bluuebunny/crossref_metadata_2025_split huggingface dataset:
A case study of an English teacher in Ukraine: Where gender and national identity intersect
Authors: Moroz Oksana 
Published: June 2024

AbstractUsing a transnational intersectionality framework that focuses on the interactions of multiple identities, this article reexamines the study conducted in 2017 on the experiences of one female in‐service English language teacher in the Ukrainian context. Employing a narrative inquiry approach, using an online autobiography, interviews, and lesson materials, Irene's multiple identities have intertwined with her gendered identity, shaping her experiences. This study's findings show that Irene's transnational gendered identity was influenced by her educational and professional paths, her position in the language teaching field, and various social identities. Irene used language strategically and negotiated power relations professionally to navigate her transition to becoming a transnational English language teacher. Moreover, she found a complex relationship between a teacher's gender and identity, which is related to the sociopolitical situation in Ukraine. The study aims to address the gap in the research on constructing a Ukrainian teacher's gendered identity. While a case study approach is not generalizable or applicable to other teachers, exploring Irene's narratives provides valuable insights into the complexities of identity and power dynamics within a transnational educational context.
Anaerobic treatment removing tetrabromobisphenol A and biota safety: How do tropical aquatic species respond to effluent toxicity over short‐ and long‐term exposures?
Authors: Bernegossi Aline Christine , Castro Gleyson Borges , Felipe Mayara Caroline , de Souza Tallyson Tavares Cunha , Macêdo Williane Vieira , Gorni Guilherme Rossi , Corbi Juliano José 
Published: June 2024

AbstractWastewater containing tetrabromobisphenol A (TBBPA), a commonly used flame retardant found in wastewater, can present significant toxic effects on biota, yet its impact on tropical freshwater environments is not well understood. This study explores the effectiveness of two independent anaerobic treatment systems, the acidogenic reactor (AR) and the methanogenic reactor (MR), for the ecotoxicity reduction of TBBPA‐rich wastewater in four tropical freshwater species. Despite presenting good physicochemical performance and reduced toxicity of the influent for most species, AR and MR treatments remain acute and chronic toxicity. Overall, MR exhibited greater efficacy in reducing influent toxicity compared with AR. TBBPA bioaccumulation was observed in Chironomus sancticaroli after short‐term exposure to 100% MR effluent. Multigenerational exposures highlighted changes in the wing length of C. sancticaroli, showing decreases after influent and AR exposures and increases after MR exposures. These findings underscore the need for ecotoxicological tools in studies of new treatment technologies, combining the removal of emerging contaminants with safeguarding aquatic biota.Practitioner Points
Acidogenic and methanogenic reactors reduced the acute and chronic toxicity of wastewater containing tetrabromobisphenol A.
Both treatments still exhibit toxicity, inducing short‐ and long‐term toxic effects on four native tropical species.
The aquatic species Pristina longiseta was most sensitive to effluents from acidogenic and methanogenic reactors.
TBBPA concentrations recovered from Chironomus sancticaroli bioaccumulation analysis ranged from 1.07 to 1.35 μg g−1.
Evaluating new treatment technologies with multiple species bioassays is essential for a comprehensive effluent toxicity assessment and ensuring aquatic safety.
Using yeasts for the studies of nonfunctional factors in protein evolution
Authors: Potera Katarzyna , Tomala Katarzyna 
Published: June 2024

AbstractThe evolution of protein sequence is driven not only by factors directly related to protein function and shape but also by nonfunctional factors. Such factors in protein evolution might be categorized as those connected to energetic costs, synthesis efficiency, and avoidance of misfolding and toxicity. A common approach to studying them is correlational analysis contrasting them with some characteristics of the protein, like amino acid composition, but these features are interdependent. To avoid possible bias, empirical studies are needed, and not enough work has been done to date. In this review, we describe the role of nonfunctional factors in protein evolution and present an experimental approach using yeast as a suitable model organism. The focus of the proposed approach is on the potential negative impact on the fitness of mutations that change protein properties not related to function and the frequency of mutations that change these properties. Experimental results of testing the misfolding avoidance hypothesis as an explanation for why highly expressed proteins evolve slowly are inconsistent with correlational research results. Therefore, more efforts should be made to empirically test the effects of nonfunctional factors in protein evolution and to contrast these results with the results of the correlational analysis approach.
Algorithmic and Implementation-Based Threats for the Security of Embedded Machine Learning Models
Authors: Moëllic Pierre-Alain , Dumont Mathieu , Hector Kevin , Hennebert Christine , Joud Raphaël , Paulin Dylan 
Published: June 2024

AbstractThe large-scale deployment of machine learning models in a wide variety of AI-based systems raises major security concerns related to their integrity, confidentiality and availability. These security issues encompass the overall traditional machine learning pipeline, including the training and the inference processes. In the case of embedded models deployed in physically accessible devices, the attack surface is particularly complex because of additional attack vectors exploiting implementation-based flaws. This chapter aims at describing the most important attacks that threaten state-of-the-art embedded machine learning models (especially deep neural networks) widely deployed in IoT applications (e.g., health, industry, transport) and highlighting new critical attack vectors that rely on side-channel and fault injection analysis and significantly extend the attack surface of AIoT systems (Artificial Intelligence of Things). More particularly, we focus on two advanced threats against models deployed in 32-bit microcontrollers: model extraction and weight-based adversarial attacks.
Reconfigurable Antennas for Trustable Things
Authors: Groth Mateusz , Rzymowski Mateusz , Nyka Krzysztof , Kulas Lukasz 
Published: June 2024

AbstractIn modern applications, the Internet of Things plays a significant role in increasing the productivity, effectiveness or safety and security of people and assets. Additionally, the reliability of Internet of Things components is crucial from the application point of view, where a resilient and low-latency network is needed. This can be achieved by utilizing reconfigurable antennas to enhance the capabilities of the wireless sensor network (WSN). Additionally, reconfigurable antennas can provide extended functionalities to the Internet of Things. One such aspect of wireless sensing is localization, where objects can be identified and positioned using radio frequency (RF) signals. For this purpose, analysis of spatial diversification of signals can be used by utilizing reconfigurable antennas. This work presents a design of a reconfigurable antenna that is applicable to Internet of Things WSNs and algorithms that utilize the antenna to provide additional localization functionalities.
Mean field theory for strongly coupled systems: Holographic approach
Authors: Sukrakarn Supalert , Yuk Taewon , Sin Sang-Jin 
Published: June 2024

Abstract
In this paper, we develop the holographic mean field theory for strongly interacting fermion systems. We investigate various types of the symmetry-breakings and their effect on the spectral function. We found analytic expressions of fermion Green’s functions in the probe-limit for all types of tensor order parameter fields. We classified the spectral shapes and singularity types from the analytic Green’s function. We calculated the fermions spectral function in the full backreacted background and then compared it with the analytic results to show the reliability of analytic results in the probe limit. The fact that all the main features of the spectral features in the current condensed matter physics including gaps of s-,p- waves, nodal rings and nodal shells, the flat band of dimension 1,2,3, can be obtained in the absence of the lattice as consequences of the order and symmetry breaking pattern, is a pleaseant surprise.
di-Langlands correspondence and extended observables
Authors: Jeong Saebyeok , Lee Norton , Nekrasov Nikita 
Published: June 2024

Abstract
We explore the difference Langlands correspondence using the four dimensional $$ \mathcal{N} $$
N
= 2 super-QCD. Surface defects and surface observables play the crucial role. As an application, we give the first construction of the full set of quantum integrals, i.e. commuting differential operators, such that the partition function of the so-called regular monodromy surface defect is their joint eigenvectors in an evaluation module over the Yangian Y$$ \left(\mathfrak{gl}(2)\right) $$

gl

2

, making it the wavefunction of a N-site $$ \mathfrak{gl}(2) $$
gl

2

spin chain with bi-infinite spin modules. We construct the Q- and $$ \overset{\sim }{\textbf{Q}} $$

Q
~

-surface observables which are believed to be the Q-operators on the bi-infinite module over the Yangian Y$$ \left(\mathfrak{gl}(2)\right) $$

gl

2

, and compute their eigenvalues, the Q-functions, as vevs of the surface observables.
Entanglement entropy and the boundary action of edge modes
Authors: Mukherjee Jyotirmoy 
Published: June 2024

Abstract
We consider an antisymmetric gauge field in the Minkowski space of d-dimension and decompose it in terms of the antisymmetric tensor harmonics and fix the gauge. The Gauss law implies that the normal component of the field strength on the spherical entangling surface will label the superselection sectors. From the two-point function of the field strength on the sphere, we evaluate the logarithmic divergent term of the entanglement entropy of edge modes of p-form field. We observe that the logarithmic divergent term in entanglement entropy of edge modes coincides with the edge partition function of co-exact p-form on the sphere when expressed in terms of the Harish-Chandra characters. We also develop a boundary path integral of the antisymmetric p-form gauge field. From the boundary path integral, we show that the edge mode partition function corresponds to the co-exact (p − 1)-forms on the boundary. This boundary path integral agrees with the direct evaluation of the entanglement entropy of edge modes extracted from the two-point function of the normal component of the field strength on the entangling surface.
Localization of scalar field on the brane-world by coupling with gravity
Authors: Guo Heng , Lu Yong-Tao , Wang Cai-Ling , Sun Yue 
Published: June 2024

Abstract
In this paper, we consider a coupling mechanism between the kinetic term and the gravity, in which a coupling function F (R) is introduced into the kinetic term of the five-dimensional scalar field. Based on this scenario, we investigate the localization of scalar fields in three specific braneworld models: the Minkowski brane, the de Sitter brane, and the Anti-de Sitter brane. The brane models considered here are regular with no singularity for scalar curvature. For the Minkowshi brane case, the zero mode can always be localized on the brane, and the massive modes can be localized or quasi-localized on the brane. For the dS4 brane case, two forms of factor F (R) is considered. The zero mode can always be localized, and the massive modes could be quasi-localized on the brane. Besides, with the second coupling factor, the scalar zero mode could be localized on the both sides of the origin of extra dimension, while the massive modes could be quasi-localized on the origin. Lastly, for the AdS4 brane case, the localization of the scalar zero mode requires the consideration of a coupling potential V (Φ, φ), while the massive modes can still be localized on the brane with an infinite number.
Skyrmion crystals stabilized by ω-mesons
Authors: Harland Derek , Leask Paul , Speight Martin 
Published: June 2024

Abstract
We investigate the ground state crystalline structure of nuclear matter in the ω-meson variant of the Skyrme model. After minimizing energy with respect to variations of both the Skyrme field and the period lattice, we find four distinct periodic solutions which are similar to those found in the standard Skyrme model. We use these crystals to calculate coefficients in the Bethe-Weizsäcker semi-empirical mass formula and the compression modulus of infinite nuclear matter, and find a significant improvement as compared with other variants of the Skyrme model.
Machine learning constructs a diagnostic prediction model for calculous pyonephrosis
Authors: Yang Bin , Zhong Jiao , Yang Yalin , Xu Jin , Liu Hua , Liu Jianhe 
Published: June 2024

AbstractIn order to provide decision-making support for the auxiliary diagnosis and individualized treatment of calculous pyonephrosis, the study aims to analyze the clinical features of the condition, investigate its risk factors, and develop a prediction model of the condition using machine learning techniques. A retrospective analysis was conducted on the clinical data of 268 patients with calculous renal pelvic effusion who underwent ultrasonography-guided percutaneous renal puncture and drainage in our hospital during January 2018 to December 2022. The patients were included into two groups, one for pyonephrosis and the other for hydronephrosis. At a random ratio of 7:3, the research cohort was split into training and testing data sets. Single factor analysis was utilized to examine the 43 characteristics of the hydronephrosis group and the pyonephrosis group using the T test, Spearman rank correlation test and chi-square test. Disparities in the characteristic distributions between the two groups in the training and test sets were noted. The features were filtered using the minimal absolute value shrinkage and selection operator on the training set of data. Auxiliary diagnostic prediction models were established using the following five machine learning (ML) algorithms: random forest (RF), xtreme gradient boosting (XGBoost), support vector machines (SVM), gradient boosting decision trees (GBDT) and logistic regression (LR). The area under the curve (AUC) was used to compare the performance, and the best model was chosen. The decision curve was used to evaluate the clinical practicability of the models. The models with the greatest AUC in the training dataset were RF (1.000), followed by XGBoost (0.999), GBDT (0.977), and SVM (0.971). The lowest AUC was obtained by LR (0.938). With the greatest AUC in the test dataset going to GBDT (0.967), followed by LR (0.957), XGBoost (0.950), SVM (0.939) and RF (0.924). LR, GBDT and RF models had the highest accuracy were 0.873, followed by SVM, and the lowest was XGBoost. Out of the five models, the LR model had the best sensitivity and specificity is 0.923 and 0.887. The GBDT model had the highest AUC among the five models of calculous pyonephrosis developed using the ML, followed by the LR model. The LR model was considered be the best prediction model when combined with clinical operability. As it comes to diagnosing pyonephrosis, the LR model was more credible and had better prediction accuracy than common analysis approaches. Its nomogram can be used as an additional non-invasive diagnostic technique.
Prognostic analysis of rectal cancer patients after neoadjuvant chemoradiotherapy: different prognostic factors in patients with different TRGs
Authors: Tang Yuan-ling , Li Dan-dan , Duan Jia-yu , Wang Xin 
Published: June 2024

Abstract
Purpose
The extent of tumor regression varies widely among locally advanced rectal cancer (LARC) patients who receive neoadjuvant chemoradiotherapy (NCRT) followed by total mesorectal excision (TME). The purpose of this retrospectively study is to assess prognostic factors in LARC patients with NCRT, and further to analyze survival outcomes in patients with different tumor regression grades (TRGs).

Methods
This study includes LARC patients who underwent NCRT and TME at our institution. We retrospectively analyzed the clinicopathological characteristics and survival of all patients, and performed subgroup analysis for patients with different TRGs. Survival differences were compared using the Kaplan-Meier method and the log rank test. Additionally, a multiple Cox proportional hazard model was used to identify independent prognostic factors.

Results
The study included 393 patients, with 21.1%, 26.5%, 45.5%, and 6.9% achieving TRG 0, TRG 1, TRG 2, and TRG 3, respectively. The overall survival (OS) rate and disease-free survival (DFS) rate for all patients were 89.4% and 70.7%, respectively. Patients who achieved TRG 0–3 had different 5-year OS rates (96.9%, 91.1%, 85.2%, and 68.8%, P = 0.001) and 5-year DFS rates (80.8%, 72.4%, 67.0%, 55.8%, P = 0.031), respectively. Multivariate analyses showed that the neoadjuvant rectal (NAR) score was an independent prognostic indicator for both overall survival (OS) (HR = 4.040, 95% CI = 1.792–9.111, P = 0.001) and disease-free survival (DFS) (HR = 1.971, 95% CI = 1.478–2.628, P ˂ 0.001). In the subgroup analyses, the NAR score was found to be associated with DFS in patients with TRG 1 and TRG 2. After conducting multivariate analysis, it was found that ypT stage was a significant predictor of DFS for TRG 1 patients (HR = 4.384, 95% CI = 1.721–11.168, P = 0.002). On the other hand, ypN stage was identified as the dominant prognostic indicator of DFS for TRG 2 patients (HR = 2.795, 95% CI = 1.535–5.091, P = 0.001). However, none of these characteristics was found to be correlated with survival in patients with TRG 0 or TRG 3.

Conclusion
NAR score, in particular, appears to be the most powerful prognostic factor. It is important to consider various prognostic predictors for patients with different TRGs.
Hilbert polynomial of length functions
Authors: Fornasiero Antongiulio 
Published: June 2024

Abstract
Let

$$\lambda $$

λ

be a general length function for modules over a Noetherian ring R. We use

$$\lambda $$

λ

to introduce Hilbert series and polynomials for R[X]-modules, measuring the growth rate of

$$\lambda $$

λ

. We show that the leading term

$$\mu $$

μ

of the Hilbert polynomial is an invariant of the module, which refines both the algebraic entropy and the receptive algebraic entropy; its degree is a suitable notion of dimension for R[X]-modules. Similar to algebraic entropy,

$$\mu $$

μ

in general is not additive for exact sequences of R[X]-modules: we demonstrate how to adapt certain entropy constructions to this new invariant. We also consider multi-variate versions of the Hilbert polynomial.
Revolutionary multi-omics analysis revealing prognostic signature of thyroid cancer and subsequent in vitro validation of SNAI1 in mediating thyroid cancer progression through EMT
Authors: Jin Xin , Fu Chunlan , Qi Jiahui , Chen Chuanzhi 
Published: June 2024

AbstractThyroid carcinoma (TC), the most commonly diagnosed malignancy of the endocrine system, has witnessed a significant rise in incidence over the past few decades. The integration of scRNA-seq with other sequencing approaches offers researchers a distinct perspective to explore mechanisms underlying TC progression. Therefore, it is crucial to develop a prognostic model for TC patients by utilizing a multi-omics approach. We acquired and processed transcriptomic data from the TCGA-THCA dataset, including mRNA expression profiles, lncRNA expression profiles, miRNA expression profiles, methylation chip data, gene mutation data, and clinical data. We constructed a tumor-related risk model using machine learning methods and developed a consensus machine learning-driven signature (CMLS) for accurate and stable prediction of TC patient outcomes. 2 strains of undifferentiated TC cell lines and 1 strain of PTC cell line were utilized for in vitro validation. mRNA, protein levels of hub genes, epithelial-mesenchymal transition (EMT)-associated phenotypes were detected by a series of in vitro experiments. We identified 3 molecular subtypes of TC based on integrated multi-omics clustering algorithms, which were associated with overall survival and displayed distinct molecular features. We developed a CMLS based on 28 hub genes to predict patient outcomes, and demonstrated that CMLS outperformed other prognostic models. TC patients of relatively lower CMLS score had significantly higher levels of T cells, B cells, and macrophages, indicating an immune-activated state. Fibroblasts were predominantly enriched in the high CMLS group, along with markers associated with immune suppression and evasion. We identified several drugs that could be suitable for patients with high CMLS, including Staurosporine_1034, Rapamycin_1084, gemcitabine, and topotecan. SNAI1 was elevated in both undifferentiated TC cell lines, comparing to PTC cells. Knockdown of SNAI1 reduced the cell proliferation and EMT phenotypes of undifferentiated TC cells. Our findings highlight the importance of multi-omics analysis in understanding the molecular subtypes and immune characteristics of TC, and provide a novel prognostic model and potential therapeutic targets for this disease. Moreover, we identified SNAI1 in mediating TC progression through EMT in vitro.
Three-Year Follow-up of PositiveLinks: Higher Use of mHealth Platform Associated with Sustained HIV Suppression
Authors: Bielick Catherine , Canan Chelsea , Ingersoll Karen , Waldman Ava Lena , Schwendinger Jason , Dillingham Rebecca 
Published: June 2024

AbstractPositiveLinks (PL) is a mHealth platform to support care engagement by people with HIV (PWH). Daily reminders prompt the user to report medication adherence, mood, and daily stress. Higher response rate to PL check-ins has been associated with better suppression of viral load over 6–18 months. We conducted a retrospective chart review for a three-year period collecting demographic information, average mood and stress scores, and all viral loads obtained in usual patient care. We performed multivariable logistic regression modeling to identify factors associated with loss of viral load suppression and a time-to-event survival analysis until first unsuppressed viral load stratified by PL usage. Of the 513 PWH included, 103 had at least one episode of viral non-suppression. Low users of PL were more likely to have an unsuppressed viral load with an adjusted Odds Ratio (aOR) of 5.8 (95% CI 3.0-11.5, p < 0.001). Protective factors included older age (aOR 0.96; 95% CI 0.93–0.98, p = 0.003) and income above the federal poverty level (FPL) (aOR 0.996; 95% CI 0.995–0.998, p < 0.001). High PL use was also associated with better viral load suppression (VLS) over time (p < 0.0001 ((aHR) of 0.437 (95% CI 0.290–0.658, p < 0.001)) after adjusting for age and FPL. High stress scores were related to subsequent loss of viral suppression in an exploratory analysis. High check-in response rate on the PL app, older age, and higher income are associated with sustained VLS over time. Conversely, lack of response to check-ins or increased reported stress may signal a need for additional support.