Miracle-dz/UniFAR-FTUs huggingface dataset:
Perceptual grouping in the cocktail party: contributions of voice-feature continuity

Cocktail parties pose a difficult yet solvable problem for the auditory system. Previous work has shown that the cocktail-party problem is considerably easier when all sounds in the target stream are spoken by the same talker (the voice-continuity benefit). The present study investigated the contributions of two of the most salient voice features — glottal-pulse rate (GPR) and vocal-tract length (VTL) — to the voice-continuity benefit. Twenty young, normal-hearing listeners participated in two experiments. On each trial, listeners heard concurrent sequences of spoken digits from three different spatial locations and reported the digits coming from a target location. Critically, across conditions, GPR and VTL either remained constant or varied across target digits. Additionally, across experiments, the target location either remained constant (Experiment 1) or varied (Experiment 2) within a trial. In Experiment 1, listeners benefited from continuity in either voice feature, but VTL continuity was more helpful than GPR continuity. In Experiment 2, spatial discontinuity greatly hindered listeners’ abilities to exploit continuity in GPR and VTL. The present results suggest that selective attention benefits from continuity in target voice features, and that VTL and GPR play different roles for perceptual grouping and stream segregation in the cocktail party.
Effective Subtree Encoding for Easy-First Dependency Parsing

Easy-first parsing relies on subtree re-ranking to build the complete parse tree. Whereas the intermediate state of parsing processing are represented by various subtrees, whose internal structural information is the key lead for later parsing action decisions, we explore a better representation for such subtrees. In detail, this work introduces a bottom-up subtree encoder based on the child-sum tree-LSTM. Starting from an easy-first dependency parser without other handcraft features, we show that the effective subtree encoder does promote the parsing process, and is able to make a greedy search easy-first parser achieve promising results on benchmark treebanks compared to state-of-the-art baselines.
High Speed Tracking With A Fourier Domain Kernelized Correlation Filter

It is challenging to design a high speed tracking approach using l1-norm due to its non-differentiability. In this paper, a new kernelized correlation filter is introduced by leveraging the sparsity attribute of l1-norm based regularization to design a high speed tracker. We combine the l1-norm and l2-norm based regularizations in one Huber-type loss function, and then formulate an optimization problem in the Fourier Domain for fast computation, which enables the tracker to adaptively ignore the noisy features produced from occlusion and illumination variation, while keep the advantages of l2-norm based regression. This is achieved due to the attribute of Convolution Theorem that the correlation in spatial domain corresponds to an element-wise product in the Fourier domain, resulting in that the l1-norm optimization problem could be decomposed into multiple sub-optimization spaces in the Fourier domain. But the optimized variables in the Fourier domain are complex, which makes using the l1-norm impossible if the real and imaginary parts of the variables cannot be separated. However, our proposed optimization problem is formulated in such a way that their real part and imaginary parts are indeed well separated. As such, the proposed optimization problem can be solved efficiently to obtain their optimal values independently with closed-form solutions. Extensive experiments on two large benchmark datasets demonstrate that the proposed tracking algorithm significantly improves the tracking accuracy of the original kernelized correlation filter (KCF) while with little sacrifice on tracking speed. Moreover, it outperforms the state-of-the-art approaches in terms of accuracy, efficiency, and robustness.
Epidermal Growth Factor Receptor Gene in Non-Small-Cell Lung Cancer: The Importance of Promoter Polymorphism Investigation

Recently, epidermal growth factor receptor (EGFR) was a key molecule in investigation of lung cancer, and it was a target for a new therapeutic strategy, based on molecular analyses. In this review, we have summarized some issues considering the role of EGFR in lung cancer, its coding gene, and its promoter gene polymorphisms (SNPs) -216G/T and -191C/A in non-small-cell lung cancer (NSCLC). The position of the SNPs indicates their significant role in EGFR regulation. The accumulation of knowledge regarding SNPs lately suggests their significant and important role in the onset of carcinogenesis, the prediction of the onset of metastases, the response to therapy with TKI inhibitors, and the onset of toxic effects of the applied therapy. Based on this, we suggest further studies of the relationship of clinical significance to SNPs in patients with lung tumors.
The use of decellularised animal tissue to study disseminating cancer cells

ABSTRACT Since the establishment of cell culture, common practice has been to grow adherent cells in 2D monolayers. Although cells behave completely differently when grown under these artificial conditions, the ease of 2D culturing has meant that this practice still prevails, and adopting conditions that more closely reflect the natural microenvironment has been met with substantial inertia. The alternative, animal models that mimic natural human physiology, are less accessible, strictly regulated and require licences and expensive facilities. Although transition from 2D to 3D cell culturing is gathering momentum, there is a clear need for alternative culturing methods that more closely resemble in vivo conditions. Here, we show that decellularised organs gleaned from discarded animal carcasses are ideal biomimetic scaffolds to support secondary tumour initiation in vitro. Further, we describe how to decellularise tissue and perform basic histochemistry and immunofluorescence procedures for cell and matrix detection. Cancer cell behaviour on this matrix is followed by way of an example. Because integration into the traditional work flow is easy and inexpensive, we hope this article will encourage other researchers to adopt this approach. Summary: Decellularised matrices derived from discarded animal-house material provide a physiologically relevant 3D-platform for studying cancer cell behaviour that is easy to use, inexpensive and embraces the 3Rs.
Polarity sorting drives remodeling of actin-myosin networks

ABSTRACT Cytoskeletal networks of actin filaments and myosin motors drive many dynamic cell processes. A key characteristic of these networks is their contractility. Despite intense experimental and theoretical efforts, it is not clear what mechanism favors network contraction over expansion. Recent work points to a dominant role for the nonlinear mechanical response of actin filaments, which can withstand stretching but buckle upon compression. Here, we present an alternative mechanism. We study how interactions between actin and myosin-2 at the single-filament level translate into contraction at the network scale by performing time-lapse imaging on reconstituted quasi-2D networks mimicking the cell cortex. We observe myosin end-dwelling after it runs processively along actin filaments. This leads to transport and clustering of actin filament ends and the formation of transiently stable bipolar structures. Further, we show that myosin-driven polarity sorting produces polar actin asters, which act as contractile nodes that drive contraction in crosslinked networks. Computer simulations comparing the roles of the end-dwelling mechanism and a buckling-dependent mechanism show that the relative contribution of end-dwelling contraction increases as the network mesh-size decreases. Summary: Experiments combined with simulations show that myosin-2 motors drive actin network contraction via a polarity-sorting mechanism that relies on dwelling of myosins at the plus end of actin filaments.
Changing Beliefs About Emotions in IBS: A Single Case Design

Background: Previous research suggests benefits of targeting beliefs about the unacceptability of emotions in treatment for irritable bowel syndrome (IBS). Aims: The current study developed and tested an intervention focusing on beliefs and behaviours around emotional expression. Method: Four participants with IBS attended five group sessions using cognitive behavioural techniques focusing on beliefs about the unacceptability of expressing emotions. Bi-weekly questionnaires were completed and a group interview was conducted. This study used an AB design with four participants. Results: Averages indicate that participants showed decreases in beliefs about unacceptability of emotions and emotional suppression during the intervention, although this was not reflected in any of the individual trends in Beliefs about Emotions Scale scores and was significant in only one individual case for Courtauld Emotional Control Scale scores. Affective distress and quality of life improved during follow-up, with only one participant not improving with regard to distress. Qualitative data suggest that participants felt that the intervention was beneficial, referencing the value in sharing their emotions. Conclusions: This study suggests the potential for beliefs about emotions and emotional suppression to be addressed in cognitive behavioural interventions in IBS. That beliefs and behaviours improved before outcomes suggests they may be important processes to investigate in treatment for IBS.
Assessment of the performance of blood glucose monitoring systems for monitoring dysglycaemia in neonatal patients

Objective To validate a three-step protocol that assesses the clinical risk associated with using blood glucose monitoring systems (BGMS) in neonates for the management of dysglycaemia. Method The three-step validation approach included confirmation of the accuracy of the reference method using National Institute of Standards and Technology (NIST) glucose standards, assessment of analytical risk performed on whole blood collected from paediatric patients routinely tested for glucose and a clinical risk assessment performed using heel stick capillary samples collected from 147 new-born babies and neonates admitted to intensive care. BGMS glucose measurements were compared with the NIST aligned laboratory reference method. Results The accuracy of the laboratory reference method was confirmed with the NIST standards. Specificity studies demonstrated that the accuracy of one of the BGMS was affected, particularly, in the hypoglycaemic range, by known interference factors including haematocrit, ascorbic acid, lactose, galactose, N-acetylcysteine and glutathione. The accuracy of the other BGMS was unaffected. The clinical performance of this BGMS in neonates met the system accuracy criteria of Clinical and Laboratory Standards Institute (CLSI) POCT 12-A3 standard for evaluating hospital BGMS with 95.1% of glucose measurements within±0.67 mmol/L for samples ≤5.55 mmol/L and 95.6% within±12.5% for samples>5.55 mmol/L. Conclusions This three-step validation protocol provides a challenging approach for determining the accuracy and reliability of BGMS for managing dysglycaemia in neonates. StatStrip BGMS achieved analytical and clinical performance criteria confirming its suitability for use in neonates. We advocate that this validation approach should be considered for performance evaluations of both BGMS and continuous glucose monitoring systems going forward.
Inversion-Based 3-D Seismic Denoising for Exploring Spatial Edges and Spatio-Temporal Signal Redundancy

Seismic data are increasingly required to be high quality for the continuous improvement of the degree of exploration. From the viewpoint of inversion, the utilization of more information is an effective way to improve the signal-to-noise ratio of seismic data. In this letter, we adopt simultaneous sparsity constraints of the first-order differences of signals along the time direction and two spatial directions, described by minimizing the Cauchy function, as a combined constraint (or regularization) term imposed on the time-domain data misfit to propose an inversion-based 3-D seismic denoising method. In this way, the redundancies among time slices and seismic sections along two spatial directions are simultaneously considered, and the edges along the spatial directions can be preserved. Through analyzing the first-order derivative of the sum of the data misfit term and the designed combined regularization term (or the objective function), we derive that the relationship between data and desired signal samples in the range of the first-order neighborhood can be expressed as a linear system with seven data-dependent coefficients. Furthermore, it can be inferred that the sparsity constraints of signal differences along different dimensional directions of 3-D data have some complementary functions of noise reduction and signal preservation. We use a 3-D synthetic data set, a 3-D real poststack data set, and a 3-D real prestack data set to determine that the proposed method is an effective amplitude-preservation denoising tool with an acceptable computational cost.
Serine protease modulation of Dependence Receptors and EMT protein expression

ABSTRACT Expression of the tumour suppressor Deleted in Colorectal Cancer (DCC) and the related protein neogenin is reduced by the mammalian serine protease chymotrypsin or the bacterial serine protease subtilisin, with increased cell migration. The present work examines whether these actions are associated with changes in the expression of cadherins, β-catenin and vimentin, established markers of the Epithelial-Mesenchymal Transition (EMT) which has been linked with cell migration and tumour metastasis. The results confirm the depletion of DCC and neogenin and show that chymotrypsin and subtilisin also reduce expression of β-catenin in acutely prepared tissue sections but not in human mammary adenocarcinoma MCF-7 or MDA-MB-231 cells cultured in normal media, or primary normal human breast cells. A loss of β-catenin was also seen in low serum media but transfecting cells with a dcc-containing plasmid induced resistance. E-cadherin was not consistently affected but vimentin was induced by low serum-containing media and was increased by serine proteases in MCF-7 and MDA-MB-231 cells in parallel with increased wound closure. Vimentin might contribute to the promotion of cell migration. The results suggest that changes in EMT proteins depend on the cells or tissues concerned and do not parallel the expression of DCC and neogenin. The increased cell migration induced by serine proteases is not consistently associated with the expression of the EMT proteins implying either that the increased migration may be independent of EMT or supporting the view that EMT is not itself consistently related to migration. (241).
Runs of homozygosity associate with decreased risks of lung cancer in never-smoking East Asian females

Although genome-wide association studies (GWASs) have identified some risk single-nucleotide polymorphisms in East Asian never-smoking females, the unexplained missing heritability is still required to be investigated. Runs of homozygosity (ROHs) are thought to be a type of genetic variation acting on human complex traits and diseases. We detected ROHs in 8,881 East Asian never-smoking women. The summed ROHs were used to fit a logistic regression model which noteworthily revealed a significant association between ROHs and the decreased risk of lung cancer (P < 0.05). We identified 4 common ROHs regions located at 2p22.1, which were significantly associated with decreased risk of lung cancer (P = 2.00 × 10-4 - 1.35 × 10-4). Functional annotation was conducted to investigate the regulatory function of ROHs. The common ROHs were overlapped with potential regulatory elements, such as active epigenome elements and chromatin states in lung-derived cell lines. SOS1 and ARHGEF33 were significantly up-regulated as the putative target genes of the identified ROHs in lung cancer samples according to the analysis of differently expressed genes. Our results suggest that ROHs could act as recessive contributing factors and regulatory elements to influence the risk of lung cancer in never-smoking East Asian females.
A study on a sulfur spring (Ain Al Kibrit) ecosystem along Tigris River Mosul, Iraq

Ain Al Kibrit sulfur spring along Tigris River in contrast with two other stations, before and after the input to the Tigris River were studied. Some chemical, physical parameters (water temperature, pH, EC, alkalinity, total hardness, and sulfate) and total number of phytoplankton were studied during 2003-2004. Samples were taken from the spring, inlet, and outlet along the Tigris River. The results show 5-20 fold differences between stations in almost all parameters, whereas, thermostatic conditions were confirmed in Ain Al Kibrit (24±1). In contrast, the total number of phytoplankton when observed to be fifty folds less in Ain Al Kibrit. This study may be regarded as a first attempt to deal with such factors in this area.
A Broadband Phototransistor Based on Three-Dimensional Reduced Graphene Oxide Foam

Three-dimensional (3D) cross-linked polymer-like reduced graphene oxide foams (rGOFs) with a seamlessly continuous graphene network, exhibit high photoresponsive and conductivity and have received much attention regarding solar cells and supercapacitors. However, little attention has been paid to photodetection applications of 3D rGOFs. Here we report a novel broadband phototransistor based on metal-3D GFs-metal, which exhibits a high light absorption and a wide spectra response ranging at least from 400 to 1600 nm wavelength with a maximum photoresponsivity of 10 mA/W at 400 nm. In particular, stable and reproducible photocurrent cycles are achieved under different light blue light (405 nm), green light (532 nm), and NIR (808 nm) irradiations. Moreover, the device displays a typical transistor characteristic with a rapid response time of 18 ms at under 532 nm irradiation. The excellent performances indicate 3D rGOF as a promising candidate for future photodetection application.
The roles of preventive and curative health care in economic development

Introduction Both the preventive and curative healthcare provisions accumulate agents’ health stock and stimulate economies’ productivities. However, with limited medical resources, increases in preventive health expenditure crowd out curative expenditure, and vice versa, which in turn impairs the population’s health and deters economic growth. This research aims to provide a empirically rigorous test on the hypothesis that optimally allocating health expenditure between prevention and cures stimulates economic growth within different countries, especially developed countries, and investigates whether health services are luxury goods on the path of economic development. Methods Based on OECD country experiences, this present study uses the system generalized method of moments (GMM) estimation method to examine the roles of preventive and curative healthcare services over the path of economic development and proves that preventive and curative health spending have non-linear effects on economic performance. Results For growth maximization, the optimal share of preventive health expenditure to GDP is 0.44% with per capita GDP at US$40,465; the real share is 0.25% with per capita GDP at US$35,230. The optimal share of curative health expenditure to GDP is 10.96% with per capita GDP at US$41,816; the real share is 8.26% with per capita GDP at US$35,230. Accordingly, the estimated optimal provision of health services are currently underprovided. This research further estimates the effects of income on demand for care and shows that the income elasticities of preventive and curative health care are greater than unity. Health services are luxury goods. Conclusions Economies with higher incomes demand such services more than those with lower incomes. The large positive effects of income on preventive care use exist.
RGB-D SLAM in Dynamic Environments Using Point Correlations

In this paper, a simultaneous localization and mapping (SLAM) method that eliminates the influence of moving objects in dynamic environments is proposed. This method utilizes the correlation between map points to separate points that are part of the static scene and points that are part of different moving objects into different groups. A sparse graph is first created using Delaunay triangulation from all map points. In this graph, the vertices represent map points, and each edge represents the correlation between adjacent points. If the relative position between two points remains consistent over time, there is correlation between them, and they are considered to be moving together rigidly. If not, they are considered to have no correlation and to be in separate groups. After the edges between the uncorrelated points are removed during point-correlation optimization, the remaining graph separates the map points of the moving objects from the map points of the static scene. The largest group is assumed to be the group of reliable static map points. Finally, motion estimation is performed using only these points. The proposed method was implemented for RGB-D sensors, evaluated with a public RGB-D benchmark, and tested in several additional challenging environments. The experimental results demonstrate that robust and accurate performance can be achieved by the proposed SLAM method in both slightly and highly dynamic environments. Compared with other state-of-the-art methods, the proposed method can provide competitive accuracy with good real-time performance.
Toward Achieving Robust Low-Level and High-Level Scene Parsing

In this paper, we address the challenging task of scene segmentation. We first discuss and compare two widely used approaches to retain detailed spatial information from pre-trained convolutional context network (CNN)—“dilation” and “skip”. Then, we demonstrate that the parsing performance of “skip” network can be noticeably improved by modifying the parameterization of skip layers. Furthermore, we introduce a “dense skip” architecture to retain a rich set of low-level information from the pre-trained CNN, which is essential to improve the low-level parsing performance. Meanwhile, we propose a CCN and place it on top of pre-trained CNNs, which is used to aggregate contexts for high-level feature maps so that robust high-level parsing can be achieved. We name our segmentation network enhanced fully convolutional network (EFCN) based on its significantly enhanced structure over FCN. Extensive experimental studies justify each contribution separately. Without bells and whistles, EFCN achieves state-of-the-arts on segmentation datasets of ADE20K, Pascal Context, SUN-RGBD, and Pascal VOC 2012.