Biomolecular analyses enable new insights into ancient Egyptian embalming.

The ability of the ancient Egyptians to preserve the human body through embalming has not only fascinated people since antiquity, but also has always raised the question of how this outstanding chemical and ritual process was practically achieved. Here we integrate archaeological, philological and organic residue analyses, shedding new light on the practice and economy of embalming in ancient Egypt. We analysed the organic contents of 31 ceramic vessels recovered from a 26th Dynasty embalming workshop at Saqqara1,2. These vessels were labelled according to their content and/or use, enabling us to correlate organic substances with their Egyptian names and specific embalming practices. We identified specific mixtures of fragrant or antiseptic oils, tars and resins that were used to embalm the head and treat the wrappings using gas chromatography-mass spectrometry analyses. Our study of the Saqqara workshop extends interpretations from a micro-level analysis highlighting the socio-economic status of a tomb owner3-7 to macro-level interpretations of the society. The identification of non-local organic substances enables the reconstruction of trade networks that provided ancient Egyptian embalmers with the substances required for mummification. This extensive demand for foreign products promoted trade both within the Mediterranean8-10 (for example, Pistacia and conifer by-products) and with tropical forest regions (for example, dammar and elemi). Additionally, we show that at Saqqara, antiu and sefet-well known from ancient texts and usually translated as 'myrrh' or 'incense'11-13 and 'a sacred oil'13,14-refer to a coniferous oils-or-tars-based mixture and an unguent with plant additives, respectively.

Effects of prey refuge and predator cooperation on a predator-prey system.

We develop and investigate a discrete-time predator-prey model with cooperative hunting among predators and a spatial prey refuge. The system can exhibit two positive equilibria if the magnitude of cooperation is large and the maximal reproduction number of predators is less than one. In such a scenario, the predator population may exhibit a strong Allee effect, and therefore the predator may survive if its density is above the threshold. When the positive equilibrium is unique, we prove that hunting cooperation can destabilize the equilibrium through a Neimark-Sacker bifurcation. Numerical findings indicate that a high degree of predator hunting cooperation can help rescue the predator population if the proportion of prey refuge is large, while hunting cooperation becomes destabilizing when the proportion of refuge is small. Despite hunting cooperation's destabilizing effect, it can facilitate predator persistence, particularly with a large proportion of prey refuge. Furthermore, there exists a wide parameter space where the predator-prey interaction may exhibit chaotic behaviour.

Deep Learning-Based Analysis of Glottal Attack and Offset Times in Adductor Laryngeal Dystonia.

OBJECTIVE: Diagnosis of adductor laryngeal dystonia (AdLD) is challenging as it mimics voice features of other voice disorders. This could lead to misdiagnosis (or delayed diagnosis) and ineffective treatments of AdLD. This paper develops automated measurements of glottal attack time (GAT) and glottal offset time (GOT) from high-speed videoendoscopy (HSV) in connected speech as objective measures that can potentially facilitate the diagnosis of this disorder in the future. METHODS: HSV data were recorded from vocally normal adults and patients with AdLD during the reading of the "Rainbow Passage" and six CAPE-V (Consensus Auditory-Perceptual Evaluation of Voice) sentences. A deep learning framework was designed and trained to segment the glottal area and detect the vocal fold edges in the HSV dataset. This automated framework allowed us to automatically measure and quantify the GATs and GOTs for the participants. Accordingly, a comparison was held between the obtained measurements among vocally normal speakers and those with AdLD. RESULTS: The automated framework was successfully developed and able to accurately segment the glottal area/edges. The precise automated measurements of GAT and GOT revealed minor, nonsignificant differences compared to the results of manual analysis-showing a strong correlation between the measures by the automated and manual methods. The results showed significant differences in the GAT values between the vocally normal subjects and AdLD patients, with larger variability in both the GAT and GOT measures in the AdLD group. CONCLUSIONS: The developed automated approach for GAT and GOT measurement can be valuable in clinical practice. These quantitative measurements can be used as meaningful biomarkers of the impaired vocal function in AdLD and help its differential diagnosis in the future.

Spatial Segmentation for Laryngeal High-Speed Videoendoscopy in Connected Speech.

OBJECTIVE: This study proposes a new computational framework for automated spatial segmentation of the vocal fold edges in high-speed videoendoscopy (HSV) data during connected speech. This spatio-temporal analytic representation of the vocal folds enables the HSV-based measurement of the glottal area waveform and other vibratory characteristics in the context of running speech. METHODS: HSV data were obtained from a vocally normal adult during production of the "Rainbow Passage." An algorithm based on an active contour modeling approach was developed for the analysis of HSV data. The algorithm was applied on a series of HSV kymograms at different intersections of the vocal folds to detect the edges of the vibrating vocal folds across the frames. This edge detection method follows a set of deformation rules for the active contours to capture the edges of the vocal folds through an energy optimization procedure. The detected edges in the kymograms were then registered back to the HSV frames. Subsequently, the glottal area waveform was calculated based on the area of the glottis enclosed by the vocal fold edges in each frame. RESULTS: The developed algorithm successfully captured the edges of the vocal folds in the HSV kymograms. This method led to an automated measurement of the glottal area waveform from the HSV frames during vocalizations in connected speech. CONCLUSION: The proposed algorithm serves as an automated method for spatial segmentation of the vocal folds in HSV data in connected speech. This study is one of the initial steps toward developing HSV-based measures to study vocal fold vibratory characteristics and voice production mechanisms in norm and disorder in the context of connected speech.

Detection of Vocal Fold Image Obstructions in High-Speed Videoendoscopy During Connected Speech in Adductor Spasmodic Dysphonia: A Convolutional Neural Networks Approach.

OBJECTIVE: Adductor spasmodic dysphonia (AdSD) is a neurogenic voice disorder, affecting the intrinsic laryngeal muscle control. AdSD leads to involuntary laryngeal spasms and only reveals during connected speech. Laryngeal high-speed videoendoscopy (HSV) coupled with a flexible fiberoptic endoscope provides a unique opportunity to study voice production and visualize the vocal fold vibrations in AdSD during speech. The goal of this study is to automatically detect instances during which the image of the vocal folds is optically obstructed in HSV recordings obtained during connected speech. METHODS: HSV data were recorded from vocally normal adults and patients with AdSD during reading of the "Rainbow Passage", six CAPE-V sentences, and production of the vowel /i/. A convolutional neural network was developed and trained as a classifier to detect obstructed/unobstructed vocal folds in HSV frames. Manually labelled data were used for training, validating, and testing of the network. Moreover, a comprehensive robustness evaluation was conducted to compare the performance of the developed classifier and visual analysis of HSV data. RESULTS: The developed convolutional neural network was able to automatically detect the vocal fold obstructions in HSV data in vocally normal participants and AdSD patients. The trained network was tested successfully and showed an overall classification accuracy of 94.18% on the testing dataset. The robustness evaluation showed an average overall accuracy of 94.81% on a massive number of HSV frames demonstrating the high robustness of the introduced technique while keeping a high level of accuracy. CONCLUSIONS: The proposed approach can be used for efficient analysis of HSV data to study laryngeal maneuvers in patients with AdSD during connected speech. Additionally, this method will facilitate development of vocal fold vibratory measures for HSV frames with an unobstructed view of the vocal folds. Indicating parts of connected speech that provide an unobstructed view of the vocal folds can be used for developing optimal passages for precise HSV examination during connected speech and subject-specific clinical voice assessment protocols.

Deep-Learning-Based Representation of Vocal Fold Dynamics in Adductor Spasmodic Dysphonia during Connected Speech in High-Speed Videoendoscopy.

OBJECTIVE: Adductor spasmodic dysphonia (AdSD) is a neurogenic dystonia, which causes spasms of the laryngeal muscles. This disorder mainly affects production of connected speech. To understand how AdSD affects vocal fold (VF) movements and hence, the speech signal, it is necessary to study VF kinematics during the running speech. This paper introduces an automated method for analysis of VF vibrations in AdSD using laryngeal high-speed videoendoscopy (HSV) in running speech. METHODS: A monochrome HSV system was used to obtain video recordings from vocally normal individuals and AdSD patients during production of the six CAPE-V sentences and the "Rainbow Passage." A deep neural network was designed based on the UNet architecture. The network was developed for glottal area segmentation in HSV data providing a tool for quantitative analysis of VF vibrations in both norm and AdSD. The network was trained and validated using the manually labeled HSV frames. After training the network, the segmentation quality was quantitatively evaluated against visual analysis results of a test dataset including segregated HSV frames and a short sequence of VF vibrations in consecutive frames. RESULTS: The developed convolutional network was successfully trained and demonstrated an accurate segmentation on the testing dataset with a mean Intersection over Union (IoU) of 0.81 and a mean Boundary-F1 score of 0.93. Moreover, the visual assessment of the automated technique showed an accurate detection of the glottal edges/area in the HSV data even with challenging image quality and excessive laryngeal maneuvers of AdSD patients during the running speech. CONCLUSION: The introduced automated approach provides an accurate representation of the glottal edges/area during connected speech in HSV data for norm and AdSD patients. This method facilitates the development of HSV-based measures to quantify VF dynamics in AdSD. Using HSV to automatically analyze VF vibrations in AdSD can allow for understanding AdSD vocal mechanisms and characteristics.

A Deep Learning Approach for Quantifying Vocal Fold Dynamics During Connected Speech Using Laryngeal High-Speed Videoendoscopy.

PURPOSE: Voice disorders are best assessed by examining vocal fold dynamics in connected speech. This can be achieved using flexible laryngeal high-speed videoendoscopy (HSV), which enables us to study vocal fold mechanics with high temporal details. Analysis of vocal fold vibration using HSV requires accurate segmentation of the vocal fold edges. This article presents an automated deep-learning scheme to segment the glottal area in HSV from which the glottal edges are derived during connected speech. METHOD: Using a custom-built HSV system, data were obtained from a vocally healthy participant reciting the "Rainbow Passage." A deep neural network was designed for glottal area segmentation in the HSV data. A recently introduced hybrid approach by the authors was utilized as an automated labeling tool to train the network on a set of HSV frames, where the glottis region was automatically annotated during vocal fold vibrations. The network was then tested against manually segmented frames using different metrics, intersection over union (IoU), and Boundary F1 (BF) score, and its performance was assessed on various phonatory events on the HSV sequence. RESULTS: The designed network was successfully trained using the hybrid approach, without the need for manual labeling, and tested on the manually labeled data. The performance metrics showed a mean IoU of 0.82 and a mean BF score of 0.96. In addition, the evaluation assessment of the network's performance demonstrated an accurate segmentation of the glottal edges/area even during complex nonstationary phonatory events and when vocal folds were not vibrating, thus overcoming the limitations of the previous hybrid approach that could only be applied to the vibrating vocal folds. CONCLUSIONS: The introduced automated scheme guarantees accurate glottis representation in challenging color HSV data with lower image quality and excessive laryngeal maneuvers during all instances of connected speech. This facilitates the future development of HSV-based measures to assess the running vibratory characteristics of the vocal folds in speakers with and without voice disorder. SUPPLEMENTAL MATERIAL: https://doi.org/10.23641/asha.19798864.

Egyptian Consensus on the Role of Lung Ultrasonography During the Coronavirus Disease 2019 Pandemic.

Background & Aims: Coronavirus disease 2019 (COVID-19) is a global health problem, presenting with symptoms ranging from mild nonspecific symptoms to serious pneumonia. Early screening techniques are essential in the diagnosis and assessment of disease progression. This consensus was designed to clarify the role of lung ultrasonography versus other imaging modalities in the COVID-19 pandemic. Methods: A multidisciplinary team consisting of experts from different specialties (ie, pulmonary diseases, infectious diseases, intensive care unit and emergency medicine, radiology, and public health) who deal with patients with COVID-19 from different geographical areas was classified into task groups to review the literatures from different databases and generate 10 statements. The final consensus statements were based on expert physically panelists' discussion held in Cairo July 2021 followed by electric voting for each statement. Results: The statements were electronically voted to be either "agree," "not agree," or "neutral." For a statement to be accepted to the consensus, it should have 80% agreement. Conclusion: Lung ultrasonography is a rapid and useful tool, which can be performed at bedside and overcomes computed tomography limitations, for screening and monitoring patients with COVID-19 with an accepted accuracy rate.

A Hybrid Machine-Learning-Based Method for Analytic Representation of the Vocal Fold Edges during Connected Speech.

Investigating the phonatory processes in connected speech from high-speed videoendoscopy (HSV) demands the accurate detection of the vocal fold edges during vibration. The present paper proposes a new spatio-temporal technique to automatically segment vocal fold edges in HSV data during running speech. The HSV data were recorded from a vocally normal adult during a reading of the "Rainbow Passage." The introduced technique was based on an unsupervised machine-learning (ML) approach combined with an active contour modeling (ACM) technique (also known as a hybrid approach). The hybrid method was implemented to capture the edges of vocal folds on different HSV kymograms, extracted at various cross-sections of vocal folds during vibration. The k-means clustering method, an ML approach, was first applied to cluster the kymograms to identify the clustered glottal area and consequently provided an initialized contour for the ACM. The ACM algorithm was then used to precisely detect the glottal edges of the vibrating vocal folds. The developed algorithm was able to accurately track the vocal fold edges across frames with low computational cost and high robustness against image noise. This algorithm offers a fully automated tool for analyzing the vibratory features of vocal folds in connected speech.

Estimation of ultrasound reference values for the ulnar nerve fascicular number and cross-sectional area in young males: A cross-sectional study.

The objective of this study is to estimate the reference values for the number of fascicles and cross-sectional area (CSA) of the ulnar nerve at a single predetermined site by ultrasound in healthy young adult males.The demographic and physical characteristics of 50 adult male volunteers were evaluated and recorded. The subjects were positioned supine with the elbow flexed at 90° and the palm of the hand placed on a hard surface. The ulnar nerve was scanned bilaterally 1 cm proximal to the medial epicondyle in projection of the cubital tunnel. The number of fascicles and mean CSA of the ulnar nerve were identified. In addition, the side-to-side differences of the estimated reference values and their correlations with the age, weight, height, and body mass index (BMI) were evaluated.The mean fascicular number was 5.66 ±â€Š1.48, the mean ultrasound-estimated CSA of the ulnar nerve was 6.54 ±â€Š1.67 mm and both sides were comparable in the mean CSA and fascicular number (6.43 ±â€Š1.80 mm and 5.88 vs 6.64 ±â€Š1.55 mm and 5.44, for right and left side, respectively). No significant correlations were observed between CSA and fascicles number and age, weight, height, or BMI of study subjects.The reference values for the number of fascicles number and the CSA of the ulnar nerve at a single predetermined site were identified. These values could be used for the sonographic diagnosis and follow-up of the ulnar nerve lesions.

High prevalence of iron deficiency anemia in infants attending a well-baby clinic in northwestern Saudi Arabia.

OBJECTIVES: To determine the prevalence of iron deficiency anemia (IDA) in infants aged 6-24 months attending the well-baby clinic in primary health care centers (PHCCs).    METHODS: This cross-sectional epidemiological study was conducted in the Northwestern region of Saudi Arabia from April 2013 to January 2014 in 5 randomly selected PHCCs. The sample size comprised 500 infants, with 100 infants screened from each PHCC. Blood samples were obtained for estimation of hemoglobin and serum ferritin levels.  RESULTS: Out of 500 infants, 246 (49%) cases had IDA with a mean age of 15.4 ± 6.5 months,  with 130 (53%) males, and 116 (47%) females (p=0367). Out of 274 Saudi infants, 126 (51%) cases were diagnosed as IDA.   CONCLUSION: Iron deficiency anemia is very common in Saudi infants aged 6-24 months. A national program directed for primary prevention and early discovery of IDA in Saudi infants is recommended at PHCCs  system. Iron supplementation is to be given at early infancy with universal screening of hemoglobin and ferritin estimation to all infants at 12 months of age.