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Objectives Guidelines that include antimicrobial recommendations should explicitly consider contextual factors that influence antimicrobial resistance and their downstream effects on resistance selection. The objectives were to analyse (1) how, and to what extent, tuberculosis, gonorrhoea and respiratory tract infection guidelines are considering antimicrobial resistance; (2) are of acceptable quality and (3) if they can be easily contextualised to fit the needs of specific populations and health systems.Methods We conducted a systematic review and searched Ovid MEDLINE and Embase from 1 January 2007 to 7 June 2019 for tuberculosis, gonorrhoea and respiratory tract infection guidelines published in English. We also searched guideline databases, key websites and reference lists. We identified guidelines and recommendations that considered contextual factors including antimicrobial resistance, values, resource use, equity, acceptability and feasibility. We assessed quality of the guidelines using the Appraisal of Guidelines for Research and Evaluation II tool focusing on the domains scope and purpose, rigour of development, and editorial independence.Results We screened 10 365 records, of which 74 guidelines met inclusion criteria. Of these guidelines, 39% (n=29/74) met acceptable quality scores. Approximately two-thirds of recommendations considered antimicrobial resistance at the population and/or outcome level. Five of the 29 guidelines reported all factors required for recommendation contextualisation. Equity was the least considered across guidelines.Discussion Relatively few guidelines for highly prevalent infectious diseases are considering resistance at a local level, and many do not consider contextual factors necessary for appropriate antimicrobial use. Improving the quality of guidelines targeting specific regional areas is required.PROSPERO registration number CRD42020145235.

Accurately detecting human trafficking is particularly challenging due to its covert nature, difficulty in distinguishing trafficking from non-trafficking exploitative conditions, and varying operational definitions. Typically, detecting human trafficking requires resource-intensive efforts from resource-constrained anti-trafficking stakeholders. Such measures may need personnel training or machine learning-based identification technologies that suffer from detection errors. Repeated usage of such measures risks biasing detection efforts and reducing detection effectiveness. Such problems raise the question: "How should imperfect detection resources be allocated to most effectively identify human trafficking?" As an answer, we construct a class of resource allocation models that considers various optimal allocation scenarios. These scenarios range from optimal location selection for monitoring to optimal allocation of a finite set of imperfect resources, given error rates. We illustrate the applicability of these models across both human and technology-facilitated detection contexts at the India–Nepal border and in the global seafood industry. Insights from our models help inform operational strategies for allocating limited anti-human trafficking resources in a way that effectively preserves human rights and dignity. [ABSTRACT FROM AUTHOR]

With the goal of understanding if the information contained in node metadata can help in the task of link weight prediction, we investigate herein whether incorporating it as a similarity feature (referred to as metadata similarity) between end nodes of a link improves the prediction accuracy of common supervised machine learning methods. In contrast with previous works, instead of normalizing the link weights, we treat them as count variables representing the number of interactions between end nodes, as this is a natural representation for many datasets in the literature. In this preliminary study, we find no significant evidence that metadata similarity improved the prediction accuracy of the four empirical datasets studied. To further explore the role of node metadata in weight prediction, we synthesized weights to analyze the extreme case where the weights depend solely on the metadata of the end nodes, while encoding different relationships between them using logical operators in the generation process. Under these conditions, the random forest method performed significantly better than other methods in 99.07% of cases, though the prediction accuracy was significantly degraded for the methods analyzed in comparison to the experiments with the original weights.

Despite signs of facial nerve recovery within a few months following face transplantation, speech deficits persist for years. Behavioral speech modifications (e.g., slower-than-normal speaking rate and increased loudness) have shown promising potential to enhance speech intelligibility in populations with dysarthric speech. However, such evidence-based practice approach is lacking in clinical management of speech in individuals with facial transplantation. Because facial transplantation involves complex craniofacial reconstruction and facial nerve coaptation, it is unknown to what extent individuals with face transplant are capable of adapting their motor system to task-specific articulatory demands. The purpose of this study was to identify the underlying articulatory mechanisms employed by individuals with face transplantation in response to speech modification cues at early and late stages of neuromotor recovery. In addition, we aimed to identify speech modifications that conferred improved speech clarity. Participants were seven individuals who underwent full or partial facial vascularized composite allografts that included lips and muscles of facial animation and were in early (~2 months) or late (~42 months) stages of recovery. Participants produced repetitions of the sentence “Buy Bobby a puppy” in normal, fast, loud, and slow speech modifications. Articulatory movement traces were recorded using a 3D optical motion capture system. Kinematic measures of average speed (mm/s) and range of movement (mm3) were extracted from the lower lip (± jaw) marker. Two speech language pathologists rated speech clarity for each speaker using a visual analog scale (VAS) approach. Results demonstrated that facial motor capacity increased from early to late stages of recovery. While individuals in the early group exhibited restricted capabilities to adjust their motor system based on the articulatory demands of each speech modification, individuals in the late group demonstrated faster speed and larger-than-normal range of movement for loud speech, and slower speed and larger-than-normal range of movement for slow speech. In addition, subjects in both groups showed overreliance on jaw rather than lip articulatory function across all speech modifications, perhaps as a compensatory strategy to optimize articulatory stability and maximize speech function. Finally, improved speech clarity was associated with loud speech in both stages of recovery.

The Scythians are frequently presented, in popular and academic thought alike, as highly mobile warrior nomads who posed a great economic risk to growing Mediterranean empires from the Iron Age into the Classical period. Archaeological studies provide evidence of first millennium BCE urbanism in the steppe while historical texts reference steppe agriculture, challenging traditional characterizations of Scythians as nomads. However, there have been few direct studies of the diet and mobility of populations living in the Pontic steppe and forest-steppe during the Scythian era. Here, we analyse strontium, oxygen, and carbon isotope data from human tooth enamel samples, as well as nitrogen and carbon isotope data of bone collagen, at several Iron Age sites across Ukraine commonly associated with 'Scythian' era communities. Our multi-isotopic approach demonstrates generally low levels of human mobility in the vicinity of urban locales, where populations engaged in agro-pastoralism focused primarily on millet agriculture. Some individuals show evidence for long-distance mobility, likely associated with significant inter-regional connections. We argue that this pattern supports economic diversity of urban locales and complex trading networks, rather than a homogeneous nomadic population.

Background: Traumatic brain injury (TBI) is one of the leading causes of neuropsychiatric disorders in young adults. Repetitive Transcranial Magnetic Stimulation (rTMS) has been shown to improve psychiatric symptoms in other neurologic disorders, such as focal epilepsy, Parkinson's disease, and fibromyalgia. However, the efficacy of rTMS as a treatment for anxiety in persons with TBI has never been investigated. This exploratory post-hoc analyzes the effects of rTMS on anxiety, depression and executive function in participants with moderate to severe chronic TBI.Methods: Thirty-six participants with moderate to severe TBI and anxiety symptoms were randomly assigned to an active or sham rTMS condition in a 1:1 ratio. A 10-session protocol was used with 10-Hz rTMS stimulation over the left dorsolateral prefrontal cortex (DLPFC) for 20 min each session, a total of 2,000 pulses were applied at each daily session (40 stimuli/train, 50 trains). Anxiety symptoms; depression and executive function were analyzed at baseline, after the last rTMS session, and 90 days post intervention.Results: Twenty-seven participants completed the entire protocol and were included in the post-hoc analysis. Statistical analysis showed no interaction of group and time (p > 0.05) on anxiety scores. Both groups improved depressive and executive functions over time, without time and group interaction (ps < 0.05). No adverse effects were reported in either intervention group.Conclusion: rTMS did not improve anxiety symptoms following high frequency rTMS in persons with moderate to severe TBI.Clinical Trial Registration:www.ClinicalTrials.gov, identifier: NCT02167971.

Ecosystem engineering is an innovative concept that recognizes that organisms impact their environment, and that these changes can be detected over time. Thus, additional datasets from the ecological longue durée are necessary, specifically in response to the onset of the Anthropocene and the impacts of humans and their commensal organisms upon ecologies of all scales. For example, the management and herding of domesticated animals are recognized as having dramatic implications for soil stability, vegetation coverage, and even atmospheric composition the world over. Yet, the point at which pastoralism became a recognizable factor in altering earth systems, with large-scale environmental ramifications, is poorly understood. Here, we respond to this by reviewing and presenting data from the archeological and paleoenvironmental record across northern Central Asia in order to assess broader ecosystem impacts of pastoralism, from time periods when this economic pattern was a relatively novel component of local ecologies and involved limited population densities, through to periods in which it became intensive, coincident with agriculture, and linked to increased sedentism. Probing diverse, published analytical datasets and case studies, we examine pastoral adaptations and environmental impacts, highlighting a region where tensions surrounding resilience and sustainability of pastoralism have peaked in modern times. We draw upon these findings to examine the challenges faced by pastoralists today, and the ways in which archeological data might inform on management decisions into the future.

Abstract The ubiquity of supply chains along with their increasingly interconnected structure has ignited interest in studying supply chain networks through the lens of complex adaptive systems. A particularly important characteristic of supply chains is the desirable goal of sustaining their operation when exposed to unexpected perturbations. Applied network science methods can be used to analyze topological properties of supply chains and propose models for their growth. Network models focusing on the critical aspect of supply chain resilience may provide insights into the design of supply networks that may quickly recover from disruptions. This is vital for understanding both static and dynamic structures of complex supply networks, and enabling management to make informed decisions and prioritizing particular operations. This paper proposes an action-based perspective for creating a compact probabilistic model for a given real-world supply network. The action-based model consists of a set of rules (actions) that a firm may use to connect with other firms, such that the synthesized networks are topologically resilient. Additionally, it captures the heterogeneous roles of different firms by incorporating domain specific constraints. Results analyzing the resilience of networks subjected to node disruptions show that networks synthesized using the proposed model can generally outperform its real-world counterpart.