bluuebunny/crossref_metadata_2025 huggingface dataset:
Medical Students' Speak-Up Barriers: A Randomized Controlled Trial With Written Vignettes
Authors: Dybdal Kayser Jesper , Kjær Ersbøll Annette , Kolbe Michaela , Østergaard Doris , Dieckmann Peter
Published: March 2024

Objectives

Little is known about medical students’ speak-up barriers upon recognizing or becoming aware of risky or deficient actions of others. Improving our knowledge on these helps in preparing student to function in actual health care organizations. The aim was to examine medical students' perceived reasons for silence in respect to different speak-up situations (i.e., vignette content) and to test if vignette difficulty had an effect on reasons indicated.

Methods

This study was a randomized, controlled, single-blind trial, with text-based vignettes to investigate speak-up barriers. Vignette contents described speak-up situations that varied systematically with respect to speak-up barrier (i.e., environmental norm, uncertainty, hierarchy) and difficulty (i.e., easy, difficult). For each vignette, participants indicated which speak-up barriers they regarded as important.

Descriptive analysis was performed for the study population, the numbers of barriers perceived and rating of vignette difficulty. Logistic regression analysis was used to examine the association between barriers perceived and vignette contents, designed vignette difficulty and subjectively rated vignette difficulty.

Results

A total of 265 students were included. The response rate was 100%. Different barriers were relevant for the different vignettes and varied in a consistent way with the theme of the vignette. Significantly more speak-up barriers were indicated for participants with the difficult version for vignette 1 (not an environmental norm) and vignette 3 (hierarchy) with odds ratio (OR) = 1.52 and 95% confidence interval (95% CI: 1.33–1.73) and OR = 1.25 (95% CI: 1.09–1.44). For (OR) estimates, confidence intervals were rather large.

Conclusions

Perceived barriers for speak-up vary consistently with the characteristics of the situation and more barriers preventing speak-up were related to the difficult versions of the vignettes.
COMPRER: A Multimodal Multi-Objective Pretraining Framework for Enhanced Medical Image Representation
Authors: Lutsker Guy , Rossman Hagai , Godneva Nastya , Segal Eran
Published: March 2024

Substantial advances in multi-modal Artificial Intelligence (AI) facilitate the combination of diverse medical modalities to achieve holistic health assessments. We present COMPRER, a novel multi-modal, multi-objective pretraining framework which enhances medical-image representation, diagnostic inferences, and prognosis of diseases. COMPRER employs a multi-objective training framework, where each objective introduces distinct knowledge to the model. This includes a multi-modal loss that consolidates information across different imaging modalities; A temporal loss that imparts the ability to discern patterns over time; Medical-measure prediction adds appropriate medical insights; Lastly, reconstruction loss ensures the integrity of image structure within the latent space. Despite the concern that multiple objectives could weaken task performance, our findings show that this combination actually boosts outcomes on certain tasks. Here, we apply this framework to both fundus images and carotid ultrasound, and validate our downstream tasks capabilities by predicting both current and future cardiovascular conditions. COMPRER achieved higher Area Under the Curve (AUC) scores in evaluating medical conditions compared to existing models on held-out data. On the Out-of-distribution (OOD) UK-Biobank dataset COMPRER maintains favorable performance over well-established models with more parameters, even though these models were trained on 75× more data than COMPRER. In addition, to better assess our model’s performance in contrastive learning, we introduce a novel evaluation metric, providing deeper understanding of the effectiveness of the latent space pairing.
MaBoSS for HPC environments: Implementations of the continuous time Boolean model simulator for large CPU clusters and GPU accelerators
Authors: Šmelko Adam , Kratochvíl Miroslav , Barillot Emmanuel , Noël Vincent
Published: March 2024

Computational models in systems biology are becoming more important with the advancement of experimental techniques to query the mechanistic details responsible for leading to phenotypes of interest. In particular, Boolean models are well fit to describe the complexity of signaling networks while being simple enough to scale to a very large number of components. With the advance of Boolean model inference techniques, the field is transforming from an artisanal way of building models of moderate size to a more automatized one, leading to very large models. In this context, adapting the simulation software for such increases in complexity is crucial. We present two new developments in the continuous time Boolean simulators: Ma-BoSS.MPI, a parallel implementation of MaBoSS which can exploit the computational power of very large CPU clusters, and MaBoSS.GPU, which can use GPU accelerators to perform these simulations.
Multi-task deep latent spaces for cancer survival and drug sensitivity prediction
Authors: Rintala Teemu J. , Napolitano Francesco , Fortino Vittorio
Published: March 2024

Motivation

Cancer is a very heterogeneous disease that can be difficult to treat without addressing the specific mechanisms driving tumour progression in a given patient. High-throughput screening and sequencing data from cancer cell-lines has driven many developments in drug development, however, there are important aspects crucial to precision medicine that are often overlooked, namely the inherent differences between tumours in patients and the cell-lines used to model themin vitro. Recent developments in transfer learning methods for patient and cell-line data have shown progress in translating results from cell-lines to individual patientsin silico. However, transfer learning can be forceful and there is a risk that clinically relevant patterns in the omics profiles of patients are lost in the process.

Results

We present MODAE, a novel deep learning algorithm to integrate omics profiles from cell-lines and patients for the purposes of exploring precision medicine opportunities. MODAE implements patient survival prediction as an additional task in a drug-sensitivity transfer learning schema and aims to balance autoencoding, domain adaptation, drugsensitivity prediction, and survival prediction objectives in order to better preserve the heterogeneity between patients that is relevant to survival. While burdened with these additional tasks, MODAE performed on par with baseline survival models, but struggled in the drug-sensitivity prediction task. Nevertheless, these preliminary results were promising and show that MODAE provides a novel AI-based method for prioritizing drug treatments for high-risk patients.

Availability

https://github.com/UEFBiomedicalInformaticsLab/MODAE
Timing of treatment shapes the path to androgen receptor signaling inhibitor resistance in prostate cancer
Authors: Lee Eugine , Zhang Zeda , Chen Chi-Chao , Choi Danielle , Rivera Aura C. Agudelo , Linton Eliot , Ho Yu-jui , Love Jillian , LaClair Justin , Wongvipat John , Sawyers Charles L.
Published: March 2024

There is optimism that cancer drug resistance can be addressed through appropriate combination therapy, but success requires understanding the growing complexity of resistance mechanisms, including the evolution and population dynamics of drug-sensitive and drug-resistant clones over time. Using DNA barcoding to trace individual prostate tumor cellsin vivo, we find that the evolutionary path to acquired resistance to androgen receptor signaling inhibition (ARSI) is dependent on the timing of treatment. In established tumors, resistance occurs through polyclonal adaptation of drug-sensitive clones, despite the presence of rare subclones with known, pre-existing ARSI resistance. Conversely, in an experimental setting designed to mimic minimal residual disease, resistance occurs through outgrowth of pre-existing resistant clones andnotby adaptation. Despite these different evolutionary paths, the underlying mechanisms responsible for resistance are shared across the two evolutionary paths. Furthermore, mixing experiments reveal that the evolutionary path to adaptive resistance requires cooperativity between subclones. Thus, despite the presence of pre-existing ARSI-resistant subclones, acquired resistance in established tumors occurs primarily through cooperative, polyclonal adaptation of drug-sensitive cells. This tumor ecosystem model of resistance has new implications for developing effective combination therapy.
Advanced median-based genetic similarity analysis in Kazakh Tazy dogs: A novel approach for breed conformity assessment
Authors: Perfilyeva Anastassiya , Mussabayev Rustam , Bespalova Kira , Kuzovleva Yelena , Bespalov Sergey , Begmanova Mamura , Amirgalyeva Almira , Vishnyakova Olga , Nazarenko Inna , Perfilyeva Yuliya , Plakhov Konstantin , Djansugurova Leyla
Published: March 2024

The breed conformity evaluation is crucial for the preservation of the traits that characterise each dog breed. The use of genetic markers for this purpose provides a precision and objectivity that can surpass the reliability of phenotypic evaluations. In this study, we present a new simple algorithm for assessing breed conformity. The algorithm creates a similarity matrix based on genotypic data and then uses the median to calculate the percentage of genetic similarity that an individual has in relation to the genetic diversity of the breed. To validate the proposed algorithm, we applied it to the genotypic data of 18 microsatellites and 43,691 single nucleotide polymorphisms (SNPs) of the Kazakh Tazy dog, a breed of great cultural and historical importance to Kazakhstan that is now threatened with extinction due to crossbreeding. The algorithm showed a moderate correlation between the microsatellite and SNP genotyping methods, reflecting the different aspects of genetic similarity. In particular, the SNP-based evaluations agreed better with the expert judgements, highlighting their potential for accurate analysis of breed conformity. The proposed algorithm provides easily interpretable results, is flexible, adapts to different genetic markers and may provide an evaluation mechanism for breed conformity in situations where there is no reference population, incomplete pedigrees, unidentified meta-founders and high genetic diversity in the population.

Author summary

Our research was initiated by the urgent concern for the possible extinction of the Kazakh Tazy dog, a breed with deep historical roots and cultural significance in Kazakhstan. Information at the DNA level may lead to faster genetic improvement of the breed than relying only on phenotypic data and pedigrees. Genotypic data can be processed to provide valuable insights into genetic diversity, relatedness and ancestry. However, existing methods do not provide a measure of percentage similarity that can be used to assess how closely a particular individual matches the typical genetic composition of the breed. These challenges have led us to propose an approach that overcomes the limitations of existing methods and allows genetic similarity to be assessed based on genotypes. It is based on a median-based approach to analyze genetic data and has been applied to microsatellite and SNP markers but can also be adapted to other genetic markers. This method provides results even in the absence of a defined reference population, complete pedigrees or identified meta-pedigrees and during high genetic diversity within a breed. It can provide breeders and researchers with a tool to maintain the genetic purity of unique breeds.
European Health Regulations Reduce Registry-Based Research
Authors: Brück Oscar , Sanmark Enni , Ponkilainen Ville , Bützow Alexander , Reito Aleksi , Kauppila Joonas H. , Kuitunen Ilari
Published: March 2024

The European Health Data Space regulation (EHDS) has been proposed to harmonize health data processing. Given its parallels with the Act on Secondary Use of Health and Social Data (Secondary Use Act) implemented in Finland in 2020, this study examines the consequences of heightened privacy constraints on registry-based medical research. Between 2020 and 2023, a median of 5.5% fewer data permits were approved annually by Finnish university hospitals. Based on linear regression modelling, we estimated a reduction of 46.9% in new data permits nationally in 2023 compared to the expected count. Similar changes were not observed in other medical research types highlighting the consequences of excessive data privacy laws on registry-based medical research.
Semantic associations restore neural encoding mechanisms
Authors: Moore Isabelle L. , Long Nicole M.
Published: March 2024

Lapses in attention can negatively impact later memory of an experience. Attention and encoding resources are thought to decline as more experiences are encountered in succession, accounting for the primacy effect in which memory is better for items encountered early compared to late in a study list. However, accessing prior knowledge during study can facilitate subsequent memory, suggesting a potential avenue to counteract this decline. Here, we investigated the extent to which semantic associations—shared meaning between experiences—can counteract declines in encoding resources. Our hypothesis is that semantic associations restore neural encoding mechanisms, which in turn improves memory. We recorded scalp electroencephalography (EEG) while male and female human participants performed a delayed free recall task. Half of the items from late in each study list were semantically associated with an item presented earlier in the list. We find that semantic associations improve memory specifically for late list items and selectively modulate the neural signals engaged during the study of late list items. Relative to other recalled items, late list items that are subsequently semantically clustered—recalled consecutively with their semantic associate—elicit increased high-frequency activity and decreased low-frequency activity, a hallmark of successful encoding. Our findings demonstrate that semantic associations restore neural encoding mechanisms and improve later memory. More broadly, these findings suggest that prior knowledge modulates the orientation of attention to influence encoding mechanisms.
Optimal individual and collective measurements for nonorthogonal quantum key distribution signals
Authors: Cerutti Isabella , Scudo Petra F.
Published: March 2024

We consider how the theory of optimal quantum measurements determines the maximum information available to the receiving party of a quantum key distribution (QKD) system employing linearly independent but nonorthogonal quantum states. Such a setting is characteristic of several practical QKD protocols. Due to nonorthogonality, the receiver is not able to discriminate unambiguously between the signals. To understand the fundamental limits that this imposes, the quantity of interest is the maximum mutual information between the transmitter (Alice) and the receiver, whether legitimate (Bob) or an eavesdropper (Eve). To find the optimal measurement—taken individually or collectively—we use a framework based on operator algebra and general results derived from singular-value decomposition, achieving optimal solutions for von Neumann measurements and positive operator-valued measures (POVMs). The formal proof and quantitative analysis elaborated for two signals allow us to conclude that optimal von Neumann measurements are uniquely defined and provide a higher information gain compared to POVMs. Interestingly, collective measurements not only do not provide additional information gain with respect to individual ones but also suffer from a gain reduction in the case of POVMs.

Published by the American Physical Society
2024
Analysis of single particle and collective beam effects in high intensity beams in a periodic quadrupole channel
Authors: Pande Rajni , Phogat Monika , Krishnagopal Srinivas
Published: March 2024

In high intensity proton accelerators, there are two main mechanisms that can cause beam degradation: incoherent and coherent effects due to nonlinear space charge forces. The incoherent effects represent the single particle dynamics while the coherent effects represent the collective response of the beam. Of particular interest is the region above a zero current phase advance (σ0) of 90° where the (coherent) second-order envelope instability and the (incoherent) fourth-order particle resonance are seen to lead to emittance growth. Large emittance growth is also seen below the envelope instability region as the full current beam phase advance (σ) decreases. In the present study, we have studied the nonlinear effects in a high intensity beam propagating through a focusing-defocusing (FD) quadrupole channel for σ0 greater than 90°, both analytically, by studying the solutions of Kapchinsky-Vladimirsky (KV) equation and the particle core model, and through detailed particle-in-cell (PIC) simulations using the code. The KV envelope equation gives the collective response of the beam while the particle core model gives the contribution of the single particle effects. With pic simulations, which resemble the behavior of the real beams more closely as compared to envelope calculations or the particle core model, it is possible to study the evolution of the beam in a self-consistent manner. Our studies show, that it is possible to identify the specific process responsible for beam degradation in high intensity beams. It is also possible to identify which process dominates under different conditions. We further show that the width of the emittance increase stop band calculated from PIC simulations is wider than that calculated by the envelope equations and that the width depends on the length of the channel being studied.

Published by the American Physical Society
2024
Quantization of Axion-Gauge Couplings and Noninvertible Higher Symmetries
Authors: Choi Yichul , Forslund Matthew , Lam Ho Tat , Shao Shu-Heng
Published: March 2024

We derive model-independent quantization conditions on the axion couplings (sometimes known as the anomaly coefficients) to the standard model gauge group [SU(3)×SU(2)×U(1)Y]/Zq with q=1, 2, 3, 6. Using these quantization conditions, we prove that any QCD axion model to the right of the E/N=8/3 line on the |gaγγ|−ma plot must necessarily face the axion domain wall problem in a postinflationary scenario. We further demonstrate the higher-group and noninvertible global symmetries in the standard model coupled to a single axion. These generalized global symmetries lead to universal bounds on the axion string tension and the monopole mass. If the axion were discovered in the future, our quantization conditions could be used to constrain the global form of the standard model gauge group.

Published by the American Physical Society
2024
Exotic symmetry breaking properties of self-dual fracton spin models
Authors: Canossa Giovanni , Pollet Lode , Martin-Delgado Miguel A. , Song Hao , Liu Ke
Published: March 2024

Fracton codes host unconventional topological states of matter and are promising for fault-tolerant quantum computation due to their large coding space and strong resilience against decoherence and noise. In this paper, we investigate the ground-state properties and phase transitions of two prototypical self-dual fracton spin models—the tetrahedral Ising model and the fractal Ising model—which correspond to error-correction procedures for the representative fracton codes of type I and type II, the checkerboard code and the Haah's code, respectively, in the error-free limit. They are endowed with exotic symmetry-breaking properties that contrast sharply with the spontaneous breaking of global symmetries and deconfinement transition of gauge theories. To show these unconventional behaviors, which are associated with subdimensional symmetries, we construct and analyze the order parameters, correlators, and symmetry generators for both models. Notably, the tetrahedral Ising model acquires an extended semilocal ordering moment, while the fractal Ising model fits into a polynomial ring representation and leads to a fractal order parameter. Numerical studies combined with analytical tools show that both models experience a strong first-order phase transition with an anomalous L−(D−1) scaling, despite the fractal symmetry of the latter. Our paper provides a unique understanding of subdimensional symmetry breaking and makes an important step for studying quantum-error-correction properties of the checkerboard and Haah's codes.

Published by the American Physical Society
2024
Nonlocality of Majorana bound states revealed by electron waiting times in a topological Andreev interferometer
Authors: Dutta Paramita , Cayao Jorge , Black-Schaffer Annica M. , Burset Pablo
Published: March 2024

The analysis of waiting times of electron transfers has recently become experimentally accessible owing to advances in noninvasive probes working in the short-time regime. We study electron waiting times in a topological Andreev interferometer: a superconducting loop with controllable phase difference connected to a quantum spin Hall edge, where the edge state helicity enables the transfer of electrons and holes into separate leads, with transmission controlled by the loop's phase difference ϕ. This setup features gapless Majorana bound states at ϕ=π. The waiting times for electron transfers across the junction are sensitive to the presence of the gapless states, but are uncorrelated for all ϕ. By contrast, at ϕ=π the waiting times of Andreev-scattered holes show a strong correlation and the crossed (hole-electron) distributions feature a unique behavior. Both effects exclusively result from the nonlocal properties of Majorana bound states. Consequently, electron waiting times and their correlations could circumvent some of the challenges for detecting topological superconductivity and Majorana states beyond conductance signatures.

Published by the American Physical Society
2024
Examining the impact of leader's paradoxical cognition on supply chain sustainability: a moderated chain mediation model
Authors: Xu Ting , Wang Jiazhan
Published: March 2024

Purpose

The COVID-19 pandemic has caused havoc on a global scale for supply chains, which put forward higher demand for organizations to reassess their global supply chain strategy and improve supply chain sustainability. The purpose of this paper is to understand how leader's paradoxical cognition affect supply chain sustainability.

Design/methodology/approach

This study conceptualizes a research model grounded in upper echelons theory and propose a chain-mediating model under the moderating effect of big data analytics. Using PLS-SEM method, we test the hypotheses using survey data collected from supply chain managers or leaders of the supply chain team from 193 firms.

Findings

The results indicate that supply chain ambidexterity and organizational learning play a mediating role in the relationship between leaders' paradoxical cognition on supply chain sustainability, respectively, and these two variables have a chain-mediating role in the relationship above. In addition, the big data analytics negatively moderates the relationship between leader's paradoxical cognition and organizational learning, and further moderates our chain mediating model.

Originality/value

This research initiatively focuses on the micro-foundations of supply chain sustainability from managerial cognition and firstly provides empirical evidence about the impact of leader's paradoxical cognition on supply chain sustainability.
Greenwashing and sustainability assurance: a review and call for future research
Authors: Free Clinton , Jones Stewart , Tremblay Marie-Soleil
Published: March 2024

Purpose

The purpose of this paper is to synthesize insights from the emerging work in accounting on greenwashing and sustainability assurance and propose an agenda for future research in this area.

Design/methodology/approach

This article offers an original analysis of papers published on greenwashing and sustainability assurance research in the field of accounting. It adopts a systematic literature review and a narrative approach to analyse the dominant themes and key findings in this new and rapidly evolving field. From this overview, specific avenues for future research are identified.

Findings

In the past few years there has been a substantial spike in concern relating to greenwashing among academics, practitioners, regulators and society. This growing concern has only partly been reflected in the research literature. To date, research has primarily focused on: (1) the characteristics of firms adopting sustainability assurance, (2) the challenges facing sustainability auditors, (3) the development of appropriate assurance standards and regulations, and (4) capital market responses to greenwashing and sustainability auditing/assurance. Three key future research issues with respect to greenwashing are identified: (1) the future of standard-setter attempts to regulate greenwashing, (2) professional jockeying in sustainability reporting assurance, and (3) capital market opportunities and challenges relating to greenwashing and assurance.

Originality/value

Despite the profound economic and reputational impact of greenwashing and the rapid development of sustainability assurance services, research in accounting remains fragmented and emergent. This review identifies avenues offering considerable scope for inter-disciplinarity and bridging the divide between academia and practice.
Characterizing the local climate of large-scale archaeological parks in the tropics
Authors: Binarti Floriberta , Pranowo Pranowo , Aditya Chandra , Matzarakis Andreas
Published: March 2024

Purpose

This study aims to compare the local climate characteristics of Angkor Wat, Borobudur and Prambanan parks and determine effective strategies for mitigating thermal conditions that could suit Borobudur and Angkor Wat.

Design/methodology/approach

The study employed local climate zone (LCZ) indicators and ten-year historical climate data to identify similarities and differences in local climate characteristics. Satellite imagery processing was used to create maps of LCZ indicators. Meanwhile, microclimate models were used to analyze sky view factors and wind permeability.

Findings

The study found that the three tropical large-scale archaeological parks have low albedo, a medium vegetation index and high impervious surface index. However, various morphological characteristics, aerodynamic properties and differences in temple stone area and altitude enlarge the air temperature range.

Practical implications

Based on the similarities and differences in local climate, the study formulated mitigation strategies to preserve the sustainability of ancient temples and reduce visitors' heat stress.

Originality/value

The local climate characterization of tropical archaeological parks adds to the number of LCZs. Knowledge of the local climate characteristics of tropical archaeological parks can be the basis for improving thermal conditions.
Beyond borders: investigating the impact of the 2023 Israeli–Palestinian conflict on global equity markets
Authors: Goyal Priyanka , Soni Pooja
Published: March 2024

Purpose

The present research study aims to explore the impact of the most recent Israeli–Palestinian conflict, which unfolded in October 2023, on global equity markets, including a wide range of both emerging and developed markets (as per the Morgan Stanley Capital Investment country classification).

Design/methodology/approach

The market model of event study methodology, with an estimation window of 200 days and 28-day event window (including event day, i.e. October 7, 2023), has been employed to investigate the event’s impact on the stock markets of different countries, with 24 emerging countries and 23 developed countries. The daily closing prices of the prominent indices of all 47 countries have been analyzed to examine the impact of the conflict on emerging markets, developed markets and overall global equity markets. Additionally, cross-sectional regression analysis has been performed to investigate the possible explanations for abnormal returns.

Findings

The findings of the study suggest the heterogeneous impact of the selected event on different markets. Notably, emerging markets and the overall global equity landscape exhibited substantial negative responses on the event day, as reflected in average abnormal returns of −0.47% and −0.397%, respectively. In contrast, developed markets displayed resilience, with no significant negative impact observed on the day of the event. A closer examination of individual countries revealed diverse reactions, with Poland, Egypt, Greece, Denmark and Portugal standing out for their positive or resilient market responses. Poland, in particular, demonstrated significantly positive cumulative abnormal returns (CARs) of 7.16% in the short-term and 8.59% in the long-term event windows (−7, +7 and −7, +20, respectively), emphasizing its robust performance amid the geopolitical turmoil. The study also found that, during various event windows, specific variables had a significant impact on the CARs.

Practical implications

The study suggests diversification and monitoring of geopolitical risks are key strategies for investors to enhance portfolio resilience during the Israeli–Palestinian conflict. This study identifies countries such as Poland, Egypt, Greece, Denmark and Portugal with positive or resilient market reactions, providing practical insights for strategic investment decisions. Key takeaways include identifying resilient markets, leveraging opportunistic strategies and navigating market dynamics during geopolitical uncertainties.

Originality/value

As per the authors’ thorough investigation and review of the literature, the present study is the earliest attempt to explore the short-term and long-term impact of the 2023 Israeli–Palestinian conflict on equity markets worldwide using the event study approach and cross-sectional regression analysis.
Experiencing the pull and push: influences on independent contractor motivation and job satisfaction
Authors: Miller Andrew , Vanhove Adam
Published: March 2024

Purpose

Drawing on organismic integration theory, we aim to examine whether the reasons independent contractors choose contract work are related to their on-the-job motivation and job satisfaction and whether their perceived support enhances positive (or buffers negative) effects.

Design/methodology/approach

We collected data at three separate time points from 241 adjunct instructors to test a moderated mediation model using bootstrapping analyses.

Findings

The positive relationship between pull factors (e.g. autonomy) and job satisfaction is fully mediated by the autonomous motivation contractors experienced at work. The inverse relationship between push factors (e.g. inability to secure desired work role) and job satisfaction is not mediated by autonomous nor controlled motivation experienced at work. Contractors' perceived organizational support does not moderate the relationship between either push or pull factors and autonomous motivation. Post hoc analysis shows a moderating effect of perceived supervisor support on the nonlinear relationship between push factors and autonomous motivation.

Practical implications

Recruiting individuals drawn to the benefits of contract work may have important implications for worker motivation, job satisfaction and potentially beyond. Moreover, organizations may consider whether existing support resources and infrastructure are appropriate for contractors.

Originality/value

Despite the abundance of evidence demonstrating the benefits of organizational and supervisor support among traditional employee populations, such support may be of limited value to those drawn to contract work.
From data to decision: distilling decision intelligence from user-generated content
Authors: Redek Tjaša , Godnov Uroš
Published: March 2024

Purpose

The Internet has changed consumer decision-making and influenced business behaviour. User-generated product information is abundant and readily available. This paper argues that user-generated content can be efficiently utilised for business intelligence using data science and develops an approach to demonstrate the methods and benefits of the different techniques.

Design/methodology/approach

Using Python Selenium, Beautiful Soup and various text mining approaches in R to access, retrieve and analyse user-generated content, we argue that (1) companies can extract information about the product attributes that matter most to consumers and (2) user-generated reviews enable the use of text mining results in combination with other demographic and statistical information (e.g. ratings) as an efficient input for competitive analysis.

Findings

The paper shows that combining different types of data (textual and numerical data) and applying and combining different methods can provide organisations with important business information and improve business performance.

Research limitations/implications

The paper shows that combining different types of data (textual and numerical data) and applying and combining different methods can provide organisations with important business information and improve business performance.

Originality/value

The study makes several contributions to the marketing and management literature, mainly by illustrating the methodological advantages of text mining and accompanying statistical analysis, the different types of distilled information and their use in decision-making.
Recovery time of the hotel and restaurant sector in Indonesia after COVID-19 crisis: a survival analysis
Authors: Sastri Ray , Li Fanglin , Naveed Hafiz Muhammad , Setiyawan Arbi
Published: March 2024

Purpose

The COVID-19 pandemic severely impacted tourism, and the hotel and restaurant industry was the most affected sector, which faced issues related to business uncertainty and unemployment during the crisis. The analysis of recovery time and the influence factors is significant to support policymakers in developing an effective response and mitigating the risks associated with the tourism crisis. This study aims to investigate numerous factors affecting the recovery time of the hotel and restaurant sector after the COVID-19 crisis by using survival analysis.

Design/methodology/approach

This study uses the quarterly value added with the observation time from quarter 1 in 2020 to quarter 1 in 2023 to measure the recovery status. The recovery time refers to the number of quarters needed for the hotel and restaurant sector to get value added equal to or exceed the value added before the crisis. This study applies survival models, including lognormal regression, Weibull regression, and Cox regression, to investigate the effect of numerous factors on the hazard ratio of recovery time of hotels and restaurants after the COVID-19 crisis. This model accommodates all cases, including “recovered” and “not recovered yet” areas.

Findings

The empirical findings represented that the Cox regression model stratified by the area type fit the data well. The priority tourism areas had a longer recovery time than the non-priority areas, but they had a higher probability of recovery from a crisis of the same magnitude. The size of the regional gross domestic product, decentralization funds, multiplier effect, recovery time of transportation, and recovery time of the service sector had a significant impact on the probability of recovery.

Originality/value

This study contributes to the literature by examining the recovery time of the hotel and restaurant sector across Indonesian provinces after the COVID-19 crisis. Employing survival analysis, this study identifies the pivotal factors affecting the probability of recovery. Moreover, this study stands as a pioneer in investigating the multiplier effect of the regional tourism and its impact on the speed of recovery.
Pilfering personalities: Effects of small mammal personality on cache pilferage
Authors: Humphreys Brigit R. , Mortelliti Alessio
Published: March 2024

Small mammals such as mice and voles play a fundamental role in the ecosystem service of seed dispersal by caching seeds in small hoards that germinate under beneficial conditions. Pilferage is a critical step in this process in which animals steal seeds from other individuals' caches. Pilferers often recache stolen seeds, which are often pilfered by new individuals, who may recache again, and so on, potentially leading to compounded increased dispersal distance. However, little research has investigated intraspecific differences in pilfering frequency, despite its importance in better understanding the role of behavioural diversity in the valuable ecosystem service of seed dispersal.

We conducted a field experiment in Maine (USA) investigating how intraspecific variation, including personality, influences pilferage effectiveness.

Within the context of a long‐term capture‐mark‐recapture study, we measured the unique personality of 3311 individual small mammals of 10 species over a 7‐year period. For this experiment, we created artificial caches using eastern white pine (Pinus strobus) seeds monitored with trail cameras and buried antennas for individual identification.

Of the 436 caches created, 83.5% were pilfered by 10 species, including deer mice ((Peromyscus maniculatus) and southern red‐backed voles (Myodes gapperi). We show how individuals differ in their ability to pilfer seeds and that these differences are driven by personality, body condition and sex. More exploratory deer mice and those with lower body condition were more likely to locate a cache, and female southern red‐backed voles were more likely than males to locate caches. Also, caches were more likely to be pilfered in areas of higher small mammal abundance.

Because the risk of pilferage drives decisions concerning where an animal chooses to store seeds, pilferage pressure is thought to drive the evolution of food‐hoarding behaviour. Our study shows that pilferage ability varies between individuals, meaning that some individuals have a disproportionately strong influence on others' caching decisions and disproportionately contribute to compounded longer‐distance seed dispersal facilitated by pilferage. Our results add to a growing body of knowledge showing that the unique personalities of individual small mammals play a critical role in forest regeneration by impacting seed dispersal.
The impact of animal protein partial substitution on the technological functionality of hybrid yoghurt
Authors: Martinussi Flávia dos Santos , Poloto Gabriel , Navarini Giulia , Marfil Paulo Henrique Mariano , Parra Baptista Débora , Gigante Mirna Lúcia , Merheb‐Dini Carolina
Published: March 2024

Following the new standard of using plant‐based ingredients, this study evaluated the effect of partially replacing milk proteins with plant proteins (almond and pumpkin seed) on yoghurt properties. Protein mixtures were prepared using rotational central composite design, investigating the percentage of animal protein replacement and the proportion between plant proteins. Milk protein substitution increased fermentation time, defined as the time needed to reach pH 4.6, and negatively impacted viscosity, firmness and syneresis of yoghurt. Nevertheless, replacing 25% of dairy proteins with an optimised plant protein blend (25% almond and 75% pumpkin seed) yielded yoghurt with typical composition and good sensory acceptance.
Pioglitazone reverses alcohol‐induced alterations in alveolar macrophage mitochondrial phenotype
Authors: Crotty Kathryn M. , Kabir Shayaan A. , Chang Sarah S. , Mehta Ashish J. , Yeligar Samantha M.
Published: March 2024

Background

People with alcohol use disorder (AUD) have an increased risk of developing pneumonia and pulmonary diseases. Alveolar macrophages (AMs) are immune cells of the lower respiratory tract that are necessary for clearance of pathogens. However, alcohol causes AM oxidative stress, mitochondrial damage and dysfunction, and diminished phagocytic capacity, leading to lung injury and immune suppression.

Methods

AMs were isolated by bronchoalveolar lavage from people with AUD and male and female C57BL/6J mice given chronic ethanol (20% w/v, 12 weeks) in drinking water. The peroxisome proliferator‐activated receptor γ ligand, pioglitazone, was used to treat human AMs ex vivo (10 μM, 24 h) and mice in vivo by oral gavage (10 mg/kg/day). Levels of AM mitochondrial superoxide and hypoxia‐inducible factor‐1 alpha (HIF‐1α) mRNA, a marker of oxidative stress, were measured by fluorescence microscopy and RT‐qPCR, respectively. Mouse AM phagocytic ability was determined by internalized Staphylococcus aureus, and mitochondrial capacity, dependency, and flexibility for glucose, long‐chain fatty acid, and glutamine oxidation were measured using an extracellular flux analyzer. In vitro studies used a murine AM cell line, MH‐S (±0.08% ethanol, 72 h) to investigate mitochondrial fuel oxidation and ATP‐linked respiration.

Results

Pioglitazone treatment decreased mitochondrial superoxide in AMs from people with AUD and ethanol‐fed mice and HIF‐1α mRNA in ethanol‐fed mouse lungs. Pioglitazone also reversed mouse AM glutamine oxidation and glucose or long‐chain fatty acid flexibility to meet basal oxidation needs. In vitro, ethanol decreased the rate of AM mitochondrial and total ATP production, and pioglitazone improved changes in glucose and glutamine oxidation.

Conclusions

Pioglitazone reversed chronic alcohol‐induced oxidative stress in human AM and mitochondrial substrate oxidation flexibility and superoxide levels in mouse AM. Decreased ethanol‐induced AM HIF‐1α mRNA with pioglitazone suggests that this pathway may be a focus for metabolic‐targeted therapeutics to improve morbidity and mortality in people with AUD.
Feeling the Vibe: Relations and Praxes of a Black Sense of Place in Oakland, California
Authors: Heitz Kaily
Published: March 2024

Black artists, activists, and residents describe their sense of place as part of efforts to claim community space in the midst of racialised dispossession in Oakland, California. “Vibe” is a term used to describe this relationship. The author explains the term by drawing on ethnographic and archival data from research conducted in Oakland between 2019 and 2020. This paper explores vibe as the means by which black life is emotionally, relationally, and geographically made possible in the midst of structural racial violence. The author breaks vibe down into three modalities, or structures of relation, through which vibe is commonly evoked: hustle, care, and openness. These forms of relation are unpacked using the story of one local Black business owner. Vibe, thus, is more than a description, but an expression of relation that is politically, culturally, and economically central to Black geographic imaginaries and struggles.
Structural change of the cytoplasmic N‐terminus and S1 segment of voltage‐sensing phosphatase reported by Anap
Authors: Tsutsui Hidekazu , Jinno Yuka , Mizutani Natsuki , Okamura Yasushi
Published: March 2024

Background

Voltage‐sensing phosphatase contains a structurally conserved S1‐S4‐based voltage‐sensor domain, which undergoes a conformational transition in response to membrane potential change. Unlike that of channels, it is functional even in isolation and is therefore advantageous for studying the transition mechanism, but its nature has not yet been fully elucidated. This study aimed to address whether the cytoplasmic N‐terminus and S1 exhibit structural change.

Methods

Anap, an environment‐sensitive unnatural fluorescent amino acid, was site‐specifically introduced to the voltage sensor domain to probe local structural changes by using oocyte voltage clamp and photometry. Tetramethylrhodamine was also used to probe some extracellularly accessible positions. In total, 51 positions were investigated.

Results

We detected robust voltage‐dependent signals from widely distributed positions including N‐terminus and S1. In addition, response to hyperpolarization was observed at the extracellular end of S1, reflecting the local structure flexibility of the voltage‐sensor domain in the down‐state. We also found that the mechanical coupling between the voltage‐sensor and phosphatase domains affects the depolarization‐induced optical signals but not the hyperpolarization‐induced signals.

Conclusions

These results fill a gap between the previous interpretations from the structural and biophysical approaches and should provide important insights into the mechanisms of the voltage‐sensor domain transition as well as its coupling with the effector.
‘Spin’ in urology non‐randomised studies comparing therapeutic interventions: a temporal analysis
Authors: Wu Jeremy , Haile Samuel S. , Ho Wilson , Klotz Laurence , Yuan Morgan , Lee Jason Y. , Krakowsky Yonah
Published: March 2024

Objective

To determine the prevalence of ‘spin’ (i.e., reporting practices that distort the interpretation of results by positively reflecting negative findings or downplaying potential harms) strategies and level of spin in urological observational studies and whether the use of spin has changed over time.

Materials and Methods

MEDLINE and Embase were searched to identify observational studies comparing therapeutic interventions in the top five urology journals and major urological subspecialty journals, published between 2000 and 2001, 2010 and 2011, and 2020 and 2021.

Results

A total of 235 studies were included. Spin was identified in 81% of studies, with a median of two strategies per study. The most commonly used strategies were inadequate implication for clinical practice (30%), causal language or causal claim (29%), and use of linguistic spin (29%). Moderate to high levels of spin were found in 55% of conclusions. From 2000 to 2020, the average number of strategies used has significantly decreased each decade (H = 27.459, P < 0.001), and the median level of spin in conclusions was significantly lower in studies published in the 2020s and 2010s than in the 2000s (H = 11.649, P = 0.003).

Conclusions

Our results suggest that 81% of urological observational studies comparing therapeutic interventions contained spin. Over the past two decades, the use of spin has significantly declined, but this remains an area for improvement, with 70% of included studies published in the 2020s employing spin. Medical writing should scrupulously avoid words or phrases that are not supported by data in the manuscript.