bluuebunny/crossref_metadata_2025_split huggingface dataset:
On the use of covariates for trachoma prevalence estimation using geostatistical methods: benefits and limitations
Authors: Sasanami Misaki , Almou Ibrahim , Diori Adam Nouhou , Bakhtiari Ana , Beidou Nassirou , Bisanzio Donal , Boyd Sarah , Burgert-Brucker Clara R , Amza Abdou , Gass Katherine , Kadri Boubacar , Kebede Fikreab , Masika Michael P , Olobio Nicholas P. , Seife Fikre , Souley Abdoul Salam Youssoufou , Tefera Amsayaw , Kello Amir B , Solomon Anthony W , Harding-Esch Emma M , Giorgi Emanuele 
Published: November 2024

Abstract

Background:
Model-based geostatistics (MBG) is increasingly used for estimating the prevalence of neglected tropical diseases, including trachoma, in low- and middle-income countries. We sought to investigate the impact of spatially referenced covariates to improve spatial predictions for trachomatous inflammation—follicular (TF) prevalence generated by MBG. To this end, we assessed the ability of spatial covariates to explain the spatial variation of TF prevalence and to reduce uncertainty in the assessment of TF elimination for pre-defined evaluation units (EUs).
Methods:
We used data from EUs in Ethiopia, Malawi, Niger, and Nigeria. For each EU, we compared two models: a model that used only age, a variable required for the standardisation of prevalence as used in the routine, standard prevalence estimation; and a model that included spatial covariates in addition to age. For each fitted model, we reported estimates of the parameters that quantify the strength of residual spatial correlation and 95% prediction intervals as the measure of uncertainty.
Results:
The strength of association between covariates and TF prevalence varied within and across countries. For some EUs, spatially referenced covariates explained most of the spatial variation and thus allowed us to generate predictive inferences for TF prevalence with a substantially reduced uncertainty, compared with models without the spatial covariates. For example, the prediction interval for TF prevalence in the areas with the lowest TF prevalence in Nigeria narrowed substantially, from a width of 2.9 to 0.7. This reduction occurred as the inclusion of spatial covariates significantly decreased the variance of the spatial Gaussian process in the geostatistical model. In other cases, spatial covariates only led to minor gains, with slightly smaller prediction intervals for the EU-level TF prevalence or even a wider prediction interval.
Conclusions:
Although spatially referenced covariates could help reduce prediction uncertainty in some cases, the gain could be very minor, or uncertainty could even increase. When considering the routine, standardised use of MBG methods to support national trachoma programmes worldwide, we recommend that spatial covariate use be avoided.
Image Shadow Removal Algorithm Based on MaskGuideAttention and Mask-Aware Enhancement Module
Authors: Wang Donghui , Wang Jinhua , He Ning , Zhang Jingzun , Zhang Sen , Liu Shuai 
Published: November 2024

Abstract

The complex and variable nature of shadows in natural environments—including diverse shapes, intensities, and intricate scene compositions—presents significant challenges for accurate shadow removal. To address these challenges, this study introduces a novel shadow removal algorithm, MGME-ShadowNet, designed to enhance image quality by restoring details within shadowed regions, thereby improving the performance of subsequent image processing tasks. MGME-ShadowNet integrates a MaskGuideAttention mechanism and a Mask-Aware Enhancement Module (MAEM) within a diffusion model framework, allowing it to effectively handle both soft and hard shadows, especially in complex scenes. The MaskGuideAttention mechanism adaptively adjusts the contributions of self-attention and spatial attention through learnable parameters, and it allocates attention to shadowed and non-shadowed regions based on shadow masks. Meanwhile, the MAEM module further refines the initial shadow masks generated by the shadow detection network. By combining dilated convolutions and pyramid pooling, MAEM improves the accuracy of the shadow masks, ensuring a clear distinction between shadowed and non-shadowed regions. Experimental evaluations on the SRD dataset demonstrate that MGME-ShadowNet outperforms current state-of-the-art methods, achieving a 2.09 dB increase in peak signal-to-noise ratio (PSNR) in shadowed regions and a 0.56-point reduction in root mean square error (RMSE) for shadow quality, surpassing the performance of ShadowDiffusion. These results confirm MGME-ShadowNet's effectiveness in managing complex shadow scenarios.The code for is available at
https://github.com/Donghui-Wang/MGMEShadowNet.
Midwives’ rural health experiences of ‘being-with’ women and newborns in labour and delivery settings, Ghana: Implications for policy and planning
Authors: Ani-Amponsah Mary , Richter Solina , Appiah Evans , Ampofo Evelyn Asamoah , Annan Emma , Mensah Gwendolyn , Badzi Caroline Dinam , Armah Deborah , Amankwah Bridget , Amegavluie Ruby Elikem Afi , Pwavra Joyce 
Published: November 2024

Abstract

Introduction: The global shortage of midwives and skilled birth attendants is a major risk factor for quality health service delivery in resource limited settings. This gap in skilled health workforce impacts quality labour and delivery services and the survival of pregnant women and their newborns, as well as midwives’ emotional and physical wellbeing. In resource-constrained contexts where midwives practice as fontline health workers, respectful maternal and newborn care delivery is compromised as midwives encounter continual distress with health system gaps. In Ghana, midwives in rural practice work as frontline health staff but their lifesaving efforts in maternal and newborn health have least been researched. We therefore aimed to explore, gain insight, and offer in-depth descriptions of the meanings embedded in midwives’ contextual experiences of being-with women and newborns in birth settings of southern rural Ghana.
Methods: Thirteen (13) midwives were purposively and conveniently sampled, and data were collected through face-to-face in-depth interviews. Data were content analysed and three major themes emerged.
Results: The study findings revealed that midwives in rural practice face complex distress circumstances that require resilience and capacity to embrace peculiarities, typically in emergency situations within resource-constrained settings where families are engaged as lifesaving companions to save maternal and newborn lives.
Conclusions: Reviewing policies on rural health care delivery focused on primary health care, planning on rural midwifery postings and creating support network for midwives are critical for saving maternal and newborn lives and improving retention of rural midwives at post. Future research is required to examine midwives’ resilience in rural practice and intention to stay for delivering quality maternal and newborn community health care service.
A multifunctional T-cell engager containing CD80 enhances prostate cancer treatment
Authors: Nie Disen , Jiang Yao , Li Hui , Zhang Keying , Li Zhengxuan , Lu Tong , Li Yu , Han Donghui , Shi Changhong , Xing Nianzeng , Yang Fa , Wen Weihong , Qin Weijun 
Published: November 2024

Abstract

Background: T-cell engagers (TCEs) are the most examined bispecific antibodies that recognize and target effector T cells to generate immune synapses against tumor cells, thereby resulting in T-cell activation and tumor killing. However, the inhibitory influence of the tumor microenvironment on TCEs' functionality has impeded their utilization in solid tumor therapies. To circumvent this limitation, we enhanced bispecific prostate-specific membrane antigen (PSMA)/CD3 TCEs by incorporating the extracellular segment of CD80 to overcome T-cell inhibition in prostate cancer by delivering a second activation signal to T cells, designating this trifunctional antibody as TriTE-N13.
Methods：Using gene editing and eukaryotic expression techniques, we developed the fully humanized fusion antibody TriTE-N13. In vitro, we assessed its effects on T-cell activation, proliferation, and cytotoxicity, and tested the T-cell targeting cytotoxicity function of TriTE-N13 against PSMA+ tumor cells in both cell co-culture models and tumor cell spheroid models. Furthermore, in humanized immune-reconstituted mouse models, we evaluated the in vivo efficacy and safety of TriTE-N13 against prostate cancer xenografts.
Results: In vitro, TriTE-N13 can notably activate human T cells and exhibits excellent binding activity to human PSMA and CD3 antigens. Meanwhile, TriTE-N13 can mediate T-cell-induced cytotoxicity against PSMA-positive prostate cancer cells. In vivo, we demonstrated that TriTE-N13 significantly reduced tumor volume compared to bispecific TCEs when treating initially large-sized tumors.
Conclusions: Our data suggest that the incorporation of CD80 as a second pathway activator significantly enhances the solid tumor-killing effect of TCEs, thereby positioning TriTE-N13 as a promising immunotherapy candidate for the treatment of advanced prostate cancer.
Industrial Mobile Robot Navigation Based on the LiDAR-IMU-Visual Measurements
Authors: Song Min , Zhang Yinlong 
Published: November 2024

Abstract

Simultaneous localization and mapping (SLAM) has emerged as a pivotal technology for enabling precise navigation and autonomy in industrial robotics. Despite its significance, SLAM systems often contend with cumulative errors over time and the lack of a fixed reference for absolute positioning, which can limit their effectiveness in complex industrial environments. This research addresses these challenges by leveraging the complementary strengths of Lidar-Inertial Measurement Unit (IMU) systems and downward-facing visual sensors for the purpose of guiding industrial robots. The data from these sensors are fused within a factor graph framework, which is a robust mathematical structure for modeling and solving SLAM problems. A novel aspect of our approach is the integration of downward visual data, specifically through the use of QR codes as factors within the graph, to achieve accurate robot positioning.Moreover, we have developed an enhanced Iterative Closest Point (ICP) algorithm tailored for refining the orientation of the robot. This refined ICP algorithm improves the alignment of Lidar scans, thereby reducing errors in the estimation of the robot’s pose. To augment the precision of the mapping process, an extended Kalman filter is implemented to mitigate Lidar measurement noise and to refine the accuracy of the maps generated by the Lidar system. This filter serves as a cornerstone for robust state estimation, ensuring that the robot maintains a high degree of localization accuracy even in the presence of environmental disturbances.The proposed SLAM system has been thoroughly evaluated on a custom-designed industrial robot platform, demonstrating its efficacy in real-world scenarios. The integration of Lidar, IMU, and visual data within the factor graph framework, along with the enhanced ICP and extended Kalman filter, has resulted in a more reliable and accurate SLAM solution for industrial robotics. Our findings suggest that this multi-sensor approach can significantly improve the performance of industrial robots in terms of navigation, mapping, and overall operational efficiency, making it a promising advancement for the automation industry.
Synthesis and diagnosis of chemical dye capable of staining some fish histological sections
Authors: Al-Waeli jassim , Abdull-majed Hasanain A. , Ibrahim Munera A. , Ismael Sadiq M-H. 
Published: November 2024

Abstract

The synthesis of dyes is crucial for the study of animal and plant tissue. We developed a method based on the creation of a new pigment and compared it with other commonly used pigments to stain histological sections of bone. The dye has been synthesized by reacting the amino part with the phenolic part of the chemical compound (2-aminophenol) at a ratio of 1:1 and by diazotization and coupling. The diagnosis was made using techniques 1HNMR, FT-IR, mass spectrum, melting point, and other analytical techniques that confirmed the formation of the dye. The dye solution was perfected in bone histological sections for some types of fish. The dye was assessed by analyzing a picture of three types of bone histological sections of the fish and comparing them with alizarin red. Alizarin red smudging techniques have been compared to determine the reliability of these methods. We found that the staining of the dye gave great differences between bone tissue and other tissue components. The performance of the created dye can be more useful than that of other pigmentation methods for several reasons. The synthesis is easy, the pigment is stable, and the pigment solution does not collapse after preparation and remains for a long time. In addition, the accuracy, clarity and contrast of the contents of the bone histological section were assessed.
Effectiveness of the Support From Community Health Workers and Health Care Professionals on the Sustained Use of Wearable Monitoring Devices Among Community-Dwelling Older Adults: Feasibility Randomized Controlled Trial
Authors: Wong Arkers Kwan Ching , Bayuo Jonathan , Su Jing Jing , Wong Frances Kam Yuet , Chow Karen Kit Sum , Wong Bonnie Po , Wong Siu Man , Hui Vivian 
Published: November 2024

Background
The wearable monitoring device (WMD) is emerging as a promising tool for community-dwelling older adults to monitor personal health, enhance awareness of their activities, and promote healthy behaviors. However, the sustained use of WMDs among this population remains a significant challenge.

Objective
This study aims to implement an interventional program that promotes and motivates the continued use of WMDs among older adults through a peer and professional support approach. This program will facilitate the integration of WMDs into their daily lives.

Methods
This feasibility trial examined the following: (1) the usability of the WMD from the users’ perspectives; (2) the feasibility of the Live With Wearable Monitoring Device program; and (3) the effectiveness of the Live With Wearable Monitoring Device program among community-dwelling older adults. The intervention, based on Self-Determination Theory, involved using the Live With Wearable Monitoring Device program over a 3-month period, with ongoing professional and peer support provided by community health workers, aided by a nurse and social workers. This support included 1 home visit and biweekly communication via WhatsApp. Data were collected at baseline and at 1, 3, and 6 months.

Results
A total of 39 participants were enrolled in the intervention group, while 37 participants were in the control group. The recruitment rate was high (76/89, 85%), and the attrition rate was low (8/76, 11%), indicating that the program is feasible for older adults. Participants in the intervention group exhibited higher self-efficacy, lower anxiety levels, and used the smartwatch more frequently, in terms of both days and hours, compared with the control group. A between-group difference was observed in self-efficacy between the intervention and control groups (β=3.31, 95% CI 0.36-6.25, P=.03), with statistically significant higher mean values recorded at all 4 time points.

Conclusions
It is clear that merely providing a WMD to older adults does not guarantee its usage, particularly for those unfamiliar with how to utilize its health-related functions in their daily routines. This study implemented a theory-based program aimed at enhancing the ongoing use of WMDs among older adults, suggesting that continuous professional and peer support may significantly influence WMD usage.

Trial Registration
ClinicalTrials.gov NCT05269303; https://clinicaltrials.gov/ct2/show/NCT05269303
Investigating the Classification of Living Kidney Donation Experiences on Reddit and Understanding the Sensitivity of ChatGPT to Prompt Engineering: Content Analysis
Authors: Nielsen Joshua , Chen Xiaoyu , Davis LaShara , Waterman Amy , Gentili Monica 
Published: November 2024

Background
Living kidney donation (LKD), where individuals donate one kidney while alive, plays a critical role in increasing the number of kidneys available for those experiencing kidney failure. Previous studies show that many generous people are interested in becoming living donors; however, a huge gap exists between the number of patients on the waiting list and the number of living donors yearly.

Objective
To bridge this gap, we aimed to investigate how to identify potential living donors from discussions on public social media forums so that educational interventions could later be directed to them.

Methods
Using Reddit forums as an example, this study described the classification of Reddit content shared about LKD into three classes: (1) present (presently dealing with LKD personally), (2) past (dealt with LKD personally in the past), and (3) other (LKD general comments). An evaluation was conducted comparing a fine-tuned distilled version of the Bidirectional Encoder Representations from Transformers (BERT) model with inference using GPT-3.5 (ChatGPT). To systematically evaluate ChatGPT’s sensitivity to distinguishing between the 3 prompt categories, we used a comprehensive prompt engineering strategy encompassing a full factorial analysis in 48 runs. A novel prompt engineering approach, dialogue until classification consensus, was introduced to simulate a deliberation between 2 domain experts until a consensus on classification was achieved.

Results
BERT and GPT-3.5 exhibited classification accuracies of approximately 75% and 78%, respectively. Recognizing the inherent ambiguity between classes, a post hoc analysis of incorrect predictions revealed sensible reasoning and acceptable errors in the predictive models. Considering these acceptable mismatched predictions, the accuracy improved to 89.3% for BERT and 90.7% for GPT-3.5.

Conclusions
Large language models, such as GPT-3.5, are highly capable of detecting and categorizing LKD-targeted content on social media forums. They are sensitive to instructions, and the introduced dialogue until classification consensus method exhibited superior performance over stand-alone reasoning, highlighting the merit in advancing prompt engineering methodologies. The models can produce appropriate contextual reasoning, even when final conclusions differ from their human counterparts.
Accuracy of Machine Learning in Discriminating Kawasaki Disease and Other Febrile Illnesses: Systematic Review and Meta-Analysis
Authors: Zhu Jinpu , Yang Fushuang , Wang Yang , Wang Zhongtian , Xiao Yao , Wang Lie , Sun Liping 
Published: November 2024

Background
Kawasaki disease (KD) is an acute pediatric vasculitis that can lead to coronary artery aneurysms and severe cardiovascular complications, often presenting with obvious fever in the early stages. In current clinical practice, distinguishing KD from other febrile illnesses remains a significant challenge. In recent years, some researchers have explored the potential of machine learning (ML) methods for the differential diagnosis of KD versus other febrile illnesses, as well as for predicting coronary artery lesions (CALs) in people with KD. However, there is still a lack of systematic evidence to validate their effectiveness. Therefore, we have conducted the first systematic review and meta-analysis to evaluate the accuracy of ML in differentiating KD from other febrile illnesses and in predicting CALs in people with KD, so as to provide evidence-based support for the application of ML in the diagnosis and treatment of KD.

Objective
This study aimed to summarize the accuracy of ML in differentiating KD from other febrile illnesses and predicting CALs in people with KD.

Methods
PubMed, Cochrane Library, Embase, and Web of Science were systematically searched until September 26, 2023. The risk of bias in the included original studies was appraised using the Prediction Model Risk of Bias Assessment Tool (PROBAST). Stata (version 15.0; StataCorp) was used for the statistical analysis.

Results
A total of 29 studies were incorporated. Of them, 20 used ML to differentiate KD from other febrile illnesses. These studies involved a total of 103,882 participants, including 12,541 people with KD. In the validation set, the pooled concordance index, sensitivity, and specificity were 0.898 (95% CI 0.874-0.922), 0.91 (95% CI 0.83-0.95), and 0.86 (95% CI 0.80-0.90), respectively. Meanwhile, 9 studies used ML for early prediction of the risk of CALs in children with KD. These studies involved a total of 6503 people with KD, of whom 986 had CALs. The pooled concordance index in the validation set was 0.787 (95% CI 0.738-0.835).

Conclusions
The diagnostic and predictive factors used in the studies we included were primarily derived from common clinical data. The ML models constructed based on these clinical data demonstrated promising effectiveness in differentiating KD from other febrile illnesses and in predicting coronary artery lesions. Therefore, in future research, we can explore the use of ML methods to identify more efficient predictors and develop tools that can be applied on a broader scale for the differentiation of KD and the prediction of CALs.
Pelatihan Penulisan Esai Destinasi Wisata di Kabupaten Gunungkidul bersama Komunitas Pelita
Authors: Cahyaningrum Dewojati  , Wulandari Ine 
Published: November 2024

Abstrak
Kabupaten Gunungkidul merupakan salah satu daerah yang kaya akan objek wisata. Objek wisata yang terdapat di Gunungkidul berupa wisata alam, religi, kuliner, edukasi, sejarah, belanja, dan lain-lain berpotensi untuk memakmurkan masyarakat dari segi ekonomi dan budaya. Namun, banyak di antara objek wisata tersebut belum dikenal dan dikembangkan dengan maksimal. Dengan demikian, perlu langkah strategis berupa penggalian potensi wisata melalui tulisan yang ditulis oleh penulis lokal dan dipublikasikan ke masyarakat umum untuk memajukan potensi tersebut. Hal ini tidak saja berguna untuk pengembangan diri penulis, tetapi juga bermanfaat untuk pengembangan sektor pariwisata dan ekonomi masyarakat Gunungkidul. Oleh karena itu, kegiatan Pengabdian kepada Masyarakat (PkM) Fakultas Ilmu Budaya berupa penulisan esai ini sangat dibutuhkan oleh masyarakat setempat guna mengasah keterampilan kreatif. Berdasarkan latar belakang tersebut, tim PkM bekerja sama dengan Komunitas Pelita (Penggiat Literasi Aktif) Gunungkidul telah mengadakan Pelatihan Esai Destinasi Wisata di Kabupaten Gunungkidul. Anggota Komunitas Pelita terdiri atas guru SD, SMP, SMA, pegawai perpustakaan, pensiunan guru, dan masyarakat umum. Melalui pendampingan pada pelatihan ini, mereka telah menghasilkan buku antologi esai yang terdiri atas 37 tulisan tentang berbagai objek wisata Gunungkidul.

Kata Kunci: Pengabdian, pelatihan, esai, wisata, Gunungkidul
Um Relações entre competências socioemocionais, adaptabilidade e autoeficácia na transição escola-trabalho
Authors: Molina Costa Fabíola Aparecida , Melo-Silva Lucy Leal , Oliveira José Egídio , Lessa João Paulo A. 
Published: November 2024

O objetivo deste estudo foi investigar possíveis correlações entre competências socioemocionais, adaptabilidade de carreira e autoeficácia na transição escola-trabalho. A amostra foi composta por 505 jovens aprendizes com idades entre 14 e 24 anos, sendo 39,4 % do sexo masculino (n = 199), 60,6 % do sexo feminino (n = 306), participantes de um programa de aprendizagem. Foram utilizados quatro instrumentos aplicados online: Instrumento para Avaliação de Competências Socioemocionais (SENNA 2.0), Escala de Adaptabilidade de Carreira (EAC), Escala de Autoeficácia na Transição Escola-Trabalho (AETBR), Questionário de Identificação e Critério de Classificação Econômica Brasil. Foram realizadas análises fatoriais confirmatórias, de confiabilidade, descritivas e correlações r de Pearson. Os resultados indicam correlações importantes entre todas as variáveis, exceto entre as competências socioemocionais, amabilidade e engajamento com os outros. Os resultados da AETT-BR e da EAC mostram relação entre confiança e adaptação. Os achados mostram implicações para as práticas e pistas para novas investigações.