Queenship, intrigue and blood-feud: deciphering the causes of the Merovingian civil wars, 561-613

The Frankish civil wars of AD 561-613 were a series of devastating encounters involving the four sons of Chlothar I and their descendants. While no party was guiltless during this period, modern scholars have tended to focus on two prominent Queens, Brunhild of Austrasia and Fredegund of Neustria, and the possibility of a blood-feud between their two families. King Sigibert of Austrasia married Brunhild because he believed she was worthy of a king, unlike many of the wives his brothers were taking. One of these women was Fredegund, who was married to King Chilperic of Neustria. Fredegund is often blamed for the assassination of Galswinth, Brunhild’s sister, even though Chilperic is the more likely culprit. This murder is what many modern scholars believe started a blood-feud between the two families, which both queens were integral in prosecuting. Even though Brunhild and Fredegund were integral figures throughout this series of bella civilia, it is apparent that the majority of the conflict which erupted during this period centered on the partition of Chlothar I’s kingdom in 561. Furthermore, the impact of the nobility, bishops, and even the armies of these kingdoms in promoting and prolonging civil war is largely ignored by modern scholars. This thesis will argue that the wars of this period cannot simply be reduced to the machinations of two queens or a blood-feud between the families. Instead, these wars were far more complex finding origins varying from scheming nobles to greed of the common soldier

Mapping Agricultural Biodiversity:Legacy data and tensions between ways of seeing fields

Mapping is a core approach used to investigate and display spatial dynamics of biological diversity and habitats. In the Netherlands, agricultural lands occupy nearly two-thirds of the land surface and provide the greatest potential for habitat restoration; particularly in grassland-based dairy production systems, which comprise the largest share of these agricultural lands. When a crop rotation is applied to a long-term grassland, the resulting disruption of ecological complexity requires years–if not decades–to restore, even after reconversion. The availability of high-quality land-use data for measuring the spatio-temporal distribution of grassland legacies is thus essential for monitoring the dynamics of biodiversity in production grasslands. In this study, we reflect on the Basic Crop Registration (BRP) of the Netherlands, an open spatial data infrastructure developed for parcel-level crop registration and examine how it shapes our spatio-temporal understanding of land use. The BRP serves as an administrative basis for numerous national and local-level regulatory and financial arrangements, mainly aimed at agricultural actors. In this study, we repurposed BRP data to introduce a new perspective on depicting the stability of grasslands in a high-intensity agricultural region. We used this data to map the frequency of grassland-to-cropland conversions using 17 years of longitudinal crop records in southwest Friesland, Netherlands. The legacy effects of grassland-to-cropland conversion were investigated in a field study, where significant differences were found between new and long-term grasslands in plant community composition, soil organic matter content, bulk density, soil penetration resistance, and pH. In our analysis of BRP data, we discovered a significant number of grasslands that were recently converted from cropland but that were recorded as long-term grasslands. This affected approximately 12% of the study area from 2005–2021, which prevents the accurate tracking of grassland stability over time. This misclassification also adds uncertainty to the temporal context of the decline in grassland-dependent species in the region. However, using a spatially-explicit mapping approach, these misclassifications can be corrected and help produce an effective measure of grassland stability with potential as an agroecosystem monitoring tool for researchers, land-use planners, and policymakers

Simulation assisted machine learning

Motivation: In a predictive modeling setting, if sufficient details of the system behavior are known, one can build and use a simulation for making predictions. When sufficient system details are not known, one typically turns to machine learning, which builds a black-box model of the system using a large dataset of input sample features and outputs. We consider a setting which is between these two extremes: some details of the system mechanics are known but not enough for creating simulations that can be used to make high quality predictions. In this context we propose using approximate simulations to build a kernel for use in kernelized machine learning methods, such as support vector machines. The results of multiple simulations (under various uncertainty scenarios) are used to compute similarity measures between every pair of samples: sample pairs are given a high similarity score if they behave similarly under a wide range of simulation parameters. These similarity values, rather than the original high dimensional feature data, are used to build the kernel. Results: We demonstrate and explore the simulation based kernel (SimKern) concept using four synthetic complex systems--three biologically inspired models and one network flow optimization model. We show that, when the number of training samples is small compared to the number of features, the SimKern approach dominates over no-prior-knowledge methods. This approach should be applicable in all disciplines where predictive models are sought and informative yet approximate simulations are available. Availability: The Python SimKern software, the demonstration models (in MATLAB, R), and the datasets are available at https://github.com/davidcraft/SimKern.Comment: This manuscript has been accepted for publication in Bioinformatics published by Oxford University Press: https://doi.org/10.1093/bioinformatics/btz199 (open access). Timo M. Deist and Andrew Patti contributed equally to this wor

Current state of scientific evidence on Internet-based interventions for the treatment of depression, anxiety, eating disorders and substance abuse: An overview of systematic reviews and meta-analyses

BACKGROUND: ICare represents a consortium of European Investigators examining the effects of online mental health care for a variety of common mental health disorders provided in a variety of settings. This article provides an overview of the evidence of effectiveness for Internet-based treatment for four common mental health disorders that are the focus of much of this work: depression, anxiety, substance abuse and eating disorders. METHODS: The overview focused primarily on systematic reviews and meta-analyses identified through PubMed (Ovid) and other databases and published in English. Given the large number of reviews specific to depression, anxiety, substance abuse and/or eating disorders, we did not focus on reviews that examined the effects of Internet-based interventions on mental health disorders in general. Each article was reviewed and summarized by one of the senior authors, and this review was then reviewed by the other senior authors. We did not address issues of prevention, cost-effectiveness, implementation or dissemination, as these are addressed in other reviews in this supplement. RESULTS: Across Internet-based intervention studies addressing depression, anxiety, substance abuse and eating disorders primarily among adults, almost all reviews and meta-analyses found that these interventions successfully reduce symptoms and are efficacious treatments. Generally, effect sizes for Internet-based interventions treating eating disorders and substance abuse are lower compared with interventions for depression and anxiety. CONCLUSIONS: Given the effectiveness of Internet-based interventions to reduce symptoms of these common mental health disorders, efforts are needed to examine issues of how they can be best disseminated and implemented in a variety of health care and other settings

A systematic digital approach to implementation and dissemination of eating disorders interventions to large populations identified through online screening: Implications for post-traumatic stress

Background: We describe an approach to implementation and dissemination that focuses on changing outcomes variables within a large, defined population and attempts to provide cost-effective opportunities and resources-which might include the provision of both digital and traditional interventions-to address individual needs and interests. We present a case example of how aspects of this model are being applied to increase reach, engagement and outcomes for individuals who complete a national eating disorders screen, and are likely to have an eating disorder but who are not in treatment. We then describe how this model can apply to post-traumatic stress (PTS) and conclude with a discussion of limitations and issues with the model. Methods: The National Eating Disorders Association (NEDA) provides online screening for eating disorders. Results: From February 2017 through March 2018, over 200,000 individuals completed the NEDA screen. Of these, 96% screened positive or at risk for an eating disorder, and most of those who screened positive for a clinical/subclinical eating disorder were not currently in treatment. Less than 10% engaged in self-help or guided self-help online digital program, or expressed interest in calling a helpline for referral to treatment. Conclusions: A systematic digital approach to implementation and dissemination has the potential to increase the number of individuals who benefit from interventions in defined populations. Uptake rates need to be improved

A framework for applying natural language processing in digital health interventions

BACKGROUND: Digital health interventions (DHIs) are poised to reduce target symptoms in a scalable, affordable, and empirically supported way. DHIs that involve coaching or clinical support often collect text data from 2 sources: (1) open correspondence between users and the trained practitioners supporting them through a messaging system and (2) text data recorded during the intervention by users, such as diary entries. Natural language processing (NLP) offers methods for analyzing text, augmenting the understanding of intervention effects, and informing therapeutic decision making. OBJECTIVE: This study aimed to present a technical framework that supports the automated analysis of both types of text data often present in DHIs. This framework generates text features and helps to build statistical models to predict target variables, including user engagement, symptom change, and therapeutic outcomes. METHODS: We first discussed various NLP techniques and demonstrated how they are implemented in the presented framework. We then applied the framework in a case study of the Healthy Body Image Program, a Web-based intervention trial for eating disorders (EDs). A total of 372 participants who screened positive for an ED received a DHI aimed at reducing ED psychopathology (including binge eating and purging behaviors) and improving body image. These users generated 37,228 intervention text snippets and exchanged 4285 user-coach messages, which were analyzed using the proposed model. RESULTS: We applied the framework to predict binge eating behavior, resulting in an area under the curve between 0.57 (when applied to new users) and 0.72 (when applied to new symptom reports of known users). In addition, initial evidence indicated that specific text features predicted the therapeutic outcome of reducing ED symptoms. CONCLUSIONS: The case study demonstrates the usefulness of a structured approach to text data analytics. NLP techniques improve the prediction of symptom changes in DHIs. We present a technical framework that can be easily applied in other clinical trials and clinical presentations and encourage other groups to apply the framework in similar contexts

Exploring social media recruitment strategies and preliminary acceptability of an mHealth tool for teens with eating disorders

(1) Background: The current study leveraged social media to connect with teens with EDs to identify population specific characteristics and to gather feedback on an mHealth intervention. (2) Methods: We recruited teens with EDs from social media in two phases: (1) Discovery Group, (2) Testing Group. The Discovery Group

Unraveling the drift behaviour of the remarkable pulsar PSR B0826-34

We present new results from high sensitivity GMRT observations of PSR B0826-34. We provide a model to explain the observed subpulse drift properties of this pulsar, including the apparent reversals of the drift direction. In this model, PSR B0826-34 is close to being an aligned rotator. We solve for the emission geometry of this pulsar and show that the angle between the rotation and the magnetic axes is less than 5 deg. We see evidence for as many as 6 to 7 drifting bands in the main pulse at 318 MHz, which are part of a circulating system of about 15 spark-associated subpulse emission beams. We provide quantitative treatments of the aliasing problem and various effects of geometry. The observed drift rate is an aliased version of the true drift rate, such that a subpulse drifts to the location of the adjacent subpulse (or a multiple thereof) in about one pulsar period. We show that small variations, of the order of 3-8%, in the mean drift rate are then enough to explain the apparent reversals of drift direction. We find the mean circulation time of the drift pattern to be significantly longer than the predictions of the original RS75 model and propose an explanation for this, based on modified models with temperature regulated partial ion flow in the polar vacuum gap. From the variation of the mean subpulse separation across the main pulse window, we show that the spark pattern is not centred around the dipole axis, but around a point much closer (within a degree or so) to the rotation axis -- we discuss the implication of this.Comment: 23 pages (including 9 figure). Submitted to Astronomy and Astrophysics on November 11, 200