Lordship and Environmental Change in Central Highland Scotland c.1300–c.1400

Whilst there has been an increasing recognition of the infl uence of natural agency on human society in Scotland in the medieval period, conventional historiography has generally presented the wholesale reconfi guration of structures of secular lordship in the Scottish central Highlands in the 14th century as an essentially political consequence of the sociopolitical dislocation associated with the Anglo-Scottish wars that occurred after 1296. The establishment within the region of militarised Gaelic kindreds from the West Highlands and Hebrides of Scotland has come to be regarded as either a symptom of efforts by externally based regional lords to bolster their authority, or an opportunistic territorial aggrandisement by newly dominant neighbouring lords. Feuding and predatory raiding associated with these kindreds is recognised as competition for resources but generally in a context of projection of superior lordship over weaker neighbours. Evidence for long-term changes in climate extrapolated from North Atlantic proxy data, however, suggests that the cattle-based economy of Atlantic Scotland was experiencing protracted environmentally induced stress in the period c.1300–c.1350. Using this evidence, we discuss whether exchange systems operating within traditional lordship structures could offset localised and short-term pressures on the livestock-based regime, but could not be sustained long-term on the reduced fodder and contracting herd sizes caused by climatic deterioration. Territorial expansion and development of a predatory culture, it is argued, were responses to an environment-triggered economic crisis

A model-based assessment of the effects of projected climate change on the water resources of Jordan

This paper is concerned with the quantification of the likely effect of anthropogenic climate change on the water resources of Jordan by the end of the twenty-first century. Specifically, a suite of hydrological models are used in conjunction with modelled outcomes from a regional climate model, HadRM3, and a weather generator to determine how future flows in the upper River Jordan and in the Wadi Faynan may change. The results indicate that groundwater will play an important role in the water security of the country as irrigation demands increase. Given future projections of reduced winter rainfall and increased near-surface air temperatures, the already low groundwater recharge will decrease further. Interestingly, the modelled discharge at the Wadi Faynan indicates that extreme flood flows will increase in magnitude, despite a decrease in the mean annual rainfall. Simulations projected no increase in flood magnitude in the upper River Jordan. Discussion focuses on the utility of the modelling framework, the problems of making quantitative forecasts and the implications of reduced water availability in Jordan

Mobility deficit – Rehabilitate, an opportunity for functionality

There are many pathological conditions that cause mobility deficits and that ultimately influence someone’s autonomy.Aims: to evaluate patients with mobility deficits functional status; to implement a Rehabilitation Nursing intervention plan; to monitor health gains through mobility deficits rehabilitation.Conclusion: Early intervention and the implementation of a nursing rehabilitation intervention plan results in health gains (direct or indirect), decreases the risk of developing Pressure Ulcers (PU) and the risk of developing a situation of immobility that affects patients’ autonomy and quality of life

Efficacy of bezlotoxumab in participants receiving metronidazole, vancomycin, or fidaxomicin for treatment of Clostridioides (Clostridium) difficile infection

Background: In phase 3 MODIFY I/II trials, bezlotoxumab significantly reduced recurrence of Methods: In MODIFY I/II (NCT01241552/NCT01513239), participants received a single infusion of bezlotoxumab (10 mg/kg) or placebo during anti-CDI treatment. Using pooled data from MODIFY I/II, initial clinical cure (ICC) and rCDI were assessed in metronidazole-, vancomycin-, and fidaxomicin-treated subgroups. Results: Of 1554 participants in MODIFY I/II, 753 (48.5%) received metronidazole, 745 (47.9%) vancomycin, and 56 (3.6%) fidaxomicin. Fewer participants receiving metronidazole had a prior CDI episode in the previous 6 months (12.9%) or ≥1 risk factor for rCDI (66.0%) vs participants receiving vancomycin (41.2% and 83.6%, respectively) and fidaxomicin (55.4% and 89.3%, respectively). ICC rates were similar in the bezlotoxumab (metronidazole, 81.0%; vancomycin, 78.5%; fidaxomicin, 86.7%) and placebo groups (metronidazole, 81.3%; vancomycin, 79.6%; fidaxomicin, 76.9%). In placebo-treated participants, the rCDI was lower in the metronidazole subgroup vs the vancomycin and fidaxomicin subgroups (metronidazole, 28.0%; vancomycin, 38.4%; fidaxomicin, 35.0%). When analyzed by subsets based on history of CDI, rCDI rates were similar in the metronidazole and vancomycin groups. rCDI rates were lower in all antibiotic subgroups for bezlotoxumab vs placebo (metronidazole: rate difference [RD], -9.7%; 95% confidence interval [CI], -16.4% to -3.1%; vancomycin: RD, -15.4%; 95% CI, -22.7% to -8.0%; fidaxomicin: RD, -11.9%; 95% CI, -38.1% to 14.3%). Conclusion: Bezlotoxumab reduces rCDI vs placebo in participants receiving metronidazole and vancomycin, with a similar effect size in participants receiving fidaxomicin

Human activity was a major driver of the mid-Holocene vegetation change in southern Cumbria: Implications for the elm decline in the British Isles

This is the author accepted manuscript. The final version is available from Wiley via the DOI in this record.The dramatic decline in elm (Ulmus) across a large swathe of north-west Europe in the mid-Holocene has been ascribed to a number of possible factors, including climate change, human activity and/or pathogens. A major limitation for identifying the underlying cause(s) has been the limited number of high-resolution records with robust geochronological frameworks. Here, we report a multiproxy study of an upland (Blea Tarn) and lowland (Urswick Tarn) landscape in southern Cumbria (British Isles) to reconstruct vegetation change across the elm decline in an area with a rich and well-dated archaeological record to disentangle different possible controls. Here we find a two-stage decline in Ulmus taking place between 6350–6150 and 6050–5850 cal a BP, with the second phase coinciding with an intensification of human activity. The scale of the decline and associated human impact is more abrupt in the upland landscape. We consider it likely that a combination of human impact and disease drove the Ulmus decline within southern Cumbria.This work was funded by a studentship for MJG from the University of Exeter and Sir John Fisher Foundation. Additional funding for 14C dating was from the Cumberland and Westmorland Antiquarian and Archaeological Society (Clare Fell Bursary to MJG), and the Australian Research Council (FL100100195)

Determinants of health seeking behaviour following rabies exposure in Ethiopia

The objective of this study was to identify factors that determine medical treatment seeking behaviour following potential rabies exposure after being bitten by a suspected dog and the likelihood of compliance to receive sufficient doses of post-exposure prophylaxis after the visit to a health centre visit. A detailed survey based on case investigation was conducted on suspected rabid dog bite cases in three areas of Ethiopia. Two multivariable logistic regression models were created with a set of putative variables to explain treatment seeking and compliance outcomes. Based on the registered bite cases at each health centre and the set of unregistered bite cases derived by contact tracing, 655 bite victim cases were identified to have occurred between September 2013 and August 2014. Of these evaluated bite incidences, 465 cases were considered to have been caused by a potentially rabid dog. About 77% of these suspected rabid dog bite victims visited a health centre, while 57% received sufficient doses of PEP. The overall likelihood of seeking medical services following rabies exposure was higher for people bitten by dogs of unknown ownership, where the bite was severe, being bitten on the leg, spend of more than 100 USD per month and where the victim lived close to the nearest health centre, while the likelihood of receiving sufficient doses of PEP was sensitive to monthly spending and distance to health centre. However, the evaluated factors did only explain a part of the variation among the three districts. The district in which victims lived appeared to have a relevant influence on the likelihood of seeking medical treatment but did not improve the prediction on the likelihood of treatment compliance. Given the insights obtained from this study, improvements in the rural districts with regard to accessibility of post-exposure prophylaxis delivering health centres in shorter distance could improve health seeking behaviour. In addition, in rural districts, majority of exposed persons who seek medical treatment tend to comply with treatment regimen, indicating that the promotion of medical treatment through awareness creation campaigns could be beneficial

Reinforcement learning or active inference?

This paper questions the need for reinforcement learning or control theory when optimising behaviour. We show that it is fairly simple to teach an agent complicated and adaptive behaviours using a free-energy formulation of perception. In this formulation, agents adjust their internal states and sampling of the environment to minimize their free-energy. Such agents learn causal structure in the environment and sample it in an adaptive and self-supervised fashion. This results in behavioural policies that reproduce those optimised by reinforcement learning and dynamic programming. Critically, we do not need to invoke the notion of reward, value or utility. We illustrate these points by solving a benchmark problem in dynamic programming; namely the mountain-car problem, using active perception or inference under the free-energy principle. The ensuing proof-of-concept may be important because the free-energy formulation furnishes a unified account of both action and perception and may speak to a reappraisal of the role of dopamine in the brain

Receptive Field Inference with Localized Priors

The linear receptive field describes a mapping from sensory stimuli to a one-dimensional variable governing a neuron's spike response. However, traditional receptive field estimators such as the spike-triggered average converge slowly and often require large amounts of data. Bayesian methods seek to overcome this problem by biasing estimates towards solutions that are more likely a priori, typically those with small, smooth, or sparse coefficients. Here we introduce a novel Bayesian receptive field estimator designed to incorporate locality, a powerful form of prior information about receptive field structure. The key to our approach is a hierarchical receptive field model that flexibly adapts to localized structure in both spacetime and spatiotemporal frequency, using an inference method known as empirical Bayes. We refer to our method as automatic locality determination (ALD), and show that it can accurately recover various types of smooth, sparse, and localized receptive fields. We apply ALD to neural data from retinal ganglion cells and V1 simple cells, and find it achieves error rates several times lower than standard estimators. Thus, estimates of comparable accuracy can be achieved with substantially less data. Finally, we introduce a computationally efficient Markov Chain Monte Carlo (MCMC) algorithm for fully Bayesian inference under the ALD prior, yielding accurate Bayesian confidence intervals for small or noisy datasets

Bayesian estimation of genomic copy number with single nucleotide polymorphism genotyping arrays

<p>Abstract</p> <p>Background</p> <p>The identification of copy number aberration in the human genome is an important area in cancer research. We develop a model for determining genomic copy numbers using high-density single nucleotide polymorphism genotyping microarrays. The method is based on a Bayesian spatial normal mixture model with an unknown number of components corresponding to true copy numbers. A reversible jump Markov chain Monte Carlo algorithm is used to implement the model and perform posterior inference.</p> <p>Results</p> <p>The performance of the algorithm is examined on both simulated and real cancer data, and it is compared with the popular CNAG algorithm for copy number detection.</p> <p>Conclusions</p> <p>We demonstrate that our Bayesian mixture model performs at least as well as the hidden Markov model based CNAG algorithm and in certain cases does better. One of the added advantages of our method is the flexibility of modeling normal cell contamination in tumor samples.</p