Miracle-dz/UniFAR-FTUs huggingface dataset:
Frequency-Domain Design of Asymmetric Circular Differential Microphone Arrays

Circular differential microphone arrays (CDMAs) facilitate compact superdirective beamformers whose beampatterns are nearly frequency invariant. In contrast to linear differential microphone arrays where the optimal steering direction is at the endfire, CDMAs provide perfect steering for all azimuthal directions. Herein, we extend the traditional symmetric model of DMAs and establish an analytical asymmetric model for $N$th-order CDMAs. This model exploits the circular geometry to eliminate the inherent limitation of symmetric beampatterns associated with a linear geometry and allows also asymmetric beampatterns. This new model is then used to develop asymmetric versions of two optimal commonly used beampatterns namely the hypercardioid and the supercardioid. Experimental results demonstrate the advantages of the asymmetric model compared to the traditional symmetric one, when additional directional constraints are imposed. The proposed model yields superior performance in terms of white noise gain, directivity factor, and front-to-back ratio, as well as more flexible design of nulls for the interfering signals.
USP11 deubiquitinates RAE1 and plays a key role in bipolar spindle formation

Correct segregation of the mitotic chromosomes into daughter cells is a highly regulated process critical to safeguard genome stability. During M phase the spindle assembly checkpoint (SAC) ensures that all kinetochores are correctly attached before its inactivation allows progression into anaphase. Upon SAC inactivation, the anaphase promoting complex/cyclosome (APC/C) E3 ligase ubiquitinates and targets cyclin B and securin for proteasomal degradation. Here, we describe the identification of Ribonucleic Acid Export protein 1 (RAE1), a protein previously shown to be involved in SAC regulation and bipolar spindle formation, as a novel substrate of the deubiquitinating enzyme (DUB) Ubiquitin Specific Protease 11 (USP11). Lentiviral knock-down of USP11 or RAE1 in U2OS cells drastically reduces cell proliferation and increases multipolar spindle formation. We show that USP11 is associated with the mitotic spindle, does not regulate SAC inactivation, but controls ubiquitination of RAE1 at the mitotic spindle, hereby functionally modulating its interaction with Nuclear Mitotic Apparatus protein (NuMA).
UvrD helicase: An old dog with a new trick

Transcription‐coupled repair (TCR) is a phenomenon that exists in a wide variety of organisms from bacteria to humans. This mechanism allows cells to repair the actively transcribed DNA strand much faster than the non‐transcribed one. At the sites of bulky DNA damage RNA polymerase stalls, initiating recruitment of the repair machinery. It is a commonly accepted paradigm that bacterial cells utilize a sole coupling factor, called Mfd to initiate TCR. According to that model, Mfd removes transcription complexes stalled at the lesion site and simultaneously recruits repair machinery. However, this model was recently put in doubt by various discrepancies between the proposed universal role of Mfd in the TCR and its biochemical and phenotypical properties. Here, I present a second pathway of bacterial TCR recently discovered in my laboratory, which does not involve Mfd but implicates a common repair factor, UvrD, in a central position in the process.
Cavitary nodule segmentation in computed tomography images based on self-generating neural networks and particle swarm optimisation

Lung nodule segmentation is an important pre-processing step for analysis of solitary pulmonary nodules in computed tomography CT imaging. However, the previous nodule segmentation methods cannot segment the cavitary nodules entirely. To address this problem, an automated segmentation method based on self-generating neural networks and particle swarm optimisation PSO is proposed to ensure the integrity of cavitary nodule segmentation. Our segmentation method first roughly segments the image using a general region-growing method. Thereafter, the PSO-self-generating neural forest SGNF-based classification algorithm is used to cluster regions. Finally, grey and geometric features are utilised to identify the nodular region. Experimental results show that our method can achieve an average pixel overlap ratio of 88.9% compared with manual segmentation results. Moreover, compared with existing methods, this algorithm has higher segmentation precision and accuracy for cavitary nodules.
Colour Coding Scrubs as a Means of Improving Perioperative Communication

Effective communication within the operating department is essential for achieving patient safety. A large part of the perioperative communication is non-verbal. One type of non-verbal communication is ‘object communication’, the most common form of which is clothing. The colour coding of clothing such as scrubs has the potential to optimise perioperative communication with the patients and between the staff. A colour contains a coded message, and is a visual cue for an immediate identification of personnel. This is of key importance in the perioperative environment. The idea of colour coded scrubs in the perioperative setting has not been much explored to date and, given the potential contribution towards improvement of patient outcomes, deserves consideration.
Software Tools for Technology Transfer Manuscript No. Fighting Livelock in the Gnu I-protocol: a Case Study in Explicit-state Model Checking

The i-protocol, an optimized sliding-window protocol for GNU uucp, first came to our attention in 1995 when we used the Concurrency Factory's local model checker to detect, locate, and correct a non-trivial live-lock in version 1.04 of the protocol. Since then, we have conducted a systematic case study on the protocol using four verification tools, viz. Cospan, Murϕ, Spin, and XMC, each of which supports some form of explicit-state model checking. Our results show that although the i-protocol is inherently complex—the size of its state space grows exponentially in the window size and it deploys several sophisticated optimizations aimed at minimizing control-message and retransmission overhead—it is nonetheless amenable to a number of general-purpose abstraction techniques whose application can significantly reduce the size of the protocol's state space. Key words: explicit-state model checking – livelock – protocol verification – sliding-window protocol
Unsupervised stance classification in online debates

This paper proposes an unsupervised debate stance classification algorithm. In other words, finding the side a post author is taking in an online debate. Stance detection has a complementary role in information retrieval, opinion mining, text summarization, etc. Existing stance detection techniques are not able to effectively handle two challenges: determine whether a given post is a debate or not? If the post is a debate on a given topic, correctly classify the side that the post author is taking. In this paper, we propose techniques that addresses both the above issues. Compared to existing technique, our technique gives 30% improvement in detection of whether a post is a debate or not. Our technique is able to find the side that an author is taking in a debate by 10% higher F1 score compared to existing work. We achieve this improvement by using new syntactic rules, better aspect popularity detection, co-reference resolution, and a novel integer linear programming model to solve the problem.
Classification & prediction methods and their application

The contemporary management paradigm is focused on strategic management. A crucial component of strategic management is the definition of Key Performance Indicators (KPIs), and the ability to predict potential future developments. These two tasks are closely intertwined, as future developments can be guided using certain KPIs, which in turn can influence the future developments. On the other hand does the future development require action which may lead to an adaption of new KPIs? This cycle of future development and guidance with the help of KPIs requires a close monitoring of the current state, and the anticipated state of the business venture as well as the relevant environment. The domain of economics has developed many tools which provide frameworks supporting the search for useful KPIs and the exploration of futures space. While the internal factors of an organization can be analyzed by management, the environment is less accessible and predictable. In this thesis we propose approaches for environmental scanning and trend detection for a medium to long term time range. We identify three different data sources: macro indicators, web data and patent data, each covering a different time range. For short term developments, web data are useful, as any new trends are propagated almost instantly, and changes in any domain are reflected by changes in web content. With its accessibility and world wide coverage, the Web takes precedence over other sources of information. We describe a system that enables decision-making based on monitoring relevant topics in several dimensions; among which are country, time and intensity [133]. Long term developments are covered by macro indicators, e.g. child mortality or GDP. We survey various data sources and quantify their quality. We propose an architecture which unifies such indicators and allows forecasting and integration into more complex models [24]. As indicator data are provided as time series, we evaluate the performance of various interand extrapolation methods. Due to the importance of time series data, we do an excursus into time series classification, using deep learning techniques. As examples we use accelerometer data from human activities. Those are commonly used as there are sufficient sample data and published methods for evaluation available. We apply the evaluation of various network architectures, required window sizes and explore the benefit of a personalized activity recognition model. With a typical time between first submission and publishing of 18 month, we use patent data to cover the medium time range. While the data is usually structured and dimensions like country of origin and certain dates are available, the challenge is the prediction of the
Effects of distance and rescue time to medical facilities on traffic mortality utilizing GIS

ABSTRACT Although supported by little scientific evidence, the concept of ‘golden hour,’ which claims that mortality for trauma patients rises significantly over rescue time, plays a vital role in the design of our current trauma system. Only the contrary results could be found by using Fatality Analysis Reporting System data from 2010 to 2012. The results of logistic regression showed that longer total time intervals were associated with lower traffic mortality, with an odds ratio (OR) of 0.993 per minute. Utilizing the closest facility solver in the Geographic Information System, the effect of ‘reverse causation’ and the difference between trauma centre and non-trauma centre on the time-mortality relationship were discussed. Furthermore, based on the Kentucky Collision Analysis for the Public data from 2010 to 2012, the results of logistic regression showed that the mortality increased with each additional mile to the trauma centre with an OR of 1.011 per mile.
Brain gray matter alterations and associated demographic profiles in adults with autism spectrum disorder: A meta-analysis of voxel-based morphometry studies

Background: There is increasing evidence that children with autism spectrum disorder are accompanied by specific anatomical alterations. However, the anatomical abnormalities in adults with autism spectrum disorder are poorly understood. This study was aimed to identify the neuroanatomical substrates underlying the pathophysiology of adults with autism spectrum disorder. We also investigated the relationship between neuroanatomical alterations and clinical and demographic characteristics. Methods: A total of 13 datasets were enrolled, of which 12 studies compared whole-brain differences of 382 adult patients with autism and 393 healthy control subjects. We conducted a meta-analysis to quantitatively estimate regional gray matter volume abnormalities in individuals with autism using the effect-size signed differential mapping. Results: The voxel-wise meta-analysis revealed that relative to controls, adults with autism spectrum disorder had significantly increased gray matter volume in the middle temporal gyrus, superior temporal gyrus, postcentral gyrus and parahippocampal gyrus, and reduced gray matter volume in the anterior cingulate cortex and cerebellum. Variations in gray matter volume were significantly associated with the mean age and mean total IQ score of the patients, as well as with the percentage of male patients with autism. Conclusion: These findings confirmed that the neuroanatomical alterations in the fronto-temporal cortices, limbic system and cerebellum in adult individuals with autism were different from the children and young adolescent’s autism. The effects of demographic characteristics on the brain morphological changes allow us to further clarify the neurobiological mechanisms and developmental trajectory in adult population with autism spectrum disorder.
Sensory sensitivity and posttraumatic stress disorder in blast exposed veterans with mild traumatic brain injury

ABSTRACT The purpose of this study was to examine the unique contribution of posttraumatic stress disorder (PTSD) symptoms on sensory sensitivity following mild traumatic brain injury (mTBI) in an Operation Enduring Freedom and Operation Iraqi Freedom (OEF/OIF) Veteran sample. We hypothesized that the effect of PTSD on noise and light sensitivity following mTBI would be largely driven by PTSD-related hyperarousal symptoms. We compared the relationships between PTSD, noise sensitivity, and light sensitivity of 49 OEF/OIF Veterans with mTBI to that of 23 OEF/OIF Veterans without mTBI. Results suggest that intrusive experiences were significantly related to noise sensitivity in the mTBI group, while light sensitivity was significantly associated with avoidance. Hyperarousal symptoms significantly accounted for noise sensitivity in the no-blast, non-TBI group, whereas PTSD did not affect light sensitivity in this group. These data suggest that PTSD symptoms may uniquely influence the experience of noise and light sensitivity. As such, treatment targeting specific PTSD symptoms may yield clinically significant improvement in sensory sensitivity.
On Voxel-by-voxel Accumulated Dose for Prostate Radiation Therapy using Deformable Image Registration

Since delivered dose is rarely the same with planned, we calculated the delivered total dose to ten prostate radiotherapy patients treated with rectal balloons using deformable dose accumulation (DDA) and compared it with the planned dose. The patients were treated with TomoTherapy using two rectal balloon designs: five patients had the Radiadyne balloon (balloon A), and five patients had the EZ-EM balloon (balloon B). Prostate and rectal wall contours were outlined on each pre-treatment MVCT for all patients. Delivered fractional doses were calculated using the MVCT taken immediately prior to delivery. Dose grids were accumulated to the last MVCT using DDA tools in Pinnacle3 TM (v9.100, Philips Radiation Oncology Systems, Fitchburg, USA). Delivered total doses were compared with planned doses using prostate and rectal wall DVHs. The rectal NTCP was calculated based on total delivered and planned doses for all patients using the Lyman model. For 8/10 patients, the rectal wall NTCP calculated using the delivered total dose was less than planned, with seven patients showing a decrease of more than 5% in NTCP. For 2/10 patients studied, the rectal wall NTCP calculated using total delivered dose was 2% higher than planned. This study indicates that for patients receiving hypofractionated radiotherapy for prostate cancer with a rectal balloon, total delivered doses to prostate is similar with planned while delivered dose to rectal walls may be significantly different from planned doses. 8/10 patients show significant correlation between rectal balloon anterior-posterior positions and some VD values.
Dabrafenib plus trametinib for compassionate use in metastatic melanoma

Abstract The main objective of the study was to evaluate the efficacy and safety of dabrafenib alone or combined with trametinib for compassionate use in patients with metastatic melanoma. This retrospective, observational study involved 135 patients with unresectable stage IIIC or stage IV melanoma from an expanded-access program at 30 Spanish centers. Forty-eight patients received dabrafenib monotherapy and 87 received combination dabrafenib and trametinib; 4.4% and 95.6% of the patients had stage IIIC and IV melanoma, respectively. All patients showed BRAF mutations in their primary or metastatic lesions; 3 were positive for V600K while the remainder had V600E or V600+. A positive response to treatment was reported in 89.3% of the patients. Overall survival rates at 12 and 24 months were 59.6% (95% confidence interval [CI], 52.5–68.9%) and 36.4% (95% CI, 27.8–45%), respectively. Progression-free survival rates at 12 and 24 months were 39.3% (95% CI, 31.1–47.5%) and 21.6% (95% CI, 14.5–28.7%), respectively. Fifty-seven patients (42.2%) reported cutaneous toxicity of any type, mainly hyperkeratosis (14.8%) and rash (11.9%). The most frequent adverse events were pyrexia (27.4%), asthenia (19.3%), arthralgia (16.9%), and diarrhoea (13.2%). Our results suggest that both dabrafenib alone or in combination with trametinib are effective for compassionate use in terms of response and/or survival rates. However, differences in patients’ prognostic features ought to be considered. No new findings were revealed regarding the safety profiles of either regimen. This is the first study to evaluate the efficacy of these 2 selective BRAF and mitogen-activated extracellular signal-regulated kinase inhibitors in a real-world setting in Spain.
Can videoconferencing affect older people's engagement and perception of their social support in long‐term conditions management: a social network analysis from the Telehealth Literacy Project

Social support is a key component in managing long-term conditions. As people age in their homes, there is a greater risk of social isolation, which can be ameliorated by informal support networks. This study examined the relationship between changes in social support networks for older people living in a regional area following weekly videoconference groups delivered to the home. Between February and June 2014, we delivered 44 weekly group meetings via videoconference to participants in a regional town in Australia. The meetings provided participants with education and an opportunity to discuss health issues and connect with others in similar circumstances. An uncontrolled, pre-post-test methodology was employed. A social network tool was completed by 45 (87%) participants either pre- or post-intervention, of which 24 (46%) participants completed the tool pre- and post-intervention. In addition, 14 semi-structured interviews and 4 focus groups were conducted. Following the intervention, participants identified increased membership of their social networks, although they did not identify individuals from the weekly videoconference groups. The most important social support networks remained the same pre- and post-intervention namely, health professionals, close family and partners. However, post-intervention participants identified friends and wider family as more important to managing their chronic condition compared to pre-intervention. Participants derived social support, in particular, companionship, emotional and informational support as well as feeling more engaged with life, from the weekly videoconference meetings. Videoconference education groups delivered into the home can provide social support and enhance self-management for older people with chronic conditions. They provide the opportunity to develop a virtual social support network containing new and diverse social connections.
Assessing small-worldness of dynamic functional brain connectivity during complex tasks

The development of network theory has introduced new approaches to understand the brain as a complex system. Currently the time-variant functional connectivity of brain networks under complex tasks is still being investigated. To explore connectivity during complex cognitive and motor tasks, this study focused on the relevance of small-worldness to human workloads using EEG signals from a dynamic analytic approach. Experiments were designed to investigate the small-worldness under two types of flight simulation tasks at two levels of difficulty - easy and hard. The results demonstrated a consistent small-world architecture of brain connectivity with time-based variance during complex tasks. We noticed an increased small-world effect especially at the alpha band when performing hard tasks compared to easy tasks, which relate to high and low workload respectively. Our results show the potential of dynamic brain network analysis in exploring time-variant and task-dependent brain connectivity during complex tasks.
DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs

In this work we address the task of semantic image segmentation with Deep Learning and make three main contributions that are experimentally shown to have substantial practical merit. First, we highlight convolution with upsampled filters, or ‘atrous convolution’, as a powerful tool in dense prediction tasks. Atrous convolution allows us to explicitly control the resolution at which feature responses are computed within Deep Convolutional Neural Networks. It also allows us to effectively enlarge the field of view of filters to incorporate larger context without increasing the number of parameters or the amount of computation. Second, we propose atrous spatial pyramid pooling (ASPP) to robustly segment objects at multiple scales. ASPP probes an incoming convolutional feature layer with filters at multiple sampling rates and effective fields-of-views, thus capturing objects as well as image context at multiple scales. Third, we improve the localization of object boundaries by combining methods from DCNNs and probabilistic graphical models. The commonly deployed combination of max-pooling and downsampling in DCNNs achieves invariance but has a toll on localization accuracy. We overcome this by combining the responses at the final DCNN layer with a fully connected Conditional Random Field (CRF), which is shown both qualitatively and quantitatively to improve localization performance. Our proposed “DeepLab” system sets the new state-of-art at the PASCAL VOC-2012 semantic image segmentation task, reaching 79.7 percent mIOU in the test set, and advances the results on three other datasets: PASCAL-Context, PASCAL-Person-Part, and Cityscapes. All of our code is made publicly available online.
Elastohydrodynamic Analysis of a Rotary Lip Seal Using Flow Factors

An elastohydrodynamic analysis of a rotary lip seal is performed numerically, incorporating both the fluid mechanics of the lubricating film and the elastic deformation of the lip. Asperities on the lip surface dominate the behavior of the flow field in the lubricating film and are taken into account through the use of flow factors. Because previous analyses treated those asperities deterministically, they required very large computation times. The present approach is much less computationally intensive because the asperities are treated statistically. Because cavitation and asperity orientation play important roles, these are taken into account in the computation of the flow factors. An asperity distortion analysis is introduced to model the complex variations in the asperity distribution on the surface of the lip. Results of the analysis show how the operating parameters of the seal and the characteristics of the asperities affect such seal characteristics as the thickness of the lubricating film, reverse pumping rate, power dissipation, and liftoff speed.