Miracle-dz/UniFAR-FTUs huggingface dataset:
Cardiovascular Risk Factors and Social Development Index

Cardiovascular diseases (CVD) are the leading causes of morbidity and mortality worldwide. The complex etiology of CVD is known to be significantly affected by environmental and social factors. There is, however, a lag in our understanding of how population level components may be related to the onset and severity of CVD, and how some indicators of unsatisfied basic needs might be related to known risk factors. Here, we present a cross-sectional study aimed to analyze the association between cardiovascular risk factors (CVRF) and Social Development Index (SDI) in adult individuals within a metropolitan urban environment. The six components of SDI as well as socioeconomic, anthropometric, clinical, biochemical, and risk behavior parameters were explored within the study population. As a result, several CVRF (waist circumference, waist-to-height ratio, body mass index, systolic blood pressure, glucose, lower high-density lipoprotein cholesterol, triglycerides, and sodium) were found in a higher proportion in the low or very low levels of the SDI, and this pattern occurs more in women than in men. Canonical analysis indicates a correlation between other socioeconomic features and anthropometric, clinical, and biochemical factors (canonical coefficient = 0.8030). Further studies along these lines are needed to fully establish how to insert such associations into the design of health policy and interventions with a view to lessen the burden of cardiovascular diseases, particularly in metropolitan urban environments.
Kefir and Intestinal Microbiota Modulation: Implications in Human Health

In the last decades changes in the pattern of health and disease in Latin America and in the world has been observed, with an increase in cases of chronic non-communicable diseases. Changes in intestinal microbiota composition can contribute to the development of these diseases and be useful in their management. In this context, the consumption of fermented foods with probiotic properties, such as kefir, stands out due to its gut microbiota-modulating capacity. There is an increasing interest in the commercial use of kefir since it can be marketed as a natural beverage containing health-promoting bacteria and has been gaining international popularity in Latin America. Also the consumption of these drinks in Latin America seems to be even more relevant, given the socioeconomic situation of this population, which highlights the need for disease prevention at the expense of its treatment. In this narrative review, we discuss how kefir may work against obesity, diabetes mellitus, liver disease, cardiovascular disorders, immunity, and neurological disorders. Peptides, bioactive compounds and strains occurring in kefir, can modulate gut microbiota composition, low-grade inflammation and intestinal permeability, which consequently may generate health benefits. Kefir can also impact on the regulation of organism homeostasis, with a direct effect on the gut-brain axis, being a possible strategy for the prevention of metabolic diseases. Further studies are needed to standardize these bioactive compounds and better elucidate the mechanisms linking kefir and intestinal microbiota modulation. However, due to the benefits reported, low cost and ease of preparation, kefir seems to be a promising approach to prevent and manage microbiota-related diseases in Latin America and the rest of the world.
The Impact of Complex Volcanic Plumbing on the Nature of Seismicity in the Developing Magmatic Natron Rift, Tanzania

Constraining the architecture of complex 3D volcanic plumbing systems within active rifts, and their impact on rift processes, is critical for examining the interplay between faulting, magmatism and magmatic fluids in developing rift segments. The Natron basin of the East African Rift System provides an ideal location to study these processes, owing to its recent magmatic-tectonic activity and ongoing active carbonatite volcanism at Oldoinyo Lengai. Here, we report seismicity and fault plane solutions from a 10 month-long temporary seismic network spanning Oldoinyo Lengai, Naibor Soito volcanic field and Gelai volcano. We locate 6,827 earthquakes with ML −0.85 to 3.6, which are related to previous and ongoing magmatic and volcanic activity in the region, as well as regional tectonic extension. We observe seismicity down to ∼17 km depth north and south of Oldoinyo Lengai and shallow seismicity (3–10 km) beneath Gelai, including two swarms. The deepest seismicity (∼down to 20 km) occurs above a previously imaged magma body below Naibor Soito. These seismicity patterns reveal a detailed image of a complex volcanic plumbing system, supporting potential lateral and vertical connections between shallow- and deep-seated magmas, where fluid and melt transport to the surface is facilitated by intrusion of dikes and sills. Focal mechanisms vary spatially. T-axis trends reveal dominantly WNW-ESE extension near Gelai, while strike-slip mechanisms and a radial trend in P-axes are observed in the vicinity of Oldoinyo Lengai. These data support local variations in the state of stress, resulting from a combination of volcanic edifice loading and magma-driven stress changes imposed on a regional extensional stress field. Our results indicate that the southern Natron basin is a segmented rift system, in which fluids preferentially percolate vertically and laterally in a region where strain transfers from a border fault to a developing magmatic rift segment.
Synthesis and antimicrobial activity of novel naphtho(2,1-b)furo-5H- (3,2-d)(1,3,4)thiadiazolo(3,2-a)pyrimidin-5-ones

Ethyl 3-aminonaphtho(2,1-b)furan-2-carboxylate 1 was converted into 3-amino-2- mercaptonaphthofuro(3,2-d)pyrimidin-4(3H)-one 4 by reacting it with carbon disulphide followed by methylation and condensation with hydrazine hydrate. The compound 4 on treatment with aryl isothiocyanates produced 2-arylaminoanilinonaphtho(2,1-b)furo-5H-(3,2- d)(1,3,4)thiadiazolo(3,2-a)pyrimidin-5-ones 5(a-h). The title compounds 6(a-h) were obtained also by reacting 4 with aromatic acids in presence of phosphorus oxychloride. The structures of the newly synthesized compounds were confirmed by IR, 1 H NMR, 13 C NMR and mass spectral studies and elemental analysis. All the synthesized compounds have been screened for antimicrobial activity.
Breaking a species barrier by enabling hybrid recombination

Hybrid sterility maintains reproductive isolation between species by preventing them from exchanging genetic material1. Anti-recombination can contribute to hybrid sterility when different species' chromosome sequences are too diverged to cross over efficiently during hybrid meiosis, resulting in chromosome mis-segregation and aneuploidy. The genome sequences of the yeasts Saccharomyces cerevisiae and Saccharomyces paradoxus have diverged by about 12% and their hybrids are sexually sterile: nearly all of their gametes are aneuploid and inviable. Previous methods to increase hybrid yeast fertility have targeted the anti-recombination machinery by enhancing meiotic crossing over. However, these methods also have counteracting detrimental effects on gamete viability due to increased mutagenesis2 and ectopic recombination3. Therefore, the role of anti-recombination has not been fully revealed, and it is often dismissed as a minor player in speciation1. By repressing two genes, SGS1 and MSH2, specifically during meiosis whilst maintaining their mitotic expression, we were able to increase hybrid fertility 70-fold, to the level of non-hybrid crosses, confirming that anti-recombination is the principal cause of hybrid sterility. Breaking this species barrier allows us to generate, for the first time, viable euploid gametes containing recombinant hybrid genomes from these two highly diverged parent species.
Artificial Light at Night Advances Spring Phenology in the United States

Plant phenology is closely related to light availability as diurnal and seasonal cycles are essential environmental cues for organizing bio-ecological processes. The natural cycles of light, however, have been dramatically disrupted by artificial light at night (ALAN) due to recent urbanization. The influence on plant phenology of ALAN and its spatial variation remain largely unknown. By analyzing satellite data on ALAN intensity across the United States, here, we showed that ALAN tended to advance the start date of the growing season (SOS), although the overall response of SOS to ALAN was relatively weak compared with other potential factors (e.g., preseason temperature). The phenological impact of ALAN showed a spatially divergent pattern, whereby ALAN mainly advanced SOS at climatically moderate regions within the United States (e.g., Virginia), while its effect was insignificant or even reversed at very cold (e.g., Minnesota) and hot regions (e.g., Florida). Such a divergent pattern was mainly attributable to its high sensitivity to chilling insufficiency, where the advancing effect on SOS was only triggered on the premise that chilling days exceeded a certain threshold. Other mechanisms may also play a part, such as the interplay among chilling, forcing and photoperiod and the difference in species life strategies. Besides, urban areas and natural ecosystems were found to suffer from similar magnitudes of influence from ALAN, albeit with a much higher baseline ALAN intensity in urban areas. Our findings shed new light on the phenological impact of ALAN and its relation to space and other environmental cues, which is beneficial to a better understanding and projection of phenology changes under a warming and urbanizing future.
Integrating Spectral Information and Meteorological Data to Monitor Wheat Yellow Rust at a Regional Scale: A Case Study

Wheat yellow rust has a severe impact on wheat production and threatens food security in China; as such, an effective monitoring method is necessary at the regional scale. We propose a model for yellow rust monitoring based on Sentinel-2 multispectral images and a series of two-stage vegetation indices and meteorological data. Sensitive spectral vegetation indices (single- and two-stage indices) and meteorological features for wheat yellow rust discrimination were selected using the random forest method. Wheat yellow rust monitoring models were established using three different classification methods: linear discriminant analysis (LDA), support vector machine (SVM), and artificial neural network (ANN). The results show that models based on two-stage indices (i.e., those calculated using images from two different days) significantly outperform single-stage index models (i.e., those calculated using an image from a single day), the overall accuracy improved from 63.2% to 78.9%. The classification accuracies of models combining a vegetation index with meteorological feature are higher than those of pure vegetation index models. Among them, the model based on two-stage vegetation indices and meteorological features performs best, with a classification accuracy exceeding 73.7%. The SVM algorithm performed best for wheat yellow rust monitoring among the three algorithms; its classification accuracy (84.2%) was ~10.5% and 5.3% greater than those of LDA and ANN, respectively. Combined with crop growth and environmental information, our model has great potential for monitoring wheat yellow rust at a regional scale. Future work will focus on regional-scale monitoring and forecasting of crop disease.
Accuracy of a Model-Free Algorithm for Temporal InSAR Tropospheric Correction

Atmospheric propagational phase variations are the dominant source of error for InSAR (interferometric synthetic aperture radar) time series analysis, generally exceeding uncertainties from poor signal to noise ratio or signal correlation. The spatial properties of these errors have been well studied, but, to date, their temporal dependence and correction have received much less attention. Here, we present an evaluation of the magnitude of tropospheric artifacts in derived time series after compensation using an algorithm that requires only the InSAR data. The level of artifact reduction equals or exceeds that from many weather model-based methods, while avoiding the need to globally access fine-scale atmosphere parameters at all times. Our method consists of identifying all points in an InSAR stack with consistently high correlation and computing, and then removing, a fit of the phase at each of these points with respect to elevation. A comparison with GPS truth yields a reduction of three, from a rms misfit of 5–6 to ~2 cm over time. This algorithm can be readily incorporated into InSAR processing flows without the need for outside information.
Effect of Hydrophilic Gums on the Quality of Frozen Dough: II. Bread Characteristics

ABSTRACT Increase in proofing periods of frozen dough subjected to extended frozen storage and freeze-thaw cycles and finally a lower loaf volume is attributed to the changes in rheological properties of frozen doughs. These changes occur due to the physical damage on the gluten network caused by ice recrystallization. The major objective of this research was to reduce ice crystallization and recrystallization in frozen dough by incorporating hydrophilic gums such as carboxy methyl cellulose (CMC), gum arabic, kappa carrageenan (κ-carrageenan), and locust bean gum at three different levels with flour derived from a Hard Red Spring wheat cultivar. The doughs were stored frozen for up to 16 weeks. Bread characteristics were analyzed after day 0, day 1, and after 4, 8, 12, and 16 weeks of frozen storage for specific loaf volume, external and internal appearance, color, texture, and shelf-life. Specific loaf volume increased significantly with the addition of different levels of all the four gums compared wit.
Development of a Miniaturized Mobile Mapping System for In-Row, Under-Canopy Phenotyping

This paper focuses on the development of a miniaturized mobile mapping platform with advantages over current agricultural phenotyping systems in terms of acquiring data that facilitate under-canopy plant trait extraction. The system is based on an unmanned ground vehicle (UGV) for in-row, under-canopy data acquisition to deliver accurately georeferenced 2D and 3D products. The paper addresses three main aspects pertaining to the UGV development: (a) architecture of the UGV mobile mapping system (MMS), (b) quality assessment of acquired data in terms of georeferencing information as well as derived 3D point cloud, and (c) ability to derive phenotypic plant traits using data acquired by the UGV MMS. The experimental results from this study demonstrate the ability of the UGV MMS to acquire dense and accurate data over agricultural fields that would facilitate highly accurate plant phenotyping (better than above-canopy platforms such as unmanned aerial systems and high-clearance tractors). Plant centers and plant count with an accuracy in the 90% range have been achieved.
Ground-Based MAX-DOAS Observations of Tropospheric NO2 and HCHO During COVID-19 Lockdown and Spring Festival Over Shanghai, China

Reduced mobility and less anthropogenic activity under special case circumstances over various parts of the world have pronounced effects on air quality. The objective of this study is to investigate the impact of reduced anthropogenic activity on air quality in the mega city of Shanghai, China. Observations from the highly sophisticated multi-axis differential optical absorption spectroscope (MAX-DOAS) instrument were used for nitrogen dioxide (NO2) and formaldehyde (HCHO) column densities. In situ measurements for NO2, ozone (O3), particulate matter (PM2.5) and the air quality index (AQI) were also used. The concentration of trace gases in the atmosphere reduces significantly during annual Spring Festival holidays, whereby mobility is reduced and anthropogenic activities come to a halt. The COVID-19 lockdown during 2020 resulted in a considerable drop in vertical column densities (VCDs) of HCHO and NO2 during lockdown Level-1, which refers to strict lockdown, i.e., strict measures taken to reduce mobility (43% for NO2; 24% for HCHO), and lockdown Level-2, which refers to relaxed lockdown, i.e., when the mobility restrictions were relaxed somehow (20% for NO2; 22% for HCHO), compared with pre-lockdown days, as measured by the MAX-DOAS instrument. However, for 2019, a reduction in VCDs was found only during Level-1 (24% for NO2; 6.62% for HCHO), when the Spring Festival happened. The weekly cycle for NO2 and HCHO depicts no significant effect of weekends on the lockdown. After the start of the Spring Festival, the VCDs of NO2 and HCHO showed a decline for 2019 as well as 2020. Backward trajectories calculated using the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model indicated more air masses coming from the sea after the Spring Festival for 2019 and 2020, implying that a low pollutant load was carried by them. No impact of anthropogenic activity was found on O3 concentration. The results indicate that the ratio of HCHO to NO2 (RFN) fell in the volatile organic compound (VOC)-limited regime.
Satellite-Based Precipitation Datasets Evaluation Using Gauge Observation and Hydrological Modeling in a Typical Arid Land Watershed of Central Asia

Hydrological modeling has always been a challenge in the data-scarce watershed, especially in the areas with complex terrain conditions like the inland river basin in Central Asia. Taking Bosten Lake Basin in Northwest China as an example, the accuracy and the hydrological applicability of satellite-based precipitation datasets were evaluated. The gauge-adjusted version of six widely used datasets was adopted; namely, Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks–Climate Data Record (CDR), Climate Hazards Group Infrared Precipitation with Stations (CHIRPS), Global Precipitation Measurement Ground Validation National Oceanic and Atmospheric Administration Climate Prediction Center (NOAA CPC) Morphing Technique (CMORPH), Integrated Multi-Satellite Retrievals for GPM (GPM), Global Satellite Mapping of Precipitation (GSMaP), the Tropical Rainfall Measuring Mission (TRMM) and Multi-satellite Precipitation Analysis (TMPA). Seven evaluation indexes were used to compare the station data and satellite datasets, the soil and water assessment tool (SWAT) model, and four indexes were used to evaluate the hydrological performance. The main results were as follows: 1) The GPM and CDR were the best datasets for the daily scale and monthly scale rainfall accuracy evaluations, respectively. 2) The performance of CDR and GPM was more stable than others at different locations in a watershed, and all datasets tended to perform better in the humid regions. 3) All datasets tended to perform better in the summer of a year, while the CDR and CHIRPS performed well in winter compare to other datasets. 4) The raw data of CDR and CMORPH performed better than others in monthly runoff simulations, especially CDR. 5) Integrating the hydrological performance of the uncorrected and corrected data, all datasets have the potential to provide valuable input data in hydrological modeling. This study is expected to provide a reference for the hydrological and meteorological application of satellite precipitation datasets in Central Asia or even the whole temperate zone.
Drivers of Bornean Orangutan Distribution across a Multiple-Use Tropical Landscape

Logging and conversion of tropical forests in Southeast Asia have resulted in the expansion of landscapes containing a mosaic of habitats that may vary in their ability to sustain local biodiversity. However, the complexity of these landscapes makes it difficult to assess abundance and distribution of some species using ground-based surveys alone. Here, we deployed a combination of ground-transects and aerial surveys to determine drivers of the critically endangered Bornean Orangutan (Pongo pygmaeus morio) distribution across a large multiple-use landscape in Sabah, Malaysian Borneo. Ground-transects and aerial surveys using drones were conducted for orangutan nests and hemi-epiphytic strangler fig trees (Ficus spp.) (an important food resource) in 48 survey areas across 76 km2, within a study landscape of 261 km2. Orangutan nest count data were fitted to models accounting for variation in land use, above-ground carbon density (ACD, a surrogate for forest quality), strangler fig density, and elevation (between 117 and 675 m). Orangutan nest counts were significantly higher in all land uses possessing natural forest cover, regardless of degradation status, than in monoculture plantations. Within these natural forests, nest counts increased with higher ACD and strangler fig density, but not with elevation. In logged forest (ACD 14–150 Mg ha−1), strangler fig density had a significant, positive relationship with orangutan nest counts, but this relationship disappeared in a forest with higher carbon content (ACD 150–209 Mg ha−1). Based on an area-to-area comparison, orangutan nest counts from ground transects were higher than from counts derived from aerial surveys, but this did not constitute a statistically significant difference. Although the difference in nest counts was not significantly different, this analysis indicates that both methods under-sample the total number of nests present within a given area. Aerial surveys are, therefore, a useful method for assessing the orangutan habitat use over large areas. However, the under-estimation of nest counts by both methods suggests that a small number of ground surveys should be retained in future surveys using this technique, particularly in areas with dense understory vegetation. This study shows that even highly degraded forests may be a suitable orangutan habitat as long as strangler fig trees remain intact after areas of forest are logged. Enrichment planting of strangler figs may, therefore, be a valuable tool for orangutan conservation in these landscapes.