Interspecies Knowledge Transfer for Facial Keypoint Detection

We present a method for localizing facial keypoints on animals by transferring knowledge gained from human faces. Instead of directly finetuning a network trained to detect keypoints on human faces to animal faces (which is sub-optimal since human and animal faces can look quite different), we propose to first adapt the animal images to the pre-trained human detection network by correcting for the differences in animal and human face shape. We first find the nearest human neighbors for each animal image using an unsupervised shape matching method. We use these matches to train a thin plate spline warping network to warp each animal face to look more human-like. The warping network is then jointly finetuned with a pre-trained human facial keypoint detection network using an animal dataset. We demonstrate state-of-the-art results on both horse and sheep facial keypoint detection, and significant improvement over simple finetuning, especially when training data is scarce. Additionally, we present a new dataset with 3717 images with horse face and facial keypoint annotations.Comment: CVPR 2017 Camera Read

Indications of enucleations and eviscerations performed at a tertiary eye care unit: a case series.

Abstract The retrospective study was planned to look at the indications of eye enucleations and eviscerations, and comprised all such procedures done at the Aga Khan University Hospital, Karachi, from January 2006 to December 2015. Data was collected regarding age at the time of procedure, gender, procedure type, indication for enucleation or evisceration, type of implant if used and complications. Of the 66 eyes of 65 individuals, 47 (71.2%) underwent evisceration and 19 (28.8%) enucleation. The mean age at the time of the procedures was 37.86±23.09 years. The indication for enucleation or evisceration were keratitis 14(21.2%), introcular tumours 13(19.7%), trauma 10(15.2%), endophthalmitis 10(15.2%), cosmesis 10(15.2%) and painful blind eye 9(13.6%). Only 5(7.5%) procedures had postoperative complications such as wound dehiscence, infected socket, loose suture, or stich abscess. Most of causes of enucleation and evisceration in this case series could have prevented through the application of evidence-based safety practices

Effect of transportation and social isolation on facial expressions of healthy horses

Horses have the ability to generate a remarkable repertoire of facial expressions, some of which have been linked to the affective component of pain. This study describes the facial expressions in healthy horses free of pain before and during transportation and social isolation, which are putatively stressful but ordinary management procedures. Transportation was performed in 28 horses by subjecting them to short-term road transport in a horse trailer. A subgroup (n = 10) of these horses was also subjected to short-term social isolation. During all procedures, a body-mounted, remote-controlled heart rate monitor provided continuous heart rate measurements. The horses' heads were video-recorded during the interventions. An exhaustive dataset was generated from the selected video clips of all possible facial action units and action descriptors, time of emergency, duration, and frequency according to the Equine Facial Action Coding System (EquiFACS). Heart rate increased during both interventions (p<0.01), confirming that they caused disruption in sympato-vagal balance. Using the current method for ascribing certain action units (AUs) to specific emotional states in humans and a novel data-driven co-occurrence method, the following facial traits were observed during both interventions: eye white increase (p<0.001), nostril dilator (p<0.001), upper eyelid raiser (p<0.001), inner brow raiser (p = 0.042), tongue show (p<0.001). Increases in 'ear flicker' (p<0.001) and blink frequency (p<0.001) were also seen. These facial actions were used to train a machine-learning classifier to discriminate between the high-arousal interventions and calm horses, which achieved at most 79% accuracy. Most facial features identified correspond well with previous findings on behaviors of stressed horses, for example flared nostrils, repetitive mouth behaviors, increased eye white, tongue show, and ear movements. Several features identified in this study of pain-free horses, such as dilated nostrils, eye white increase, and inner brow raiser, are used as indicators of pain in some face-based pain assessment tools. In order to increase performance parameters in pain assessment tools, the relations between facial expressions of stress and pain should be studied further

Equine Facial Action Coding System for determination of pain-related facial responses in videos of horses

During the last decade, a number of pain assessment tools based on facial expressions have been developed for horses. While all tools focus on moveable facial muscles related to the ears, eyes, nostrils, lips, and chin, results are difficult to compare due to differences in the research conditions, descriptions and methodologies. We used a Facial Action Coding System (FACS) modified for horses (EquiFACS) to code and analyse video recordings of acute short-term experimental pain (n = 6) and clinical cases expected to be in pain or without pain (n = 21). Statistical methods for analyses were a frequency based method adapted from human FACS approaches, and a novel method based on co-occurrence of facial actions in time slots of varying lengths. We describe for the first time changes in facial expressions using EquiFACS in video of horses with pain. The ear rotator (EAD104), nostril dilation (AD38) and lower face behaviours, particularly chin raiser (AU17), were found to be important pain indicators. The inner brow raiser (AU101) and eye white increase (AD1) had less consistent results across experimental and clinical data. Frequency statistics identified AUs, EADs and ADs that corresponded well to anatomical regions and facial expressions identified by previous horse pain research. The co-occurrence based method additionally identified lower face behaviors that were pain specific, but not frequent, and showed better generalization between experimental and clinical data. In particular, chewing (AD81) was found to be indicative of pain. Lastly, we identified increased frequency of half blink (AU47) as a new indicator of pain in the horses of this study

Sharing Pain: Using Pain Domain Transfer for Video Recognition of Low Grade Orthopedic Pain in Horses

Orthopedic disorders are common among horses, often leading to euthanasia, which often could have been avoided with earlier detection. These conditions often create varying degrees of subtle long-term pain. It is challenging to train a visual pain recognition method with video data depicting such pain, since the resulting pain behavior also is subtle, sparsely appearing, and varying, making it challenging for even an expert human labeller to provide accurate ground-truth for the data. We show that a model trained solely on a dataset of horses with acute experimental pain (where labeling is less ambiguous) can aid recognition of the more subtle displays of orthopedic pain. Moreover, we present a human expert baseline for the problem, as well as an extensive empirical study of various domain transfer methods and of what is detected by the pain recognition method trained on clean experimental pain in the orthopedic dataset. Finally, this is accompanied with a discussion around the challenges posed by real-world animal behavior datasets and how best practices can be established for similar fine-grained action recognition tasks. Our code is available at https://github.com/sofiabroome/painface-recognition

Evaluation of serum ferritin for prediction of severity and mortality in COVID-19- A cross sectional study

Background: Ferritin even though widely recognized as a representative of total body iron stores, its prognostic utility is linked with COVID-19. This study was aimed at evaluation of the association of ferritin with severity in Coronavirus disease 2019 (COVID-19), hospitalized patients and to test the hypothesis that it is an independent predictor of mortality.Material and methods: This study was conducted at Chemical Pathology, Department of Pathology and Laboratory Medicine, Aga Khan University (AKU), Karachi. Medical records of all in-patients including both genders, and all age groups with documented COVID-19 from 1st March to 10th August 2020 were reviewed. The subjects were divided into two categories severe and non-severe COVID-19; and survivors and non-survivors. The details were recorded on a pre-structured performa. Between-group differences were tested using the Mann-Whitney\u27s U-test. The receiver operating characteristic curve was plotted for ferritin with severity and mortality. A binary logistic regression was used to identify variables independently associated with mortality. The data was analyzed using Statistical Package for the Social Sciences (SPSS).Results: A total of 336 in patients were reviewed as declared COVID-19 positive during the study duration, and 157 were included in the final analysis including 108 males and 49 females. Statistically significant difference in ferritin was found in the two categories based on severity and mortality. Binary logistic regression showed ferritin to be an independent predictor of all-cause mortality supplemented with an AUC of 0.69 on ROC analysis.Conclusions: Serum ferritin concentration is a promising predictor of mortality in COVID-19 cases

Intersecting Disadvantages for Married Adolescents: Life After Marriage Pre- and Post-COVID-19 in Contexts of Displacement.

PURPOSE: Although there is a growing evidence base on the drivers of child marriage, comparatively little is known about the experiences of married girls in refugee settings and how their development trajectories diverge from those of their nonmarried peers, particularly in the context of the COVID-19 pandemic. Drawing on cross-national panel data from Bangladesh and Jordan, this article explores diversity in child marriage experiences in contexts affected by forced displacement, highlighting how married girls\u27 well-being differs from that of their unmarried peers, and how COVID-19 has reinforced these differences. METHODS: We analyzed longitudinal survey data-collected pre- and post-COVID-19-from the Gender and Adolescence: Global Evidence study with 293 ever-married and 1,102 never-married adolescent girls. Multivariate regression analysis assessed the well-being of married and unmarried girls across contexts and refugee status, both prior to and during the COVID-19 pandemic. These quantitative data are complemented by in-depth qualitative data from adolescents (n = 112), and key informant interviews with service providers and community leaders (n = 62). RESULTS: Our findings highlight that married girls in contexts affected by displacement are disadvantaged in multiple ways, but that the patterning of disadvantage varies across contexts, and that marriage can also have protective effects in certain contexts. The COVID-19 pandemic has, however, served to exacerbate existing inequalities in all contexts. DISCUSSION: Although child marriage prevention efforts remain critical, there is also an urgent need for programming that targets married girls in refugee and host communities to mitigate negative outcomes among this vulnerable group

Towards Automatic Visual Recognition of Horse Pain

Pain is a manifestation of disease and decreases welfare. Early detection of animal pain can not only improve animal well being by enabling early diagnosis and treatment of disease, but can also reduce healthcare costs for livestock owners. A video based animal pain detection system can provide a reliable and scalable means to unobtrusively monitor animals round the clock for signs of pain behavior, and enable the timely provision of medical treatment and pain management. This thesis presents the first steps towards creating such an automated visual system for animal pain detection. In particular, it presents computer vision techniques for pain recognition in the horse, and addresses the challenges of reliably determining pain when working with small scale and sparsely annotated datasets. We first present two methods that address challenges in veterinary research on equine pain detection by transferring techniques from computer vision and graph theory. We present a unifying description of the equine pain face by use of the biologically grounded language of Equine Facial Action Coding System (EquiFACS) to identify facial changes most correlated with pain in horses. In addition, we develop a novel graph based method that deduces the components of pain expression in horses by inspecting correlations between facial changes. Following, we develop an automatic and easy to use application for finding horse faces in videos that allows veterinary researchers to quickly identify time segments suitable for facial expression annotation from long videos. The application uses a deep convolution network for fast and reliable detection of horse faces, saves veterinary researchers hours in valuable annotation time, enables blinding during the data selection process, and has been instrumental in the description of both the pain and the stress face in horses in terms of EquiFACS. Beyond veterinary science, we present novel computer vision methods for automatic horse behavior understanding that use small and sparsely annotated datasets. We present a means for identifying facial keypoints in animal faces that enables accurate detection of horse facial parts without requiring large amounts of training data by transferring knowledge from large, readily available human facial keypoint datasets via face structure warping. Apart from facial keypoints, collecting detailed horse pain annotation in videos is cumbersome, and unscalable. We address this problem by developing two different methods that are capable of identifying pain behavior with crude -- weak -- video level training labels. First, we present a graph convolution based method for action localization that, by the explicit use of similarity relationships between time segments in videos, temporally localizes the extent of actions in videos despite not being trained with any such annotation. Finally, we present a method for pain detection in horses that uses horse pose cues, learned via multi-view surveillance footage, in a weakly supervised setting to deduce the pain status of the horse. The method identifies pain features that align well with pain scales currently used by veterinary practitioners, with impressive accuracy

Correction to: ‘Do Poor People’s Dreams Ever Come True?’ Educational Aspirations and Lived Realities in Urban Slums in Dhaka, Bangladesh (The European Journal of Development Research, (2021), 33, 5, (1409-1428), 10.1057/s41287-021-00439-4)

Due to an unfortunate oversight an author name has been misspelt. It should read: Jennifer Seager. The original article has been corrected