Miracle-dz/UniFAR-FTUs huggingface dataset:
A streamlined workflow for single-cells genome-wide copy-number profiling by low-pass sequencing of LM-PCR whole-genome amplification products

Chromosomal instability and associated chromosomal aberrations are hallmarks of cancer and play a critical role in disease progression and development of resistance to drugs. Single-cell genome analysis has gained interest in latest years as a source of biomarkers for targeted-therapy selection and drug resistance, and several methods have been developed to amplify the genomic DNA and to produce libraries suitable for Whole Genome Sequencing (WGS). However, most protocols require several enzymatic and cleanup steps, thus increasing the complexity and length of protocols, while robustness and speed are key factors for clinical applications. To tackle this issue, we developed a single-tube, single-step, streamlined protocol, exploiting ligation mediated PCR (LM-PCR) Whole Genome Amplification (WGA) method, for low-pass genome sequencing with the Ion Torrent™ platform and copy number alterations (CNAs) calling from single cells. The method was evaluated on single cells isolated from 6 aberrant cell lines of the NCI-H series. In addition, to demonstrate the feasibility of the workflow on clinical samples, we analyzed single circulating tumor cells (CTCs) and white blood cells (WBCs) isolated from the blood of patients affected by prostate cancer or lung adenocarcinoma. The results obtained show that the developed workflow generates data accurately representing whole genome absolute copy number profiles of single cell and allows alterations calling at resolutions down to 100 Kbp with as few as 200,000 reads. The presented data demonstrate the feasibility of the Ampli1™ WGA-based low-pass workflow for detection of CNAs in single tumor cells which would be of particular interest for genome-driven targeted therapy selection and for monitoring of disease progression.
Overview of the NTCIR-13 We Want Web Task

In this paper, we provide an overview of the NTCIRWeWant Web (WWW) task, which comprises the Chinese and the English subtasks. The WWW task is a classical ad-hoc textual retrieval task. This round ofWWWreceived 19 runs from 4 teams for the Chinese subtask, and 13 runs from 3 teams for the English subtask. In this overview paper, we describe the task details, data and evaluation methods, as well as the report on the official results.
Detection of Narcissus Latent Virus Isolates Using One-Step Rt-Pcr Assay

Abstract A one-step RT-PCR procedure was developed for specific detection of Narcissus latent virus (NLV) isolates. Following alignment of RNA sequences of three NLV isolates, the conserved sequence fragments were identified in viral polyprotein gene and 3’UTR region. Based on those fragments, a forward NLs1 and two reverse: NLa1 and NLSCPR2 primers were designed. Primer pairs NLs1-NLa1 and NLs1- NLSCPR2 proved to be effective in amplification of a 669 bp and 1295 bp products, respectively. Sequence analysis of amplified products confirmed their specificity.
Determining the Number of Ischemic Strokes Potentially Eligible for Endovascular Thrombectomy: A Population-Based Study

Background and Purpose— Endovascular thrombectomy (ET) is standard-of-care for ischemic stroke patients with large vessel occlusion, but estimates of potentially eligible patients from population-based studies have not been published. Such data are urgently needed to rationally plan hyperacute services. Retrospective analysis determined the incidence of ET-eligible ischemic strokes in a comprehensive population-based stroke study (Adelaide, Australia 2009–2010). Methods— Stroke patients were stratified via a prespecified eligibility algorithm derived from recent ET trials comprising stroke subtype, pathogenesis, severity, premorbid modified Rankin Score, presentation delay, large vessel occlusion, and target mismatch penumbra. Recognizing centers may interpret recent ET trials either loosely or rigidly; 2 eligibility algorithms were applied: restrictive (key criteria modified Rankin Scale score 0–1, presentation delay <3.5 hours, and target mismatch penumbra) and permissive (modified Rankin Scale score 0–3 and presentation delay <5 hours). Results— In a population of 148 027 people, 318 strokes occurred in the 1-year study period (crude attack rate 215 [192–240] per 100 000 person-years). The number of ischemic strokes eligible by restrictive criteria was 17/258 (7%; 95% confidence intervals 4%–10%) and by permissive criteria, an additional 16 were identified, total 33/258 (13%; 95% confidence intervals 9%–18%). Two of 17 patients (and 6/33 permissive patients) had thrombolysis contraindications. Using the restrictive algorithm, there were 11 (95% confidence intervals 4–18) potential ET cases per 100 000 person-years or 22 (95% confidence intervals 13–31) using the permissive algorithm. Conclusions— In this cohort, ≈7% of ischemic strokes were potentially eligible for ET (13% with permissive criteria). In similar populations, the permissive criteria predict that ⩽22 strokes per 100 000 person-years may be eligible for ET.
Discrete Geodesic Nets for Modeling Developable Surfaces

We present a discrete theory for modeling developable surfaces as quadrilateral meshes satisfying simple angle constraints. The basis of our model is a lesser-known characterization of developable surfaces as manifolds that can be parameterized through orthogonal geodesics. Our model is simple and local, and, unlike in previous works, it does not directly encode the surface rulings. This allows us to model continuous deformations of discrete developable surfaces independently of their decomposition into torsal and planar patches or the surface topology. We prove and experimentally demonstrate strong ties to smooth developable surfaces, including a theorem stating that every sampling of the smooth counterpart satisfies our constraints up to second order. We further present an extension of our model that enables a local definition of discrete isometry. We demonstrate the effectiveness of our discrete model in a developable surface editing system, as well as computation of an isometric interpolation between isometric discrete developable shapes.
An Improved Social Media Analysis on 3 Layers: A Real Time Enhanced Recommendation System

The Internet can be considered as an open field for expression regarding products, politics, ideas, and people. Those expressive interactions generate a large amount of data pinned per users and groups. In that scope, Big data along with various technologies, such as social media, cloud computing, and machine learning can be used as a toolbox to make sense of the data and give the opportunity to generate efficient analysis and studies of the individuals and crowds regarding market orientation, politics, and industry. The recommendation system for this acts as the pillars of technology, in the field of sentiment analysis and predictive analysis to make sense of user’s data. However, this complex operation comes at the price of this. To each analysis, there is a personalized architecture and tool. In this paper, a novel design of a recommender system is provided powered by sentiment analysis and predictive models applied onto an example of data flow from the social media Twitter.
Exercise Leads to Better Clinical Outcomes in Those Receiving Medication Plus Cognitive Behavioral Therapy for Major Depressive Disorder

Objective The aim of this study is to investigate the effects of exercise as an add-on therapy with antidepressant medication and cognitive behavioral group therapy (CBGT) on treatment outcomes in low-active major depressive disorder (MDD) patients. We also explored whether exercise reduces the residual symptoms of depression, notably cognitive impairment and poor sleep quality, and aimed to identify putative biochemical markers related to treatment response. Methods Sixteen low-active MDD patients were recruited from a mental health day treatment program at a local hospital. Eight medicated patients performed an 8-week exercise intervention in addition to CBGT, and eight medicated patients attended the CBGT only. Twenty-two low-active, healthy participants with no history of mental health illness were also recruited to provide normal healthy values for comparison. Results Results showed that exercise resulted in greater reduction in depression symptoms (p = 0.007, d = 2.06), with 75% of the patients showing either a therapeutic response or a complete remission of symptoms vs. 25% of those who did not exercise. In addition, exercise was associated with greater improvements in sleep quality (p = 0.046, d = 1.28) and cognitive function (p = 0.046, d = 1.08). The exercise group also had a significant increase in plasma brain-derived neurotrophic factor (BDNF), p = 0.003, d = 6.46, that was associated with improvements in depression scores (p = 0.002, R2 = 0.50) and sleep quality (p = 0.011, R2 = 0.38). Conclusion We provide evidence that exercise as an add-on to conventional antidepressant therapies improved the efficacy of standard treatment interventions. Our results suggest that plasma BDNF levels and sleep quality appear to be good indicators of treatment response and potential biomarkers associated with the clinical recovery of MDD.
Protective effect of human serum amyloid P on CCl4-induced acute liver injury in mice

Human serum amyloid P (hSAP), a member of the pentraxin family, inhibits the activation of fibrocytes in culture and inhibits experimental renal, lung, skin and cardiac fibrosis. As hepatic inflammation is one of the causes of liver fibrosis, in the present study, we investigated the hepatoprotective effects of hSAP against carbon tetrachloride (CCl4)-induced liver injury. Our data indicated that hSAP attenuated hepatic histopathological abnormalities and significantly decreased inflammatory cell infiltration and pro-inflammatory factor expression. Moreover, CCl4-induced apoptosis in the mouse liver was inhibited by hSAP, as measured by terminal-deoxynucleotidyl transferase mediated nick-end labeling (TUNEL) assay and cleaved caspase-3 expression. hSAP significantly restored the expression of B cell lymphoma/leukemia (Bcl)-2 and suppressed the expression of Bcl-2-associated X protein (Bax) in vivo. The number of hepatocytes in early apoptosis stained with Annexin V was significantly reduced by 28–30% in the hSAP treatment group compared with the CCl4 group, and the expression of Bcl-2 was increased, whereas the expression of Bax and cleaved caspase-3 were significantly inhibited in the hSAP pre-treatment group compared with the CCl4 group. hSAP administration also inhibited the migration and activation of hepatic stellate cells (HSCs) in CCl4-injured liver and suppressed the activation of isolated primary HSCs induced by transforming growth factor (TGF)-β1 in vitro. Collectively, these findings suggest that hSAP exerts a protective effect againts CCl4-induced hepatic injury by suppressing the inflammatory response and hepatocyte apoptosis, potentially by inhibiting HSC activation.
Ca2+ tunnelling through the ER lumen as a mechanism for delivering Ca2+ entering via store‐operated Ca2+ channels to specific target sites

Ca2+ signalling is perhaps the most universal and versatile mechanism regulating a wide range of cellular processes. Because of the many different calcium‐binding proteins distributed throughout cells, signalling precision requires localized rises in the cytosolic Ca2+ concentration. In electrically non‐excitable cells, for example epithelial cells, this is achieved by primary release of Ca2+ from the endoplasmic reticulum via Ca2+ release channels placed close to the physiological target. Because any rise in the cytosolic Ca2+ concentration activates Ca2+ extrusion, and in order for cells not to run out of Ca2+, there is a need for compensatory Ca2+ uptake from the extracellular fluid. This Ca2+ uptake occurs through a process known as store‐operated Ca2+ entry. Ideally Ca2+ entering the cell should not diffuse to the target site through the cytosol, as this would potentially activate undesirable processes. Ca2+ tunnelling through the lumen of the endoplasmic reticulum is a mechanism for delivering Ca2+ entering via store‐operated Ca2+ channels to specific target sites, and this process has been described in considerable detail in pancreatic acinar cells and oocytes. Here we review the most important evidence and present a generalized concept.
A critical analysis of computational protein design with sparse residue interaction graphs

Protein design algorithms enumerate a combinatorial number of candidate structures to compute the Global Minimum Energy Conformation (GMEC). To efficiently find the GMEC, protein design algorithms must methodically reduce the conformational search space. By applying distance and energy cutoffs, the protein system to be designed can thus be represented using a sparse residue interaction graph, where the number of interacting residue pairs is less than all pairs of mutable residues, and the corresponding GMEC is called the sparse GMEC. However, ignoring some pairwise residue interactions can lead to a change in the energy, conformation, or sequence of the sparse GMEC vs. the original or the full GMEC. Despite the widespread use of sparse residue interaction graphs in protein design, the above mentioned effects of their use have not been previously analyzed. To analyze the costs and benefits of designing with sparse residue interaction graphs, we computed the GMECs for 136 different protein design problems both with and without distance and energy cutoffs, and compared their energies, conformations, and sequences. Our analysis shows that the differences between the GMECs depend critically on whether or not the design includes core, boundary, or surface residues. Moreover, neglecting long-range interactions can alter local interactions and introduce large sequence differences, both of which can result in significant structural and functional changes. Designs on proteins with experimentally measured thermostability show it is beneficial to compute both the full and the sparse GMEC accurately and efficiently. To this end, we show that a provable, ensemble-based algorithm can efficiently compute both GMECs by enumerating a small number of conformations, usually fewer than 1000. This provides a novel way to combine sparse residue interaction graphs with provable, ensemble-based algorithms to reap the benefits of sparse residue interaction graphs while avoiding their potential inaccuracies.
Automatic Inference of Translation Rules for Native Cross-Platform Mobile Applications

ABSTRACT A native cross-platform mobile app has multiple platform-specic implementations. Typically, an app is developed for one platform and then ported to the remaining ones. Translating an app from one language (e.g., Java) to another (e.g., Swi) by hand is tedious and error-prone, while automated translators either require manually dened translation rules or focus on translating APIs. To automate the translation of native cross-platform apps, we present tInferer, a novel approach that iteratively infers syntactic transformation rules and API mappings from Java to Swi. Given a soware corpus in both languages, tInferer rst identies the semantically equivalent code based on braces and string similarity. For each pair of similar code segments, tInferer then creates syntax trees of both languages, leveraging the minimalist domain knowledge of language correspondence (e.g., operators and markers) to iteratively align syntax tree nodes, and to infer both syntax and API mapping rules. tInferer represents inferred rules as string templates, stored in a database, to translate code from Java to Swi. We evaluated tInferer with four applications, using one part of the data to infer translation rules, and the other part to apply the rules. With 76% in-project accuracy and 65% cross-project accuracy, tInferer outperforms in accuracy j2swi, a state-of-the-art Java-to-Swi conversion tool. As native cross-platform mobile apps grow in popularity, tInferer can shorten their time to market by automating the tedious and error prone task of source-to-source translation.
Receive Antenna Selection for Multiuser MIMO Systems with Tomlinson-Harashima Precoding

SUMMARY In this letter, we discuss the problem of receive antenna selection in the downlink of multiuser multiple-input multiple-output (MIMO) systems with Tomlinson-Harashima precoding (THP), where the number of receivers is assumed equal to that of transmit antennas. Based on the criterion of maximum system sum-capacity, a per-layer receive antenna selection scheme is proposed. This scheme, which selects one receive antenna for each receiver, can well exploit the nonlinear and successive characteristics of THP. Two models are established for the proposed per-layer scheme and the conventional per-user scheme. Both the theoretical analysis and simulation results indicate that the proposed scheme can greatly
Diffuse brain damage in normal tension glaucoma

Brain changes within and beyond the visual system have been demonstrated in primary open angle glaucoma (POAG), the most common type of glaucoma. These changes have been often interpreted as a neurodegenerative process due, at least partially, to the raised intraocular pressure (IOP). In this context, normal tension glaucoma (NTG), a form of POAG with IOP <21 mm Hg despite the typical glaucomatous findings, represents the most suitable model of glaucoma to test the validity of this hypothesis. We acquired multimodal brain MRI in NTG patients (n = 17) and compared them with demographically matched groups of POAG patients with raised IOP (n = 17) and normal controls (NC, n = 29). Voxelwise statistics was performed with nonparametric permutation testing.
DIVAS: Outreach to the Natural Sciences through Image Processing

The DIVAS (Digital Imaging and Vision Applications in Science) project addresses workforce challenges in science, technology, engineering, and mathematics by creating a pedagogical and programmatic "on-ramp" that empowers natural science majors to engage in authentic computational problems as members of skilled, professional teams. We are developing and testing institutional practices and curricular innovations that engage and train STEM undergraduate students to use Python programming, and image processing in particular, in their undergraduate research projects. Students are recruited into the DIVAS program in the first semester of their first year. DIVAS scholars and other participating students can experience a variety of interventions including: 1) a one-credit DIVAS seminar exploring several imaging and computing topics; 2) image capture and analysis modules in introductory- or upper-level biology and chemistry courses; 3) a week-long, intensive coding bootcamp that introduces bash, git, Python programming, and the OpenCV image processing library; 4) pair programming exercises to solve genuine morphometric and colorimetric problems; 5) an extended summer research project involving image processing; and 6) weekly code reviews to check on progress and provide guidance. The DIVAS projects measures the impact of these interventions on students' self-reported efficacy in using computation to solve problems, their attitudes towards computation, and their computational thinking skills, using both established and newly developed instruments. Our first year results show that multiple interventions have had significant positive impact on students' self-efficacy and interest in using computing in their future careers, and certain computational thinking skills.
Effect of the Consonant–Vowel Structure of Written Words in Italian

Surprisingly little is known about the nature of intermediary sublexical units in visual word recognition in Italian, a language with a highly consistent print-to-sound mapping, which should enhance reliance on grapheme-to-phoneme correspondences. In the present study, we examined whether Italian readers are sensitive to large orthographic units defined by the consonant–vowel (CV) pattern of words and that do not directly map onto linguistic constituents. Participants had to judge the number of syllables in written words matched for the number of spoken syllables but comprising either one orthographic vowel cluster less than the number of syllables (hiatus words, e.g., teatro) or as many vowel clusters as syllables (e.g., agosto). Relative to control words, readers were slower and less accurate for hiatus words, for which they underestimated the number of syllables. This underestimation bias demonstrates that Italian readers are sensitive to large orthographic units stemming from a parsing process based on the CV pattern—that is, the arrangement of consonant and vowel letters.
Severity grading of diabetic retinopathy using extreme learning machine

A computationally effective method for diagnosing the severity of Diabetic Retinopathy is proposed. The proposed approach follows 3 stages; the preprocessing, feature extraction and classification. The purpose of the first step is to make the image suitable for subsequent process. In feature extraction, number of micro aneurysms and texture features were extracted by morphological process and the last step is the classification, in which the images are categorized by these features with the help of ELM classifier. The above procedure was tested and analyzed using images in DIARETDB0 and DRIVE database and we were able to achieve accuracy of 95% with good training speed.
A 3D circular object detection method based on binocular stereo vision

Circular object detection is of great significance in wide areas, such as image processing, pattern recognition, computer vision, etc. Most present methods for circle detection are 2D-based algorithms, which means that only ideal 2D circles in the image plane can be detected. Circular objects in 3D physical world are usually ellipses in 2D images rather than standard 2D circles. Consequently, most common methods based on 2D images are unable to distinguish real space circles from ellipses. To solve the problem, in this paper, a 3D circular object detection method based on binocular stereo vision is proposed. It detects and fits ellipses/circles in stereo images firstly, then obtains sub-pixel-level disparity data between two images after stereo matching with the fitted mathematical model. Next, according to the binocular stereo vision model, the disparity data is reversely projected into 3D space. By the preset thresholds for parameters to evaluate the extent deviated from circular and coplanar objects, space circular objects can finally be detected based on those 3D data. Numerous experimental results demonstrate that the proposed method can detect circular objects successfully with high precision and efficiency.
A selectivity filter at the intracellular end of the acid-sensing ion channel pore

Increased extracellular proton concentrations during neurotransmission are converted to excitatory sodium influx by acid-sensing ion channels (ASICs). 10-fold sodium/potassium selectivity in ASICs has long been attributed to a central constriction in the channel pore, but experimental verification is lacking due to the sensitivity of this structure to conventional manipulations. Here, we explored the basis for ion selectivity by incorporating unnatural amino acids into the channel, engineering channel stoichiometry and performing free energy simulations. We observed no preference for sodium at the “GAS belt” in the central constriction. Instead, we identified a band of glutamate and aspartate side chains at the lower end of the pore that enables preferential sodium conduction. DOI: http://dx.doi. org/10.7554/eLife.24630. 001