Miracle-dz/UniFAR-FTUs huggingface dataset:
LCD Benchmark: Long Clinical Document Benchmark on Mortality Prediction

Natural Language Processing (NLP) is a study of automated processing of text data. Application of NLP in the clinical domain is important due to the rich unstructured information implanted in clinical documents, which often remains inaccessible in structured data. Empowered by the recent advance of language models (LMs), there is a growing interest in their application within the clinical domain. When applying NLP methods to a certain domain, the role of benchmark datasets are crucial as benchmark datasets not only guide the selection of best-performing models but also enable assessing of the reliability of the generated outputs. Despite the recent availability of LMs capable of longer context, benchmark datasets targeting long clinical document classification tasks are absent. To address this issue, we propose LCD benchmark, a benchmark for the task of predicting 30-day out-of-hospital mortality using discharge notes of MIMIC-IV and statewide death data. Our notes have a median word count of 1687 and an interquartile range of 1308 to 2169. We evaluated this benchmark dataset using baseline models, from bag-of-words and CNN to Hierarchical Transformer and an open-source instruction-tuned large language model. Additionally, we provide a comprehensive analysis of the model outputs, including manual review and visualization of model weights, to offer insights into their predictive capabilities and limitations. We expect LCD benchmarks to become a resource for the development of advanced supervised models, prompting methods, or the foundation models themselves, tailored for clinical text. The benchmark dataset is available at https://github.com/Machine-Learning-for-Medical-Language/long-clinical-doc
Differences in oscillatory whistles produced by spinner (Stenella longirostris) and pantropical spotted (Stenella attenuata) dolphins

Acoustic recordings of two closely related species, spinner dolphin (Stenella longirostris) and pantropical spotted dolphin (Stenella attenuata), were investigated from four different geographic locations: two in the Central Tropical Pacific, one in the Eastern Tropical Pacific and one in the Indian Ocean. The two delphinid species occur in tropical and warm temperate waters, with overlapping ranges. They produce very similar vocalizations, but at the same time their calls exhibit a certain degree of intraspecific variation among different geographic locations as has been observed in other delphinid species. Oscillatory whistles (whistles with at least two oscillations in their frequency contours) were identified and manually extracted from the recordings. Whistles with four or more maxima (oscillations) occurred only in spinner dolphins and they were present in all geographic regions investigated. In addition, the oscillatory whistles with two and three maxima were significantly more frequent in spinner than in spotted dolphins. The differences in oscillatory whistles for these two species seem to be consistent across study areas and therefore, could be used in addition to other whistle features to help distinguish between them
Using Transactional Memory to Synchronize an Adaptive Garbage Collector in Real-Time Java

Currently multicore systems start to be used even in low-end devices such as embedded systems controllers requiring real-time guarantees. Hardware Transactional Memory (TM) is a new synchronization paradigm for this architecture, allowing problems of lock-based methods and making easer programming. We propose to use TM to synchronize the concurrent/parallel execution of the Garbage Collector (GC) and Java applications, within the context of an embedded real-time environment. GC gives robust programming, and performance advantages. Since in the context of real-time systems, a collector must guarantee a worst-case in it performance, the worst-case time for a committed transaction must be limited; we achieve this by limiting its size (i.e., the size of the explored object).
Prognostic analysis of sex and age in hepatocellular carcinoma: a SEER study

Objectives This study aimed to explore the impact of sex on clinical features and survival among hepatocellular carcinoma (HCC) patients. Methods HCC case data from the Surveillance, Epidemiology, and End Results (SEER) database for the period 2010 to 2015 were selected for analysis. Kaplan–Meier curves displayed overall survival. Univariate cox regression examined the prognostic characteristics of individual features, and multivariate Cox regression assessed hazard ratios. Results This study comprised 3486 HCC patients, with 2682 males and 804 females. Across all age groups, there was a higher prevalence of males compared to females. Survival curves among female patients showed no significant differences across various age groups. However, among male patients, those under 60 demonstrated notably higher survival rates compared to those aged 60 and above. Regarding various ethnicities, TNM staging systems, tumor sizes, the presence of lung/bone/brain metastases, location in Purchased/Referred Care Delivery Areas, SEER historic stages, tumor grades, and individuals receiving chemotherapy, the proportion of male patients consistently exceeded that of female patients. Within the female patient group, individuals receiving chemotherapy exhibited significantly higher survival rates compared to those who did not. However, the administration of chemotherapy showed no significant impact on the survival rate of male patients. Multivariate Cox regression analysis revealed age, gender, and the administration of chemotherapy key factors influencing the overall survival prognosis. Conclusion Age, gender, and the administration of chemotherapy are influential factors in the prognosis of both male and female HCC patients.
Scaling and biomechanics of surface attachment in climbing animals

Attachment devices are essential adaptations for climbing animals and valuable models for synthetic adhesives. A major unresolved question for both natural and bioinspired attachment systems is how attachment performance depends on size. Here, we discuss how contact geometry and mode of detachment influence the scaling of attachment forces for claws and adhesive pads, and how allometric data on biological systems can yield insights into their mechanism of attachment. Larger animals are expected to attach less well to surfaces, due to their smaller surface-to-volume ratio, and because it becomes increasingly difficult to distribute load uniformly across large contact areas. In order to compensate for this decrease of weight-specific adhesion, large animals could evolve overproportionally large pads, or adaptations that increase attachment efficiency (adhesion or friction per unit contact area). Available data suggest that attachment pad area scales close to isometry within clades, but pad efficiency in some animals increases with size so that attachment performance is approximately size-independent. The mechanisms underlying this biologically important variation in pad efficiency are still unclear. We suggest that switching between stress concentration (easy detachment) and uniform load distribution (strong attachment) via shear forces is one of the key mechanisms enabling the dynamic control of adhesion during locomotion.
Suicidal ideation and attempts among Nigerian undergraduates: Exploring the relationships with depression, hopelessness, perceived burdensomeness, and thwarted belongingness

Objective: The study investigated the prevalence of suicidal behavior and its association with depression, hopelessness, perceived burdensomeness, and thwarted belongingness in a sample of undergraduates in Nigeria. Introduction: Suicide is a leading cause of death among young adults globally. However, information about suicidal ideation, attempts, and relationships with psychiatric factors, perceived burdensomeness, and thwarted belongingness is sparse in Nigeria. The study investigated the prevalence of suicidal ideation and attempts and associated factors in a sample of undergraduates in Nigeria. Methods: We collected data from a cross-sectional survey of 625 undergraduate students primarily recruited from universities, colleges of education, and polytechnics in Adamawa Central Senatorial Zone, Adamawa State, northeast Nigeria. The Suicidal Behaviors Questionnaire-Revised, the 15-item Interpersonal Needs Questionnaire, the 9-item Patient Health Questionnaire, and the 20-item version of the Beck Hopelessness Scale were administered to the participants. We used descriptive statistics and binary and multivariate logistic regressions for data analysis. Results: A total of 616 students with a mean age of 21.41 ± 4.72 years completed the study. The prevalence of suicidal behavior was 34.9% (215/616). Having a female sex (adjusted odds ratio [AOR] = 8.37, 95% confidence interval (CI): 2.06, 34.03), depression (AOR = 45.15, 95% CI: 0.95, 5.11), hopelessness (AOR = 20.10, 95% CI: 5.56, 72.41), and perceived burdensomeness (AOR = 89.15, 95% CI: 29.63, 268.30) were associated with suicidal ideation. In addition, being a female (AOR = 1.69, 95% CI = 1.02, 2.83), being a 200-level student (AOR = 3.41, 95% CI = 1.46, 7.96), and being a 300-level student (AOR = 0.28, 95% CI = 0.11, 0.74) were associated with suicidal attempt. Conclusion: The study’s findings show that suicidal behaviors (ideations and attempts) are prevalent among undergraduate students in northeast Nigeria. The findings underline the need for the development of mental health services and early identification and intervention for at-risk young people in Nigeria.
Detecting MRI-Invisible Prostate Cancers Using a Weakly Supervised Deep Learning Model

Background MRI is an important tool for accurate detection and targeted biopsy of prostate lesions. However, the imaging appearances of some prostate cancers are similar to those of the surrounding normal tissue on MRI, which are referred to as MRI-invisible prostate cancers (MIPCas). The detection of MIPCas remains challenging and requires extensive systematic biopsy for identification. In this study, we developed a weakly supervised UNet (WSUNet) to detect MIPCas. Methods The study included 777 patients (training set: 600; testing set: 177), all of them underwent comprehensive prostate biopsies using an MRI-ultrasound fusion system. MIPCas were identified in MRI based on the Gleason grade (≥7) from known systematic biopsy results. Results The WSUNet model underwent validation through systematic biopsy in the testing set with an AUC of 0.764 (95% CI: 0.728-0.798). Furthermore, WSUNet exhibited a statistically significant precision improvement of 91.3% (p < 0.01) over conventional systematic biopsy methods in the testing set. This improvement resulted in a substantial 47.6% (p < 0.01) decrease in unnecessary biopsy needles, while maintaining the same number of positively identified cores as in the original systematic biopsy. Conclusions In conclusion, the proposed WSUNet could effectively detect MIPCas, thereby reducing unnecessary biopsies.
Ambient and Unobtrusive Cardiorespiratory Monitoring Techniques

Monitoring vital signs through unobtrusive means is a goal which has attracted a lot of attention in the past decade. This review provides a systematic and comprehensive review over the current state of the field of ambient and unobtrusive cardiorespiratory monitoring. To this end, nine different sensing modalities which have been in the focus of current research activities are covered: capacitive electrocardiography, seismo- and ballistocardiography, reflective photoplethysmography (PPG) and PPG imaging, thermography, methods relying on laser or radar for distance-based measurements, video motion analysis, as well as methods using high-frequency electromagnetic fields. Current trends in these subfields are reviewed. Moreover, we systematically analyze similarities and differences between these methods with respect to the physiological and physical effects they sense as well as the resulting implications. Finally, future research trends for the field as a whole are identified.
How can macroscopic models reveal self-organization in traffic flow?

In this paper we propose a new modeling technique for vehicular traffic flow, designed for capturing at a macroscopic level some effects, due to the microscopic granularity of the flow of cars, which would be lost with a purely continuous approach. The starting point is a multiscale method for pedestrian modeling, recently introduced in [1], in which measure-theoretic tools are used to manage the microscopic and the macroscopic scales under a unique framework. In the resulting coupled model the two scales coexist and share information, in the sense that the same system is simultaneously described from both a discrete (microscopic) and a continuous (macroscopic) perspective. This way it is possible to perform numerical simulations in which the single trajectories and the average density of the moving agents affect each other. Such a method is here revisited in order to deal with multi-population traffic flow on networks. For illustrative purposes, we focus on the simple case of the intersection of two roads. By exploiting one of the main features of the multiscale method, namely its dimension-independence, we treat one-dimensional roads and two-dimensional junctions in a natural way, without referring to classical network theory. Furthermore, thanks to the coupling between the microscopic and the macroscopic scales, we model the continuous flow of cars without losing the right amount of granularity, which characterizes the real physical system and triggers self-organization effects, such as, for example, the oscillatory patterns visible at jammed uncontrolled crossroads.
Edinburgh Research Explorer A polygenic risk score analysis of psychosis endophenotypes across brain functional, structural, and cognitive domains

This large multi-center study investigates the relationships between genetic risk for schizophrenia and bipolar disorder, and multi-modal endophenotypes for psychosis. The sample included 4,242 individuals; 1,087 patients with psychosis, 822 unaffected first-degree relatives of patients, and 2,333 controls. Endophenotypes included the P300 event-related potential ( N = 515), lateral ventricular volume ( N =798), and the cognitive measures block design ( N = 3,089), digit span ( N = 1,437), and the Ray Auditory Verbal Learning Task ( N = 2,406). Data were collected across 11 sites in Europe and Australia; all genotypingandgeneticanalysesweredoneatthesamelaboratoryintheUnitedKingdom. Wecalculatedpolygenicriskscoresforschizophreniaandbipolardisorderseparately,and used linear regression to test whether polygenic scores influenced the endophenotypes. Results showed that higher polygenic scores for schizophrenia were associated with poorer performance on the block design task and explained 0.2% ( p = 0.009) of the variance.Associationsinthesamedirectionwerefoundforbipolardisorderscores,butthis wasnotstatisticallysignificantatthe1%level( p = 0.02). Theschizophreniascoreexplained 0.4%ofvarianceinlateralventricularvolumes,thelargestacrossallphenotypesexamined, although this was not significant ( p = 0.063). None of the remaining associations reached significance after correction for multiple testing (with alpha at 1%). These results indicate that common genetic variants associated with schizophrenia predict performance in spatial visualization, providing additional evidence that this measure is an endophenotype for the disorder withshared genetic riskvariants. The useofendophenotypessuch as this will help to characterize the effects of common genetic variation in psychosis.
What Sensing Tells Us: Towards a Formal Theory of Testing for Dynamical Systems

Just as actions can have indirect effects on the state of the world, so too can sensing actions have indirect effects on an agent’s state of knowledge. In this paper, we investigate “what sensing actions tell us”, i.e., what an agent comes to know indirectly from the outcome of a sensing action, given knowledge of its actions and state constraints that hold in the world. To this end, we propose a formalization of the notion of testing within a dialect of the situation calculus that includes knowledge and sensing actions. Realizing this formalization requires addressing the ramification problem for sensing actions. We formalize simple tests as sensing actions. Complex tests are expressed in the logic programming language Golog. We examine what it means to perform a test, and how the outcome of a test affects an agent’s state of knowledge. Finally, we propose automated reasoning techniques for test generation and complex-test verification, under certain restrictions. The work presented in this paper is relevant to a number of application domains including diagnostic problem solving, natural language understanding, plan recognition, and active vision.
The Women4Health cohort: a unique cohort to study women-specific mechanisms of cardio-metabolic regulation

Abstract Aims Epidemiological research has shown relevant differences between sexes in clinical manifestations, severity, and progression of cardiovascular and metabolic disorders. To date, the mechanisms underlying these differences remain unknown. Given the rising incidence of such diseases, gender-specific research on established and emerging risk factors, such as dysfunction of glycaemic and/or lipid metabolism, of sex hormones and of gut microbiome, is of paramount importance. The relationships between sex hormones, gut microbiome, and host glycaemic and/or lipid metabolism are largely unknown even in the homoeostasis status. Yet this knowledge gap would be pivotal to pinpoint to key mechanisms that are likely to be disrupted in disease context. Methods and results Here we present the Women4Health (W4H) cohort, a unique cohort comprising up to 300 healthy women followed up during a natural menstrual cycle, set up with the primary goal to investigate the combined role of sex hormones and gut microbiota variations in regulating host lipid and glucose metabolism during homoeostasis, using a multi-omics strategy. Additionally, the W4H cohort will take into consideration another ecosystem that is unique to women, the vaginal microbiome, investigating its interaction with gut microbiome and exploring—for the first time—its role in cardiometabolic disorders. Conclusion The W4H cohort study lays a foundation for improving current knowledge of women-specific mechanisms in cardiometabolic regulation. It aspires to transform insights on host–microbiota interactions into prevention and therapeutic approaches for personalized health care.
INNOVATION ECOSYSTEMS IN BRAZIL: RESEARCH UNIVERSITIES AS DRIVERS OF COMPETITIVENESS

Universities are increasingly perceived as agents involved with regional development. It is now recognized that academic contributions to the socioeconomic environment go well beyond scientific investigations and teaching activities, incorporating market-oriented initiatives to the academic mission. However, these effects are geographically bounded. Given these conditions, this article aims at addressing universities’ impacts upon output vectors of localized innovation ecosystems. Using data from cities and microregions in the State of São Paulo, Brazil, we address universities impacts on the local-level generation of patents and utility models, software production and emergence of knowledge-intensive entrepreneurship. Findings support the proposition that major academic institutions lie at the heart of innovation ecosystems, but impacts are mostly local, happening more strongly at the level of cities.
Structurally constrained and pathology-aware convolutional transformer generative adversarial network for virtual histology staining of human coronary optical coherence tomography images

Abstract. Significance There is a significant need for the generation of virtual histological information from coronary optical coherence tomography (OCT) images to better guide the treatment of coronary artery disease (CAD). However, existing methods either require a large pixel-wise paired training dataset or have limited capability to map pathological regions. Aim The aim of this work is to generate virtual histological information from coronary OCT images, without a pixel-wise paired training dataset while capable of providing pathological patterns. Approach We design a structurally constrained, pathology-aware, transformer generative adversarial network, namely structurally constrained pathology-aware convolutional transformer generative adversarial network (SCPAT-GAN), to generate virtual stained H&E histology from OCT images. We quantitatively evaluate the quality of virtual stained histology images by measuring the Fréchet inception distance (FID) and perceptual hash value (PHV). Moreover, we invite experienced pathologists to evaluate the virtual stained images. Furthermore, we visually inspect the virtual stained image generated by SCPAT-GAN. Also, we perform an ablation study to validate the design of the proposed SCPAT-GAN. Finally, we demonstrate 3D virtual stained histology images. Results Compared to previous research, the proposed SCPAT-GAN achieves better FID and PHV scores. The visual inspection suggests that the virtual histology images generated by SCPAT-GAN resemble both normal and pathological features without artifacts. As confirmed by the pathologists, the virtual stained images have good quality compared to real histology images. The ablation study confirms the effectiveness of the combination of proposed pathological awareness and structural constraining modules. Conclusions The proposed SCPAT-GAN is the first to demonstrate the feasibility of generating both normal and pathological patterns without pixel-wisely supervised training. We expect the SCPAT-GAN to assist in the clinical evaluation of treating the CAD by providing 2D and 3D histopathological visualizations.
Dual contrastive learning for synthesizing unpaired fundus fluorescein angiography from retinal fundus images

Background Fundus fluorescein angiography (FFA) is an imaging method used to assess retinal vascular structures by injecting exogenous dye. FFA images provide complementary information to that provided by the widely used color fundus (CF) images. However, the injected dye can cause some adverse side effects, and the method is not suitable for all patients. Methods To meet the demand for high-quality FFA images in the diagnosis of retinopathy without side effects to patients, this study proposed an unsupervised image synthesis framework based on dual contrastive learning that can synthesize FFA images from unpaired CF images by inferring the effective mappings and avoid the shortcoming of generating blurred pathological features caused by cycle-consistency in conventional approaches. By adding class activation mapping (CAM) to the adaptive layer-instance normalization (AdaLIN) function, the generated images are made more realistic. Additionally, the use of CAM improves the discriminative ability of the model. Further, the Coordinate Attention Block was used for better feature extraction, and it was compared with other attention mechanisms to demonstrate its effectiveness. The synthesized images were quantified by the Fréchet inception distance (FID), kernel inception distance (KID), and learned perceptual image patch similarity (LPIPS). Results The extensive experimental results showed the proposed approach achieved the best results with the lowest overall average FID of 50.490, the lowest overall average KID of 0.01529, and the lowest overall average LPIPS of 0.245 among all the approaches. Conclusions When compared with several popular image synthesis approaches, our approach not only produced higher-quality FFA images with clearer vascular structures and pathological features, but also achieved the best FID, KID, and LPIPS scores in the quantitative evaluation.