Miracle-dz/UniFAR-FTUs huggingface dataset:
Forcings and chaos in interannual to decadal climate change

We investigate the roles of climate forcings and chaos (unforced variability) in climate change via ensembles of climate simulations in which we add forcings one by one. The experiments suggest that most interannual climate variability in the period 1979–1996 at middle and high latitudes is chaotic. But observed SST anomalies, which themselves are partly forced and partly chaotic, account for much of the climate variability at low latitudes and a small portion of the variability at high latitudes. Both a natural radiative forcing (volcanic aerosols) and an anthropogenic forcing (ozone depletion) leave clear signatures in the simulated climate change that are identified in observations. Pinatubo aerosols warm the stratosphere and cool the surface globally, causing a tendency for regional surface cooling. Ozone depletion cools the lower stratosphere, troposphere and surface, steepening the temperature lapse rate in the troposphere. Solar irradiance effects are small, but our model is inadequate to fully explore this forcing. Well-mixed anthropogenic greenhouse gases cause a large surface wanning that, over the 17 years, approximately offsets cooling by the other three mechanisms. Thus the net calculated effect of all measured radiative forcings is approximately zero surface temperature trend and zero heat storage in the ocean for the period 1979–1996. Finally, in addition to the four measured radiative forcings, we add an initial (1979) disequilibrium forcing of +0.65 W/m2. This forcing yields a global surface warming of about 0.2°C over 1979–1996, close to observations, and measurable heat storage in the ocean. We argue that the results represent evidence of a planetary radiative imbalance of at least 0.5° W/m2; this disequilibrium presumably represents unrealized wanning due to changes of atmospheric composition prior to 1979. One implication of the disequilibrium forcing is an expectation of new record global temperatures in the next few years. The best opportunity for observational confirmation of the disequilibrium is measurement of ocean temperatures adequate to define heat storage.
Microevolutionary patterns in ecotypes of the symbiotic cyanobacterium UCYN-A revealed from a Northwest Atlantic coastal time series

UCYN-A is a globally important nitrogen-fixing symbiotic microbe often found in colder regions and coastal areas where nitrogen fixation has been overlooked. We present a 3-year coastal Northwest Atlantic time series of UCYN-A by integrating oceanographic data with weekly nifH and16S rRNA gene sequencing and quantitative PCR assays for UCYN-A ecotypes. High UCYN-A relative abundances dominated by A1 to A4 ecotypes reoccurred annually in the coastal Northwest Atlantic. Although UCYN-A was detected every summer/fall, the ability to observe separate ecotypes may be highly dependent on sampling time given intense interannual and weekly variability of ecotype-specific occurrences. Additionally, much of UCYN-A’s rarer diversity was populated by short-lived neutral mutational variants, therefore providing insight into UCYN-A’s microevolutionary patterns. For instance, rare ASVs exhibited community composition restructuring annually, while also sharing a common connection to a dominant ASV within each ecotype. Our study provides additional perspectives for interpreting UCYN-A intraspecific diversity and underscores the need for high-resolution datasets when deciphering spatiotemporal ecologies within UCYN-A.
Comparing emergency department visits 10-year apart at a tertiary care center in Lebanon

Presentations to the emergency department (ED) are growing worldwide. With the increasing risk factors of non-communicable disease (NCD) and communicable diseases (CD) in low- and middle-income countries, it is crucial to understand how ED presentations are changing with time to meet patients’ needs and allocate acute care resources. The aim of this study is to compare the changes in patient and diseases characteristics over 2 time periods 10 year apart at the largest tertiary care center in Lebanon. This was a retrospective descriptive study of patients presenting to the ED at a large tertiary care center in 2009/2010 and 2018/2019. The discharge diagnoses were coded into Clinical Classification Software codes. We used descriptive statistics, odds ratios (OR), and non-parametric test to compare the different diagnoses. The total number of ED visits increased by 33% from 2009/2010 to 2018/2019. The highest increase rate was among patients older than 65 years (2.6%), whereas the percentage of pediatric patients decreased from 30.8% to 25.3%. ED presentations shifted from NCD to CD. A shift in the discharge diagnoses was also noted within age groups, specifically a shift in cardiovascular diseases to a younger age. Our study suggests that the role of the ED is changing and moving towards treating the aging population and CD. There is a need to invest and mitigate CD, better allocate resources to accommodate the aging population, focus on awareness campaigns targeting early detection of cardiovascular diseases and modifying its risk factors.
Short text classification with machine learning in the social sciences: The case of climate change on Twitter

To analyse large numbers of texts, social science researchers are increasingly confronting the challenge of text classification. When manual labeling is not possible and researchers have to find automatized ways to classify texts, computer science provides a useful toolbox of machine-learning methods whose performance remains understudied in the social sciences. In this article, we compare the performance of the most widely used text classifiers by applying them to a typical research scenario in social science research: a relatively small labeled dataset with infrequent occurrence of categories of interest, which is a part of a large unlabeled dataset. As an example case, we look at Twitter communication regarding climate change, a topic of increasing scholarly interest in interdisciplinary social science research. Using a novel dataset including 5,750 tweets from various international organizations regarding the highly ambiguous concept of climate change, we evaluate the performance of methods in automatically classifying tweets based on whether they are about climate change or not. In this context, we highlight two main findings. First, supervised machine-learning methods perform better than state-of-the-art lexicons, in particular as class balance increases. Second, traditional machine-learning methods, such as logistic regression and random forest, perform similarly to sophisticated deep-learning methods, whilst requiring much less training time and computational resources. The results have important implications for the analysis of short texts in social science research.
Biological functions and molecular subtypes regulated by miR-142-3p in colon cancer

MicroRNA-142-3p (miR-142-3p) has been reported to be implicated in colon cancer; however, the possible regulatory mechanisms and molecular subtypes regulated by miR-142-3p have not been fully elucidated. This study aimed to investigate the biological functions and regulatory mechanism of miR-142-3p in colon cancer. The expression level of miR-142-3p in colon cancer was analyzed based on the mRNA and miRNA expression datasets of colon cancer retrieved from The Cancer Genome Atlas. Target genes of miR-142-3p were also predicted. Based on these target genes, the functions and subtypes of miR-142-3p were investigated. The metabolic and tumor-related pathways, immune microenvironment, and target gene expression between the 2 subtypes were analyzed. MiR-142-3p was upregulated in tumor tissues, and its high expression indicated a poor prognosis. A total of 39 target genes were predicted, which were significantly involved in autophagy- and metabolism-related functions and pathways. Based on these target genes, the colon cancer samples were clustered into 2 subtypes. There were 35 metabolism-related pathways that were significantly different between the 2 clusters. The immune and stromal scores in cluster 2 were higher than those in cluster 1, whereas the tumor purity of cluster 2 was significantly lower than that of cluster 1. TP53INP2 expression in cluster 2 was higher than that in cluster 1. MiR-142-3p may promote colon cancer progression via autophagy- and metabolism-related pathways. MiR-142-3p may be served as a candidate target for the treatment of colon cancer.
Prioritising cardiovascular disease risk assessment to high risk individuals based on primary care records

Objective To provide quantitative evidence for systematically prioritising individuals for full formal cardiovascular disease (CVD) risk assessment using primary care records with a novel tool (eHEART) with age- and sex- specific risk thresholds. Methods and analysis eHEART was derived using landmark Cox models for incident CVD with repeated measures of conventional CVD risk predictors in 1,642,498 individuals from the Clinical Practice Research Datalink. Using 119,137 individuals from UK Biobank, we modelled the implications of initiating guideline-recommended statin therapy using eHEART with age- and sex-specific prioritisation thresholds corresponding to 5% false negative rates to prioritise adults aged 40–69 years in a population in England for invitation to a formal CVD risk assessment. Results Formal CVD risk assessment on all adults would identify 76% and 49% of future CVD events amongst men and women respectively, and 93 (95% CI: 90, 95) men and 279 (95% CI: 259, 297) women would need to be screened (NNS) to prevent one CVD event. In contrast, if eHEART was first used to prioritise individuals for formal CVD risk assessment, we would identify 73% and 47% of future events amongst men and women respectively, and a NNS of 75 (95% CI: 72, 77) men and 162 (95% CI: 150, 172) women. Replacing the age- and sex-specific prioritisation thresholds with a 10% threshold identify around 10% less events. Conclusions The use of prioritisation tools with age- and sex-specific thresholds could lead to more efficient CVD assessment programmes with only small reductions in effectiveness at preventing new CVD events.
Comparison of Knee Pain and Difficulty With Kneeling Between Patellar Tendon and Hamstring Tendon Autografts After Anterior Cruciate Ligament Reconstruction: A Study From the New Zealand ACL Registry

Background: The bone–patellar tendon–bone (BTB) autograft is associated with difficulty with kneeling after anterior cruciate ligament (ACL) reconstruction; however, it is unclear whether it results in a more painful or symptomatic knee compared with the hamstring tendon autograft. Purpose: To identify the rate and risk factors for knee pain and difficulty with kneeling after ACL reconstruction. Study Design: Cohort study; Level of evidence, 3. Methods: Primary ACL reconstruction procedures prospectively recorded in the New Zealand ACL Registry from April 2014 to May 2021 were analyzed. The Knee injury and Osteoarthritis Outcome Score (KOOS) was used to identify patients reporting consequential knee pain (CKP), defined as a KOOS Pain subscore of ≤72 points, and severe kneeling difficulty (SKD), defined as a self-report of “severe” or “extreme” difficulty with kneeling. Absolute values of the KOOS Pain and Symptoms subscales were also compared. Results: A total of 10,999 patients were analyzed. At 2-year follow-up, 9.3% (420/4492) reported CKP, and 12.0% (537/4471) reported SKD. The most important predictor of CKP at 2-year follow-up was having significant pain before surgery (adjusted odds ratio, 4.10; P < .001). The most important predictor of SKD at 2-year follow-up was the use of a BTB autograft rather than a hamstring tendon autograft (21.3% vs 9.4%, respectively; adjusted odds ratio, 3.12; P < .001). There was no difference between the BTB and hamstring tendon grafts in terms of CKP (9.9% vs 9.2%, respectively; P = .494) or in absolute values of the KOOS Pain (mean, 88.7 vs 89.0, respectively; P = .37) and KOOS Symptoms (mean, 82.5 vs 82.1, respectively; P = .49) subscales. Conclusion: At 2-year follow-up after primary ACL reconstruction, 9.3% of patients reported CKP, and 12.0% reported SKD. The BTB autograft was associated with difficulty with kneeling, but it did not result in a more painful or symptomatic knee compared with the hamstring tendon autograft.
Adenylosuccinate lyase deficiency affects neurobehavior via perturbations to tyramine signaling in Caenorhabditis elegans

Adenylosuccinate lyase deficiency is an ultrarare congenital metabolic disorder associated with muscle weakness and neurobehavioral dysfunction. Adenylosuccinate lyase is required for de novo purine biosynthesis, acting twice in the pathway at non-sequential steps. Genetic models can contribute to our understanding of the etiology of disease phenotypes and pave the way for development of therapeutic treatments. Here, we establish the first model to specifically study neurobehavioral aspects of adenylosuccinate lyase deficiency. We show that reduction of adsl-1 function in C. elegans is associated with a novel learning phenotype in a gustatory plasticity assay. The animals maintain capacity for gustatory plasticity, evidenced by a change in their behavior in response to cue pairing. However, their behavioral output is distinct from that of control animals. We link substrate accumulation that occurs upon adsl-1 deficiency to an unexpected perturbation in tyrosine metabolism and show that a lack of tyramine mediates the behavioral changes through action on the metabotropic TYRA-2 tyramine receptor. Our studies reveal a potential for wider metabolic perturbations, beyond biosynthesis of purines, to impact behavior under conditions of adenylosuccinate lyase deficiency.
Prevalence and associated factors of cognitive frailty in community-dwelling older adults: Results from the Korean longitudinal study of aging.

BACKGROUND Cognitive frailty, a condition characterized by physical frailty with cognitive impairment, is emerging as a determinant of adverse health outcomes in older adults. However, its prevalence and correlation with associated factors are unknown in the aging population of Korea. OBJECTIVES To estimate the prevalence of cognitive frailty and identify factors associated with it among older Korean adults. METHODS A secondary analysis was performed using the Korean Longitudinal Study of Aging seventh survey dataset collected in 2018. Multinomial logistic regression analyses were conducted to examine the association between cognitive frailty and demographic, psychosocial, oral health and physical function factors. Individuals aged ≥65 years and without dementia were included (N = 1024). Participants were classified into four groups based on the presence or absence of physical frailty and mild cognitive impairment. This article is executed in accordance with the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement. RESULTS The prevalence of cognitive frailty in the study sample was 11.2%. The results of multinomial logistic regression showed that advanced age, being female, lower education levels, heart disease, arthritis or rheumatoid arthritis, underweight, depression, non-social activity, poor oral health and functional limitation were significantly associated with cognitive frailty. CONCLUSIONS Cognitive frailty is prevalent among community-dwelling older adults in Korea. The findings provide primary care providers with insights about effective strategies for identifying at-risk individuals and will guide the development of population-level interventions to prevent or delay the onset of physical frailty and cognitive impairment in older adults. IMPLICATIONS FOR PRACTICE The findings provide practical information to healthcare providers for identifying cognitive frailty in older adults. The risk factors of cognitive frailty, such as psychosocial, oral health, and physical function factors, should be thoroughly monitored for older adults. Health personnel working in primary care have a critical role in identifying risk and beneficial factors and promoting preventative strategies that enhance health outcomes.
Stationary distribution of a reaction-diffusion hepatitis B virus infection model driven by the Ornstein-Uhlenbeck process

A reaction-diffusion hepatitis B virus (HBV) infection model based on the mean-reverting Ornstein-Uhlenbeck process is studied in this paper. We demonstrate the existence and uniqueness of the positive solution by constructing the Lyapunov function. The adequate conditions for the solution’s stationary distribution were described. Last but not least, the numerical simulation demonstrated that reversion rates and noise intensity influenced the disease and that there was a stationary distribution. It was concluded that the solution tends more toward the stationary distribution, the greater the reversion rates and the smaller the noise.
Computational Study of Complex Formation between Hyaluronan Polymers and Polyarginine Peptides at Various Ratios

Hyaluronic acid, a naturally occurring carbohydrate biopolymer in human tissues, finds wide application in cosmetics, medicine, and material science. Its anionic properties play a crucial role in its interaction with positively charged macromolecules and ions. Among these macromolecules, positively charged arginine molecules or polyarginine peptides demonstrate potential in drug delivery when complexed with hyaluronan. This study aimed to compare and elucidate the results of both experimental and computational investigations on the interactions between hyaluronic acid polymers and polyarginine peptides. Experimental findings revealed that by varying the length of polyarginine peptides and the molar ratio, it is possible to modulate the size, solubility, and stability of hyaluronan–arginine particles. To further explore these interactions, molecular dynamics simulations were conducted to model the complexes formed between hyaluronic acid polymers and arginine peptides. The simulations are considered in different molar ratios and lengths of polyarginine peptides. By analysis of the data, we successfully determined the shape and size of the resulting complexes. Additionally, we identified the primary driving forces behind complex formation and explained the observed variations in peptide interactions with hyaluronan.
Programmable optical meta-holograms

Abstract The metaverse has captured significant attention as it provides a virtual realm that cannot be experienced in the physical world. Programmable optical holograms, integral components of the metaverse, allow users to access diverse information without needing external equipment. Meta-devices composed of artificially customized nano-antennas are excellent candidates for programmable optical holograms due to their compact footprint and flexible electromagnetic manipulation. Programmable optical meta-holograms can dynamically alter reconstructed images in real-time by directly modulating the optical properties of the metasurface or by modifying the incident light. Information can be encoded across multiple channels and freely selected through switchable functionality. These advantages will broaden the range of virtual scenarios in the metaverse, facilitating further development and practical applications. This review concentrates on recent advancements in the fundamentals and applications of programmable optical meta-holograms. We aim to provide readers with general knowledge and potential inspiration for applying programmable optical meta-holograms, both intrinsic and external ways, into the metaverse for better performance. An outlook and perspective on the challenges and prospects in these rapidly growing research areas are provided.
A Deep Learning-based VNF Placement Approach for SFC Requests in MEC-NFV Enabled Networks

The Service Function Chain (SFC) has become a popular paradigm to complete mobile services due to the advancements in Mobile Edge Computing (MEC) and Network Function Virtualization (NFV). This new computing and networking paradigm allows Virtual Network Functions (VNFs) to be placed in physical devices within MEC-NFV networks cost-effectively and flexibly. However, most existing VNF placement algorithms are complex, unscalable, and time-consuming. In this paper, we investigate the VNF placement problem in MEC-NFV networks and formulate an optimization model to optimize network resource utilization. We introduce a novel Deep Learning-based VNF Placement Approach (DLVPA) that intelligently selects nodes and places VNFs for SFC requests. Performance evaluations demonstrate that DLVPA can effectively improve network resource utilization.
AgriTera: Accurate Non-Invasive Fruit Ripeness Sensing via Sub-Terahertz Wireless Signals

The ability to assess the quality of fruit and vegetables at scale can revolutionize the agriculture sector and significantly reduce food waste. In this paper, we present AgriTera, a novel solution for accurate non-invasive, and contract-free fruit ripeness sensing via sub-terahertz wireless signals. The key idea is that sugar and water concentrations in fruit (that are associated with fruit ripening) leave unique non-uniform footprints in the wide band spectrum of the reflected signal off of the fruits. AgriTera utilizes the sub-THz bands for its wide bandwidth, sensitivity to water, mm-scale penetration depth, and non-ionizing features that offer high-resolution inferences from the peel as well as the pulp underneath the peel. We develop a chemometric model that translates the reflection spectra to well-known ripeness metrics, namely Dry Matter and Brix. We conduct extensive over-the-air experiments with commercially available sub-THz transceivers. We compare our results with ground truth values captured by a specialized quality sensor and a vision-based scheme that infers ripeness based on changes in the appearance of the fruit. We demonstrate that AgriTera can accurately estimate Brix and Dry Matter in three different types of fruit with an average Normalized RMSE value of 0.55%, an error that yields a negligible impact on taste and is imperceivable by the consumer.
Robust Real-time Multi-vehicle Collaboration on Asynchronous Sensors

Cooperative perception significantly enhances the perception performance of connected autonomous vehicles. Instead of purely relying on local sensors with limited range, it enables multiple vehicles and roadside infrastructures to share sensor data to perceive the environment collaboratively. Through our study, we realize that the performance of cooperative perception systems is limited in real-world deployment due to (1) out-of-sync sensor data during data fusion and (2) inaccurate localization of occluded areas. To address these challenges, we develop RAO, an innovative, effective, and lightweight cooperative perception system that merges asynchronous sensor data from different vehicles through our novel designs of motion-compensated occupancy flow prediction and on-demand data sharing, improving both the accuracy and coverage of the perception system. Our extensive evaluation, including real-world and emulation-based experiments, demonstrates that RAO outperforms state-of-the-art solutions by more than 34% in perception coverage and by up to 14% in perception accuracy, especially when asynchronous sensor data is present. RAO consistently performs well across a wide variety of map topologies and driving scenarios. RAO incurs negligible additional latency (8.5 ms) and low data transmission overhead (10.9 KB per frame), making cooperative perception feasible.