China’s business cycles since 1979: a chronology and comparative analysis

The path to emerging as the world’s second largest economy (in PPP terms) has not been a smooth one. This paper seeks to provide a detailed chronology of China’s business cycles since 1979. It also considers whether their volatility has changed over time, and how their volatility compares with those in the world’s largest and third largest economies, the U.S and Japan. In the process, several puzzles relating to China’s business cycles are observed that warrant further research attention.

A European Termite Reticulotermes Lucifugus Rossi in the Vicinity of Boston

Volume: 25Start Page: 99End Page: 10

ENGL64.05: Cultural Analytics

Syllabus for ENGL 64.05 "Cultural Analytics," taught Fall 2019 at Dartmouth College

Global analysis of SNPs, proteins and protein-protein interactions: approaches for the prioritisation of candidate disease genes.

PhDUnderstanding the etiology of complex disease remains a challenge in biology. In recent years there has been an explosion in biological data, this study investigates machine learning and network analysis methods as tools to aid candidate disease gene prioritisation, specifically relating to hypertension and cardiovascular disease. This thesis comprises four sets of analyses: Firstly, non synonymous single nucleotide polymorphisms (nsSNPs) were analysed in terms of sequence and structure based properties using a classifier to provide a model for predicting deleterious nsSNPs. The degree of sequence conservation at the nsSNP position was found to be the single best attribute but other sequence and structural attributes in combination were also useful. Predictions for nsSNPs within Ensembl have been made publicly available. Secondly, predicting protein function for proteins with an absence of experimental data or lack of clear similarity to a sequence of known function was addressed. Protein domain attributes based on physicochemical and predicted structural characteristics of the sequence were used as input to classifiers for predicting membership of large and diverse protein superfamiles from the SCOP database. An enrichment method was investigated that involved adding domains to the training dataset that are currently absent from SCOP. This analysis resulted in improved classifier accuracy, optimised classifiers achieved 66.3% for single domain proteins and 55.6% when including domains from multi domain proteins. The domains from superfamilies with low sequence similarity, share global sequence properties enabling applications to be developed which compliment profile methods for detecting distant sequence relationships. Thirdly, a topological analysis of the human protein interactome was performed. The results were combined with functional annotation and sequence based properties to build models for predicting hypertension associated proteins. The study found that predicted hypertension related proteins are not generally associated with network hubs and do not exhibit high clustering coefficients. Despite this, they tend to be closer and better connected to other hypertension proteins on the interaction network than would be expected by chance. Classifiers that combined PPI network, amino acid sequence and functional properties produced a range of precision and recall scores according to the applied 3 weights. Finally, interactome properties of proteins implicated in cardiovascular disease and cancer were studied. The analysis quantified the influential (central) nature of each protein and defined characteristics of functional modules and pathways in which the disease proteins reside. Such proteins were found to be enriched 2 fold within proteins that are influential (p<0.05) in the interactome. Additionally, they cluster in large, complex, highly connected communities, acting as interfaces between multiple processes more often than expected. An approach to prioritising disease candidates based on this analysis was proposed. Each analyses can provide some new insights into the effort to identify novel disease related proteins for cardiovascular disease

The use of needle guidance software within interventional radiology

Cone Beam CT (CBCT) has allowed for the expansion of the examinations/procedures that can be performed within the interventional radiology suite. Many of these procedures were once only possible within CT, however with the availability of CBCT and needle guidance software within the interventional suite these exams can be brought into the Interventional setting. This has allowed for the improved safety and care of the patients whilst not limiting the 89 imaging facilities available to the radiologist. With improved experience, knowledge and confidence in using needle guidance even the most complex cases have the possibility of being performed within the interventional suite

Discharge Summary Hospital Course Summarisation of In Patient Electronic Health Record Text with Clinical Concept Guided Deep Pre-Trained Transformer Models

Brief Hospital Course (BHC) summaries are succinct summaries of an entire hospital encounter, embedded within discharge summaries, written by senior clinicians responsible for the overall care of a patient. Methods to automatically produce summaries from inpatient documentation would be invaluable in reducing clinician manual burden of summarising documents under high time-pressure to admit and discharge patients. Automatically producing these summaries from the inpatient course, is a complex, multi-document summarisation task, as source notes are written from various perspectives (e.g. nursing, doctor, radiology), during the course of the hospitalisation. We demonstrate a range of methods for BHC summarisation demonstrating the performance of deep learning summarisation models across extractive and abstractive summarisation scenarios. We also test a novel ensemble extractive and abstractive summarisation model that incorporates a medical concept ontology (SNOMED) as a clinical guidance signal and shows superior performance in 2 real-world clinical data sets

Benefits of Exercise Intervention in Reducing Neuropathic Pain

Peripheral neuropathy is a widespread and potentially incapacitating pathological condition that encompasses more than 100 different forms and manifestations of nerve damage. The diverse pathogenesis of peripheral neuropathy affects autonomic, motor and/or sensory neurons, and the symptoms that typify the condition are abnormal cutaneous sensation, muscle dysfunction and, most notably, chronic pain. Chronic neuropathic pain is difficult to treat and is often characterized by either exaggerated responses to painful stimuli (hyperalgesia) or pain resulting from stimuli that would not normally provoke pain (allodynia). The objective of this review is to provide an overview of some pathways associated with the development of peripheral neuropathy and then discuss the benefits of exercise interventions. The development of neuropathic pain is a highly complex and multifactorial process, but recent evidence indicates that the activation of spinal glial cells via the enzyme glycogen synthase kinase 3 and increases in the production of both pro-inflammatory cytokines and brain derived neurotropic factor are crucial steps. Since many of the most common causes of peripheral neuropathy cannot be fully treated, it is critical to understand that routine exercise may not only help prevent some of those causes, but that it has also proven to be an effective means of alleviating some of the condition’s most distressing symptoms. More research is required to elucidate the typical mechanisms of injury associated with peripheral neuropathy and the exercise-induced benefits to those mechanisms

Response of aquatic hyphomycete communities to enhanced stream retention in areas impacted by commercial forestry

1. Aquatic hyphomycetes are an important component of detritus processing in streams. Their response to enhanced stream retentiveness was tested by manipulating three streams located in Kielder Forest (northern England), a large plantation of exotic conifers, and two streams in Montagne Noire (south-west France) dominated by native broadleaf woodland. Treatment was by placement of logs or plastic litter traps into a 10–20 m stream section. Fungal spores were collected from stream water upstream and downstream of the treated sections over 1–2 years. 2.The average concentration of fungal spores in reference sections was nearly 10x greater in the French streams than in the English streams. The number of hyphomycete species was also higher in the French streams. These differences between regions were probably a consequence of the much lower standing stock and diversity of leaf litter in the English streams. 3. Despite these large regional differences, the treatment had a clear effect in all streams. Detrital standing stocks were enhanced in treated sections by up to 90% in French streams and 70% in English streams. 4. Mean spore density below treated sections increased by 1.8–14.8% in French streams and 10.2–28.9% in the naturally less retentive English streams. The number of fungal species increased significantly below the treated sections of the English streams, although not the French ones. 5. In biologically impoverished plantation streams, input of woody debris can increase detritus retention and enhance hyphomycete diversity and productivity. This may have consequent benefits for detritus processing and macroinvertebrate production

Bayesian estimates of transmission line outage rates that consider line dependencies

Transmission line outage rates are fundamental to power system reliability analysis. Line outages are infrequent, occurring only about once a year, so outage data are limited. We propose a Bayesian hierarchical model that leverages line dependencies to better estimate outage rates of individual transmission lines from limited outage data. The Bayesian estimates have a lower standard deviation than estimating the outage rates simply by dividing the number of outages by the number of years of data, especially when the number of outages is small. The Bayesian model produces more accurate individual line outage rates, as well as estimates of the uncertainty of these rates. Better estimates of line outage rates can improve system risk assessment, outage prediction, and maintenance scheduling