Selection of Fusion-Site Overhang Sets for High-Fidelity and High-Complexity Golden Gate Assembly.

Golden Gate Assembly depends on the accurate ligation of overhangs at fragment fusion sites to generate full-length products with all parts in the desired order. Traditionally, fusion-site sequences are selected by using validated sets of overhang sequences or by applying a handful of semi-empirical rules to guide overhang choice. While these approaches allow dependable assembly of 6-8 fragments in one pot, recent work has demonstrated that comprehensive measurement of ligase fidelity allows prediction of high-fidelity junction sets that enable much more complex assemblies of 12, 24, or even 36+ fragments in a single reaction that will join with high accuracy and efficiency. In this chapter, we outline the application of a set of online tools that apply these comprehensive datasets to the analysis of existing junction sets, the de novo selection of new high-fidelity overhang sets, the modification and expansion of existing sets, and the principles for dividing known sequences at an arbitrary number of high-fidelity breakpoints.

Optimized aptamer-based next generation biosensor for the ultra-sensitive determination of SARS-CoV-2 S1 protein in saliva samples.

COVID-19 is an infectious disease caused by the SARS-CoV-2 virus, which rapidly spread worldwide and resulted in a pandemic. Efficient and sensitive detection techniques have been devised since the onset of the epidemic and continue to be improved at present. Due to the crucial role of the SARS-CoV-2 S1 protein in facilitating the virus's entry into cells, efforts in detection and treatment have primarily centered upon this protein. In this study, a rapid, ultrasensitive, disposable, easy-to-use, cost-effective next generation biosensor based on optimized aptamer (Optimer, OPT) was developed by using a disposable pencil graphite electrode (PGE) and applied for the impedimetric determination of SARS-CoV-2 S1 protein. The S1 protein interacted with the OPT in the solution phase and then immobilized onto the PGE surface. Subsequently, measurements using electrochemical impedance spectroscopy (EIS) were conducted in a solution containing a redox probe of 1 mM [Fe(CN)6]3-/4-. Under optimum conditions, the limit of detection (LOD) for the S1 protein in buffer medium at concentrations ranging from 101 to 106 ag/mL was calculated as 8.80 ag/mL (0.11 aM). The selectivity of the developed biosensor was studied against MERS-CoV-S1 protein (MERS) and Influenza Hemagglutinin antigen (HA). Furthermore, the application of the biosensor in artificial saliva medium is demonstrated. The LOD was also calculated in artificial saliva medium in the concentration range of 101-105 ag/mL and calculated as 2.01 ag/mL (0.025 aM). This medium was also used to assess the selectivity of optimized-aptamer based biosensor.

Development of optimized ensemble machine learning-based prediction models for wire electrical discharge machining processes.

This paper proposes development of optimized heterogeneous ensemble models for prediction of responses based on given sets of input parameters for wire electrical discharge machining (WEDM) processes, which have found immense applications in many of the present-day manufacturing industries because of their ability to generate complicated 2D and 3D profiles on hard-to-machine engineering materials. These ensembles are developed combining predictions of the three base models, i.e. random forest, support vector machine and ridge regression. These three base models are first framed utilizing the training datasets, providing predictions for all the responses under consideration. Based on these predictions, two optimization problems are formulated for each of the responses, while minimizing root mean squared error and mean absolute error, for subsequent development of two optimized ensembles whose predictions are the weighted sum of the predictions of the base models. The prediction performance of all the five models is ascertained through nine statistical metrics, after which a cumulative quality loss-based multi-response signal-to-noise (MRSN) ratio for each model is computed, for each of the responses, where a higher MRSN ratio indicates greater accuracy in prediction. This study is conducted using two experimental datasets of WEDM process. Overall, the optimized ensemble models having higher MRSN ratios than the base models are indicated to deliver better prediction accuracy.

Potency of butylated hydroxytoluene and optimized black pepper extract as additives on quality characteristics of stored (4 °C) pork on various days.

The potency of butylated hydroxytoluene and black pepper extract (BPE) as additives was explored. BPE was produced through upscale of desirability from 72 to 91.9%, yielding sixty-three bio-compounds. Next, control (0% antioxidant), vitamin E, and BPE groups were formed, each replicated five times in a Completely Randomized Design. Meat physico-chemical and fatty acid (FA) indices, malondialdehyde (MDA) content, and microbial profile were evaluated [day (d) 0, 5, and 10]. Results indicate BPE had lower (p < 0.05) pH (5.85) and least cooking loss (19.30%). BPE had reduced (p < 0.05) saturated FA (SFA) of 2.18 but higher poly unsaturated FA (PUFA)-13.17; UFA (18.82), PUFA: SFA (6.05), and omega 6 (7.69). Also, BPE had lowest (p < 0.05) MDA on d 5 (2.16) and 10 (3.45) of storage (4 °C), while coliform was not seen (p < 0.05) on BPE-incorporated samples on d 0 and 5. BPE contains bio-compounds that are effective and safe for meat storage.

Visceral Fat-Reducing Effect of Novel Dietary Intervention Program: A Randomized Controlled Trial in Japanese Males.

Background/Objectives: Reducing visceral fat is a critical factor in preventing obesity-related health complications. In this study, we evaluated the effectiveness of our previously reported novel dietary intervention program, "Optimized Nutri-Dense Meals", designed according to the Dietary Reference Intakes (DRIs) in Japan in reducing visceral fat. Method: This open-label, randomized controlled trial included 100 Japanese males with obesity or overweight. In total, 50 participants were assigned to a control group that continued their usual diets, and the other 50 consumed test meals twice per week for four weeks (test group). Result: The primary outcome, visceral fat area, significantly decreased in the test group compared to that in the control group (-7.5 cm2, confidence interval [CI]: -14.3 to -0.6). In addition, we measured the changes in gut flora and work productivity. The abundance of Bifidobacterium (+1.5%, CI: 0.3-2.7) and Christensenellaceae (+0.4%, CI: 0.01-0.8) increased significantly in the test group compared with those in the control group. Work Limitation Questionnaire Japanese version scores representing presenteeism also significantly increased in the test group (+1.2 points, CI: 0.2-2.3) compared with the control group. Conclusions: These findings suggest that dietary interventions based on Japanese DRIs can effectively decrease visceral fat and improve health outcomes over the short term without the need for a complete dietary overhaul.

Efficient and robust reverse genetics system for bovine rotavirus generation and its application for antiviral screening.

Unveiling the molecular mechanisms underlying rotavirus replication and pathogenesis has been hampered by the lack of a reverse genetics (RG) system in the past. Since 2017, multiple plasmid-based RG systems for simian, human, and murine-Like rotaviruses have been established. However, none of the described methods have supported the recovery of bovine rotaviruses (BRVs). Here, we established an optimized plasmid-based RG system for BRV culture-adapted strain (BRV G10P [15] BLR) and clinical isolates (BRV G6P[1] C73, G10P[11] HM26) based on a BHK-T7 cell clone stably expressing T7 polymerase. Furthermore, using this optimized RG system, we successfully rescued the reporter virus BRV rC73/Zs, rHM26/Zs and rBLR/Zs, harboring a genetically modified 1.8-kb segment 7 encoding full-length nonstructural protein 3 (NSP3) fused to ZsGreen, a 232-amino acid green fluorescent protein. Analysis of the stability of genomic insertions showed that the rC73/Zs and rBLR/Zs replicated efficiently and were genetically stable in seven rounds of serial passaging, while rHM26/Zs can be stabilized only up to the third generation, indicating that the BRV segment composition may influence the viral fitness. In addition, we adopted the recombinant reporter viruses for high-throughput screening application and discovered 12 candidates out of 1440 compounds with potential antiviral activities against rotavirus. In summary, this improved RG system of BRVs represents an important tool with great potential for understanding the molecular biology of BRV and facilitates the development of novel therapeutics and vaccines for BRV.

3D printed optimized electrodes for electrochemical flow reactors.

Recent advances in 3D printing have enabled the manufacture of porous electrodes which cannot be machined using traditional methods. With micron-scale precision, the pore structure of an electrode can now be designed for optimal energy efficiency, and a 3D printed electrode is not limited to a single uniform porosity. As these electrodes scale in size, however, the total number of possible pore designs can be intractable; choosing an appropriate pore distribution manually can be a complex task. To address this challenge, we adopt an inverse design approach. Using physics-based models, the electrode structure is optimized to minimize power losses in a flow reactor. The computer-generated structure is then printed and benchmarked against homogeneous porosity electrodes. We show how an optimized electrode decreases the power requirements by 16% compared to the best-case homogeneous porosity. Future work could apply this approach to flow batteries, electrolyzers, and fuel cells to accelerate their design and implementation.

Equation-of-motion orbital-optimized coupled-cluster doubles method with the density-fitting approximation: An efficient implementation.

Orbital-optimized coupled-cluster methods are very helpful for theoretical predictions of the molecular properties of challenging chemical systems, such as excited states. In this research, an efficient implementation of the equation-of-motion orbital-optimized coupled-cluster doubles method with the density-fitting (DF) approach, denoted by DF-EOM-OCCD, is presented. The computational cost of the DF-EOM-OCCD method for excitation energies is compared with that of the conventional EOM-OCCD method. Our results demonstrate that DF-EOM-OCCD excitation energies are dramatically accelerated compared to EOM-OCCD. There are almost 17-fold reductions for the C 5 H 12 $$ {\mathrm{C}}_5{\mathrm{H}}_{12} $$ molecule in an aug-cc-pVTZ basis set with the RHF reference. This dramatic performance improvement comes from the reduced cost of integral transformation with the DF approach and the efficient evaluation of the particle-particle ladder (PPL) term, which is the most expensive term to evaluate. Further, our results show that the DF-EOM-OCCD approach is very helpful for the computation of excitation energies in open-shell molecular systems. Overall, we conclude that our new DF-EOM-OCCD implementation is very promising for the study of excited states in large-sized challenging chemical systems.

Increased coherence predicts medical refractoriness in patients with temporal lobe epilepsy on monotherapy.

Among patients with epilepsy, 30-40% experience recurrent seizures even after adequate antiseizure medications therapies, making them refractory. The early identification of refractory epilepsy is important to provide timely surgical treatment for these patients. In this study, we analyze interictal electroencephalography (EEG) data to predict drug refractoriness in patients with temporal lobe epilepsy (TLE) who were treated with monotherapy at the time of the first EEG acquisition. Various EEG features were extracted, including statistical measurements and interchannel coherence. Feature selection was performed to identify the optimal features, and classification was conducted using different classifiers. Functional connectivity and graph theory measurements were calculated to identify characteristics of refractory TLE. Among the 48 participants, 34 (70.8%) were responsive, while 14 (29.2%) were refractory over a mean follow-up duration of 38.5 months. Coherence feature within the gamma frequency band exhibited the most favorable performance. The light gradient boosting model, employing the mutual information filter-based feature selection method, demonstrated the highest performance (AUROC = 0.821). Compared to the responsive group, interchannel coherence displayed higher values in the refractory group. Interestingly, graph theory measurements using EEG coherence exhibited higher values in the refractory group than in the responsive group. Our study has demonstrated a promising method for the early identification of refractory TLE utilizing machine learning algorithms.

A hyper-fast gas chromatograph coupled to an ion mobility spectrometer with high repetition rate and flow-optimized ion source to resolve the short chromatographic peaks.

By combining the high selectivity of a gas chromatograph (GC) with the high sensitivity and decent selectivity of an ion mobility spectrometer (IMS), GC-IMS have become increasingly popular in many applications. However, most GC suffer from long analysis times. In contrast, an hyper-fast GC allows for extremely fast analysis in the tens of seconds while reaching comparably high resolution. In turn, coupling such hyper-fast GC with IMS requires sufficiently high repetition rate of recording full IMS spectra to resolve the short GC peaks. Therefore, we present a drift tube IMS with 100 Hz repetition rate. Key is a small effective detector volume combined with short drift length. Therefore, the ion source of the IMS combines a small reaction region with an extended field-switching ion shutter and optimized gas flows. To resolve even the shortest GC peaks with a full width at half maximum of 100 ms, a short drift length of just 41 mm was used, achieving a measurement time of 10 ms per spectrum and hence ten data points across the shortest GC peak. To avoid condensation of the sample, the entire IMS was heated isothermally to 120 °C. Despite short drift times and high temperatures, the IMS still reaches high resolving power of Rp = 60. The hyper-fast GC-IMS reaches low detection limits in the low ppbV range. For demonstration, ketone mixes and three different hop varieties were analyzed in <30 s.

A novel super-resolution microscopy platform for cutaneous alpha-synuclein detection in Parkinson's disease.

Alpha-synuclein (aSyn) aggregates in the central nervous system are the main pathological hallmark of Parkinson's disease (PD). ASyn aggregates have also been detected in many peripheral tissues, including the skin, thus providing a novel and accessible target tissue for the detection of PD pathology. Still, a well-established validated quantitative biomarker for early diagnosis of PD that also allows for tracking of disease progression remains lacking. The main goal of this research was to characterize aSyn aggregates in skin biopsies as a comparative and quantitative measure for PD pathology. Using direct stochastic optical reconstruction microscopy (dSTORM) and computational tools, we imaged total and phosphorylated-aSyn at the single molecule level in sweat glands and nerve bundles of skin biopsies from healthy controls (HCs) and PD patients. We developed a user-friendly analysis platform that offers a comprehensive toolkit for researchers that combines analysis algorithms and applies a series of cluster analysis algorithms (i.e., DBSCAN and FOCAL) onto dSTORM images. Using this platform, we found a significant decrease in the ratio of the numbers of neuronal marker molecules to phosphorylated-aSyn molecules, suggesting the existence of damaged nerve cells in fibers highly enriched with phosphorylated-aSyn molecules. Furthermore, our analysis found a higher number of aSyn aggregates in PD subjects than in HC subjects, with differences in aggregate size, density, and number of molecules per aggregate. On average, aSyn aggregate radii ranged between 40 and 200 nm and presented an average density of 0.001-0.1 molecules/nm2. Our dSTORM analysis thus highlights the potential of our platform for identifying quantitative characteristics of aSyn distribution in skin biopsies not previously described for PD patients while offering valuable insight into PD pathology by elucidating patient aSyn aggregation status.

Experiences of Using a Digital Guidance and Assessment Tool (the Technology-Optimized Practice Process in Nursing Application) During Clinical Practice in a Nursing Home: Focus Group Study Among Nursing Students.

BACKGROUND: Nursing students' learning during clinical practice is largely influenced by the quality of the guidance they receive from their nurse preceptors. Students that have attended placement in nursing home settings have called for more time with nurse preceptors and an opportunity for more help from the nurses for reflection and developing critical thinking skills. To strengthen students' guidance and assessment and enhance students' learning in the practice setting, it has also been recommended to improve the collaboration between faculties and nurse preceptors. OBJECTIVE: This study explores first-year nursing students' experiences of using the Technology-Optimized Practice Process in Nursing (TOPP-N) application in 4 nursing homes in Norway. TOPP-N was developed to support guidance and assessment in clinical practice in nursing education. METHODS: Four focus groups were conducted with 19 nursing students from 2 university campuses in Norway. The data collection and directed content analysis were based on DeLone and McLean's information system success model. RESULTS: Some participants had difficulties learning to use the TOPP-N tool, particularly those who had not attended the 1-hour digital course. Furthermore, participants remarked that the content of the TOPP-N guidance module could be better adjusted to the current clinical placement, level of education, and individual achievements to be more usable. Despite this, most participants liked the TOPP-N application's concept. Using the TOPP-N mobile app for guidance and assessment was found to be very flexible. The frequency and ways of using the application varied among the participants. Most participants perceived that the use of TOPP-N facilitated awareness of learning objectives and enabled continuous reflection and feedback from nurse preceptors. However, the findings indicate that the TOPP-N application's perceived usefulness was highly dependent on the preparedness and use of the app among nurse preceptors (or absence thereof). CONCLUSIONS: This study offers information about critical success factors perceived by nursing students related to the use of the TOPP-N application. To develop similar learning management systems that are usable and efficient, developers should focus on personalizing the content, clarifying procedures for use, and enhancing the training and motivation of users, that is, students, nurse preceptors, and educators.

Conduction time from left bundle branch pacing to the left ventricular lateral wall in two patients in whom cardiac resynchronization therapy pacemaker was implanted.

The efficacy of left bundle branch pacing (LBBP) as cardiac resynchronization therapy (CRT) has been reported, but LBBP may not always improve conduction disturbance in the left ventricle (LV). To evaluate LV electrical conduction delay during LBBP, we measured conduction time from the pacing at left bundle branch to LV lead sensing (LBBP-LV) in two patients in whom left bundle branch-optimized cardiac resynchronization therapy (LOT-CRT) was attempted. Case 1 was a 77-year-old female with dilated cardiomyopathy (DCM) and left bundle branch block. The QRS duration during LBBP was 160 ms and the interval between the stimulus artifact and peak of the R wave in lead V6 (Stim-V6RWPT) was 74 ms. LBBP-LV at the LV mid-lateral wall and LV mid-posterolateral wall were 112 ms and 102 ms, respectively. Case 2 was a 75-year-old female with DCM and nonspecific intraventricular conduction delay. The QRS duration during LBBP was 156 ms and Stim-V6RWPT was 66 ms. LBBP-LV at the LV mid-anterolateral wall, LV mid-lateral wall, and LV mid-posterolateral wall were 96 ms, 107 ms, and 121 ms, respectively. In conclusion, LBBP-LV at the LV mid-lateral area was relatively long. If LBBP does not improve LV conduction disturbances, LOT-CRT may be more effective. Learning objective: The efficacy of left bundle branch pacing (LBBP) for cardiac resynchronization therapy (CRT) has been reported. However, if the conduction time from pacing to the left ventricular (LV) lateral area during LBBP is long, LBBP may not improve LV conduction disturbance, resulting in ineffective CRT. In such cases, more effective CRT would be expected with LV lead implantation at the area of the LV conduction delay during LBBP.

When Revascularization May Be Appropriate in Atherosclerotic Renal Artery Stenosis.

Renal artery stenosis (RAS) is a condition that involves the narrowing of one or both renal arteries, most commonly caused by either atherosclerosis or fibroplasia. RAS can present in a multitude of clinical manifestations involving hypertension (HTN), heart failure, and renal failure. Current recommendations for treating patients with RAS involve strict medical therapy often without invasive therapies. However, in more complicated patients with RAS, recent clinical studies and guidelines have offered varying recommendations, which has presented challenges in managing these cases. This review aims to summarize current evidence to best evaluate which patients with RAS may benefit from renal artery revascularization as opposed to medical therapy alone.

Accuracy Validation of the New Barrett True Axial Length Formula and the Optimized Lens Factor Using Sum-of-Segment Biometry.

Objectives: This study aims to verify the accuracy of a new calculation formula, Barrett true axial length formula (T-AL), and the optimized lens factor (LF) for predicting postoperative refraction after cataract surgery. Methods: We included 156 Japanese patients who underwent cataract surgery using Clareon monofocal intraocular lenses at our clinic between January 2022 and June 2023. Postoperative spherical equivalent was calculated using subjective refraction values obtained 1 month post-surgery. The LFs were optimized so that the mean prediction error (PE) of each calculation formula was zero (zero optimization). We calculated the mean absolute PE (MAE) to assess accuracy and used a Friedman test for statistical comparisons. The accuracy of T-AL and the optimized LFs was compared with that of the conventional Barrett Universal II formula for ARGOS (AR-B) and OA-2000 (OA-B) with equivalent refractive index. Results: For T-AL, AR-B, and OA-B, the MAEs ± standard deviations were 0.225 ± 0.179, 0.219 ± 0.168, and 0.242 ± 0.206 D, respectively. The Friedman test showed no statistically significant differences among the three groups. The device-optimized LFs were 2.248-2.289 (T-AL), 2.236-2.246 (AR-B), and 2.07-2.08 (OA-B); the corresponding zero-optimized LFs were 2.262-2.287 (T-AL), 2.287-2.303 (AR-B), and 2.160-2.170 (OA-B). Conclusion: There were no significant differences in prediction accuracy among the formulas. However, the accuracy of LF optimization varied by device, with T-AL being closest to the value under zero optimization. This suggests that T-AL is clinically useful for predicting an accurate postoperative refraction without zero optimization.

Tool State Recognition Based on POGNN-GRU under Unbalanced Data.

Accurate recognition of tool state is important for maximizing tool life. However, the tool sensor data collected in real-life scenarios has unbalanced characteristics. Additionally, although graph neural networks (GNNs) show excellent performance in feature extraction in the spatial dimension of data, it is difficult to extract features in the temporal dimension efficiently. Therefore, we propose a tool state recognition method based on the Pruned Optimized Graph Neural Network-Gated Recurrent Unit (POGNN-GRU) under unbalanced data. Firstly, design the Improved-Majority Weighted Minority Oversampling Technique (IMWMOTE) by introducing an adaptive noise removal strategy and improving the MWMOTE to alleviate the unbalanced problem of data. Subsequently, propose a POG graph data construction method based on a multi-scale multi-metric basis and a Gaussian kernel weight function to solve the problem of one-sided description of graph data under a single metric basis. Then, construct the POGNN-GRU model to deeply mine the spatial and temporal features of the data to better identify the state of the tool. Finally, validation and ablation experiments on the PHM 2010 and HMoTP datasets show that the proposed method outperforms the other models in terms of identification, and the highest accuracy improves by 1.62% and 1.86% compared with the corresponding optimal baseline model.

Recent microencapsulation trends for enhancing the stability and functionality of anthocyanins: a review.

Anthocyanins (ACNs) are water-soluble pigments in various fruits and vegetables known for their high antioxidant activity. They are used as natural food colorants and preservatives and have several medicinal benefits. However, their application in functional foods and nutraceuticals is often compromised by their low stability to heat, oxygen, enzymes, light, pH changes, and solubility issues. Spray drying has emerged as an effective microencapsulation technique to enhance the shelf life, quality, and stability of ACNs. This manuscript reviews the latest scientific developments in spray drying microencapsulation of ACNs-rich fruit extracts. Process optimization and the stability and physicochemical properties of the spray-dried, microencapsulated ACNs-rich powders are discussed. This review also covers functional food and nutraceutical applications and introduces novel encapsulation methods, such as freeze-drying, supercritical carbon dioxide (SC-CO2), coacervation, drum drying, and electrospraying, highlighting their potential in improving the utility of ACNs-rich fruit extracts.

Comparative assessment of three commercial kits and in house optimized PCR assays for GMO screening in food and feed.

Screening strategies for GMO detection in food and feed are a crucial aspect in GMO testing laboratories for streamlining the analytical workflow and reducing turnaround time and costs. These strategies can be more or less complex or even be targeted according to the ingredients in the product, but whatever the choice, a good basic approach is generally based on the search for 35S promoter (P35S), nos-terminator (T-nos) and FMV promoter (P-FMV). In this study, we compare the singleplex real time PCR method for P35S, T-nos and P-FMV detection currently adopted by the Italian National Reference Laboratory for GM food and feed (NRL) with three commercial kits available on the market for giving a greater choice to consider the best approach suitable to the official control laboratories that are different from each other.•The NRL optimized singleplex PCR methods and the three commercial kits fully respect all the validation parameters criteria according to the minimum performance requirements (MPR) of ENGL [1]•Screening strategies for GMO detection in food and feed are a crucial aspect in GMO testing laboratories and being the commercial kits different from each other, the laboratory can choose the methods best suit their needs reducing turnaround time and costs.

Bridging instrumental and visual perception with improved color difference equations: A multi-center study.

OBJECTIVES: This multicenter study aimed to evaluate visual-instrumental agreement of six color measurement devices and optimize three color difference equations using a dataset of visual color differences (∆V) from expert observers. METHODS: A total of 154 expert observers from 16 sites across 5 countries participated, providing visual scaling on 26 sample pairs of artificial teeth using magnitude estimation. Three color difference equations (ΔE*ab, ∆E00, and CAM16-UCS) were tested. Optimization of all three equations was performed using device-specific weights, and the standardized residual sum of squares (STRESS) index was used to evaluate visual-instrumental agreement. RESULTS: The ΔE*ab formula exhibited STRESS values from 18 to 40, with visual-instrumental agreement between 60 % and 82 %. The ∆E00 formula showed STRESS values from 26 to 32, representing visual-instrumental agreement of 68 % to 74 %. CAM16-UCS demonstrated STRESS values from 32 - 39, with visual-instrumental agreement between 61-68 %. Following optimization, STRESS values decreased for all three formulas, with ΔE' demonstrating average visual-instrumental agreement of 79 % and ∆E00 of 78 %. CAM16-UCS showed average visual-instrumental agreement of 76 % post optimization. SIGNIFICANCE: Optimization of color difference equations notably improved visual-instrumental agreement, overshadowing device performance. The optimzed ΔE' formula demonstrated the best overall performance combining computational simplicty with outstanding visual-instrumental agreement.

Breeding Value Estimation Based on Morphological Evaluation of the Maremmano Horse Population through Factor Analysis.

Morphological scoring is a common evaluation method for domestic animals. The National Association of Maremmano Breeders (ANAM) has provided a dataset containing the records of 600 horses, four metric measurements (cm) and 24 traits with a continuous evaluation scale, each one with 15 classes. Moreover, a body condition score (BCS) with five classes is included. In this study, factor analysis was conducted to create a small number of informative factors (3) obtained from these traits, and a new BLUP-AM-MT index was established. The New Estimated Breeding Value (NEBV1) of each horse was computed by adding the genetic indexes of the three factors, with each one multiplied using a coefficient indicated by ANAM. The practical feasibility of the NEBV1 was evaluated through Spearman correlations between the rankings of the NEBV1 and the rankings of the BLUP-AM-MT, estimated through the four biometric measures and the morphological score (MS) assigned to each horse by the ANAM judges. The factorial analysis was used to estimate three factors: the "Trunk Dimension", "Legs" and "Length". As the explained variance was only 32%, the model was rotated, and the heritability of the three factors were 0.51, 0.05 and 0.41, respectively. After rotation, the estimated correlations between the new NEBV1 and the biometric measures were improved. These results should encourage breeders to adopt a breeding value index that takes into consideration the factors derived from all the variables observed in the morphological evaluation of the Maremmano. In this way, breeders can use it to select the best animals for breeding.