Selection Index Research Articles

Using Multi-Sensor Data Fusion Techniques and Machine Learning Algorithms for Improving UAV-Based Yield Prediction of Oilseed Rape

Accurate and timely prediction of oilseed rape yield is crucial in precision agriculture and field remote sensing. We explored the feasibility and potential for predicting oilseed rape yield through the utilization of a UAV-based platform equipped with RGB and multispectral cameras. Genetic algorithm–partial least square was employed and evaluated for effective wavelength (EW) or vegetation index (VI) selection. Additionally, different machine learning algorithms, i.e., multiple linear regression (MLR), partial least squares regression (PLSR), least squares support vector machine (LS-SVM), back propagation neural network (BPNN), extreme learning machine (ELM), and radial basis function neural network (RBFNN), were developed and compared. With multi-source data fusion by combining vegetation indices (color and narrow-band VIs), robust prediction models of yield in oilseed rape were built. The performance of prediction models using the combination of VIs (RBFNN: Rpre = 0.8143, RMSEP = 171.9 kg/hm2) from multiple sensors manifested better results than those using only narrow-band VIs (BPNN: Rpre = 0.7655, RMSEP = 188.3 kg/hm2) from a multispectral camera. The best models for yield prediction were found by applying BPNN (Rpre = 0.8114, RMSEP = 172.6 kg/hm2) built from optimal EWs and ELM (Rpre = 0.8118, RMSEP = 170.9 kg/hm2) using optimal VIs. Taken together, the findings conclusively illustrate the potential of UAV-based RGB and multispectral images for the timely and non-invasive prediction of oilseed rape yield. This study also highlights that a lightweight UAV equipped with dual-image-frame snapshot cameras holds promise as a valuable tool for high-throughput plant phenotyping and advanced breeding programs within the realm of precision agriculture.

Revealing host genome–microbiome networks underlying feed efficiency in dairy cows

Ruminants have the ability to digest human-inedible plant materials, due to the symbiotic relationship with the rumen microbiota. Rumen microbes supply short chain fatty acids, amino acids, and vitamins to dairy cows that are used for maintenance, growth, and lactation functions. The main goal of this study was to investigate gene-microbiome networks underlying feed efficiency traits by integrating genotypic, microbial, and phenotypic data from lactating dairy cows. Data consisted of dry matter intake (DMI), net energy secreted in milk, and residual feed intake (RFI) records, SNP genotype, and 16S rRNA rumen microbial abundances from 448 mid-lactation Holstein cows. We first assessed marginal associations between genotypes and phenotypic and microbial traits through genomic scans, and then, in regions with multiple significant hits, we assessed gene-microbiome-phenotype networks using causal structural learning algorithms. We found significant regions co-localizing the rumen microbiome and feed efficiency traits. Interestingly, we found three types of network relationships: (1) the cow genome directly affects both rumen microbial abundances and feed efficiency traits; (2) the cow genome (Chr3: 116.5 Mb) indirectly affects RFI, mediated by the abundance of Syntrophococcus, Prevotella, and an unknown genus of Class Bacilli; and (3) the cow genome (Chr7: 52.8 Mb and Chr11: 6.1–6.2 Mb) affects the abundance of Rikenellaceae RC9 gut group mediated by DMI. Our findings shed light on how the host genome acts directly and indirectly on the rumen microbiome and feed efficiency traits and the potential benefits of the inclusion of specific microbes in selection indexes or as correlated traits in breeding programs. Overall, the multistep approach described here, combining whole-genome scans and causal network reconstruction, allows us to reveal the relationship between genome and microbiome underlying dairy cow feed efficiency.

Adding gastrointestinal parasite resistance to the breeding objective in hair sheep: initial steps.

The U.S. Maternal Hair index was designed by the National Sheep Improvement Program (NSIP) to increase total weight of lamb weaned per ewe lambing (TW). Producers are interested in adding gastrointestinal nematode (GIN) resistance to this breeding objective since parasitism causes substantial economic losses. The NSIP provides producers with estimated breeding values (EBV) for post-weaning fecal egg count (PFEC), an indicator of GIN resistance. Our objective was to gauge the effects of including PFEC as another selection criterion and goal trait. Selection index theory was used to construct 11 indexes. First was a benchmark index with TW as the goal. Weaning weight, and number of lambs born and weaned, were selection criteria. An index was then designed with PFEC added as a selection criterion. In 9 more indices, PFEC was also included as a goal trait, where the economic value of TW was scaled relative to that of PFEC. PFEC received a scaled economic value of -1 with that of TW increasing from +1 to +5 at +0.5 increments. Selection criteria were modelled as EBV or phenotypes. Annual genetic responses in goal traits were predicted. The top 3% of males and 26% of females were selected. Breeding values and phenotypes were simulated for 200 lambs by Cholesky decomposition and used to generate index scores, with 100 replicates run. Concordances with the animals selected in the benchmark scenario were determined. Using EBV as selection criteria, TW increased by 1.46kg/yr in the benchmark scenario. However, unfavorably, PFEC increased by 2.24%/yr. When using phenotypic criteria, TW increased by 0.52kg/yr and PFEC by 0.28%/yr. Once added as a goal trait, PFEC decreased regardless of the scaled economic value of TW. However, responses in TW were also reduced, although less so as its scaled economic value increased. A scaled economic value of +3 for TW appeared reasonable over other choices with 79% of the emphasis placed on TW in the breeding objective. With EBV as selection criteria, PFEC declined by 7.96%/yr with 98% of the response in TW retained. Also, on average, 64% of males and 80% of females were chosen in common with the benchmark. With phenotypic selection criteria, PFEC declined by 5.13%/yr and 94% of the response in TW was retained; 61% of males and 80% of females were chosen in common with the benchmark. Implementing an index with scaled economic values of +3 for TW and -1 for PFEC would balance gains in TW with reductions in PFEC.

Comparative analysis of ride comfort evaluation indices of high-speed vehicles based on a vehicle-seat-human body coupled dynamics model

As high-speed vehicles become increasingly lightweight, the influence of passengers on the vehicle vibration system cannot be overlooked. In this paper, a 3D vehicle-seat-human body coupled dynamics model is established to comprehensively evaluate the ride comfort of high-speed vehicles. The model combines the passenger biomechanics with the carbody flexibility. This is often neglected in previous studies and only the carbody vibrations are considered. Applying the validated model, four types of ride comfort evaluation indices are calculated and compared at 250–400 km/h. Results show that the ride comfort of high-speed vehicles at different speeds is excellent when the carbody acceleration and Sperling indices are used. While the results are significantly different when the total equivalent weighted acceleration and annoyance rate indices based on human body vibrations are used. At full load and speed of 400 km/h, the passengers sitting near the windows at both ends of the carbody felt ‘slightly uncomfortable’ or ‘less comfortable’ with an annoyance rate of about 30%, which indicates that up to 30% of the passengers might find the vibrations intolerable. This study underscores the necessity of integrating human body vibrations and passenger subjective psychology into ride comfort assessments, which can provide theory references for the selection and optimization of evaluation indices for high-speed lightweight vehicles.

Unsupervised selection of acoustic indices: An experimental comparison for characterizing unlabeled audio recordings from sub-Andean forest soundscapes

We present an experimental comparison of applying six unsupervised (i.e. not relying on class labels) and almost parameterless feature selection methods for ranking acoustic indices. The study is aimed at guiding the practitioner in choosing appropriate acoustic indices when, in absence of class labels, a small but meaningful number of features to characterize soundscapes is desired. Forty acoustic indices were considered, which correspond to seventeen temporal, spectral and soundscape features, along with their basic statistics. Three publicly available soundscape datasets, registered in a sub-Andean forest, were used for the experiments; moreover, several subsets were considered according to the different times of the day. Results reveal that the Acoustic Evenness Index is the most important one in terms of representational power according to the six considered selection criteria, followed by the Acoustic Complexity Index when conditions are relaxed to examine features ranked among the top-five. Besides, the Bioacoustic Index, the Acoustic Diversity Index and the Root Mean Square Energy stand out as important features when characterizing days in their separated parts.

Character association and path analysis for yield and its attributing traits among urdbean landraces

In the present study, character association and path analysis were studied among 23 urdbean genotypes for 11 quantitative traits. Correlation analysis divulged that seed yield/plant proclaimed a significant positive relationship between branches/plant, biological yield/plant, pods/plant, and pod length at both genotypic and phenotypic levels and also showed a direct effect on yield. Biological yield/plant, branches/plant and pods/plant had the most beneficial direct and indirect influences on the overall relationship between yield and other component attributes, indicating the high inter-dependent interaction among these traits. Therefore, these characteristics could be considered as optimum selection indices on a priority basis, which would be remarkable for improving genotype efficiency during genetic improvement.

Early Flowering and Maturity Promote the Successful Adaptation and High Yield of Quinoa (Chenopodium quinoa Willd.) in Temperate Regions

Quinoa (Chenopodium quinoa Willd.) can offer an alternative for staple food considering its tolerance to abiotic stresses and high seed quality. However, its cultivation in temperate regions has not been successful due to its photoperiod sensitivity and low seed yield. This study investigated the agronomical performance and quality traits of 48 accessions for cultivation in northern Europe. We conducted two-year field trials and phenotyped traits related to phenological development, plant architecture, yield components, seed quality, and disease resistance. The major determinants of seed yield in this study were days to flowering, days to maturity, thousand-kernel weight, and panicle density, while downy mildew susceptibility and stem lodging showed a negative correlation with seed yield. We developed a selection index to enable simultaneous selection based on different important agronomical traits. We evaluated the stability of different accessions over the two years of the experiment. Finally, we provided a list of 10 selected accessions that can be directly integrated and serve as new crossing parents in quinoa breeding programs for temperate regions.

Trait Association Studies to Determine Selection Indices in F5 Segregating Populations of Rice (Oryza sativa L.)

The experiment consisted of 38 promising F5 breeding lines developed and evaluated for correlation coefficient, path coefficient and selection indices studies with four check varieties viz., NVSR-2435, Sardar, Lalkada Gold and GR-18 in RBD with three replications during kharif-2023 at RRRS, Vyara, Gujarat. Genotypic correlation analysis revealed that grain yield per plant appeared to be positively and significantly correlated with productive tillers per plant, plant height, panicle length, straw yield per plant and 100 grain weight, which suggested that these characters can be improved simultaneously towards higher grain yield per plant by direct selection. Genotypic path analysis revealed that positive and direct effects on grain yield per plant were exhibited by days to maturity, productive tillers per plant, panicle length, 100 grain weight and straw yield per plant. Among these traits, panicle length, productive tillers per plant, 100 grain weight and straw yield per plant exhibited direct effect along with highly significant and positive association with grain yield per plant. Therefore, selection for such traits may be considered as most important yield attributing characters. The negligible residual effect of 0.037 was observed in path analysis, indicates that the model explained large proportion of variance. Thirty-one selection indices were constructed in all possible combinations of grain yield per plant (X1) along with four components viz., productive tiller per plant (X2), panicle length (X3), 100 grain weight (X4) and straw yield per plant (X5) through equal weight method. Among all the selection indices, the relative efficiency (%) and expected genetic advance (GA) was noted maximum with four characters selection index (I1245) compared to grain yield per plant (I1).

Genetic Studies on Yield and Its Components in Pigeonpea (Cajanus cajan L. Millsp.)

A field trial was carried out at the International Crop Research Institute for the Semi-Arid Tropics, Patancheru, Hyderabad during Kharif, 2023-24, with the aim of studying genetic parameters for yield and its components among pigeonpea genotypes which provides in developing an apt selection index to be included in the breeding programs. This research was conducted on 200 genotypes of Indian pigeon pea on seven traits. Data were analysed to calculate analysis of variance (ANOVA), phenotypic coefficient of variation (PCV), genetic coefficient of variation (GCV), broad sense heritability (h2bs) and estimated genetic progression as percentage mean using R software v 4.3.1. The results of the analysis of variance showed that there was a significant difference for days to 50% flowering, plant height (cm), hundred seed weight and seed yield per plant among the genotypes. The genotypic coefficient of variance was lower than the genotypic coefficient of variance for all the traits under selection. Higher extent of genotypic coefficient of variation is manifested by days to 50% flowering, plant height, seeds per pod, hundred seed weight and seed yield per plant. High heritability coupled with genetic advance as per cent mean was observed in seed yield per plant, number of days to 50% flowering and plant height. The results concluded that seed yield per plant, number of days to flowering up to 50%, and plant height are the traits that should be emphasized as selection indicator in Indian pigeon pea breeding programs.

The Azawak zebu bred at the Toukounous experimental centre in Niger: reproduction performance and estimation of genetic parameters.

The aim of this study was to estimate the genetic parameters of the reproductive performance of the Azawak zebu, bred on station during the period 1981 to 2007, in order to contribute to the implementation of an effective selection programme for improving the zootechnical performance of cattle herds. The data contained records of 2453 calvings from 650 Azawak cows, daughters of 55 bulls, all bred at the Toukounous experimental centre (Niger). Non-genetic factors were evaluated using linear mixed models and genetic parameters were estimated using REML procedures with a multi-trait animal model. The effects of year and calving rank were significant (P < 0.001) for the three reproductive traits studied. Heritability was low for age at first calving (h2 = 0.09 ± 0.03) and calf birth weight (h2 = 0.07 ± 0.03). It was moderate for calving interval (h2 = 0.30 ± 0.04) indicating the possibility of genetic improvement of this trait by selection. Repeatability estimates for calf birth weight and calving interval were 0.10 ± 0.02 and 0.42 ± 0.03 respectively. Calving interval had very strong and significant genetic correlations with age at first calving (0.84 ± 0.08) and calf birth weight (-0.40 ± 0.20). There was also a significantly favourable genetic correlation between calf weight at birth and age at first calving (-0.32 ± 0.25), implying a simultaneous improvement in all three reproductive traits through selection. An increase in heritability of calf birth weight was observed after the active phase of the program, reflecting an overly open selection scheme. These genetic parameter estimates suggest that the three reproductive traits could be used in selection index with the aim to improve the three reproductive traits of Azawak cattle.

Exploring collective activity space and its spatial heterogeneity using mobile phone signaling Data: A case of Shenzhen, China

The planning ethos of providing proximity-based services to all inhabitants has been prevailing recently, and underlines the importance of knowing areal differences in collective activity space (AS) of populations. Mobile phone signaling data (MSD) has great potentials for this end, especially in mega-cities with fast changing and spatially varying demographic composition. However, two problems need to be addressed when applying MSD-based AS measurement for planning practices, including the identification of regularly visited locations and the selection of measure indices. This paper proposes a three-step workflow to apply the MSD to measure local collective AS with considering addressing the problems. This three-step workflow aims to illustrate the procedure of using MSD to measure collective AS for supporting planning practice in urban China, with clarifying some key concerns when doing so. We apply the workflow to examine the spatial heterogeneity of the collective AS in Shenzhen City and discuss the transferability of the workflow in different social and institutional contexts.

Improving the process of identification of superior pearl millet populations using Genotype by Yield × Trait (GYT) biplot

Abstract Utilizing a trait-based breeding approach is crucial for developing diverse materials with various agronomic traits, emphasizing a common focus on achieving high grain yield, as seen in many breeding programs. Genotype evaluation based on yield × trait combinations is essential, with yield as the primary variable. Genotype by yield × traits biplot analysis was used in pearl millet to assess associations among yield-trait combinations, trait profiles, and superiority rankings. The Biplot analysis reveals prevalent positive associations among yield-trait combinations, implying that selecting multiple traits can augment grain yield productivity. Notably, ICMV 221 and Dhanashakti exhibit elevated levels of YLD×Fe and YLD×Zn, while SOSAT C88, EC C6, Raj 171, and CZIC 618 excel in combining grain yield with traits such as plant height (PH), panicle length (PL), and days to maturity (DM). Based on overall superiority in yield-trait combinations, populations were ranked as follows: AIMP 92901 &gt; ICMV 221 &gt; SOSAT C88 &gt; CZIC 618 &gt; Raj 171 &gt; ICMP 87307 &gt; EC C6. This study demonstrates the practical utility of the genotype by yield × traits biplot approach for selecting pearl millet germplasm with diverse trait combinations, enhancing the breeding program's effectiveness. Keywords: Trait Association, Trait Profile, Selection Index, Multiple Trait-Based Selection, GYT-Biplot

Maximizing genetic gains across agronomic and consumer preference traits in St. Augustinegrass breeding

AbstractCombining large multi‐environment trial (MET) datasets to decide which genotypes to move forward in the breeding process can be challenging, especially when dealing with negatively correlated traits. The use of a selection index has long been identified as an effective strategy in these situations. However, the method has found limited application in turfgrass breeding. The objective of this study was to use MET data for St. Augustinegrass [Stenotaphrum secundatum (Walt.) Kuntze] breeding lines evaluated across the southern United States to compare genetic gains achieved with the additive additive genetic index (AI) versus the turf performance index (TPI) incorporating agronomic as well as consumer preference traits. The use of either selection index produced more positive genetic gains across traits than direct selection even in the presence of negative correlations. However, the higher genetic gains obtained with AI versus TPI indicate that the use of an index that weighs traits according to their importance is a better approach for selection. Moreover, under a more stringent selection intensity, none of the best lines identified with AI would have been selected with TPI emphasizing the importance of choosing selection criteria that provide a more nuanced ranking of lines. Additionally, higher heritability values and gains from selection were obtained for turfgrass quality under stress (drought and shade) than under normal conditions indicating that selection under stress environments might be more efficient. Most of the evaluated St. Augustinegrass lines outperformed the checks, further supporting the value of cross‐institutional breeding collaborations.

Comparative Phenotypic Traits and Performances of Native Sheep in Naikhongchhari Hilly Area of Bangladesh

Background: Many studies have been conducted to investigate the performance of native sheep in plains and coastal regions, but no studies have been conducted in hilly regions of Bangladesh. Considering this fact, Coastal, Barind and Jamuna River Basin sheep were evaluated for their phenotypic traits, productive and reproductive abilities to gain insights into their performance in the hilly region. Methods: The Coastal, Barind and Jamuna River Basin sheep were collected from Bangladesh Livestock Research Institute (BLRI), Savar, Dhaka for the first time to Regional Station, Naikhongchhari, BLRI and separated based on their phenotypic characteristics. A breeding program was established to ensure purity, using independent culling levels and selection indexes. Ewes were allowed to lamb throughout the year, mated with rams in a 15-20 to 1 ratio. The sheep were kept in a semi-intensive system. Result: The highest lamb birth weight was observed in coastal Region sheep, both male (1.43 kg) and female (1.22 kg), followed by Barind males (1.36 kg) and females (1.15 kg), respectively. The highest average daily gain was found in coastal region sheep (41.87 g/d), which was significantly higher compared to others. The lowest age at first heat, gestation length, age at first lambing, post-partum heat period, days open, lambing interval was observed in Barind sheep. Finally, the study offers valuable insights into the potentialities of native sheep in hilly regions, guiding future research and extension efforts for sheep farming in these areas.

Variability and Correlation Studies in Dolichos bean (Dolichos lablab L.)

Background: Dolichos bean (Dolichos lablab L.), 2n = 22 is an important leguminous vegetable crop which provides protein, minerals and vitamins. The exploitation of variability is of great importance and is a prerequisite for the effective screening of superior genotypes. Keeping this in view, the present investigation was carried out to assess the variability and correlation for yield and other components which are useful to select the promising genotypes. Methods: The study on variability and correlation in Dolichos Bean genotypes for yield and quality was conducted at the College Orchard, Tamil Nadu Agricultural University, Coimbatore. Twenty lablab genotypes, sourced from various locations, were planted in a randomized block design with three replications during the kharif and rabi seasons of 2021 and 2022. Observations on growth, yield and quality traits were recorded and analysed for variance, variability and correlations. Result: In the kharif season, Ankur Gold yielded the highest green pods per plant (487.50 g), while Acc.7 yielded the lowest (89.59 g). In the pooled analysis, Ankur Gold (469.80 g) had the highest and Acc.6 (87.49 g) had the lowest green pod yield per plant. The study suggests using a selection index based on the number of racemes per plant, raceme length, number of pods per plant, green pod yield per plant and pod length to identify high yielding lablab genotypes.

12426 Performance Of Dehydroepiandrosterone-Sulphate In Diagnosing Mild Autonomous Cortisol Secretion: A Systematic Review And Meta-Analysis

Abstract Disclosure: J. Saini: None. B. Salama: None. S.R. Chacko: None. K. Yu: None. I. Bancos: None. Objective: Measurement of baseline dehydroepiandrosterone sulfate (DHEAS) is suggested by the guidelines in support of diagnosing mild autonomous cortisol secretion (MACS). However, evidence on the diagnostic accuracy of DHEAS in MACS is difficult to interpret due to differences in the diagnostic cutoffs, small sample size, and heterogeneous reference standards. We aimed to conduct a systematic review and meta-analysis of published literature to assess the performance of DHEAS in diagnosing MACS, as defined in the 2023 guidelines. Methods: A comprehensive search was conducted using several databases from each database’s inception to January 8th, 2024. Databases include Ovid MEDLINE (R) and Epub Ahead of Print, In-Process &amp; Other Non-Indexed Citations, and Daily, Ovid EMBASE, Ovid Cochrane Central Register of Controlled Trials, Ovid Cochrane Databases of Systematic Reviews, and Scopus. Only original studies of at least 20 participants were included. MACS was defined as post-dexamethasone cortisol &amp;gt;1.8 mcg/dL. QUADAS-2 was used to assess the risk of bias. Bivariate and random effects meta-analysis was used to generate pooled diagnostic accuracy estimates. Results: The initial search yielded 285 articles, and only 7 studies met the inclusion criteria. In 7 studies (1 from North America, 3 from Asia, and 3 from Europe), a total of 574 patients with MACS and 830 referent subjects (patients with adrenal tumors without MACS) were included. The mean age was specified only in 5 studies and was similar in patients with MACS (55.5 years) and referent group (53.3 years). Patients with MACS had a higher number of women (323/472, 68.4%) as compared to the referent group (352/658, 53.4%). DHEAS cutoffs studied included: lower cutoff of age-and sex-specific ranges (3 studies), 40 mcg/dL (3 studies), 60 mcg/dL (1 study), and 67.5 mcg/dL (1 study). With increasing DHEAS cutoff, sensitivity for MACS diagnosis increased, and specificity decreased. Most studies (4 studies) used the chemiluminescent immunoassay to measure DHEAS. A meta-analysis of studies using DHEAS cut-off between 40-70 mcg/dL was performed, with a pooled sensitivity of 68% (95% CI: 51-81) and a pooled specificity of 85% (78-89). The positive likelihood ratio was 4 (95% CI: 3-6; I2=36.94, P=0.175) and the negative likelihood ratio was 0.4 (0.3-0.6; I2=49.97, P=0.092). Most studies were at a high risk of bias for selection of patients, index, or reference standard. Conclusion: Based on limited heterogeneous evidence, measurement of DHEAS provides additional value in diagnosing MACS. Further larger diagnostic accuracy studies should explore various DHEAS cutoffs and consider assay variability. Presentation: 6/3/2024

Genetic Variability in the Physicochemical Characteristics of Cultivated Coffea canephora Genotypes.

The objective of this study was to characterize the genetic divergence and selection gains of the physicochemical grains traits of 68 genotypes of C. canephora most cultivated in the Western Amazon. For this purpose, the following characteristics were evaluated over two harvests: aqueous extract, ash, acidity, pH, protein, ether extract, soluble solids, phenolic compounds, soluble sugars, reducing sugars, and non-reducing sugars. The genotype × measurement interaction effect was significant for all characteristics, with a predominant simple interaction, resulting in smaller changes in the ranking of genotypes. Out of a total of 45 genotypic correlation estimates, 8 were significant, of which 5 were related to acidity. The dispersion of the first two components associated with reference points shows that the genotypes BRS3193, AS1, AS2, AS3, N16, CA1, and AS7 were closest to the ideal type of higher performance. Selection for the main characteristic of soluble sugars resulted in estimates of genetic progress lower than those observed using selection indices. The genetic materials present high genetic diversity, allowing the selection of reference plants with high levels of sugars (BRS3193, AS3, GJ25, and LB30), proteins (BRS2357), lipids (GJ30), and phenolic compounds in their green beans (BRS3193) and high water solubility (AS2).

Comparing Deprivation vs Vulnerability Index Performance Using Medicare Beneficiary Surgical Outcomes

Health care researchers, professionals, payers, and policymakers are increasingly relying on publicly available composite indices of area-level socioeconomic deprivation to address health equity. Implications of index selection, however, are not well understood. To compare the performance of 2 frequently used deprivation indices using policy-relevant outcomes among Medicare beneficiaries undergoing 3 common surgical procedures. This cross-sectional study examined outcomes among Medicare beneficiaries (65 to 99 years old) undergoing 1 of 3 common surgical procedures (hip replacement, knee replacement, or coronary artery bypass grafting) between 2016 and 2019. Index discriminative performance was compared for beneficiaries residing in tracts with high- and low-deprivation levels (deciles) according to each index. Analyses were conducted between December 2022 and August 2023. Tract-level deprivation was operationalized using 2020 releases of the area deprivation index (ADI) and the social vulnerability index (SVI). Binary outcomes were unplanned surgery, 30-day readmissions, and 30-day mortality. Multivariable logistic regression models, stratified by each index, accounted for beneficiary and hospital characteristics. A total of 2 433 603 Medicare beneficiaries (mean [SD] age, 73.8 [6.1] years; 1 412 968 female beneficiaries [58.1%]; 24 165 Asian [1.0%], 158 582 Black [6.5%], and 2 182 052 White [89.7%]) were included in analyses. According to both indices, beneficiaries residing in high-deprivation tracts had significantly greater adjusted odds of all outcomes for all procedures when compared with beneficiaries living in low-deprivation tracts. However, compared to ADI, SVI resulted in higher adjusted odds ratios (adjusted odds ratios, 1.17-1.31 for SVI vs 1.09-1.23 for ADI), significantly larger outcome rate differences (outcome rate difference, 0.07%-5.17% for SVI vs outcome rate difference, 0.05%-2.44% for ADI; 95% CIs excluded 0), and greater effect sizes (Cohen d, 0.076-0.546 for SVI vs 0.044-0.304 for ADI) for beneficiaries residing in high- vs low-deprivation tracts. In this cross-sectional study of Medicare beneficiaries, SVI had significantly better discriminative performance-stratifying surgical outcomes over a wider range-than ADI for identifying and distinguishing between high- and low-deprivation tracts, as indexed by outcomes for common surgical procedures. Index selection requires careful consideration of index differences, index performance, and contextual factors surrounding use, especially when informing resource allocation and health care payment adjustment models to address health equity.

Genomic prediction and validation strategies for reproductive traits in Holstein cattle across different Chinese regions and climatic conditions

Accurate genomic predictions of breeding values for traits included in the selection indexes are paramount for optimizing genetic progress in populations under selection. The size of the reference populations is a major factor influencing the accuracy of genomic predictions, which is even more important for lowly-heritable traits such as fertility and reproduction indicators. Combining data from different geographical regions or countries can be beneficial for genomic prediction of these lowly heritable traits. Therefore, the objectives of this study were to: 1) evaluate the benefits of performing across-regional genomic evaluations for reproduction traits in Chinese Holstein cattle; and, 2) assess the feasibility of validating genomic predictions across environments based on reaction norm models (RNM) and the Linear Regression (LR) method after accounting for genotype-by-environment interactions. Phenotypic records from 194,574 cows collected across 47 farms located in 2 regions of China were used for this study. The reference and validation populations were defined based on birth year for applying the LR validation method. The traits evaluated included: interval from first to last insemination (IFL), conception rate at the first insemination (CR_f), and number of inseminations (NS) recorded in heifers and first-parity cows. The results indicated that combining data from different regions resulted in greater genomic prediction accuracies compared with using data from single regions, with increases ranging from 2.74% to 93.81%. This improvement was particularly notable for the region with the least amount of available data, where the increases ranged from 26.49% to 93.81%. Furthermore, the predictive abilities could be validated for all studied traits based on the LR method across different environments when fitting RNM. The prediction accuracies and bias of genomic breeding values based on RNM were better than regular single-trait animal models in extreme climatic conditions for IFL and NS, whereas limited increases in predictive abilities were observed for CR_f. Across-regional genomic prediction by RNM can account for genotype-by-environment interactions, potentially increase the accuracy of genomic prediction, and predict the performances of individuals in the environments with limited phenotypic data available.

Exploring Scotopic Microperimetry as an Outcome Measure in Choroideremia.

Choroideremia is an X-linked outer retinal degeneration. Early symptoms include nyctalopia and progressive visual field loss, but visual acuity is preserved until late disease stages. Dark-adapted two-color fundus-controlled perimetry (also known as scotopic microperimetry) has been developed to enable spatial assessment of rod and cone photoreceptor function. This study explores the use of scotopic microperimetry in patients with choroideremia. Twenty patients with choroideremia and 21 age-matched healthy controls completed visual acuity and scotopic microperimetry testing, which used the Scotopic Macular Integrity Assessment (S-MAIA) microperimeter. A subset of participants completed repeat scotopic testing to enable Bland-Altman repeatability analyses. Test reliability was assessed using fixation stability, fixation losses, and assessment of the rod-free zones. Pointwise sensitivity, mean sensitivity, and volume sensitivity indices were analyzed. False positive responses were the main source of poor test reliability, indicated by stimuli responses in the physiological blind spot and lack of rod-free mapping. Scotopic cyan and red sensitivities were significantly reduced in choroideremia participants (n = 17) compared to healthy controls (n = 16) (P < 0.01, Mann-Whitney U test). Scotopic cyan sensitivity was statistically lower than scotopic red sensitivity in both healthy controls and choroideremia (P < 0.01, Wilcoxon signed rank test). Interpretation of scotopic cyan-red differences should be used with caution due to high test-retest variability. Scotopic microperimetry could be a useful outcome measure in patients with early choroideremia. Careful selection of test grid design and sensitivity indices is required. Scotopic microperimetry may be a useful outcome measure in clinical trials for patients with early stage choroideremia.