Miracle-dz/UniFAR-FTUs huggingface dataset:
The Temporal and Geographical Dynamics of Potato Virus Y Diversity in Russia

Potato virus Y, an important viral pathogen of potato, has several genetic variants and geographic distributions which could be affected by environmental factors, aphid vectors, and reservoir plants. PVY is transmitted to virus-free potato plants by aphids and passed on to the next vegetative generations through tubers, but the effects of tuber transmission in PVY is largely unknown. By using high-throughput sequencing, we investigated PVY populations transmitted to potato plants by aphids in different climate zones of Russia, namely the Moscow and Astrakhan regions. We analyzed sprouts from the tubers produced by field-infected plants to investigate the impact of tuber transmission on PVY genetics. We found a significantly higher diversity of PVY isolates in the Astrakhan region, where winters are shorter and milder and summers are warmer compared to the Moscow region. While five PVY types, NTNa, NTNb, N:O, N-Wi, and SYR-I, were present in both regions, SYRI-II, SYRI-III, and 261-4 were only found in the Astrakhan region. All these recombinants were composed of the genome sections derived from PVY types O and N, but no full-length sequences of such types were present. The composition of the PVY variants in the tuber sprouts was not always the same as in their parental plants, suggesting that tuber transmission impacts PVY genetics.
Leveraging digital technology for social connectedness among adults with chronic conditions: A systematic review

Purpose To review the evidence about the impact of digital technology on social connectedness among adults with one or more chronic health conditions. Methods PubMed, Embase, Social Sciences, CINAHL, and Compendex were systematically searched for full-text, peer-reviewed empirical evidence published between 2012 and 2023 and reported using the PRISMA flow diagram. Articles were critically appraised applying the Joanna Briggs Institute checklists. Specific data were extracted based on the framework for social identity and technology approaches for health outcomes and then analyzed and synthesized. Results Thirty-four studies met study criteria. Evidence showed heterogeneity among research methodology, chronic health conditions, digital technology, and health outcomes. Technology use was influenced by factors such as usability, anonymity, availability, and control. More advanced digital technologies require higher digital literacy and improved accessibility features/modifications. Social support was the most measured aspect of social connectedness. The emotional and informational forms of social support were most reported; instrumental support was the least likely to be delivered. Self-efficacy for using technology was considered in seven articles. Sixteen articles reported health outcomes: 31.2% (n = 5) described mental health outcomes only, 18.8% (n = 3) reported physical health outcomes only, 31.2% (n = 5) detailed both physical and mental health outcomes, whereas 18.8% (n = 3) denoted well-being or quality-of-life outcomes. Most often, health outcomes were positive, with negative outcomes for selected groups also noted. Conclusion Leveraging digital technology to promote social connectedness has the potential to affect positive health outcomes. Further research is needed to better understand the social integration of technology among populations with different contexts and chronic health conditions to enhance and tailor digital interventions.
Determinants of risk perception and willingness to tackle climate change. A pilot study

Abstract Different research papers have demonstrated that risk perception related to climate change can be attributed to different variables such as general environmental beliefs, knowledge about climate change and self-efficacy (Heath and Gifford, 2006; Sundblad, Biel and Gärling, 2007). However, the lack of agreement in the work carried out so far and the need to define specific scales of attitudes towards climate change indicate that further research must be conducted along these lines. This pilot study analyzes the determinants of risk perception and behavioral intentions in relation to climate change found in the literature. A Likert-type scale was developed to assess the following variables: knowledge, attitudes towards climate change and self-efficacy as predictive variables and risk perception and behavioral intentions as criteria variables. This scale was tested on 84 university students. The internal consistency of the scale was acceptable for all variables with the exception of attitude toward climate change where consistency was low. The results of the regression analyses showed that 44% of the variance observed in risk perception related to climate change can be explained by the level of knowledge, attitudes toward climate change and self-efficacy. 17% of the variance observed in relation to behavioral intentions can be predicted by attitudes toward climate change and self-efficacy.
Exploring the Interrelationships between Diabetes, Nutrition, Anxiety, and Depression: Implications for Treatment and Prevention Strategies

Across the world, diabetes, depression, and anxiety symptoms have gained widespread recognition as significant public health issues. Recent research has unveiled a mutually influential relationship between diabetes and these two mental health conditions, where each disorder impacts the course and outcomes of the others. The role of nutrition emerges as pivotal in preventing and treating depression, anxiety, and diabetes. A thorough literature review was undertaken to investigate the reciprocal effects between anxiety, depression, and diabetes, including their impact on the development and severity of each condition. Additionally, the effects of nutrition on the prevention and management of depression, anxiety, diabetes, and related complications in at-risk individuals were assessed. Our findings show that mental disorders, such as depression and anxiety, increase the risk of developing type 2 diabetes and are associated with poorer glycemic control, increased diabetes-related complications, and higher mortality rates. Conversely, diabetes is also linked with an increased risk of developing depression and anxiety. The biological, psychological, and social factors that contribute to the comorbidity between these two conditions are complex and multifaceted. Therefore, an integrated approach to the management of both conditions is critical for improving patient outcomes and reducing the overall burden of disease. Nutritional interventions should be utilized to reduce the risk of diabetes in patients with anxiety and depression as well as enhance mental health in patients with diabetes.
The Physiological and Pathological Role of Acyl-CoA Oxidation

Fatty acid metabolism, including β-oxidation (βOX), plays an important role in human physiology and pathology. βOX is an essential process in the energy metabolism of most human cells. Moreover, βOX is also the source of acetyl-CoA, the substrate for (a) ketone bodies synthesis, (b) cholesterol synthesis, (c) phase II detoxication, (d) protein acetylation, and (d) the synthesis of many other compounds, including N-acetylglutamate—an important regulator of urea synthesis. This review describes the current knowledge on the importance of the mitochondrial and peroxisomal βOX in various organs, including the liver, heart, kidney, lung, gastrointestinal tract, peripheral white blood cells, and other cells. In addition, the diseases associated with a disturbance of fatty acid oxidation (FAO) in the liver, heart, kidney, lung, alimentary tract, and other organs or cells are presented. Special attention was paid to abnormalities of FAO in cancer cells and the diseases caused by mutations in gene-encoding enzymes involved in FAO. Finally, issues related to α- and ω- fatty acid oxidation are discussed.
Metabolomic biomarkers in autism: identification of complex dysregulations of cellular bioenergetics

Autism Spectrum Disorder (ASD or autism) is a phenotypically and etiologically heterogeneous condition. Identifying biomarkers of clinically significant metabolic subtypes of autism could improve understanding of its underlying pathophysiology and potentially lead to more targeted interventions. We hypothesized that the application of metabolite-based biomarker techniques using decision thresholds derived from quantitative measurements could identify autism-associated subpopulations. Metabolomic profiling was carried out in a case–control study of 499 autistic and 209 typically developing (TYP) children, ages 18–48 months, enrolled in the Children’s Autism Metabolome Project (CAMP; ClinicalTrials.gov Identifier: NCT02548442). Fifty-four metabolites, associated with amino acid, organic acid, acylcarnitine and purine metabolism as well as microbiome-associated metabolites, were quantified using liquid chromatography-tandem mass spectrometry. Using quantitative thresholds, the concentrations of 4 metabolites and 149 ratios of metabolites were identified as biomarkers, each identifying subpopulations of 4.5–11% of the CAMP autistic population. A subset of 42 biomarkers could identify CAMP autistic individuals with 72% sensitivity and 90% specificity. Many participants were identified by several metabolic biomarkers. Using hierarchical clustering, 30 clusters of biomarkers were created based on participants’ biomarker profiles. Metabolic changes associated with the clusters suggest that altered regulation of cellular metabolism, especially of mitochondrial bioenergetics, were common metabolic phenotypes in this cohort of autistic participants. Autism severity and cognitive and developmental impairment were associated with increased lactate, many lactate containing ratios, and the number of biomarker clusters a participant displayed. These studies provide evidence that metabolic phenotyping is feasible and that defined autistic subgroups can lead to enhanced understanding of the underlying pathophysiology and potentially suggest pathways for targeted metabolic treatments.
The Lomonosov ridge experiment: “Lorex 79”

In the spring of 1979 the Department of Energy, Mines and Resources (EMR) undertook a large-scale, multidisciplinary project to study the nature and origin of the Lomonosov Ridge. The scientific program was planned and coordinated by the Earth Physics Branch, and the logistic support was provided by the Polar Continental Shelf Project/Scientists from other branches of EMR, from the Department of Fisheries and Oceans (DFO), and from a number of universities in Canada and the U.S.A. took part in the project code named Lorex 79. The Lomonosov Ridge is an aseismic submarine mountain range that bisects the Arctic Ocean into the Amerasia and Eurasia Basins (Figure 1). It rises sharply to a height of some 3 km above the adjacent abyssal plains, the Makarov Basin to the west, and the Fram Basin to the east. It is close to 200 km wide where it approaches the North American and Eurasian continental shelves but narrows to 25 km at its midpoint near the North Pole. Its relatively flat top, the steepness of its flanks, and results from previous geophysical measurements indicate that it may be a sedimentary structure of continental origin rather than a structure of oceanic origin [Sweeney et al., 1978]. In this context it has been suggested on several occasions that it is a rafted fragment split off the Eurasian continental shelf at the time of the opening of the North Atlantic and the Eurasia Basin in Early Tertiary times. However, since the available geophysical, geological, and bathymetric data are relatively sparse and often of doubtful quality (because of the enormously difficult operating conditions), the continental nature of the Lomonosov Ridge has never been unambiguously demonstrated.
Distal volcano-tectonic seismicity near Augustine Volcano

Clustered earthquakes located 25 km northeast of Augustine Volcano occurred more frequently beginning about 8 months before the volcano’s explosive eruption in 2006. This increase in distal seismicity was contemporaneous with an increase in seismicity directly below the volcano’s vent. Furthermore, the distal seismicity intensified penecontemporaneously with signals in geodetic data that appear to reveal a transition from magmatic inflation of the volcano to dike injection. Focal mechanisms for five events within the distal cluster show strike-slip-fault movement. Directly above the earthquake cluster, shallow (<5 km deep) folds and faults mapped using multichannel seismic-reflection data strike northeast, parallel to the regional structural grain. About 10 km northeast of Augustine Volcano, however, the AugustineSeldovia arch, an important trans-basin feature, strikes west and intersects the northeast-striking structural zone. We propose that the fault causing the distal earthquake cluster strikes northwest, subparallel to the arch, and is a right-lateral strike-slip fault. Future earthquake monitoring might show whether increasing activity in the remote cluster can aid in making eruption forecasts.
Automatic geo-registration of aerial image sequence with untextured LIDAR data using line features

This paper presents one from-Coarse-to-Fine approach to geo-register photogrammetry image sequence to un-textured LIDAR point clouds. First, Photogrammetric Automatic Aerial Triangulation (AAT) Technique is altered to coarsely geo-register image sequence to LIDAR points; After that, LIDAR-Image-Mutual line feature extraction and correspondence strategy is proposed to generate 3D space line from LIDAR points, extract image lines and establish correspondence effectively; Next, "Line Photogrammetric" technique is utilized to map generated conjunctive line features and to refine previous geo-register result under Least Squares adjustment; Finally, geo-registration experiment with developed approach is conducted and contributions are concluded as well. The experimental result shows that proposed approach is robust and effective.
Energy Landscape of Relaxation and Interaction of an Amino Acid, Glutamine (L), on Pristine and Au/Ag/Cu-Doped TiO2 Surfaces

Studying the interaction of inorganic systems with organic ones is a highly important avenue for finding new drugs and treatment methods. Tumor cells show an increased demand for amino acids due to their rapid proliferation; thus, targeting their metabolism is becoming a potential oncological therapeutic strategy. One of the inorganic materials that show antitumor properties is titanium dioxide, while its doping was found to enhance interactions with biological systems. Thus, in this study, we investigated the energy landscape of glutamine (L), an amino acid, on pristine and doped TiO2 surfaces. We first locally optimized 2D-slab structures of pristine and Au/Ag/Cu-doped anatase (001 and 101 surfaces) and similarly optimized a single molecule of glutamine in vacuum. Next, we placed the pre-optimized glutamine molecule in various orientations and on a variety of locations onto the relaxed substrate surfaces (in vacuum) and performed ab initio relaxations of the molecule on the substrate slabs. We employed the DFT method with a GGA-PBE functional implemented in the Quantum Espresso code. Comparisons of the optimized conformations and electronic structures of the amino acid in vacuum and on the surfaces yield useful insights into various biological processes.
Table-top demonstration of interferometric imaging using path-entangled single photons

Very-long baseline interferometry has been one of the major astronomical imaging techniques used in the last century for tasks ranging from measuring diameters of stars to imaging black holes at the center of galaxies. However, the usual heterodyne technique is typically limited to radio wavelengths for the longest baselines due to fundamental noise from the local oscillator, which is used to measure the collected electric field in time at each aperture. Further, the visible and near-infrared (V-NIR) wavelengths do not easily allow such measurements due to their higher frequency; so, for optimal performance, the collected fields must be directly interfered with each other to measure the spatial correlation of the stellar light between each aperture. This implies, at V-NIR wavelengths, a practical limitation on the distance between the receivers and the brightness of stellar sources since bringing the fields together is lossy. Several theoretical proposals have promised reduction of this loss by using single photons along with quantum networks and/or quantum memories. We demonstrate a proof-of-principle, table-top experiment of one proposal by interfering path-entangled single photons generated from parametric down conversion and the light collected from a quasi-thermal source occupying a single spectral-temporal mode representing light from a star. The interference signal was then used to recover the spatial autocorrelation of two source distributions: 1 and 2 mm separated double slits. We compare this to a theoretical model and see good agreement. This model allows further comparison to other weak, non-single-photon, local-oscillator sources such as coherent states.
Production of high-resolution reference polarization images from real world scenes

A few polarization image datasets depicting real world scenes have been reported. Some of them are available on an open-data basis. Some databases contain color images, often with color bands reconstructed from a sensor equipped with a Bayer filter. Unfortunately, even if these real-world images depict a variety of objects and situations and have a good overall quality (ie the spectral bands and the various polarization channels are or can be registered, noise is reduced), they often have a low definition (smaller than 1 Mp), a low bit depth and are captured with a large lens aperture, resulting in very band-limited images. Moreover, the demosaicing procedure used to reconstruct the various color bands has a smoothing effect, reducing their resolution. This latter point proves detrimental when it comes to use these images as references for demosaicing algorithms, especially for RGB images: since each channel combining polarization direction and spectral band is very sparse in the base mosaic pattern, artifacts likely to appear are considerably underestimated with band-limited images. In this work, we review existing polarization image databases, focus on non-mosaiced datasets and propose a technique to produce HD polarization images with superior quality.
Secular Trends in Tidal Parameters along the Coast of Japan

Abstract The secular trends for local tidal datums, tide ranges, and tidal harmonic constituents at 13 tidal gauge stations located along the coast of Japan were analyzed in this study. The general trends for mean higher high water (MHHW), mean high water (MHW), diurnal tide range (DTR), mean tide range (MTR), and relative mean sea level (RMSL) were positive, and for mean lower low water (MLLW) and mean low water (MLW) the trends were negative. The variation patterns were largest at Kushiro and Ofunato stations, both located in the open waters of the North Pacific Ocean. The tidal datums and tide ranges remained fairly stable at the Hakodate station, and an opposite trend was observed at the Maisaka station. The analysis of tidal harmonic constituents revealed a less pronounced pattern. The most distinct trend was observed for the M2 amplitude, which appears to be negative at a majority of the stations. The largest decrease in the M2 amplitude was observed at Kushimoto and Mera stations, also located in the open waters of the North Pacific Ocean. The negative trend is attributed to decreasing water depths at these stations, owing to small-scale local processes.
A conceptual model for hydrocarbon accumulation and seepage processes around Chapopote asphalt site, southern Gulf of Mexico: From high resolution seismic point of view

[1] As part of the German R/V Meteor M67/2 expedition in 2006 to the southern Gulf of Mexico, a set of 2D high resolution seismic profiles was acquired across the Chapopote knoll to study seafloor asphalt occurrences. On the basis of regional seismostratigraphic studies and DSDP drilling, a more highly reflective, coarse-grained sediment unit of late Miocene age is identified as a potential shallow hydrocarbon reservoir. Although a unit of that age would typically be buried by Pliocene and Pleistocene sediment cover, at Chapopote, local salt tectonism has highly elevated the structure since the late Miocene, and the Miocene reservoir is locally above present-day regional datum. The elevation resulted in a thin (100–200 m), fine grained sediment cover on the crest of the knoll above the reservoir. Because oil and gas production can be expected at depth in Jurassic, Cretaceous and Tertiary source rocks, the presence of high-amplitude reflector packages within the reservoir unit is interpreted as an evidence of hydrocarbons. This is variously supported by observations of crosscutting reflectors, polarity reversal, and drops in instantaneous frequency. The thin seal above the reservoir unit facilitates leakage of trapped hydrocarbons. Hydrocarbons apparently invaded into the seal sediments in the wider vicinity around the crest of the knoll, even extending beyond the area where seafloor asphalt is known. The asphalt site thus may be a currently active spot, while the rest of the crest may be temporarily sealed by solid phase hydrocarbons. We propose that a shallow, large reservoir with deeply sourced, relatively heavy petroleum is principally responsible for the formation of asphalts on the seafloor.
Semi-empirical models for estimating canopy chlorophyll content: the importance of prior information

ABSTRACT The canopy chlorophyll content (CCC) provides valuable information about the crop growth status. CCC can be estimated using remote sensing techniques, such as through the red-edge-based chlorophyll index (CIRE). The empirical model between CCC and CIRE calibrated using the measured dataset lacks generality. Therefore, the semi-empirical model is a better choice, which is calibrated on the physical model simulations. However, the effect of parameter settings of physical models on semi-empirical models is not clear. This study first investigated the effects of dry matter content (LMA) and mesophyll structural coefficient (N s ) on the CCC-CIRE relationships and then evaluated CCC estimation using the CIRE-based semi-empirical model calibrated on simulated datasets with different ranges of LMA and N s . The results showed that the relationships between CCC and CIRE were sensitive to N s and LMA. Therefore, after considering the prior information of N s (1.0–1.5) and LMA (20–80 g m−2) for the crop, the best estimation of CCC was obtained with an R 2 of 0.82 and an RMSE of 0.36 g m−2, which were substantially better than the model without considering the prior information (R 2 = 0.40 and RMSE = 0.67 g m−2). These findings improved our understanding of CCC estimation using the semi-empirical model and would facilitate the accurate mapping of CCC for agricultural management.