Participatory epidemiological study on the burden of rabies in animals and humans in three districts of Buno Bedele Zone, West Ethiopia

Rabies is one of the priority zoonotic diseases and a major public health challenge in Ethiopia. Dog plays an important role in the transmission of the disease to humans. With this point in mind, this study was conducted in selected districts of Buno Bedele Zone namely Bedele, Gechi, and Dabo Hana districts, Western Oromia regional state from December 2019 to April 2020. The objective of the study was to know the status and burden of rabies in the community using the participatory approach and retrospective record assessment from animal and human health facilities. Twelve interview meetings were conducted in 12 Kebeles, which involved 156 informants. In this survey authors learned that dogs were the species most affected by rabies followed by cattle, human, cat, equine, and shoats. Accordingly, rabies in dog had an average score of 64 out of 100 with a range of 50-80. Besides, rabies outbreak was frequently noted between June to October in the study areas. Slaughter and sharing of the meat for household consumption was the most common practice taken to salvage bitten cattle. As per the available record in the study area, the estimated rabies cases incidence was 1.75 bovines, 18 dogs, 2.37 equines, 2.28 cats, and 0.37 shoats per 10,000 animals annually. The annual average post-exposure rabies vaccinations records were 75, 39, and 63 in Bedele, Dabo Hana, and Gechi districts, respectively. On the other hand, the average annual rabies death in humans was 2.2, 1.4, and 1.8 in Bedele, Dabo Hana, and Gechi districts, respectively. In general, this study shows that rabies is a disease that worth serious attention in the study areas

Examining a possible association between human papilloma virus (HPV) vaccination and migraine: results of a cohort study in the Netherlands

Since the introduction of the bivalent human papilloma virus (HPV) vaccine in the Netherlands, migraine has been reported as a notable event in the passive safety surveillance system. Research on the association between HPV vaccination and migraine is needed. Therefore, potential migraine cases in 2008–2010 were selected from a group of general practitioners and linked to the vaccination registry. Data were analysed in three ways: (i) incidences of migraine postvaccination (2009/2010) were compared to pre-vaccination incidences (2008); (ii) in a cohort, incidence rates of migraine in vaccinated and unvaccinated girls were compared and (iii) in a self-controlled case series analysis, the relative incidence of migraine in potentially high-risk periods was compared to non-high-risk periods. Incidence rates of migraine for 12- to 16-year-old girls and boys postvaccination were slightly higher than pre-vaccination incidence rates. Incidence rate ratios (IRRs) for vaccinated compared to unvaccinated girls were not statistically significantly higher. Furthermore, the RR for migraine in the high-risk period of 6 weeks following each dose versus non-high-risk period was 4.3 (95% confidence interval (CI) 0.69–26.6) for certain migraine. Conclusion: Using different methods, no statistically significant association between HPV vaccination and incident migraine was found. However, the number of cases was low; to definitively exclude the risk, an increased sample size is needed

The value of intra-operative electrographic biomarkers for tailoring during epilepsy surgery: from group-level to patient-level analysis

Signal analysis biomarkers, in an intra-operative setting, may be complementary tools to guide and tailor the resection in drug-resistant focal epilepsy patients. Effective assessment of biomarker performances are needed to evaluate their clinical usefulness and translation. We defined a realistic ground-truth scenario and compared the effectiveness of different biomarkers alone and combined to localize epileptogenic tissue during surgery. We investigated the performances of univariate, bivariate and multivariate signal biomarkers applied to 1 min inter-ictal intra-operative electrocorticography to discriminate between epileptogenic and non-epileptogenic locations in 47 drug-resistant people with epilepsy (temporal and extra-temporal) who had been seizure-free one year after the operation. The best result using a single biomarker was obtained using the phase-amplitude coupling measure for which the epileptogenic tissue was localized in 17 out of 47 patients. Combining the whole set of biomarkers provided an improvement of the performances: 27 out of 47 patients. Repeating the analysis only on the temporal-lobe resections we detected the epileptogenic tissue in 29 out of 30 combining all the biomarkers. We suggest that the assessment of biomarker performances on a ground-truth scenario is required to have a proper estimate on how biomarkers translate into clinical use. Phase-amplitude coupling seems the best performing single biomarker and combining biomarkers improves localization of epileptogenic tissue. Performance achieved is not adequate as a tool in the operation theater yet, but it can improve the understanding of pathophysiological process

Effector-triggered defence against apoplastic fungal pathogens

Copyright 2014 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license CC BY 3.0 (http://creativecommons.org/licenses/by/3.0/). hR gene-mediated host resistance against apoplastic fungal pathogens is not adequately explained by the terms pathogen-associated molecular pattern (PAMP)-triggered immunity (PTI) or effector-triggered immunity (ETI). Therefore, it is proposed that this type of resistance is termed ‘effector-triggered defence’ (ETD). Unlike PTI and ETI, ETD is mediated by R genes encoding cell surface-localised receptor-like proteins (RLPs) that engage the receptor-like kinase SOBIR1. In contrast to this extracellular recognition, ETI is initiated by intracellular detection of pathogen effectors. ETI is usually associated with fast, hypersensitive host cell death, whereas ETD often triggers host cell death only after an elapsed period of endophytic pathogen growth. In this opinion, we focus on ETD responses against foliar fungal pathogens of cropsPeer reviewe

Block-Diagonalization and f-electron Effects in Tight-Binding Theory

We extend a tight-binding total energy method to include f-electrons, and apply it to the study of the structural and elastic properties of a range of elements from Be to U. We find that the tight-binding parameters are as accurate and transferable for f-electron systems as they are for d-electron systems. In both cases we have found it essential to take great care in constraining the fitting procedure by using a block-diagonalization procedure, which we describe in detail.Comment: 9 pages, 6 figure

Robust compression and detection of epileptiform patterns in ECoG using a real-time spiking neural network hardware framework

Interictal Epileptiform Discharges (IED) and High Frequency Oscillations (HFO) in intraoperative electrocorticography (ECoG) may guide the surgeon by delineating the epileptogenic zone. We designed a modular spiking neural network (SNN) in a mixed-signal neuromorphic device to process the ECoG in real-time. We exploit the variability of the inhomogeneous silicon neurons to achieve efficient sparse and decorrelated temporal signal encoding. We interface the full-custom SNN device to the BCI2000 real-time framework and configure the setup to detect HFO and IED co-occurring with HFO (IED-HFO). We validate the setup on pre-recorded data and obtain HFO rates that are concordant with a previously validated offline algorithm (Spearman’s ρ = 0.75, p = 1e-4), achieving the same postsurgical seizure freedom predictions for all patients. In a remote on-line analysis, intraoperative ECoG recorded in Utrecht was compressed and transferred to Zurich for SNN processing and successful IED-HFO detection in real-time. These results further demonstrate how automated remote real-time detection may enable the use of HFO in clinical practice

Nowcasting pandemic influenza A/H1N1 2009 hospitalizations in the Netherlands

During emerging epidemics of infectious diseases, it is vital to have up-to-date information on epidemic trends, such as incidence or health care demand, because hospitals and intensive care units have limited excess capacity. However, real-time tracking of epidemics is difficult, because of the inherent delay between onset of symptoms or hospitalizations, and reporting. We propose a robust algorithm to correct for reporting delays, using the observed distribution of reporting delays. We apply the algorithm to pandemic influenza A/H1N1 2009 hospitalizations as reported in the Netherlands. We show that the proposed algorithm is able to provide unbiased predictions of the actual number of hospitalizations in real-time during the ascent and descent of the epidemic. The real-time predictions of admissions are useful to adjust planning in hospitals to avoid exceeding their capacity