bluuebunny/crossref_metadata_2025_split huggingface dataset:
Patients with advanced cancer in Uganda: Gender, social norms, and family relationship icebergs in the face of terminal illness
Authors: Kulikowski Julia D , Namisango Eve , Rosa William E. 
Published: January 2025

Abstract

Objectives
Cancer is associated with physical, social, spiritual, and psychological changes in patients and their caregivers. However, in sub-Saharan Africa, there is lack of evidence on the impact of gender, social norms, and relationship dynamics in the face of terminal illness. The aim of this paper is to explore how gender identity, social norms, and power relations are impacted when a person is living in Uganda with advanced cancer.

Methods
Focus groups with adult men and women living with advanced cancer in Uganda were conducted. Interviews explored the social effects of cancer and common challenges, including how both disease and treatment affect the patient and marital relationships within their families. Participants’ recommendations were sought to improve the social well-being of patients and their families. Data were analyzed using inductive thematic analysis.

Results
Men and women experienced negative changes in their roles and identities, often feeling unable to fulfill their marital duties in terms of intimacy, their social roles and responsibilities based on societal expectations. Men expressed loss of a “masculine” identity when unable to provide economically for the household. This led to tension in the familial power dynamics, contributing to relationship breakdown and gender-based violence (GBV) against spouses. Women noted challenges with parenting, relationship breakdowns, and increased GBV.

Significance of results
Gender impacts the patient and the family dynamic throughout the life course, including during advanced cancer. Patients and caregivers experience a change in their roles and identities while coping with existential distress and end-of-life tasks. Given these results, gender considerations and dynamics should be incorporated into overall palliative care provision. In addition, there is a need to integrate GBV screening and support in cancer services to address social health and safety needs in the context of serious illness.
Humanized dual-targeting antibody–drug conjugates specific to MET and RON receptors as a pharmaceutical strategy for the treatment of cancers exhibiting phenotypic heterogeneity
Authors: Wang Minghai , Ma Qi , Suthe Sreedhar Reddy , Hudson Rachel E. , Pan Jing-ying , Mikelis Constantinos , Zhu Miao-jin , Wu Zhi-gang , Shi Dan-rong , Yao Hang-ping 
Published: January 2025

Abstract
Cancer heterogeneity, characterized by diverse populations of tumorigenic cells, involves the occurrence of differential phenotypes with variable expressions of receptor tyrosine kinases. Aberrant expressions of mesenchymal–epithelial transition (MET) and recepteur d’origine nantais (RON) receptors contribute to the phenotypic heterogeneity of cancer cells, which poses a major therapeutic challenge. This study aims to develop a dual-targeting antibody–drug conjugate (ADC) that can act against both MET and RON for treating cancers with high phenotypic heterogeneity. Through immunohistochemical staining, we show that MET and RON expressions are highly heterogeneous with differential combinations in more than 40% of pancreatic and triple-negative breast cancer cases. This expressional heterogeneity provides the rationale to target both receptors for cancer therapy. A humanized bispecific monoclonal antibody specific to both MET and RON (PCMbs–MR) is generated through IgG recombination using monoclonal antibody sequences specific to MET and RON, respectively. Monomethyl auristatin E is conjugated to PCMbs–MR to generate a dual-targeting ADC (PCMdt–MMAE), with a drug-to-antibody ratio of 4:1. Various cancer cell lines were used to determine PCMdt-MMAE-mediated biological activities. The efficacy of PCMdt–MMAE in vivo is evaluated using multiple xenograft tumor models. PCMdt–MMAE shows a favorable pharmacokinetic profile, with a maximum tolerated dose of ~30 mg/kg in mice. Toxicological studies using Sprague–Dawley rats reveal that PCMdt–MMAE is relatively safe with slight-to-moderate, temporary, and reversible adverse events. Functionally, PCMdt-MMAE induces a robust internalization of both MET and RON and causes a large-scale cell death in cancer cell lines exhibiting MET and RON heterogeneous co-expressions. Both in vitro and in vivo studies demonstrate that the dual-targeting approach in the form of an ADC is highly effective with a long-lasting effect against tumors exhibiting MET/RON heterogeneous phenotypes. Hence, we can suggest that a dual-targeting ADC specific to both MET and RON can be employed as a novel therapeutic strategy for tumors with expressional phenotypic heterogeneity.
Noise amplification and ill-convergence of Richardson-Lucy deconvolution
Authors: Liu Yiming , Panezai Spozmai , Wang Yutong , Stallinga Sjoerd 
Published: January 2025

Abstract
Richardson-Lucy (RL) deconvolution optimizes the likelihood of the object estimate for an incoherent imaging system. It can offer an increase in contrast, but converges poorly, and shows enhancement of noise as the iteration progresses. We have discovered the underlying reason for this problematic convergence behaviour using a Cramér Rao Lower Bound (CRLB) analysis. An analytical expression for the CRLB diverges for spatial frequency components that approach the diffraction limit from below. The resulting mean noise variance per pixel diverges for large images. These results imply that a regular optimum of the likelihood does not exist, and that RL deconvolution is necessarily ill-convergent.
Understanding metro station areas’ functional characteristics via embedding representation: A case study of shanghai
Authors: Jiang Heping , Liu Ruihua , Luo Shijia , Yi Disheng , Zhang Jing 
Published: January 2025

Abstract
As crucial transportation hubs for urban travel, metro stations catalyze the transformation of their surrounding areas into highly prominent locations where many activities converge. Uncovering the functional attributes of station areas holds immense significance in comprehending citizens’ activity demands, thereby offering valuable insights for regional development and planning in proximity to metro stations. This study introduces a framework that improves the process of accurately representing station areas. On the basis of the semantic vectors of point of interests (POI) categories trained by the GloVe model, the partition smooth inverse frequency (P-SIF) model and affinity propagation (AP) are employed to generate the embedding representations of station areas and categorize. Finally, we classify the station areas into 9 functional groups: and analyse the spatial distribution characteristics of each group. It is found that most of the station areas in Shanghai show the characteristics of mixed type, in which the characteristics of residential type and commercial type are obvious. In terms of spatial, the stations with commercial characteristics are mainly distributed in the central area of the city, while those with residential and working characteristics are scattered.
Current applications and challenges in large language models for patient care: a systematic review
Authors: Busch Felix , Hoffmann Lena , Rueger Christopher , van Dijk Elon HC , Kader Rawen , Ortiz-Prado Esteban , Makowski Marcus R. , Saba Luca , Hadamitzky Martin , Kather Jakob Nikolas , Truhn Daniel , Cuocolo Renato , Adams Lisa C. , Bressem Keno K. 
Published: January 2025

Abstract

Background
The introduction of large language models (LLMs) into clinical practice promises to improve patient education and empowerment, thereby personalizing medical care and broadening access to medical knowledge. Despite the popularity of LLMs, there is a significant gap in systematized information on their use in patient care. Therefore, this systematic review aims to synthesize current applications and limitations of LLMs in patient care.

Methods
We systematically searched 5 databases for qualitative, quantitative, and mixed methods articles on LLMs in patient care published between 2022 and 2023. From 4349 initial records, 89 studies across 29 medical specialties were included. Quality assessment was performed using the Mixed Methods Appraisal Tool 2018. A data-driven convergent synthesis approach was applied for thematic syntheses of LLM applications and limitations using free line-by-line coding in Dedoose.

Results
We show that most studies investigate Generative Pre-trained Transformers (GPT)-3.5 (53.2%, n = 66 of 124 different LLMs examined) and GPT-4 (26.6%, n = 33/124) in answering medical questions, followed by patient information generation, including medical text summarization or translation, and clinical documentation. Our analysis delineates two primary domains of LLM limitations: design and output. Design limitations include 6 second-order and 12 third-order codes, such as lack of medical domain optimization, data transparency, and accessibility issues, while output limitations include 9 second-order and 32 third-order codes, for example, non-reproducibility, non-comprehensiveness, incorrectness, unsafety, and bias.

Conclusions
This review systematically maps LLM applications and limitations in patient care, providing a foundational framework and taxonomy for their implementation and evaluation in healthcare settings.
Visual Stimulus Materials Used in Spoken Narrative Discourse Elicitation After Traumatic Brain Injury: A Scoping Review
Authors: Steel Joanne , Hoffman Rhianne , Bogart Elise 
Published: January 2025

Purpose:
Management of discourse is acknowledged as a critical component of speech-language pathology practice with cognitive communication after traumatic brain injury (TBI). This scoping review aimed to collate the visual materials that are being used in empirical research for spoken narrative elicitation post-TBI, in both assessment and treatment contexts. We aimed to examine the format, structure, and sources for visuals used. Discourse analyses were also investigated.

Method:
The research was conducted and reported as per the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews between September 26, 2023, and March 1, 2024. Four electronic databases were searched for peer-reviewed original research studies reporting spoken narrative tasks using visual elicitation stimuli after TBI. Secondary searches of backward and forward citations were also undertaken.

Results:
Of the 1,461 studies found in the search, 60 studies were eligible for data extraction. Eleven visual stimuli were used in 51 of the studies to elicit narratives. The visual stimulus reported in the highest number of studies was The Flowerpot Incident, a six-picture black-and-white picture sequence. Overall, the most frequently used format was a wordless storybook, analyzed most frequently at the macrostructural level.

Conclusions:
Research studies are using picture sequences or wordless storybooks to elicit discourse samples after TBI. This contrasts with a recent survey of clinical practice with discourse post-TBI, where The Cookie Theft picture was most reported in use (Steel et al., 2024). We discuss the relevance of findings in relation to recent INCOG 2.0 guidelines (Togher et al., 2023) and speech-language pathology practice, and make recommendations for clinical and research future directions.

Supplemental Material:

https://doi.org/10.23641/asha.28098113
The prevalence of gas exchange data processing methods: a
semi-automated scoping review
Authors: Hesse Anton , White Manix , Lundstrom Christopher 
Published: January 2025

AbstractCardiopulmonary exercise testing involves collecting variable breath-by-breath
data and sometimes requiring data processing of outlier removal, interpolation,
and averaging before later analysis. These data processing choices, such as
averaging duration, affect calculated values such as ˙VO2max.
However, assessing the implications of data processing without knowing popular
methods worth comparing is difficult. In addition, such details aid study
reproduction. We conducted a semi-automated scoping review of articles with
exercise testing that collected data breath-by-breath from three databases. Of
the 8,344 articles, 376 (mean: 4.5% and 95% confidence interval: 4.1–5.0%) and
581 (mean: 7.0% and 95% confidence interval: 6.4–7.5%) described outlier removal
and interpolation, respectively. A random subset of 1,078 articles revealed
(mean: 60.9% and 95% confidence interval: 57.9–63.7%) the reported averaging
methods. The commonly documented outlier cutoffs were±3 or 4 SD (39.1 and 51.6%,
respectively). The dominating interpolation duration and procedure were 1 s
(93.9%) and linear interpolation (92.5%). Averaging methods commonly described
were 30 (30.9%), 60 (12.4%), 15 (11.6%), 10 (11.0%), and 20 (8.1%) second bin
averages. This shows that studies collecting breath-by-breath data often lack
detailed descriptions of data processing methods, particularly for outlier
removal and interpolation. While averaging methods are more commonly reported,
improved documentation across all processing steps will enhance reproducibility
and facilitate future research comparing data processing choices.
Prospects for cosmological research with the FAST array: 21-cm intensity mapping survey observation strategies
Authors: Pan Jun-Da , Wu Peng-Ju , Du Guo-Hong , Li Yichao , Zhang Xin 
Published: January 2025

Abstract
Precise cosmological measurements are essential for understanding the evolution of the universe and the nature of dark energy. The Five-hundred-meter Aperture Spherical Telescope (FAST), the most sensitive single-dish radio telescope, has the potential to provide the precise cosmological measurements through neutral hydrogen 21 cm intensity mapping sky survey. This paper primarily explores the potential of technological upgrades for FAST in cosmology. The most crucial upgrade begins with equipping FAST with a wide-band receiver (0 < z < 2.5). This upgrade can enable FAST to achieve higher precision in cosmological parameter estimation than the Square Kilometre Array Phase 1 Mid-Frequency Array. On this basis, expanding to a FAST array (FASTA) consisting of six identical FASTs would offer significant improvements in precision compared to FAST. Additionally, compared with the current results from the data combination of cosmic microwave background, baryon acoustic oscillations (optical galaxy surveys), and type Ia supernovae, FASTA can provide comparable constraints. Specifically, for the dark-energy equation-of-state parameters, FASTA can achieve σ(w
0) = 0.09 and σ(wa
) = 0.33.
High-redshift cosmography with a possible cosmic distance duality relation violation
Authors: Jesus José F. , Gomes Mikael J.S. , Holanda Rodrigo F.L. , Nunes Rafael C. 
Published: January 2025

Abstract
In this study, we used geometric distances at high redshifts (both luminosity and angular) to perform a cosmographic analysis with the Padé method, which stabilizes the behaviour of the cosmographic series in this redshift regime. However, in our analyses, we did not assume the validity of the Cosmic Distance Duality Relation (CDDR), but allowed for potential violations, such as d

L
(z) = η(z)(1+z)2
d

A
(z), where three different functional forms of η(z) are considered. By incorporating updated data from supernovae (SN), baryon acoustic oscillations (BAO), and cosmic chronometers (CC), we obtained observational constraints on cosmographic models alongside possible CDDR violations. Interestingly, we found that potential CDDR violations introduce new statistical correlations among cosmographic parameters such as H
0, q
0, and j
0. Nonetheless, within this framework, we did not observe significant deviations from the CDDR, and our results remain consistent with the predictions of the ΛCDM model. In the same time, this work provides a novel and straightforward method for testing the CDDR by fixing the background evolution through cosmographic techniques, paving the way for new geometric observational tests of possible deviations from standard cosmology.
Hybrid summary statistics: neural weak lensing inference beyond the power spectrum
Authors: Lucas Makinen T. , Heavens Alan , Porqueres Natalia , Charnock Tom , Lapel Axel , Wandelt Benjamin D. 
Published: January 2025

Abstract
Cosmological inference relies on compressed forms of the raw data for analysis, with traditional methods exploiting physics knowledge to define summary statistics, such as power spectra, that are known to capture much of the information. An alternative approach is to ask a neural network to find a set of informative summary statistics from data, which can then be analysed either by likelihood- or simulation-based inference. This has the advantage that for non-Gaussian fields, they may capture more information than two-point statistics. However, a disadvantage is that the network almost certainly relearns that two-point statistics are informative. In this paper, we introduce a new hybrid method, which combines the best of both: we use our domain knowledge to define informative physics-based summary statistics, and explicitly ask the network to augment the set with extra statistics that capture information that is not already in the existing summaries. This yields a new, general loss formalism that reduces both the number of simulations and network size needed to extract useful non-Gaussian information from cosmological fields, and guarantees that the resulting summary statistics are at least as informative as the power spectrum. In combination, they can then act as powerful inputs to implicit inference of model parameters. We use a generalisation of Information Maximising Neural Networks (IMNNs) to obtain the extra summaries, and obtain parameter constraints from simulated tomographic weak gravitational lensing convergence maps. We study several dark matter simulation resolutions in low- and high-noise regimes. We show that i) the information-update formalism extracts at least 3× and up to 8× as much information as the angular power spectrum in all noise regimes, ii) the network summaries are highly complementary to existing 2-point summaries, and iii) our formalism allows for networks with extremely lightweight architectures to match much larger regression networks with far fewer simulations needed to obtain asymptotically optimal inference.
Optimal probe states for phase estimation with a fixed mean particle number
Authors: Qin Jinfeng , Liu Bo 
Published: January 2025

Abstract
Quantum phase estimation reveals the power of quantum resources to beat the standard quantum limit and has been widely used in many fields. To improve the precision of phase estimation, we discuss the optimal probe states for phase estimation with a fixed mean particle number. By searching for the maximum quantum Fisher information, we optimize the probe states, which are superior to the path-entangled Fock states. Compared the mean particle number $(\bar{n})$ with the dimension of the probe states in Fock space $(N+1)$, when $\bar{n}\leq N$, our optimal probe states can provide better performance than the $\bar{n}00\bar{n}$ states; When $\bar{n}> N$, our optimal probe states can also remain optimal if the dimension of the probe states is large enough.
Direct ink writing of nickel oxide-based thin films for room temperature gas detection
Authors: Thakur Neha , Murthy Hari , Arumugam Sudha , Thomas Neethu , Koshy Aarju Mathew , Swaminathan Parasuraman 
Published: January 2025

Abstract
The rapid industrial growth and increasing population have led to significant pollution and deterioration of the natural atmospheric environment. Major atmospheric pollutants include NO2 and CO2. Hence, it is imperative to develop NO2 and CO2 sensors for ambient conditions, that can be used in indoor air quality monitoring, breath analysis, food spoilage detection, etc. In the present study, two thin film nanocomposite (nickel oxide-graphene and nickel oxide-silver nanowires) gas sensors are fabricated using direct ink writing. The nano-composites are investigated for their structural, optical, and electrical properties. Later the nano-composite is deposited on the interdigitated electrode (IDE) pattern to form NO2 and CO2 sensors. The deposited films are then exposed to NO2 and CO2 gases separately and their response and recovery times are determined using a custom-built gas sensing setup. Nickel oxide-graphene provides a good response time and recovery time of 10 and 9 s, respectively for NO2, due to the higher electron affinity of graphene towards NO2. Nickel oxide-silver nanowire nano-composite is suited for CO2 gas because silver is an excellent electrocatalyst for CO2 by giving response and recovery times of 11 s each. This is the first report showcasing NiO nano-composites for NO2 and CO2 sensing at room temperature.
A smart finger patch with coupled magnetoelastic and resistive bending sensors
Authors: Dai Ziyi , Wang Mingrui , Wang Yu , Yu Zechuan , Li Yan , Qin Weidong , Qian Kai 
Published: January 2025

Abstract
In the era of Metaverse and virtual reality (VR)/augmented reality (AR), capturing finger motion and force interactions is crucial for immersive human-machine interfaces. This study introduces a flexible electronic skin for the index finger, addressing coupled perception of both state and process in dynamic tactile sensing. The device integrates resistive and giant magnetoelastic sensors, enabling detection of surface pressure and finger joint bending. This e-skin identifies three phases of finger action: bending state, dynamic normal force and tangential force (sweeping). The system comprises resistive carbon nanotubes (CNT)/polydimethylsiloxane (PDMS) films for bending sensing and magnetoelastic sensors (NdFeB particles, EcoFlex, and flexible coils) for pressure detection. The inward bending resistive sensor, based on self-assembled microstructures, exhibits directional specificity with a response time under 120 ms and bending sensitivity from 0° to 120°. The magnetoelastic sensors demonstrate specific responses to frequency and deformation magnitude, as well as sensitivity to surface roughness during sliding and material hardness. The system’s capability is demonstrated through tactile-based bread type and condition recognition, achieving 92% accuracy. This intelligent patch shows broad potential in enhancing interactions across various fields, from VR/AR interfaces and medical diagnostics to smart manufacturing and industrial automation.
Graphene/F16CuPc synaptic transistor for the emulation of multiplexed neurotransmission
Authors: Xu Zhipeng , Ni Yao , Sun Mingxin , Yuan Yiming , Wu Ning , Xu Wentao 
Published: January 2025

Abstract
We demonstrate a bipolar graphene/F16CuPc synaptic transistor (GFST) with matched p-type and n-type bipolar properties, which emulates multiplexed neurotransmission of the release of two excitatory neurotransmitters in graphene and F16CuPc channels, separately. This process facilitates fast-switching plasticity by altering charge carriers in the separated channels. The complementary neural network for image recognition of Fashion-MNIST dataset was constructed using the matched relative amplitude and plasticity properties of the GFST dominated by holes or electrons to improve the weight regulation and recognition accuracy, achieving a pattern recognition accuracy of 83.23%. These results provide new insights to the construction of future neuromorphic systems.