P < 1 × 10−5). Among them, rs3967317 (P = 1.21 × 10−8) exceeded the genome-wide significance level (P < 5 × 10−8) and was also demonstrated to be associated with depression and memory in pleiotropy analysis, followed by rs9863698, rs3967316, and rs9261381 (P = 7.80 × 10−8−5.68 × 10−7), which were associated with all three phenotypes. After imputation, a total of 457 SNPs reached suggestive significance. The top SNP chr6:24597173 was located in the KIAA0319 gene, which had biased expression in brain tissues. Genes and pathways related to metabolism, immunity, and neuronal systems demonstrated nominal significance (P < 0.05) in gene-based and pathway enrichment analyses. In the validation phase, 12 of the abovementioned SNPs reached the nominal significance level (P < 0.05) in the UKB. Among them, three SNPs were located in the KIAA0319 gene, and four SNPs were identified as significant expression quantitative trait loci in brain tissues. These findings may provide evidence for pleiotropic variants among depression, cognition, and memory and clues for further exploring the shared genetic pathogenesis of depression with Alzheimer’s disease.To date, little is known about the pleiotropic genetic variants among depression, cognition, and memory. The current research aimed to identify the potential pleiotropic single nucleotide polymorphisms (SNPs), genes, and pathways of the three phenotypes by conducting a multivariate genome-wide association study and an additional pleiotropy analysis among Chinese individuals and further validate the top variants in the UK Biobank (UKB). In the discovery phase, the participants were 139 pairs of dizygotic twins from the Qingdao Twins Registry. The genome-wide efficient mixed-model analysis identified 164 SNPs reaching suggestive significance ( Depression and Alzheimer’s disease (AD) are two common mental disorders that pose a serious threat to the physical and mental health of the public, especially elderly persons , 2, resuIn addition to environmental factors, genetic factors play an important etiologic role in both depression and AD. According to a meta-analysis of twin studies, the heritability of depression ranges from 31% to 42% . As the hNP gene was associated with both depression [APOE-4 allele gene was linked to lower cognitive ability, faster cognitive decline [DISC1 gene polymorphisms were associated with depression [rGE)’ and can distinguish direct genetic effects from indirect genetic effects [However, to date, little is known about the shared genetic variants among depression, cognition, and memory. A depression genome-wide meta-analysis identified 269 genes associated with depression . Interespression  and memopression . The APO decline , and poo decline . Studiespression , cognitipression , and mempression , 28. Thepression , 30. Notpression . The com effects . Additio effects . Human g effects .Thus, we performed a multivariate GWAS among Qingdao twins in China to explore the potential pleiotropic single nucleotide polymorphisms (SNPs), genes, and pathways among depression, cognition, and memory. An additional pleiotropy analysis was also performed for interpreting the possibility of pleiotropy. Then, to determine if these findings in Chinese can be generalized to different ethnic groups , we further validated the top variants in an independent UK Biobank (UKB) population to identify cross-ethnic associations.In the discovery phase, the participants were adult twins from the Qingdao Twins Registry, China, and the details have been described in previous literature . Blood sDepression was assessed using the 30-item Geriatric Depression Scale , which consists of 30 questions, with an overall score of 0-30 points, and a higher score indicated more severe depressive symptoms. The GDS-30 is especially suitable for the assessment of depression in middle-aged and elderly individuals and is also highly valid in the Chinese population , 36.Cognition was measured using the Montreal Cognitive Assessment  with high reliability and acceptance in Chinese adults , 38. ThiMemory was assessed by the backward and forward digit span tasks of the Wechsler Adult Intelligence . The total score ranging from 0-17 was obtained by summing the scores of backward and forward digit span, and a lower score indicated worse memory. Digit span tasks have widely been used to reflect short-term memory, and the backward digit span also reflects working memory .−4, and minor allele frequency (MAF) > 0.05 were included in this multivariate GWAS.Infinium Omni2.5Exome-8v1.2 BeadChip from Illumina was used for genotyping in dizygotic twins. After quality control, 1,338,905 SNPs with calling rate >0.98, locus missing <0.05, the significance of Hardy-Weinberg equilibrium (HWE) significance >1 × 10−4, MAF > 0.05, and R2 > 0.6, a total of 7,399,084 SNPs were finally used in the post-imputation multivariate GWAS.On the basis of the linkage disequilibrium (LD) principle, IMPUTE2 software  was utilP value threshold of <5 × 10−8  [P value threshold of <1 × 10−5 (a commonly utilized threshold in GWAS) [P values) associated with depression-cognition-memory to HaploReg v4.1 [P value of <0.05 were reported. All genomic coordinates were based on human genome Build 37 (NCBI GRCh37).The genome-wide efficient mixed-model association (GEMMA)  was usedreshold) , and thein GWAS) . QuantilReg v4.1 , and theP value greater than 0.05 was derived, the sequential testing stopped. If the P value of test 2 remained <0.05, it implied that all three phenotypes were associated with this variant.For interpreting the possibility of pleiotropy, we further performed a pleiotropy analysis by using the R package “pleio”  to test P value of <2.61 × 10−6  was regarded as a significant threshold by Bonferroni correction due to the 19,152 genes tested, and the nominal significance level was defined as a P value of <0.05 [VEGAS2  was applof <0.05 .P value of <4.64 × 10−5  was regarded as a significant threshold by Bonferroni correction due to the 1,077 pathways tested, and the nominal significance level was defined as an emp-P value of <0.05.PASCAL  was utiln = 355) were defined as participants with depression scores ≥3 and cognition and memory performance scores lower than the 25th percentile of their score distributions. Controls (n = 1775) were selected by matching individuals’ age and sex (ratio = 1:5) with those of the participants  with depression scores <3 and cognition and memory performance ≥25th percentile of their score distributions. Finally, 2130 individuals (355 cases and 1775 controls) with a median age (interquartile range) of 55 (15) years were included in the validated sample. The top SNPs were validated by logistic regression analysis of the additive effect model, adjusting for the first 10 genetic PCs. A total of 469 of 481 SNPs (the union number before and after imputation) with P values lower than 1 × 10−5 in the discovery set were typed in the UKB data set and selected for validation. Thus, a P value < 1.07 × 10−4 (0.05/469) was regarded as a significant threshold by Bonferroni correction, and the nominal significance level was defined as a P value <0.05. Statistical analyses were performed utilizing R version 4.1.0.To identify the cross-ethnicity generalizability and cross-ethnic associations, we validated the top variants in an independent UKB population, which is a population-based cohort of 488,377 individuals with genotypic data across the United Kingdom; more details of genotyping, quality control, and imputation have been described elsewhere . The pheP value lower than 0.05 was regarded as significant in the single-tissue eQTL analysis. The posterior probability m-value that the eQTL effect existed in each tissue of a cross-tissue meta-analysis higher than 0.9 indicated that the tissue had an eQTL effect [For SNPs with nominal significance in the validation set, we further checked their functional consequences by eQTL analysis across tissues using data from the GTEx portal (version 8) . A P valL effect . An outlThere were 139 pairs of dizygotic twins in the final discovery sample. The median (interquartile range) age was 49 (11) years, and the median scores (interquartile ranges) for depression, cognition, and memory for participants were 7 (7), 22 (5), and 12 (3) points, respectively (Supplementary Table P < 1 × 10−5) (Supplementary Table P = 1.21 × 10−8) on the CNTN4 gene on chromosome 3 exceeded the genome-wide significance level (P < 5 × 10−8). In addition, rs9863698 (P = 7.80 × 10−8) and rs3967316 (P = 1.33 × 10−7) on the CNTN4 gene, rs9261381 (P = 5.68 × 10−7) on the TRIM31 gene, rs11577464 (P = 5.82 × 10−7) on the LINC02567 gene, and rs73198369 (P = 7.00 × 10−7) on the RNU6-1325P gene reached suggestive significance. The top 20 SNPs ranked by P values are shown in Table P < 0.05), with rs3967317 associated with depression and memory, rs9863698, rs3967316, and rs9261381 associated with all three phenotypes  , were located in the KIAA0319 gene, which had biased expression in multiple brain tissues  in the LINC02567 gene. The top 20 SNPs after imputation are shown in Supplementary Table P < 0.05) , but 1107 genes reached the nominal significance level (P < 0.05). Most of these genes are known to be involved in metabolism, immunity, and neuronal systems, and the top 20 genes are shown in Table In the gene-based study, no statistically significant gene was found (P < 4.64 × 10−5), but 587 pathways were found to be nominally associated with depression-cognition-memory (emp-P < 0.05), and most of these pathways were involved in the metabolism of amino acids, lipids and RNA, the immune system, and the neuronal system. The top 20 pathways are shown in Table In the pathway-based analysis, no statistically significant pathway was found , three of them  were located in the KIAA0319 gene, and one (rs9261134) was located in the ZNRD1ASP gene , rs17337582 , and rs62358383  were significantly associated with the expression of MAP3K1 gene in brain tissues. Rs9261134 located at the ZNRD1ASP gene was significantly associated with the expression of 15 genes , and ZNRD1ASP expression with rs9261134 across tissues is shown in Supplementary Fig The eQTL analysis across tissues found that four  of 12 SNPs with nominal significance in the validation set were significant eQTLs in multiple tissues, specifically in brain tissues  with cognitive function among European cohorts. The study revealed that MDD status had a moderating effect on the associations of variants with cognitive function, with some SNPs associated with cognitive domains in the context of MDD . Anotherpulation .CNTN4 gene on chromosome 3, which exceeded the genome-wide significance level. The following were rs9863698 and rs3967316, which were also located in the CNTN4 gene. The CNTN4-encoded protein belongs to the contactin family and is involved in neuronal network development and plasticity. Pertinently, studies have found that CNTN4 is associated with mental retardation [TRIM31 gene on chromosome 6. TRIM31 encodes a protein that functions as an E3 ubiquitin-protein ligase and can regulate cell growth. Studies have shown that the TRIM31 gene was associated with intelligence only in the background of a psychiatric disorder [In the SNP-based analysis, the strongest association signal was rs3967317 located in the ardation  and affeardation . Rs92613disorder . Furtherdisorder , which fKLAA0319 gene showed the strongest association, although this might be mainly driven by the association of the SNP with cognition. However, the KLAA0319 gene demonstrated a biased expression in multiple brain tissues, and three of 12 SNPs with nominal significance in the validation phase among the UKB population whose cases were abnormal on all three phenotypes and controls were normal were located in the KIAA0319 gene, indicating a potential cross-ethnic association of KIAA0319 with depression-cognition-memory. The protein encoded by KIAA0319 can regulate cell adhesion and neuronal migration processes to influence the growth of the cerebral cortex, and KIAA0319 has been found to be associated with category fluency, recall process, and verbal learning [MAP3K1 gene in the brain-substantia nigra and brain-nucleus accumbens. Pertinently, the protein encoded by MAP3K1 is part of the nuclear factor kappa beta (NF-κB) pathway [After imputing untyped SNPs, more SNPs with suggestive significance were identified. The SNP chr6:24597173 located in the  pathway . NF-κB h pathway .MEDAG is an adipogenic gene that can promote the formation of adipocytes [C3 can modulate the inflammation process which is closely related to depression and AD [TET1 influences gene activation and the process of DNA methylation. Studies have shown that the expression level of TET1 is significantly increased in psychotic participants [TET1 variation is associated with late-onset AD [FGF1 is related to the survival of neurons and is involved in various biological processes. Furthermore, FGF1 has been reported to be associated with AD in Han Chinese individuals [Most genes with nominal significance levels in the gene-based analysis are known to be involved in metabolism, immunity, and neuronal systems. The potential mechanisms of several interesting genes other than those mentioned above were as follows: (1) ipocytes . The lipipocytes , 77; (2)n and AD ; and (3)icipants , and TETonset AD ; (4) FGFividuals .P values . Except for the limitation of the small sample size, another possible reason was that although several physiological processes were thought to be the common mechanisms of both depression and AD, they might be mainly caused by different genes in depression and AD, rather than pleiotropic genes, and further research is required in the future.In the pathway enrichment analysis, most pathways that reached nominal significance were related to metabolism, immune, and neuronal system, and several important pathways were revealed as follows: (1) metabolism of amino acids and derivatives, various metabolites of this pathway including glutamate and glycine are involved in the pathophysiology of depression and AD , 82; (2)rGE and can identify direct genetic effects [KIAA0319. However, the strongest SNP (rs3967317), which demonstrated genome-wide significance (P < 5 × 10−8) and still showed suggestive significance after imputation (P < 1 × 10−5) in the discovery set, was not replicated in the UKB validation sample, but it may still be worth being further validated in the Asian population as a promising candidate variant.The current multivariate GWAS had several strengths. First, this is the first study to identify potential pleiotropic SNPs, genes, and pathways among depression, cognition, and memory phenotypes in Chinese individuals, which may not only provide insight into a common genetic basis of these phenotypes but also make a little contribution in shifting the Eurocentric bias of GWASs. Second, this current GWAS was performed in twin samples. The twin-based GWAS design has been demonstrated excellence in controlling population stratification and passive  effects , which r effects , which mIn conclusion, this multivariate GWAS identified some pleiotropic SNPs, genes, and pathways among depression, cognition, and memory, which provided evidence for a common genetic basis of the three phenotypes and clues for further exploring the shared genetic pathogenesis of depression with AD, and it might be helpful in the search for new therapeutic targets for both diseases.Supplementary Table 1Supplementary Table 2Supplementary Table 3Supplementary Table 4Supplementary Table 5Supplementary Table 6Supplementary figure and table legendsSupplementary Figure 1Supplementary Figure 2Supplementary Figure 3Supplementary Figure 4Supplementary Figure 5


Zea mays) influenced soil enzyme activity. Enzyme activity was determined by monitoring fluorescein diacetate (FDA) hydrolysis, ß-glucosidase, acid-phosphomonoesterase, and arylsulfatase activities. Experiments were conducted from 2014 through 2016 to compare single  and split applications of N at varying N rates under irrigation (Becker) and rainfed conditions (Lamberton and Waseca) in MN, USA. Nitrogen rates varied by location and were based on University of Minnesota guidelines. Soil samples were collected seven times each season. Nitrogen application split into two applications increased FDA activity by 10% compared with fall and spring applied N at Waseca. Fall or spring N application decreased arylsulfatase activity by 19% at Becker and by between 13% and 16% at Lamberton. ß-Glucosidase and acid-phosphomonoesterase activities were unaffected by N application. Sampling time and year had the greatest impact on enzyme activity, but the results varied by location. A negative linear relationship occurred between FDA and ß-glucosidase activity at all three sites. In summary, urea N application had small effects on enzyme activity at the sites studied, suggesting that some form of organic N could be more important than the ammonium provided by urea.Understanding how fertilizer application  interacts with soil microbes is important for the development of best management practices that target improved microbial activity to enhance sustainable food production. This study was conducted to determine whether urea N rate and time of application to maize ( Current estimates indicate that the global population could reach 10 billion people by 2050. This increase in population will require a much more efficient and sustainable food production system. Our food production system relies heavily on chemical inputs, such as N-containing fertilizer and pesticides, for maximum plant yield. In a more integrated food production system, the soil microbial communities will likely play a significant role in achieving and maintaining system sustainability. Soil organisms can play a pivotal role in nutrient cycling. Soil microflora (bacteria and fungi) and fauna  can influence the availability of nutrients for crop production through the decomposition of crop residues, mineralization and immobilization of nutrients, biological N fixation, and bioturbation. Mineralization or immobilization of nutrients by soil microbes influences temporal nutrient availability, soil nutrient status, and net productivity of agroecosystems -maize-maize-maize (SB-M-M-M) production system. Our hypothesis is that the addition of N fertilizer would stimulate microbial growth, resulting in an increase in the activity of the enzymes targeted in this research. The enzyme activities selected for study are responsible for C turnover (β-glucosidase), organic P turnover (acid phosphomonoesterase), and organic S turnover (arylsulfatase); in addition, fluorescein diacetate (FDA) was studied as a surrogate for overall microbial activity.Over the years, correlations have been established on the effect of tillage, pH, and residue management on soil enzyme activities involved in C, N, P, and S cycling in soils ,25. More−1; rainfed Normania loam  at Lamberton, with an OM level of 23 g kg−1; and rainfed Nicollet clay loam  at Waseca, with an OM level of 36 g kg−1. The experimental design was a randomized complete block with four replications of 10 N fertilizer management treatments. Treatments were applied to the same plots in all years, making this a repeated measures design, and were analyzed according to the methodology of Pagliari et al. , capped, and then incubated at 37 °C for 1 h. After incubation, buffer solutions  at wavelengths of 400 nm, 410 nm, and 420 nm for β-glucosidase, phosphatase, and arylsulfatase, respectively. Enzyme activity per kg of soil per hour was expressed as the release of p-nitrophenol per hour.The procedures for ß-1,4-glucosidase (hereinafter β-glucosidase), acid phosphomonoesterase (hereinafter phosphatase), and arylsulfatase were adapted from . Brieflyons . Using the correlation matrix, principal components (factors) with eigenvalues >1 were retained. The correlation coefficients between the soil enzymes, maize dry matter yield, and soil nitrogen were interpreted as ordinary Pearson correlation coefficients.A principal component analysis (PCA) was also carried out using data for soil enzyme activity, maize dry matter yield (DM), and soil nitrogen  and lowest at PM (640 mg of fluorescein released kg−1 soil h−1) (−1 soil h−1) was always significantly lower than post-N application (mean = 775 mg of fluorescein released kg−1 soil h−1) in the split treatments.Fluorescein diacetate hydrolysis was not significantly affected by treatment or interactions with treatment at Becker; however, the main effects of sampling time and year significantly affected FDA activity . In 2014oil h−1) . Fluores−1 soil h−1, significantly greater than that for other sampling times (−1 soil h−1) was significantly greater than that at PM (487 mg of fluorescein kg−1 soil h−1). In 2016, there were no differences in FDA activity between sampling times. Among all years, FDA was greater in 2014 (717 mg of fluorescein kg−1 soil h−1) than in 2015 and 2016 (mean = 559 mg of fluorescein kg−1 soil h−1).Fluorescein diacetate hydrolysis was not affected by treatment at Lamberton; however, there were significant effects for sampling time, year, and the year × sampling time interaction . In 2014ng times . In 2014ng times , suggest−1 soil h−1) than most of the other treatments receiving N fertilizer (mean = 417 mg of fluorescein kg−1 soil h−1) (−1 soil h−1), with less significant differences among samples collected during the growing season (−1 soil h−1) to PM (322 mg of fluorescein kg−1 soil h−1) compared with the other sampling times in 2015. In 2016, soil samples taken at Post-V6 and 3wk-V6 recorded 410 and 438 mg of fluorescein kg−1 soil h−1, greater than at later sampling times. Similar to the other locations, FDA hydrolysis at Waseca was greater at the beginning of each season and decreased over the growing season  . In 2014g season . The onlg season . Fluores−1 soil h−1) and lower at the Post-V6 (average 158 mg p-nitrophenyl released kg−1 soil h−1) compared with the other samplings (mean = 203 mg p-nitrophenyl released kg−1 soil h−1) (−1 soil h−1) and lowest at the Pre-R1 sampling (198 mg p-nitrophenyl released kg−1 soil h−1). In 2016, β-glucosidase activity tended to be higher at the first two (mean = 193 mg p-nitrophenyl released kg−1 soil h−1) and last (221 mg p-nitrophenyl released kg−1 soil h−1) sampling times compared with the other sampling times (mean = 151 mg p-nitrophenyl released kg−1 soil h−1). Β-Glucosidase activity was the lowest at the three weeks after N application sampling time (130 mg p-nitrophenyl released kg−1 soil h−1).β-Glucosidase activity was not significantly influenced by treatment at Becker but was affected by sampling time, year, and the year × sampling time interaction . In 2014oil h−1) . β-Gluco−1 soil h−1) and least for samples collected at Post-V6 (421 mg of p-nitrophenyl released kg−1 soil h−1)  than all other sampling times. In 2016, β-glucosidase activity in soil samples taken at Post-V6 and Post-R1 (mean = 691 mg of p-nitrophenyl released kg−1 soil h−1) were greater than those of samples collected at Pre-V6 and Pre-R1 (mean = 605 mg of p-nitrophenyl released kg−1 soil h−1) and at 3wk-V6 and 3wk-R1 (mean = 611 mg of p-nitrophenyl released kg−1 soil h−1).β-Glucosidase activity at Lamberton was not significantly affected by treatment but was affected by sampling time, year, and the year × sampling time interaction . In 2014oil h−1) . After t−1 soil h−1, greater than that at all other sampling times . In 2015, there were no changes in β-glucosidase activity throughout the growing season, with the exception that it was lower at Post-R1 than at PM, with 822 and 929 mg of p-nitrophenyl released kg−1 soil h−1, respectively . β-Glucosidase activity after Post-V6 decreased and remained low until 3wk-R1, when it increased to levels similar to those at Post-V6  than in 2014 and 2016 .Acid phosphatase activity at Lamberton was not significantly influenced by treatment effects but was affected by sampling time, year, and the year × sampling time interaction . In 2014ectively . In 2014−1 soil h−1) showed greater acid phosphatase activity than soil samples taken Post-V6, 3wk-V6, 3wk-R1, and PM (−1 soil kg−1) than in 2014 and 2016 .There was no significant effect of treatment on phosphatase activity at Waseca, but there were significant effects for sampling time, year, and the year × sampling time interaction . In 2014, and PM . Phospha−1 soil h−1) was greater than that with the F100, F125, PP100, PP125, and Sp100 treatments (mean = 76 mg of p-nitrophenyl released kg−1 soil h−1) (−1 soil h−1)  . Arylsuloil h−1) .−1 soil h−1) than at any other sampling time (mean = 88 mg of p-nitrophenyl released kg−1 soil h−1), with no differences among the remaining sampling times (−1 soil h−1) compared with the early samplings (mean = 76 mg of p-nitrophenyl released kg−1 soil h−1). In 2016, arylsulfatase activity had more variability throughout the season, being greater in the first and last sampling times compared with the mid-season samplings  than at any other sampling time (−1 soil h−1) was greater than that of samples collected at later times, with the exception of a significant high arylsulfatase activity observed at 3wk-R1 (210 mg of p-nitrophenyl released kg−1 soil h−1). In 2016, the results were similar to those observed in 2014 and 2015, where arylsulfatase activity was highest at the first sampling (160 mg of p-nitrophenyl released kg−1 soil h−1). In 2016, there was also an increase in arylsulfatase activity at the Post-R1 sampling time (162 mg of p-nitrophenyl released kg−1 soil h−1).In 2014, arylsulfatase activity was greater at Pre-V6 (224 mg of p-nitrophenyl released kging time . For the−1 soil kg−1) was greater than at all other sampling times (mean = 423 mg of p-nitrophenyl released kg−1 soil h−1) (−1 soil h−1), which was greater than all other sampling times (263 to 464 mg of p-nitrophenyl released kg−1 soil h−1).Arylsulfatase activity was not significantly influenced by treatment but was affected by sampling time and the year × sampling time interaction . In 2014oil h−1) . In 2015Principal component analysis was carried out to determine the relationships between soil enzyme activity, soil nitrogen, and DM yield. Eigenvalues from the principal component analysis indicate that the first four principal components accounted for 66.08% of the variance of data . β-Glucosidase activity (PNG) had a positive effect on Factor 1 (>0.900), while sampling time and maize dry matter yield had a positive effect on Factor 2 (>0.900) .3, soil NH4, and maize dry matter yield , increases in soil C input would likely improve soil microbial activity [−1 is used for over five years [Fluoroscein diacetate hydrolysis was not affected by the N rate and time of application at any location, but it did fluctuate across the growing season and was affected by year . For exaactivity ,36. The activity . Long-teactivity ,35,37. Ive years . Nitrogeve years . The enz−1 soil h−1) compared with 2014 and 2016 . Other studies have failed to find differences in β-glucosidase activity in continuous maize over the years [In our study, β-glucosidase activity in the first year after soybean was always greater at the Pre-V6 sampling than at the Post-V6 sampling . This rehe years .Acid phosphatase was the enzyme least affected by N fertilizer rate, time of application, and year. At Waseca, differences among sampling times only occurred in 2014. At Lamberton, N application often decreased acid phosphatase activity soon after application; however, in the early sampling times it tended to increase after this initial drop and then remain low during the rest of the season. In contrast, N application has been reported to be directly linked to soil phosphatase activity in soils from different regions of the planet ,46. DiffArylsulfatase activity was affected by N application at Becker and Lamberton. At Becker, N application reduced arylsulfatase activity in comparison with the control plots, which showed the greatest levels of arylsulfatase activity. The results at Lamberton were not as clear-cut; the control plot did tend to have the greatest arylsulfatase activity compared with other treatments, but the differences were only significant when compared with the PP125, Sp75, and TSp100 treatments. While some studies have also reported that N addition decreased arylsulfatase activity by 39.6% ,23, otheAt Lamberton, soil moisture seemed to have had a more pronounced effect on arylsulfatase activity than N fertilization. A spike in arylsulfatase activity was often preceded by precipitation in the days leading up to the sampling date. At Lamberton in 2014, there was a sharp increase in arylsulfatase activity at the 3wks-R1 sampling; this increase in activity could have been a result of the 14 mm of precipitation that occurred two to five days before sampling. In 2015, a similar spike was observed at the 3wks-R1 sampling, where 8.1 mm of rain fell three days before the sampling date. The addition of water to soils has also resulted in increased arylsulfatase activity . Water aIn general, the results of this research showed that the addition of N does not have a significant and consistent impact on soil enzyme activity, although sometimes the addition of N did cause a significant change. We found that having residue from different crops or soil moisture  played a much larger role in soil microbial activity (as measured by enzyme activity) than N application. This result seems to suggest that continuous corn has the potential, in the long run, to limit soil function by reducing microbial activity.Nitrogen application to continuous maize production was found to have small effects on soil enzyme activities. Split application of N was found to increase FDA activity at the Waseca site, and the application of N was found to reduce arylsulfatase activity at the Becker site. Collecting soil samples for enzyme activity tests during the growing season showed that enzyme activity varied, in most cases randomly, throughout the year and on a year-to-year basis. For example, samples collected after N application at Becker were found to have greater FDA activity than samples collected prior to N application. In contrast, at Lamberton and Waseca, sampling after N application was found to show lower FDA activity than samples collected prior to N application in some years, but the results were not consistent across all years and treatments. A common trend observed at all locations was a decline in FDA and β-glucosidase activity from 2014 to 2016, suggesting that the soybean residue provided ideal conditions for high FDA and β-glucosidase activity at all locations. The decline in FDA and β-glucosidase activity from 2014 to 2016 suggests that crop rotation could be beneficial at Becker, Lamberton, and Waseca for increased soil enzyme activity. Phosphatase activity was the least affected by the parameters measured in this study. The results of this study indicate that arylsulfatase activity was affected by soil moisture content and rainfall more than by N application, sampling time, and year. Overall, the variability of soil enzyme activity under maize in field conditions suggests that urea N application has a small effect on enzyme activity. The lack of enzyme response to N application in combination with higher enzyme activity when corn followed soybean in the first year of the study suggests that some form of organic N could have a larger impact on microbial activity.


Alveolar soft part sarcoma (ASPS) is a rare soft tissue sarcoma with unique tumor characteristics, which is rare in children. Herein, we present the immunophenotype, treatment, and prognosis of three children with ASPS from The Second Xiangya Hospital of Central South University, and 51 children with ASPS have been reported in the previous literature, along with a focused review of the clinical features, pathological features, differential diagnosis, treatment, and prognosis of ASPS in pediatric patients. Alveolar soft part sarcoma (ASPS) is a rare soft tissue sarcoma reported by Christopher et al. in 1952 . Therefo2,d1, d8, d15;Pirarubicin (THP) 30 mg/(m2*d),d1–d2;Cyclophosphamide (CTX) 1.2 g/m2,d1] and IE chemotherapy regimen  and Sunitinib 15 mg/m2 for four consecutive weeks, but the mass did not shrink significantly  d1–d3, Cisplatin (DDP) 30 mg/(m2*d) d1–d3, Epirubicin (E-DAM) 70 mg/(m2*d), d1] from 28 September 2009. After four cycles of IEP chemotherapy, CT revealed that the nodules in the lower lobe of both lungs were smaller than before but did not achieve CR. In April 2013, the patient presented again with left chest and back pain, paroxysmal, progressive aggravation of more than one month, accompanied by slight chest tightness, shortness of breath, slight cough, less phlegm, and no fever. CT showed right supraclavicular lymph node metastasis. On September 10 of the same year, a biopsy of the chest and back mass (2*d), d1–d4; Epirubicin (E-DAM) 70 mg/(m2*d),d1] for three cycles, then lost follow-up.During the physical examination of a 17-year-old female, a mass in the left posterior chest wall was discovered, and CT suggested that the nature of the space-occupying lesion in the left upper chest wall remained unknown. A resection of the left upper posterior chest wall mass and left lower lung nodule was performed on 2 September 2009. The mass was located in the 4th–5th intercostal space of the left posterior chest wall, and the size of the mass was about 4 cm × 5 cm × 5 cm, white, with a rich blood supply, granulation protruded on the surface of the mass, the pedicle extended into the intercostal space, and two 1 cm × 1 cm × 1 cm-sized nodules were palpable around the left lower lobe. Postoperative pathology:  malignant tumor, tumor cells showed acinar arrangement, rich cytoplasm, transparent or eosinophilic, vascular rich. Immunohistochemical: CT (−), HMB45 (−), CEA (−), CD68 (+ −), Syn (−), CD34 (−), CgA (−), F8 (−), TG (−), 5HT (−), GFAP (−), CD1a (−), Ki-67 (−), HHF35 (−), Lys (−), CD10 (−). CK (−), EMA (−), HPC (−), NSE (−), Vim (−), S100 (−), NF (−), and alveolar soft tissue sarcoma was considered. (Left lower lung nodule) two, tumor acinar, rich cytoplasm, red staining or transparent, combined with chest wall mass section immunohistochemical results, consistent with metastatic alveolar soft part sarcoma. She recovered after the operation and was discharged from the hospital on 9 September 2009. The discharge diagnosis was alveolar soft part sarcoma of the left upper posterior chest wall with multiple metastases in both lungs. The patient began to receive four cycles of postoperative IEP chemotherapy  and IEV chemotherapy regimen  were performed in turn from 12 July 2018. On 13 September 2018, an MRI of the neck  transcription family of microphthalmia-associated transcription factor/transcription factor E (MITF-TFE). Other members of the same family are MITF, TFEB, and TFEC subtypes . The genASPS is rare in pediatrics, and often misdiagnosed, especially in unusual sites. The diagnosis of ASPS should be combined with MRI, histopathology, and clinical features. Immunohistochemical and imaging methods play an important role in differential diagnosis. Because ASPS is a highly vascular tumor, MRI is often the first choice in imaging examination . ASPS sh2 for four consecutive weeks and rest for 2 weeks  Imm 2 weeks .The prognosis of ASPS depends on the patient's age, tumor size, and stage at the time of diagnosis. Many literature have reported that the prognosis of children with ASPS is better than that of adults, and the possible reasons are as follows: (1) age is an important factor determining the prognosis, and the younger the child is, the higher the survival rate is ; (2) comTherefore, ASPS in children has unique characteristics. When a painless soft tissue mass with slow growth in the head and neck is found in adolescent patients, we should be alert to the possibility of ASPS. Early detection, early diagnosis and early treatment are of great significance for the prognosis of children.


Biochemical mechanisms by which beneficial Cl− nutrition improves NUE in plants are poorly understood. First, we determined that Cl− nutrition at beneficial macronutrient levels did not impair the − in parallel with an increase in − nutrition reduces leaf −-treated plants. Chloride, in turn, promoted higher transcript levels of genes encoding enzymes of the photorespiration pathway. Accordingly, microscopy observations suggested strong interactions between different cellular organelles involved in photorespiration. Therefore, in this work we demonstrate for the first time that the greater − nutrition is mainly due to the stimulation of − functions and remarks on its relevance as a potential tool to manipulate NUE in plants.Chloride (Cl PE) or to convert the acquired N to biomass or grain yield . These parameters are considered important traits in agriculture, since crops with higher NUE promote greater yields under limited N in soil, or require lower N fertilization to reach similar yield. Therefore, new strategies to enhance NUE in plants are required to reduce the abusive use of N fertilizers or to improve plant growth when low N is available, with substantial benefits for farmers and to the environment (Nitrogen (N) is an essential macronutrient for plant growth and development , requireironment .In aerobic soils, nitrate  are the most abundant inorganic anions in plants, but contrary to − is not metabolized. Both anions share very similar physical properties in solution, playing important roles in charge balance and cell osmoregulation, showing strong dynamic interactions in land plants  than those required as a micronutrient (−), Cl− is accumulated at typical macronutrient concentrations, promoting plant growth by specifically improving the efficiency in the use of water, N and C, the main components of plant nutrition  were sown in flat trays  and grown under experimental greenhouse conditions with controlled temperature between 25 ± 3/18 ± 1 °C (day/night), a relative humidity of 60 ± 10% , 16/8 h photoperiod with a photosynthetic proton flux density  of 300–350 μmol m–2 s–1 , and a luminous emittance of 9,000–10,000 lx . At 15 days after sowing (DAS), seedlings were transferred to 7.5 L pots (20 cm x 17 cm x 25 cm) with a soil substrate containing a mixture of washed vermiculite:perlite (7:3). During the experiments, plants were watered with a basal nutrient solution containing: 4 mM KNO3, 1 mM Mg(NO3)2, 1 mM Ca(NO3)2, 0.1 mM FeNa-EDTA, 50 μM KCl, 0.1 mM H3BO3, 0.1 mM MnSO4, 29 μM ZnSO4, 0.11 μM CoCl2 0.1 μM CuSO4, 1 μM Na2MoO4, and 5 μM KI. In addition, three nutritional treatments were applied to evaluate the effect of Cl− nutrition: 0 mM Cl− , 2 mM Cl− , and 6 mM Cl− . To counterbalance all cations supplemented with the three CL treatments and to fulfil plant nutritional requirements, phosphate and sulphate salts were added to 0CL, 2CL and 6CL treatments as follows: 4.32, 3.24 and 1.08 mM KH2PO4; 0.48, 0.36 and 0.12 K2HPO4; 1.6, 1.2 and 0.4 mM MgSO4; and 1.6, 1.2, and 0.4 mM CaSO4; respectively. Considering that 50 µM Cl− was reported to ensure Cl− micronutrient requirements in different plant species  to fulfil micronutrient Cl− functions in low Cl− treatments  and to avoid both water deficit and flooding events during the course of the experiments. When the flowering stage started (60 DAS), half of the plants used in the assay were randomly harvested and weighted before noon (between 10:00-12:00). This material was further divided into two parts: one part was frozen with liquid nitrogen and conserved at -80°C in an ultra-low temperature freezer for further measurements of enzymatic activities; the other part was dried in a forced-air oven at 75°C for 48-72 hours, grounded to powder in a mortar and preserved dry for further recording of plant organ dry weight (DW) and for determination of different nutrient contents. The other half of the plant samples were used for xylem sap extraction and ion content determination.Pots were watered every two days, allowing adequate drainage to keep the soil close to field capacity (3.5 mL gn = 6-12), which confirmed similar tendencies between the three independent experiments.Experiments were performed in three independent times, showing in figures results from one representative experiment (–), 2CL and 6CL treatments. Pots were transferred to a airtight container, and submerged in the appropriate nutrient solution almost up to substrate level. Each plant was detached using a razor blade, and the root sap exudate was collected 1h later. Shoots were harvested and weighted to obtain fresh weight, and samples were dried in a force-oven at 75°C for 48-72 hours to obtain dry weight.Xylem sap was collected from tobacco plants of 60 DAS grown in the 0CL .−, −1 DW. Total N accumulation (TNA) was calculated as the result of TNC multiplied by total DW as described in −1 N; TE) calculated as total DW divided by TNC  calculated as TNA divided by root DW , 2 mM dithiothreitol (DTT) and 1% (w/v) polyvinylpolypyrrolidone (PVPP). Homogenate was centrifuged for 20 min at 30,000 g. The upper fraction was used to quantify nitrate reductase , nitrite reductase , glutamate synthase , and glutamate dehydrogenase  by spectrophotometry, following methods described by g for 20 min. Supernatant was used for colorimetric and kinetic assays , based on methods described by Frozen leaf tissues were ground in a chilled mortar with 1 mL of extract buffer (pH 7.5), which contained 50 mM KHProtein assay was done according to the colorimetric method g for 10 minutes. Samples were derivatized by reaction with diethyl ethoxymethylenemalonate and analyzed by reverse-phase high-performance liquid chromatography as described by Lyophilized leaf tissues (2 mg) were powdered and homogenized in 1 mL ethanol:water (3:7 v/v). The mixture was stirred for 30 minutes at room temperature and centrifuged at 8,850 NtL25 (Nitab4.5_0002995g0050.1) and NtGAPGH (Nitab4.5_0005327g0090.1). Reactions were performed on two independent RNA batches and results were comparable in the different assays. Results were also given as “Fold Change” when the transcript abundance of the treatment was divided by the control value.Total RNA was extracted from ground leaf samples using TRIsure reagent procedure . One µg of total RNA was used as a template for first strand cDNA synthesis in a 10 μl reaction using the iScript cDNA synthesis kit . Diluted cDNA was used as a template for gene expression level quantification by quantitative real-time reverse transcription (Q-RT)-PCR. The iTaq Universal SYBR Green Supermix (BIO-RAD) was used for Q-PCR analyses, using a CFX Connect Real-Time Detection System (BIO-RAD Laboratories) and following the manufacturer’s instructions. PCR was carried out with previously designed gene-specific primers S2. Data2 were fixed for 24h at room temperature in 4% (v/v) glutaraldehyde in 0.1 M sodium cacodylate buffer (pH 7.4), and washed in the same buffer. Subsequently, leaf sections were post fixed for 2 h at 4°C in 1% (w/v) osmium tetroxide in 0.1 M sodium cacodylate buffer (pH 7.4) and washed in the same buffer. After fixation, fragments were dehydrated through graded alcohols and embedded in Epon resin . Sections were cut with an ultramicrotome (Reichert-Jung Ultracut) with a diamond knife and mounted on nickel grids. The grids were contrasted with 2% aqueous uranyl acetate and observed with a Zeiss Libra 120 Electron Microscope at 80 kV.For transmission electron microscopy (TEM), leaves sections (60 DAS) around 1–2 mmP > 0.05, and levels of significance were indicated as followed: *P ≤ 0.05; **P ≤ 0.01; ***P ≤ 0.001. The Shapiro–Wilk (W) test was used to verify the normality of the datasets; and the Levene test to determine the homogeneity of variance. Data would be transformed if homogeneity of variance or normality could not be achieved using the functions 1/x, √x, and ln(x). Each variable assayed was analysed using one-way Analysis of variance (ANOVA) or multivariate ANOVA (MANOVA) including treatments as grouping factors. Means were compared by Tukey’s HSD post hoc test, with different letters indicating significant differences between groups of samples. Whether homogeneity of variance or normality was not reached after data transformation, non-parametric Kruskal-Wallis ANOVA was followed by post hoc Mann-Whitney (U test). Results represent the mean of at least six tobacco plants in each treatment (n = 6-12), which were reproducible in three independent experiments.Statistical analysis was performed using STATGRAPHICS Centurion XVI software . Results were considered non-significant (ns) when − nutrition at beneficial macronutrient levels has a positive impact on N assimilation, we conducted several greenhouse experiments with adult tobacco plants supplemented with 0, 2 and 6 mM Cl− salts . These nutritional replacements did not induce nutritional deficiencies since no symptoms of wilting, bronzing or chlorosis were observed  the treatment that reached the highest values of total, leaf and root biomass  values , which is in agreement with the range of typical macronutrient levels  values Figure 1o plants . No difft levels . These r− on either the uptake or assimilation of − -treated plants  nitrate uptake efficiency (NUPE), indicating how efficiently is N transported into the plant, and ii) nitrate utilization efficiency (NUTE), indicating how efficiently the transported N is used by the plant  were supplied with similar he plant . Accordi− treatment stimulates the − treatment on the transcript abundance and activity of key enzymes involved in − treatment induced higher accumulation of of transcripts encoding for NR, GS, GOGAT, GDH and AAT enzymes, showing lower changes in NiR transcript accumulation  in mitochondria through the photorespiratory conversion of Gly into Ser, with the latter generating up to 20 times more −-treatment . Interestingly, the expression level of all photorespiration-related genes was up-regulated in CL plants  in plants treated with 6CL  did not cause significant reductions in −, maintaining active transport mechanisms that are more selective for −  are considered important crop traits and a potential tool to reduce the abusive use of N fertilizers or to improve plant growth when low N is available , showing a repression of N allocation and assimilation mainly related to the inhibition of the activity and expression pattern of key enzymes such as NR, NiR, GS, GOGAT and GDH in leaves of several crop species  alongside the occurrence of ionic or osmotic stress conditions (up to 20 mM), beyond the beneficial macronutrient range (5-6 mM), concealed the impact of Cl− over the cation. Conversely, no NaCl effect on Sophora japonica  from Gly decarboxylation  without impairing photosynthesis rates (AN), and improving water use efficiency (WUE). Nonetheless, the low gs rates were counteracted by the higher mesophyll diffusion conductance to CO2 (gm), avoiding a negative effect on photosynthetic performance, due to a higher surface of chloroplasts exposed to the intercellular airspace of mesophyll cells. Consistently, 2 uptake increases when assimilating N via the photorespiratory pathway, by fixing C as amino acids in addition to carbohydrates. Therefore, these results support that the stronger Cl−-dependent assimilation of N is coordinated with a higher assimilation of C to improve growth in tobacco plants. Taken together, and after having ruled out the occurrence of Cl−/ − of biochemical pathways involved on − over the key genes of photorespiration pathway, deeper research must be done. As a future perspective, the deletion/overexpression of RuBisCO subunits, SHMT or GO genes in transformed tobacco lines could clearly demonstrate the induction of macronutrient Cl– and therefore reinforce the current evidence observed in our research. The complex interaction of this process with chloroplast, peroxisomes, mitochondria, and cytosol compartments led us to believe that macronutrient Cl– could acquire other specific roles in higher plants.The co-ordination of C and N assimilation has been studied extensively over many years because it is a key determinant of plant productivity. The integration of primary N assimilation with the re-assimilation of tabolism , being tlacetate , and parlacetate . In this− nutrition supplied at beneficial macronutrient levels improves NUE via the concurrent induction of both − on the synthesis and activity of enzymes involved in these N-metabolism pathways. The increment of − nutrition. And the higher organic N content in leaf tissue combined with higher content of soluble proteins and lower content in free amino acids in Cl−-treated plants indicate that Cl− promotes the synchronized assimilation of N and C, therefore producing higher biomass in plants. Thus, we propose that the optimal Cl− implementation in crop fields could be useful to improve biomass production even when N availability is scarce. Therefore, Cl− must be considered a good candidate to reduce both N application in farmlands and deleterious Finally, we conclude in this work that ClThe raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.PP-T, RA, ML, JDF-N, and FD-G performed the experiments, participated in the conception of experiments and research plans, analyzed and plotted the data; PP-T participated in the writing of the article; JC-F participated in the conception of research plans, co-funded to finance the project, supervised the experiments, and participated in the writing of the article; and MR supervised and participated in the performance of the experiments, conceived research plans, co-funded the project and wrote the article. All authors contributed to the article and approved the submitted version.


Objective: To examine the associations of mobile phone use and its use characteristics with new-onset CKD.Methods: 408,743 participants without prior CKD in the UK Biobank were included. The primary outcome was new-onset CKD.Results: During a median follow-up of 12.1 years, 10,797 (2.6%) participants occurred CKD. Compared with mobile phone non-users, a significantly higher risk of new-onset CKD was found in mobile phone users . Moreover, among mobile phone users, compared with participants with weekly usage time of mobile phone making or receiving calls <30 min, a significantly higher risk of new-onset CKD was observed in those with usage time ≥30 min . Moreover, participants with both high genetic risks of CKD and longer weekly usage time of mobile phones had the highest risk of CKD. Similar results were found using the propensity score matching methods. However, there were no significant associations of length of mobile phone use, and hands-free device/speakerphone use with new-onset CKD among mobile phone users.Conclusion: Mobile phone use was significantly associated with a higher risk of new-onset CKD, especially in those with longer weekly usage time of mobile phones making or receiving calls. Our findings and the underlying mechanisms should be further investigated. Chronic kidney disease (CKD) has a major impact on global health, both as a direct cause of morbidity and as an important risk factor for cardiovascular disease and premature death . In 2017In recent years, one phenomenon worthy of our attention is the sharp increase in the number of mobile phone users around the world, with an estimated 8.2 billion subscriptions worldwide in 2020 . This raTo address these gaps in knowledge, we aimed to examine the associations of mobile phone use, and characteristics of mobile phone use, including the frequency of making or receiving calls, the length of mobile phone use and hands-free device/speakerphone use, with risk of new-onset CKD in general population, using data from the large-scale, observational UK Biobank. Moreover, since it has been established that genetic factors may contribute to the development of CKD, we further investigated the joint effect of mobile phone use and genetic susceptibility of CKD on the risk of new-onset CKD.The UK Biobank is a large prospective, observational, population-based cohort designed to provide a resource for investigation of the genetic, environmental, and lifestyle factors associated with health and a wide range of diseases. Details of the study design and data collection have been described previously , 18. Bri2, or urine albumin: creatinine ratio (UACR) ≥30 mg/g]. Therefore, a total of 408,743 participants were enrolled in the final analysis  < 60 mL/min/1.73 manalysis . The UK In the UK Biobank, mobile phone use characteristics  were self-reported and assessed through the touchscreen questionnaire on the initial assessment visit (2006–2010).Length of mobile phone use was assessed using the following question, “For approximately how many years have you been using a mobile phone at least once per week to make or receive calls?”, and 7 options were given to respond: “Never used mobile phone at least once per week,” “One year or less,” “Two to four years,” “Five to eight years,” “More than eight years,” “Do not know,” and “Prefer not to answer.” According to the answers to the above question, those, who reported that they have used a mobile phone at least once per week in the past, were defined as mobile phone users. Mobile phone users were further asked for weekly usage of mobile phone making or receiving calls, and hands-free device/speakerphone use with mobile phone, while others did not.Weekly usage of mobile phones making or receiving calls was obtained using the following question, “Over the last 3 months, on average how much time per week did you spend making or receiving calls on a mobile phone?”, and 8 options were given to respond: “Less than 5 min,” “5–29 min,” “30–59 min,” “1–3 h,” “4–6 h,” “More than 6 h,” “Do not know,” and “Prefer not to answer.”Hands-free device/speakerphone use to make or receive calls was assessed using the following question, “Over the last 3 months, how often have you used a hands-free device/speakerphone when making or receiving calls on your mobile?”, and 7 options were given to respond: “Never or almost never,” “Less than half the time,” “About half the time,” “More than half the time,” “Always or almost always,” “Do not know,” and “Prefer not to answer.”Detailed information about genotyping and quality control in the UK Biobank study has been described previously . GeneticDetailed information on covariates was available through standardized questionnaires, including age, sex, ethnicities, education, smoking, alcohol drinking, income, and the uses of antihypertensive, cholesterol-lowering and glucose-lowering medications. Area-based socioeconomic status was derived from postal code of residence by using the Townsend deprivation score. BMI was calculated from weight (kg)/height(m) . PrevaleThe UK biobank blood collection sampling procedures have previously been described and validated . BiochemThe study outcome was new-onset CKD, which was defined according to the International Classification of Diseases (ICD) edition 9 code of 585 and 5859, ICD edition 10 code of N12.0, N13.1, N13.2, N18.0, N18.3, N18.4, N18.5, N18.8, and N18.9, and the Office of Population Censuses and Surveys Classification of Interventions and Procedures, version 4 (OPCS-4) code of M01 . The folBaseline characteristics of study participants were presented as mean ± standard deviation (SD) for continuous variables and proportions for categorical variables. Comparisons of the characteristics according to the weekly usage time of mobile phones making or receiving calls  were performed by chi-square tests for categorical variables and one-way analysis of variance (ANOVA) for continuous variables among mobile phone users.Cox proportional hazards models were used to investigate the relations of mobile phone use [users  vs. non-users] with new-onset CKD in the total participants, and the associations of weekly usage time of mobile phones making or receiving calls, length of mobile phone use  and hands-free device/speakerphone use to make or receive calls  with new-onset CKD in mobile phone users. Model 1 adjusted for age and sex. Model 2 adjusted for age, sex, BMI, ethnicities, Townsend deprivation index, income, education, smoking, alcohol drinking, systolic blood pressure (SBP), triglycerides, low-density lipoprotein (LDL) cholesterol, high-density lipoprotein (HDL) cholesterol, C-reactive protein, glycosylated hemoglobin (HbA1c), eGFR, uses of antihypertensive medications, cholesterol-lowering medications and glucose-lowering medications. Model 3 included all the covariates in Model 2 plus mutually adjustments for different characteristics of mobile phone use  among mobile phone users. The proportional hazards assumptions for the Cox model were tested using Schoenfeld residuals method and no violation of this assumption was detected.2), smoking status , SBP (<140 or ≥140 mmHg), eGFR (<90 or ≥90 mL/min/1.73 m2), diabetes (no or yes), length of mobile phone use, and hands-free device/speakerphone use to make or receive calls. Interactions were examined by likelihood ratio testing.Moreover, we also estimated the joint effect of weekly usage time of mobile phones making or receiving calls and genetic risks of CKD on the risk of new-onset CKD using weekly usage time <5 min with a low genetic risk of CKD as reference. Stratified analysis was conducted to assess potential modification effects of weekly usage time of mobile phones making or receiving calls (<30 or ≥30 min) with new-onset CKD according to age (<60 or ≥60 years), sex, BMI .A two-tailed As illustrated in the flow chart , a totalCompared with mobile phone non-users, mobile phone users were younger, more likely to be smokers, had higher BMI, eGFR, income levels, lower SBP levels, and lower usage of antihypertensive medications, cholesterol-lowering medications and glucose-lowering medications .Moreover, among mobile phone users, participants with longer weekly usage time of mobile phones making or receiving calls were younger, more likely to be male, current smokers and to use hands-free device/speakerphone, had lower SBP levels, lower use of antihypertensive and cholesterol-lowering medications, higher Townsend deprivation index, income, BMI, eGFR, TG levels, and higher length of mobile phone use .During a median follow-up duration of 12.1 years, a total of 10,797 (2.6%) participants occurred new-onset CKD. Compared with mobile phone non-users, a significantly higher risk of new-onset CKD was found in mobile phone users  .Overall, there were no significant associations of the length of mobile phone use, and hands-free device/speakerphone use with new-onset CKD among mobile phone users .P for trend <0.001). Consistently, compared with participates with weekly usage time of mobile phone making or receiving calls <30 min, a significantly higher risk of new-onset CKD was found in those with weekly usage time ≥30 min  , 1–3 h , 4–6 h  and >6 h  (07–1.18) .Further adjustments for physical activity, healthy diet scores, healthy sleep scores, self-reported depression, total mental health complaints and genetic risk scores of kidney function , or exclMoreover, a significantly higher risk of new-onset CKD was found in participants with diabetes , hypertension , and high genetic risks of CKD  .After propensity score matching, 101,816 participants  were included in the analysis for mobile phone uses (non-users vs. users) and new-onset CKD, and 183,068 participants  were included in the analysis for weekly usage time of mobile phone making or receiving calls (<30 min vs. ≥30 min) and new-onset chronic kidney disease. All the post-matched participant characteristics were highly balanced . ConsistP-interaction = 0.610).Compared with those with a low genetic risk of CKD and weekly usage time of mobile phones making or receiving calls <30 min, participants with a high genetic risk of CKD and weekly usage time ≥30 min had the highest risk of CKD  and new-onset CKD in various subgroups .p values for interaction >0.05).None of the variables, including age, sex, BMI, smoking, SBP, eGFR, diabetes, length of mobile phone use, and hands-free device/speakerphone uses, significantly modify the association between weekly usage time of mobile phones and new-onset CKD   and causThe major strengths of the current study include a prospective design, a large sample size, a long follow-up, and the ability to simultaneously consider multiple mobile phone use characteristics. Our study also had some limitations. First, in the current study, the information on mobile phone use were based on the questionnaires at baseline, and the weekly usage time making or receiving calls was only considered for the 3 months prior to the interview. As such, we could not evaluate the association between cumulative exposure of mobile phone use or lifetime usage of mobile phones and new-onset CKD. However, due to the continuous increase in the number of mobile phone users over the years, mobile phone non-users may possibly have used mobile phones subsequently. Moreover, with the pace of work and life accelerating worldwide, mobile phone users are likely to spend more time making or receiving calls. Therefore, our study may possibly underestimate the relationship of mobile phone use and weekly usage time making or receiving calls with the risk of CKD. Second, the participants were predominantly of European descent and were healthier than the general UK population , which mIn summary, mobile phone use was significantly associated with a higher risk of new-onset CKD, especially in those with longer weekly usage time of mobile phones making or receiving calls, among the general population. Of note, there is no established biological mechanism for the results. Our findings and the underlying mechanisms should be further investigated in more studies. If further confirmed, our study highlights the importance of reducing usage time of mobile phones making or receiving calls for primary prevention of CKD in the general population.


Immune checkpoint inhibitors, such as programmed death-1/ligand-1 (PD-1/L1), exhibited autoimmune-like disorders, and hyperglycemia was on the top of grade 3 or higher immune-related adverse events. Machine learning is a model from past data for future data prediction. From post-marketing monitoring, we aimed to construct a machine learning algorithm to efficiently and rapidly predict hyperglycemic adverse reaction in patients using PD-1/L1 inhibitors.  In original data downloaded from Food and Drug Administration Adverse Event Reporting System (US FAERS), a multivariate pattern classification of support vector machine (SVM) was used to construct a classifier to separate adverse hyperglycemic reaction patients. With correct core SVM function, a 10-fold 3-time cross validation optimized parameter value composition in model setup with R language software. r2 in curve regressions (nu = 0.5649 × e(− (case/6984)), gamma = 9.005 × 10−4 × case − 4.877 × 10−8 × case2). This SVM model with computable parameters greatly improved the assessing indexes  as well as coequal sensitivity and the area under the curve (AUC).  The SVM prediction model was set up from the number type/number optimization method, as well as the kernel and type of “rbf” and “nu-regression” composition. Two key values (nu and gamma) and case number displayed high adjusted  We constructed an effective machine learning model based on compositions of exact kernels and computable parameters; the SVM prediction model can noninvasively and precisely predict hyperglycemic adverse drug reaction (ADR) in patients treated with PD-1/L1 inhibitors, which could greatly help clinical practitioners to identify high-risk patients and perform preventive measurements in time. Besides, this model setup process provided an analytic conception for promotion to other ADR prediction, such ADR information is vital for outcome improvement by identifying high-risk patients, and this machine learning algorithm can eventually add value to clinical decision making. Diabetes mellitus and cancer are among the leading causes of death worldwide. As a major contributor to neoplastic transformation and an important prediabetes index, hyperglycemia (high blood glucose) is also influenced by cancer treatment. Higher blood glucose level, dealing with or without insulin, may lead to serious complications of diabetes, such as ketoacidosis, and is correlated with cancer risk, progression, and mortality at a higher degree –3.Recently, immunotherapy is a vast improvement among anticancer therapies . In 2018Worldwide public database on adverse events could provide many drug-usage information , 12, havMachine learning (ML) “learns” a model from past data in order to predict future data. The key process is the learning which is one of the artificial intelligences . ArtificMany different statistical, probabilistic, and optimization techniques can be implemented as learning methods such as the logistic regression, artificial neural networks, K-nearest neighbor, decision trees, and Naïve Bayes . HoweverSVM-based approach performances well in managing sparse data in high dimensions, in that it overcomes the other state-of-the-art competitors providing the best compromise between prediction and computation time . It has To date, the correlation of hyperglycemic occurrences and personal-related features, or which kinds of special features could mostly correlate with hyperglycemia, has become an urgent problem, for which all of these predictions have not been reported yet. Predicting and avoiding terrible ADR can provide better guidance for clinical decision making. In this study, we aimed to construct an effective SVM-based machine learning model with high accuracy, low computational costs, and computable parameters to predict hyperglycemia in PD-1/PD-L1–treated patients.Original data was downloaded from US FAERS public dashboard  , Cemiplimab , Avelumab , Durvalumab , Atezolizumab , Pembrolizumab , Nivolumab , and Ipilimumab .Positive cases: Items in “Reaction” variable including at least one of the following: “Diabetes Mellitus,” “Diabetic,” “Hyperglycaemia,” “Hyperglycinaemia,” “Glucose Tolerance Impaired,” and “Blood Glucose Increased.”Inclusion criteria were as follows:Cases with wrong report year.Variables with no healthcare information (Step 1.1).Cases with missing value(s), excluded in complete data (Step 1.2).Exclusion criteria were as follows:  Step 1.1. Variable primary screening  Variables (13 columns) were obtained from the downloaded Excel files: Case ID, Reason for Use, Reactions, Serious, Outcomes, Sex, Event Date, Case Priority, Patient Age, Patient Weight, Reporter Type, Report Source, and Country where event occurred.  Variables (11 columns) with no more healthcare information were excluded: Suspected Product Names, Suspected Product Active Ingredients, Latest FDA Received Date, Sender, Concomitant Product Names, Latest Manufacturer Received Date, Initial FDA Received Date, Reported to Manufacturer, Manufacturer Control Number, Literature Reference, and Compounded Flag.  Step 1.2. Raw data and complete data  Original data were fixed into two versions: raw data  and complete data ). The two versions of data contained above 13 variables.Step 1. Data VersionStep 2. Core SVM algorithmPositive and negative cases could be substituted as 2-category factor type (“Yes” and “No”) or number type (“1” and “0”) in SVM model setup.R-function optimization. In number optimization, the best values were defined by the composition of parameters, according to the best range of each parameter. In R-function optimization, the parameter range was input to a built-in function (R library: tune.svm). If the best values were close to range boundary, the new range would be adjusted for optimization again. The factorized data were optimized via both number and function methods.  Step 2.1. Number/factorf_raw” and “n_raw.” As R-tune optimization needs variables in form of factor, there were no results of “nr” (number type/R-tune optimization), but only “nn” (number type/number optimization).  Each drug with 600 cases  was extracted from raw data and complete data. All the type-optimization compositions were tested. In raw data, the results of factor number were very close to those of factor tune, so they are only displayed as “  The data version was also determined at this step.  Step 2.2. VariablesR library: nearZeroVar; “T” means deletable) and model assessment .  The key to construct an SVM model that can screen the active markers accurately is to select the appropriate variables. Variable selection was according to two methods: near zero variance method (  Step 2.3. Type and kernelR language library SVM (e1071) official Arguments are as follows: SVM-Type  and SVM-Kernel , “r”; sigmoid, “s”).  Parameter selection: parameters from C” classification can accept both numeric and factorial dependent variables.  Selection is also based on data types (number and factor). “eps” and “nu” regression need numeric dependent variables, while “one” and “There are two parameter optimization methods: number optimization and Step 3. Key parameters  Step 3.1. Parameter composition  Parameters  optimized separately: their value ranges were determined by the best outputs.  Parameters' best composition : parameters were set by training data through 10-fold 3-time cross validation. Accuracy , F1 score, kappa (consistence), and sensitivity (positive precise rate) values were selected as evaluation indicators.  Parameter value determination : gamma and cost were set as the minimal values; nu was set as the mean value in parameter composition.  Step 3.2. Regressions for selected parameters  Regression analysis of parameters  and corresponding case number were tested for computable correlations.With complete data, the general modeling set was from stratifying random-split cross validation into training data (70% data) and testing data (30% data), containing proportional positive and negative cases, respectively.Step 4. Model prediction performanceThe model prediction was performed on testing data and other testing drugs. Four indexes  and ROC were checked for its effect.The procedure comprises data download, algorithm selection, key parameter regression, and model prediction performance .Step 1. T-test was performed for comparing normal distributions and defining 95% confidence intervals, and Wilcoxon rank test was used for comparing other unknown distributions. R language (version 4.2.0 for Windows) was used for statistics, and its library of e1071 was used for SVM model buildup.Descriptive analysis was used to summarize patient demographic characteristics, with mean values for continuous variables and ratios for categorical variables. To explain the impacting factors in hyperglycemia, “Reaction” is defined as a dependent variable and others as response variables. A demographic summary of complete data for analysis is provided in Pilot assay was used for algorithm selection based on proportional 600 cases of each drug. Positive and negative cases were classified as number type  or factor type . Optimization methods were combined as number and R-tune. Accuracy , F1 score, kappa (consistence), and sensitivity  values were evaluated as indicators of performance. Confusion matrix was calculated according to One key to constructing an SVM model that can accurately screen the active markers is to select the appropriate variable indexes . Classicl,” “p,” “r,” “s”) and type . The five PD-1/L1 drugs were test by the 4 × 4 kernel-type cross compositions. As the index range span is too long, y-axis values were logged in the boxplots. In number type, “r-nu” (“rbf” and “nu-regression”) displayed better performances, especially at the important indexes of F1 score and kappa. In factor type, “p-C” displayed better performances, except for being lower at sensitivity. Moreover, “r-nu” performed better than “p-C” at F1 score and kappa  and testing data (30% data), containing proportional positive and negative cases, respectively.Parameters of SVM mainly included degree, cost, gamma, nu, coef0. From pilot study, nu was defined by the mean value and cost (=1) and gamma by the minimal value in the best prediction range (1∼10); while other parameters did not need to be adjusted. The main model setup algorithm is shown in Algorithm 1.Main model setup indicated the critical points depended on the patterns of nu and gamma. In regression analysis, we found out that the nu values 0 < nu ≤ 1) were exponential and gamma values (1 ≤ gamma ≤ 10) were quadratic to case number, regression formula, and curves as in  were exp and FiguThe setup model with optimized parameters ))2)) was Receiver operating characteristic (ROC) analysis was used to describe the discrimination accuracy of a diagnostic test or prediction model . The diaThe application of machine learning in healthcare delivery presents unique challenges that require data preprocessing, model training, and refinement of the system with respect to the actual clinical problem . In thisHyperglycemia is a serious ADR in cancer treatment , and it R library: createDataPartition. SVM is powerful in data mining for better classification; however, it is greatly influenced by the parameters. It is inefficient to use traditional grid search, learning curve, and other parameter adjustment methods [SVM is a kind of structural dependence model to find maximum margin hyperplane with ADR and reported features. To train the algorithm, new cases are projected in the same situation to test which side of the hyperplane they are located on . As the  methods . SVM is  methods , while oR-tune optimization, in that R-tune leans toward various minor compositions other than the comprehensive adjustment in number optimization .SVM model is usually based on kernel and type kinds. From crossing composition, “tor type . FurtherFive parameters  were adjusted in parameter selection (Step 3.1). As no results changed in regulating degree and coef0, the two parameters were set as the function default. To avoid overfitting, cost value was defined as “1” (the function default number). Adjusting the other two parameters (nu and gamma) could improve model performance in 3 × 10 (10-fold 3-time) cross validation. Then, nu was defined by the mean value and gamma by the minimal value from model. Because the whole range of nu (0∼1) had been checked in cross validation, its mean value could cover entire situations. However, gamma determines distribution of new feature space, meaning that the smaller the gamma value, the more the support vectors. So, the choice of gamma on smallest value would not be effected by its primeval testing range. Different from previously published preprint , model re(−case number/constant)(iterated highest r2).In the exponential curves , the cony-axis against 1–specificity  on the x-axis for the various values tabulated [The positive hyperglycemic ADR case ratio is relatively low <5%, , so the abulated . Areas uOne SVM's downside is that finding the best model requires testing of various compositions of kernels and model parameters. The success or failure of machine learning approaches on a given problem may vary strongly with the expertise of the user . Our modThough SVMs are extremely powerful classifiers and there are no medicolegal implications, clinicians' understanding, or privacy and security on public database, several limitations must be addressed. One thing that needs to be clear is that pharmacovigilance databases can just be used to predict correlations rather than causality. FAERS (including spontaneous reporting systems) declares no certainty that the reported event was due to the product, insufficient details, incomplete reports, and duplicate reports . Other dDespite the limitations described above, this algorithm has provided meaningful information in order to adjust care goals for these patients or provide signs for further well-organized clinical studies.ADR is one of the pharmacovigilance keys during drug post-marketing phase. On the one hand, it is challengeable to find out the effective and fast means from clinical heterogeneity results. On the other hand, interestingly, ADR prediction is more important in that its occurrence is usually unknown in clinical treatment, since real time monitoring is expensive and inconvenient for discovering ADRs. This algorithm model from clinical available features at the time of presentation was proved robust and generalizable in later testing-part sequence. This noninvasive and precise prediction could greatly help clinical practitioners to distinguish high-risk patients. Therefore, this study provided an orientation to predict hyperglycemic ADR with these drugs.In summary, the SVM model established here can noninvasively and precisely predict hyperglycemic ADR in patients treated with PD-1/PD-L1 inhibitors from given personal-related features and given case number. An SVM model was set up based on compositions of correct kernels and computable parameters. The SVM model showed good prediction performance in testing data, which proved that this model is robust and generalizable in this field. This model setup process provided an analytic conception for promotion to other ADR prediction. We also believe that the availability of medical and personal information will further facilitate this model. Such information from prediction is vital for preventing or even overcoming ADR and to improve patient outcomes by distinguishing high hyperglycemia-risk patients, and this machine learning algorithm can eventually add value to clinical decision making.


We propose a new method that is efficient and applicable for estimating photosynthetic performance and photosystem status using remote sensing hyperspectroscopy with visible, near-infrared and shortwave spectroscopy (Vis-NIR-SWIR) based on rapid multivariate partial least squares regression (PLSR) as a tool to estimate biomass production, calorimetric energy content and chlorophyll a fluorescence parameters. The results showed the presence of typical inflections associated with chemical and structural components present in plants, enabling us to obtain PLSR models with R2P and RPDP values greater than >0.82 and 3.33, respectively. The most important wavelengths were well distributed into 400 (violet), 440 (blue), 550 (green), 670 (red), 700–750 (red edge), 1330 (NIR), 1450 (SWIR), 1940 (SWIR) and 2200 (SWIR) nm operating ranges of the spectrum. Thus, we report a methodology to simultaneously determine fifteen attributes , ΔH°area, ΔH°mass, Fv/Fm, Fv’/Fm’, ETR, NPQ, qP, qN, ΦPSII, P, D, SFI, PI(abs), D.F.) with high accuracy and precision and with excellent predictive capacity for most of them. These results are promising for plant physiology studies and will provide a better understanding of photosystem dynamics in tobacco plants when a large number of samples must be evaluated within a short period and with remote acquisition data.High-throughput and large-scale data are part of a new era of plant remote sensing science. Quantification of the yield, energetic content, and chlorophyll  Plants perceive light in the surrounding environment in very specific and sensitive ways, which induces biochemical, physiological and morphological changes at the individual level ,2. Lighta fluorometer devices, such as pulse-amplitude-modulation (PAM) fluorometry in conjunction with the saturation pulse method, remain the most utilized approaches , with light irradiance fixed at 200 µmol m−2 s−1, individually adjusted by a LI-190R quantum sensor  under a 12 h/12 h photoperiod (light/dark) at 25 °C (±3 °C) with 70% relative humidity. The experimental design was a random scheme with tobacco plants grown under an LED to produce a broad spectrum of datasets. On the 28th day after transplantation, the plants were collected and freshly analyzed, frozen in liquid N2, stored at −80 °C, and freeze-dried or oven-dried (70 °C) for the various analyses . White (W—peak at 443 and 580 nm), blue (B—peak at 450 nm), red (R—peak at 658 nm) and red added to far-red (FR—peak at 742 nm) values (data not shown) were measured as described in a previous study .Destructive growth analyses were conducted on days 30–35 after seedling transplantation as described following ,67. Leaf−1) and area .A calorimetric energetic analysis (ΔH°) was performed to evaluate the differential construction costs of leaf plant tissue. For the quantification of the calorimetric content, the dry matter of leaves was oven-dried at 70 °C until reaching a constant weight. The leaf fractions were ground in a knife mill . Oven-dried samples were calcined using a Parr 6100 adiabatic calorimetric bomb . The calorimetric power of leaves  was estimated by calculating the sum of the respective mass fraction  was used according to the manufacturer’s light source: red:blue ratio (90:10), 2000 µmol m−2 s−1 light source, 400 µmol mol−1 constant in the chamber sample (CO2_sample), 60% sample chamber relative humidity (%RH_sample), flow rate of 700 µmol s−1, fan speed of 10,000 rpm, and 25 °C heat exchanger temperature of the sample (Theat_sample) with a 6 cm2 sample chamber coupled to chlorophyll a fluorescence measurements simultaneously for all parameters.An infrared gas exchange analyzer (IRGA) was used to measure 10- to 28-day-old experimental plants using the healthy, young, expanded leaf of the 4th or 5th leaf . An IRGA  coupled with a Multiphase Flasha fluorescence was measured in leaves previously acclimated (12 h) to the dark to measure “dark acclimated” fluorescence parameters . Variable fluorescence (Fv) was estimated using the equation Fv = Fm − Fo, allowing an estimation of the Fv/Fm ratio (maximum quantum efficiency of PSII photochemistry). Other chlorophyll a fluorescence measurements were performed on light acclimated leaves using the multiphase flash fluorescence protocol (MPF) with a saturating intensity of 15,000 µmol m−2s−1, dark modulation rate of 5 kHz, and light modulation rate of 50 kHz. Maximum Chl fluorescence (Fm’) was measured at 250 kHz during the saturating pulse, and fluorescence was detected at >700 nm (Li-Cor Inc.). The PSII maximum efficiency (Fv’/Fm’), quantum yield of photosystem II photochemistry (Φ PSII), electron transport rate through photosystem PSII (ETR) (µmol m−2 s−1), non-photochemical quenching (NPQ), photochemical quenching factor (qP) and non-photochemical quenching factor (qN) were estimated using LiCor® software simultaneously with gas exchange measurements of light [Chlorophyll of light ,69.a fluorescence transient induction was performed on the same leaves as “classical” fluorescence measurements using an LI-6800 instrument. Before being measured, each selected leaf was acclimated in the dark overnight (12 h). Afterward, a clipping chamber and a saturating light pulse of 15,000 µmol m−2 s−1 were applied for 1 s in induction mode, which closed all of the reaction centers, and the following fluorescence parameters were measured [(abs), Performance index for energy conservation from photons absorbed by PSII antenna until the reduction of PSI acceptors; D.F., Driving force on light absorption by a leaf cross-section. Data analysis and equations for calculating JIP-test parameters are explained in a previous study [®  was used to estimate the JIP-test parameters associated with the electron transport chain in plants.Chlorophyll measured ,70: SFI,us study ,71. Biol®; ASD Inc., Boulder, CO, USA) that is used as a reflectance standard, and an opaque background of 1% reflectance was used in the plant probe to ensure the collection of pure reflectance spectra from the leaf-based methods following [Tobacco adaxial leaf spectral reflectance data were obtained using a FieldSpec 3 Jr. spectroradiometer  with a spectral resolution of 3 nm between 350 and 1400 nm and 10 nm between 1400 and 2500 nm. The equipment was programmed to perform 50 readings for each sample, thereby generating an average reflectance hyperspectral curve. Readings (scans) were recorded with the plant-probe device connected to the spectroradiometer by an optical fiber to avoid atmospheric influences and external interference. The plant probe has an artificial light consisting of a 4.5 W halogen lamp, which allows its operation regardless of the light conditions in the laboratory, greenhouse and environmental measures. The plant-probe device has an internal 99% white reference , Statistica® 12.0 software , The Unscramber X10.4®  and the R package R-Core-Team (2020)  [All statistical analyses of yield, calorimetric energy and chlorophyll st 2022) .a fluorescence parameters derived from the hyperspectral data using the PLSR models. Data transformation was not needed. The effects of light qualities  on the leaf traits were analyzed using one-way analysis of variance (ANOVA). Duncan’s post-hoc test was used. The effects of light on the untransformed reflectance profiles (averaged per plant) were assessed using PERMANOVA by employing Euclidian measurements of dissimilarity using the Euclidean distance with the “vegan” package in R-Core Team (2020).The Shapiro-Wilk test was used to assess the normal distribution of data, and Bartlett’s test was used to assess the homogeneity of variance of hyperspectral indices, yield, calorimetric energy and chlorophyll p < 0.05) was performed using “The Unscrambler X” software, version 10.4  as an indicator of whether the variance in the reflectance hyperspectral between the varieties could be explained or not and how effectively tobacco plant varieties could be grouped. The numbers of components based on the highest average accuracy (Acc) and Kappa (K) values obtained for the validation models from partial least squares regression (PLSR) are reported. Partial least squares discriminant analysis was performed with the “caret” and “vegan” packages in R-Core Team (2020).Principal component analysis (a fluorescence). No transformation was needed. For all variables, the marginal wavelengths recorded (350–399 and 2401–2500 nm), which are the nosiest ones, were removed to improve the accuracy of the data. Afterward, the data  were centered on the mean and subjected to a partial least squares regression (PLSR) analysis. The algorithm for model inputs was NIPALS, and output outlier limits were defined by Leverage’s type and analyzed using Leverage and Hotelling’s T2 test (limit of 5%). For that purpose, the spectral data of the 260 samples of different parameters collected were divided into two groups. The first group consisted of 70% (182) of the samples with the aim of creating the model (training), while the second group was represented by 30% (78) of the remaining samples with the aim of testing (prediction) the adjusted PLSR model. These samples were randomly selected from the dataset, avoiding bias that might influence the quality of the model following [The data were subjected to a Shapiro–Wilk test to assess the normal distribution and homogeneity of variance with Bartlett’s test and to obtain the prediction models of quality ; ΔH°mass , calorimeter energy content by mass; ΔH°area , calorimetry energy content by area; Fv/Fm: Maximum quantum efficiency of PSII photochemistry in the dark-acclimated state; Fv’/Fm’: Maximum efficiency of PSII in light-acclimated state; ETR, electron transport rate (µmol m−2 s−1); NPQ, non-photochemical quenching calculated as (Fm − Fm’)/Fm’; qP: Photochemical quenching calculated as (Fm’–Fs)/(Fm’ − Fo’); qN: Non-photochemical quenching calculated as (Fm − Fm’)/(Fm − Fo’); ΦPSII, PSII operating efficiency under light conditions as calculated as (1 − Fs/Fm’); P, fraction of light absorbed in PSII antennae that is utilized in PSII photochemistry; D, fraction of light absorbed in PSII antennae that is dissipated thermally; SFI, Structure function index; PI(abs), Performance index for energy conservation from photons absorbed by PSII antenna until the reduction of PSI acceptors; D.F., Driving force on light absorption by the leaf cross-section).The calibration  and leave-one-out cross-validation (Cva) methods were used to predict the quality attributes based on the yield, calorimetry energy and chlorophyll 2 (coefficient of determination), offset, RMSE (root mean square error) and RPD (ratio of performance to deviation), and bias was determined to assess the quality and accuracy of the model. According to [2 values are classified as R2 < 0.50 , R2 between 0.50 and 0.65 (models with moderate predictions that indicate the possibility of discriminating high and low concentrations in the model), R2 between 0.66 and 0.81 (good prediction models that facilitate quantitative predictions), R2 between 0.82 and 0.90 (very good quantitative prediction models) and R2 ≥ 0.91 (excellent prediction models). In addition, [The predictive ability of the calibration models was evaluated by calculating metrics such as Rrding to ,72, R2 vddition, ,41. β-CoThese randomized investigations generated a distribution of fit statistics simplifying the evaluation of model stability, as well as uncertainty in the model predictions. The strength contribution of PLSR loadings by individual wavelengths was also assessed using the VIP selection statistics, which highlight the importance of individual wavelengths in explaining the variation in both the response and predictor variables: larger weights provide higher values to the contribution of individual wavelengths to the predictive model . The modIn addition, before developing the prediction model, all preliminary models were investigated to identify poorly predicted outliers. However, prediction residuals were explored to identify potential outliers. Following the method proposed following , the spe2, Offset, RMSE, SEP, RPD, and bias) were investigated to assess the accuracy of the model-derived estimate . Relations between predicted and observed values were tested by performing a regression analysis, and fit statistics  with hyperspectral techniques. The most important wavelengths for the construction of the PLSR model of the evaluated parameters were well distributed within the three operating ranges of the spectroradiometer (Vis-NIR-SWIR). According to the regression coefficients, the most important bands were generally close to 400 (violet), 440 (blue), 550 (green), 670 (red), 700–750 (red edge), 1330 (NIR), 1450 (SWIR), 1940 (SWIR) and 2200 (SWIR) nm.We were able to adjust PLSR models in the prediction (test) phase with R(abs) and D.F.) in tobacco plants. Therefore, this technique is a promising alternative for the routine analysis of the aforementioned parameters, as it provides advantages such as rapid data acquisition over a wide range. Based on the information provided above, since the Vis-NIR-SWIR spectroradiometer facilitates data acquisition and processing, it can be directly applied by field or plant physiologists (fast inline classification) with an excellent capacity for monitoring of the photodevelopment and understanding photosystem dynamics in tobacco, and other, plants.This study confirms the potential of a Vis-NIR-SWIR spectroradiometer to estimate the physiological parameters (yield, ΔH°area, ΔH°mass, Fv/Fm, Fv’/Fm’, ETR, NPQ, qP, qN, ΦPSII, P, D, SFI, PI