Miracle-dz/UniFAR-FTUs huggingface dataset:
Biomolecules

: In this study, a previously little-studied group of viruses—virophages—was searched for and identiﬁed in the viromes of the ancient oligotrophic Lake Baikal. Virophages are small dsDNA viruses that parasitize giant viruses (e.g., Mimiviridae ), which in turn affect unicellular eukaryotes. We analyzed eight viromes obtained from the deep-water areas of three basins of Lake Baikal and the shallow-water strait Maloye More in different seasons. The sequences of virophages were revealed in all viromes and were dominant after bacteriophages and algal viruses. Sixteen putative complete genomes of virophages were assembled, all of which contained four conserved genes encoding major capsid protein (MCP), minor capsid protein (mCP), maturation cysteine protease (PRO), and FtsK-HerA family DNA-packaging ATPase (ATPase). The MCP-based cluster analysis showed a sequence separation according to seasons, and a dependence on the geographical localization was not detected.
S3-Net: A Self-Supervised Dual-Stream Network for Radiology Report Generation

Intelligent medicine is eager to automatically generate radiology reports to ease the tedious work of radiologists. Previous researches mainly focused on the text generation with encoder-decoder structure, while CNN networks for visual features ignored the long-range dependencies correlated with textual information. Besides, few studies exploit cross-modal mappings to promote radiology report generation. To alleviate the above problems, we propose a novel end-to-end radiology report generation model dubbed Self-Supervised dual-Stream Network (S3-Net). Specifically, a Dual-Stream Visual Feature Extractor (DSVFE) composed of ResNet and SwinTransformer is proposed to capture more abundant and effective visual features, where the former focuses on local response and the latter explores long-range dependencies. Then, we introduced the Fusion Alignment Module (FAM) to fuse the dual-stream visual features and facilitate alignment between visual features and text features. Furthermore, the Self-Supervised Learning with Mask(SSLM) is introduced to further enhance the visual feature representation ability. Experimental results on two mainstream radiology reporting datasets (IU X-ray and MIMIC-CXR) show that our proposed approach outperforms previous models in terms of language generation metrics.
MsSVT++: Mixed-scale Sparse Voxel Transformer with Center Voting for 3D Object Detection

Accurate 3D object detection in large-scale outdoor scenes, characterized by considerable variations in object scales, necessitates features rich in both long-range and fine-grained information. While recent detectors have utilized window-based transformers to model long-range dependencies, they tend to overlook fine-grained details. To bridge this gap, we propose MsSVT++, an innovative Mixed-scale Sparse Voxel Transformer that simultaneously captures both types of information through a divide-and-conquer approach. This approach involves explicitly dividing attention heads into multiple groups, each responsible for attending to information within a specific range. The outputs of these groups are subsequently merged to obtain final mixed-scale features. To mitigate the computational complexity associated with applying a window-based transformer in 3D voxel space, we introduce a novel Chessboard Sampling strategy and implement voxel sampling and gathering operations sparsely using a hash map. Moreover, an important challenge stems from the observation that non-empty voxels are primarily located on the surface of objects, which impedes the accurate estimation of bounding boxes. To overcome this challenge, we introduce a Center Voting module that integrates newly voted voxels enriched with mixed-scale contextual information towards the centers of the objects, thereby improving precise object localization. Extensive experiments demonstrate that our single-stage detector, built upon the foundation of MsSVT++, consistently delivers exceptional performance across diverse datasets.
Underestimated Cervical Cancer among Women over 65 Years Old: Is It Time to Revise the Screening Target Age Group?

Abstract Objective To compare cytological and histological results from women > 64 years old who followed the Brazilian national cervical cancer screening guidelines with those who did not. Methods The present observational retrospective study analyzed 207 abnormal cervical smear results from women > 64 years old in a mid-sized city in Brazil over 14 years. All results were reported according to the Bethesda System. The women were divided into those who followed the screening guidelines and those who did not. Results Atypical squamous cells of undetermined significance and low-grade squamous intraepithelial lesion cytology results were found in 128 (62.2%) cases. Of these, 112 (87.5%) had repeated cytology with positive results. The other 79 (38.1%) with abnormal results should have been referred to colposcopy and biopsy. Out of 41 (51.9%) biopsied women, 23 (29.1%) had a confirmed diagnosis of neoplasia or precursor lesion. In contrast, among the 78 (37.7%) biopsied patients, 40 (51.3%) followed the guideline recommendations, with 9 (22.5%) positive biopsies. Of the 38 (48.7%) women who did not follow the guidelines, there were 24 (63.1%) positive results. Women who did not follow the guidelines demonstrated higher chances of cancer and precursor lesions (odds ratio [OR]: 5.904; 95% confidence interval [CI]: 2.188–15.932; p = 0.0002). Conclusion Women > 64 years old who did not follow the national screening protocol showed significant differences in the frequency of abnormal results and severity of diagnosis compared with those who followed the protocol.
Contribution of automated feedback to the English writing competence of distance foreign language learners

In recent years, automated feedback (AF) has greatly increased its applicability as a mean to support development of English as a foreign language (EFL) learners’ writing competence in the context of face-to-face education. This study examines contribution of AF to the development of writing competence of EFL learners in open and distance learning context. Anadolu University, Open Education Faculty learners received regular feedback on their writing tasks by an AF tool, Write & Improve. The contribution of AF to academic writing success was observed by statistically comparing the grades of the learners for their writing tasks at the beginning and at the end of the process and also by correlation analysis between AF frequency and achievement scores. In the second stage of the study, detailed data about learners’ experiences were obtained by interviewing volunteer participants. The quantitative and qualitative findings of the study showed that AF significantly contributed to the development of writing competence of EFL learners at a distance. Interviews with learners revealed themes such as effective learning process, contribution to foreign language development, motivation increase, effective and fast feedback, user-friendly learning environment, support for individual learning, and contribution to distance learning as the strengths of AF. The interviews also included participants’ suggestions on how to use these tools more effectively. Based on the findings, pedagogical implications are discussed regarding the incorporation of AF in distance EFL learning.
Hierarchical Modularity in Human Brain Functional Networks

The idea that complex systems have a hierarchical modular organization originated in the early 1960s and has recently attracted fresh support from quantitative studies of large scale, real-life networks. Here we investigate the hierarchical modular (or “modules-within-modules”) decomposition of human brain functional networks, measured using functional magnetic resonance imaging in 18 healthy volunteers under no-task or resting conditions. We used a customized template to extract networks with more than 1800 regional nodes, and we applied a fast algorithm to identify nested modular structure at several hierarchical levels. We used mutual information, 0 < I < 1, to estimate the similarity of community structure of networks in different subjects, and to identify the individual network that is most representative of the group. Results show that human brain functional networks have a hierarchical modular organization with a fair degree of similarity between subjects, I = 0.63. The largest five modules at the highest level of the hierarchy were medial occipital, lateral occipital, central, parieto-frontal and fronto-temporal systems; occipital modules demonstrated less sub-modular organization than modules comprising regions of multimodal association cortex. Connector nodes and hubs, with a key role in inter-modular connectivity, were also concentrated in association cortical areas. We conclude that methods are available for hierarchical modular decomposition of large numbers of high resolution brain functional networks using computationally expedient algorithms. This could enable future investigations of Simon's original hypothesis that hierarchy or near-decomposability of physical symbol systems is a critical design feature for their fast adaptivity to changing environmental conditions.
Hierarchical modularity in human brain functional networks

systems and thereby to feedback on theoretical considerations (Strogatz, 2001; Amaral and Ottino, 2004). Many complex systems can be represented using tools drawn from graph theory as networks of nodes linked by edges. Such networks have been used to represent a broad variety of systems, ranging from genetic and protein networks to the World Wide Web. The huge size of some of these systems (∼10 billion nodes in the WWW) has driven the development of new statistical tools in order to characterize their topological properties (Newman, 2003). A quantity called modularity has been introduced in order to measure the decomposability of a network into modules (Guimerà et al., 2004; Newman and Girvan, 2004). Modularity can be used as a merit function to fi nd the optimal partition of a network. The resulting partition has been shown to reveal important network community structures in a variety of contexts, e.g. the global air transportation network (Guimerà et al., 2005) and gene expression interactomes (Oldham et al., 2008) are two diverse examples of complex systems with topological modularity. However, in systems having an intrinsic hierarchical structure, fi nding a single partition is not satisfactory. Several approaches have therefore been proposed in order to allow for more fl exibility and to uncover communities at different hierarchical levels. Among those multi-scale approaches, there are algorithms searching for local minima of INTRODUCTION Almost 50 years ago, Herbert Simon wrote an essay entitled “The architecture of complexity” (Simon, 1962). In this prescient analysis, he argued that most complex systems, such as social, biological and physical symbolic systems, are organized in a hierarchical manner. He introduced the notion of “nearly-decomposable systems”, i.e. systems where elements have most of their interactions (of any kind) with a subset of elements in some sense close to them, and much less interaction with elements outside this subset. In mainstream contemporary parlance, Simon’s near-decomposability is closely analogous to the concept of topological modularity: nodes in the same module have dense intra-modular connectivity with each other and sparse inter-modular connectivity with nodes in other modules (Newman, 2004, 2006). Simon argued that near-decomposability was a virtually universal property of complex systems because it conferred a very important evolutionary or adaptive advantage. Decomposability, or modularity, accelerates the emergence of complex systems from simple systems by providing stable intermediate forms (component modules) that allow the system to adapt one module at a time without risking loss of function in other, already-adapted modules. Our understanding of complexity has progressed considerably since that time, partly due to the availability of large data-sets that now allow us to explore empirically the architecture of complex Hierarchical modularity in human brain functional networks
Structural and depositional controls on Plio-Pleistocene submarine channel geometry ( Taranaki 1 Basin , New Zealand ) 2 3

Structural and depositional controls on Plio-Pleistocene submarine channel geometry (Taranaki 1 Basin, New Zealand) 2 3 Nathalia H. Mattos1*, Tiago M. Alves1 and Aisling Scully2 4 5 13D Seismic Lab, School of Earth and Ocean Sciences, Cardiff University, Main Building-Park Place, CF10 6 3AT Cardiff, United Kingdom (mattosnh@cardiff.ac.uk) 7 2School of Biological, Earth and Environmental Sciences, University College Cork, Cork, Ireland 8 9 Abstract 10
Prevalence of sexual dysfunction, factors, and psychological effects on adult males in the Buea Health District, Cameroon

The aim of this study was to determine the prevalence of sexual dysfunction, associated factors, quality of life and health seeking habit of males in the Buea Health District (BHD). A community-based cross-sectional study was carried out on sexually active males. Sexual dysfunction and quality of life were assessed using the International Index for Erectile Function (IIEF) and the Sexual Quality of Life Scale-Male (SQOL-M) questionnaire for psychological effect, respectively. The health seeking habits of participants was also assessed. The data were analysed using IBM SPSS Statistics (Version 26). The prevalence of sexual dysfunction was 18%. Erectile dysfunction (48%) was the most reported, whereas premature ejaculation (3.1%) was the least prevalent. Poor intercourse satisfaction and orgasmic dysfunction were linked to masturbation, whereas smoking, anxiety, and age above 50 were associated to poor erectile dysfunction. There was a significant association between all types of sexual dysfunction and the quality of life. For the health seeking habits, 45.7% of the participants sought traditional medication, 40% medical care, and 11.4% roadside medication. Male sexual dysfunction is prevalent in all its forms in the BHD, and it is affected by both life style and demographic factors. The perceived SQOL of affected males is poor and they exhibit different health seeking habits.
Understanding High-Speed Rail Passengers in China: A Segmentation Approach

China has established the world’s largest high-speed rail (HSR) system, which has fundamentally changed the way people travel in the domestic market. As China aims to double its HSR capacity in the next few years, the HSR population will continue to grow, which calls for an in-depth understanding of HSR passengers. While HSR has been of academic interest for many years, existing research has not provided meaningful demographic segmentation in the HSR context. This paper collected empirical data from HSR passengers in Beijing and Shanghai, the largest HSR markets in China, and performed a cluster analysis based collectively on three demographic variables—age, income, and education, which led to the formation of four segments—High-Ed Youths, Mature Travelers, New Starters, and Elite Travelers. Significant differences were found in terms of the passenger demographics and travel experiences across the four segments, to support the validity of the clustering solutions. The multivariate analysis of variance (MANOVA) test further revealed cross-segment differences in terms of passenger evaluation of five HSR variables—reasonableness of price, reliability, food choices, employee service, and likelihood for recommendation, suggesting the possibility of predicting passenger perceptions and behaviors based on their cluster membership. The findings demonstrate that passenger segmentation based on multiple demographic variables can provide deeper insights into the HSR population. For HSR providers in China, an understanding of the characteristics of the four passenger segments can assist them in developing service and communication strategies to cater to the different passenger needs.
Inspiratory flows through dry powder inhaler in chronic obstructive pulmonary disease: age and gender rather than severity matters

Background: Dry powder inhalers (DPIs) are inspiratory flow driven and hence flow dependent. Most patients with chronic obstructive pulmonary disease (COPD) are elderly and have poor lung function. The factors affecting their inspiratory flows through inhalers are unclear. Objective: To study peak inspiratory flows (PIFs) and their determinants through a DPI in COPD patients of varying age and severity. Methods: Flow-volume spirometry was performed in 93 COPD patients. Maximum PIF rates were recorded through an empty Easyhaler ® (PIF EH ; Orion Corporation, Espoo, Finland), a DPI that provides consistent dose delivery at inhalation rates through the inhaler of 28 L/min or higher. Results: The mean PIF EH was 54 L/min (range 26–95 L/min) with a coefficient of variation of 7%. All but two patients were able to generate a flow of $ 28 L/min. In a general linear model, the independent determinants for PIF EH were age ( P = 0.02) and gender ( P = 0.01), and forced expiratory volume in 1 s (FEV 1 ) expressed as percent predicted was not a significant factor. The regression model accounted only for 18% of the variation in PIF EH . Conclusion: In patients with COPD, age and gender are more important determinants of inspiratory flow through DPIs than the degree of expiratory airway obstruction. Most COPD patients with varying age and severity are able to generate inspiratory flows through the test inhaler that is sufficient for optimal drug delivery to the lower airways.
GPS PPP‐derived precipitable water vapor retrieval based on Tm/Ps from multiple sources of meteorological data sets in China

Weighted mean temperature (Tm) and pressure (Ps) are two parameters of great relevance to precipitable water vapor (PWV) retrieval from global positioning system (GPS) data. However, information about the Tm and Ps cannot be available for those GPS stations that are not colocated with meteorological sensors. To investigate the optimal GPS‐PWV retrieval method for China, two enhanced Tm models, GM‐Tm (temperature dependent) and GH‐Tm (temperature independent), are developed. Additionally, the potentials of the Ps data from the two reanalysis data sets, the National Centers for Environmental Prediction (NCEP)‐Department of Energy (DOE) Reanalysis II (NCEP II) and ERA‐Interim, and from the empirical model GPT2w for GPS‐PWV retrieval are investigated over China. To evaluate the performances of multisources Tm and Ps data for GPS‐PWV retrieval, GPS data (2011–2013) collected from 22 stations of the Crustal Movement Observation Network of China (CMONOC) were processed by using the precise point positioning (PPP) technique, estimating the zenith tropospheric delay (ZTD) so as to be subsequently converted to GPS‐PWV. The retrieved GPS‐PWVs are compared with their counterparts derived from NCEP II and radiosonde data over China. The results show that (1) the GM‐Tm model consistently shows the highest accuracy (with root mean square error of 2.3 K), and the GH‐Tm model should be selected when temperature observations are not available, and that (2) the performances of Ps from NCEP II and ERA‐Interim differ marginally for GPS‐PWV retrieval, and significant seasonal variations are found in the agreement between the GPS‐PWVs and the PWVs derived from NCEP II and radiosonde data over China.
DigitalCommons@UNO DigitalCommons@UNO

Stationary and moving surfaces impose different constraints on walking. In this study we investigated within-participants differences between walking on a ship before (at the dock) and during (at sea) a sea voyage. Four individuals participated in the study. While on the ship they wore a tri-axial accelerometer (ActiGraph GT3Xþ; ActiGraph LLC, Pensacola, FL) on their waists. Activity data were sampled at 30 Hz. Data were collected on the day before the voyage began and on several days at sea. The number of steps per day was greater at the dock than at sea. The net resultant force per day also was greater at the dock than at sea. However, resultant force per step was greater at sea (79.97 ^ 8.50 vector magnitude counts/ step) than on land (62.94 ^ 10.03 vector magnitude counts/step). In addition, we observed variations in resultant force per step across days at sea. Ship motion decreased overall activity but increased the force per step. Motion of a ship at sea mandates changes in the control of the body. In qualitative terms, the phenomena of living and working on ships at sea have been known for millennia. Perhaps surprisingly, there has been very little controlled scientific research in which quantitative data on human movement have been collected on ships at sea. Research has shown that standing body sway is powerfully affected by ship motion (e.g., Mayo, Wade, & Stoffregen, 2011) and that sway changes with continued exposure as people get their sea legs (Stoffregen, Chen, Varlet, Alcantara, & Bardy, 2013).
DIFFICULTIES MET BY UNICAMP STUDENTS IN THE TR ANSITION TO REMOTE LEARNING

This paper conducts an exploratory analysis of data collected from a survey with Brazilian students at Universidade Estadual de Campinas (Unicamp) about the difficulties observed in the transition from in-person to remote learning due to the Covid-19 pandemic. We conducted bibliographic research and applied exploratory factor analysis (EFA) to responses from 1,776 students. Our goal was to obtain a model that explains most of the variance in the data. Upon analyzing the EFA results, two groups of difficulties stood out: those related to course planning and those related to access and the provision of adequate infrastructure for remote learning. These findings can contribute to higher education institutions’ institutional plans. Importantly, we are not inferring a prevalence of difficulties, but rather emphasizing that university stakeholders should actively analyze and explore these challenges.
Advances in Rice Plant Disease Detection: A Survey of Machine Learning and Deep Learning Approaches

: Rice is the major crop worldwide, which provides sustenance for a substantial part of the world's population. But the rice plant is prone to different diseases that seriously damage the quality and crop yield. Accurate and earlier detection of this disease is crucial to implement effective and timely disease management strategy. Recently, the incorporation of machine learning (ML) and deep learning (DL) approaches has exposed great potential to automate and enhance the uncovering of crop diseases. This survey paper's aim is to provide a detailed study of the present research on crop disease recognition using ML and DL algorithms. We begin by deliberating the significance of earlier disease recognition in rice plants and the challenges related to the conventional technique. Consequently, we presented a review of the challenges and need to be associated with the crop disease recognition system. Moreover, we analyze and review the present study that utilizes ML and DL approaches for detecting rice plant diseases. Furthermore, we inspect the evaluation methodologies, performance metrics, and datasets applied in this case for evaluating the efficiency of the presented method. Additionally, we highlight the emerging trends and recent advancements in this field, such as the incorporation of image augmentation approaches, transfer learning, and ensemble algorithms.