Introduction: Physical distancing during the coronavirus Covid-19 pandemic has brought telehealth to the forefront to keep up with patient care amidst an international crisis that is exhausting healthcare resources. Understanding and managing health-related concerns resulting from physical distancing measures are of utmost importance.Objectives: To describe and analyze the volume, content, and geospatial distribution of tweets associated with telehealth during the Covid-19 pandemic.Methods: We inquired Twitter public data to access tweets related to telehealth from March 30, 2020 to April 6, 2020. We analyzed tweets using natural language processing (NLP) and unsupervised learning methods. Clustering analysis was performed to classify tweets. Geographic tweet distribution was correlated with Covid-19 confirmed cases in the United States. All analyses were carried on the Google Cloud computing service “Google Colab” using Python libraries (Python Software Foundation).Results: A total of 41,329 tweets containing the term “telehealth” were retrieved. The most common terms appearing alongside ‘telehealth’ were “covid”, “health”, “care”, “services”, “patients”, and “pandemic”. Mental health was the most common health-related topic that appeared in our search reflecting a high need for mental healthcare during the pandemic. Similarly, Medicare was the most common appearing health plan mirroring the accelerated access to telehealth and change in coverage policies. The geographic distribution of tweets related to telehealth and having a specific location within the United States  was significantly associated with the number of confirmed Covid-19 cases reported in each state (p<0.001).Conclusion: Social media activity is an accurate reflection of disease burden during the Covid-19 pandemic. Widespread adoption of telehealth-favoring policies is necessary and mostly needed to address mental health problems that may arise in areas of high infection and death rates. The novel coronavirus outbreak that started in Wuhan, China entered a new phase after the World Health Organization (WHO) officially declared it as a global pandemic on March 11, 2020 . As the Study design and data collectionThis cross-sectional study was conducted from March 30, 2020 to April 6, 2020. This study was exempted from the Institutional Review Board at Harvard Medical School because the data used are publicly available. In this instance, a consent form was not necessary. This study followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guidelines.Twitter is an online public social media platform that allows users to post 280-character posts. Publicly posted “tweets” from March 30, 2020 to April 6, 2020, were collected through the public streaming API (application programming interface).Text miningFull-text tweets were preprocessed by converting the sentences to words (Tokenization), removing unnecessary punctuations, tags, and stop words that do not have a specific semantic meaning . We applied a stemming function with lexicon normalization to reduce related words to a common word root . We isolated tweets with an identifiable location within the United States and constructed a density map. Preprocessing was done using the Natural Language Toolkit (NLTK) on Python 3.0 . Mapping Covid-19 confirmed caseshttps://github.com/CSSEGISandData/COVID-19) published by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University, Baltimore, MD, USA. The numbers presented in this manuscript were posted on April 4, 2020. The U.S. map illustrating the number of confirmed Covid-19 cases in each state was generated using Plotly; a Python open-source graphing library [Information on Covid-19 cases in the United States was obtained from the freely available public database  using Python 3.0 programming language .Descriptive analytics were performed to study the data collected. Tweet characteristics included account median number and range of followers. Generalized linear regression was performed to study the association between “telehealth” tweets and the number of confirmed Covid-19 infections. Statistical significance was set at p<0.05. Unsupervised learning was performed using K-means clustering algorithms to classify tweets into topics. We used the Elbow method to define the number of K dimensions where K depends on the number of topics. All data preprocessing, analysis, and visualization were performed on the Google Cloud computing service “Google Colab” . Every post containing the word “telehealth” was retweeted with a median number of two times (range 0-7556). The 10 most common words apart from “telehealth” that appeared in these tweets were “COVID”, “health”, “care”, “services”, “patients”, “pandemic”, “coronavirus”, “healthcare”, “access”, “need”. Mental health was the most common health-related topic that appeared in our search. Similarly, Medicare was the most common appearing health policy-related topic mirroring the accelerated response to telehealth and the changes in coverage policies. Unsupervised machine learning classification of tweets identified six clusters of tweets that contained words mostly related to: (1) mental health services, (2) digital health, (3) policies and advocacies, (4) hydroxychloroquine, (5) technology, and (6) general opinions.Geospatial distribution of tweets in the United StatesIn the United States, our analysis showed that tweets were most commonly posted from California (CA), New York (NY), Texas (TX), Florida (FL), and Pennsylvania (PA). In contrast, among the 20 most common tweeting U.S locations, tweets having “telehealth” were less likely to be posted from Tennessee (TN), Indiana (IN), Michigan (MI), Minnesota (MN), and Maryland (MD). The geographical distribution of tweets was illustrated on a U.S. density map Figure .In parallel, New York (NY) State has recorded the highest number of confirmed cases, followed by New Jersey (NJ), Michigan (MI), and California (CA). Among the 20 most infected states in the United States, Wisconsin (WI), North Carolina (NC), Maryland (MD), Tennessee (TN), Ohio (OH), and Indiana (IN) have the lowest numbers of confirmed cases Figure .Our analysis revealed an association between the number of tweets related to telehealth posted in a certain state and the number of confirmed Covid-19 cases in that particular state (p<0.001) Figure . This vaSocial media platforms have proven to be very engaging with the public . Data anIn fact, efforts to implement telehealth were rapidly mobilized across the United States on many levels. Healthcare plans and agencies provided regulatory relief and reimbursement policies to provide telehealth services during this public health emergency . Major MTelehealth services have rapidly and largely transformed healthcare delivery in areas of high infection rates. In parallel, social media platforms became an immense source of information on day-to-day events and a reflection of social interactions and responses. In this study, we showed that such platforms can be used to assess the needs of our communities and to embrace the healthcare response, resilience, and preparedness during pandemics. Nationwide efforts should focus on lifting provisions and scaling up resources to expand digital health implementation to address crisis-related sequelae.


Neither predictor accounted for significant amounts of variance, leaving the wide variability unexplained and likely arising from complex individual effects of the DS genotype. There was weak evidence that early development was faster for the APOE genotype conferring greater AD risk (ε4 carriers) consistent with recent observations in infant attention  and genetic  which modulates risk for AD in adulthood. There was no reliable effect of APOE genotype, though weak evidence that development was faster for the genotype conferring greater AD risk (ε4 carriers), consistent with recent observations in infant attention , exemplifying the multi-level, multi-method, lifespan developmental approach advocated by Karmiloff-Smith . We evaluate the possibility of linking variations in infant and child development with variations in the (elevated) risk for Alzheimer’s disease (AD) in adults with DS. We review the theoretical basis for this argument, considering genetics, epigenetics, brain, behaviour and environment. In studies 1 and 2, we focus on variation in language development. We utilise data from the MacArthur-Bates Communicative Development Inventories CDI; , and Multtention . Study 3ttention . We esta Neither predictor accounted for significant amounts of variance, leaving the wide variability unexplained and likely arising from complex individual effects of the DS genotype.Evaluation of environmental (socioeconomic status) and genetic , arising from trisomy of chromosome 21, is the most common genetic syndrome causing intellectual disability, with an incidence of between 1 in 750 and 1 in 1000 live births . Some ch1While general intellectual disability in DS is described as falling within the mild to severe range, there is marked individual variability; around 80% of individuals have moderate intellectual disability, but for some it is in the severe range and others have IQ scores falling within the normal range . The genAPP) gene, which is implicated in the brain pathology of AD, lies on the triplicated chromosome 21. Reports of the prevalence of dementia between the ages of 30 and 39 years range between 0 to 33% of individuals with DS, between 40-49 years of age from 5.7-55%, and between 50-59 years from 4-55% . This is largely due to the fact that the amyloid precursor protein  studies of adults have indicated differences in the size of macro-structures, with reduced cerebellar, hippocampal, and cortical volumes, and enlarged ventricles . DivergeAt the environmental level, differences may exist in parental behaviour in response to a child with a disability , or in the parents’ mental health such as parental depression  gene on chromosome 19. This is because it has been identified as a genetic risk factor for AD in the general population , and because it demonstrates notable individual variation across children with DS. In Section 2Language is a domain of vulnerability in DS . ReceptiIn the following study, we began by considering the extent to which SES level, measured by highest parental occupation, predicted variance in DS receptive and expressive vocabulary scores in early childhood.2.12.1.1The sample comprised 84 infants and young children with DS , recruited as part of the larger LonDownS study see . The chi2.1.2A parental questionnaire, the MacArthur-Bates Communicative Development Inventories: Words and Gestures version CDI;  was used2.1.3gross motor ; (2) fine motor ; (3) visual reception ; (4) receptive language ; and (5) expressive language (the ability to use sounds and language productively). MSEL is standardised for TD children between 0 and 68 months (from 0 to 33 months for gross motor domain). All participants scored under test ceiling on the MSEL language scales. The receptive and expressive language raw scores were normalised to the range 0 to 100, and averaged with the CDI scores of receptive and expressive vocabularies, similarly normalised, to create a language composite score. . Parental SES groups were: 1 managers / directors / senior officials, 2 professional occupations, 3 associate professional and technical occupations, 4 administrative and secretarial occupations, 5 skilled trade occupations, 6 caring, leisure and other service occupations, 7 sales and customer service occupations, 8 process, plant and machine operatives, 9 elementary occupations. The distribution of SES scores is shown in 2.22 = .751, F = 247.93, p < .001). The number of words produced was .57  times the number comprehended, with an intercept of -16.99 . A similar regression from the CDI TD norms generated the equation: number of words produced = .40 X number comprehended – 20.98. Thus, the DS 95% confidence intervals overlapped with the TD intercept, but the gradient was higher for DS than that for TD . The higher than expected expressive vocabulary given the level receptive vocabulary may have arisen because parents were allowed to include manual signs, increasing estimates of expressive vocabulary.Parental ratings of their children’s receptive and expressive vocabulary sizes are shown in There was large individual variability in the DS sample, evident in 2.3The current data, from a reasonably sized sample of infants and young children with DS compared to the literature, confirmed extensive individual differences in both receptive and expressive vocabulary. It is possible that some of the variance arises from limitations / noise in the CDI measure itself, though one would need to explain why this noise was larger for parental ratings of children with DS than those of TD children. Perhaps this could be due to difficulties in discerning poor articulation or the use of gestures in some children with DS. The relationship between receptive and expressive vocabulary showed a higher expressive vocabulary given receptive vocabulary than would be expected from the CDI norms. This differs from patterns reported elsewhere, of an expressive vocabulary deficit . Children were categorised according to whether they carried the ε4 risk allele (either heterozygously or homozygously) or did not carry the ε4 allele (ε2 and ε3 genotypes were not distinguished). There were 25 ε4 carriers  and 59 non-carriers , a proportion that reflects the distribution in the typical population  are included in p2 = .671; excluding participants at floor which may flatten the developmental trajectory: F = 76.57, p < .001, ηp2 = .519; expressive: F = 72.37, p < .001, ηp2 = .488; excluding floor: F = 35.00, p < .001, ηp2 = .330). There was no reliable effect of APOE genotype or interaction with age  = .75, p = .389, ηp2 = .010; interaction with age: F = 2.02, p = .159, ηp2 = .026; excluding floor: main effect F = 1.31, p = .256, ηp2 = .018, interaction F = 2.40, p = .126, ηp2 = .033; expressive, main effect: F = .78, p = .379, ηp2 = .010; interaction with age; F = .519, p = .473, ηp2 = .007; excluding floor: main effect F = 1.20, p = .277, ηp2 = .017, interaction F = 2.16, p = .146, ηp2 = .030). There were no reliable main effects of gender  = .33, p = .569, ηp2 = .004 ; expressive: F = .65, p = .423, ηp2 = .008) or interactions.Separate fully factorial analyses of covariance (ANCOVAs) were used to assess the role of  in days . Linear APOE genotype on our language composite measure, which was potentially more robust to measurement noise  = 1.49, p = .226, ηp2 = .018; interaction with age: F = 2.17, p = .145, ηp2 = .026). Finally, we entered SES into the model as an additional co-variate. SES showed no reliable main effect or interactions. In this more complex model, the main effect of APOE showed a trend  = 3.68, p = .059, ηp2 = .051), with higher scores for ε4 carriers compared to non-carriers, and there was also a trend for an interaction between APOE and age, with ε4 carriers scoring higher than non-carriers at younger ages and lower at older ages  = 3.60, p = .062, ηp2 = .050).We carried out a similar fully factorial ANCOVA assessing the effect of nt noise . There w3.3APOE genotype was deemed a possible predictor of individual differences since in adulthood in DS, it increases the risk of AD by a hazard ratio of 1.8 and there is evidence that the genotype also influences early brain development   were use4.2In order to contextualise the results for the five children with DS, we plot their MRI data against the larger cohort of fetuses and neonates with DS reported in Comparison of neonatal brain volume and MSEL score plots case by case suggest different patterns of relationship. The participant labelled DS_1 had below average brain volumes, which matched to below average scores in all MSEL scores at 6 months old. A similar consistent pattern was found in the participant labelled DS_2, but in this case, all values were above or around the group’s mean. However, this pattern did not hold in the other three cases, where being below the group mean for brain volume did not match to being below the group average in the cognitive outcome, and vice versa. In this small sample, therefore, the results were inconclusive.4.3The pilot data presented here demonstrate the viability of collecting the data to evaluate whether differences in neonatal brain volumes in DS are predictive of early cognitive development, but initial data are too few to draw conclusions, and work is still on going. It does appear possible to test the relationship between perinatal brain volumes and childhood cognitive outcome in children with DS, both of which reflect the net genetic effect of the DS mutation. For example, one could test the hypothesis that specific abnormalities in the cerebellum might predict subsequent motor deficits, or that cortical volumes would predict overall IQ or core cognitive measures such as language. Should such relationships arise, the mechanisms by which brain volumes relate to cognitive outcomes would need to be further established, as this may be driven by several factors, such as volumes of cellular bodies, neural synapses, white matter tracts or even extracellular matrix.5We focused on individual differences in DS – rather than differences compared to typically developing controls – seeking to integrate causes across multiple levels including genes, brain, behaviour, and environment. We also sought to conceptualise individual differences in a lifespan developmental framework, seeking potential links between the marked variation observed in early development and the elevated risk for dementia in adulthood. We asked whether it was possible to identify already in infancy and early childhood in this very high-risk population, risk or protective factors for later AD . Such a Beginning at the environmental level, we investigated whether SES would demonstrate the predictive power for variations in rate of vocabulary development that is observed in typical development e.g., . Karmiloeven though SES was not a reliable predictor in our data, there was extensive individual variability in vocabulary development. While our CDI data likely included measurement noise, SES predicted no more of the variance in a composite language measure that should have been more robust to noise.Nevertheless, our data suggest any such effects are weak, with the causes of DS variability either generated by the genotype or by environmental measures not indexed by parental occupation – that is, Lastly, connecting with the lifespan developmental perspective, we note that APOE genotype, and assessed whether it explained any of the variation we observed in early vocabulary development. There are at least three ways that early development and adult resilience to AD pathology may be linked: (1) development and pathological ageing may involve separate mechanisms, so that individual differences in the two would be independent; (2) development may contribute to resilience – in the form of brain reserve  or in the form of cognitive reserve    – but otAPOE genotype, so that children with the risk ε4 allele (N = 25) did not show different levels or rates of vocabulary development than those who were not carriers (N = 59). It is possible the sample was underpowered to detect a small effect size, so that the null result was therefore insufficient to distinguish the possibilities at this stage. The strongest effects were observed in the language composite measure, which combined parental ratings of vocabulary size with lab-based standardised assessments of receptive and expressive language skills. For this measure, a trend was observed towards an advantage for ε4 carriers that reduced with age . The fact that differences have been observed in the brain development of typically developing infant ε4 carriers compared to non-carriers . Recent data suggest that in DS, divergence from typical patterns of prenatal brain development can be observed as early as 22 weeks gestation , althougtography . This wotography . This wi5.1neuroconstructivism; The research reported here was inspired by the theoretical framework of Annette Karmiloff-Smith, both in its emphasis on a multi-level, multi-method approach and its commitment to placing developmental mechanisms at the heart of explanations of disorders and variability (Michael S.C. Thomas: Conceptualization, Formal analysis, Funding acquisition, Investigation, Methodology, Supervision, Writing - original draft. Olatz Ojinaga Alfageme: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Visualization, Writing - original draft. Hana D’Souza: Conceptualization, Data curation, Funding acquisition, Investigation, Project administration, Supervision, Writing - review & editing. Prachi A. Patkee: Conceptualization, Data curation, Formal analysis, Methodology, Project administration, Visualization. Mary A. Rutherford: Conceptualization, Funding acquisition, Investigation, Methodology, Supervision, Writing - review & editing. Kin Y. Mok: Data curation, Formal analysis, Methodology, Validation. John Hardy: Conceptualization, Funding acquisition, Investigation, Methodology. the LonDownS Consortium: Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing - original draft, Writing - review & editing. Annette Karmiloff-Smith: Conceptualization, Funding acquisition, Investigation, Methodology, Resources, Supervision.


The utilization of nickel slag has attracted much attention due to its high-content of valuable elements. As a part of these efforts, this work focuses on whether magnetite crystals, obtained from nickel slag via molten oxidation, magnetic separation, and ball-milling can be used as a microwave absorber. The composition, morphology, microstructure, magnetic properties, and microwave absorption performance were characterized by X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS), field emission scanning electron microscopy (FE-SEM), transmission electron microscopy (TEM), vibrating sample magnetometer (VSM), and vector network analysis (VNA). The results reveal that the magnetite crystals exhibit excellent microwave absorption properties because of the synergistic action between dielectric loss and magnetic loss. The minimum reflection loss (RL) of the particles obtained after 6 h ball-milling reaches −34.0 dB at 16.72 GHz with thickness of 5 mm. The effective frequency bandwidth (RL ≤ −10 dB) is 4.8–5.4 GHz and 15.9–17.6 GHz. Interfacial polarization of the particles could play a crucial role in improving absorbing properties because several components contained in the particles can dissipate electromagnetic wave effectively. The current study could show great potential in the preparation of magnetite crystals and utilization of nickel slag. As the demand for information technology and electronic equipment continues to rise, the utilization of high-performance broad-band microwave absorbing materials (MAM) becomes more critical in decreasing or even eliminating electromagnetic (EM) pollution, which impacts human health and disrupts electronic equipment functioning ,2,3,4. M2, ZnO, SnO2, and MnO2 [Magnetite is very attractive as an MAM because of its low cost and excellent magnetic properties. However, its direct use is difficult because of an impedance mismatch and poor antioxidation. Therefore, to solve this problem, magnetite can be combined with dielectric materials, such as SiOand MnO2 ,9,10,11.and MnO2 .Nickel slag is produced during the nickel metallurgy process as industrial solid waste. The large amount of nickel slag in the environment is a serious problem, resulting from largescale occupation of farmland and causing secondary environmental pollution ,14. ThusMagnetite particles (MPs) obtained from the oxidized nickel slag possess strong magnetic properties and hetero-interfaces formed by impurities, which are expected to be helpful in improving EM wave absorption. So far, however, there has been little discussion on the EM wave absorption of MPs gained from molten slag. Furthermore, considering microwave absorption strongly correlates with particle size , we condThe raw material was water-quenched nickel slag, acquired from flash furnaces in Jinchuan Group . Its composition is given in Iron recovered from the Ni-slag was oxidized into magnetite and then magnetically separated to obtain MIPs. For this purpose, Ni-slag was crushed into particles with size below 74 μm, then placed into an alumina crucible, and heated to 1450 ℃ for 30 min. The oxidized slag was then subjected to magnetic separation. 200 mesh MPs (marked as sample M0) were then ball milled at 300 rpm for 3 h (sample M1), 6 h (sample M2), and 12 h (sample M3), respectively. The ball-to-powder ratio was 30:1. After the ball milling, the resulting material was washed several times with ethanol and then dried at 60 °C for 12 h in a vacuum furnace.X-ray diffraction (XRD) patterns were recorded on the D8 ADVANCE instrument  with Cu-Kα radiation (λ = 1.5418Å) as an X-ray source. Crystal morphology and elemental analysis were obtained using an electron probe microanalysis (EPMA) performed using the EPMA-1600 instrument . X-ray photoelectron spectroscopy (XPS) measurements were performed on PHI-5000 Versa probe instrument . Field emission scanning electron microscopy (FE-SEM), transmission electron microscopy (TEM), high resolution transmission electron microscopy (HRTEM), and selected area electron diffraction (SAED) images were collected on JSM-6700F  and JEM-2100 (JEOL) instruments, respectively. Room temperature magnetic hysteresis loops were conducted by vibrating sample magnetometer (VSM), Lake Shore 7307 . Electric resistivity was measured by a four-probe method using a double electrical RTS-9 tester . EM parameters were tested by a vector network analysis (VNA) coupled with a coaxial line method performed on the Agilent N5244A instrument . All the powders were uniformly dispersed in paraffin matrix with a filling ratio of 70 wt% and pressed into a toroidal ring with outer diameter of 7.00 mm, inner diameter of 3.04 mm, and thickness of 2.00 mm. Complex permeability and permittivity were obtained from experimental scattering parameters according to the standard Nicolson-Ross and Weir theoretical calculations.2+ from the nickel slag was able to dissolve and replace Fe2+ in magnetite.Nickel slag was crushed into particles with specific size and then oxidized during the heating process, causing magnetite formation, as shown in Fayalite, the most common iron-bearing phase in Ni-slags, transforms to magnetite below 1723 K, as shown in hklβ is the full width at the half-maximum (FWHM) of the corresponding diffraction peaks, , θ represents the Bragg’s angle, k is the shape factor , λ stands for the X-ray wavelength, δ means the grain size, and ε is the internal stain.The Hall-Williamson equation ,23 is ofThe grain size decreases slightly during the early stages of the ball-milling process due to the silicate separation from the samples, as shown in 2+ approaches that of Mg2+.The backscattering electron image of the oxidized nickel slag shown in d demonst2+ in Fe3O4, while peaks at 714.18 and 725.57 eV are assigned to Fe3+ in Fe3O4. The satellite peak at 719.05 eV indicates the co-existence of Fe2+ and Fe3+ [XPS analysis shows the presence of Mg, Fe, O, Ca, and Si in the M2 sample see a, which and Fe3+ . AnotherThe particle size of the M0 sample is in the range of 20–60 μm see . It can Ms) is about 30.8 emu/g for M0, and increases to 70.4 emu/g for M1 and up to 103.4 emu/g for M2. This is probably attributed to the formation of small clusters with high Ms, for example, α-Fe3Si has a Ms of 150 emu/g. On the contrary, Ms of the M3 decreases down to 39 emu/g, which is mainly ascribed to its increasing defects density and the amorphous layer on its surface [The magnetic properties of materials directly correlate with their ability to absorb electromagnetic wave energy.  surface ,27.c) of M0, M1, M2, and M3 samples are 83.4, 187.3, 203.3, and 264.2 Oe, respectively. The M3 sample shows the highest Hc value, resulting from the increase of defect density and decrease in grain sizes, which cause the material to become hard to magnetization for the pinning effect [The coercivity forces . Therefore, M2 has the highest μ″ value in the 2.0–8.0 GHz. The variation of dielectric and magnetic loss tangents  are shown in δμ value is larger than the tan εδ value at low frequencies, indicating that magnetic loss is stronger than dielectric loss for all samples. However, dielectric loss prevails in the 12.0–18.0 GHz. follows :(4)μi=Mssε and ∞ε are the static dielectric constant and the dielectric constant at an infinite frequency, respectively. Thus, ε′ plotted as a function of ε″ would be a single semicircle, known as a Cole-Cole semicircle. Each semicircle represents a Debye dipolar relaxation. Experimentally obtained ε′ values plotted as a function of the corresponding ε″ values for M0, M1, M2, and M3 are given in Many studies have reported that Debye dipolar relaxation can promote EM wave absorption. The relationship between ε′ and ε″ can be described by the Debye theory  as folloμ0 is the permeability in a vacuum, and ƒ is frequency. −(μ″(μ′)2−ƒ1) in Equation (6) would be constant if the magnetic losses are mostly caused by Eddy current losses. This phenomenon can be found after 16 GHz in Magnetic loss also plays a crucial role in EM wave absorption. The natural and exchange resonances, as well as eddy current loss, contribute to magnetic loss in the GHz region. The eddy current losses can be expressed by the following equation :(6)μ″ values for our samples using the transmission line model. Based on the model of the metal-backed materials acting as a microwave-absorption layer, the reflection loss (RL) values were calculated according to the transmission-line theory by Equations (7) and (8) :(7)Zin=Zλ/4 model) is used to optimize the material thickness according to the best performance data. The relationship between peak frequency and material thickness (tm) can be described by Equation (9) [n represents the uneven number, λ is the wavelength, and ƒm is frequency. When an EM wave enters a material, two reflection microwaves are generated from the interfaces of absorber–metal and absorber–air. If the matching thickness of the absorber obeys Equation (9), the interference cancellation of EM waves occur at the interface of the air–absorber. The RL peaks are consistent with the λ/4 model when their thickness changes from 1.0 to 5.0 mm, as shown in λ/4 model.The thickness of the absorber significantly affects the microwave absorption performance. Therefore, the quarter-wavelength matching model (tion (9) :(9)tm=nλZ = |Zin/Z0|. When Z approaches 1, optimal impedance matching is obtained. Therefore, the smallest RL value can be obtained at 16.7 GHz corresponding Z close to 1 α=2πThus, ball-milling processing can improve RL values and expansion of the effective bandwidth. For example, the M3 RL value is close to M2, which could be attributed to its smaller particle size, higher defect density, and lower silicates content. The EM wave absorption performances of MPs and other related composites are shown in In short, the advantages of using as-prepared MPs to design MAM are as follows: (i) ball-milling can not only reduce particle size but also introduce numerous defects, which strengthens the dielectric relaxation; (ii) the multi-interfacial polarization induced between silicates and magnetite is beneficial for improving their microwave absorption performance; (iii) the synergy effect of dielectric loss and magnetic loss can be optimized to form good impedance matching. These descriptions are graphically shown in This work demonstrates an inexpensive and environment-friendly method to prepare magnetite particles that can be used as MAM. Ball milling is used to tailor the MPs’ morphology and size—that which improves their microwave absorption properties—attributable to particle size effect and silicates’ dissociation. The results indicate that particle size and heterogeneous interface structures significantly affect MPs’ microwave absorbing properties. MPs milled for 6 h showed the lowest RL value −34 dB at 16.72 GHz with thickness of 5 mm; effective absorption bandwidth was about 2.3 GHz. As the ball-milling time increases to 12 h, the microwave absorption capacity decreases to −31.4 dB. This work provided insights into the preparation of MAM from slags and tunable absorbing properties by the ball-milling method.


Sweet cherry production faces new challenges that necessitate the exploitation of genetic resources such as varietal collections and landraces in breeding programs. A harmonized approach to characterization is key for an optimal utilization of germplasm in breeding. This study reports the genotyping of 63 sweet cherry accessions using a harmonized set of 11 simple sequence repeat (SSR) markers optimized in two multiplexed PCR reactions. Thirty-eight distinct allelic profiles were identified. The set of SSR markers chosen proved highly informative in these germplasm; an average of 6.3 alleles per locus, a PIC value of 0.59 and above-average expected and observed heterozygosity levels were detected. Additionally, 223 amplified fragment length polymorphism (AFLP) markers derived from eight selective primer combinations were employed to further differentiate 17 closely related accessions, confirming the SSR analysis. Genetic relationships between internationally known old cultivars were revealed: SSR fingerprints of “Schneiders Späte Knorpelkirsche” and “Germersdorfer” were found to be identical to those of the standard cultivar “Noire de Meched”, among others, whereas four accessions known as “Hedelfinger Riesenkirsche” and four known as “Große Schwarze Knorpelkirsche” showed allelic differences at various loci. The genetic diversity of locally-grown cultivars worldwide might be currently underestimated. Several autochthonous Austrian sweet cherry germplasm accessions were genotyped for the first time and their genetic relationships analyzed and discussed. Interestingly, seven Austrian sweet cherry landraces were shown to be clearly genetically separated from international and modern varieties, indicating that Austrian germplasm could include valuable genetic resources for future breeding efforts.  Autochthonous varieties and their wild relatives are valuable genetic resources for the breeding and development of any domesticated crop ,2,3,4. GDespite a long-standing history of sweet cherry growing , the ecoRecent surveys of sweet cherry landraces in the provinces Burgenland ,12 and UEnvironmental and phytosanitary factors can lead to morphological variation in plants , potentiThe deployment of genetic markers such as simple sequence repeats (SSRs), or single nucleotide polymorphisms (SNPs) to reveal a characteristic genetic fingerprint, considerably accelerates and simplifies the process of cultivar grouping and identification ,18,19,20SSR markers inherently cover a very limited part of the target genome. They are not universally polymorphic. A defined set of markers could reveal genetic polymorphism within one population but be less informative for a second group of genotypes, e.g., closely related landraces from another gene pool ,26,27. AThis is the first study reporting genetic fingerprints of autochthonous Austrian landraces and germplasm accessions of sweet cherry. The research questions we wanted to address were the following: (1) Are the SSR marker set and the multiplex method we chose effective? Do they provide fast and reliable results that can be harmonized? (2) Can Austrian sweet cherry landraces be successfully differentiated by the selected set of SSR markers? (3) Have the selected accessions and landraces been correctly identified? n = 29) representing old landraces and probably regional selections of known cultivars in the traditional high-stem meadow orchard growing system; and (2) Sample Set BOK: University of Natural Resources and Life Sciences, Vienna (BOKU) germplasm collection, Vienna, AT (n = 23) including Austrian landraces and some modern varieties. Additionally, ECPGR reference genotypes from East Malling Research, United Kingdom (GB) (n = 5) and United States of America (US) reference genotypes (n = 6) were included in Sample Set IS. These reference genotypes were used to standardize the SSR data as described by [Plant material for SSR-analysis was collected at two locations. (1) Sample Set STB: Stoob, Burgenland, AT  (2) Scharten, Upper-Austria (n = 5) and (3) BOKU germplasm collection (n = 8), Vienna. ECPGR reference genotypes “Noire de Meched” and “Noble” were included in the analysis  Leithaberg, Burgenland  and diluted to a concentration of 2.5–4 ng/µL. For the reference accessions, lyophilized leaf-samples were extracted and diluted accordingly. The genomic DNA for the US reference accessions was kindly provided by Dr Amy Iezzoni’s team in Michigan State University.For AFLP analysis, DNA was extracted from lyophilized leaf-samples according to  with minTwelve primer pairs recommended by the ECPGR  and suitPrimer concentrations in PCR reactions were optimized to produce similar intensity of chromatogram peaks to facilitate scoring. The final volume of PCR reactions was 13 µL containing 5–8 ng genomic DNA, 2× Type-it Multiplex PCR Master Mix , 1.25–5 nmol of primer, the exact quantity depending on each primer .PCR was started with a denaturing step at 95 °C for 5 min, followed by 10 circles of touchdown-PCR: 95 °C for 30 s, 60 °C for 1.5 min (−1 °C per cycle) and 72 °C for 30 s, followed by 18 cycles: 95 °C for 30 s, 50 °C for 1.5 min and 72 °C for 30 s, with a final elongation step of 60 °C for 30 min.® and GENOTYPER® . Scored fragment sizes were harmonized with those of the reference genotypes using the allele sizes published by [The amplicon sizes were measured against a LIZ 500 standard with an ABI 3130 Genetic Analyzer . Subsequent scoring was done with GENESCANished by . 2, 0.2 mM of dNTPs. The pre-amplified template was diluted 1:10 and 4 µL were incubated in a 10µL-PCR reaction with 0.5 pM of each of the selective primers. For this final selective amplification, 18 different primer combinations were tested on nine varieties. The eight most promising combinations were selected for the analysis: ATC/ATC, ACC/ATC, ACC/CAG, AGG/AGT, AGG/CAG, ATA/ATC, ATA/AGT, ATA/CAG. AFLP-fragments were run on a LI-COR (NEN Model 4300) analyzer  and scored for presence (1) or absence (0) by hand with SagaTM (Version 3.0) . For all samples, except two, biological replicates  for estimation of clonal variation were done and run side by side. Additionally, a technical control was run for four samples . Three types of negative controls, one from the start and one for each of the PCRs were included. Only clearly visible bands between 90 and 400 bp of length were scored.AFLP analysis was performed following the protocol by Vos et al. with the following modifications: Genomic DNA (0.3 µg) was incubated with 3.6 U Tru1I and 45 U EcoRI in 25 µL Tango-Buffer  for 1 hour at 37 °C, followed by two hours at 65 °C and 15 min at 85 °C. 5 µL of the adapter-ligation-solution as described by Vos et al.  were addFor SSR data and allele frequencies, the identification of unique alleles was done in GenAlEx version 6.5 ,42. polyThe calculation of the genotype association curve , frequenThe genotype association curve  reveals The DAPC was done in R, package adegenet version 2.1.3  for SSR For AFLP-scores the PCoA was done in GenAlEx ,42. ThisPrunus avium, obtaining reliable results [Eleven of twelve SSR markers gave unambiguous results in the multiplex approach, which could be standardized. Scores of BPPCT034  could no results ,19.The number of alleles per locus ranged from 3 to 9, with an average of 6.3; PIC values ranged between 0.22 (EMPa017) and 0.78 (BPPCT037), with an average of 0.59. The observed and expected heterozygosity ranged between 0.06 resp. 0.23 (EMPA017) and 0.89 resp. 0.81 (BPPCT037) depending on the SSR marker . Eight different genotypes  show unique alleles in up to three markers. The best markers in terms of detecting unique alleles in different genotypes were CPPCT022 and EMPaS10, with unique alleles amplified in six and five genotypes, respectively.The genotypes obtained by SSR fingerprinting allowed us to confirm or reject the morphological identification of landraces and germplasm accessions and revealed the occurrence of homonyms and synonyms in otherwise well-known varieties. Some phenotyping, grafting or labelling errors were also detected . Four trees in Stoob were phenotyped as cultivar “Große Schwarze Knorpelkirsche”; only two showed the same genotype and all of them differed compared to the reference accession in the BOKU germplasm collection. Two of three trees phenotyped as “Hedelfinger Riesenkirsche” and collected in Stoob had the same fingerprint, but all of them differed from the two accessions of the same name in the BOKU germplasm collection, which also turned out to show two distinct genetic profiles. Some clonal variants were also identified such as the samples phenotyped as landrace “Butterkirsche” and the two types of “Kritzendorfer Einsiedekirsche”. Six of the sampled Austrian landraces showed to be identical to several germplasm accessions and standard cultivars from different origins. This group of seemingly identical genotypes includes the accessions “Germersdorfer”, “Schneiders Späte Knorpelkirsche”, the ECPGR reference cultivar “Noire de Meched” and the US-reference “Schneiders”, as well as traditional Austrian cultivars like “Melker Riesenkirsche” and “Horitschoner Herzkirsche”. For simplification purposes this group of accessions will be mentioned as the “Schneiders-Group” throughout this study.An AFLP-analysis including further samples from two other sampling sites was conducted to confirm if these cultivars are true clones i.e., synonyms (see below). The Discriminant Analysis of Principal Components  was used to identify groups of genetically related individuals in all three sample sets. This analysis has been developed and is suitable for clonal or partly clonal populations, since it does not rely on Hardy–Weinberg or linkage disequilibria . The varCluster 1 consists of nine multi-locus-genotypes (MLGs) and contains landraces from Burgenland  as well as the rootstocks “NY54” and “F12/1” . Only onAccording to Simpson’s diversity index lambda, , DAPC-clThe expected heterozygosity (Hexp) of all three clusters were very similar, with values ranging from 0.595 (cluster 2) to 0.63 (cluster 1).The dendrogram  was calcDAPC-cluster 1 (red) is represented on the upper end missing “Kritzenorfer Einsiedekirsche”, which groups with the varieties of the cluster below. NY45 groups together with Goodnestone Black (uppermost end), and F12/1 appears separated from all other varieties on the bottom end of the dendrogram. The other two DAPC clusters appear to be admixed, whereas notably all genotypes of “Hedelfinger Riesenkirsche” appear in one cluster and all genotypes of “Große Schwarze Knorpelkirsche” in another. A separate cluster consists of “Hybrid 222”, i.e., “Burlat VG”. Unlike DAPC analysis, the dendrogram shows the rootstock “F12/1” as more genetically distant from the rest of the varieties . This min = 7), “Horitschoner Herzkirsche” (n = 3), “Melker Riesenkirsche” (n = 2), “Schneiders Späte Knorpelkirsche” (n = 3) of three origins and the ECPGR standard “Noire de Meched” were tested. We also included the clearly distinct phenotypes “Noble” , rootstock “F12/1” as well as the Austrian landrace “Rainkirsche” (K02)  . Four ruAn AFLP analysis with eight selective primer combinations and 17 samples of four different origins resulted in 223 markers, of which 64 (28.7%) were polymorphic. The error-rate was moderate, with 1.36–1.79% of difference in band-occurrence between technical replicates. Biological replicates differed up to 6.73%. The samples of “Noble” could clearly be separated from the other samples with PCoA . The perThe optimized multiplex-PCR approach to SSR genotyping was broadly successful; 11 of the 12 SSR-markers were easily scored and standardized against published data for five reference accessions (data not shown). This gives us confidence in the quality of the data generated which should prove straightforward to compare with similarly standardized data for other germplasm collections in the future The number of markers was comparable to that of similar studies ,23,27,51The chosen primer combination was found to be effective for differentiating between most of the Austrian sweet cherry landraces tested. Regarding the accurate identification of duplicates (i.e. synonyms) and homonyms , both could be detected by SSR-analysis, providing essential information for the optimal management of the germplasm collection and future breeding approaches.In DAPC cluster 2 , there aFour of five samples phenotyped as “Hedelfinger Riesenkirsche” showed differences in allele sizes. The cultivar “Hedelfinger Riesenkirsche” originates from Hedelfingen in Germany where it was selected from seedlings and taken to Hohenheim (Germany) around 1850. Thereafter, this cultivar has been widely distributed by tree nurseries [“Große Schwarze Knorpelkirsche” as well as “Hedelfinger Riesenkirsche” were assigned to the same DAPC cluster; both have been referred to as “population cultivar”, indicating that the cultivar consists of several phenotypes . This coOn the other hand, three not previously identified genotypes of distinct phenotypes found in Stoob showed the same fingerprint  are found in this group, along with the ECPGR reference cultivar “Noire de Meched” and the US-standard “Schneiders”. Although these findings could be due to limited SSR-marker resolution, suspicions that some of the cultivars could be clones and their names therefore synonyms have been raised before. Braun-Lüllemann and Bannier  found thThis finding is especially intriguing since this latter cultivar was found to be comparable in fruit size to widely distributed modern cultivars like “Regina” or “Kordia”, and thus represents a potential candidate for breeding. Considering that maximizing fruit size still is one of the most important objectives in sweet cherry breeding , it is eTo our knowledge, until now there has been no such study. To gather additional evidence on the correct genetic identity of the above-mentioned varieties which have identical SSR profiles, an AFLP analysis was conducted.We compared the ECPGR standard “Noire de Meched”, accessions of the Austrian germplasm collection “Germersdorfer”, “Schneiders Späte Knorpelkirsche”, “Melker Riesenkirsche” and “Horitschoner Herzkirsche”, as well as samples from two different sites consisting of several landraces identified as one of the just mentioned cultivars. Three genetically and phenotypically different accessions were included for comparison. Prunus avium varieties, which puts more weight on such technical errors. To sum up: based on the results shown in this study genetic differences among the tested varieties exist. These genotypes might represent valuable resources for future breeding efforts, if they have superior traits, e.g., disease resistance or superior fruit size. Based on the results of the AFLP analysis, the tested varieties in general show low genetic variation; only about 29% of markers were shown to be polymorphic. This is in agreement with reported polymorphic rates of 21% for AFLP markers in sweet cherry . TechnicThe genetic diversity of the Austrian landraces evaluated was subsequently compared to that of international standard cultivars based on the results of the DAPC and calculated diversity indices. The DAPC sorted the samples into three clusters. In cluster 2, all samples of “Hedelfinger Riesenkirsche” and “Große Schwarze Knorpelkirsche” group together with “Goodnestone Black”, “Napoleon”, “Stella Spur”, “Lambert”, “Lapins”, “Ulster”, “Tavriczskai” and “Sarga Dragan”. “Chelan” groups with “Noble”, “Early Burlat”, “Burlat”, “Jaboulay” and “Früheste der Mark” in DAPC cluster 3, whereas DAPC cluster 1 comprised only varieties from Burgenland, i.e., landraces not mentioned in the available literature. These autochthonous varieties seemed clearly distinct from the other groups and may therefore constitute a valuable germplasm for breeding as members of a regional gene pool. Details on valuable phenotypic and physicochemical characteristics of these varieties such as unique taste, low susceptibility to rain-induced cracking or high content of polyphenols in the fruit have been recorded in prior studies ,12,58. TA successful method of fast and easy-to-use multiplex SSR analysis for international harmonization of sweet cherry accessions was presented. The investigated collection of autochthonous Austrian sweet cherry landraces is highly diverse and could constitute a valuable germplasm for future breeding programs, since Austrian landraces were shown to represent a regional gene pool. They exhibit interesting traits that might be valuable for breeders  and are Concerning the various genotypes of “Große Schwarze Knorpelkirsche” and 2Hedelfinger Riesenkirsche”, marker assisted selection (MAS), field trials and cultivar evaluations should be conducted to identify the most valuable of the clones for breeding purposes.It would be interesting to compare the genetic diversity of Austrian landraces with those from the French collection described by Mariette et al. . Does itPhenotype-based surveys on sweet cherry diversity have been conducted for some Austrian regions ,11,12,13Part of these gaps could effectively be filled by genetic evaluation, as has been shown in this study. Homonyms, synonyms and labeling errors were detected. The genetic data help to evaluate the genetic diversity, identity and trueness to type of Austrian sweet cherry accessions and thus serves as an important and valuable tool for the management of Austrian germplasm collections.


For instance, allopregnanolone and 7α-hydroxypregnenolone are actively produced in the pineal gland, unlike in other brain regions. Pineal 7α-hydroxypregnenolone, an up-regulator of locomotion, enhances locomotor activity in response to light stimuli in birds. Additionally, pineal allopregnanolone acts on Purkinje cells in the cerebellum and prevents neuronal apoptosis within the developing cerebellum in juvenile birds. Furthermore, exposure to light during nighttime hours can cause loss of diurnal variations of pineal allopregnanolone synthesis during early posthatch life, eventually leading to cerebellar Purkinje cell death in juvenile birds. In light of these new findings, this review summarizes the biosynthesis and physiological functions of pineal neurosteroids. Given that the circadian rhythms of individuals in modern societies are constantly interrupted by artificial light exposure, these findings in birds, which are excellent model diurnal animals, may have direct implications for addressing problems regarding the mental health and brain development of humans.Similar to the adrenal glands, gonads, and placenta, vertebrate brains also produce various steroids, which are known as “neurosteroids.” Neurosteroids are mainly synthesized in the hippocampus, hypothalamus, and cerebellum; however, it has recently been discovered that in birds, the pineal gland, a photosensitive region in the brain, produces more neurosteroids than other brain regions. A series of experiments using molecular and biochemical techniques have found that the pineal gland produces various neurosteroids, including sex steroids,  Similar to the gonads and placenta, vertebrate brains actively also produce various steroid hormones. These steroid hormones produced in the brain are named “neurosteroids.” The production of neurosteroids was demonstrated firstly in mammals, and then in other vertebrates –5. Thus,It is known that neurosteroids are produced in glial cells and neurons of the central and peripheral nervous systems , 5. HoweThe pineal glands of vertebrates respond to light stimuli and fulfill important functions in the organization of circadian rhythms. The secretion of melatonin, a major hormone produced by the pineal gland, shows a clear daily rhythm with its peak concentration occurring at night , 9. HoweCyp11a gene). We have demonstrated by transcription-polymerase chain reaction (RT-PCR) that the pineal gland in juvenile birds expresses P450scc mRNA , 3α-hydroxysteroid dehydrogenase/Δ5-Δ4-isomerase , 3β-hydroxysteroid dehydrogenase/Δ5-Δ4-isomerase , 5α-reductase (encoded by the Srd5a gene), 5β-reductase (encoded by the Srd5b gene), cytochrome P450 17α-hydroxylase/c17,20-lyase , 17β-hydroxysteroid dehydrogenase , and cytochrome P450 aromatase , are expressed in the pineal gland of birds  receptor and may also act as an agonist of the membrane progesterone receptors α (mPRα), as well as the mPRβ and mPRγ (Allopregnanolone acts mainly as a ligand of the γ-aminobutyric acid type A (GABAand mPRγ –21. Therand mPRγ . Furtherand mPRγ –26 has band mPRγ . It has and mPRγ . These fand mPRγ .It is known that environmental stimuli affect the development of animals including humans. In vertebrate brain development, a natural light-dark cycle promotes better brain development than constant conditions, such as constant light or constant darkness –31. HoweThis review summarized the recent data on pineal neurosteroids. Studies have indicated that the pineal gland produces neurosteroids from cholesterol in birds. Pineal 7α-hydroxypregnenolone regulates locomotion in response to light stimuli in birds. Pineal allopregnanolone prevents the death of developing Purkinje cells by suppressing neuronal apoptosis during development. In addition, circadian disruption by light exposure during nighttime leads to cell death of developing Purkinje cells through pineal allopregnanolone-dependent mechanisms in juvenile birds. These observations suggest that nighttime artificial light exposure in modern societies may also perturb the development of the human brain.Almost all animals have circadian rhythms. However, modern life conditions chronically disrupt circadian rhythm through artificial light exposure. The disruption of circadian rhythm is associated with a decline in mental and physical health –34. The SH and KT wrote the manuscript. All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Data in this article is associated with our research article, Electronic Properties of Fe Charge Transfer Complexes – a Combined Experimental and Theoretical Approach [1].II complexes are presented here, as extracted from the redox data obtained from its associated cyclic voltammograms, which were measured at scan rates varying from 0.05 V.s−1 to 5.00 V.s−1, under similar experimental conditions. Acetonitrile was used as solvent, and tetrabutylammonium hexafluorophosphate as supporting electrolyte. All data are reported versus the FeII redox couple in ferrocene.The oxidation and reduction potentials of fourteen Fe Specifications TableValue of the Data•II/III redox couple of fourteen polypyridine FeII complexes at scan rates 0.05 – 5.0 Vs−1.Cyclic voltammograms provide oxidation potential data of the Fe•II in the fourteen FeII polypyridine complexes are determined by the functional groups on the polypyridine ligands.The ease of oxidation of Fe•The suitability of the complex as a catalyst is determined by the redox data of the complexes. As opposed to the platinum group metals, iron is an earth-abundant and eco-friendly metal.•The suitability of the complex as redox indicator is determined by the redox data of the complexes.•Oxidation potential data, amongst others, of metal-to-ligand charge transfer complexes determine if complexes qualify for application as photo-active mediators or dyes in dye sensitized solar cells.•2 and/or H2O in the production of environmentally friendly fuels.Metal-to-ligand charge transfer complexes also serve as important catalysts in photo-catalytic reduction of CO1II complexes are presented in this report. These complexes, 1–14, contain 1,10-phenanthroline and substituted 1,10-phenanthroline, bipyridine and substituted bipyridine, and terpyridine ligands, see 1 – 14 vary from electron donating (OMe and Me) to electron withdrawing (Cl and NO2). Comparative CVs of 1 – 14, at a scan rate of 0.10 Vs−1 CVs II complexes are of practical use in its applications as redox indicators, catalysts and photo-active mediators in, amongst others, dye sensitized solar cells −1 to 5.00 Vs−1, See −1 CVs from The redox data of fourteen octahedral Fe2via cyclic voltammetric measurements were done as described previously [−3 (or saturated) solutions of complex. Dissolution was complete, in CH3CN, containing 0.100 mol dm−3 tetrabutylammonium hexafluorophosphate  as supporting electrolyte. A three-electrode cell was used, consisting of a glassy carbon (surface area 3.14 × 10−6 m2) working electrode, a Pt auxiliary electrode and a Ag/Ag+ (0.010 mol dm−3 AgNO3 in CH3CN) reference electrode −1. All experimental potentials were referenced against the redox couple of ferrocene, FcH/FcH+ (IUPAC), as internal standard Epa (Epc) = anodic (cathodic) peak potential and pai (pci) = anodic (cathodic) peak current. The peak potential separation is determined by ΔEp = Epa – Epc and peak current ratio by pc/ipai.Electrochemical studies eviously ,5,6. A BThe authors declare that there is no conflict of interest regarding the publication of this article.


To establish such a system, we worked with the broad host-range, highly destructive, soil-borne oomycete pathogen, Phytophthora capsici. We used an isolate expressing red fluorescence protein (RFP) to establish a microtiter plate, real-time assay to quantify pathogen growth in live tissue. The system was successfully used to monitor P. capsici growth in planta on cucumber (Cucumis sativus) fruit and pepper (Capsicum annuum) leaf samples in relation to different levels of host susceptibility. These results demonstrate usefulness of the method in different species and tissue types, allowing for highly replicated, quantitative time-course measurements of pathogen growth in vivo. Analyses of pathogen growth during initial stages of infection preceding symptom development show the importance of very early stages of infection in determining disease outcome, and provide insight into points of inhibition of pathogen growth in different resistance systems.Effective assessment of pathogen growth can facilitate screening for disease resistance, mapping of resistance loci, testing efficacy of control measures, or elucidation of fundamental host-pathogen interactions. Current methods are often limited by subjective assessments, inability to detect pathogen growth prior to appearance of symptoms, destructive sampling, or limited capacity for replication and quantitative analysis. In this work we sought to develop a real-time,  Measurement of pathogen growth on plant tissue is an important tool in developing disease management strategies. It can facilitate phenotyping to identify resistance loci, test efficacy of control measures, or elucidate fundamental host-pathogen interactions. Traditional methods to measure disease response, such as calculation of host survival rates, scoring of visual symptoms, proportion of plants exhibiting symptoms, or symptom severity provide useful information about disease progression. While effective and useful in the field, these visual approaches have limited accuracy, require training, and can be affected by inherent bias of the researcher . In receQuantification of pathogen growth typically relies upon the ability to measure pathogen-specific biochemical components, proteins, or nucleic acid sequences . If pathPhytophthora capsici developed by in vivo, quantitative assessment and multiple replications, if possible, are labor- and time-intensive. The microplate experiments provided quantitative analyses over time in culture conditions, but not in live plant tissue.A non-destructive approach that can facilitate real-time monitoring of specific pathogen growth is the use of reporter genes, such as genes encoding fluorescent proteins, often in combination with microscopy to observe the pathogen infection process and location. This approach has been used in numerous fungal, oomycete and bacterial plant pathogen systems e.g., . Prior sP. capsici, which was first identified in pepper, has a broad host range causing severe damage in many solanaceous, cucurbitaceous, and legume crops  plants can be infected at all growth stages, and roots, stems, foliage and fruit are all susceptible. Symptoms include damping off, stunted growth, wilting, and in severe cases, death (Cucumis sativus), very young fruit are most susceptible relative to other parts of the plant such as leaves and vines which remain largely unaffected  tdTomato gene ; the slicing cucumber cultivar, “Poinsett 76” , with age-related resistance (ARR) to PI109483 . The douPI109483  were harTwo pepper cultigens were used: the susceptible cultivar “ACE” , and the resistant breeding line Criollo de Morelos-334 (“CM-334”) , generouP. capsici isolate NY0664-1 expressing RFP gene tdTomato, kindly provided by C. Smart, Cornell University . Excised cucumber peels of susceptible “Gy14” were affixed to the lid of a 100 mm petri dish using petroleum jelly  and inoculated with 10 μL of 5 × 104 zoospores/mL of P. capsici isolate NY 0664-1-RFP (as per 5 or 5 × 105 zoospores/ml (as per 2O was added to each plate to maintain a high relative humidity (>70%) and incubated at room temperature. A 5 μL droplet of 2 × 105 zoospores/mL suspension was placed on the adaxial side of the leaf on each side of the midvein as per 2O. A total of 15 plants of each accession were used per replication. Two mature leaves per plants were selected for inoculation. The experiment was replicated three times. The disease level was evaluated on a 5 point scale according to Harvested cucumber fruits and pepper leaves were washed and surface sterilized as previously described . Young cP as per ; fruits l as per . Fruit wFor cucumber fruit, samples were prepared from the central portion of the fruit using a 6 mm biopsy punch . Samples were cut to 5–6 mm thickness, and placed with external surface to the top, into a 96-well (6.5 mm diameter wells), flat bottom, black Greiner microplate . The bottom and sides of the wells were coated with petroleum jelly prior to placement of the samples. For pepper leaves, two disks, one from the center of either side of the midvein, were sampled from each leaf with a 6 mm biopsy punch. Leaf samples were placed adaxial side up into wells containing 150 μL 6% agar .5, 5 × 105, and 1 × 106 zoospores/mL). Zoospore concentrations within the droplets were chosen to provide equivalent inoculum pressure as used for intact fruits or leaves as determined in our prior experiments or from the literature  and zoospore concentrations . Black, flat-bottom microplates were used to minimize background signals and fluorescence. The excitation, emission wavelength, and bandwidth settings to maximize signal to background were optimized using the pathogen. The following optimized process was used for detection of fluorescence: excitation wavelength = 536 nm, emission wavelength = 586 nm, both excitation and emission bandwidth = 20 nm, number of flashes = 30, integration time = 40 μs, top reading. For each sample well, multiple points were recorded using the optimal read function to ensure that the full surface of the sample was measured. Unless stated otherwise, the maximum detected relative fluorescence units (RFU) within each well was used at each timepoint. To verify suitability of the fluorescence emission intensity for linearity of response and quantification of relative pathogen levels, a range of pathogen concentration was tested using a dilution series of 5 × 104, 1 × 105, 2 × 105, 2.5 × 105, 4 × 105, 4.5 × 105, 5 × 105, 6 × 105, 6.5 × 105, and 1 × 106 zoospores/mL. The dilution series test was repeated three times. Each bioassay experiment included a sample that was inoculated 2 days in advance to calibrate the detection range. The Z position was set automatically for each plate based on the control pathogen well.Florescence was measured by the Tecan Spark5 zoospores/mL P. capsici; the other two samples received 3 μl sterile ddH2O as a control. For “Poinsett 76” at 8 dpp vs. 16 dpp, and “Poinsett 76” at 16 dpp vs. “Gy14” at 16 dpp, three fruits were harvested for each genotype/age for each experiment. Sixteen samples were taken from each fruit; twelve were inoculated with 5 μl 1 × 106 zoospores/mL P. capsici, the other four samples received 5 μl ddH2O.For the cucumber young fruit comparison, six fruits of “A4-3” and “Gy14” were used for each experiment. Eight samples were taken from each fruit. Six of the samples were inoculated with 3 μl 1 × 10P. capsici 2 × 105 zoospores/mL and two samples inoculated with 5 μl ddH2O as control. Fluorescence measurements were taken for each well every hour for the first 24 or 48 h post inoculation (hpi). Each experiment was repeated three times.For the pepper “CM-334” and “ACE” comparison, 12 plants were used for each experiment for each line. Two young, fully expanded leaves were taken from each plant, one sample from each side of the leaf as described above. For every column of the microplate, six samples were inoculated with 5 μl all_growthmodels and the grow_gompertz3 functions in the growthrates R package  were determined by examining data for the full plate. For the pepper leaf experiment, the parameter values were set as y0 = 1000, mumax = 0.15, K = 10000, lambda = 12 for start parameters and an upper bound of y0 = 3000, mumax = 0.5, K = 15000, lambda = 24. A value of 0 was set to all parameters as a lower bound, to avoid non-biologically relevant predictions. The results for each well were returned and the mean and standard error were calculated per treatment and genotype. Results were plotted using ggplot2. Parameter estimate means were compared using Wilcoxon Signed Rank Test.After recording the data, the highest value from each well was used for further analysis. Data were analyzed using R and Origin 8.5  and plots were made using the ggplot2 and Tidyverse packages in R studio . The relationship between the concentration of the pathogen and the relative intensity of the fluorescence was drawn by Origin 8.5.0 Software. Gompertz growth models were fit to maximum RFU values for each well and modeled using the  package . For eacP. capsici  . For leaP. capsici is via appressorial-mediated penetration, however, P. capsici also can infect via wound sites, and in doing so, overcome host resistances .The primary mode of infection by istances . To previstances . TestingtdTomato . A gradient of wavelengths 530–550 nm for excitation and 580–590 nm for emission showed that the optimum excitation and emission wavelength in our conditions were 536 nm and 586 nm, respectively (R2 = 0.9735), indicating that this method can be used to quantify relative pathogen levels.The excitation, emission wavelength and bandwidth setting to maximize signal relative to background were optimized using zoospores suspended in water. The excitation and emission wavelengths tested were based on values for isolated RFP encoded by ectively . Equivalectively  showed tThe saturation threshold used to amplify and calibrate fluorescence signals was based on maximum detected fluorescence at the first measured timepoint. Thus, if the gain is based on a sample with low fluorescent signal, it could quickly become saturated and uninformative as the pathogen grows. To avoid this problem and maximize signal detection over time, we included a sample with high fluorescence at time-point zero, using either fluorescent mycelia-covered agar plugs, or fruit samples inoculated 2 days prior to the experiment. Timepoint zero gain thresholds set on either of those options successfully allowed for maximum detection without saturation as the fluorescence of freshly inoculated experimental samples increased throughout the experiments.Ability to detect growth of the pathogen on the host surface can be influenced by location within the well. As the initial foci of infection are microscopic, if fluorescence is measured at a single point, early growth might not be detected. To overcome this limitation, emission measurements were performed using the optimal read function, that measures fluorescence at multiple, spatially separated spots optimized to cover the full well as determined by the beam diameter . For a 96-well plate, five read positions are recorded for each well . Figure t-test, n = 36, P < 0.001). Furthermore, 11 of the 36 “A4-3” samples essentially had no increase (less than 1.5-fold increase during the time period 15–30 hpi). The remainder exhibited an average increase of 3.7-fold; thus, even those that did enter exponential growth had a significantly lower rate of increase than for “Gy-14” . In contrast, none of the “Gy14” samples increased less than 1.5×. Similar results were observed in all three experiments with 2.06-, 2.00-, and 1.61-fold greater rate of increase on “Gy14” than “A4-3.” No increase in fluorescence was detected in the control.Quantification of pathogen growth was performed on samples of young cucumber fruit (5–7 dpp) from the susceptible “Gy14” and moderately resistant “A4-3” breeding lines. Typical of growth patterns showing a lag phase followed by exponential increase, during the first 10–15 hpi, pathogen growth was slow and similar for both “Gy14” and “A4-3” . After tP. capsici, wherein young fruit are highly susceptible, but become resistant as they transition away from the period of exponential fruit growth, at approximately 10–12 dpp , extensively used in pepper breeding . Followior brown . Water-sor brown . At 3 dpor brown . In contor brown . Initialor brown . In contor brown  that shogrowthrates R package  indicating that the Gompertz model is appropriate for our pathogen growth data , and maximum growth rate (mumax) in the resistant pepper genotype “CM-334” ((lambda) between the two genotypes. These observations suggest that response processes subsequent to the lag phase, contributed to inhibition of disease development in “CM-344.” Model analysis for the cucumber experiments also showed excellent fit (R2 values 0.945–0.997; As a proof of utility for data obtained from our bioassay system, data from the pepper leaf microtiter plate experiments were analyzed using the Gompertz model as implemented in the  package . Model f“CM-334” , There win planta and demonstrated usefulness in different tissue types and species. In each case, samples from susceptible tissues showed significantly greater pathogen growth as measured by fluorescence emissions than those from resistant tissues. These differences were observable very early in the infection process, prior to the appearance of visible symptoms. As many fluorescently labeled pathogen strains have been developed for microscopy studies, this approach should be transferable and broadly applicable to many plant-pathogen systems. Such analyses can provide highly replicated data allowing comparisons across treatment variables such as genotype, tissue type, age, or experimental treatment conditions. The highly replicated data are also amenable to modeling approaches that can provide insight into host pathogen interactions and critical stages influencing success or inhibition of infection.We have developed a real-time microtiter bioassay to quantitatively measure pathogen growth The original contributions presented in the study are included in the article/BM, CZ, and RG conceived the project and wrote the manuscript. CZ and BM developed and performed the microplate bioassay and statistical analyses. BM and Y-CL performed the microscopy experiments. All authors have read and approved the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Within the observation period of 3.0 ± 0.9 years, the incidence of ≥10% weight gain was observed in 1896 (10.8%) men and 1518 (17.1%) women, respectively. Skipping dinner was identified as a significant predictor of weight gain in multivariable-adjusted Poisson regression models for both men and women  and 1.67 (1.33–2.09) in male and female students, respectively), whereas skipping breakfast and lunch were not. Similarly, skipping dinner, not breakfast or lunch, was associated with overweight/obesity (1.74 (1.07–2.84) and 1.68 (1.02–2.78) in men and women, respectively). In conclusion, skipping dinner predicted the incidence of weight gain and overweight/obesity in university students.Although multiple studies have identified skipping breakfast as a risk factor for weight gain, there is limited evidence on the clinical impact of skipping lunch and dinner on weight gain. This retrospective cohort study including 17,573 male and 8860 female university students at a national university in Japan, assessed the association of the frequency of breakfast, lunch, and dinner with the incidence of weight gain (≥10%) and overweight/obesity (body mass index ≥ 25 kg/m Obesity, defined as a disease resulting from excess body weight, is one of the major global health burdens, along with smoking, hypertension, and diabetes . Obesity2) in 3598 young adults [One of the dietary risk factors for overweight/obesity is meal frequency . Multiplg adults . A systeg adults . Few stug adults ,14, partg adults .The aim of this retrospective cohort study was to assess the clinical impact of skipping breakfast, lunch, and dinner on weight gain and overweight/obesity in 26,433 university students within their 6-year college life.Over 30,144 university students enrolled at Osaka University, one of the largest national universities in Japan, between 2007 and 2015 who underwent baseline health checkup on admission at the Osaka University Healthcare Center in April or October were eligible for inclusion in this retrospective cohort study. We excluded 15 (0.0%) students aged 17 years and 1062 students (3.5%) with missing baseline data . Osaka UThe study protocol was approved by the Ethics Committee of the Health and Counseling Center at Osaka University (No. 2020-7) and the Osaka University Hospital (No. 18352). Informed consent was not obtained from all the participants according to the Japanese Ethical Guidelines for Medical and Health Research Involving Human Subjects. All the data were retrieved from the electronic database of the Health and Counseling Center, Osaka University.2 (m2)), and questionnaires on meal frequency, dinner time, sleep duration on weekdays, smoking status, and drinking status. Questions related to these items were included in the general health questionnaires administered at the baseline health checkup. Breakfast, lunch, and dinner frequency were determined by asking the question “How often did you have breakfast/lunch/dinner over the past year?”; the possible responses were “Eating almost every day”, “Skipping occasionally”, “Skipping often”, and “Usually skipping.” Dinner time was determined by asking the question “What time did you have dinner?”; the four possible answers were as follows: “Before 7 PM”, “7–9 PM”, “9–11 PM”, and “After 11 PM.” Sleep duration on weekdays was determined by asking the question “How long do you sleep on weekdays?”; the possible answers were “< 5 h”, “5–6 h”, “6–7 h”, “7–8 h”, and “≥8 h.” Smoking status was determined by asking the question “Do you smoke?”; the possible answers were “I do not smoke”, “I quitted smoking”, “I would like to quit smoking”, and “I smoke.” Drinking status was determined by asking the question “Do you drink alcohol?”; the possible answers were “I do not drink”, “I drink occasionally”, “I drink 1 day/week”, “I drink 2–3 days/week”, and “I drink ≥4 days/week.”The baseline health checkup variables at admission included age, BMI (body weight (kg)/height2) [2. Observational time was defined as the period from the health checkup at admission to (i) the incidence of each outcome or (ii) the last body weight measurement at the annual Osaka University health checkups within 6 years (=2192 days) of the baseline checkup before April 2019, whichever came first.The outcome measures of the present study were weight gain ≥10%  in all s2)  in studeTo assess the degree to which the baseline dinner frequencies reflected the dinner frequency during the observation period, the answers to “How often did you have dinner during the past year?” at the annual health checkups for university students 1 year (365 days) and 3 years (1095 days) after the baseline checkup, with a window period of ±182 days, were retrieved.Because of the small number of students skipping dinner often ; women, n = 32 (0.4%)) and usually ; women, n = 11 (0.1%)), dinner frequency was divided into two groups: eating every day and skipping dinner ≥ occasionally. Similarly, students skipping lunch often ; women, n = 20 (0.2%)), and usually ; women, n = 7 (0.1%)) were categorized as skipping lunch ≥ occasionally. Breakfast frequency was classified as eating every day, skipping occasionally, and skipping ≥ often, including skipping often ; women, n = 209 (2.4%)) and usually ; women, n = 76 (0.9%)). Because 17,476 (99.4%) men and 8847 (99.9%) women answered “I do not smoke”, subjects were categorized into non-smokers with “I do not smoke” and smokers with other answers. Drinking status was classified into two categories: non-drinkers who answered “I do not drink” ; women, n = 8442 (95.3%)) and drinkers who answered “I drink occasionally” ; women, n = 378 (4.3%)), “I drink 1 day/week” ; women, n = 21 (0.2%)), “I drink 2–3 day/week” ; women, n = 16 (0.2%)), and “I drink ≥4 days/week” ; women, n = 3 (0.0%)). The clinical characteristics of the different meal frequency categories were compared using the Student’s t-test, analysis of variance, and the chi-squared test, as appropriate.2 was calculated using the Kaplan–Meier method and compared using the log-rank test among the categories of meal frequency. To assess the association between the baseline meal frequency and the incidence of each outcome, unadjusted and multivariable-adjusted incidence rate ratios (IRR) with the corresponding 95% confidence interval (CI) of each meal frequency category were calculated using Poisson regression models. Model 1 was an unadjusted model including frequency of each meal as an independent variable. In model 2, the association between frequency of each meal and the outcomes were assessed after adjusting for admission year , age , BMI (kg/m2), smoking status (non-smokers and smokers), drinking status (non-drinkers and drinkers), sleep duration [The cumulative probability of the incidence of weight gain ≥10% and BMI ≥ 25 kg/mTo evaluate an association between the baseline dinner frequency and the dinner frequency during the observation period, reproducibility of the baseline dinner frequency with the dinner frequency 1 and 3 years after admission was assessed using the Gwet’s AC1 coefficient, which was calculated using Stata’s kappaetc command . ReproduTo identify the characteristics of students who kept skipping dinner ≥ occasionally, the baseline characteristics were compared between students who kept skipping dinner ≥ occasionally at admission and 1 year after admission and those who skipped dinner ≥ occasionally at admission but ate dinner every day 1 year after admission using the Student’s t-test and the chi-squared test.p value <0.05 was considered significant. All statistical analyses were performed using Stata version 16.1 .Continuous variables were expressed as the mean ± standard deviation, while categorical variables were expressed as numbers and proportions. A p < 0.05). With regard to the frequency of breakfast and lunch frequency, similar trends were observed in male students (p < 0.05), except for BMI (p < 0.05) (p < 0.05)  . Female  < 0.05) .p = 0.040) (p = 0.059) (p = 0.029) (p = 0.222)  and 39 (14.7%) male students who ate dinner every day and skipped dinner ≥ occasionally, respectively (= 0.040) . Male st= 0.059) a. Consid= 0.029) . By cont= 0.222) . Unadjusp = 0.002) (p = 0.464) (p = 0.677)  and 82 (22.9%) students, respectively . The cum= 0.002) b; by con= 0.464)  and lunc= 0.677) . In unad2, we assessed the association between meal frequency and the incidence of overweight/obesity (BMI ≥ 25 kg/m2) in students with a BMI of <25 kg/m2. The cumulative probability of male and female students skipping dinner ≥ occasionally was significantly higher than of those eating dinner every day    ; female students, 1.68 (1.02–2.78)), while breakfast and lunch frequencies were not (After excluding 1917 (10.9%) male and 395 (4.5%) female students with BMI ≥ 25 kg/mctively) c,d. No sctively)  and femactively) . In multTo assess how strongly baseline dinner frequency reflected dinner frequency during the observation period, dinner frequency 1 and 3 years after admission was assessed . Of the p < 0.05). In contrast, no significant difference was observed between the 116 female students who kept skipping dinner ≥ occasionally and the 171 female students who skipped dinner ≥ occasionally at admission but ate dinner every day 1 year after admission  in both male and female students. These results suggest that skipping dinner, which was much less prevalent than skipping breakfast, has a stronger association with weight gain and overweight/obesity than skipping breakfast. The strengths of the present study are as follows: cohort study design with the mean observation period of 3 years; large sample size, which allowed the assessment of the clinical impact of low prevalence of skipping dinner; and two outcomes (≥10% weight gain and BMI ≥ 25 kg/m2).This retrospective large cohort study revealed that skipping dinner was significantly associated with ≥10% weight gain and overweight/obesity (BMI ≥ 25 kg/m2), while there was no increased prevalence of obesity among the men who skipped dinner [Few studies have previously reported the association between skipping dinner and overweight/obesity. An Iranian cross-sectional study, the Childhood and Adolescence Surveillance and Prevention of Adult Non-communicable Disease (CASPIAN-III) study, including 5642 school students aged 10–18 years reported that students who skipped dinner had a significantly high prevalence of overweight/obesity . A Spanid dinner . This red dinner . CompareOne of the plausible mechanisms for the association between skipping dinner and weight gain may be an excess of energy intake due to upregulation of appetite after skipping dinner, leading to high total energy intake. Regarding breakfast, a cross-sectional study using 24-h dietary recall for two days reported that Brazilian adults who skipped breakfast at least 5 days/week had significantly higher total energy intake than those who did not . Only a Another potential candidate for a link between skipping dinner and weight gain may be low diet quality. The NHANES showed that skipping dinner led to a deterioration in the healthy eating index (HEI) , the indThe present study has several limitations. Firstly, it included university students aged 18–19 years mainly at a single national university in Japan. The generalizability of our results should be assessed in different cohorts. Secondly, a dose-dependent association between dinner frequency and weight gain was not assessed, because dinner frequency was determined based on a simple question with only four possible answers: “Eating almost every day”, “Skipping occasionally”, “Skipping often”, and “Usually skipping.” After asking how many times students eat dinner per week strictly, the dose-dependent association between the frequency of dinner and weight gain should be assessed in more detail. Thirdly, the association between skipping dinner and weight gain may be confounded by unmeasured factors. For example, a previous cohort study suggested that depression modified the association between evening chronotype and weight gain . BecauseThe present retrospective cohort study identified skipping dinner as a significant predictor of weight gain and overweight/obesity. These results suggest that dinner frequency may be a critical lifestyle factor for the prevention of obesity in addition to breakfast frequency. However, the clinical impact of dinner frequency should be clarified in further studies.