bluuebunny/crossref_metadata_2025_split huggingface dataset:
Noninvasive and affordable type 2 diabetes screening by deep learning-based risk assessment and detection using ophthalmic images inspired by traditional Chinese medicine
Authors: Zhang Zhi , Wang Han , Chen Lei , Cao Chensi , Liu Tengwen , Ren Ruifang , Zhou Ruixing , Huang Rudan , Hu Dan , Zhu Chenxing , Lu Chong , Xu Yunsheng , Fang Zhaohui , Lu Fuer , Pan Huimin , Su Yanjin , Fu Nanlin , Zhan Huixia , Si Qin , Bai Chenze , Le Ge Ri , Cao Hongmei , Dong Wei , Yang Guohui , Wu Lan , Guo Jiao , Cheng Jing 
Published: July 2023

AbstractThe global prevalence of diabetes is steadily increasing, with a high percentage of patients unaware of their disease status. Screening for diabetes is of great significance in preventive medicine and may benefit from deep learning technology. In traditional Chinese medicine, specific features on the ocular surface have been explored as diagnostic indicators for systemic diseases. Here we explore the feasibility of using features from the entire ocular surface to construct deep learning models for risk assessment and detection of type 2 diabetes (T2DM). We performed an observational, multicenter study using ophthalmic images of the ocular surface to develop a deep convolutional network, OcularSurfaceNet. The deep learning system was trained and validated with a multicenter dataset of 416580 images from 67151 participants and tested independently using an additional 91422 images from 12544 participants, and can be used to identify individuals at high risk of T2DM with areas under the receiver operating characteristic curve (AUROC) of 0.89–0.92 and T2DM with AUROC of 0.70–0.82. Our study demonstrated a qualitative relationship between ocular surface images and T2DM risk level, which provided new insights for the potential utility of ocular surface images in T2DM screening. Overall, our findings suggest that the deep learning framework using ocular surface images can serve as an opportunistic screening toolkit for noninvasive and low-cost large-scale screening of the general population in risk assessment and early identification of T2DM patients.
The effectiveness of anti-stigma interventions for reducing mental health stigma in young people: A systematic review and meta-analysis
Authors: Song Ning , Hugh-Jones Siobhan , West Robert M. , Pickavance John , Mir Ghazala 
Published: July 2023

Abstract
Experiencing mental health stigma during adolescence can exacerbate mental health conditions, reduce quality of life and inhibit young people’s help-seeking for their mental health needs. For young people, education and contact have most often been viewed as suitable approaches for stigma reduction. However, evidence on the effectiveness of these anti-stigma interventions has not been consistent. This systematic review evaluated the effectiveness of interventions to reduce mental health stigma among youth aged 10–19 years. The review followed Cochrane and PRISMA guidelines. Eight databases were searched: PubMed, PsycINFO, MEDLINE, Web of Science, Scopus, EMBASE, British Education Index and CNKI. Hand searching from included studies was also conducted. Randomised controlled trials and experimental designs that included randomised allocation to interventions and control groups were included in the review. Narrative synthesis was employed to analyse the results. A meta-analysis was conducted to determine the effectiveness of included interventions. Twenty-two studies were included in the review. Eight studies reported positive effects, 11 studies found mixed effects and 3 studies reported no effect on indicators of mental health stigma among youth. Seven of the effective studies were education-based. Eleven studies were suitable for meta-analysis, and the multivariate meta-analytic model indicated a small, significant effect at post-intervention (d = .21, p < .001), but not at follow-up (d = .069, p = .347). Interventions to reduce stigma associated with mental health conditions showed small, short-term effects in young people. Education-based interventions showed relatively more significant effects than other types of interventions.
Epidemiology of patients harboring carbapenemase-producing bacteria and comparison with patients with detection of extended-spectrum beta-lactamase–producing Enterobacterales—A retrospective cohort study
Authors: Vock Isabelle , Aguilar-Bultet Lisandra , Goldenberger Daniel , Ragozzino Silvio , Kuster Sabine , Tschudin-Sutter Sarah 
Published: July 2023

AbstractObjective:We evaluated the epidemiology of carbapenemase-producing bacteria (CPB) in Switzerland by comparing risk factors between patients colonized with CPB and patients colonized with extended-spectrum β-lactamase–producing Enterobacterales (ESBL-PE).Methods:This retrospective cohort study was conducted at the University Hospital Basel in Switzerland. Hospitalized patients with CPB in any sample between January 2008 and July 2019 were included. The ESBL-PE group consisted of hospitalized patients with detection of ESBL-PE from any sample between January 2016 and December 2018. Comparisons of risk factors for acquisition of CPB and ESBL-PE were performed by logistic regression.Results:Inclusion criteria were met for 50 patients in the CPB group and 572 in the ESBL-PE group. In the CPB group, 62% had a travel history and 60% had been hospitalized abroad. When comparing the CPB group to the ESBL-PE group, hospitalization abroad (odds ratio [OR], 25.33; 95% confidence interval [CI], 11.07–57.98) and prior antibiotic therapy (OR, 4.76; 95% CI, 2.15–10.55) remained independently associated with CPB colonization. Hospitalization abroad (P < .001) and prior antibiotic therapy (P < .001) predicted CPB in the comparison of CPB with ESBL Escherichia coli, whereas hospitalization abroad was associated with CPB in comparison to ESBL Klebsiella pneumoniae.Conclusions:Although CPB still seem to be mainly imported from areas of higher endemicity, local acquisition of CPB is emerging, especially in patients with close and/or frequent contact with healthcare services. This trend resembles the epidemiology of ESBL K. pneumoniae, supporting mainly healthcare-associated transmission. Frequent evaluation of CPB epidemiology is required to improve detection of patients at risk of CPB carriage.
Hypolipidaemic effects of papaya (Carica papayaL.) juice on rats fed on a high fat and fructose diet
Authors: Matsuane Christinah , Kiage Beatrice N. , Karanja Josephine , Kavoo Agnes M. , Rimberia Fredah K. 
Published: July 2023

AbstractPapaya (Carica papayaL.) is a highly nutritious and less-caloric fruit, commonly consumed for its minerals and vitamins and hence may help in controlling obesity and abdominal discomforts. The present study investigated the hypolipidaemic effects of papaya juice extract on male Albino Wistar rats (7 weeks old; 185 ± 17 g) fed on a high fat and fructose diet (HFFD) for 6 weeks. The rats were divided into groups I–IV of five rats each and fed on either a HFFD (i.e. the Control), HFFD + 200 mg papaya, HFFD + 350 mg papaya or a HFFD + 500 mg papaya. On day 34, after an overnight fast, blood samples were obtained by cardiac puncture under 99⋅8 % Chloroform anaesthesia for the determination of serum triglyceride (TG), total cholesterol (TC), low-density lipoprotein cholesterol (LDL-c) and high-density cholesterol (HDL-c). The atherogenic (AI) and coronary risk (CRI) indices were also calculated. Statistical analysis was performed using ANOVA where means were separated using Tukey's HSD test. Resulted showed that all rats given papaya juice had an increasing, non-significant HDL-c and reduced LDL-c levels while rats fed on HFFD had the highest TC (53⋅2 mg/dl) and TG (37⋅6 mg/dl) levels. Papaya juice statistically reduced the AI and CRI of the rats. In conclusion, consumption of HFFD + 500 mg was the most effective in the reduction of rats’ blood lipids and fats, due to its anti-obesity and hypolipidaemic properties, thus can be used in the management of dyspilidaemic disorders.
Altered topology of individual brain structural covariance networks in major depressive disorder
Authors: Ping Liangliang , Sun Shan , Zhou Cong , Que Jianyu , You Zhiyi , Xu Xiufeng , Cheng Yuqi ,   the DIRECT Consortium
Published: July 2023

AbstractBackgroundThe neurobiological pathogenesis of major depression disorder (MDD) remains largely controversial. Previous literatures with limited sample size utilizing group-level structural covariance networks (SCN) commonly generated mixed findings regarding the topology of brain networks.MethodsWe analyzed T1 images from a high-powered multisite sample including 1173 patients with MDD and 1019 healthy controls (HCs). We used regional gray matter volume to construct individual SCN by utilizing a novel approach based on the interregional effect size difference. We further investigated MDD-related structural connectivity alterations using topological metrics.ResultsCompared to HCs, the MDD patients showed a shift toward randomization characterized by increased integration. Further subgroup analysis of patients in different stages revealed this randomization pattern was also observed in patients with recurrent MDD, while the first-episode drug naïve patients exhibited decreased segregation. Altered nodal properties in several brain regions which have a key role in both emotion regulation and executive control were also found in MDD patients compared with HCs. The abnormalities in inferior temporal gyrus were not influenced by any specific site. Moreover, antidepressants increased nodal efficiency in the anterior ventromedial prefrontal cortex.ConclusionsThe MDD patients at different stages exhibit distinct patterns of randomization in their brain networks, with increased integration during illness progression. These findings provide valuable insights into the disruption in structural brain networks that occurs in patients with MDD and might be useful to guide future therapeutic interventions.
Experimental studies of paranoid thinking in clinical and nonclinical populations: a systematic review and meta-analysis
Authors: Ellett Lyn , Varese Filippo , Owens Jane , Rafiq Sonya , Penn Georgia , Berry Katherine 
Published: July 2023

AbstractParanoia is common in clinical and nonclinical populations, consistent with continuum models of psychosis. A number of experimental studies have been conducted that attempt to induce, manipulate or measure paranoid thinking in both clinical and nonclinical populations, which is important to understand causal mechanisms and advance psychological interventions. Our aim was to conduct a systematic review and meta-analysis of experimental studies (non-sleep, non-drug paradigms) on psychometrically assessed paranoia in clinical and nonclinical populations. The review was conducted using PRISMA guidelines. Six databases (PsycINFO, PubMed, EMBASE, Web of Science, Medline and AMED) were searched for peer-reviewed experimental studies using within and between-subject designs to investigate paranoia in clinical and nonclinical populations. Effect sizes for each study were calculated using Hedge's g and were integrated using a random effect meta-analysis model. Thirty studies were included in the review (total n = 3898), which used 13 experimental paradigms to induce paranoia; 10 studies set out to explicitly induce paranoia, and 20 studies induced a range of other states. Effect sizes for individual studies ranged from 0.03 to 1.55. Meta-analysis found a significant summary effect of 0.51 [95% confidence interval 0.37–0.66, p < 0.001], indicating a medium effect of experimental paradigms on paranoia. Paranoia can be induced and investigated using a wide range of experimental paradigms, which can inform decision-making about which paradigms to use in future studies, and is consistent with cognitive, continuum and evolutionary models of paranoia.
Neural mechanisms of acceptance-commitment therapy for obsessive-compulsive disorder: a resting-state and task-based fMRI study
Authors: Lee Sang Won , Kim Seungho , Lee Sangyeol , Seo Ho Seok , Cha Hyunsil , Chang Yongmin , Lee Seung Jae 
Published: July 2023

AbstractBackgroundThere is growing evidence for the use of acceptance-commitment therapy (ACT) for the treatment of obsessive-compulsive disorder (OCD). However, few fully implemented ACT have been conducted on the neural mechanisms underlying its effect on OCD. Thus, this study aimed to elucidate the neural correlates of ACT in patients with OCD using task-based and resting-state functional magnetic resonance imaging (fMRI).MethodsPatients with OCD were randomly assigned to the ACT (n = 21) or the wait-list control group (n = 21). An 8-week group-format ACT program was provided to the ACT group. All participants underwent an fMRI scan and psychological measurements before and after 8 weeks.ResultsPatients with OCD showed significantly increased activation in the bilateral insula and superior temporal gyri (STG), induced by the thought-action fusion task after ACT intervention. Further psycho-physiological interaction analyses with these regions as seeds revealed that the left insular–left inferior frontal gyrus (IFG) connectivity was strengthened in the ACT group after treatment. Increased resting-state functional connectivity was also found in the posterior cingulate cortex (PCC), precuneus, and lingual gyrus after ACT intervention Most of these regions showed significant correlations with ACT process measures while only the right insula was correlated with the obsessive-compulsive symptom measure.ConclusionsThese findings suggest that the therapeutic effect of ACT on OCD may involve the salience and interoception processes (i.e. insula), multisensory integration (i.e. STG), language (i.e. IFG), and self-referential processes (i.e. PCC and precuneus). These areas or their interactions could be important for understanding how ACT works psychologically.
Bayesian optimization for assist-as-needed controller in robot-assisted upper limb training based on energy information
Authors: Zhang Jianxi , Zeng Hong , Li Xiao , Xu Guozheng , Li Yongqiang , Song Aiguo 
Published: July 2023

AbstractThe assist-as-needed (AAN) controller is effective in robot-assisted rehabilitation. However, variations of the engagement of subjects with fixed controller often lead to unsatisfying results. Therefore, adaptive AAN that adjusts control parameters based on individualized engagement is essential to enhance the training effect further. Nevertheless, current approaches mainly focus on the within-trial real-time engagement estimation, and the presence of measurement noise may cause improper evaluation of engagement. In addition, most studies on human-in-loop optimization strategies modulate the controller by greedy strategies, which are prone to fall into local optima. These shortcomings in previous studies could significantly limit the efficacy of AAN. This paper proposes an adaptive AAN to promote engagement by providing subjects with a subject-adaptive assistance level based on trial-wise engagement estimation and performance. Firstly, the engagement is estimated from energy information, which assesses the work done by the subject during a full trial to reduce the influence of measurement outliers. Secondly, the AAN controller is adapted by Bayesian optimization (BO) to maximize the subject’s performance according to historical trial-wise performance. The BO algorithm is good at dealing with noisy signals within limited steps. Experiments with ten healthy subjects resulted in a decrease of 34.59$\%$in their average trajectory error, accompanied by a reduction of 9.71$\%$in their energy consumption, thus verifying the superiority of the proposed method to prior work. These results suggest that the proposed method could potentially improve the effect of upper limb rehabilitation.
Navigation pattern extraction from AIS trajectory big data via topic model
Authors: Fujino Iwao , Claramunt Christophe 
Published: July 2023

AbstractThis paper introduces a novel approach for extracting vessel navigation patterns from very large automatic identification system (AIS) trajectory big data. AIS trajectory data records are first converted to a series of code documents using vector quantisation, such as k-means and PQk-means algorithms, whose performance is evaluated in terms of precision and computational time. Therefore, a topic model is applied to these code documents from which vessels’ navigation patterns are extracted and identified. The potential of the proposed approach is illustrated by several experiments conducted with a practical AIS dataset in a region of North West France. These experimental results show that the proposed approach is highly appropriate for mining AIS trajectory big data and outperforms common DBSCAN algorithms and Gaussian mixture models.
Psychological service provision in Australian Palliative Care Services
Authors: Brook Emma , Chur-Hansen Anna , Chan Lily , Crawford Gregory 
Published: July 2023

AbstractObjectivesPsychological distress is common among palliative care patients. Despite this, little is known about the availability of psychological services to support palliative care patients within Australia. This study aimed to determine the level of psychological support services available within Australian Palliative Care Services. The study was based on a similar study in Australia by Crawford in 1999, allowing differences over time to be examined.MethodsA 12-item online survey was distributed to adult Palliative Care Services throughout Australia from November 2021 to January 2022. Quantitative and qualitative analysis of responses was conducted, with comparisons made with the 1999 study using a 2-proportions z-test.ResultsSocial workers were the most available professionals delivering psychological care (prevalence of 94.1%), followed by spiritual care workers (62.5%), creative therapists (43.8%), counselors (36.4%), psychiatrists (31.3%), complementary therapists (28.1%), and psychologists (25.0%). Nearly 60% of services had no access to a psychiatrist or a psychologist. The proportion of Palliative Care Services that had access to a psychiatrist, psychologist, or counselor was significantly less in 2021/22 compared to 1999, with differences of 29.4% (p = 0.002), 23.4% (p = 0.015), and 26.1% (p = 0.006), respectively.Significance of resultsLack of access to psychiatrists, psychologists, and counselors in Australian Palliative Care Services remains a significant issue and has become more prevalent since 1999. Ongoing advocacy and increased government funding to enable psychological health professionals to be readily employed in Palliative Care Services is vital.
A NATURAL DEDUCTION SYSTEM FOR ORTHOMODULAR LOGIC
Authors: KORNELL ANDRE 
Published: July 2023

AbstractOrthomodular logic is a weakening of quantum logic in the sense of Birkhoff and von Neumann. Orthomodular logic is shown to be a nonlinear noncommutative logic. Sequents are given a physically motivated semantics that is consistent with exactly one semantics for propositional formulas that use negation, conjunction, and implication. In particular, implication must be interpreted as the Sasaki arrow, which satisfies the deduction theorem in this logic. As an application, this deductive system is extended to two systems of predicate logic: the first is sound for Takeuti’s quantum set theory, and the second is sound for a variant of Weaver’s quantum logic.
Diagnosing the Radiation Biases in Global Climate Models Using Radiative Kernels
Authors: Huang Han , Huang Yi 
Published: July 2023

AbstractRadiation energy balance at the top of the atmosphere (TOA) is a critical boundary condition for the Earth climate. It is essential to validate it in the global climate models (GCM) on both global and regional scales. However, the comparison of overall radiation field is known to conceal compensating errors. Here we use a new set of radiative kernels to diagnose the radiation biases caused by different geophysical variables in the GCMs of the Coupled Model Intercomparison Project. We find although clouds remain a primary cause of radiation biases, the radiation biases caused by non‐cloud variables are of comparable magnitudes. Many GCMs have a cold air temperature bias and a moist tropospheric humidity bias, which lead to considerable biases in TOA radiation budget but are compensated by cloud biases. These findings signify the importance of validating the GCM simulations in terms of both the overall and component radiation biases.
On the Relation Between Jupiter's Aurora and the Dawnside Current Sheet
Authors: Xu Y. , Yao Z. H. , Zhang B. , Delamere P. A. , Ray L. C. , Dunn W. R. , Badman S. V. , Feng E. H. , Zheng Z. Q. , Bolton S. J. , Grodent D. , Bonfond B. , Wei Y. 
Published: July 2023

AbstractJupiter's auroral emission is a spectacular phenomenon that provides insight into energy release processes related to the coupling of its magnetosphere and ionosphere. This energy release is influenced by solar wind conditions. Using joint observations from Juno and the Hubble Space Telescope (HST), we statistically investigate the relationship between auroral power and current sheet variations under different solar wind conditions. In this study, we reveal that during global main auroral brightening events that are closely connected to solar wind compressions, the dawn side current sheet is substantially thinner than during times when a quiet auroral morphology is present. Furthermore, the total current intensity in the current sheet is found to increase under solar wind compression conditions compared to the quiet period. These findings provide important observational evidence for how magnetospheric dynamics driven by solar wind behavior affect auroral activity, deepening our understanding of the coupling between Jupiter's magnetosphere and ionosphere.
Effects of Dead‐End Fractures on Non‐Fickian Transport in Three‐Dimensional Discrete Fracture Networks
Authors: Yoon Seonkyoo , Hyman Jeffrey D. , Han Weon Shik , Kang Peter K. 
Published: July 2023

AbstractUnderstanding mechanistic causes of non‐Fickian transport in fractured media is important for many hydrogeologic processes and subsurface applications. This study elucidates the effects of dead‐end fractures on non‐Fickian transport in three‐dimensional (3D) fracture networks. Although dead‐end fractures have been identified as low‐velocity regions that could delay solute transport, the direct relation between dead‐end fractures and non‐Fickian transport has been elusive. We systematically generate a large number of 3D discrete fracture networks with different fracture length distributions and fracture densities. We then identify dead‐end fractures using a novel graph‐based method. The effect of dead‐end fractures on solute residence time maximizes at the critical fracture density of the percolation threshold, leading to strong late‐time tailing. As fracture density increases beyond the percolation threshold, the network connectivity increases, and dead‐end fractures diminish. Consequently, the increase in network connectivity leads to a reduction in the degree of late‐time tailing. We also show that dead‐end fractures can inform about main transport paths, such as the mean tortuosity of particle trajectories. This study advances our mechanistic understanding of solute transport in 3D fracture networks.