Miracle-dz/UniFAR-FTUs huggingface dataset:
Applications of Self-Supervised Learning to Biomedical Signals: A Survey

Over the last decade, deep learning applications in biomedical research have exploded, demonstrating their ability to often outperform previous machine learning approaches in various tasks. However, training deep learning models for biomedical applications requires large amounts of data annotated by experts, whose collection is often time- and cost- prohibitive. Self-Supervised Learning (SSL) has emerged as a prominent solution for such problems, as it allows learning powerful representations from vast unlabeled data by producing supervisory signals directly from the data. The high number of recent works employing the self-supervised learning paradigm for the analysis of biomedical signals (biosignals) can make it difficult for researchers to have a complete picture of the current research state. Therefore, this paper aims at outlining and clarifying the state-of-the-art in the domain. The article: briefly summarizes the nature and acquisition modality of the main biosignals; introduces the self-supervised learning method, focusing on the different pretraining strategies; provides a concise overview of the works employing SSL for the analysis of different types of biosignals; provides an overall analysis of critical aspects to consider when employing SSL to biosignals, also highlighting current open challenges. The analysis of the scientific literature highlights the importance of SSL, confirming its potential to improve models’ performance and robustness, and to promote the integration of deep learning into clinical tasks.
12. PALYNOLOGICAL STRATIGRAPHY OF DEEP SEA DRILLING PROJECT SITE 416

Samples from DSDP Hole 416A, Cores 53 to 9, contain both spores and dinoflagellate cysts. The palynomorphs indicate the presence of Kimmeridgian-Portlandian sediments (Cores 52 and 51) which are overlain by Berriasian (Cores 50 to 32), Valanginian (Cores 32 to 10), and Hauterivian (Core 9) strata.
Uncertainty in Pacific decadal oscillation indices does not contribute to teleconnection instability

The Pacific decadal oscillation (PDO) has been used extensively to understand low‐frequency climate variability and its impact on ecological and hydrological systems. In the last few years, however, numerous studies have identified various weaknesses in the PDO paradigm. These range from disagreement between proxy‐based reconstructions to instability in teleconnections to discrepancy between the publicly available indices. The potential impact of differences between indices on teleconnection stability is evaluated using the 20th Century Reanalysis and Climatic Research Unit Time‐Series (CRU TS) 3.22 data. While disagreements between the indices may influence the interpretation of climate drivers in particular years, they do not appear to contribute to teleconnection instability in any significant way. Ruling out this simple explanation for teleconnection instability provides further evidence that the PDO is too simplistic a framework for understanding Pacific atmosphere–ocean impacts on remote climate and suggests that variable relationships between the PDO and climate are likely related to the complex origins of this variability.
Effect of foliar application of KNO3 on fruit yield and quality in litchi

The present investigation was carried out to study the „Effect of foliar application of KNO3 on fruit yield and quality in litchi‟ during the year 2015-16. Thirty five year‟s old uniformly grown „Dehradun‟ litchi plants established at MS Randhawa FRS, Gangian (Hoshiarpur) were sprayed with KNO3 @ 1.0, 1.5 and 2% at three different stages i.e. single spray 10 days after fruit set (DAFS) (S1); double spray after 10 and 20 DAFS (S2) and triple spray after 10, 20 and 30 DAFS (S3) and the control (water spray). The plants treated with K as foliar feeding significantly improved fruit pericarp colour, marketable fruit yield and quality attributes over the control. Increment in fruit yield to the tune of 13.9% was recorded in trees treated with double spray of KNO3 1% over the control. Fruit weight, size and fruit retention were also improved with different doses of K as foliar feeding. Fruit colour coordinates estimated in terms of ‘L‟ value was recorded the highest with one spray of KNO3 1.5% and ‘a’ value also increased from 17.9 to 18.6 with the increased in dose of KNO3 from 1 to 2 per cent as well as single spray of KNO3 1.5%, however; ‘b’ colour value was noted maximum with single spray of KNO31%. Fruit quality characteristics viz. soluble solids content (SSC), SSC/acid ratio and total sugars were also enhanced with single, double and triple sprays of different concentrations of K fertilizers over untreated trees. Leaf N and K contents were significantly higher with foliar feeding of KNO3, however, effect on leaf P content was statistically non significant. From the present studies, it is concluded that single foliar spray of KNO3 1% after 10 DAFS considerably improved fruit size, fruit weight, pericarp colour, pulp/stone ratio and fruit yield in litchi cv. Dehradun.
Peripheral Neuropathy in Virologically Suppressed People Living with HIV: Evidence from the PIVOT Trial

The aim of this study is to identify the factors associated with peripheral neuropathy and to explore neurofilament light chain (NfL) as a biomarker for peripheral neuropathy (PN) in effectively virologically suppressed adults living with HIV. All protease inhibitor monotherapy versus ongoing triple therapy in the long-term management of HIV infection (PIVOT) trial participants with data on PN at baseline were included in the study. NfL plasma levels (pNfL) were measured in a sub-set of participants. Multivariable logistic regression was used to examine the associations of PN with potential risk factors (including age, sex, nadir CD4 cell count, history of dideoxynucleoside (d-drugs) exposure, and blood glucose levels) and NfL levels. Of the 585 participants included, 131 (22.4%) reported PN during the study period (median of 44 months). The participants were predominantly male (76.6%), White (68.2%), and virologically suppressed for a median period of 37 months (range of 20–63) before recruitment. The age at baseline was 44.3 years (standard deviation (SD) of 9.2). PN was independently associated with age (adjusted odds ratio (aOR) = 1.35, 95% CI of 1.20–1.52; additional 5 years), history of d-drugs (aOR 1.88, 95% CI of 1.12–3.16), height (aOR 1.19, 95% CI of 1.05–1.35; additional 5 cm), nadir CD4 cell count (aOR 1.10 CI of 1.00–1.20; 50 cells fewer), and metabolic syndrome (aOR 2.31, 95% CI of 1.27 4.20), but not pNfL. The excess risk for PN associated with d-drug use remains after the exposure has stopped for years, suggesting non-reversible toxicity. In people with HIV, metabolic syndrome is independently associated with PN. There was no additional value for pNfL as a screening test for peripheral neuropathy in effectively virologically suppressed adults living with HIV.
The complete mitochondrial genome of Nycteribia formosana (Diptera, Nycteribiidae)

Abstract The family Hippoboscidae is an ectoparasite that primarily inhabits bats and relies on the host’s blood for sustenance. This research provides the first complete mitochondrial genome of Nycteribia formosana, which shares similar characteristics with other dipteran insects. The circularized mitochondrial genome, spanning 15,107 bp, encompasses 13 protein-coding genes (PCGs), 22 transfer RNA genes (tRNA), two ribosomal RNA genes, and a control region. The nucleotide composition of A, C, G, and T is 40.4%, 10.9%, 6.7%, and 42.0%, respectively. The findings from the phylogenetic analysis suggest that the species under investigation forms a cluster with other species belonging to the family Nycteribiidae. Consequently, this study provides valuable insights for the identification of N. formosana.
Multilevel Features Fused and Change Information Enhanced Neural Network for Hyperspectral Image Change Detection

Hyperspectral image change detection (HSI CD) refers to identifying and analyzing differences between two HSIs acquired in the same area but at different times. However, current deep learning (DL)-based methods have limitations in fully exploiting the change information between bitemporal images and utilizing multilevel features. To address these issues, we propose a novel Multi-level features Fused and Change information Enhanced neural Network (MFCEN) for HSI CD. The proposed MFCEN method leverages the hierarchical low- and high-level features of bitemporal images, allowing for the direct capture and enhancement of change features. First, a Siamese-based network is employed to extract multilevel features from the bitemporal images, including low-level spatial details and high-level semantic features. Within the temporal change information branch (TCIB) at each level, the change features are reinforced by the semantic features of each image, and the change features act as a guiding force to direct the feature extraction of each bitemporal image to focus more on the change region. Next, the enhanced change features of each level are fed into the multilevel features fusion module (MFFM) to aggregate the fine-grained details and high-level semantics. Finally, the fused features, enriched with multilevel change information, are utilized for CD. The experiments demonstrate that our proposed MFCEN outperforms existing methods on three public datasets. The code will be available at https://github.com/Ding201901/MFCEN.
Weight reduction and dietary improvements in a cluster-randomised controlled trial for adults with intellectual disabilities

Background People with intellectual disabilities (IDs) have an increased risk of obesity and health concerns related to their nutritional status and dietary intake. Objective To assess the effectiveness of a multi-component intervention on weight, waist circumference (WC), clinical health parameters and dietary habits in a group of overweight and obese adults with mild-to-moderate ID. Design A 7-month cluster-randomised trial and a 7-month follow-up of the intervention group after the end of intervention when the group received usual care. The intervention consisted of monthly dietary-group courses tailored to the participants’ cognitive abilities and practical skills, monthly nutritional courses for staff, use of behaviour change techniques and nudging. The control group received usual care during the intervention. Results There were 32 participants aged 22–61 years: 15 in the intervention group and 17 in the control group. After 7 months, a non-significant weight difference (median difference = −1.25 kg; 95% confidence interval [CI] = −2.00; 0.95 vs. +1.00 kg; CI = −1.15; 3.00, P = 0.08) and a significant WC difference were observed between the intervention and control groups (median difference = −3.75 cm; CI: −7.68; 0.11 vs. 0 cm; CI = −3.99; 1.00, P = 0.03), respectively. The median reduction in WC continued in the intervention group during the 7-month follow-up (median difference = −7.50 cm; CI: −13.57; −3.16, P = 0.002). A significant difference in frequency intake of fruit (P = 0.03) and berries (P = 0.004) was observed between the groups after 7 months, supported by a significant increase in measured serum-carotenoid levels in the intervention group after 7 months (median difference = 0.26 mmol/L; CI: −0.12; 0.52, P = 0.007). Conclusions A significant difference in WC was observed between the groups, accompanied by changes in blood parameters and dietary habits.
A Life-Long Learning XAI Metaheuristic-based Type-2 Fuzzy System for Solar Radiation Modelling

— Solar photovoltaic (PV) power generation is one of the most important sources for renewable energy. However, PV power generation is entirely dependent on the amount of downward solar radiation reaching the solar cells. This is determined by uncertain and uncontrollable meteorological factors such as temperature, humidity, wind speed, and direction, as well as other factors such as topographical characteristics. Good solar radiation prediction models can increase energy output while decreasing the operation costs of photovoltaic power generation. For example, in some provinces in China, PV stations are required to upload short-term online power forecast information to power dispatching agencies. Numerous AI, statistical, and numerical weather prediction models have been used in many real-world renewable energy applications, with a focus on modeling accuracy. However, there is a need for Explainable AI (XAI) models that could be easily understood, analyzed, and augmented by the stakeholders. In this paper, we present a compact, explainable, and lifelong learning metaheuristic-based Interval Type-2 Fuzzy Logic System (IT2FLS) for Solar Radiation Modeling. The generated model will be composed of a small number of short IF-Then rules that have been optimized via simulated annealing to produce models with high prediction accuracy. These models are updated through a life-long learning approach to maximize their accuracy and maintain interpretability. In the process of lifelong learning, the proposed method transferred the model's knowledge to new geographical locations with minimal forgetting. The proposed method achieved good prediction accuracy and outperformed on new geographical locations other transparent and black-box models by 13.2% as well as maintaining excellent generalization ability. The resulting models have been evaluated and accepted by experts, and thanks to the generated transparency, the experts were able to augment the models with their expertise, which increased the models' accuracy.
Cloud–Atmospheric Boundary Layer–Surface Interactions on the Greenland Ice Sheet during the July 2012 Extreme Melt Event

AbstractRegional model simulations of the 10–13 July 2012 extreme melt event over the Greenland Ice Sheet (GIS) are used to investigate how low-level liquid-bearing clouds impact surface energy fluxes, and therefore the energy available for melt. A sensitivity study in which the radiation code is modified so that cloud liquid and ice do not emit, absorb, or reflect radiation is used to identify cloud impacts beyond the cloud radiative effect. It is found that Arctic mixed-phase stratocumuli are not produced in the sensitivity experiment, highlighting that cloud radiative fluxes are required to maintain the clouds. A number of feedbacks are found that damp the warming effect of the clouds. Thin mixed-phase clouds increase the downward longwave fluxes by 100 W m−2, but upward daytime surface longwave fluxes increase by 20 W m−2 (60 W m−2 at night) and net shortwave fluxes decrease by 40 W m−2 (partially due to a 0.05 increase in surface albedo), leaving only 40 W m−2 available for melt. This 40 W m−2 is dis.
Non-surgical Rhinoplasty (NSR): A Systematic Review of Its Techniques, Outcomes, and Patient Satisfaction

Surgical rhinoplasty (SR), commonly known as nose job, is a widely practiced cosmetic surgery globally, aimed at addressing diverse aesthetic and functional concerns related to the nose. In recent years, non-surgical rhinoplasty (NSR) has gained popularity due to advanced techniques involving hyaluronic acid (HA) dermal fillers, offering advantages such as affordability, reduced side effects, and faster results. However, concerns persist about the suitability of dermal fillers for nasal anatomy and potential complications, prompting this comprehensive review. This study systematically evaluated the techniques, fillers, safety, and patient satisfaction associated with NSR, with the intent of providing valuable insights for clinicians and patients considering NSR or SR for improved aesthetic outcomes. The literature search, following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) criteria, yielded 16 relevant studies from an initial pool of 1002 articles. These studies covered various aspects of NSR, including techniques, complications, limitations, and positive results. In conclusion, NSR appears to be a quick and safe option for addressing minor nose shape issues, particularly through the use of HA fillers, but further discussion and standardization are necessary to address risks and limitations. A randomized controlled trial (RCT) using photographic evidence could significantly propel the progress of this evolving treatment. RCTs offer an optimal method to assess NSR's adverse effects and overall outcomes by allowing controlled comparisons between treatment and control groups. This approach minimizes biases and generates reliable statistical data, which is critical for evaluating safety, efficacy, and potential risks, thereby guiding informed clinical decisions.
Sonographic, Demographic, and Clinical Characteristics of Pre- and Postmenopausal Women with Endometrial Cancer; Results from a Post Hoc Analysis of the IETA4 (International Endometrial Tumor Analysis) Multicenter Cohort

In this study, we conducted a comparative analysis of demographic, histopathological, and sonographic characteristics between pre- and postmenopausal women diagnosed with endometrial cancer, while also examining sonographic and anthropometric features in ‘low’ and ‘intermediate/high-risk’ cases, stratified by menopausal status. Our analysis, based on data from the International Endometrial Tumor Analysis (IETA) 4 cohort comprising 1538 women (161 premenopausal, 1377 postmenopausal) with biopsy-confirmed endometrial cancer, revealed that premenopausal women, compared to their postmenopausal counterparts, exhibited lower parity (median 1, IQR 0–2 vs. 1, IQR 1–2, p = 0.001), a higher family history of colon cancer (16% vs. 7%, p = 0.001), and smaller waist circumferences (median 92 cm, IQR 82–108 cm vs. 98 cm, IQR 87–112 cm, p = 0.002). Premenopausal women more often had a regular endometrial–myometrial border (39% vs. 23%, p < 0.001), a visible endometrial midline (23% vs. 11%, p < 0.001), and undefined tumor (73% vs. 84%, p = 0.001). Notably, despite experiencing a longer duration of abnormal uterine bleeding (median 5 months, IQR 3–12 vs. 3 months, 2–6, p < 0.001), premenopausal women more often had ‘low’ risk disease (78% vs. 46%, p < 0.001). Among sonographic and anthropometric features, only an irregular endometrial–myometrial border was associated with ‘intermediate/high’ risk in premenopausal women. Conversely, in postmenopausal women, multiple features correlated with ‘intermediate/high’ risk disease. Our findings emphasize the importance of considering menopausal status when evaluating sonographic features in women with endometrial cancer.
Basin‐scale methods for predicting salmonid spawning habitat via grain size and riffle spacing, tested in a California coastal drainage

Basin‐scale predictive geomorphic models for river characteristics, particularly grain size, can aid in salmonid habitat identification. However, these basin‐scale methods are largely untested with actual habitat usage data. Here, we develop and test an approach for predicting grain size distributions from high resolution LiDAR (Light Detection and Ranging)‐derived topographic data for a 77 km2 watershed along the central California Coast. This approach improves on previous efforts in that it predicts the full grain size distribution and incorporates an empirically calibrated shear stress partitioning factor. The predicted grain size distributions are used to calculate the fraction of the bed area movable by spawning fish. We then compare the ‘movable fraction’ with 7 years of observed spawning data. We find that predicted movable fraction explains the paucity of spawning in the upper reaches of the study drainage, but does not explain variation along the mainstem. In search of another morphologic characteristic that may help explain the variation within the mainstem, we measure riffle density, a proxy for physical habitat complexity. We find that field surveys of riffle density explain 64% of the variation in spawning in these mainstem reaches, suggesting that within reaches of appropriate sized gravel, spawning density is related to riffle density. Because riffle density varies systematically with channel width, predicting riffle spacing is straightforward with LiDAR data. Taken together, these findings demonstrate the efficacy of basin‐scale spawning habitat predictions made using high‐resolution digital elevation models. Copyright © 2016 John Wiley & Sons, Ltd.
Problematic Use of Internet Among Indonesia University Students: Psychometric Evaluation of Bergen Social Media Addiction Scale and Internet Gaming Disorder Scale-Short Form

Objective Social media addiction and internet gaming disorder may cause mental health problems among a minority of university students. The Bergen Social Media Addiction Scale (BSMAS) and the 9-item Internet Gaming Disorder Scale-Short Form (IGDS9-SF) are commonly used worldwide. However, they have not been translated or validated into Indonesian. The present study aimed to translate and validate the BSMAS and IGDS9-SF in an Indonesian context among young adults. Methods A multi-center, web-based cross-sectional study was conducted among 458 university students (74% female; mean age 22.5 years) in Indonesia from June to December 2021. The BSMAS and IGDS9-SF were translated into Indonesian. Internal consistency (using Cronbach’s α and McDonald’s ω) and factor structure (using confirmatory factor analysis) of the two instruments were examined. Concurrent validity of BSMAS and IGDS9-SF was examined using their correlations with two external concepts: nomophobia and psychological distress. Results Internal consistency of the Indonesian BSMAS and IGDS9-SF were both acceptable (Cronbach’s α=0.80 and 0.90; McDonald’s ω=0.86 and 0.92). Both instruments were unidimensional with good factor loadings (0.54–0.78 for BSMAS; 0.63–0.79 for IGDS9-SF). Moreover, BSMAS and IGDS9-SF had stronger associations with nomophobia (r=0.58 and 0.12; p<0.001) than with psychological distress (r=0.43 and 0.15; p<0.001). Conclusion The Indonesian versions of the BSMAS and IGDS9-SF had good psychometric properties in terms of linguistic validity, unidimensionality, and reliability. The findings indicate the tools are appropriate for assessing the risk of social media addiction and internet gaming disorder among university students in Indonesia.
An Evaluation of Life History Parameters and Management of White-tailed Deer (Odocoileus virginianus) in the Red River Valley of Northeastern North Dakota

xv CHAPTER 1: GENERAL INTRODUCTION GENERAL INTRODUCTION . 1 LITERATURE CITED . 3 CHAPTER 2: MOVEMENT, HOME RANGES, AND DISTRIBUTION DURING FIREARMS SEASON OF WHITE-TAILED DEER IN NORTHEAST NORTH