Securing Internet-of-Medical-Things networks using cancellable ECG recognition.

Reinforcement of the Internet of Medical Things (IoMT) network security has become extremely significant as these networks enable both patients and healthcare providers to communicate with each other by exchanging medical signals, data, and vital reports in a safe way. To ensure the safe transmission of sensitive information, robust and secure access mechanisms are paramount. Vulnerabilities in these networks, particularly at the access points, could expose patients to significant risks. Among the possible security measures, biometric authentication is becoming a more feasible choice, with a focus on leveraging regularly-monitored biomedical signals like Electrocardiogram (ECG) signals due to their unique characteristics. A notable challenge within all biometric authentication systems is the risk of losing original biometric traits, if hackers successfully compromise the biometric template storage space. Current research endorses replacement of the original biometrics used in access control with cancellable templates. These are produced using encryption or non-invertible transformation, which improves security by enabling the biometric templates to be changed in case an unwanted access is detected. This study presents a comprehensive framework for ECG-based recognition with cancellable templates. This framework may be used for accessing IoMT networks. An innovative methodology is introduced through non-invertible modification of ECG signals using blind signal separation and lightweight encryption. The basic idea here depends on the assumption that if the ECG signal and an auxiliary audio signal for the same person are subjected to a separation algorithm, the algorithm will yield two uncorrelated components through the minimization of a correlation cost function. Hence, the obtained outputs from the separation algorithm will be distorted versions of the ECG as well as the audio signals. The distorted versions of the ECG signals can be treated with a lightweight encryption stage and used as cancellable templates. Security enhancement is achieved through the utilization of the lightweight encryption stage based on a user-specific pattern and XOR operation, thereby reducing the processing burden associated with conventional encryption methods. The proposed framework efficacy is demonstrated through its application on the ECG-ID and MIT-BIH datasets, yielding promising results. The experimental evaluation reveals an Equal Error Rate (EER) of 0.134 on the ECG-ID dataset and 0.4 on the MIT-BIH dataset, alongside an exceptionally large Area under the Receiver Operating Characteristic curve (AROC) of 99.96% for both datasets. These results underscore the framework potential in securing IoMT networks through cancellable biometrics, offering a hybrid security model that combines the strengths of non-invertible transformations and lightweight encryption.

Assessment of biometrics and stress indicators of the adult Village Weaver birds (Ploceus cucullatus) during breeding and post-breeding seasons in Jos, Nigeria.

In this era of climate change, some biological conservationists' concerns are based on seasonal studies that highlight how wild birds' physiological fitness are interconnected with the immediate environment to avoid population decline. We investigated how seasonal biometrics correlated to stress parameters of the adult Village Weavers (Ploceus cucullatus) during breeding and post-breeding seasons of the Weaver birds in Amurum Forest Reserve. Specifically, we explored the following objectives: (i) the seasonal number of birds captured; (ii) whether seasonal baseline corticosterone (CORT), packed cell volume (PCV), and heterophil to lymphocytes ratio (H:L) were sex-dependent; (iii) whether H:L ratio varied with baseline (CORT); (iv) whether phenotypic condition (post-breeding moult) and brood patch varied with baseline (CORT) and H:L ratio; and (v) how body biometrics co-varied birds' seasonal baseline (CORT), (PCV) and (H:L) ratio. Trapping of birds (May-November) coincided with breeding and post-breeding seasons. The birds (n = 53 males, 39 females) were ringed, morphologically assessed (body mass, wing length, moult, brood patch) and blood collected from their brachial vein was used to assess CORT, PCV and H:L ratio. Although our results indicated more male birds trapped during breeding, the multiple analyses of variance (MANOVA) indicated that the seasonal temperature of the trapping sites correlated (P < 0.05) significantly to baseline (CORT). The general linear mixed model analyses (GLMMs) indicated that the baseline (CORT) also correlated significantly to H:L ratio of the male and female birds. However, PCV correlated significantly to body size of the birds (wing length) and not body mass. Haematological parameters such as the baseline CORT and the H:L ratio as indicators of stress in wild birds. Hence, there is the possibility that the Village Weaver birds suffered from seasonally induced stress under the constrained effect of environmental temperature. Hence, future studies should investigate whether the effect observed is also attributable to other passerine species.

Enhancing Resilience in Biometric Research: Generation of 3D Synthetic Face Data Using Advanced 3D Character Creation Techniques from High-Fidelity Video Games and Animation.

Biometric authentication plays a vital role in various everyday applications with increasing demands for reliability and security. However, the use of real biometric data for research raises privacy concerns and data scarcity issues. A promising approach using synthetic biometric data to address the resulting unbalanced representation and bias, as well as the limited availability of diverse datasets for the development and evaluation of biometric systems, has emerged. Methods for a parameterized generation of highly realistic synthetic data are emerging and the necessary quality metrics to prove that synthetic data can compare to real data are open research tasks. The generation of 3D synthetic face data using game engines' capabilities of generating varied realistic virtual characters is explored as a possible alternative for generating synthetic face data while maintaining reproducibility and ground truth, as opposed to other creation methods. While synthetic data offer several benefits, including improved resilience against data privacy concerns, the limitations and challenges associated with their usage are addressed. Our work shows concurrent behavior in comparing semi-synthetic data as a digital representation of a real identity with their real datasets. Despite slight asymmetrical performance in comparison with a larger database of real samples, a promising performance in face data authentication is shown, which lays the foundation for further investigations with digital avatars and the creation and analysis of fully synthetic data. Future directions for improving synthetic biometric data generation and their impact on advancing biometrics research are discussed.

FetSAM: Advanced Segmentation Techniques for Fetal Head Biometrics in Ultrasound Imagery.

Goal: FetSAM represents a cutting-edge deep learning model aimed at revolutionizing fetal head ultrasound segmentation, thereby elevating prenatal diagnostic precision. Methods: Utilizing a comprehensive dataset-the largest to date for fetal head metrics-FetSAM incorporates prompt-based learning. It distinguishes itself with a dual loss mechanism, combining Weighted DiceLoss and Weighted Lovasz Loss, optimized through AdamW and underscored by class weight adjustments for better segmentation balance. Performance benchmarks against prominent models such as U-Net, DeepLabV3, and Segformer highlight its efficacy. Results: FetSAM delivers unparalleled segmentation accuracy, demonstrated by a DSC of 0.90117, HD of 1.86484, and ASD of 0.46645. Conclusion: FetSAM sets a new benchmark in AI-enhanced prenatal ultrasound analysis, providing a robust, precise tool for clinical applications and pushing the envelope of prenatal care with its groundbreaking dataset and segmentation capabilities.

Wearable Biosensor Technology in Education: A Systematic Review.

Wearable Biosensor Technology (WBT) has emerged as a transformative tool in the educational system over the past decade. This systematic review encompasses a comprehensive analysis of WBT utilization in educational settings over a 10-year span (2012-2022), highlighting the evolution of this field to address challenges in education by integrating technology to solve specific educational challenges, such as enhancing student engagement, monitoring stress and cognitive load, improving learning experiences, and providing real-time feedback for both students and educators. By exploring these aspects, this review sheds light on the potential implications of WBT on the future of learning. A rigorous and systematic search of major academic databases, including Google Scholar and Scopus, was conducted in accordance with the PRISMA guidelines. Relevant studies were selected based on predefined inclusion and exclusion criteria. The articles selected were assessed for methodological quality and bias using established tools. The process of data extraction and synthesis followed a structured framework. Key findings include the shift from theoretical exploration to practical implementation, with EEG being the predominant measurement, aiming to explore mental states, physiological constructs, and teaching effectiveness. Wearable biosensors are significantly impacting the educational field, serving as an important resource for educators and a tool for students. Their application has the potential to transform and optimize academic practices through sensors that capture biometric data, enabling the implementation of metrics and models to understand the development and performance of students and professors in an academic environment, as well as to gain insights into the learning process.

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).

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.

Customizable Presentation Attack Detection for Improved Resilience of Biometric Applications Using Near-Infrared Skin Detection.

Due to their user-friendliness and reliability, biometric systems have taken a central role in everyday digital identity management for all kinds of private, financial and governmental applications with increasing security requirements. A central security aspect of unsupervised biometric authentication systems is the presentation attack detection (PAD) mechanism, which defines the robustness to fake or altered biometric features. Artifacts like photos, artificial fingers, face masks and fake iris contact lenses are a general security threat for all biometric modalities. The Biometric Evaluation Center of the Institute of Safety and Security Research (ISF) at the University of Applied Sciences Bonn-Rhein-Sieg has specialized in the development of a near-infrared (NIR)-based contact-less detection technology that can distinguish between human skin and most artifact materials. This technology is highly adaptable and has already been successfully integrated into fingerprint scanners, face recognition devices and hand vein scanners. In this work, we introduce a cutting-edge, miniaturized near-infrared presentation attack detection (NIR-PAD) device. It includes an innovative signal processing chain and an integrated distance measurement feature to boost both reliability and resilience. We detail the device's modular configuration and conceptual decisions, highlighting its suitability as a versatile platform for sensor fusion and seamless integration into future biometric systems. This paper elucidates the technological foundations and conceptual framework of the NIR-PAD reference platform, alongside an exploration of its potential applications and prospective enhancements.

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.

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.

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.

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.

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.

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.

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.

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.

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.