Centrifugal melt spinning of polyvinylpyrrolidone (PVP)/triacontene copolymer fibres

Polyvinylpyrrolidone/1-triacontene (PVP/TA) copolymer fibre webs produced by centrifugal melt spinning were studied to determine the influence of jet rotation speed on morphology and internal structure as well as their potential utility as adsorbent capture media for disperse dye effluents. Fibres were produced at 72 C with jet head rotation speeds from 7000 to 15,000 r min-1. The fibres were characterised by means of SEM, XRD and DSC. Adsorption behaviour was investigated by means of an isothermal bottle point adsorption study using a commercial disperse dye, Dianix AC-E. Through centrifugal spinning nanofibers and microfibers could be produced with individual fibres as fine as 200–300 nm and mean fibre diameters of ca. 1–2 lm. The PVP/TA fibres were mechanically brittle with characteristic brittle tensile fracture regions observed at the fibre ends. DSC and XRD analyses suggested that this brittleness was linked to the graft chain crystallisation where the PVP/TA was in the form of a radial brush copolymer. In this structure, the triacontene branches interlock and form small lateral crystals around an amorphous backbone. As an adsorbent, the PVP/TA fibres were found to adsorb 35.4 mg g-1 compared to a benchmark figure of 30.0 mg g-1 for a granular-activated carbon adsorbent under the same application conditions. PVP/TA is highly hydrophobic and adsorbs disperse dyes through the strong ‘‘hydrophobic bonding’’ interaction. Such fibrous assemblies may have applications in the targeted adsorption and separation of non-polar species from aqueous or polar environments

Viral Etiology of Influenza-Like Illnesses in Antananarivo, Madagascar, July 2008 to June 2009

In Madagascar, despite an influenza surveillance established since 1978, little is known about the etiology and prevalence of viruses other than influenza causing influenza-like illnesses (ILIs).From July 2008 to June 2009, we collected respiratory specimens from patients who presented ILIs symptoms in public and private clinics in Antananarivo (the capital city of Madagascar). ILIs were defined as body temperature ≥38°C and cough and at least two of the following symptoms: sore throat, rhinorrhea, headache and muscular pain, for a maximum duration of 3 days. We screened these specimens using five multiplex real time Reverse Transcription and/or Polymerase Chain Reaction assays for detection of 14 respiratory viruses. We detected respiratory viruses in 235/313 (75.1%) samples. Overall influenza virus A (27.3%) was the most common virus followed by rhinovirus (24.8%), RSV (21.2%), adenovirus (6.1%), coronavirus OC43 (6.1%), influenza virus B (3.9%), parainfluenza virus-3 (2.9%), and parainfluenza virus-1 (2.3%). Co-infections occurred in 29.4% (69/235) of infected patients and rhinovirus was the most detected virus (27.5%). Children under 5 years were more likely to have one or more detectable virus associated with their ILI. In this age group, compared to those ≥5 years, the risk of detecting more than one virus was higher (OR = 1.9), as was the risk of detecting of RSV (OR = 10.1) and adenovirus (OR = 4.7). While rhinovirus and adenovirus infections occurred year round, RSV, influenza virus A and coronavirus OC43 had defined period of circulation.In our study, we found that respiratory viruses play an important role in ILIs in the Malagasy community, particularly in children under 5 years old. These data provide a better understanding of the viral etiology of outpatients with ILI and describe for the first time importance of these viruses in different age group and their period of circulation

Real‐time alerts from AI‐enabled camera traps using the Iridium satellite network: A case‐study in Gabon, Central Africa

Efforts to preserve, protect and restore ecosystems are hindered by long delays between data collection and analysis. Threats to ecosystems can go undetected for years or decades as a result. Real-time data can help solve this issue but significant technical barriers exist. For example, automated camera traps are widely used for ecosystem monitoring but it is challenging to transmit images for real-time analysis where there is no reliable cellular or WiFi connectivity. We modified an off-the-shelf camera trap (Bushnell™) and customised existing open-source hardware to create a ‘smart’ camera trap system. Images captured by the camera trap are instantly labelled by an artificial intelligence model and an ‘alert’ containing the image label and other metadata is then delivered to the end-user within minutes over the Iridium satellite network. We present results from testing in the Netherlands, Europe, and from a pilot test in a closed-canopy forest in Gabon, Central Africa. All reference materials required to build the system are provided in open-source repositories. Results show the system can operate for a minimum of 3 months without intervention when capturing a median of 17.23 images per day. The median time-difference between image capture and receiving an alert was 7.35 min, though some outliers showed delays of 5-days or more when the system was incorrectly positioned and unable to connect to the Iridium network. We anticipate significant developments in this field and hope that the solutions presented here, and the lessons learned, can be used to inform future advances. New artificial intelligence models and the addition of other sensors such as microphones will expand the system's potential for other, real-time use cases including real-time biodiversity monitoring, wild resource management and detecting illegal human activities in protected areas

First-trimester cesarean scar pregnancy: a comparative analysis of treatment options from the international registry

Background: A cesarean scar pregnancy is an iatrogenic consequence of a previous cesarean delivery. The gestational sac implants into a niche created by the incision of the previous cesarean delivery, and this carries a substantial risk for major maternal complications. The aim of this study was to report, analyze, and compare the effectiveness and safety of different treatments options for cesarean scar pregnancies managed in the first trimester through a registry. Objective: This study aimed to evaluated the ultrasound findings, disease behavior, and management of first-trimester cesarean scar pregnancies. Study design: We created an international registry of cesarean scar pregnancy cases to study the ultrasound findings, disease behavior, and management of cesarean scar pregnancies. The Cesarean Scar Pregnancy Registry collects anonymized ultrasound and clinical data of individual patients with a cesarean scar pregnancy on a secure, digital information platform. Cases were uploaded by 31 participating centers across 19 countries. In this study, we only included live and failing cesarean scar pregnancies (with or without a positive fetal heart beat) that received active treatment (medical or surgical) before 12+6 weeks' gestation to evaluate the effectiveness and safety of the different management options. Patients managed expectantly were not included in this study and will be reported separately. Treatment was classified as successful if it led to a complete resolution of the pregnancy without the need for any additional medical interventions. Results: Between August 29, 2018, and February 28, 2023, we recorded 460 patients with cesarean scar pregnancies (281 live, 179 failing cesarean scar pregnancy) who fulfilled the inclusion criteria and were registered. A total of 270 of 460 (58.7%) patients were managed surgically, 123 of 460 (26.7%) patients underwent medical management, 46 of 460 (10%) patients underwent balloon management, and 21 of 460 (4.6%) patients received other, less frequently used treatment options. Suction evacuation was very effective with a success rate of 202 of 221 (91.5%; 95% confidence interval, 87.8-95.2), whereas systemic methotrexate was least effective with only 38 of 64 (59.4%; 95% confidence interval, 48.4-70.4) patients not requiring additional treatment. Overall, surgical treatment of cesarean scar pregnancies was successful in 236 of 258 (91.5%, 95% confidence interval, 88.4-94.5) patients and complications were observed in 24 of 258 patients (9.3%; 95% confidence interval, 6.6-11.9). Conclusion: A cesarean scar pregnancy can be managed effectively in the first trimester of pregnancy in more than 90% of cases with either suction evacuation, balloon treatment, or surgical excision. The effectiveness of all treatment options decreases with advancing gestational age, and cesarean scar pregnancies should be treated as early as possible after confirmation of the diagnosis. Local medical treatment with potassium chloride or methotrexate is less efficient and has higher rates of complications than the other treatment options. Systemic methotrexate has a substantial risk of failing and a higher complication rate and should not be recommended as first-line treatment

Skin tribology: Science friction?

The application of tribological knowledge is not just restricted to optimizing mechanical and chemical engineering problems. In fact, effective solutions to friction and wear related questions can be found in our everyday life. An important part is related to skin tribology, as the human skin is frequently one of the interacting surfaces in relative motion. People seem to solve these problems related to skin friction based upon a trial-and-error strategy and based upon on our sense for touch. The question of course rises whether or not a trained tribologist would make different choices based upon a science based strategy? In other words: Is skin friction part of the larger knowledge base that has been generated during the last decades by tribology research groups and which could be referred to as Science Friction? This paper discusses the specific nature of tribological systems that include the human skin and argues that the living nature of skin limits the use of conventional methods. Skin tribology requires in vivo, subject and anatomical location specific test methods. Current predictive friction models can only partially be applied to predict in vivo skin friction. The reason for this is found in limited understanding of the contact mechanics at the asperity level of product-skin interactions. A recently developed model gives the building blocks for enhanced understanding of friction at the micro scale. Only largely simplified power law based equations are currently available as general engineering tools. Finally, the need for friction control is illustrated by elaborating on the role of skin friction on discomfort and comfort. Surface texturing and polymer brush coatings are promising directions as they provide way and means to tailor friction in sliding contacts without the need of major changes to the produc

Assessing the digenic model in rare disorders using population sequencing data

An important fraction of patients with rare disorders remains with no clear genetic diagnostic, even after whole-exome or whole-genome sequencing, posing a difficulty in giving adequate treatment and genetic counseling. The analysis of genomic data in rare disorders mostly considers the presence of single gene variants in coding regions that follow a concrete monogenic mode of inheritance. A digenic inheritance, with variants in two functionally-related genes in the same individual, is a plausible alternative that might explain the genetic basis of the disease in some cases. In this case, digenic disease combinations should be absent or underrepresented in healthy individuals. We develop a framework to evaluate the significance of digenic combinations and test its statistical power in different scenarios. We suggest that this approach will be relevant with the advent of new sequencing efforts including hundreds of thousands of samples

Imidacloprid-Induced Impairment of Mushroom Bodies and Behavior of the Native Stingless Bee Melipona quadrifasciata anthidioides

Declines in pollinator colonies represent a worldwide concern. The widespread use of agricultural pesticides is recognized as a potential cause of these declines. Previous studies have examined the effects of neonicotinoid insecticides such as imidacloprid on pollinator colonies, but these investigations have mainly focused on adult honey bees. Native stingless bees (Hymenoptera: Apidae: Meliponinae) are key pollinators in neotropical areas and are threatened with extinction due to deforestation and pesticide use. Few studies have directly investigated the effects of pesticides on these pollinators. Furthermore, the existing impact studies did not address the issue of larval ingestion of contaminated pollen and nectar, which could potentially have dire consequences for the colony. Here, we assessed the effects of imidacloprid ingestion by stingless bee larvae on their survival, development, neuromorphology and adult walking behavior. Increasing doses of imidacloprid were added to the diet provided to individual worker larvae of the stingless bee Melipona quadrifasciata anthidioides throughout their development. Survival rates above 50% were only observed at insecticide doses lower than 0.0056 µg active ingredient (a.i.)/bee. No sublethal effect on body mass or developmental time was observed in the surviving insects, but the pesticide treatment negatively affected the development of mushroom bodies in the brain and impaired the walking behavior of newly emerged adult workers. Therefore, stingless bee larvae are particularly susceptible to imidacloprid, as it caused both high mortality and sublethal effects that impaired brain development and compromised mobility at the young adult stage. These findings demonstrate the lethal effects of imidacloprid on native stingless bees and provide evidence of novel serious sublethal effects that may compromise colony survival. The ecological and economic importance of neotropical stingless bees as pollinators, their susceptibility to insecticides and the vulnerability of their larvae to insecticide exposure emphasize the importance of studying these species

Robust ecological analysis of camera trap data labelled by a machine learning model

1. Ecological data are collected over vast geographic areas using digital sensors such as camera traps and bioacoustic recorders. Camera traps have become the standard method for surveying many terrestrial mammals and birds, but camera trap arrays often generate millions of images that are time‐consuming to label. This causes significant latency between data collection and subsequent inference, which impedes conservation at a time of ecological crisis. Machine learning algorithms have been developed to improve the speed of labelling camera trap data, but it is uncertain how the outputs of these models can be used in ecological analyses without secondary validation by a human. 2. Here, we present our approach to developing, testing and applying a machine learning model to camera trap data for the purpose of achieving fully automated ecological analyses. As a case‐study, we built a model to classify 26 Central African forest mammal and bird species (or groups). The model generalizes to new spatially and temporally independent data (n = 227 camera stations, n = 23,868 images), and outperforms humans in several respects (e.g. detecting ‘invisible’ animals). We demonstrate how ecologists can evaluate a machine learning model's precision and accuracy in an ecological context by comparing species richness, activity patterns (n = 4 species tested) and occupancy (n = 4 species tested) derived from machine learning labels with the same estimates derived from expert labels. 3. Results show that fully automated species labels can be equivalent to expert labels when calculating species richness, activity patterns (n = 4 species tested) and estimating occupancy (n = 3 of 4 species tested) in a large, completely out‐of‐sample test dataset. Simple thresholding using the Softmax values (i.e. excluding ‘uncertain’ labels) improved the model's performance when calculating activity patterns and estimating occupancy but did not improve estimates of species richness. 4. We conclude that, with adequate testing and evaluation in an ecological context, a machine learning model can generate labels for direct use in ecological analyses without the need for manual validation. We provide the user‐community with a multi‐platform, multi‐language graphical user interface that can be used to run our model offline.Additional co-authors: Cisquet Kiebou Opepa, Ross T. Pitman, Hugh S. Robinso

Translational models for vascular cognitive impairment: a review including larger species.

BACKGROUND: Disease models are useful for prospective studies of pathology, identification of molecular and cellular mechanisms, pre-clinical testing of interventions, and validation of clinical biomarkers. Here, we review animal models relevant to vascular cognitive impairment (VCI). A synopsis of each model was initially presented by expert practitioners. Synopses were refined by the authors, and subsequently by the scientific committee of a recent conference (International Conference on Vascular Dementia 2015). Only peer-reviewed sources were cited. METHODS: We included models that mimic VCI-related brain lesions (white matter hypoperfusion injury, focal ischaemia, cerebral amyloid angiopathy) or reproduce VCI risk factors (old age, hypertension, hyperhomocysteinemia, high-salt/high-fat diet) or reproduce genetic causes of VCI (CADASIL-causing Notch3 mutations). CONCLUSIONS: We concluded that (1) translational models may reflect a VCI-relevant pathological process, while not fully replicating a human disease spectrum; (2) rodent models of VCI are limited by paucity of white matter; and (3) further translational models, and improved cognitive testing instruments, are required

Real-time alerts from AI-enabled camera traps using the Iridium satellite network: a case-study in Gabon, Central Africa

Efforts to preserve, protect, and restore ecosystems are hindered by long delays between data collection and analysis. Threats to ecosystems can go undetected for years or decades as a result. Real-time data can help solve this issue but significant technical barriers exist. For example, automated camera traps are widely used for ecosystem monitoring but it is challenging to transmit images for real-time analysis where there is no reliable cellular or WiFi connectivity. Here, we present our design for a camera trap with integrated artificial intelligence that can send real-time information from anywhere in the world to end-users. We modified an off-the-shelf camera trap (Bushnell) and customised existing open-source hardware to rapidly create a 'smart' camera trap system. Images captured by the camera trap are instantly labelled by an artificial intelligence model and an 'alert' containing the image label and other metadata is then delivered to the end-user within minutes over the Iridium satellite network. We present results from testing in the Netherlands, Europe, and from a pilot test in a closed-canopy forest in Gabon, Central Africa. Results show the system can operate for a minimum of three months without intervention when capturing a median of 17.23 images per day. The median time-difference between image capture and receiving an alert was 7.35 minutes. We show that simple approaches such as excluding 'uncertain' labels and labelling consecutive series of images with the most frequent class (vote counting) can be used to improve accuracy and interpretation of alerts. We anticipate significant developments in this field over the next five years and hope that the solutions presented here, and the lessons learned, can be used to inform future advances. New artificial intelligence models and the addition of other sensors such as microphones will expand the system's potential for other, real-time use cases. Potential applications include, but are not limited to, wildlife tourism, real-time biodiversity monitoring, wild resource management and detecting illegal human activities in protected areas