Carcinoma of the Fallopian Tube

Seven cases of primary carcinoma of the fallopian tube have been reviewed and compared with cases previously reported in the literature. Sterility was present in 51% of patients. There was a variety of symptoms, but the most frequent was vaginal discharge, in 42% of patients. Two patients manifested urinary symptoms and one patient ascites. In six of our seven patients, an adnexal mass was present. Although clinical diagnosis will always be difficult one should be highly suspicious of this disease in patients between 40 and 60 years of age with these symptoms

Spatiotemporal dispersion and wave envelopes with relativistic and pseudorelativistic characteristics

A generic nonparaxial model for pulse envelopes is presented. Classic Schro¨dinger-type descriptions of wave propagation have their origins in slowly-varying envelopes combined with a Galilean boost to the local time frame. By abandoning these two simplifications, a picture of pulse evolution emerges in which frame-of-reference considerations and space-time transformations take center stage. A wide range of effects, analogous to those in special relativity, then follows for both linear and nonlinear systems. Explicit demonstration is presented through exact bright and dark soliton pulse solutions

Blending of nanoscale and microscale in uniform large-area sculptured thin-film architectures

The combination of large thickness ($>3$ $\mu$m), large--area uniformity (75 mm diameter), high growth rate (up to 0.4 $\mu$m/min) in assemblies of complex--shaped nanowires on lithographically defined patterns has been achieved for the first time. The nanoscale and the microscale have thus been blended together in sculptured thin films with transverse architectures. SiO$_x$ ($x\approx 2$) nanowires were grown by electron--beam evaporation onto silicon substrates both with and without photoresist lines (1--D arrays) and checkerboard (2--D arrays) patterns. Atomic self--shadowing due to oblique--angle deposition enables the nanowires to grow continuously, to change direction abruptly, and to maintain constant cross--sectional diameter. The selective growth of nanowire assemblies on the top surfaces of both 1--D and 2--D arrays can be understood and predicted using simple geometrical shadowing equations.Comment: 17 pages, 9 figure

Dispelling the myths of online education: learning via the information superhighway

There continues to be a perception that online education is inferior to traditional education. In the U.S. online learning is more developed than in the U.K. This paper provides insights into a U.S. provision and takes a close look at what are perceived as weaknesses of on line learning and argues that these are not necessarily inherent weaknesses of this form of educational delivery. Then, results of two major studies, undertaken in the U.S. are provided comparing the effectiveness of online education to traditional education as perceived by current MBA students and past graduates. Results of these studies suggest that students of MBA modules and MBA graduates perceive the quality and effectiveness of online education to be similar to, if not higher than, the quality and effectiveness of traditional modules and programmes

ensemblQueryR: fast, flexible and high-throughput querying of Ensembl LD API endpoints in R

We present ensemblQueryR, a package providing an R interface to the Ensembl REST API that facilitates flexible, fast, user-friendly and R workflow integrable querying of Ensembl REST API linkage disequilibrium (LD) endpoints, optimised for high-throughput querying. ensemblQueryR achieves this through functions that are intuitive and amenable to custom code integration, use of familiar R object types as inputs and outputs, code optimisation and optional parallelisation functionality. For each LD endpoint, ensemblQueryR provides two functions, permitting both single-query and multi-query modes of operation. The multi-query functions are optimised for large query sizes and provide optional parallelisation to leverage available computational resources and minimise processing time. We demonstrate that ensemblQueryR has improved performance in terms of random access memory (RAM) usage and speed, delivering a 10-fold speed increase over analogous software whilst using a third of the RAM. Finally, ensemblQueryR is near-agnostic to operating system and computational architecture through availability of Docker and singularity images, making this tool widely accessible to the scientific community

Scalable design of tailored soft pulses for coherent control

We present a scalable scheme to design optimized soft pulses and pulse sequences for coherent control of interacting quantum many-body systems. The scheme is based on the cluster expansion and the time dependent perturbation theory implemented numerically. This approach offers a dramatic advantage in numerical efficiency, and it is also more convenient than the commonly used Magnus expansion, especially when dealing with higher order terms. We illustrate the scheme by designing 2nd-order pi-pulses and a 6th-order 8-pulse refocusing sequence for a chain of qubits with nearest-neighbor couplings. We also discuss the performance of soft-pulse refocusing sequences in suppressing decoherence due to low-frequency environment.Comment: 4 pages, 2 tables. (modified first table, references added, minor text changes

Perspective: Current advances in solid-state NMR spectroscopy

In contrast to the rapid and revolutionary impact of solution-state Nuclear Magnetic Resonance (NMR) on modern chemistry, the field of solid-state NMR has matured more slowly. This reflects the major technical challenges of much reduced spectral resolution and sensitivity in solid-state as compared to solution-state spectra, as well as the relative complexity of the solid state. In this perspective, we outline the technique developments that have pushed resolution to intrinsic limits and the approaches, including ongoing major developments in the field of Dynamic Nuclear Polarisation, that have enhanced spectral sensitivity. The information on local structure and dynamics that can be obtained using these gains in sensitivity and resolution is illustrated with a diverse range of examples from large biomolecules to energy materials and pharmaceuticals and from both ordered and highly disordered materials. We discuss how parallel developments in quantum chemical calculation, particularly density functional theory, have enabled experimental data to be translated directly into information on local structure and dynamics, giving rise to the developing field of “NMR crystallography

Informing the future of Australian mining through climate change scenarios

Abstract: Mining value chains are vulnerable to a changing climate mainly due to the likelihood of increases in the incidence of extreme weather events. As such events will potentially become more frequent and more intense, the associated impacts such as infrastructure damage, production delays and downtime may damage mine profitability, staff safety, company reputation, regional 'liveability' and government revenues. Mining adaptation strategies to better deal with such impacts can be developed but the options available cannot simply be applied 'across the board' at all mines and in all situations. Various types of mining in Australia occur across 11 main geographic areas, each with its own processes and needs, its own climate signature and its own extreme-event profile. To provide some context for the likely changes in future climate, CSIRO has developed mining region-specific scenarios in association with the OzClim Climate Change Scenario Generator. OzClim generates climate change scenarios using pattern scaling where the change at a particular grid point is normalised by the mean global warming produced by the model for a doubled CO 2 concentration in the atmosphere. The patterns of change are produced for each of the 23 global climate models and for the purposes of the Australian mining regions, we have expressed changes consistent with an historical baseline in order to make the projection information as contextually relevant as possible. To bridge the gap between scenarios and users, CSIRO facilitated workshop events in mining regions. Representatives of a cross-section of the mining chain (including energy, mining, transport, research, water and community stakeholders) were invited to attend, some of whom were first interviewed by facilitators to gain an insight into their operations, understandings, and needs with regard to the workshop. The attendees were presented with future regional climate scenarios, additional information from other studies and climate location analogues helping to further 'set the scene' for the future and helping to facilitate discussion around potential impacts and adaptation needs. Discussions at the workshops provided the means for the scenarios to be placed in their local context, whilst hearing how others in the chain may be directly and indirectly impacted and how they may adapt. Mines and their related infrastructure are frequently long-term investments for all concerned. Therefore, future climate scenarios are valuable for mining value chains and the decision-makers to envisage and plan the future, including adaptation at established sites, alternative processes at new sites and contingency plans that accommodate new levels of variability. Utilising workshops to link future climate scenarios to the value chain and its operational components assisted the end-users to visualise, conceptualise and engage with adaptation decision-making scenarios. The event also brought together participants from different parts of the mining chain who were able to share knowledge and discuss needs that may in the future aid adaptation and avoid maladaptation

The role of mutation rate variation and genetic diversity in the architecture of human disease

Background We have investigated the role that the mutation rate and the structure of genetic variation at a locus play in determining whether a gene is involved in disease. We predict that the mutation rate and its genetic diversity should be higher in genes associated with disease, unless all genes that could cause disease have already been identified. Results Consistent with our predictions we find that genes associated with Mendelian and complex disease are substantially longer than non-disease genes. However, we find that both Mendelian and complex disease genes are found in regions of the genome with relatively low mutation rates, as inferred from intron divergence between humans and chimpanzees, and they are predicted to have similar rates of non-synonymous mutation as other genes. Finally, we find that disease genes are in regions of significantly elevated genetic diversity, even when variation in the rate of mutation is controlled for. The effect is small nevertheless. Conclusions Our results suggest that gene length contributes to whether a gene is associated with disease. However, the mutation rate and the genetic architecture of the locus appear to play only a minor role in determining whether a gene is associated with disease

Evaluating natural language processing models with generalization metrics that do not need access to any training or testing data

The search for effective and robust metrics has been the focus of recent theoretical and empirical work on generalization of deep neural networks (NNs). In this paper, we discuss the performance of natural language processing (NLP) models, and we evaluate various existing and novel generalization metrics. Compared to prior studies, we (i) focus on NLP instead of computer vision (CV), (ii) focus on generalization metrics that predict test error instead of the generalization gap, (iii) focus on generalization metrics that do not need the access to data, and (iv) focus on the heavy-tail (HT) phenomenon that has received comparatively less attention in the study of NNs. We extend recent HT-based work which focuses on power law (PL) distributions, and we study exponential and exponentially truncated power law (E-TPL) fitting to the empirical spectral densities (ESDs) of weight matrices. Our empirical studies are carried on (i) hundreds of Transformers trained in different settings, in which we systematically vary different hyperparameters, (ii) a total of 51 pretrained Transformers from eight families of Huggingface NLP models, including BERT, GPT2, etc., and (iii) a total of 28 existing and novel generalization metrics. From our empirical analyses, we show that shape metrics, or the metrics obtained from fitting the shape of the ESDs, perform uniformly better at predicting generalization performance than scale metrics commonly studied in the literature, as measured by the rank correlations with the generalization performance. We also show that among the three HT distributions considered in our paper, the E-TPL fitting of ESDs performs the most robustly when the models are trained in experimental settings, while the PL fitting achieves the best performance on well-trained Huggingface models, and that both E-TPL and PL metrics (which are both shape metrics) outperform scale metrics