Spectral-Domain OCT Lens Meridian Position as a Metric to Estimate Postoperative Anatomical Lens Position.

PURPOSE: To evaluate the prediction of postoperative anatomical lens position (ALP) using intraoperative spectral-domain optical coherence tomography (SD-OCT) lens anatomy metrics in patients who underwent femtosecond laser-assisted cataract surgery. METHODS: Intraoperative SD-OCT (Catalys; Johnson & Johnson Vision) and postoperative optical biometry (IOLMaster 700; Carl Zeiss Meditec AG) were used to assess anterior segment landmarks, including lens thickness, lens volume, anterior chamber depth, lens meridian position (LMP), and measured ALP. LMP was defined as the distance from the corneal epithelium to the lens equator, and ALP was defined as the distance from the corneal epithelium to the IOL surface. Eyes were divided into groups according to axial length (> 22.5 mm, 22.5 to 24.5 mm, and > 24.5 mm) and IOL type (Tecnis ZCB00 [Johnson & Johnson Vision]; AcrySof SN-60WF [Alcon Laboratories, Inc], or enVista MX60E [Bausch & Lomb]) to further analyze the correlation between LMP and ALP. Theoretical effective lens position was back-calculated using a specific formula. Primary outcome was correlation between postoperative measured ALP and LMP. RESULTS: A total of 97 eyes were included in this study. Linear regression analysis displayed a statistically significant correlation between intraoperative LMP and postoperative ALP (R2 = 0.522; P < .01). No statistically significant correlation was observed between LMP and lens thickness (R2 = 0.039; P = .06) or between ALP and lens thickness (R2 = 0.02; P = .992). The greatest predictor for ALP was LMP (ß = 0.766, P < .001; R2 = 0.523). CONCLUSIONS: Intraoperative SD-OCT-measured LMP correlated better than anterior chamber depth and axial length to postoperative ALP. Further studies are necessary to analyze the impact of preoperative or intraoperative LMP measurements on postoperative refractive outcomes. [J Refract Surg. 2023;39(3):165-170.].

Accuracy of Nine Formulas to Calculate the Powers of an Extended Depth-of-Focus IOL Using Two SS-OCT Biometers.

PURPOSE: To evaluate the accuracy of nine formulas to calculate the power of a new extended depth-of-focus intraocular lens (EDOF IOL), the AcrySof IQ Vivity (Alcon Laboratories, Inc), using measurements from two optical biometers, the IOLMaster 700 (Carl Zeiss Meditec AG) and Anterion (Heidelberg Engineering GmbH). METHODS: After constant optimization, the accuracy of these formulas was analyzed in 101 eyes: Barrett Universal II, EVO 2.0, Haigis, Hoffer Q, Holladay 1, Kane, Olsen, RBF 3.0, and SRK/T. Both standard and total keratometry from the IOLMaster 700 and standard keratometry from the Anterion were used for each formula. RESULTS: Constant optimization provided slightly different values for the A-constant, which ranged between 118.99 and 119.16, depending on the formula and the optical biometer. According to the heteroscedastic test, within each keratometry modality the standard deviation of the SRK/T was significantly higher compared to that of the Holladay 1, Kane, Olsen, and RBF 3.0 formulas. The SRK/T formula provided less accurate results also when the absolute prediction errors were compared by Friedman test. According to McNemar's test with Holm corrections, statistically significant differences were found within each keratometry modality between the percentage of eyes with a prediction error within ±0.25 diopters obtained with the Olsen formula compared to the Holladay 1 and Hoffer Q formulas. CONCLUSIONS: Constant optimization remains a mandatory step to achieve the best outcomes with the new EDOF IOL: the same constant should not be used for all formulas and for both optical biometers. Different statistical tests revealed that older IOL formulas have lower accuracy compared to newer formulas. [J Refract Surg. 2023;39(3):158-164.].

Assessment of Firearm Storage Practices in the US, 2022.

Importance: Secure firearm storage may help reduce firearm injury and death. Broad implementation requires more granular assessments of firearm storage practices and greater clarity on circumstances that may prevent or promote the use of locking devices. Objective: To develop a more thorough understanding of firearm storage practices, obstacles to using locking devices, and circumstances in which firearm owners would consider locking unsecured firearms. Design, Setting, and Participants: A cross-sectional, nationally representative survey of adults residing in 5 US states who owned firearms was administered online between July 28 and August 8, 2022. Participants were recruited via probability-based sampling. Main Outcomes and Measures: Firearm storage practices were assessed via a matrix provided to participants in which firearm-locking devices were described both via text and images. Locking mechanisms (key/personal identification number [PIN]/dial vs biometric) were specified for each type of device. Obstacles to the use of locking devices and circumstances in which firearm owners would consider locking unsecured firearms were assessed via self-report items developed by the study team. Results: The final weighted sample included 2152 adult (aged ≥18 years), English-speaking firearm owners residing in the US; the sample was predominantly male (66.7%). Among the 2152 firearm owners, 58.3% (95% CI, 55.9%-60.6%) reported storing at least 1 firearm unlocked and hidden, with 17.9% (95% CI, 16.2%-19.8%) reporting storing at least 1 firearm unlocked and unhidden. Gun safes were the most frequently used device both among participants who use keyed/PIN/dial locking mechanisms (32.4%; 95% CI, 30.2%-34.7%) and those who use biometric locking mechanisms (15.6%; 95% CI, 13.9%-17.5%). Those who do not store firearms locked most frequently noted a belief that locks are unnecessary (49.3%; 95% CI, 45.5%-53.1%) and a fear that locks would prevent quick access in an emergency (44.8%; 95% CI, 41.1%-48.7%) as obstacles to lock usage. Preventing access by children was the most often reported circumstance in which firearm owners would consider locking unsecured firearms (48.5%; 95% CI, 45.6%-51.4%). Conclusions and Relevance: In this survey study of 2152 firearm owners, consistent with prior research, unsecure firearm storage was common. Firearm owners appeared to prefer gun safes relative to cable locks and trigger locks, indicating that locking device distribution programs may not match firearm owners' preferences. Broad implementation of secure firearm storage may require addressing disproportionate fears of home intruders and increasing awareness of the risks associated with household firearm access. Furthermore, implementation efforts may hinge on broader awareness of the risks of ready firearm access beyond unauthorized access by children.

A Fusion-Assisted Multi-Stream Deep Learning and ESO-Controlled Newton-Raphson-Based Feature Selection Approach for Human Gait Recognition.

The performance of human gait recognition (HGR) is affected by the partial obstruction of the human body caused by the limited field of view in video surveillance. The traditional method required the bounding box to recognize human gait in the video sequences accurately; however, it is a challenging and time-consuming approach. Due to important applications, such as biometrics and video surveillance, HGR has improved performance over the last half-decade. Based on the literature, the challenging covariant factors that degrade gait recognition performance include walking while wearing a coat or carrying a bag. This paper proposed a new two-stream deep learning framework for human gait recognition. The first step proposed a contrast enhancement technique based on the local and global filters information fusion. The high-boost operation is finally applied to highlight the human region in a video frame. Data augmentation is performed in the second step to increase the dimension of the preprocessed dataset (CASIA-B). In the third step, two pre-trained deep learning models-MobilenetV2 and ShuffleNet-are fine-tuned and trained on the augmented dataset using deep transfer learning. Features are extracted from the global average pooling layer instead of the fully connected layer. In the fourth step, extracted features of both streams are fused using a serial-based approach and further refined in the fifth step by using an improved equilibrium state optimization-controlled Newton-Raphson (ESOcNR) selection method. The selected features are finally classified using machine learning algorithms for the final classification accuracy. The experimental process was conducted on 8 angles of the CASIA-B dataset and obtained an accuracy of 97.3, 98.6, 97.7, 96.5, 92.9, 93.7, 94.7, and 91.2%, respectively. Comparisons were conducted with state-of-the-art (SOTA) techniques, and showed improved accuracy and reduced computational time.

Biometric Authentication and Correlation Analysis Based on CNN-SRU Hybrid Neural Network Model.

With the continuous development of computer technology, many institutions in society have higher requirements for the efficiency and reliability of identification systems. In sectors with a high-security level, the use of traditional key and smart card system has been replaced by the identification system of biometric technology. The use of fingerprint and face recognition in biometric technology is a biometric technology that does not constitute an infringement on the human body and is convenient and reliable. The biometric technology has been continuously improved, and the existing biometric technologies are based on unimodal biometric features. The unimodal biometric technology has its own limitations such as proposing single information and checking data affected by the environment, which makes it difficult for the technology to play its advantages in practical applications. In this paper, we use CNN-SRU deep learning to preprocess a large amount of complex data in the perceptual layer. The data collected in the perceptual layer are first transmitted to CNN convolutional neural network for simple classification and analysis and then arrives at the LSTM session to update again and optimize the screening to improve the biometric performance. The results show that the CNN-LSTM, CNN-GRU, and CNN algorithms show a decreasing trend in accuracy under the three error evaluation criteria of RMSE, MAE, and ME, from 0.35 to 0.07, 0.58 to 0.19, and 0.38 to 0.15, respectively. The recognition rate of multifeature fusion can reach 95.2%; the recognition efficiency of the multibiometric authentication system and accuracy rate has been significantly improved. It provides a strong guarantee for the regional standardization, high integration, generalization, and modularization of multibiometric identification system application products.

Evaluating the influence of environmental variables on the length-weight relationship and prediction modelling in flathead grey mullet, Mugil cephalus Linnaeus, 1758.

Fish stocks that are grown under diverse environmental conditions have different biometric relationships and growth patterns. The biometric length-weight relationship (LWR) is an essential fishery assessment tool, as fish growth is continuous and depends on genetic and environmental factors. The present study attempts to understand the LWR of the flathead grey mullet, Mugil cephalus Linnaeus, 1758, from different locations. The study area encompassed its distribution in the wild across freshwater location (one), coastal habitats (eight locations), and estuaries (six locations) in India to determine the relationship between various environmental parameters. Specimens (n = 476) of M. cephalus were collected from commercial catches and the length and weight of individual specimens were recorded. Monthly data from the study locations were extracted for nine environmental variables from the datasets downloaded from the Physical Oceanography Distributed Active Archive Center (PO.DAAC) and the Copernicus Marine Environment Monitoring Service (CMEMS) over 16 years (2002 to 2017) on the Geographical Information System platform. The parameters of the LWR, intercept 'a' and slope or regression coefficient 'b', varied from 0.005321 to 0.22182 and 2.235 to 3.173, respectively. The condition factor ranged from 0.92 to 1.41. The partial least squares (PLS) score scatter plot matrix indicated differences in the environmental variables between the locations. PLS analysis of the regression coefficient and environment parameters revealed that certain environment variables viz., sea surface temperature, salinity, dissolved oxygen, nitrate, and phosphate, played a positive role. However, chlorophyll, pH, silicate, and iron played a negative role in influencing weight growth across various locations. The results revealed that the M. cephalus specimens from three locations, Mandapam, Karwar, and Ratnagiri, possessed significantly higher fitness to their environment than those from the other six locations. The PLS model can be used to predict weight growth under the various environmental conditions of different ecosystems. The three identified locations are useful sites for the mariculture of this species considering their growth performance, the environmental variables, and their interactions. The results of this study will improve the management and conservation of exploited stocks in regions affected by climate change. Our results will also aid in making environment clearance decisions for coastal development projects and will improve the efficiency of mariculture systems.

Standard vs total keratometry for intraocular lens power calculation in cataract surgery combined with DMEK.

PURPOSE: To compare the prediction accuracy of standard keratometry (K) and total keratometry (TK) for intraocular lens (IOL) power calculation in eyes undergoing combined cataract surgery and Descemet membrane endothelial keratoplasty (triple DMEK). SETTING: Tertiary care academic referral center. DESIGN: Retrospective case series. METHODS: Review of 83 eyes (63 patients) that underwent triple DMEK between 2019 and 2021. Biometry measurements were obtained using a swept-source optical biometer (IOLMaster 700). 63 eyes were used for statistical analysis. Mean error, mean absolute error (MAE), SD, median absolute error, maximum absolute error, root mean squared prediction error, and the percentage of eyes within prediction errors of ±0.50 diopters (D) and ±1.00 D were calculated for 9 multivariate and third-generation formulas using K and TK values (Barrett Universal II, Yeo EVO 2.0, Cooke K6, Kane, Pearl-DGS, Haigis, Holladay 1, Hoffer Q, and SRK/T). Formulas were additionally tested by using the prediction for an IOL power 1 D below the IOL used (IOLup1D). RESULTS: For all formulas, MAE was lower for K than for TK by an average of 0.21 D. The lowest MAE value observed was 0.67 D for "adjusted" SRK/T using K, and the highest MAE values observed were 1.24 D and 1.24 D for nonadjusted Hoffer Q and Haigis using TK, respectively. Overall, lower MAE values were observed for multivariate formulas and SRK/T. CONCLUSIONS: In triple DMEK eyes, the prediction accuracy of K was higher than that of TK. The most accurate formulas were SRK/T and multivariate formulas using K with the IOLup1D adjustment.

Formula constant optimisation techniques including variation of keratometer or corneal refractive index and consideration for classical and modern IOL formulae.

BACKGROUND: To investigate whether variation of the keratometer/corneal refractive index nK/nC improves the performance (prediction error PE) of classical and a modern intraocular lens (IOL) power calculation formula and further, to establish whether any trend error of PE for corneal radius R could be eliminated using formula constant and nK/nC optimisation. METHODS: Based on 2 large datasets (1: N = 888 Hoya Vivinex aberration-correcting and 2: N = 822 Alcon SA60AT spherical lens) a classical formula constant optimisation has been performed for the Hoffer Q, Holladay 1, Haigis and Castrop formulae, to minimise the root mean squared (rms) PE (situation A). In two further optimisations, the formula constants and the formula specific nK/nC value were optimised to minimise the rms PE (situation B) or rms PE and trend error of PE for R (situation C). Nonlinear iterative optimisation strategy was applied according to Levenberg-Marquardt. RESULTS: Optimising for rms PE and trend error (C) mainly improved the performance of the Holladay 1. The Haigis formula also showed a slight improvement compared to (A). The Hoffer Q formula shows no relevant trend error of PE for R. In contrast, the Holladay shows a positive and the Haigis (and the Castrop a slight) negative trend error of PE for R. The trend error could be fully eliminated by optimising formula constants and nK/nC in (B), but this was at the cost of overall performance in the case of the Holladay 1 formula. CONCLUSION: Classical IOL calculation concepts should be critically examined for potential improvement of formula performance by variation of the empirical nK/nC value defined in the formula. With additional degrees of freedom additional optimisation terms such as trend errors might be considered in new intelligent optimisation strategies.

A New Post-Processing Proposal for Improving Biometric Gait Recognition Using Wearable Devices.

In this work, a novel Window Score Fusion post-processing technique for biometric gait recognition is proposed and successfully tested. We show that the use of this technique allows recognition rates to be greatly improved, independently of the configuration for the previous stages of the system. For this, a strict biometric evaluation protocol has been followed, using a biometric database composed of data acquired from 38 subjects by means of a commercial smartwatch in two different sessions. A cross-session test (where training and testing data were acquired in different days) was performed. Following the state of the art, the proposal was tested with different configurations in the acquisition, pre-processing, feature extraction and classification stages, achieving improvements in all of the scenarios; improvements of 100% (0% error) were even reached in some cases. This shows the advantages of including the proposed technique, whatever the system.

Using Gaze for Behavioural Biometrics.

A principled approach to the analysis of eye movements for behavioural biometrics is laid down. The approach grounds in foraging theory, which provides a sound basis to capture the uniqueness of individual eye movement behaviour. We propose a composite Ornstein-Uhlenbeck process for quantifying the exploration/exploitation signature characterising the foraging eye behaviour. The relevant parameters of the composite model, inferred from eye-tracking data via Bayesian analysis, are shown to yield a suitable feature set for biometric identification; the latter is eventually accomplished via a classical classification technique. A proof of concept of the method is provided by measuring its identification performance on a publicly available dataset. Data and code for reproducing the analyses are made available. Overall, we argue that the approach offers a fresh view on either the analyses of eye-tracking data and prospective applications in this field.

Biometric Recognition: A Systematic Review on Electrocardiogram Data Acquisition Methods.

In the last decades, researchers have shown the potential of using Electrocardiogram (ECG) as a biometric trait due to its uniqueness and hidden nature. However, despite the great number of approaches found in the literature, no agreement exists on the most appropriate methodology. This paper presents a systematic review of data acquisition methods, aiming to understand the impact of some variables from the data acquisition protocol of an ECG signal in the biometric identification process. We searched for papers on the subject using Scopus, defining several keywords and restrictions, and found a total of 121 papers. Data acquisition hardware and methods vary widely throughout the literature. We reviewed the intrusiveness of acquisitions, the number of leads used, and the duration of acquisitions. Moreover, by analyzing the literature, we can conclude that the preferable solutions include: (1) the use of off-the-person acquisitions as they bring ECG biometrics closer to viable, unconstrained applications; (2) the use of a one-lead setup; and (3) short-term acquisitions as they required fewer numbers of contact points, making the data acquisition of benefit to user acceptance and allow faster acquisitions, resulting in a user-friendly biometric system. Thus, this paper reviews data acquisition methods, summarizes multiple perspectives, and highlights existing challenges and problems. In contrast, most reviews on ECG-based biometrics focus on feature extraction and classification methods.

Spherical Aberration of Cataractous Eyes and Its Relationship With Age, Ocular Biometry, and Various IOL Platforms.

PURPOSE: To determine the median spherical aberration (SA) of the cataractous population, how it relates to biometry, and the theoretical effect of different intraocular lens (IOL) platforms. METHODS: A retrospective chart review of patients undergoing cataract surgery evaluation with a high quality Pentacam (Oculus Optikgeräte GmbH) were included. Age, gender, Q-value, mean total SA, higher order aberration root mean square wavefront error, and equivalent keratometry were collected from the Holladay report and axial length and anterior chamber depth (ACD) from the IOLMaster 700 (Carl Zeiss Meditec AG). RESULTS: Data from 1,725 eyes of 999 patients were collected. SA had a median of 0.37 µm (95% confidence interval: 0.36 to 0.38. Age (r = .136, P < .001), Q-factor (r = .743, P < .001), and higher order aberration root mean square wavefront error (r = .307, P < .001) were positively correlated with SA. Average equivalent keratometry (r = -.310, P < .001) was negatively correlated with SA. Axial length (r = -0.037, P = .120) and ACD (r = .004, P = .856) had no association with SA. Up to 1,499 (86.9%) theoretically had SA moved closer to zero with IOLs that had negative SA. Up to 102 (5.9%) had SA theoretically worsened. CONCLUSIONS: SA is not normally distributed, suggesting that there may be no "average" SA that IOLs should aim to correct. Patients might benefit from tailoring IOL choice to individual SA. Without access to SA data, eyes with steeper average keratometry or younger patients may have less SA, which could influence IOL choice. [J Refract Surg. 2023;39(2):89-94.].

Using the artificial bee colony technique to optimize machine learning algorithms in estimating the mature weight of camels.

This paper aims to predict male and female camels' mature weight (MW) through various morphological traits using hybrid machine learning (ML) algorithms. For this aim, biometrical measurements such as birth weight (BW), length of face (FL), length of the neck (NL), a girth of the heart (HG), body length (BL), withers height (WH), and hind leg length (HLL) were used to estimate the mature weight for eight camel breeds of Pakistan. In this study, multivariate adaptive regression splines (MARS), random forest (RF), and support vector machine (SVM) were applied to develop prediction models. Furthermore, the artificial bee colony (ABC) algorithm is employed to optimize ML models' internal parameters and improve prediction accuracy. The predictive performance of ML and hybrid models was evaluated on a testing dataset using goodness-of-fit measures such as mean absolute deviation (MAD), mean absolute percentage error (MAPE), coefficient of determination (R2), and root mean square error (RMSE). The results of the study revealed the ABC-SVM model was the best predictive model. The experimental results of this study showed that the proposed ABC-SVM method could effectively improve the accuracy for MW prediction of camels, thus having a research and practical value.

Measurement of change in angle kappa and its correlation with ocular biometric parameters pre- and post-phacoemulsification.

Purpose: Angle kappa has been considered to play a role in causing glare and haloes despite accurate centration during implantation of multifocal intraocular lenses following phacoemulsification. There is a lack of substantial data regarding whether angle kappa is a constant entity or changes following ocular surgical procedures. To answer this question, in this prospective observational study, we measured change in angle kappa following phacoemulsification, and studied the ocular biometric parameters correlating with this change. Methods: Angle kappa was measured objectively using synoptophore. Ocular Biometric parameters (Anterior Chamber Depth, Corneal White-to-White measurement, Lens Thickness, and Axial Length) using LenStar LS 900 Haag Streit Anterior Segment imaging system. outcome measures were a quantitative change in angle kappa from the preoperative value by one degree or more and observation of correlation between change in angle kappa and ocular biometric parameters. The Wilcoxin Signed Rank Test was used to determine the difference between pre-operative and post-operative measurements for angle kappa. A p-value of less than 0.05 was considered statistically significant. Pearson's correlation coefficient was employed to find the relationship between preoperative ocular biometric parameters and a change in angle kappa. A linear regression model was used to derive an equation considering corneal white-to-white measurement as the predictor and change in angle kappa as the outcome measure. Results: A significant change in angle kappa was recorded, and a significant correlation was found with corneal white to white measurements. This change could be predicted preoperatively, for a known corneal white to white measurement using the standard equation y=mx+c. Conclusion: This study explains the possible cause of dissatisfaction among seemingly ideal patients who undergo multifocal IOL implantation and the potential for better decision-making during patient selection for multifocal IOL implantation.

Intra-operative aberrometry versus conventional biometry for intraocular lens power calculation: A prospective observational study from a tertiary referral center.

Purpose: To evaluate the per operative intra-ocular lens (IOL) power calculation using intra-operative aberrometry (ORA) and its comparison with conventional methods. Methods: Patients with cataract planned for phacoemulsification by a single surgeon under topical anesthesia were enrolled in this prospective observational study in this prospective observational study. All patients underwent pre-operative biometry (Manual SRK-II and IOLMaster® 500) to determine the intra-ocular lens (IOL) power. Intra-operative aberrometry using ORA was also performed; however, IOL was inserted according to IOLMaster® (SRK/T). Spherical equivalent (SE) was recorded on post-operative days 1, 7, and 30. Patients were divided into three groups based on axial lengths for analysis. Comparative analysis was performed for the calculated IOL powers and prediction errors of ORA with conventional methods. Adjusted IOL power to calculate the emmetropic IOL using the LiHue formula was also determined and was compared with existing methods. A P-value less than 0.05 was considered statistically significant. Results: A total of 115 eyes from 113 patients were included, with a median age of 54.90 ± 14.3 years. The mean axial length was found to be 23.94 ± 2.3 mm. There was good agreement (87%) between ORA and IOLMaster® for calculated IOL powers with a mean difference of 0.047 ± 0.5D between the two (P = 0.33). A positive correlation was found between IOL power calculated using ORA, IOLMaster®, SRK-II, and adjusted IOL. Conclusion: The use of intra-operative aberrometry (ORA) to calculate IOL power in patients undergoing uncomplicated phacoemulsification is non-inferior relative to standard pre-operative measurement and planning.

Understanding consumer liking of beef patties with different firmness among younger and older adults using FaceReader™ and biometrics.

Sensorial perceptions change as people age and biometrics analysis can be used to explore the unconscious consumer responses. Investigation was conducted of effects of consumer age (younger, 22-52 years; older, 60-76 years) on facial expression response (FER) during consumption of beef patties with varying firmness (soft, medium, hard) and taste (±plum sauce). Video images were collected and FERs analysed using FaceReader™. Younger people exhibited higher intensity for happy/sad/scared and lower intensity for neutral/disgusted, relative to older people. Interactions between age and texture/sauce showed little FER variation in older people, whereas younger people showed considerable FER variation. Younger people, but not older people, had lowest intensity of happy FER and highest intensity of angry FER for the hard patty. Sauce addition resulted in higher intensity of happy/contempt in younger consumers, but not older consumers. FER collected using FaceReader™ was successfully used to differentiate between the unconscious responses of younger and older consumers.

Alternative additives associated in the feeding of laying hens: performance, biometrics, bone traits, and economic evaluation-an unsupervised machine learning approach.

Given the current bans on the use of some growth promoting antibiotics in poultry nutrition, the need to use alternative additives which could replace traditional promoters in diets has arisen. The objective of this study was to evaluate the effect of alternative additives, associated or not, in replacing the antibiotic growth promoter in the diets of laying hens on performance, egg quality, biometry, bone characteristics, and economic viability. A total of 378 birds at 97 weeks of age, weighing 1691 ± 80g with an average production of 79.96 ± 4.9%, were randomly distributed and submitted to different diets: negative control - NC (no additive); positive control - PC, conventional growth promoter (Enramycin); associated organic acids (OA); symbiotic (S); Essential oil (EO); OA + S; and S+EO. The diet did not influence (P > 0.05) performance, egg quality, biometry, and bone traits. However, the use of alternative additives and their associations with the exception of S+OA, provided better economic indices when compared to NC and CP. The first component showed a negative relationship between feed conversion per mass and dozen eggs with gut length, Seedor index, egg production, and egg mass; the second component showed a positive relationship between yolk, pancreas, proventriculus, and gizzard; and, finally, the third component showed that feed consumption has a negative relationship with bone strength and deformity. The first two canonical functions were significant and discriminated 100% of the differences between the diets. Moreover, 50% of the birds were correctly classified in their group of origin, in which the positive control group (83.3%) and OA+S presented the highest rates of correct responses (66.7%). Bone deformity and bowel length were the only two variables with discriminatory power. Natural growth promoters alone or in association do not harm performance, egg quality, digestive organs biometry or bird bone characteristics, in addition to promoting greater economic return. Thus, they can be considered possible substitutes for traditional antibiotics. Finally, unsupervised machine learning methods are useful statistical techniques to study the relationship of variables and point out the main biomarkers of poultry production.

A Study of 42 Partially Edentulous Patients with Single-Crown Restorations and Implants to Compare Bone Loss Between Crestal and Subcrestal Endosseous Implant Placement.

BACKGROUND The purpose of the study was to evaluate the influence of dental implant placement at different bone levels upon the resultant postoperative peri-implant bone loss. MATERIAL AND METHODS Forty-two partially edentulous patients seeking implant-supported single-crown restorations were screened followed by segregation into 2 groups (GP), GP E (equicrestal) and GP S (subcrestal) (n=21 each). Sixty endosseous implants (30 each) (Adin Tourage-S, Israel), size 3.5/8 and 4/10 mm for mandibles, were placed using a 2-stage surgical procedure. At 4 to 6 months, straight abutments were attached followed by restoration (Vita Zahnfabrik, Germany). Crestal bone levels (mesial/distal) of implant fixtures were assessed at 5 time intervals (after surgery, and at 3, 6, 9, and 12 months) using digital radiography. Means and standard deviations were calculated, following which the differences were statistically analyzed using ANOVA at P value of <0.05. RESULTS The mean annual bone loss for GP S (1.96 mm) was higher than GP E (1.10 mm). At all studied time intervals, the bone loss for implants in GP S was higher than in GP E (P<0.05). Between time intervals, lowest bone loss was observed on the distal side in GP E (0.11 mm/6-9 month) and the highest bone loss was observed on the distal side of GP S (0.6 mm/9-12 month). Differences in the means between the 2 groups on mesial and distal sides were statistically significant at all time intervals (P<0.05). CONCLUSIONS Subcrestal implant placement was associated with more bone loss than when implants are placed at the crestal level.

Secure biometric systems based on bio-signals and DNA encryption of optical spectrograms.

Recently, biometrics has become widely used in applications to verify an individual's identity. To address security issues, biometrics presents an intriguing window of opportunity to enhance the usability and security of the Internet of Things (IoT) and other systems. It can be used to secure a variety of newly emerging IoT devices. However, biometric scenarios need more protection against different hacking attempts. Various solutions are introduced to secure biometrics. Cryptosystems, cancelable biometrics, and hybrid systems are efficient solutions for template protection. The new trend in biometric authentication systems is to use bio-signals. In this paper, two proposed authentication systems are introduced based on bio-signals. One of them is unimodal, while the other is multimodal. Protected templates are obtained depending on encryption. The deoxyribonucleic acid (DNA) encryption is implemented on the obtained optical spectrograms of bio-signals. The authentication process relies on the DNA sensitivity to variations in the initial values. In the multimodal system, the singular value decomposition (SVD) algorithm is implemented to merge bio-signals. Different evaluation metrics are used to assess the performance of the proposed systems. Simulation results prove the high accuracy and efficiency of the proposed systems as the equal error rate (EER) value is close to 0 and the area under the receiver operator characteristic curve (AROC) is close to 1. The false accept rate (FAR), false reject rate (FRR), and decidability (D) are also estimated with acceptable results of 1.6 × 10-8, 9.05 × 10-6, and 29.34, respectively. Simulation results indicate the performance stability of the proposed systems in the presence of different levels of noise.