Miracle-dz/UniFAR-FTUs huggingface dataset:
Observation and Modeling of Low Saline Water Flow through Gulf of Mannar during December 2016

ABSTRACT Anand, P.; Anubhav, C.A. , and Albert, P.I. , 2020. Observation and modeling of low saline water flow through Gulf of Mannar during December 2016. In: Sheela Nair, L. ; Prakash, T.N. ; Padmalal, D. , and Kumar Seelam, J. (eds.), Oceanic and Coastal Processes of the Indian Seas. Journal of Coastal Research, Special Issue No. 89, pp. 20-25. Coconut Creek (Florida), ISSN 0749-0208. Indian Ocean is unique as it features the reversal of currents in relation to the monsoonal winds. During the north-east monsoon season (November-February), the East Indian Coastal Current (EICC) is known to flow round Srilanka into the South Eastern Arabian Sea (SEAS) which transports low saline waters from the Bay of Bengal (BoB). Till now, due to lack of observational data along the Gulf of Mannar (GoM), it is not evident, whether part of the water that flows from BoB is passing through the Palk Strait/Bay and GoM to reach SEAS. There are two schools of thought existing, one is that, the flow exists through this passage. Efforts have been made to prove this using simulation studies. The second thought is that the Pamban Pass as well as the Adam's Bridge act as barriers for this passage of water. To study the subject in detail, CTD observations during December 2016 along GoM and west coast of India are utilized. Analysis shows that, the surface waters along transect of GoM is lower in salinity (33 psu) than that of SEAS (34 psu) with the lowest surface salinity along the easternmost transect. Different case studies, carried out using high resolution (10 km) Princeton Ocean Model (POM), suggested that the flow exists through the GoM. The signature of low saline water (33.2 psu) in this region is also well matched with that of the observations. The study concludes that, the flow through the Palk Bay/Strait and GoM have a distinct role in bringing the water from the Bay to SEAS.
A Revision of the Didelphid Marsupial Genus MarmosaPart 2. Species of the Rapposa Group (Subgenus Micoureus)

ABSTRACT In this report, the second of a revisionary series on mouse opossums (Marmosa), we analyze cytochrome b sequence data from 166 specimens of the subgenus Micoureus and delimit putative species using the multirate Poisson Tree Processes (mPTP) method. That analysis identifies 21 putative species, many of which can be matched with available names, including alstoni, constantiae, demerarae, limae, germana, meridae, paraguayana, parda, perplexa, phaea, rapposa, and rutteri. However, some of these nominal taxa are not morphologically diagnosable, and in the absence of other corroborating evidence, we do not recommend that they all be recognized as valid. Phylogenetic analyses of a multigene dataset suggest that putative species of Micoureus belong to several well-supported clades, one of which (the “Rapposa Group”) is revised in this report. As defined herein, the Rapposa Group includes at least three valid species: M. rapposa Thomas, 1899 (including budini Thomas, 1920); M. parda Tate, 1931; and M. rutteri Thomas, 1924. Herein we document their ecogeographic distributions and diagnostic traits, comment on their taxonomic histories, and list the specimens we examined (including all relevant type material).
Numerical simulation of the nanoparticle-surface collision in a liquid jet and its effects on material removal

Abstract. In ultrasmooth surface machining technologies, such as fluid jet polishing (FJP), a better understanding of the material removal mechanism is urgently required for the improvement of surface quality. The shape of the material removal on a polished surface after impact of a liquid jet containing nanoparticles was first measured and then compared with the calculated spatial distribution of the particle-wall collision along the solid surface. The consistency implies that the impact of particles on the workpiece surface is the main reason for material removal in FJP. Thus, to further understand the mechanism of the material removal of FJP, the characteristics of the particle-surface collision of a liquid jet are essential. For this, the effects of jet velocity, particle diameter, and particle density on the particle-surface collisions of a liquid jet were numerically investigated. The results show that the probability of the nanoparticle-surface collision of a liquid jet has a linear relationship with both the jet speed and particle density. Meanwhile, for the particle diameter, the relationship is nonlinear when the particle diameter is <500 nm and has little influence on the collision probability (PC); whereas, the PC increases almost cubically when the particle diameter is >500 nm. The present results provide more knowledge about the particle-surface collisions in a liquid jet and may be helpful for further understanding of the material removal mechanism in ultrasmooth surface processing technologies.
Sub-Mesoscale Circulation Features along the Andhra Pradesh Coast, Bay of Bengal: Observations from HF Radars

ABSTRACT Mandal, S. ; Pramanik, S. ; Sil, S. ; Arunraj, K.S. , and Jena, B.K. , 2020. Sub-mesoscale circulation features along the Andhra Pradesh coast, Bay of Bengal: Observations from HF radars. In: Sheela Nair, L. ; Prakash, T.N. ; Padmalal, D. , and Kumar Seelam, J. (eds.), Oceanic and Coastal Processes of the Indian Seas. Journal of Coastal Research, Special Issue No. 89, pp. 132-138. Coconut Creek (Florida), ISSN 0749-0208. This work presents the coastal circulation variability along the Andhra coast in western Bay of Bengal (BoB) using hourly high frequency radar (HFR, 4.4 MHz) derived ocean surface currents during 2015. The comparison of daily averaged HFR surface currents with satellite derived currents shows high correlations (0.86, 0.70) and low errors (0.28 m s-1, 0.25 m s-1) for the zonal and meridional components. The HFRs captured and quantified the dominant circulation features associated with the reversal of boundary currents. The northward (southward) currents are observed from mid-March till May end (October to November with highest current speed of ∼1.58 (1.24) m s-1 along the Andhra coast (between 15-17°N). In addition, the sub-mesoscale features associated with dominant circulation features are observed, like, the propagation of an anticyclonic eddy (81.80°E, 15.25°N) during March and cyclonic eddy (82.25°E, 15.40°N) during November. The mean vorticity pattern from the HFR currents shows the dipole nature during April - May to indicate the near coast upwelling signatures which are also supported by chlorophyll observations from satellite. The tidal driven currents, extracted from the HFRs, show that the semi-diurnal tidal constituent, M2 dominates during both the seasons with less intensity in March than in November. The distributions of the M2 tidal ellipses match well with the broad-scale current pattern in the offshore region. They follow the bathymetry in the nearshore region to indicate that it is driven by tides resulting in the generation of the shallow water tidal constituents (M3, M4, MS4 and M8) with significant amount of amplitudes.
Deep learning to calculate breast density from processed mammography images

Purpose: To calculate continuous breast density measures from processed images using deep learning. Method: Processed and unprocessed mammograms were collected for 3251 women attending the UK NHS Breast Screening Programme (NHSBSP). The breast density measures investigated included volumetric breast density, fibroglandular volume and breast volume. The ground truth for these measures was calculated using Volpara software on unprocessed mammograms. A deep learning model was trained and validated to predict each breast density measure. The performance of the deep learning model was assessed using a hold-out test set. Results: The breast volume and fibroglandular volume predicted with deep learning were strongly correlated with the ground truth (r=0.96 and r=0.88 respectively). The volumetric breast density had a Pearson correlation coefficient of 0.90. Conclusions: It is possible to predict volumetric breast density from processed images using deep learning.
Nonea pisidica (Boraginaceae-Boragineae), a new species from southwest Anatolia and its relationships inferred from karyology and cpDNA sequences

The new species Nonea pisidica (Boraginaceae-Boragineae) is described based on two collections by the authors from the region of Lake Burdur in southwest Anatolia, Turkey. It shows a combination of morphological characteristics concerning habit, flower, and fruit that distinguishes it from N. caspica and N. pallens, the two more similar taxa in Nonea sect. Nonea. Karyological analyses corroborate the separation of the three species, which have different chromosome complements and even base numbers. N. pisidica is characterised by 2n = 30, a complement previously unknown for west Asiatic species of Nonea. The dibasic haploid number x = 15 may have originated through amphidiploid hybridisation between two annual, diploid taxa with x = 7 and x = 8, such as N. lutea and N. caspica. The relationships of the new species were further analysed using trnLUAA sequences of the chloroplast genome, which were obtained for 16 selected species of Nonea. The resulting phylogeny confirms that it is related to N. caspica and N. lutea, but not to N. pallens, in spite of morphological resemblance. Lack of relationship with the south Mediterranean N. vesicaria, the only other species of Nonea known to have 2n = 30, suggests that amphidiploidy may have played an important role in speciation processes through recurrent and polytopical occurrence.
Automatic density prediction in low dose mammography

Estimation of breast density for cancer risk prediction is generally achieved by analysis of full-field digital mammograms. Conventional digital mammography should be avoided if possible in young women because of concerns about potential cancer induction, particularly in those with dense breasts who receive higher doses. This precludes repeated examinations over a short timescale to assess density change. We assess whether density can be accurately estimated in low dose mammograms with one-tenth of the standard dose, with the aim of providing a safe and effective method for use in younger women which is suitable for serial density measurement. We present analysis of data from an on-going clinical trial in which both standard and low dose mammograms are acquired under the same compression. We used both an existing convolutional neural network model designed to estimate breast density and a new model developed using a transfer learning approach. We then applied three methods to estimate density on the low dose mammograms: training on a different mammogram dataset; using simulated low dose data; and training directly on low dose mammograms using cross-validation. Pearson correlation coefficients between measurements on full dose and low dose mammograms ranged from 0.92 to 0.98 with the root mean squared error ranging between 3.37 and 7.27. Our results indicate that accurate density measurements can be made using low dose mammograms.
Tumor Necrosis Factor-α (TNF-α) Stimulates Proliferation of Mouse Primordial Germ Cells in Culture

Abstract Recent studies have shown that stem cell factor, leukemia inhibitory factor, and basic fibroblast growth factor increase proliferation and survival of the mouse primordial germ cells (PGCs) in culture. We now show that addition of tumor necrosis factor-α (TNF-α) to culture medium stimulates proliferation of PGCs without transforming them into embryonic stem cells and that its effect is specific for the PGCs at younger stages before and during their migration to gonads. A previously reported finding that TNF-α is expressed at these stages in mouse embryos suggests possible involvement of TNF-α in the proliferative regulation of the PGCs in the embryo.
Characterization of laser thermal loading on microelectromechanical systems-based fast steering mirror in vacuum

Abstract. Microelectromechanical systems (MEMS) have produced high-quality, high-bandwidth, small form factor, and inexpensive fast steering mirror (FSM) devices potentially suitable for a large variety of applications, such as image stabilization and beam pointing in satellite-based and ground-based, free-space optical communication systems. However, one outstanding question for this application is power handling. The absorption of the mirror substrate is low, but non-negligible, so the question remains of whether thermal loading from laser radiation on a MEMS mirror will deform its surface and, if so, to what extent. We show experimental results of optical performance changes due to thermal loading for MEMS two-axis FSM devices from Mirrorcle Technologies, Inc. Results and reproducible behavior are reported and compared in ambient versus vacuum conditions, where the benefits of convective cooling are absent. Finite element analyses corroborate the experimental results and show that the mirror substrate can deform due to thermal expansion imbalances. The deformation changes the focusing characteristics of the mirror, with a peak to valley defocus (second-order Zernike mode) of up to 50 nm when the mirrors are tested in ambient and up to approximately 450 nm when under vacuum. Such defocusing negatively impacts the link budget for laser-based satellite communications.
Influence of Rare Species on Electrofishing Distance When Estimating Species Richness of Stream and River Reaches

Abstract The electrofishing distance needed to estimate fish species richness at the stream or river reach scale is an important question in fisheries science. This distance is governed by the shape of the species accumulation curve, which, in turn, is influenced by a combination of factors, including the number of species, their overall abundances, habitat associations, the efficiency of the sampling method, and the occurrence of rare species. In this study we document the influence of rare species on the species accumulation curves from stream and river sites in data sets from five dispersed regions of the USA. Spatial discontinuity (i.e., a noncontinuous distribution within reaches) was observed in four of the five data sets, and the four data sets contained numerically rare species represented by one or two individuals (termed singletons and doubletons, respectively). Numerically rare species were typically proportionately rare (i.e., <1% of the total number of individuals captured), but proportionate.
Daily number of bee louse (Braula coeca) in honey bee (Apis mellifera carnica and A. m. syriaca) colonies maintained under semi‐arid conditions

Experimental work was conducted at two apiaries located in Irbid district and in Shuna North, Jordan, during the years 2004–2006. The aims of these investigations were to estimate the seasonal changes in the infestation rates of the bee louse (Braula sp.) and to develop an easy and rapid method of estimating the infestation rate on workers with bee Braula. Two major honey bee subspecies are reared in Jordan; Apis mellifera carnica and Apis mellifera syriaca were used in this study. The results showed that the infestation rate began to increase rapidly in May, reaching the season's maximum rate of 16.2%, 15.8% and 17.4% for A. m. carnica and 22.6%, 23.9% and 22.9% for A. m. syriaca in December of 2004, 2005 and 2006, respectively. The maximum adult numbers of bees were found in April and June, whereas the minimum for the year was in January in both honey bee subspecies colonies during the study period. The actual population of the bee louse could be estimated by counting the daily dropped lice and multiplying by a factor of 158. This factor is valid for the experimental colonies of both subspecies kept for 3 years under semi‐arid Mediterranean conditions.
Laboratory Automation and High-Throughput Biology Brief Clinical Evaluation of Six High-Throughput SARS-CoV-2 IgG Antibody Assays

Serological severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) assays are urgently needed for diagnosis, for contact tracing, and for epidemiological studies. So far, there are limited data on how recently commercially available high-throughput immunoassays, using different recombinant SARS-CoV-2 antigens, perform with clinical samples. Focusing on immunoglobulin G (IgG) and total antibodies, Kohmer et al. demonstrate the performance of four automated immunoassays—the Abbott Architect i2000 (N protein based), Roche cobas e 411 analyzer (N protein based, not differentiating between IgA, IgM, or IgG antibodies), LIAISON XL platform (S1 and S2 protein based), and VIRCLIA automation system (S1 and N protein based)—in comparison to two enzyme-linked immunosorbent assays (ELISAs) (Euroimmun SARS-CoV-2 IgG, S1 protein based; and Virotech SARS-CoV-2 IgG ELISA, N protein based) and an in-house–developed plaque reduction neutralization test (PRNT). The authors tested follow-up serum/plasma samples of individuals who were diagnosed with novel coronavirus 2019 (COVID-19) by PCR. When calculating the overall sensitivity in a time frame of 49 days after first PCR positivity, the PRNT as the gold standard showed the highest sensitivity with 93.3%, followed by the dual-target assay for the VIRCLIA automation system with 89%. The overall sensitivity in the group of N-proteinbased assays ranged from 66.7% to 77.8%, and in the S-protein-based assays from 71.1% to 75.6%. Five followup samples of three individuals were detected in either an Sor N-protein-based assay only, indicating different individual immune responses to SARS-CoV-2 and the influence of the assay used in the detection of IgG antibodies. This should be further analyzed. The specificity of the examined assays was ≥97%. Because of the low or unknown prevalence of SARS-CoV-2, however, the examined assays in this study are currently primarily eligible for epidemiological investigations, because they have limited information in individual testing (Kohmer, N., et al. J. Clin. Virol. 2020, 129, 104480). Understanding Metabolic Adaptation by Using Bacterial Laboratory Evolution and Trans-Omics Analysis
Antihypertensive Effects of Unripe Persimmon (Diospyros kaki L. cv. Hiratanenashi) Fruit and Its Component in Spontaneously Hypertensive Rats

1 Department of Food, Life and Environmental Sciences, Faculty of Agriculture, Yamagata University, 1-23 Wakaba-machi, Tsuruoka, Yamagata 997-8555, Japan. 2 Nittobest Co., 4-27 Saiwai-cho, Sagae 911-8610, Japan 3 Institute for Advanced Biosciences, Keio University, 403-1 Nipponkoku, Daihoji, Tsuruoka, Yamagata 997-0017, Japan 4 RIKEN Plant Science Center (Tsuruoka), 403-1 Nipponkoku, Daihoji, Tsuruoka, Yamagata 997-0017, Japan
Artificial Intelligence in Skin Cancer Diagnostics: The Patients' Perspective

Background: Artificial intelligence (AI) has shown promise in numerous experimental studies, particularly in skin cancer diagnostics. Translation of these findings into the clinic is the logical next step. This translation can only be successful if patients' concerns and questions are addressed suitably. We therefore conducted a survey to evaluate the patients' view of artificial intelligence in melanoma diagnostics in Germany, with a particular focus on patients with a history of melanoma. Participants and Methods: A web-based questionnaire was designed using LimeSurvey, sent by e-mail to university hospitals and melanoma support groups and advertised on social media. The anonymous questionnaire evaluated patients' expectations and concerns toward artificial intelligence in general as well as their attitudes toward different application scenarios. Descriptive analysis was performed with expression of categorical variables as percentages and 95% confidence intervals. Statistical tests were performed to investigate associations between sociodemographic data and selected items of the questionnaire. Results: 298 individuals (154 with a melanoma diagnosis, 143 without) responded to the questionnaire. About 94% [95% CI = 0.91–0.97] of respondents supported the use of artificial intelligence in medical approaches. 88% [95% CI = 0.85–0.92] would even make their own health data anonymously available for the further development of AI-based applications in medicine. Only 41% [95% CI = 0.35–0.46] of respondents were amenable to the use of artificial intelligence as stand-alone system, 94% [95% CI = 0.92–0.97] to its use as assistance system for physicians. In sub-group analyses, only minor differences were detectable. Respondents with a previous history of melanoma were more amenable to the use of AI applications for early detection even at home. They would prefer an application scenario where physician and AI classify the lesions independently. With respect to AI-based applications in medicine, patients were concerned about insufficient data protection, impersonality and susceptibility to errors, but expected faster, more precise and unbiased diagnostics, less diagnostic errors and support for physicians. Conclusions: The vast majority of participants exhibited a positive attitude toward the use of artificial intelligence in melanoma diagnostics, especially as an assistance system.
Raiz tuberosa de yacon (Smallanthus sonchifolius): potencialidade de cultivo, aspectos tecnológicos e nutricionais

Yacon (Smallanthus sonchifolius) is a plant that belongs to Asteraceae family, originated from Andean regions. The tuberous roots produced by this plant are similar to sweet potatoes in appearance, have a sweet taste and a crunch pulp, being often consumed "in natura". Yacon was not usually consumed until the 80s decade, when some peculiarities were discovered in its chemical composition that could be beneficial to human health. The tubers contain high water content, reduced energetic value and, unlikely most tuberous species which store the carbohydrates in the form of starch, yacon has fructooligosaccharides (FOS) as its main reserve carbohydrate, being highlighted by its bifidogenic activity. This paper presents a literature review on many aspects about this specie, such as historical origin, botanical features, chemical composition and post harvest effects.