Miracle-dz/UniFAR-FTUs huggingface dataset:
Primary adenoid cystic carcinoma of the tracheobronchial tree: A decade-long experience at a health centre in Mexico

Background: Mexico's National Institute of Respiratory Diseases (NIRD) is a third-level national reference center. Primary adenoid cystic carcinoma (PACC) is an uncommon neoplastic disorder; hence improvements in the description of this disease are needed. Materials and Methods: This is a retrospective clinical study based on all consecutive patients with pathological diagnoses of PACC seen at the NIRD between January 1, 2000 and December 31, 2009. Results: We identified 9 cases of PACC (67% female) out of a total of 2,634 patients with lung cancer seen during the period analyzed. The mean age of those 9 patients was 41 years (IQR 36-57), and the frequency of PACC at our center was 0.3%. It is important to note that 67% of those patients had a history of smoking and that 6 of the 9 had the antecedent of previous exposure to biomass fuel smoke. Baseline arterial blood gas analyses revealed a median of 61 mmHg for pO2 and 28.5 mmHg for pCO2. Median FVC was 78%, while FEV1 was 77% with an FEV1 /FVC ratio of 78. Death occurred in 56% of cases, and the median survival time was 17 months (IQR 6-26) after the initial diagnosis. Conclusions: The frequency of tracheobronchial PACC among patients with lung cancer was similar to that previously reported (0.3%). According to our results, lung function has no specific phenotype in this disease; however, some abnormalities could be related to potential risk factors such as tobacco use and exposure to biomass fuel smoke.
An architecture of Distributed Beta Wavelet Networks for large image classification in MapReduce

MapReduce has become a dominant parallel computing paradigm for storing and processing massive data due to its excellent scalability, reliability, and elasticity. In this paper, we present a new architecture of Distributed Beta Wavelet Networks {DBWN} for large image classification in MapReduce model. First to prove the performance of wavelet networks, a parallelized learning algorithm based on the Beta Wavelet Transform is proposed. Then the proposed structure of the {DBWN} is itemized. However the new algorithm is realized in MapReduce model. Comparisons with Fast Beta Wavelet Network {FBWN} are presented and discussed. Results of comparison have shown that the {DBWN} model performs better than {FBWN} model in classification rate and in the context of training run time.
Emergency department triage revisited

Triage is a process that is critical to the effective management of modern emergency departments. Triage systems aim, not only to ensure clinical justice for the patient, but also to provide an effective tool for departmental organisation, monitoring and evaluation. Over the last 20 years, triage systems have been standardised in a number of countries and efforts made to ensure consistency of application. However, the ongoing crowding of emergency departments resulting from access block and increased demand has led to calls for a review of systems of triage. In addition, international variance in triage systems limits the capacity for benchmarking. The aim of this paper is to provide a critical review of the literature pertaining to emergency department triage in order to inform the direction for future research. While education, guidelines and algorithms have been shown to reduce triage variation, there remains significant inconsistency in triage assessment arising from the diversity of factors determining the urgency of any individual patient. It is timely to accept this diversity, what is agreed, and what may be agreeable. It is time to develop and test an International Triage Scale (ITS) which is supported by an international collaborative approach towards a triage research agenda. This agenda would seek to further develop application and moderating tools and to utilise the scales for international benchmarking and research programmes.
Physical Activity of Young Overweight and Obese Children

The purpose of this study was to compare objectively measured physical activity (PA) and parent-reported assessments of the children’s PA in an overweight/obese child sample. A total of 67 child–parent dyads were recruited for study participation. Child anthropometric measures and parent-reported questionnaires were completed, and then PA was measured by accelerometers for 48 consecutive hours. These children demonstrated moderate-to-vigorous PA for 17.32 min per day on average. The children spent 86.7% of their time in sedentary activities. Parents’ reports of hyperactivity were significantly related to the objectively measured PA; however, the parents significantly overestimated the average amount of time children spent in each activity level. These findings suggest that although parents may be able to generally characterize their child’s PA, they may not be able to accurately report specific information and thus may struggle to identify clear activity goals for their child and/or be unable to assess intervention effects.
Regulation of expression of the ftsA cell division gene by sequences in upstream genes

The essential cell division genes ftsQ and ftsA overlap by 1 bp (A. C. Robinson, D. J. Kenan, G. F. Hatfull, N. F. Sullivan, R. Spiegelberg, and W. D. Donachie. J. Bacteriol. 160:546-555, 1984; Q.-M. Yi, S. Rockenbach, J. E. Ward, and J. F. Lutkenhaus. J. Mol. Biol. 184:399-412, 1985). We have previously shown that ftsA can be expressed from a weak promoter located within the ftsQ gene (Robinson et al., J. Bacteriol. 160:546-555, 1984). We report here the effects on ftsA expression of a series of deletions within ftsQ. We find that two regions upstream of the promoter are important in its expression. When both are present, ftsA is expressed, as is also the case when both are absent. The two regulatory elements (O1 and O2) have 9-bp sequences, of which 8 bp are identical.
A sporulation-specific, sigF-dependent protein, SspA, affects septum positioning in Streptomyces coelicolor

The RNA polymerase sigma factor SigF controls late development during sporulation in the filamentous bacterium Streptomyces coelicolor. The only known SigF‐dependent gene identified so far, SCO5321, is found in the biosynthetic cluster encoding spore pigment synthesis. Here we identify the first direct target for SigF, the gene sspA, encoding a sporulation‐specific protein. Bioinformatic analysis suggests that SspA is a secreted lipoprotein with two PepSY signature domains. The sspA deletion mutant exhibits irregular sporulation septation and altered spore shape, suggesting that SspA plays a role in septum formation and spore maturation. The fluorescent translational fusion protein SspA–mCherry localized first to septum sites, then subsequently around the surface of the spores. Both SspA protein and sspA transcription are absent from the sigF null mutant. Moreover, in vitro transcription assay confirmed that RNA polymerase holoenzyme containing SigF is sufficient for initiation of transcription from a single sspA promoter. In addition, in vivo and in vitro experiments showed that sspA is a direct target of BldD, which functions to repress sporulation genes, including whiG, ftsZ and ssgB, during vegetative growth, co‐ordinating their expression during sporulation septation.
Exploiting Saliency for Object Segmentation from Image Level Labels

There have been remarkable improvements in the semantic labelling task in the recent years. However, the state of the art methods rely on large-scale pixel-level annotations. This paper studies the problem of training a pixel-wise semantic labeller network from image-level annotations of the present object classes. Recently, it has been shown that high quality seeds indicating discriminative object regions can be obtained from image-level labels. Without additional information, obtaining the full extent of the object is an inherently ill-posed problem due to co-occurrences. We propose using a saliency model as additional information and hereby exploit prior knowledge on the object extent and image statistics. We show how to combine both information sources in order to recover 80% of the fully supervised performance &#x2013; which is the new state of the art in weakly supervised training for pixel-wise semantic labelling.
Networking as a job search behaviour: A social network perspective

Although networking is typically recommended as a job search strategy in the popular press, research on networking as a job search behaviour is scarce. On the basis of social network theory, the present study investigated whether the structure and composition of job seekers' social network determined their networking behaviour and moderated its relationship with job search and employment outcomes. The data were collected in a large, representative sample of 1,177 unemployed Flemish job seekers, using a two-wave longitudinal design. Job seekers with a larger social network and with stronger ties in their network spent more time networking, beyond individual differences in extraversion and conscientiousness. Networking explained incremental variance in job offers beyond job seekers' use of print advertising, the internet, and public employment services, but not in employment outcomes. Some evidence was found indicating that networking might be more effective for job seekers whose social network contains weaker and higher-status ties.
An Energy Harvesting 2$\times$2 60 GHz Transceiver With Scalable Data Rate of 38–2450 Mb/s for Near-Range Communication

A fully integrated 2 ×2 CMOS transceiver at 60 GHz with energy harvesting capability in the transmitter mode and on-chip dipole antennas is demonstrated. The radio supports on-off-keying (OOK) modulation and a programmable data rate of 38 to 2450 Mb/s at a BER of less than 5 ×10 -4. The power consumption of the transmitter scales with data rate from 100 μW to 6.3 mW at 5 cm range and from 260 μW to 11.9 mW at 10 cm range. This yields an energy efficiency of 2.6 pJ/b at 5 cm and 4.9 pJ/b at 10cm. The energy harvesting circuits operate at 2.45 GHz with an average efficiency of 33%. The harvesting antenna and its matching components are off-chip. The complete transceiver including the energy harvesting block and on-chip antennas occupies 1.62 mm 2 in 40 nm CMOS.
Learning to Manipulate Articulated Objects in Unstructured Environments Using a Grounded Relational Representation

We introduce a learning-based approach to manipulation in unstructured environments. This approach permits autonomous acquisition of manipulation expertise from interactions with the environment. The resulting expertise enables a robot to perform effective manipulation based on partial state information. The manipulation expertise is represented in a relational state representation and learned using relational reinforcement learning. The relational representation renders learning tractable by collapsing a large number of states onto a single, relational state. The relational state representation is carefully grounded in the perceptual and interaction skills of the robot. This ensures that symbolically learned knowledge remains meaningful in the physical world. We experimentally validate the proposed learning approach on the task of manipulating an articulated object to obtain a model of its kinematic structure. Our experiments demonstrate that the manipulation expertise acquired by the robot leads to substantial performance improvements. These improvements are maintained when experience is applied to previously unseen objects.
Scaling Usability-Evaluation to a Minimum ?

Today there are several approaches available to develop usable software, but many products still lack a minimum of usability. One reason may be the lack of usability in some “heavyweight” usability engineering and evaluation methods themselves. It seems that many development teams think such elaborate process models are too complex to be applied, so they just don’t do usability engineering. This paper offers a scalable “lightweight” approach to usability engineering. It starts from the idea that an easy and imperfect but used method is better than a complex unused method. The paper gives a short overview on the intentions, the basic ideas, the implementation, as well as experiences in using the method and discusses their possible application in product certification.
The interactional community: a structural network analysis of community action in three Midwestern towns

iv CHAPTER L INTRODUCTION I CHAPTER! LITERATURE AND THEORY 18 CHAPTERS. METHODOLOGY 53 CHAPTER 4. ANALYSIS OF RESIDENT SURVEY 72 CHAPTERS. NETWORK ANAL YSIS 97 CHAPTER 6. COMMUNITY ACTION ANAL YSIS 145 CHAPTER 7. CONCLUSIONS 166 APPENDIX A. RESIDENT SURVEY 180 APPENDDCB. INFORMANT SURVEY 190
Posterior Expectation of Regularly Paved Random Histograms

We present a novel method for averaging a sequence of histogram states visited by a Metropolis-Hastings Markov chain whose stationary distribution is the posterior distribution over a dense space of tree-based histograms. The computational efficiency of our posterior mean histogram estimate relies on a statistical data-structure that is sufficient for nonparametric density estimation of massive, multidimensional metric data. This data-structure is formalized as statistical regular paving (SRP). A regular paving (RP) is a binary tree obtained by selectively bisecting boxes along their first widest side. SRP augments RP by mutably caching the recursively computable sufficient statistics of the data. The base Markov chain used to propose moves for the Metropolis-Hastings chain is a random walk that data-adaptively prunes and grows the SRP histogram tree. We use a prior distribution based on Catalan numbers and detect convergence heuristically. The performance of our posterior mean SRP histogram is empirically assessed for large sample sizes simulated from several multivariate distributions that belong to the space of SRP histograms.
Pectoral Muscle Attenuation as a Marker for Breast Cancer Risk in Full-Field Digital Mammography

Background: Mammographic percent density is an established marker of breast cancer risk. In a study of screen film mammograms, we recently reported a novel feature from the pectoral muscle region to be associated with breast cancer risk independently of area percent density (APD). We now investigate whether our novel feature is associated with risk in a study based on full-field digital mammography (FFDM). Methods: We carried out a breast cancer risk analysis using a data set of 3,552 healthy controls and 278 cases. We included three image-based measures in our analyses: volumetric percent density (VPD), APD, and the mean intensity of the pectoral muscle (MIP). The datasets comprised different machine vendors and models. In addition, the controls dataset was used to test for the association of our measures against rs10995190, in the ZNF365 gene, a genetic variant known to be associated with mammography density and breast cancer risk. Results: MIP was associated with breast cancer risk [per SD OR, 0.811; 95% confidence interval (CI), 0.707–0.930; P = 0.0028] after adjusting for conventional covariates and VPD. It was also associated with the genetic variant rs10995190 after adjusting for VPD and other covariates (per allele effect = 0.111; 95% CI, 0.053–0.170; P = 1.8 × 10−4). Results were similar when adjusting for APD instead of VPD. Conclusion: MIP is a novel mammographic marker, which is associated with breast cancer risk and the genetic variant rs10995190 independently of PD measures. Impact: Inclusion of MIP in risk models should be considered for studies using PD from FFDM. Cancer Epidemiol Biomarkers Prev; 24(6); 985–91. ©2015 AACR.
Sincerity and Deception in Speech: Two Sides of the Same Coin? A Transfer- and Multi-Task Learning Perspective

In this work, we investigate the coherence between inferable de-ception and perceived sincerity in speech, as featured in the De-ception and Sincerity tasks of the INTERSPEECH 2016 Computational Paralinguistics ChallengE (ComParE). We demonstrate an effective approach that combines the corpora of both Challenge tasks to achieve higher classiﬁcation accuracy. We show that the na¨ıve label mapping method based on the assumption that sincerity and deception are just ‘two sides of the same coin’, i.e., taking deceptive speech as equivalent to non-sincere speech and vice versa, does not yield satisfactory results. However, we can exploit the interplay and synergies between these characteristics. To achieve this, we combine our previously introduced approach for data aggregation by semi-supervised cross-task label completion with multi-task learning, and knowledge-based instance selection. In the result, our approach achieves significant error rate reductions compared to the ofﬁcial Challenge baseline.
Polyblock Algorithm-Based Robust Beamforming for Downlink Multi-User Systems With Per-Antenna PowerConstraints

In this paper, we investigate the robust beamforming for multi-user multiple-input single-output systems under quantized channel direction information (CDI) with per-antenna power constraints. The robustness of the considered beamforming design is achieved in the sense that the stochastic interference leakage is below a certain level by a given probability. Our design objective is to maximize the expectation of the weighted sum-rate performance. From the discussion of the non-robust optimal beamforming based on the polyblock algorithm, we propose a robust beamforming scheme for the quantized CDI case with per-antenna power constraints. In the proposed beamforming scheme, we use Jensen's inequality to generate a tractable feasibility problem for the polyblock algorithm and apply the semi-definite programming relaxation, as well as the randomization technique to find its approximate rank-one matrix solution and user equipments' beamforming vectors. Simulation results show that substantial gains can be achieved by the proposed scheme compared with the existing schemes in terms of the average weighted sum-rate performance. Although very high complexity is required for the implementation of the proposed scheme, it stands as a good benchmark for robust beamforming designs.
Of mice and (wo)men: Mouse models of breast cancer metastasis to bone

Approximately 200,000 new cases of breast cancer and 40,000 breast cancer deaths occur annually in the United States. Among breast cancers that become aggressive, metastasis to bone marrow is common. In many respects, the problem of metastasis to bone is more serious than the original tumor, and its detection often signals the onset of the life-threatening phase of this disease. Long before death, however, a number of other serious complications threaten the well-being of the patient: These include fractures, disability, pain, and hypercalcemia. While important advances have been made in understanding and treating the primary malignancy, relatively little progress has beenmade in understanding the pathogenesis of metastasis and developing effective therapies for these disseminated growths. To date, relatively few validated targets have been identified in breast cancer metastases, precluding efforts to discover metastasis-specific therapies. Such treatments, combined with strategies to eradicate the primary malignancy, would have high clinical impact. These considerations highlight the compelling need to understand the molecular mechanisms by which breast cancer metastasizes to bone and the urgent need for safe and effective agents to prevent or treat breast cancer skeletal metastases. One barrier to identifying the mechanisms of osteotropic metastasis has been the lack of animal models that reflect the complex biology of metastasis in humans. A number of experimental models of murine mammary tumor development have been developed, but these models rarely generate bone metastases from the primary site. The most commonly used models of breast cancer metastasis employ intravenous administration of cancer cells, a route that bypasses the early steps of the invasion-metastasis cascade, which include local invasion and intravasation. Unfortunately, these experimental models typically generate lung metastases, with few concomitantly developed skeletal metastases. In order to circumvent the trapping of cells in the lung microvasculature, thereby permitting alternative sites of metastasis formation, alternative routes of administration have been employed. Thus human breast cancer cells can be administered via intracadiac injection or are introduced into the mouse bone marrow through direct inoculation. While these models of human breast cancer to the mouse bone fail to recapitulate the early steps of the invasion-metastatic cascade, they often can generate bone metastases at high frequency and have proven to be valuable for elucidating the later steps of this progression, specifically the process of metastatic colonization, in which initially formed micrometastases succeed in colonizing the marrow and generating osteolytic metastases. These models have been improved further by the use of mice bearing engrafted human bone fragments, allowing migration of human cancer cells to a human target organ.
Four-State Magnetoresistance in Epitaxial CoFe-Based Magnetic Tunnel Junctions

A four-state magnetic random access memory (MRAM) was developed using an epitaxial Co<sub>50</sub>Fe<sub>50</sub>-MgO-Co<sub>50</sub>Fe <sub>50</sub> magnetic tunnel junction (MTJ) with a tunnel magnetoresistance (TMR) ratio of 145% at room temperature (RT). Four remanent magnetization states in the single-crystalline Co<sub>50</sub>Fe<sub>50</sub> electrode, due to the cubic anisotropy with easy axes of the lang110rang directions, result in four possible angular-dependent TMRs, each separated by more than 20% at RT. Analysis of the asteroid curve for Co<sub>50</sub>Fe<sub>50</sub> indicated that the magnetic field along 22.5deg from the lang110rang directions made it possible to change the magnetization direction of the selected cell without disturbing those of the half-selected cells
On the Laplace-Beltrami operator and brain surface flattening

In this paper, using certain conformal mappings from uniformization theory, the authors give an explicit method for flattening the brain surface in a way which preserves angles. From a triangulated surface representation of the cortex, the authors indicate how the procedure may be implemented using finite elements. Further, they show how the geometry of the brain surface may be studied using this approach.
Partial Similarity of Shapes Using a Statistical Significance Measure

Partial matching of geometric structures is important in computer vision, pattern recognition and shape analysis applications. The problem consists of matching similar parts of shapes that may be dissimilar as a whole. Recently, it was proposed to consider partial similarity as a multi-criterion optimization problem trying to simultaneously maximize the similarity and the significance of the matching parts. A major challenge in that framework is providing a quantitative measure of the significance of a part of an object. Here, we define the significance of a part of a shape by its discriminative power with respect do a given shape database — that is, the uniqueness of the part. We define a point-wise significance density using a statistical weighting approach similar to the term frequency-inverse document frequency (tf-idf) weighting employed in search engines. The significance measure of a given part is obtained by integrating over this density. Numerical experiments show that the proposed approach produces intuitive significant parts, and demonstrate an improvement in the performance of partial matching between shapes.