Miracle-dz/UniFAR-FTUs huggingface dataset:
Telomere-to-telomere carrot (Daucus carota) genome assembly reveals carotenoid characteristics

Abstract Carrot (Daucus carota) is an Apiaceae plant with multi-colored fleshy roots that provides a model system for carotenoid research. In this study, we assembled a 430.40 Mb high-quality gapless genome to the telomere-to-telomere (T2T) level of “Kurodagosun” carrot. In total, 36 268 genes were identified and 34 961 of them were functionally annotated. The proportion of repeat sequences in the genome was 55.3%, mainly long terminal repeats. Depending on the coverage of the repeats, 14 telomeres and 9 centromeric regions on the chromosomes were predicted. A phylogenetic analysis showed that carrots evolved early in the family Apiaceae. Based on the T2T genome, we reconstructed the carotenoid metabolic pathway and identified the structural genes that regulate carotenoid biosynthesis. Among the 65 genes that were screened, 9 were newly identified. Additionally, some gene sequences overlapped with transposons, suggesting replication and functional differentiation of carotenoid-related genes during carrot evolution. Given that some gene copies were barely expressed during development, they might be functionally redundant. Comparison of 24 cytochrome P450 genes associated with carotenoid biosynthesis revealed the tandem or proximal duplication resulting in expansion of CYP gene family. These results provided molecular information for carrot carotenoid accumulation and contributed to a new genetic resource.
Physical Activity, Body Image, and Emotional Intelligence Differences in Adults with Overweight and Obesity

Physical activity (PA) and emotional intelligence (EI) are integral parts of human nature. Body image (BI) and body mass index (BMI) may be indications of the psycho-emotional and physical health of human beings. The purpose of this study was to investigate the relationship between PA and EI of Greek adults living with overweight and obesity, as well as to identify the BI and EI differences in this population. A cross-sectional study design was used, involving 216 participants (65% females) of whom 51.4% were young adults (20–40 years), 48.6% were middle-aged adults (41–60 years), while 51.4% of participants were living with overweight or obesity. According to the results, all indicators of PA had very low correlations with EI factors, while statistically significant correlations were observed only for PA at work and the total score of the International Physical Activity Questionnaire with the use of emotions (r = 0.16 and r = 0.17, respectively, p < 0.05). Women had significantly higher EI scores than men regarding the care and empathy factor, while the individuals with obesity had lower scores in the use of emotions factor. Regarding BI, young adults who were satisfied with their BI had better control of feelings than the middle-aged adult counterparts. In conclusion, BI satisfaction and EI may differ between individuals living with overweight and obesity in both genders. Younger individuals with obesity may compensate better for their BI and better control their emotions. On the other side, PA does not seem to have an important role in these associations.
Subwavelength interference with an effective entangled source

We propose a two-photon subwavelength interference scheme for classical light in which multiple quantum-like entangled two-photon paths play an essential role. These entangled two-photon paths are introduced through a specially designed source composed of many point sources j with j ’s complex amplitude being a superposition of modes ej and e (1) j , where φj and φ (1) j are temporally random phases but satisfying φj + φ j = φ0, with φ0 being either a constant or a random phase in time. Interference between the entangled two-photon paths could lead to second-order subwavelength interference of an object put in front of the source plane. In a proof-of-principle experiment, by using a spatial light modulator to modulate the wave front of a coherent light, we have generated such a source and observed subwavelength interference of a double-slit mask via two-photon measurement.
Exploring BenzylethoxyAryl Urea Scaffolds for Multitarget Immunomodulation Therapies

Thirteen benzylethoxyaryl ureas have been synthesized and biologically evaluated as multitarget inhibitors of VEGFR-2 and PD-L1 proteins to overcome resistance phenomena offered by cancer. The antiproliferative activity of these molecules on several tumor cell lines (HT-29 and A549), on the endothelial cell line HMEC-1, on immune cells (Jurkat T) and on the non-tumor cell line HEK-293 has been determined. Selective indexes (SI) have been also determined and compounds bearing p-substituted phenyl urea unit together with a diaryl carbamate exhibited high SI values. Further studies on these selected compounds to determine their potential as small molecule immune potentiators (SMIPs) and as antitumor agents have been performed. From these studies, we have concluded that the designed ureas have good tumor antiangiogenic properties, exhibit good inhibition of CD11b expression, and regulate pathways involved in CD8 T-cell activity. These properties suggest that these compounds could be potentially useful in the development of new cancer immune treatments.
Rapid Measurement of Magnetic Particle Concentrations in Wildland–Urban Interface Fire Ashes and Runoff Using Compact NMR

Wildfires are increasing in size, frequency, and intensity, releasing increased amounts of contaminants, including magnetic particles (MPs), into the surrounding environment. The aim of this article is to develop a sensing method for the detection and quantification of MPs in fire ash and fire runoff using a compact time-domain nuclear magnetic resonance (TD-NMR) system. The system is made up of custom nuclear magnetic resonance (NMR) electronics with a compact and rugged permanent magnet array designed to enable future deployment as an in situ sensor. A signal-to-noise ratio (SNR) of 25 dB was measured for a single scan, and sufficient data can be acquired in one minute. A linear relationship with an ${R}^{{2}}$ value of 0.9699 was established between transverse relaxation rates and MP concentrations in ash samples. This was validated by testing known dilutions of pure magnetite particles and showing that they fit within the same linear curve. The developed approach was then applied to detect MPs in surface water, where changes in the relaxation rates as high as 400% were observed before and after a wildfire event. MPs were removed from the surface water using a MP separator to confirm that observed changes were solely due to the presence of MPs. The compact NMR system can be used as a simple and rapid approach to track and quantify the concentrations of MPs released from fire ashes and also from other sources such as discharges from coal ash and other combustion ashes.
Why we should care about moral foundations when preparing for the next pandemic: Insights from Canada, the UK and the US

Health behaviors that do not effectively prevent disease can negatively impact psychological wellbeing and potentially drain motivations to engage in more effective behavior, potentially creating higher health risk. Despite this, studies linking “moral foundations” (i.e., concerns about harm, fairness, purity, authority, ingroup, and/or liberty) to health behaviors have generally been limited to a narrow range of behaviors, specifically effective ones. We therefore explored the degree to which moral foundations predicted a wider range of not only effective but ineffective (overreactive) preventative behaviors during the COVID-19 pandemic. In Study 1, participants from Canada, the United Kingdom, and the United States reported their engagement in these preventative behaviors and completed a COVID-specific adaptation of the Moral Foundations Questionnaire during the pandemic peak. While differences occurred across countries, authority considerations consistently predicted increased engagement in both effective preventative behaviors but also ineffective overreactions, even when controlling for political ideology. By contrast, purity and liberty considerations reduced intentions to engage in effective behaviors like vaccination but had no effect on ineffective behaviors. Study 2 revealed that the influence of moral foundations on U.S participants’ behavior remained stable 5-months later, after the pandemic peak. These findings demonstrate that the impact of moral foundations on preventative behaviors is similar across a range of western democracies, and that recommendations by authorities can have unexpected consequences in terms of promoting ineffective—and potentially damaging—overreactive behaviors. The findings underscore the importance of moral concerns for the design of health interventions that selectively promote effective preventative behavior.
Efficient cancer modeling through CRISPR-Cas9/HDR-based somatic precision gene editing in mice

CRISPR-Cas9 has been used successfully to introduce indels in somatic cells of rodents; however, precise editing of single nucleotides has been hampered by limitations of flexibility and efficiency. Here, we report technological modifications to the CRISPR-Cas9 vector system that now allows homology-directed repair–mediated precise editing of any proto-oncogene in murine somatic tissues to generate tumor models with high flexibility and efficiency. Somatic editing of either Kras or Pik3ca in both normal and hyperplastic mammary glands led to swift tumorigenesis. The resulting tumors shared some histological, transcriptome, and proteome features with tumors induced by lentivirus-mediated expression of the respective oncogenes, but they also exhibited some distinct characteristics, particularly showing less intertumor variation, thus potentially offering more consistent models for cancer studies and therapeutic development. Therefore, this technological advance fills a critical gap between the power of CRISPR technology and high-fidelity mouse models for studying human tumor evolution and preclinical drug testing.
Illness perceptions of occupational hand eczema in German patients based on the common-sense model of self-regulation: A qualitative study

Background Occupational skin diseases (OSD) in the form of hand eczema (HE) are a common work-related disease. Illness perceptions as presented in Leventhal’s Common-Sense Model (CSM) are important for patients’ self-management of diseases. Understanding these illness perceptions is essential for patient communicating. No quantitative or qualitative studies which investigated subjective illness perceptions in patients with occupational HE utilized the CSM as theoretical framework. The Objective of this study is to investigate illness perceptions of patients with occupational hand eczema (HE) using the CSM. Methods We applied an exploratory qualitative approach and conducted purposive sampling. Thirty-six patients with occupational HE were interviewed using an interview guide based on the dimensions of the CSM, including coherence and emotional representation. All participants participated in a three-week inpatient program at a clinic specialized on occupational dermatology. One interview had to be excluded before analysis, since one participant’s diagnosis was retrospectively changed from ICD to tinea and hence did not match the inclusion criteria. Thirty-five interviews were transcribed verbatim and analyzed. Data was analyzed deductively and inductively using qualitative text analysis. MAXQDA 2018 (Verbi, Berlin, Germany), a software for qualitative data analysis, was applied for coding and summarizing of results. All dimensions of the CSM were explored for occupational HE. Results Several sub-categories could be identified. Participants named a variety of causes in different areas (e. g. external irritants and other hazardous factors, psycho-social factors, allergies, having a ‘bad immune system’ or lifestyle). The great impact of the disease on the participants’ life is shown by the wide range of consequences reported, affecting all areas of life (i. e. psychological, physical, occupational, private). Considering coherence, an ambivalence between comprehensibility and non-comprehensibility of the disease is apparent. Discussion The complexity of illness perceptions presented in this paper is relevant for those involved in HE patient education and counseling, e. g, health educators, dermatologists, and, occupational physicians. Future research might further investigate specific aspects of illness perceptions in patients with occupational HE, especially considering the complexity of coherence and overlapping dimensions (i. e. emotional representation and psychological consequences).
A Study on the Intensity Change of Typhoon Nakri (0208)

The characteristics of environmental influences and lower tropospheric vertical structure over ocean associated with the intensity change of typhoon Nakri were investigated using the observation data by two Aerosondes and GDAPS(Global Data Analysis and Prediction System) data. Typhoon Nakri (0208), which formed as a Tropical Storm (TS) at about 400 ㎞ southwest of Taiwan on 1800UTC July 8, 2002, has experienced an intensity change from Tropical Storm (TS) on 1200UTC 12 July to Tropical Depression (TD) on 1200UTC 13 July. The evolutions of 200-850 h㎩ vertical wind shear and 200 h㎩ eddy relative angular momentum flux convergence (EFC) computed from the GDAPS analysis data showed that vertical wind shear exceeded 10 ㎳? ¹ from 1200UTC 12 July to 0000UTC 13 July. Positive values of EFC were larger during the period of 1800UTC 12 July to 0600UTC 13 July than other times. This analysis indicates that typhoon Nakri interacts with the upper tropospheric trough, so it becomes weak rapidly. We also found from the Aerosonde observations that lower tropospheric vertical structure for about three hours before the typhoon weakened was different from that of other times. That is, the inversion base and top were much lower than the other periods but its intensity was stronger. Also, northwesterly was appeared at altitude between 1.5 ㎞ and 3.5 ㎞. It means that the southward movement of low at 850 h㎩ before the extension of western North Pacific subtropical high affects lower tropospheric vertical structure over ocean ahead of typhoon. This southward movement might be regarded as a precursor process of typhoon-trough interaction.
Can Facial Expressions Induce Haptic Perception?

A key challenge in haptics is designing human–human communications involving touch to facilitate positive effects on social interactions. An important consideration in designing social touch is understanding the effect of social stimuli on perception, in addition to that of a physical stimulus, because social touch always involves a partner. This study presents an experiment to demonstrate that facial expressions induce haptic perception. We developed a human–agent interaction system on a display in which participants moved the mouse cursor to click the target icon while the agent behaved as if it pulled the cursor back in the opposite direction, showing either a negative or neutral face. The perceived force during the interaction was quantified by the control display ratio using a psychophysical approach. The results show that the negative face induced a significantly greater perceived force than the neutral face. In addition, the perceived force correlated with the individual's evaluation of the facial expression; that is, the more unpleasant or aroused they perceived the facial expression to be, the more force they perceived. This study sheds light on the design of social touch performed by people who have physical or mediated contact with each other in physical space or cyberspace.
Theoretical and Data-Driven Approaches for Biomolecular Condensates

Biomolecular condensation processes are increasingly recognized as a fundamental mechanism that living cells use to organize biomolecules in time and space. These processes can lead to the formation of membraneless organelles that enable cells to perform distinct biochemical processes in controlled local environments, thereby supplying them with an additional degree of spatial control relative to that achieved by membrane-bound organelles. This fundamental importance of biomolecular condensation has motivated a quest to discover and understand the molecular mechanisms and determinants that drive and control this process. Within this molecular viewpoint, computational methods can provide a unique angle to studying biomolecular condensation processes by contributing the resolution and scale that are challenging to reach with experimental techniques alone. In this Review, we focus on three types of dry-lab approaches: theoretical methods, physics-driven simulations and data-driven machine learning methods. We review recent progress in using these tools for probing biomolecular condensation across all three fields and outline the key advantages and limitations of each of the approaches. We further discuss some of the key outstanding challenges that we foresee the community addressing next in order to develop a more complete picture of the molecular driving forces behind biomolecular condensation processes and their biological roles in health and disease.
Dual Compensation Residual Networks for Class Imbalanced Learning

Learning generalizable representation and classifier for class-imbalanced data is challenging for data-driven deep models. Most studies attempt to re-balance the data distribution, which is prone to overfitting on tail classes and underfitting on head classes. In this work, we propose Dual Compensation Residual Networks to better fit both tail and head classes. First, we propose dual Feature Compensation Module (FCM) and Logit Compensation Module (LCM) to alleviate the overfitting issue. The design of these two modules is based on the observation: an important factor causing overfitting is that there is severe feature drift between training and test data on tail classes. In details, the test features of a tail category tend to drift towards feature cloud of multiple similar head categories. So FCM estimates a multi-mode feature drift direction for each tail category and compensate for it. Furthermore, LCM translates the deterministic feature drift vector estimated by FCM along intra-class variations, so as to cover a larger effective compensation space, thereby better fitting the test features. Second, we propose a Residual Balanced Multi-Proxies Classifier (RBMC) to alleviate the under-fitting issue. Motivated by the observation that re-balancing strategy hinders the classifier from learning sufficient head knowledge and eventually causes underfitting, RBMC utilizes uniform learning with a residual path to facilitate classifier learning. Comprehensive experiments on Long-tailed and Class-Incremental benchmarks validate the efficacy of our method.
Wireless Dynamic Light Scattering Sensors Detect Microvascular Changes Associated With Ageing and Diabetes

This article presents clinical results of wireless portable dynamic light scattering sensors that implement laser Doppler flowmetry signal processing. It has been verified that the technology can detect microvascular changes associated with diabetes and ageing in volunteers. Studies were conducted primarily on wrist skin. Wavelet continuous spectrum calculation was used to analyse the obtained time series of blood perfusion recordings with respect to the main physiological frequency ranges of vasomotions. In patients with type 2 diabetes, the area under the continuous wavelet spectrum in the endothelial, neurogenic, myogenic, and cardio frequency ranges showed significant diagnostic value for the identification of microvascular changes. Aside from spectral analysis, autocorrelation parameters were also calculated for microcirculatory blood flow oscillations. The groups of elderly volunteers and patients with type 2 diabetes, in comparison with the control group of younger healthy volunteers, showed a statistically significant decrease of the normalised autocorrelation function in time scales up to 10 s. A set of identified parameters was used to test machine learning algorithms to classify the studied groups of young controls, elderly controls, and diabetic patients. Our conclusion describes and discusses the classification metrics that were found to be most effective.
Cyclic Learning: Bridging Image-Level Labels and Nuclei Instance Segmentation

Nuclei instance segmentation on histopathology images is of great clinical value for disease analysis. Generally, fully-supervised algorithms for this task require pixel-wise manual annotations, which is especially time-consuming and laborious for the high nuclei density. To alleviate the annotation burden, we seek to solve the problem through image-level weakly supervised learning, which is underexplored for nuclei instance segmentation. Compared with most existing methods using other weak annotations (scribble, point, etc.) for nuclei instance segmentation, our method is more labor-saving. The obstacle to using image-level annotations in nuclei instance segmentation is the lack of adequate location information, leading to severe nuclei omission or overlaps. In this paper, we propose a novel image-level weakly supervised method, called cyclic learning, to solve this problem. Cyclic learning comprises a front-end classification task and a back-end semi-supervised instance segmentation task to benefit from multi-task learning (MTL). We utilize a deep learning classifier with interpretability as the front-end to convert image-level labels to sets of high-confidence pseudo masks and establish a semi-supervised architecture as the back-end to conduct nuclei instance segmentation under the supervision of these pseudo masks. Most importantly, cyclic learning is designed to circularly share knowledge between the front-end classifier and the back-end semi-supervised part, which allows the whole system to fully extract the underlying information from image-level labels and converge to a better optimum. Experiments on three datasets demonstrate the good generality of our method, which outperforms other image-level weakly supervised methods for nuclei instance segmentation, and achieves comparable performance to fully-supervised methods.
An Averaging Method for Phase Noise Studies of Quartz Phononic Frequency Combs

A fast and accurate averaging method was derived and developed for the analysis and design of quartz phononic frequency combs. The phononic frequency combs were obtained from a pair of coupled nonlinear Duffing equations for quartz resonators by solving the equations in the time domain and performing a fast Fourier transformation (FFT) of the steady-state vibrations of the time series. Noise simulations were added to the drive frequency to study noise transfer characteristics between the drive signal and the resonances of the phononic frequency combs produced in 100-MHz quartz shear-mode resonators. Our new method averaged out the carrier frequency, thus allowing for a fast and efficient computation at parts per million accuracies of noise close to the carrier ( $\sim $ 10 Hz). The goal of our study was to develop methods and resonator requirements for engineering the properties of the phononic frequency combs for low-noise clock applications.