bluuebunny/crossref_metadata_2025_split huggingface dataset:
Counting Objects in Images using DeepLearning: Methods and Current Challenges
Authors: D'Alessandro Adriano , Mahdavi-Amiri Ali , Hamarneh Ghassan 
Published: June 2023

Abstract
Object counting is an important computer vision application and research topic, which typically involves enumerating the number of objects in an image. Methodologies spanning a broad set of strategies have been proposed for solving object counting problems. These methods have seen an increase in relevance with the recent emergence of several highly successful deep learning techniques, which have led to significant performance improvements on a growing number of annotated counting benchmark datasets. However, despite the recent advancements in deep learning and computer vision, object counting remains a challenging problem with several open research directions. Datasets often contain objects that are highly occluded and which occur across a range of scales and perspectives. Further, popular annotation strategies, like density map annotations, suffer from annotator noise and inconsistency, which creates a performance bottleneck. These annotation strategies also have a high annotation burden, which leads to datasets that are very small when compared to common benchmark datasets in domains like image classification. Given both the significant progress and continued challenges of object counting, this task continues to be an interesting and ongoing research problem. This overview explores the historical context of object counting methods, the fundamental methodologies driving progress, the state of the art methods, and the significant open problems. In particular, we focus on recent trends that attempt to alleviate the problem of the annotation burden for object counting problems.
Spatial proteomics reveals human microglial states shaped by anatomy and neuropathology
Authors: Mrdjen Dunja , Amouzgar Meelad , Cannon Bryan , Liu Candace , Spence Angie , McCaffrey Erin , Bharadwaj Anusha , Tebaykin Dmitry , Bukhari Syed , Hartmann Felix , Kagel Adam , Vijayaragavan Kausalia , Oliveria John Paul , Yakabi Koya , Serrano Geidy , Corrada Maria , Kawas Claudia H , Fullaway Christine , Bosse Marc , Tibshirani Robert , Beach Thomas , Angelo Michael , Montine Thomas , Bendall Sean 
Published: June 2023

Abstract
Microglia are implicated in aging, neurodegeneration, and Alzheimer’s disease (AD). Traditional, low-plex, imaging methods fall short of capturing in situ cellular states and interactions in the human brain. We utilized Multiplexed Ion Beam Imaging (MIBI) and data-driven analysis to spatially map proteomic cellular states and niches in healthy human brain, identifying a spectrum of microglial profiles, called the microglial state continuum (MSC). The MSC ranged from senescent-like to active proteomic states that were skewed across large brain regions and compartmentalized locally according to their immediate microenvironment. While more active microglial states were proximal to amyloid plaques, globally, microglia significantly shifted towards a, presumably, dysfunctional low MSC in the AD hippocampus, as confirmed in an independent cohort (n=26). This provides an in situ single cell framework for mapping human microglial states along a continuous, shifting existence that is differentially enriched between healthy brain regions and disease, reinforcing differential microglial functions overall.
WITHDRAWN: Model prediction of suicidal behavior in first hospitalization patients with major depression disorder: a cross-sectional study based on a large sample
Authors: Hu Suoya , Zhang Lin , Liu Xuebing , Li Yi , Ma Jun 
Published: June 2023

Abstract

Background
Major depressive disorder (MDD) is one of the common and major causes of suicidal behavior (SB). The purpose of this study was to determine the prevalence of SB and its factors of influence among first hospitalized MDD patients in China, and to develop a predictive model for SB in the target population.
Methods
This study recruited 981 patients with MDD who were experiencing their first hospitalization. The patients' socio-pathological and general clinical data were gathered, along with common metabolic parameters and thyroid function. In addition, patients were assessed for depressive, anxiety, and psychotic symptoms, as well as the severity of their disease.
Results
Our study revealed a 13.46% prevalence of SB among MDD first hospitalized patients. A variety of clinical symptom scores and metabolic indicators were identified as risk factors for SB, and based on the identified risk factors we further obtained a predictive model with good ability to differentiate SB.
Conclusion
This study provides a validated predictive model for SB in the first hospitalized MDD population and suggests that the factors addressed in our model could be better used for early implementation of preventive measures.
A latent class analysis of psycho-behavioural adaptation in children and its relationship with sense of parenting competence and psychological distress of parents
Authors: Shan Li , Li Mingyu , Cui Dalei , Xu Simeng , Wang Jie , Li lezhi , Zhang Aidi , He Li 
Published: June 2023

Abstract
Background
The healthy development of psychological behaviour among children has been widely concerned by the whole society. However, little is known about different categories or attributes of their psycho-behavioural adaptation. The purpose of this study to identifies the latent classes of psycho-behavioural adaptation in children and explores the relationships of these classes with sense of parenting competence and psychological distress of parents
Methods
The cross-sectional study was conducted in one primary and secondary education school in mainland China from September 2022 to December 2022. The parents of children (3–18 years old) were invited to participate in the study. We used standardized questionnaires to collect data. A latent class analysis was performed to identify children's psycho-behavioural adaptation classes.
Results
The study recruited 242 participants. Two classes of psycho-behavioural adaptation were identified, namely, the psycho-behavioural problems group and the psycho-behavioural health group. The differences between latent classes were statistically significant regarding the child's age and parental health. The differences between latent classes were statistically significant in terms of sense of parenting competence, satisfaction, efficacy, and psychological distress score. The sense of parenting competence and parental psychological distress influenced the latent class of psycho-behavioural adaptation in children.
Conclusion
There is heterogeneity in the psycho-behavioural adaptation of children and differences in the sense of parenting competence and psychological distress of parents in each category. Targeted interventions should be developed and implemented based on the characteristics of each psycho-behavioural adaptation class to maximize interventions' effectiveness and promote parental and child well-being.
Assessing landscape fragmentation and its implications for biodiversity conservation in the Greater Accra Metropolitan Area (GAMA) of Ghana
Authors: Bonsu Kofi , Bonin Olivier 
Published: June 2023

Abstract
Context
This paper discusses the impact of urban expansion on the environment, including its effects on biodiversity conservation. As urban areas develop, natural habitats such as forests and agricultural lands are transformed into industrial, commercial, and residential uses, which alters landscape patterns and can lead to fragmentation. Fragmentation can have negative impacts on biodiversity, such as deforestation, alteration of watercourses leading to flooding, reduced availability of food, and increased pollution.
Objectives
To provide a comprehensive and data-driven analysis of the urban expansion of Greater Accra Metropolitan Area (GAMA) over time, and its effects on biodiversity. The idea is to capture the changes in land cover and landscape patterns in GAMA from 1991 to 2022 and predict the landscape patterns for 2030.
Methods
We perform a land cover change analysis of GAMA from 1991 to 2022. The evolution of the landscape pattern is captured by calculating landscape metrics. Multiradial fractal dimension is used as a transition variable to model and predict the land cover and landscape pattern of GAMA for 2030.
Results
The landscape metrics revealed extensive fragmentation of the Vegetation land cover type of GAMA, indicating a negative impact on biodiversity conservation, species movements, ecosystem functioning, and long-term ecological resilience.
Conclusion
The trends highlight the importance of considering landscape connectivity, habitat fragmentation and spatial arrangement of land cover types for assessing ecological processes and urban development impacts on the environment. Additionally, multiradial fractal dimension of built-up areas is recommended as an explanatory variable for urban growth modelling.
Validity of measures to indirectly determine maximal oxygen uptake in healthy and clinical subjects
Authors: Ribeiro Leonardo , Silva Luan R. S. , Souza Wallace M. M. , Miranda Humberto , Reis Michel S. 
Published: June 2023

Abstract
Background
The peak oxygen uptake (VO2peak) is considered the most reliable parameter for evaluating cardiorespiratory fitness and guiding physical training. However, not everyone has access to VO2peak measurements. As a more accessible alternative, heart rate (HR) variables have emerged.
Aim
To explore the correlation between HR responses during rest, exercise, recovery, and VO2peak.
Methods
Thirteen healthy men participated in the study. Resting HR variability and parameters were recorded for 10 minutes using a heart rate monitor while participants were seated. A maximal intermittent treadmill test was conducted to determine the maximum rate of HR increase (rHRI) and obtain VO2peak data (33.53 ± 8.22 mL/kg/min). The test speed was set based on the International Physical Activity Questionnaire (IPAQ) classification: 8 km/h for inactive or insufficiently active individuals, and 10 km/h for active and highly active individuals. Additionally, HR recovery (HRR) was measured during a 10-minute seated recovery period.
Results
Significant correlations were observed between resting HR (r = -0.78, p = 0.001), average RR interval (iRR) (r = 0.73, p = 0.004), percentage of iRR differences exceeding 50 ms (pNN50) (r = 0.64, p = 0.001), HR (r = -0.60, p = 0.003), and VO2peak. However, no significant correlation was found between rHRI and VO2peak (r = 0.38, p = 0.2).
Conclusion
Resting HR parameters, HR variability, and HRR showed significant associations with VO2peak. These HR variables can be useful alternatives for assessing and prescribing physical training when direct measurement of VO2peak is not feasible.
Risk Prediction of Pneumonia in Maintenance Hemodialysis Patients with COVID-19: A Single-center Retrospective Study in China
Authors: Jiang Zhenbin , Pan Yuejuan , Zhu Jinrong , Bi Shuhong , He Lian , Shi Junbao , Tian Xinkui , Xie Qian , Yang Wenling , Su Chunyan , Wang Song , Wang Yue 
Published: June 2023

Abstract
Objective:
The infection rate and mortality of COVID-19 in hemodialysis patients are extremely high. In this study, we analyzed the risk prediction of pneumonia in Chinese’s hemodialysis patients with COVID-19.
Method:
We conducted a retrospective analysis of maintenance hemodialysis patients with COVID-19 admitted to Peking University Third Hospital from December 1, 2022 to January 31, 2023. We collected demographic data, underlying diseases, dialysis treatment data, and laboratory test results of these patients; logistic regression and ROC curve were used to analyze the risk factors of pneumonia.
Results:
A total of 209 hemodialysis patients with COVID-19 were enrolled in this study, of whom 80(38.3%) had pneumonia and 129(61.7%) normal. Multivariate logistic analysis revealed that older age (OR=1.030, 95%CI 1.002~1.059, p=0.036), lower hemoglobin (OR=0.968, 95%CI 0.942~0.995, p=0.019), lower albumin (OR=0.834, 95%CI 0.738-0.943, p=0.004) were risk factors for pneumonia. Patients with age >65 years old, hemoglobin <115g/L or albumin <36.8g/L had a higher risk of pneumonia, and we combined the three indexes to predict the risk of pneumonia (P= elogit(P) / 1+ elogit(P) , logit(P)=9.593 +0.031×Age (years old) -0.038×Hemoglobin (g/L) -0.210×Albumin (g/L)), drew the ROC curve with the risk P value (AUC=0.756, 95% CI 0.687~0.825, p<0.001), when P=0.575 was selected as the cut-off value, the sensitivity and specificity of the three indexes combined to predict pneumonia were 44.0% and 94.5% respectively.
Conclusion:
Older age, anemia and hypoalbuminemia were risk factors for pneumonia in MHD patients. We could reduce the incidence of pneumonia and improve the prognosis of MHD patients by correcting anemia and hypoalbuminemia.
Five-axis toolpath re-scheduling for facilitating assisted supporting thin-walled blade machining by decreasing axis sensitivity
Authors: Li Pei-Yao , Song De-Ning , Li Jing-Hua , Zhong Yu-Guang , Ma Jian-Wei , Ma Xin 
Published: June 2023

Abstract
Existing works in the optimization of five-axis machining mainly focus on the efficiency, precision, or dynamic performance of the machine tools, while the performance of other equipment which assists the machining process has not been considered. This paper takes the robot assisted supporting machining of thin-walled blades as the research objective, and proposes an axial-sensitivity-reduction based five-axis toolpath re-scheduling method for facilitating the collaborative support machining with a machine-tool and a robot. The input of the method is lenient, merely including the original toolpath and the workpiece point-cloud model. The output of the method is the re-scheduled toolpath which requires lower motion ability of the assisted support equipment. This is realized by the following approaches. First, an axial sensitivity concept is defined, which quantificationally reflects the influence extent of the machine-tool axis motion on the assisted supporter motion, thus the most sensitive axis which affects the assisted process maximally can be identified. Then, an optimal-partition dual-domain-assisted-fitting method is provided to reconstruct the parameterized geometrical model of blades according to the point-cloud model, thus the multi-value property of the blades which troubles the surface construction is solved. After that, a sensitive-axis-calming bidirectional-tangent-bug-searching method is proposed to re-schedule the toolpath of five-axis machining, thus reducing the sensitivity of the most-sensitive axis. The whole method is universal, because a general cutter model is used and the cutter-contact point keeps invariant after re-scheduling. The correctness and effectiveness of the proposed method are verified by illustration machining tests of a steam turbine blade using an AB-type five-axis machine tool.
A Cloud Edge based Intelligent System for Detection of Grape Diseases
Authors: Khan Murtaza Ali , Zakarya Muhammad , Haleem Muhammad 
Published: June 2023

Abstract
SegmentatFinding plant diseases early on is essential for reducing damage while improving the quality of the yield. This paper describes an intelligent cloud-edge-based system for identifying and categorizing diseases in grape plants. The system is based on the support vector machine (SVM), a supervised machine learning technique to classify data. The traits of healthy and diseased plants are identified using digital photographs of grape plants. To determine color and texture information, we retrieved global features from the grape images. Patterns or structures (such as corners or edges) are discovered using the speeded-up robust features (SURF) method. The K-means clustering approach is used to quantify feature space, which lowers the number of feature descriptors. The training set for the SVM classifier is made up of feature descriptors. The system classified the unlabeled grape images using the trained SVM classifier during testing. 1600 RGB pictures from four classes: Black-rot, Black-measles, Leaf-blight, and Healthy-leaf make up the original data set. To evaluate the system and provide accuracy and confusion matrices, simulations are run in four different color spaces (grayscale, RGB, YCbCr, and L*a*b*). In the L*a*b* color space, the system attained a maximum average accuracy of up to 90.63% at a ratio of 70:30 training to testing data.
Fluvoxamine restores TFEB-mediated autophagy through Sigma-1R-controlled POM121 expression
Authors: Lin Chun-Yu , Wu Hsiang-En , Weng Eddie Feng-Ju , Su Tsung-Ping , Wang Shao-Ming 
Published: June 2023

Abstract
Expansion of the GGGGCC-RNA repeat is a known cause of amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD), two conditions that currently have no cure. Recent studies have shown that activation of the Sigma-1 receptor plays an important role in providing neuroprotection, particularly in ALS and Alzheimer’s disease. However, the mechanisms underlying Sigma-1R activation and its effect on (G4C2)n-RNA-induced cell death remain unclear. In this study, we demonstrated that fluvoxamine is a Sigma-1R agonist that can increase chaperone activity and rescue Pom121 intensity in (G4C2)31-RNA-expressing NSC34 cells, thereby stabilizing the protein expression of Pom121. Interestingly, fluvoxamine treatment increased Pom121 protein expression without affecting transcription. In C9orf72-ALS, the TFEB autophagy factor translocation decreased owing to nucleocytoplasmic transport defects. Our results showed that pretreatment of NSC34 cells with fluvoxamine promoted TFEB translocation into the nucleus and increased LC3-II expression compared to the overexpression of (G4C2)31-RNA alone. Collectively, fluvoxamine may act as a promising repurposed medicine for patients with C9orf72-ALS, as it stabilizes the nucleoporin Pom121 and promotes the translocation of TFEB in (G4C2)31-RNA-expressing NSC34 cells.
KLF4 as a Novel Prognostic Biomarker and Its Correlation with Immune Infiltrates in Lung Adenocarcinoma
Authors: Shen Jia-Xin , Wen Xiaofen , Zhu Jian-Ling , Chen Min-Na , Lin Dan-Xia , Xue Wen-Wu 
Published: June 2023

Abstract
Background: Improving the treatment of lung adenocarcinoma (LUAD) requires urgent exploration of molecular mechanisms underlying its progression and new prognostic predictors due to low long-term survival rates. Carcinogenesis studies have highlighted the clinical implications of KLF4, a member of the Krüppel-like transcription factor family. In this study, we aimed to investigate the expression of KLF4 in LUAD, assess its correlation with clinicopathological characteristics, prognosis and tumor immune cell infiltration, and analyze its potential advantages as a prognostic marker for LUAD patients.
Methods: This study examined KLF4 expression and clinical characteristics in normal and LUAD patients using TCGA and GEO datasets. 25 pairs of LUAD and normal tissues were then validated by IHC. Then we utilized analyses including PPI, GO, KEGG, GSEA and assessment of methylation status, to explore the potential mechanism of KLF4 in LUAD. Differences in the abundance of immune cell infiltration with high and low KLF4 expression were assessed by ssGSEA. Finally, univariate and multivariate Cox regression analysis were performed to construct a nomogram for evaluating the correlation between KLF4 expression and clinical prognosis.
Results: KLF4 was downregulated among tumors compared with normal tissue in multiple LUAD cohorts (P < 0.001). KLF4-related genes were then identified through differential analysis, and functional annotation revealed its significant enrichment in the processes of cornification and peptidase activity. GSEA indicated a potential involvement of KLF4 in tumor immunoregulation. Further analysis revealed a significant correlation between the expression of KLF4 and the infiltration levels of neutrophils, mast cells, eosinophils, and NK CD56bright cells (P < 0.001). Additionally, KLF4 in LUAD was found associated with a wide range of immune markers. Notably, higher level of KLF4 significantly reduced prognostic factors including OS, DSS and PFI. Finally, a nomogram incorporating KLF4, pathologic T and N stage, and residual tumor was built and exhibited promising predictive power for assessing the survival outcomes of LUAD patients.
Conclusion: High expression of KLF4 is an independent adverse prognostic factor in LUAD, and is strongly associated with aggressive clinical features and unfavorable immune inﬁltration. Therefore, KLF4 can be used as a novel prognostic biomarker for predicting patient outcomes.
Teaching in Evidence-Based Practice among undergraduate healthcare students. Which interventions, methods and outcome measures are being used in teaching EBP among undergraduate healthcare students: An overview of systematic reviews
Authors: Nielsen Lea D. , Løwe Mette M. , Castaño Francisco M. , Jørgensen Rene B. , Ramachandran Asviny , Noe Bodil B. , Egebaek Heidi K. 
Published: June 2023

Abstract
Background: To fully implement the internationally acknowledged requirements for teaching in evidence-based practice, and support the student’s development of core competencies in evidence-based practice, educators at professional bachelor degree programs in healthcare needs a systematic overview of evidence-based teaching and learning interventions. The purpose of this study was to summarize and synthesize the current evidence from systematic reviews on educational interventions being used by educators to teach evidence-based practice to professional bachelor-degree healthcare students and to identify the evidence-based practice-related learning outcomes used.Methods An overview of systematic reviews. Four databases (PubMed/Medline, CINAHL, ERIC and the Cochrane library) were searched from May 2013 to May 2022. Additional sources were checked for unpublished or ongoing systematic reviews. Eligibility criteria included systematic reviews of studies among undergraduate nursing, physiotherapist, occupational therapist, midwife, nutrition and health, and biomedical laboratory science students, evaluating educational interventions aimed at teaching evidence-based practice in classroom or clinical practice setting, or a combination. Two authors independently performed initial eligibility screening of title/abstracts. Four authors independently performed full-text screening and assessed the quality of selected systematic reviews using standardized instruments. Data was extracted and synthesized using a narrative approach.Results A total of 524 references were retrieved, and 6 systematic reviews (with a total of 39 primary studies) were included. Overlap between the systematic reviews was minimal. All the systematic reviews were of low methodological quality. Synthesis and analysis revealed a variety of teaching modalities and approaches. The outcomes were to some extend assessed in accordance with the Sicily group`s categories; “skills”, “attitude” and “knowledge”. Whereas “behaviors”, “reaction to educational experience”, “self-efficacy” and “benefits for the patient” were rarely used.Conclusions Teaching evidence-based practice is widely used in undergraduate healthcare students and a variety of interventions are used and recognized. Not all categories of outcomes suggested by the Sicily group are used to evaluate outcomes of evidence-based practice teaching. There is a need for studies measuring the effect on outcomes in all the Sicily group categories, to enhance sustainability and transition of evidence-based practice competencies to the context of healthcare practice.