Genome-wide identification and expression analysis of SMALL AUXIN UP RNA (SAUR) genes in rice (Oryza sativa).

SMALL AUXIN UP RNAs (SAURs), the largest family of early auxin response genes, plays crucial roles in multiple processes, including cell expansion, leaf growth and senescence, auxin transport, tropic growth and so on. Although the rice SAUR gene family was identified in 2006, it is necessary to identify the rice SAUR gene due to the imperfection of its analysis methods. In this study, a total of 60 OsSAURs (including two pseudogenes) distributed on 10 chromosomes were identified in rice (Oryza sativa). Bioinformatics tools were used to systematically analyze the physicochemical properties, subcellular localization, motif compositions, chromosomal location, gene duplication, evolutionary relationships, auxin-responsive cis-elements of the OsSAURs. In addition, the expression profiles obtained from microarray data analysis showed that OsSAUR genes had different expression patterns in different tissues and responded to auxin treatment, indicating functional differences among members of OsSAUR gene family. In a word, this study provides basic information for SAUR gene family of rice and lays a foundation for further study on the role of SAUR in rice growth and development.

Classification method based on Siamese-like neural network for inter-species blood Raman spectra similarity measure.

Analysis of blood species is an extremely important part in customs inspection, forensic investigation, wildlife protection and other fields. In this study, a classification method based on Siamese-like neural network (SNN) for interspecies blood (22 species) was proposed to measure Raman Spectra similarity. The average accuracy was above 99.20% in the test set of spectra (known species) that did not appear in the training set. This model could detect species not represented in the dataset underlying the model. After adding new species to the training set, we can update the training based on the original model without retraining the model from scratch. For species with lower accuracy, SNN model can be trained intensively in the form of enriched training data for that species. A single model can achieve both multiple-classification and binary classification functions. Moreover, SNN showed higher accuracy rates when trained with smaller datasets compared to other methods.

Age estimation of bloodstains based on Raman spectroscopy and chemometrics.

The accurate estimation of the bloodstain age, which is one of the important biological evidence of crime scene, can provide a lot of information related to crime. How to extract the information quickly and accurately from bloodstains without damage has been a focused problem. In this study, a bloodstains age estimation method based on Raman spectroscopy and chemometrics was developed. As many as 11 simulated environments based on different temperature and humidity were constructed in the method, and bloodstains of three species including human were studied. The influence of environmental factors such as temperature and humidity on the variation of Raman spectral peaks during the aging process of bloodstains was analyzed using the e-index fitting. When the humidity was kept constant, the increase of temperature generally promoted the changes of the spectral peaks. When the temperature was kept constant, the increase of humidity generally slowed the changes of spectral peaks. These works provide data support for the further development of Raman spectroscopy for bloodstain age estimation and could accelerate its application in actual scenes.

Identification of blood species based on surface-enhanced Raman scattering spectroscopy and convolutional neural network.

The identification of blood species is of great significance in many aspects such as forensic science, wildlife protection, and customs security and quarantine. Conventional Raman spectroscopy combined with chemometrics is an established method for identification of blood species. However, the Raman spectrum of trace amount of blood could hardly be obtained due to the very small cross-section of Raman scattering. In order to overcome this limitation, surface-enhanced Raman scattering (SERS) was adopted to analyze trace amount of blood. The 785 nm laser was selected as the optimal laser to acquire the SERS spectra, and the blood SERS spectra of 19 species were measured. The convolutional neural network (CNN) was used to distinguish the blood of 19 species including human. The recognition accuracy of the blood species was obtained with 98.79%. Our study provides an effective and reliable method for identification and classification of trace amount of blood.

Accumulation of Wheat Phenolic Acids under Different Nitrogen Rates and Growing Environments.

The health benefits of whole wheat grains are partially attributed to their phenolic acid composition, especially that of trans-ferulic acid (TFA), which is a powerful natural antioxidant. Breeders and producers are becoming interested in wheat with enhanced health-promoting effects. This study investigated the effects of different nitrogen (N) application rates (0, 42, 84, 126, and 168 N kg ha-1) on the phenolic acid composition of three wheat varieties in four locations for two years. The results indicate that the different N rates did not affect the TFA concentration but that they significantly affected the concentrations of para-coumaric acid, sinapic acid, and cis-ferulic acid in the wheat grains. A statistical analysis suggested that the wheat phenolic acid composition was predominantly determined by wheat variety, though there existed some interaction effect between the wheat variety and environments. The TFA concentration of the variety Jimai 22 was generally higher (with a mean value of 726.04 µg/g) but was easily affected by the environment, while the TFA concentration of the variety Zhongmai 578 (with a mean value of 618.01 µg/g) was more stable across the different environments. The results also suggest that it is possible to develop new wheat varieties with high yield potential, good end-use properties, and enhanced nutraceutical values.

Determination of blood species using echelle Raman spectrometer and surface enhanced Raman spectroscopy.

Blood species identification of human and animals has attracted much attention in the areas of customs inspection and forensic science. The combination of vibrational spectroscopy and machine learning has been proven to be feasible and effective for this purpose. However, the popularization of this technology needs instrument which is compact, robust and more suitable for field application. Besides the quantity of the blood sample should be as little as possible. In this study, we proposed a system using echelle Raman spectrometer combined with surface enhanced Raman spectroscopy (SERS), which protocol combines the advantages of broadband and high resolution of echelle Raman spectrometer with the advantages of high SERS spectral sensitivity. The SERS spectra of 26 species including human were collected with echelle Raman spectrometer, and the convolutional neural network was used for species identification, with an accuracy rate of over 94%. The feasibility, validity and reliability of the combination of echelle Raman spectrometer and SERS for blood species identification were realized.

QTL Mapping for Grain Zinc and Iron Concentrations in Bread Wheat.

Deficiency of micronutrient elements, such as zinc (Zn) and iron (Fe), is called "hidden hunger," and bio-fortification is the most effective way to overcome the problem. In this study, a high-density Affymetrix 50K single-nucleotide polymorphism (SNP) array was used to map quantitative trait loci (QTL) for grain Zn (GZn) and grain Fe (GFe) concentrations in 254 recombinant inbred lines (RILs) from a cross Jingdong 8/Bainong AK58 in nine environments. There was a wide range of variation in GZn and GFe concentrations among the RILs, with the largest effect contributed by the line × environment interaction, followed by line and environmental effects. The broad sense heritabilities of GZn and GFe were 0.36 ± 0.03 and 0.39 ± 0.03, respectively. Seven QTL for GZn on chromosomes 1DS, 2AS, 3BS, 4DS, 6AS, 6DL, and 7BL accounted for 2.2-25.1% of the phenotypic variances, and four QTL for GFe on chromosomes 3BL, 4DS, 6AS, and 7BL explained 2.3-30.4% of the phenotypic variances. QTL on chromosomes 4DS, 6AS, and 7BL might have pleiotropic effects on both GZn and GFe that were validated on a germplasm panel. Closely linked SNP markers were converted to high-throughput KASP markers, providing valuable tools for selection of improved Zn and Fe bio-fortification in breeding.

Discordance Between Advanced Cancer Patients' Perceived and Preferred Roles in Decision Making and its Association with Psychological Distress and Perceived Quality of Care.

OBJECTIVE: We investigated patient-reported roles of families, physicians, and patients themselves in treatment decision making and whether discordance between perceived and preferred roles is associated with psychological distress and perceived quality of care among patients with cancer. METHODS: We analyzed cross-sectional survey data from 599 adults with stage IV solid malignancy in Singapore. Stuart-Maxwell tests were used to compare patients' perceived and preferred roles in decision making. Types of discordance were categorized as follows: involvement at a lesser level than preferred, involvement at a greater level than preferred, and no change in patient involvement. Ordinary least squares regressions examined the associations between types of discordance and patient outcomes, controlling for patient characteristics. RESULTS: Discordance between perceived and preferred roles was observed in 16% of patients. Amongst patients with discordance, 33% reported being involved at a lesser level than they preferred, 47% reported being involved at a greater level than they preferred, and 19% reported discordance where level of patient involvement did not change. Multivariable analyses showed that lesser involvement than preferred and discordance with no change in patient involvement were associated with poorer quality of physician communication (ß = - 9.478 [95% confidence interval {CI} - 16.303 to - 2.653] and ß = - 9.184 [95% CI - 18.066 to - 0.301]) and poorer care coordination (ß = - 11.658 [95% CI - 17.718 to - 5.597] and ß = - 8.856 [95% CI - 16.744 to - 0.968]) compared with concordance. CONCLUSIONS: Most patients reported participating at their desired level. Despite this finding, our results suggest that involving patients at a lesser level than they prefer can lead to poorer perceived quality of physician communication and care coordination and that encouraging patient participation is a safe approach to minimizing poor outcomes.

Identification and validation of genetic loci for tiller angle in bread wheat.

KEY MESSAGE: Two major QTL for tiller angle were identified on chromosomes 1AL and 5DL, and TaTAC-D1 might be the candidate gene for QTA.caas-5DL. An ideal plant architecture is important for achieving high grain yield in crops. Tiller angle (TA) is an important factor influencing yield. In the present study, 266 recombinant inbred lines (RILs) derived from a cross between Zhongmai 871 (ZM871) and its sister line Zhongmai 895 (ZM895) was used to map TA by extreme pool-genotyping and inclusive composite interval mapping (ICIM). Two quantitative trait loci (QTL) on chromosomes 1AL and 5DL were identified with reduced tiller angle alleles contributed by ZM895. QTA.caas-1AL was detected in six environments, explaining 5.4-11.2% of the phenotypic variances. The major stable QTL, QTA.caas-5DL, was identified in all eight environments, accounting for 13.8-24.8% of the phenotypic variances. The two QTL were further validated using BC1F4 populations derived from backcrosses ZM871/ZM895//ZM871 (121 lines) and ZM871/ZM895//ZM895 (175 lines). Gene TraesCS5D02G322600, located in the 5DL QTL and designated TaTAC-D1, had a SNP in the third exon with 'A' and 'G' in ZM871 and ZM895, respectively, resulting in a Thr169Ala amino acid change. A KASP marker based on this SNP was validated in two sets of germplasm, providing further evidence for the significant effects of TaTAC-D1 on TA. Thus extreme pool-genotyping can be employed to detect QTL for plant architecture traits and KASP markers tightly linked with the QTL can be used in wheat breeding programs targeting improved plant architecture.

Cost-effectiveness of Intravitreal Ranibizumab With Verteporfin Photodynamic Therapy Compared With Ranibizumab Monotherapy for Patients With Polypoidal Choroidal Vasculopathy.

Importance: The EVEREST II trial showed that for patients with polypoidal choroidal vasculopathy (PCV), intravitreal ranibizumab in combination with verteporfin photodynamic therapy improves visual acuity relative to ranibizumab monotherapy. However, whether combination therapy is incrementally cost-effective relative to monotherapy during a lifetime is unclear. Objective: To assess the incremental cost-effectiveness of combination therapy compared with ranibizumab monotherapy in patients with PCV. Design, Setting, and Participants: This model-based, economic evaluation used 2018 unit cost data from a tertiary eye hospital in Singapore, first- and second-year outcomes and resource use data from a multicenter trial across various Asian countries (EVEREST II) to model a hypothetical cohort of patients with symptomatic PCV. Scenario analyses and deterministic and probabilistic sensitivity analyses were performed to examine uncertainty. Data were collected from October 2018 through April 2019 and analyzed from March through October 2019. Interventions: This model used data from the EVEREST II trial, in which all participants were given 0.5 mg of intravitreal ranibizumab once every 4 weeks for the first 3 months. Subsequent administration occurred as needed. For participants receiving combination therapy, standard fluence (50 J/cm3) photodynamic therapy with 6-mg/m2 verteporfin was administered once during the first 3 months and thereafter as needed. Main Outcomes and Measures: Incremental cost per quality-adjusted life-year (QALY) gained for combination therapy relative to monotherapy for patients with PCV. Results: In this model based on a cohort of 1000 patients aged 68 years, a patient with PCV incurred a total cost in Singapore dollars (SGD) of 92 327 (US $67 399) with combination therapy and SGD 92 371 (US $67 431) with monotherapy during a lifetime horizon, generating a modest cost savings of SGD 44 (US $32) per patient undergoing combination therapy. Lifetime QALYs were estimated to be 7.87 for combination therapy and 7.85 for monotherapy, for an incremental gain of 0.02 QALYs. Combination therapy remained cost-saving or cost-effective in all lifetime scenarios modeled, but during shorter time horizons and at lower monotherapy costs, it may not be cost-effective. Conclusions and Relevance: This study found combination therapy to be a dominant (more effective and less costly) strategy, being similar in costs and slightly more effective than ranibizumab monotherapy during a lifetime horizon. However, decreasing the time horizon to less than 10 years and/or reductions in the cost of monotherapy may result in combination therapy no longer being cost-effective.

QTL mapping for grain yield-related traits in bread wheat via SNP-based selective genotyping.

KEY MESSAGE: We identified four chromosome regions harboring QTL for grain yield-related traits, and breeder-friendly KASP markers were developed and validated for marker-assisted selection. Identification of major stable quantitative trait loci (QTL) for grain yield-related traits is important for yield potential improvement in wheat breeding. In the present study, 266 recombinant inbred lines (RILs) derived from a cross between Zhongmai 871 (ZM871) and its sister line Zhongmai 895 (ZM895) were evaluated for thousand grain weight (TGW), grain length (GL), grain width (GW), and grain number per spike (GNS) in 10 environments and for grain filling rate in six environments. Sixty RILs, with 30 higher and 30 lower TGW, respectively, were genotyped using the wheat 660 K SNP array for preliminary QTL mapping. Four genetic regions on chromosomes 1AL, 2BS, 3AL, and 5B were identified to have a significant effect on TGW-related traits. A set of Kompetitive Allele Specific PCR markers were converted from the SNP markers on the above target chromosomes and used to genotype all 266 RILs. The mapping results confirmed the QTL named Qgw.caas-1AL, Qgl.caas-3AL, Qtgw.caas-5B, and Qgl.caas-5BS on the targeted chromosomes, explaining 5.0-20.6%, 5.7-15.7%, 5.5-17.3%, and 12.5-20.5% of the phenotypic variation for GW, GL, TGW, and GL, respectively. A novel major QTL for GNS on chromosome 5BS, explaining 5.2-15.2% of the phenotypic variation, was identified across eight environments. These QTL were further validated using BC1F4 populations derived from backcrosses ZM871/ZM895//ZM871 (121 lines) and ZM871/ZM895//ZM895 (175 lines) and 186 advanced breeding lines. Collectively, selective genotyping is a simple, economic, and effective approach for rapid QTL mapping and can be generally applied to genetic mapping studies for important agronomic traits.

Blood species identification based on deep learning analysis of Raman spectra.

Blood analysis is an indispensable means of detection in criminal investigation, customs security and quarantine, anti-poaching of wildlife, and other incidents. Detecting the species of blood is one of the most important analyses. In order to classify species by analyzing Raman spectra of blood, a recognition method based on deep learning principle is proposed in this paper. This method can realize multi-identification blood species, by constructing a one-dimensional convolution neural network and establishing a Raman spectra database containing 20 kinds of blood. The network model is obtained through training, and then is employed to predict the testing set data. The average accuracy of blind detection is more than 97%. In this paper, we try to increase the diversity of data to improve the robustness of the model, optimize the network and adjust the hyperparameters to improve the recognition ability of the model. The evaluation results show that the deep learning model has high recognition performance to distinguish the species of blood.

A Mobile Lifestyle Management Program (GlycoLeap) for People With Type 2 Diabetes: Single-Arm Feasibility Study.

BACKGROUND: Singapore's current prevalence of diabetes exceeds 13.6%. Although lifestyle modification can be effective for reducing the risks for complications of type 2 diabetes mellitus (T2DM), traditional lifestyle interventions are often difficult to administer in the primary care setting due to limited resources. Mobile health apps can address these limitations by offering low-cost, adaptable, and accessible platforms for disseminating lifestyle management interventions. OBJECTIVE: Using the RE-AIM evaluation framework, this study assessed the potential effectiveness and feasibility of GlycoLeap, a mobile lifestyle management program for people with T2DM, as an add-on to standard care. METHODS: This single-arm feasibility study recruited 100 patients with T2DM and glycated hemoglobin (HbA1c) levels of ≥7.5% from a single community health care facility in Singapore. All participants were given access to a 6-month mobile lifestyle management program, GlycoLeap, comprising online lessons and the Glyco mobile phone app with a health coaching feature. The GlycoLeap program was evaluated using 4 relevant dimensions of the RE-AIM framework: (1) reach (percentage who consented to participate out of all patients approached), (2) effectiveness (percentage point change in HbA1c [primary outcome] and weight loss [secondary outcome]), (3) implementation (program engagement as assessed by various participatory metrics), and (4) maintenance (postintervention user satisfaction surveys to predict the sustainability of GlycoLeap). Participants were assessed at baseline and at follow-up (≥12 weeks after starting the intervention). RESULTS: A total of 785 patients were approached of whom 104 consented to participate, placing the reach at 13.2%. Four were excluded after eligibility screening, and 100 patients were recruited. Program engagement (implementation) started out high but decreased with time for all evaluated components. Self-reported survey data suggest that participants monitored their blood glucose on more days in the past week at follow-up compared to baseline (P<.001) and reported positive changes to their diet due to app engagement (P<.001) (implementation). Primary outcome data were available for 83 participants. Statistically significant improvements were observed for HbA1c (-1.3 percentage points, P<.001) with greater improvements for those who logged their weight more often (P=.007) (effectiveness). Participants also had a 2.3% reduction in baseline weight (P<.001) (effectiveness). User satisfaction was high with 74% (59/80) and 79% (63/80) of participants rating the app good or very good and claiming that they would probably or definitely recommend the app to others, respectively (maintenance). CONCLUSIONS: Although measures of program engagement decreased with time, clinically significant improvements in HbA1c were achieved with the potential for broader implementation. However, we cannot rule out that these improvements were due to factors unrelated to GlycoLeap. Therefore, we would recommend evaluating the effectiveness and cost effectiveness of GlycoLeap using a randomized controlled trial of at least 12 months. TRIAL REGISTRATION: ClinicalTrials.gov NCT03091517; https://clinicaltrials.gov/ct2/show/NCT03091517 (Archived by WebCite at http://www.webcitation.org/77rNqhwRn).

Dual-model analysis for improving the discrimination performance of human and nonhuman blood based on Raman spectroscopy.

The discrimination accuracy for human and nonhuman blood is important for customs inspection and forensic applications. Recently, Raman spectroscopy has shown effectiveness in analyzing blood droplets and stains with an excitation wavelength of 785 nm. However, the discrimination of liquid whole blood in a vacuum blood tube using Raman spectroscopy, which is a form of noncontact and nondestructive detection, has not been achieved. An excitation wavelength of 532 nm was chosen to avoid the fluorescent background of the blood tube, at the cost of reduced spectroscopic information and discrimination accuracy. To improve the accuracy and true positive rate (TPR) for human blood, a dual-model analysis method is proposed. First, model 1 was used to discriminate human-unlike nonhuman blood. Model 2 was then used to discriminate human-like nonhuman blood from the "human blood" obtained by model 1. A total of 332 Raman spectra from 10 species were used to build and validate the model. A blind test and external validation demonstrated the effectiveness of the model. Compared with the results obtained by the single partial least-squares model, the discrimination performance was improved. The total accuracy and TPR, which are highly important for practical applications, increased to 99.1% and 97.4% from 87.2% and 90.6%, respectively.

Discrimination of human and nonhuman blood using Raman spectroscopy with self-reference algorithm.

We report a self-reference algorithm to discriminate human and nonhuman blood by calculating the ratios of identification Raman peaks to reference Raman peaks and choosing appropriate threshold values. The influence of using different reference peaks and identification peaks was analyzed in detail. The Raman peak at 1003 cm-1 was proved to be a stable reference peak to avoid the influencing factors, such as the incident laser intensity and the amount of sample. The Raman peak at 1341 cm-1 was found to be an efficient identification peak, which indicates that the difference between human and nonhuman blood results from the C-H bend in tryptophan. The comparison between self-reference algorithm and partial least square method was made. It was found that the self-reference algorithm not only obtained the discrimination results with the same accuracy, but also provided information on the difference of chemical composition. In addition, the performance of self-reference algorithm whose true positive rate is 100% is significant for customs inspection to avoid genetic disclosure and forensic science.

Continuous-wave yellow-green laser at 0.56 µm based on frequency doubling of a diode-end-pumped ceramic Nd:YAG laser.

We present what is, to the best of our knowledge, the first report on yellow-green laser generation based on the frequency doubling of the 1.1 µm transitions in Nd:YAG ceramics. By employing an 885 nm diode laser as the end-pumping source and a lithium triborate crystal as the frequency doubler, the highest continuous wave output powers of 1.4, 0.5, and 1.1 W at 556, 558, and 561 nm are achieved, respectively. These result in optical-to-optical efficiencies of 6.9%, 2.5%, and 5.4% with respect to the absorbed pump power, respectively.