Associations between corneal curvature and other anterior segment biometrics in young myopic adults.

To investigate the associations between corneal curvature (CC) and other anterior segment biometrics in young myopic adults. In this retrospective multi-center study, 7893 young myopic adults were included. CC and other anterior segment biometrics were measured by Scheimpflug imaging (Pentacam). CC was defined as SimK at central 3 mm area, and other anterior segment biometrics included white-to-white corneal diameter (WTW), central corneal thickness (CCT), corneal volume (CV) at 3 mm, 5 mm, and 7 mm area, anterior corneal astigmatism (ACA), posterior corneal astigmatism (PCA), anterior corneal eccentricity (ACE) and asphericity (ACAP), posterior corneal eccentricity (PCE) and asphericity (PCAP), anterior chamber depth (ACD), and anterior chamber volume (ACV). Univariate regression analyses were used to assess the associations between CC and other anterior segment biometrics, and multivariate regression analyses were further performed to adjusted for age, gender and spherical equivalent. CC was higher in patients of female gender and higher myopia (all P < 0.05). Eyes in higher CC quartiles had lower WTW, thinner CCT, lower CV at 3 mm and 5 mm, lower ACD, and lower ACV (all P < 0.001), but had larger ACA, larger PCA, less PCE and less PCAP (all P < 0.001), compared to eyes in lower CC quartiles. The trends of CV at 7 mm, ACE and ACAP were inconsistent in different CC quartiles. After adjusting for age, gender and spherical equivalent with multivariate linear regression, CC was positively correlated to CV at 7 mm (ßs = 0.069), ACA (ßs = 0.194), PCA (ßs = 0.187), ACE (ßs = 0.072), PCAP (ßs = 0.087), and ACD (ßs = 0.027) (all P < 0.05), but was negatively correlated to WTW (ßs = - 0.432), CCT (ßs = - 0.087), CV-3 mm (ßs = - 0.066), ACAP (ßs = - 0.043), PCE (ßs = - 0.062), and ACV (ßs = - 0.188) (all P < 0.05). CC was associated with most of the other anterior segment biometrics in young myopic adults. These associations are important for better understanding of the interactions between different anterior segment structures in young myopic patients, and are also useful for the exploration of the pathogenesis of myopia.

Synthetic Iris Images: A Comparative Analysis between Cartesian and Polar Representation.

In recent years, the advancement of generative techniques, particularly generative adversarial networks (GANs), has opened new possibilities for generating synthetic biometric data from different modalities, including-among others-images of irises, fingerprints, or faces in different representations. This study presents the process of generating synthetic images of human irises, using the recent StyleGAN3 model. The novelty presented in this work consists in producing generated content in both Cartesian and polar coordinate representations, typically used in iris recognition pipelines, such as the foundational work proposed by John Daugman, but hitherto not used in generative AI experiments. The main objective of this study was to conduct a qualitative analysis of the synthetic samples and evaluate the iris texture density and suitability for meaningful feature extraction. During this study, a total of 1327 unique irises were generated, and experimental results carried out using the well-known OSIRIS open-source iris recognition software and the equivalent software, wordlcoin-openiris, newly published at the end of 2023 to prove that (1) no "identity leak" from the training set was observed, and (2) the generated irises had enough unique textural information to be successfully differentiated between both themselves and between them and real, authentic iris samples. The results of our research demonstrate the promising potential of synthetic iris data generation as a valuable tool for augmenting training datasets and improving the overall performance of iris recognition systems. By exploring the synthetic data in both Cartesian and polar representations, we aim to understand the benefits and limitations of each approach and their implications for biometric applications. The findings suggest that synthetic iris data can significantly contribute to the advancement of iris recognition technology, enhancing its accuracy and robustness in real-world scenarios by greatly augmenting the possibilities to gather large and diversified training datasets.

Pattern analysis using lower body human walking data to identify the gaitprint.

All people have a fingerprint that is unique to them and persistent throughout life. Similarly, we propose that people have a gaitprint, a persistent walking pattern that contains unique information about an individual. To provide evidence of a unique gaitprint, we aimed to identify individuals based on basic spatiotemporal variables. 81 adults were recruited to walk overground on an indoor track at their own pace for four minutes wearing inertial measurement units. A total of 18 trials per participant were completed between two days, one week apart. Four methods of pattern analysis, a) Euclidean distance, b) cosine similarity, c) random forest, and d) support vector machine, were applied to our basic spatiotemporal variables such as step and stride lengths to accurately identify people. Our best accuracy (98.63%) was achieved by random forest, followed by support vector machine (98.40%), and the top 10 most similar trials from cosine similarity (98.40%). Our results clearly demonstrate a persistent walking pattern with sufficient information about the individual to make them identifiable, suggesting the existence of a gaitprint.

Emotion detection from handwriting and drawing samples using an attention-based transformer model.

Emotion detection (ED) involves the identification and understanding of an individual's emotional state through various cues such as facial expressions, voice tones, physiological changes, and behavioral patterns. In this context, behavioral analysis is employed to observe actions and behaviors for emotional interpretation. This work specifically employs behavioral metrics like drawing and handwriting to determine a person's emotional state, recognizing these actions as physical functions integrating motor and cognitive processes. The study proposes an attention-based transformer model as an innovative approach to identify emotions from handwriting and drawing samples, thereby advancing the capabilities of ED into the domains of fine motor skills and artistic expression. The initial data obtained provides a set of points that correspond to the handwriting or drawing strokes. Each stroke point is subsequently delivered to the attention-based transformer model, which embeds it into a high-dimensional vector space. The model builds a prediction about the emotional state of the person who generated the sample by integrating the most important components and patterns in the input sequence using self-attentional processes. The proposed approach possesses a distinct advantage in its enhanced capacity to capture long-range correlations compared to conventional recurrent neural networks (RNN). This characteristic makes it particularly well-suited for the precise identification of emotions from samples of handwriting and drawings, signifying a notable advancement in the field of emotion detection. The proposed method produced cutting-edge outcomes of 92.64% on the benchmark dataset known as EMOTHAW (Emotion Recognition via Handwriting and Drawing).

Association between body stature with ocular biometrics and refraction among Chinese preschoolers.

PURPOSE: To evaluate the association of body stature with ocular biometrics and refraction in preschool children. METHODS: A cross-sectional, school-based study was conducted in Shenzhen, China. Preschool children aged 3 to 6 from 10 randomly-selected kindergartens were recruited. Ocular biometric parameters, including axial length (AL), anterior chamber depth (ACD), vitreous chamber depth (VCD), corneal radius curvature (CR), axial length to corneal radius ratio (AL-to-CR ratio) and lens thickness (LT) were measured using non-contact partial-coherence laser interferometry. Cycloplegic refractions were obtained by a desktop autorefractor. Body height and weight were measured using standard procedures. The association between body stature and ocular biometrics were analyzed with univariable and multivariable regression model. RESULTS: A total of 373 preschoolers were included. AL, ACD, VCD, CR, and AL-to-CR ratio, were positively associated with height and weight (p < 0.05), whereas LT was negatively associated with height and weight (p < 0.01). No association was observed between stature and central cornea thickness and refraction. After adjusted for age and gender in a multivariable regression model, AL had positive associations with height (p < 0.01) and weight (p < 0.01). However, refraction had no significant association with stature parameters. CONCLUSION: Taller and heavier preschoolers had eyes with longer AL, deeper vitreous chamber, and flatter cornea. The significant associations between body stature and ocular biometric parameters reveal the driving influence of body development on the growth of eyeballs in preschoolers.

Experiment data: Human-in-the-loop decision support in process control rooms.

These datasets contain measures from multi-modal data sources. They include objective and subjective measures commonly used to determine cognitive states of workload, situational awareness, stress, and fatigue using data collection tools such as NASA-TLX, SART, eye tracking, EEG, Health Monitoring Watch, a survey to assess training, and a think-aloud situational awareness assessment following the SPAM methodology. Also, data from a simulation formaldehyde production plant based on the interaction of the participants in a controlled control room experimental setting is included. The interaction with the plant is based on a human-in-the-loop alarm handling and process control task flow, which includes Monitoring, Alarm Handling, Recovery planning, and intervention (Troubleshooting, Control and Evaluation). Data was collected from 92 participants, split into four groups while they underwent the described task flow. Each participant tested three scenarios lasting 15-18 min with a -10-min survey completion and break period in between using different combinations of decision support tools. The decision support tools tested and varied for each group include alarm prioritisation vs. none, paper-based vs. Digitised screen-based procedures, and an AI recommendation system. This is relevant to compare current practices in the industry and the impact on operators' performance and safety. It is also applicable to validate proposed solutions for the industry. A statistical analysis was performed on the dataset to compare the outcomes of the different groups. Decision-makers can use these datasets for control room design and optimisation, process safety engineers, system engineers, human factors engineers, all in process industries, and researchers in similar or close domains.

Preliminary Study of Novel Bio-Crypto Key Generation Using Clustering-Based Binarization of ECG Features.

In modern society, the popularity of wearable devices has highlighted the need for data security. Bio-crypto keys (bio-keys), especially in the context of wearable devices, are gaining attention as a next-generation security method. Despite the theoretical advantages of bio-keys, implementing such systems poses practical challenges due to their need for flexibility and convenience. Electrocardiograms (ECGs) have emerged as a potential solution to these issues but face hurdles due to intra-individual variability. This study aims to evaluate the possibility of a stable, flexible, and convenient-to-use bio-key using ECGs. We propose an approach that minimizes biosignal variability using normalization, clustering-based binarization, and the fuzzy extractor, enabling the generation of personalized seeds and offering ease of use. The proposed method achieved a maximum entropy of 0.99 and an authentication accuracy of 95%. This study evaluated various parameter combinations for generating effective bio-keys for personal authentication and proposed the optimal combination. Our research holds potential for security technologies applicable to wearable devices and healthcare systems.

FV-EffResNet: an efficient lightweight convolutional neural network for finger vein recognition.

Several deep neural networks have been introduced for finger vein recognition over time, and these networks have demonstrated high levels of performance. However, most current state-of-the-art deep learning systems use networks with increasing layers and parameters, resulting in greater computational costs and complexity. This can make them impractical for real-time implementation, particularly on embedded hardware. To address these challenges, this article concentrates on developing a lightweight convolutional neural network (CNN) named FV-EffResNet for finger vein recognition, aiming to find a balance between network size, speed, and accuracy. The key improvement lies in the utilization of the proposed novel convolution block named the Efficient Residual (EffRes) block, crafted to facilitate efficient feature extraction while minimizing the parameter count. The block decomposes the convolution process, employing pointwise and depthwise convolutions with a specific rectangular dimension realized in two layers (n × 1) and (1 × m) for enhanced handling of finger vein data. The approach achieves computational efficiency through a combination of squeeze units, depthwise convolution, and a pooling strategy. The hidden layers of the network use the Swish activation function, which has been shown to enhance performance compared to conventional functions like ReLU or Leaky ReLU. Furthermore, the article adopts cyclical learning rate techniques to expedite the training process of the proposed network. The effectiveness of the proposed pipeline is demonstrated through comprehensive experiments conducted on four benchmark databases, namely FV-USM, SDUMLA, MMCBNU_600, and NUPT-FV. The experimental results reveal that the EffRes block has a remarkable impact on finger vein recognition. The proposed FV-EffResNet achieves state-of-the-art performance in both identification and verification settings, leveraging the benefits of being lightweight and incurring low computational costs.

Rescue and rehabilitation of maned wolf (Chrysocyon brachyurus) in Paraguay: Case description.

The maned wolf, Chrysocyon brachyurus, is the largest South American canid, with a natural distribution that stretches across Peru, Bolivia, Brazil, Argentina, Paraguay and Uruguay. The present study reports the case of a rescued specimen of maned wolf that underwent a rehabilitation process in Paraguay, starting in October 2020 with its rescue, and finalising in May 2021 with the reintroduction. Herein, we document findings regarding the general management, biometrics, feeding and environmental enrichment; chemical immobilisation and monitoring; haematology, blood biochemistry and specific serology-relevant pathogens; skin examination and bone marrow cytology; orthopaedic, ophthalmological and dental evaluation; abdominal and cardiac ultrasonography; radiology and copro-parasitology. Main findings include the feeding habits of the individual and enrichment opportunities. The animal weighed 7 kg on arrival, with an estimated age of 5 months, and 18 kg on reintroduction, with an estimated age of 1 year. The animal tested negative to serologic tests for Brucella canis, Dirofilaria, canine distemper, Toxoplasmosis and canine parvovirus. Leptospira testing showed antibodies against L. grippotyphosa on both samplings, L. wolffi and L. ictero on the first sampling, and L. pomona on the second sampling. Abdominal organs were examined and measured through ultrasound evaluation and kidneys showed no alterations. Echocardiography showed preserved mitral, tricuspid and aortic valve flows, but turbulent pulmonary valve flow. Copro-parasitology reported the presence of Lagochilascaris sp. and Balantidium sp. All the information gathered aided in diagnosing the health status of the individual, and the response to environmental enrichment helped assess the behaviour, which led to the suggestion of reintroducing the animal. These data constitute the first published health check of a maned wolf in Paraguay, which can contribute to the species' conservation in the country. The protocol presented in this study can serve as a basis for developing an action plan for the maned wolf in Paraguay.

Association Between Peripheral Retinal Defocus and Myopia by Multispectral Refraction Topography in Chinese Children.

Objective: To investigate the association between the peripheral refractive errors of the fundus in different regions and moderate and high myopia. Methods: In this case-control study, 320 children and adolescents aged 6 to 18 years were recruited. Peripheral refractive errors were measured using multispectral retinal refractive topography (MRT). Spherical equivalent (SE) and cylinder errors were classified into low, moderate, and high categories based on the magnitude range. Logistic regression was performed to test the factors associated with myopia. Results: There were 152 participants with low myopia and 168 participants with moderate and high myopia included in the current study. Participants with moderate and high myopia were most likely to be older, with larger axial length (AL), lower SE, less time to watch electronic devices on the weekend, a higher difference between central refractive error and paracentral refractive error from the superior side of the retina (RDV-S), but a smaller difference between the central refractive error and paracentral refractive error from the inferior side of the retina (RDV-I) than those with low myopia (all P <0.05). After logistic analysis, female sex (odds ratio [OR] = 4.14; 95% confidence interval [CI] = 2.16-7.97, P <0.001), AL (OR = 6.88, 95% CI = 4.33-10.93, P <0.001), and RDV-I (OR = 0.52, 95% CI = 0.32-0.86, P = 0.010) were independent factors for moderate and high myopia. Conclusion: Our study demonstrated that the retina peripheral refraction of the eyes (RDV-I) was associated with moderate and high myopia, and RDV-S was only associated with high myopia.

Enhanced Multitask Learning for Hash Code Generation of Palmprint Biometrics.

This paper presents a novel multitask learning framework for palmprint biometrics, which optimizes classification and hashing branches jointly. The classification branch within our framework facilitates the concurrent execution of three distinct tasks: identity recognition and classification of soft biometrics, encompassing gender and chirality. On the other hand, the hashing branch enables the generation of palmprint hash codes, optimizing for minimal storage as templates and efficient matching. The hashing branch derives the complementary information from these tasks by amalgamating knowledge acquired from the classification branch. This approach leads to superior overall performance compared to individual tasks in isolation. To enhance the effectiveness of multitask learning, two additional modules, an attention mechanism module and a customized gate control module, are introduced. These modules are vital in allocating higher weights to crucial channels and facilitating task-specific expert knowledge integration. Furthermore, an automatic weight adjustment module is incorporated to optimize the learning process further. This module fine-tunes the weights assigned to different tasks, improving performance. Integrating the three modules above has shown promising accuracies across various classification tasks and has notably improved authentication accuracy. The extensive experimental results validate the efficacy of our proposed framework.

Effect of Prenatal Iron Supplementation Adapted to Hemoglobin Levels in Early Pregnancy on Fetal and Neonatal Growth-ECLIPSES Study.

In this randomized clinical trial, we evaluated the effects of prenatal iron supplementation adapted to pregnant women's initial hemoglobin (Hb) levels on fetal growth parameters until birth in women from the Mediterranean coast of northern Spain. All (n = 791) women were iron-supplemented during pregnancy according to Hb levels at the 12th gestational week: stratum 1 (Hb: 110-130 g/L) received 40 or 80 mg iron daily; stratum 2 (Hb > 130 g/L) received 40 or 20 mg iron daily. Fetal biometric and anthropometric measurements were evaluated in the three trimesters and at birth, respectively. In stratum 1, using 80 mg/d instead of 40 mg/d increased the risk of fetal head circumference > 90th percentile (OR = 2.49, p = 0.015) at the second trimester and fetal weight (OR = 2.36, p = 0.011) and femur length (OR = 2.50, p = 0.018) < 10th percentile at the third trimester. For stratum 2, using 40 mg/d instead of 20 mg/d increased the risk of fetal head circumference > 90th percentile (OR = 3.19, p = 0.039) at the third trimester. A higher risk of delivering an LGA baby (OR = 2.35, p = 0.015) for birthweight was also observed in stratum 1 women receiving 80 mg/d. It is crucial to adjust the prenatal iron supplementation to each pregnant woman's needs, i.e., adapted to their initial Hb levels, to achieve optimal fetal development, since excessive iron doses appear to adversely influence fetal growth.

Ocular biometrics in eyes with different white-to-white corneal diameter in young myopic adults.

The interactions between white-to-white corneal diameter (WTW) and other ocular biometrics are important for planning of refractive surgery and understanding of ocular structural changes in myopia, but such interactions are rarely investigated in young myopic adults. This is a retrospective study involving 7893 young myopic adults from five centers. WTW and other ocular biometrics were measured by Pentacam. The ocular biometrics included anterior corneal curvature (AK) and posterior corneal curvature (PK), central corneal thickness (CCT) and corneal volume (CV), anterior and corneal eccentricity and asphericity, anterior corneal astigmatism (ACA) and posterior corneal astigmatism, anterior chamber depth (ACD), and anterior chamber volume (ACV). The ocular biometrics were compared among eyes of different WTW quartiles. Multivariate linear regression was used to assess the linear associations between WTW and other ocular biometrics adjusting for age, gender and spherical equivalent. In eyes of different WTW quartiles, other ocular biometrics were also significantly different (all P < 0.05). After adjusting for age, gender and spherical equivalent, WTW was positively correlated to AK (ß = 0.26 to 0.29), ACA (ß = 0.13), anterior corneal asphericity (ß = 0.05), PK (ß = 0.33 to 0.34), posterior corneal asphericity (ß = 0.13), ACD (ß = 0.29), and ACV (ß = 40.69), and was negatively correlated to CCT (ß = - 6.83), CV (ß = - 0.06 to - 0.78), anterior corneal eccentricity (ß = - 0.035), and posterior corneal eccentricity (ß = - 0.14) (all P < 0.001). In conclusion, we found that in young myopic adults, larger WTW was associated with thinner corneal thickness, flatter corneal curvature, more anterior corneal toricity, less corneal eccentricity and asphericity, and broader anterior chamber. Our findings may fill in the gap of literature, and help us better understand how the anterior segment structures interact with the WTW in myopia.

Reversible gender privacy enhancement via adversarial perturbations.

The significant advancement in deep learning has made it feasible to extract gender from faces accurately. However, such unauthorized extraction would pose potential threats to individual privacy. Existing protection schemes for gender privacy have exhibited satisfactory performance. Nevertheless, they suffer from gender inference from gender-related attributes and fail to support the recovery of the original image. In this paper, we propose a novel gender privacy protection scheme that aims to enhance gender privacy while supporting reversibility. Firstly, our scheme utilizes continuously optimized adversarial perturbations to prevent gender recognition from unauthorized classifiers. Meanwhile, gender-related attributes are concealed for classifiers, which prevents the inference of gender from these attributes, thereby enhancing gender privacy. Moreover, an identity preservation constraint is added to maintain identity preservation. Secondly, reversibility is supported by a reversible image transformation, allowing the perturbations to be securely removed to losslessly recover the original face when required. Extensive experiments demonstrate the effectiveness of our scheme in gender privacy protection, identity preservation, and reversibility.

An electronic nose can identify humans by the smell of their ear.

Terrestrial mammals identify conspecifics by body odor. Dogs can also identify humans by body odor, and in some instances, humans can identify other humans by body odor as well. Despite the potential for a powerful biometric tool, smell has not been systematically used for this purpose. A question arising in the application of smell to biometrics is which bodily odor source should we measure. Breath is an obvious candidate, but the associated humidity can challenge many sensing devices. The armpit is also a candidate source, but it is often doused in cosmetics. Here, we test the hypothesis that the ear may provide an effective source for odor-based biometrics. The inside of the ear has relatively constant humidity, cosmetics are not typically applied inside the ear, and critically, ears contain cerumen, a potent source of volatiles. We used an electronic nose to identify 12 individuals within and across days, using samples from the armpit, lower back, and ear. In an identification setting where chance was 8.33% (1 of 12), we found that we could identify a person by the smell of their ear within a day at up to ~87% accuracy (~10 of 12, binomial P < 10-5), and across days at up to ~22% accuracy (~3 of 12, binomial P < 0.012). We conclude that humans can indeed be identified from the smell of their ear, but the results did not imply a consistent advantage over other bodily odor sources.

Performance counter dataset for behavioural biometric purpose.

In the pursuit of advancing research in continuous user authentication, we introduce COUNT-OS-I and COUNT-OS-II, two distinct performance counter datasets from Windows operating systems, crafted to bolster research in continuous user authentication. Encompassing data from 63 computers and users, the datasets offer rich, real-world insights for developing and evaluating authentication models. COUNT-OS-I spans 26 users in an IT department, capturing 159 attributes across diverse hardware and software environments over 26 h on average per user. COUNT-OS-II, on the other hand, encompasses 37 users with identical system configurations, recording 218 attributes per sample over a 48-hour period. Both datasets utilize pseudonymization to safeguard user identities while maintaining data integrity and statistical accuracy. The well-balanced nature of the data, confirmed by comprehensive statistical analysis, positions these datasets as reliable benchmarks for the continuous user authentication domain. Through their release, we aim to empower the development of robust, real-world applicable authentication models, contributing to enhanced system security and user trust.

Fingerprint Recognition in Forensic Scenarios.

Fingerprints are unique patterns used as biometric keys because they allow an individual to be unambiguously identified, making their application in the forensic field a common practice. The design of a system that can match the details of different images is still an open problem, especially when applied to large databases or, to real-time applications in forensic scenarios using mobile devices. Fingerprints collected at a crime scene are often manually processed to find those that are relevant to solving the crime. This work proposes an efficient methodology that can be applied in real time to reduce the manual work in crime scene investigations that consumes time and human resources. The proposed methodology includes four steps: (i) image pre-processing using oriented Gabor filters; (ii) the extraction of minutiae using a variant of the Crossing Numbers method which include a novel ROI definition through convex hull and erosion followed by replacing two or more very close minutiae with an average minutiae; (iii) the creation of a model that represents each minutia through the characteristics of a set of polygons including neighboring minutiae; (iv) the individual search of a match for each minutia in different images using metrics on the absolute and relative errors. While in the literature most methodologies look to validate the entire fingerprint model, connecting the minutiae or using minutiae triplets, we validate each minutia individually using n-vertex polygons whose vertices are neighbor minutiae that surround the reference. Our method also reveals robustness against false minutiae since several polygons are used to represent the same minutia, there is a possibility that even if there are false minutia, the true polygon is present and identified; in addition, our method is immune to rotations and translations. The results show that the proposed methodology can be applied in real time in standard hardware implementation, with images of arbitrary orientations.

A comparative analysis of multi-biometrics performance in human and action recognition using silhouette thermal-face and skeletal data.

Biometrics is a field that has been given importance in recent years and has been extensively studied. Biometrics can use physical and behavioural differences that are unique to individuals to recognize and identify them. Today, biometric information is used in many areas such as computer vision systems, entrance systems, security and recognition. In this study, a new biometrics database containing silhouette, thermal face and skeletal data based on the distance between the joints was created to be used in behavioural and physical biometrics studies. The fact that many cameras were used in previous studies increases both the processing intensity and the material cost. This study aimed to both increase the recognition performance and reduce material costs by adding thermal face data in addition to soft and behavioural biometrics with the optimum camera. The presented data set was created in accordance with both motion recognition and person identification. Various data loss scenarios and multi-biometrics approaches based on data fusion have been tried on the created data sets and the results have been given comparatively. In addition, the correlation coefficient of the motion frames method to obtain energy images from silhouette data was tested on this dataset and yielded high-accuracy results for both motion and person recognition.

How to enhance the applicability of a risk prediction model for term small-for-gestational-age neonates in clinical settings?

OBJECTIVE: To construct a simple term small-for-gestational-age (SGA) neonate prediction model that is clinically practical. METHODS: This analysis was based on the Born in Guangzhou Cohort Study (BIGCS). Mothers who had a singleton pregnancy, delivered a term neonate, and had an ultrasonography within 30 + 0 to 32 + 6 weeks of gestation were included. Term SGA was defined with customized population percentiles. Prediction models were constructed with backward selection logistic regression in a four-step approach, where model 1 contained fetal biometrics only, models 2 and 3 included maternal features and a time factor (weeks between ultrasonography and delivery), respectively; and model 4 contained all features mentioned. The prediction performance of individual models was evaluated based on area under the curve (AUC) and a calibration test was performed. RESULTS: The prevalence of SGA in the study population of 21 346 women was 11.5%. With a complete-case analysis approach, data of 19 954 women were used for model construction and validation. The AUC of the four models were 0.781, 0.793, 0.823, and 0.834, respectively, and all were well-calibrated. Model 3 consisted of fetal biometrics and corrected for time to delivery was chosen as the final model to build risk prediction graphs for clinical use. CONCLUSION: A prediction model derived from fetal biometrics in early third trimester is satisfactory to predict SGA.

Relationships between body composition, anthropometrics, and standard lipid panels in a normative population.

Introduction: More than one third of adults in the United States (US) meet the clinical criteria for a diagnosis of metabolic syndrome, but often diagnosis is challenging due to healthcare access, costs and discomfort with the process and invasiveness associated with a standard medical examination. Less invasive and more accessible approaches to collecting biometric data may have utility in identifying individuals at risk of diagnoses, such as metabolic syndrome or dyslipidemia diagnoses. Body composition is one such source of biometric data that can be non-invasively acquired in a home or community setting that may provide insight into an individual's propensity for a metabolic syndrome diagnosis. Here we investigate possible associations between body composition, anthropometrics and lipid panels in a normative population. Methods: Healthy participants visited the Lab100 clinic location at a hospital setting in New York City and engaged in a wellness visit led by a nurse practitioner. Blood was analyzed at point-of-care using the Abbott Piccolo Xpress portable diagnostic analyzer (Abbott Laboratories, IL, USA) and produced direct measures of total cholesterol (TC), high density lipoprotein (HDL-C), low density lipoprotein (LDL-C), very-low density lipoprotein (VLDL-C), and triglycerides (TG). Body composition and anthropometric data were collected using two separate pieces of equipment during the same visit (Fit3D and InBody570). Regression analysis was performed to evaluate associations between all variables, after adjusting for age, sex, race, AUDIT-C total score (alcohol use), and current smoking status. Results: Data from 199 participants were included in the analysis. After adjusting for variables, percentage body fat (%BF) and visceral fat levels were significantly associated with every laboratory lipid value, while waist-to-hip ratio also showed some significant associations. The strongest associations were detected between %BF and VLDL-C cholesterol levels (t = 4.53, p = 0.0001) and Triglyceride levels (t = 4.51, p = 0.0001). Discussion: This initial, exploratory analysis shows early feasibility in using body composition and anthropometric data, that can easily be acquired in community settings, to identify people with dyslipidemia in a normative population.