-free tumor DNA to predict relapse in early colorectal cancer) included patients with stage I to III resectable CRC. Prospective longitudinal plasma collection for ctDNA occurred pre- and postsurgery, post-ACT, every 3 months for year 1 and every 6 months in years 2 and 3 with imaging annually. The Guardant Reveal assay evaluated genomic and methylation signals. The primary endpoint was 2-year recurrence-free survival (RFS) by postoperative ctDNA detection (NCT04050345).

Results:

Between December 2016 and August 2022, 1,203 were patients enrolled. Plasma samples (n = 997) from 214 patients were analyzed. One hundred forty-three patients were evaluable for the primary endpoint; 92 (64.3%) colon, 51 (35.7%) rectal; two (1.4%) stage I, 64 (44.8%) stage II, and 77 (53.8%) stage III. Median follow-up was 30.3 months (95% CI, 29.5–31.3). Two-year RFS was 91.1% in patients with ctDNA not detected postoperatively and 50.4% in those with ctDNA detected [HR, 6.5 (2.96–14.5); P < 0.0001]. Landmark negative predictive value (NPV) was 91.2% (95% CI, 83.9–95.9). Longitudinal sensitivity and specificity were 62.1% (95% CI, 42.2–79.3) and 85.9% (95% CI, 78.9–91.3), respectively. The median lead time from ctDNA detection to radiological recurrence was 7.3 months (IQR, 3.3–12.5; n = 9).

Conclusions:

Tissue-free MRD detection with longitudinal sampling predicts recurrence in patients with stage I to III CRC without the need for tissue sequencing. The UK TRACC Part C study is currently investigating the potential for ACT de-escalation in patients with undetectable postoperative ctDNA, given the high NPV indicating a low likelihood of residual disease.
A Novel Approach to Quantify Heterogeneity of Intrahepatic Cholangiocarcinoma: The Hidden-Genome Classifier
Authors: Song Yi , Boerner Thomas , Drill Esther , Shin Paul , Kumar Sandeep , Sigel Carlie , Cercek Andrea , Kemeny Nancy , Abou-Alfa Ghassan , Iacobuzio-Donahue Christine , Cowzer Darren , Schultz Nikolaus , Walch Henry , Balachandran Vinod , Groot Koerkamp Bas , Kingham Peter , Soares Kevin , Wei Alice , D’Angelica Michael , Drebin Jeffrey , Chandwani Rohit , Harding James J. , Jarnagin William
Published: June 2024

Purpose:

Intrahepatic cholangiocarcinoma (IHC) is a heterogeneous tumor. The hidden-genome classifier, a supervised machine learning–based algorithm, was used to quantify tumor heterogeneity and improve classification.

Experimental Design:

A retrospective review of 1,370 patients with IHC, extrahepatic cholangiocarcinoma (EHC), gallbladder cancer (GBC), hepatocellular carcinoma (HCC), or biphenotypic tumors was conducted. A hidden-genome model classified 527 IHC based on genetic similarity to EHC/GBC or HCC. Genetic, histologic, and clinical data were correlated.

Results:

In this study, 410 IHC (78%) had >50% genetic homology with EHC/GBC; 122 (23%) had >90% homology (“biliary class”), characterized by alterations of KRAS, SMAD4, and CDKN2A loss; 117 IHC (22%) had >50% genetic homology with HCC; and 30 (5.7%) had >90% homology (“HCC class”), characterized by TERT alterations. Patients with biliary- versus non-biliary-class IHC had median overall survival (OS) of 1 year (95% CI, 0.77, 1.5) versus 1.8 years (95% CI, 1.6, 2.0) for unresectable disease and 2.4 years (95% CI, 2.1, NR) versus 5.1 years (95% CI, 4.8, 6.9) for resectable disease. Large-duct IHC (n = 28) was more common in the biliary class (n = 27); the HCC class was composed mostly of small-duct IHC (64%, P = 0.02). The hidden genomic classifier predicted OS independent of FGFR2 and IDH1 alterations. By contrast, the histology subtype did not predict OS.

Conclusions:

IHC genetics form a spectrum with worse OS for tumors genetically aligned with EHC/GBC. The classifier proved superior to histologic subtypes for predicting OS independent of FGFR2 and IDH1 alterations. These results may explain the differential treatment responses seen in IHC and may direct therapy by helping stratify patients in future clinical trials.
Table S17 from Deep-Learning Model for Tumor-Type Prediction Using Targeted Clinical Genomic Sequencing Data
Authors: Darmofal Madison , Suman Shalabh , Atwal Gurnit , Toomey Michael , Chen Jie-Fu , Chang Jason C. , Vakiani Efsevia , Varghese Anna M. , Balakrishnan Rema Anoop , Syed Aijazuddin , Schultz Nikolaus , Berger Michael F. , Morris Quaid
Published: June 2024

CUP Later confirmed Diagnoses
Table S11A/B from Deep-Learning Model for Tumor-Type Prediction Using Targeted Clinical Genomic Sequencing Data
Authors: Darmofal Madison , Suman Shalabh , Atwal Gurnit , Toomey Michael , Chen Jie-Fu , Chang Jason C. , Vakiani Efsevia , Varghese Anna M. , Balakrishnan Rema Anoop , Syed Aijazuddin , Schultz Nikolaus , Berger Michael F. , Morris Quaid
Published: June 2024

S11A: Expected Panel Performance (Masked Analysis), S11B: Performance of GDD-ENS on UCSF test set. Expected Acc: Recalibrated accuracy comparable to accuracy on GDD-ENS cohort after correction for difference in distribution of cancer types within the UCSF cohort
Supplementary Figure 4 from Mechanistic Characterization of Cancer-associated Fibroblast Depletion via an Antibody–Drug Conjugate Targeting Fibroblast Activation Protein
Authors: Gallant Joseph P. , Hintz Hallie M. , Gunaratne Gihan S. , Breneman Matthew T. , Recchia Emma E. , West Jayden L. , Ott Kendahl L. , Heninger Erika , Jackson Abigail E. , Luo Natalie Y. , Rosenkrans Zachary T. , Hernandez Reinier , Zhao Shuang G. , Lang Joshua M. , Meimetis Labros , Kosoff David , LeBeau Aaron M.
Published: June 2024

Proliferation and survival marker gene expression
The Study of SRSF1 Regulates Abnormal Alternative Splicing of BCL2L11 and the Role in Refractory Acute Myeloid Leukemia
Authors: Wang Qirong , Duan Yu , Zan Zhifang , Yang Kai , Wang Jinjuan , Jia Fengfeng , Tan Yanhong , Wang Hongwei , Li Li
Published: June 2024

Introduction: Abnormalities in splicing factors, such as mutations or deregulated expression, can lead to aberrant splicing of target genes, potentially contributing to the pathogenesis of acute myeloid leukemia (AML). Despite this, the precise mechanism underlying the abnormal alternative splicing (AS) induced by SRSF1, a splicing factor associated with poor AML prognosis, remains elusive. Methods: Using strict splicing criteria, we globally screened for AS events in NPMc-positive and NPMc-negative AML samples from TCGA. An AS network associated with AML prognosis was then established. Functional assays, including CCK-8, flow cytometry, and Western blot, were conducted on K562 and THP-1 cells overexpressing SRSF1. Cell viability following 72-h Omipalisib treatment was also assessed. To explore the mechanism of SRSF1-induced AS, we created a BCL2L11 miniGene with a site-specific mutation at its branch point. The AS patterns of both wild-type and mutant miniGenes were analyzed following SRSF1 overexpression in HEK-293T, along with the subcellular localization of different spliceosomes. Results: SRSF1 was significantly associated with AML prognosis. Notably, its expression was markedly upregulated in refractory AML patients compared to those with a favorable chemotherapy response. Overexpression of SRSF1 promoted THP-1 cell proliferation, suppressed apoptosis, and reduced sensitivity to Omipalisib. Mechanistically, SRSF1 recognized an aberrant branch point within the BCL2L11 intron, promoting the inclusion of a cryptic exon 3, which in turn led to apoptosis arrest. Conclusion: Overexpression of SRSF1 and the resulting abnormal splicing of BCL2L11 are associated with drug resistance and poor prognosis in AML.
Vitamin D and Breast Cancer Risk: Evaluating the Association and Effective Risk Reduction
Authors: Pereira Talita Siemann Santos , Marques Steffanni Sayala Andrade , Olandoski Marcia , Polakowski Camila Brandão , Beltrame Olair Carlos , Elifio-Esposito Selene , Casali-da-Rocha José Claudio
Published: June 2024

Introduction: Breast cancer (BC) is the most common cancer among women globally. Vitamin D has been considered a protective factor; however, its relationship with any aspect of the disease remains controversial. Methods: A cross-sectional, single-center clinical study was conducted between 2015 and 2018, including 141 women diagnosed with BC and 239 women in the control group, with mean ages of 43.1 and 41.7 years, respectively (p = 0.103). Serum levels of vitamin D and lipid profile were measured. Clinical and nutritional data were obtained through interviews and medical records. Results: The vitamin D dosage presented an average value of 25.5 ng/mL and 31.0 ng/mL in the case and control groups, respectively (p < 0.001). The vitamin D cut-off point for discriminating the presence of BC was 27.45 ng/mL. Additionally, low-density lipoprotein cholesterol levels were higher in the case group (121.4 mg/dL) compared to the control group (110.7 mg/dL) (p = 0.002), whereas high-density lipoprotein cholesterol levels were lower in the case group (47.6 mg/dL) compared to the control group (53.3 mg/dL) (p = 0.001). Alcohol consumption was significantly higher in the case group than in the control group (2.7 vs. 5.3 doses/day; p < 0.001). Conclusion: The results indicate a significant association between lower vitamin D levels and BC, persisting after multivariate analysis (p < 0.001). These findings could inform prevention strategies, highlighting the importance of maintaining adequate vitamin D levels and potentially identifying a risk group.