bluuebunny/crossref_metadata_2025_split huggingface dataset:
Robust Automated Assessment of Human Blastocyst Quality using Deep Learning
Authors: Khosravi Pegah , Kazemi Ehsan , Zhan Qiansheng , Toschi Marco , Malmsten Jonas E. , Hickman Cristina , Meseguer Marcos , Rosenwaks Zev , Elemento Olivier , Zaninovic Nikica , Hajirasouliha Iman 
Published: August 2018

AbstractMorphology assessment has become the standard method for evaluation of embryo quality and selecting human blastocysts for transfer inin vitro fertilization(IVF). This process is highly subjective for some embryos and thus prone to human bias. As a result, morphological assessment results may vary extensively between embryologists and in some cases may fail to accurately predict embryo implantation and live birth potential. Here we postulated that an artificial intelligence (AI) approach trained on thousands of embryos can reliably predict embryo quality without human intervention.To test this hypothesis, we implemented an AI approach based on deep neural networks (DNNs). Our approach called STORK accurately predicts the morphological quality of blastocysts based on raw digital images of embryos with 98% accuracy. These results indicate that a DNN can automatically and accurately grade embryos based on raw images. Using clinical data for 2,182 embryos, we then created a decision tree that integrates clinical parameters such as embryo quality and patient age to identify scenarios associated with increased or decreased pregnancy chance. This IVF data-driven analysis shows that the chance of pregnancy varies from 13.8% to 66.3%.In conclusion, our AI-driven approach provides a novel way to assess embryo quality and uncovers new, potentially personalized strategies to select embryos with an improved likelihood of pregnancy outcome.
ML-DSP: Machine Learning with Digital Signal Processing for ultrafast, accurate, and scalable genome classification at all taxonomic levels
Authors: Randhawa Gurjit S. , Hill Kathleen A. , Kari Lila 
Published: August 2018

AbstractBackgroundAlthough methods and software tools abound for the comparison, analysis, identification, and taxonomic classification of the enormous amount of genomic sequences that are continuously being produced, taxonomic classification remains challenging. The difficulty lies within both the magnitude of the dataset and the intrinsic problems associated with classification. The need exists for an approach and software tool that addresses the limitations of existing alignment-based methods, as well as the challenges of recently proposed alignment-free methods.ResultsWe combine supervisedMachineLearning withDigitalSignalProcessing to designML-DSP, an alignment-free software tool for ultrafast, accurate, and scalable genome classification at all taxonomic levels.We test ML-DSP by classifying 7,396 full mitochondrial genomes from the kingdom to genus levels, with 98% classification accuracy. Compared with the alignment-based classification tool MEGA7 (with sequences aligned with either MUSCLE, or CLUSTALW), ML-DSP has similar accuracy scores while being significantly faster on two small benchmark datasets (2,250 to 67,600 times faster for 41 mammalian mitochondrial genomes). ML-DSP also successfully scales to accurately classify a large dataset of 4,322 complete vertebrate mtDNA genomes, a task which MEGA7 with MUSCLE or CLUSTALW did not complete after several hours, and had to be terminated. ML-DSP also outperforms the alignment-free tool FFP (Feature Frequency Profiles) in terms of both accuracy and time, being three times faster for the vertebrate mtDNA genomes dataset.ConclusionsWe provide empirical evidence that ML-DSP distinguishes complete genome sequences at all taxonomic levels. Ultrafast and accurate taxonomic classification of genomic sequences is predicted to be highly relevant in the classification of newly discovered organisms, in distinguishing genomic signatures, in identifying mechanistic determinants of genomic signatures, and in evaluating genome integrity.
Occurrence of non-western magic in the European brain
Authors: Koten Jan Willem , Schüppen André , Kumar Vinod , Wood Guilherme 
Published: August 2018

ABSTRACTTimecourses that exhibit identical behaviour at distinct measurement occasions are reliable. Voodoo connectivity occurs when connectivity among brain regions exceeds within subject timecourse reliability. Thus, timecourse reliability limits the true detectable connectivity. We reproduced a working memory related connectome consisting of 561 paths obtained from 67 individuals. We tested >100000 fc-MRI pipelines and show that Savitzky Golay (SG) filters maximize true connectivity while conserving cognitively relevant changes of signals. This is noteworthy for approaches that focus on rapidly changing aspects of connectomes. Furthermore, SG filters detect zombie activity. These “resting state oscillations” are not under human control and contaminate working state signals. SPM pipelines exhibit more voodoo connectivity than SG pipelines. With the SPM pipeline, we observed a connectivity of r=0.44 and a poor true connectivity of r=0.23, but with the SG pipeline we observed a connectivity of r=0.59 and a fair true connectivity of r=0.43. The number of paths detected with fair true connectivity (r >0.4) was 4 for the SPM pipeline but 352 for the SG based pipeline. However, superior statistical properties of SG pipelines may not reflect neural reality. Hence, causal external validation of fc-MRI pipelines is crucial. Without such studies, different pipelines produce at best “alternative maps”.
High Postoperative Monocyte Indicated Inferior Clinicopathological Characteristics and Worse Prognosis in Lung Adenocarcinoma or Squamous Cell Carcinoma after Lobectomy
Authors: Hai Yang , Chen Nan , Wu Wenwen , Wang Zihuai , Lin Feng , Guo Chenglin , Liu Chengwu , Li Weiming , Liu Lunxu 
Published: August 2018

AbstractBackgroundPeripheral monocyte count is an assessable parameter. Recently, evidence suggested an elevated preoperative monocyte counts predicting poor prognosis in malignancies. The aim of this study was to determine the prognostic effect of early postoperative blood monocyte count in patients with lung adenocarcinoma or squamous cell carcinoma following lobectomy.MethodsWe retrospectively reviewed patients with operated lung adenocarcinoma or squamous cell carcinoma from 2006 to 2011 in Western China Lung Cancer database. Univariate analysis on disease-free survival (DFS) and overall survival (OS) was performed using the Kaplan-Meier and log-rank tests, and multivariate analysis was conducted using the Cox proportional hazards regression model.ResultsThere were 433 patients enrolled in our analysis. High postoperative elevated monocyte was associated with male gender (P<0.001), positive smoking history (P=0.005), and higher N stage (P=0.002) and higher tumor stage (P=0.026). Two-tailed log-rank test indicated patients with an early postoperative elevated monocyte count predicted a poor DFS and OS overall (P<0.001,P<0.001, respectively) as well as in subgroup analysis, and further presented as a promising independent prognostic factor for both DFS and OS (HR=2.991, 95%CI: 2.243-3.988,P<0.001; HR=2.705, 95%CI: 1.977-3.700,P<0.001, respectively) on multivariate analysis. However, no significance was detected for preoperative monocyte in multivariate analysis.ConclusionsElevated early postoperative peripheral monocyte count was an independent prognostic factor of poor prognosis and inferior clinicopathological features for patients with operable lung adenocarcinoma or squamous cell carcinoma by lobectomy.
Learning unsupervised feature representations for single cell microscopy images with paired cell inpainting
Authors: Lu Alex X , Kraus Oren Z , Cooper Sam , Moses Alan M 
Published: August 2018

AbstractCellular microscopy images contain rich insights about biology. To extract this information, researchers use features, or measurements of the patterns of interest in the images. Here, we introduce a convolutional neural network (CNN) to automatically design features for fluorescence microscopy. We use a self-supervised method to learn feature representations of single cells in microscopy images without labelled training data. We train CNNs on a simple task that leverages the inherent structure of microscopy images and controls for variation in cell morphology and imaging: given one cell from an image, the CNN is asked to predict the fluorescence pattern in a second different cell from the same image. We show that our method learns high-quality features that describe protein expression patterns in single cells both yeast and human microscopy datasets. Moreover, we demonstrate that our features are useful for exploratory biological analysis, by capturing high-resolution cellular components in a proteome-wide cluster analysis of human proteins, and by quantifying multi-localized proteins and single-cell variability. We believe paired cell inpainting is a generalizable method to obtain feature representations of single cells in multichannel microscopy images.Author SummaryTo understand the cell biology captured by microscopy images, researchers use features, or measurements of relevant properties of cells, such as the shape or size of cells, or the intensity of fluorescent markers. Features are the starting point of most image analysis pipelines, so their quality in representing cells is fundamental to the success of an analysis. Classically, researchers have relied on features manually defined by imaging experts. In contrast, deep learning techniques based on convolutional neural networks (CNNs) automatically learn features, which can outperform manually-defined features at image analysis tasks. However, most CNN methods require large manually-annotated training datasets to learn useful features, limiting their practical application. Here, we developed a new CNN method that learns high-quality features for single cells in microscopy images, without the need for any labeled training data. We show that our features surpass other comparable features in identifying protein localization from images, and that our method can generalize to diverse datasets. By exploiting our method, researchers will be able to automatically obtain high-quality features customized to their own image datasets, facilitating many downstream analyses, as we highlight by demonstrating many possible use cases of our features in this study.
Endogenous viral elements are widespread in arthropod genomes and commonly give rise to piRNAs
Authors: ter Horst Anneliek M. , Nigg Jared C. , Falk Bryce W. 
Published: August 2018

ABSTRACTArthropod genomes contain sequences derived from integrations of DNA and non-retroviral RNA viruses. These sequences, known as endogenous viral elements (EVEs), have been acquired over the course of evolution and have been proposed to serve as a record of past viral infection. Recent evidence indicates that EVEs can function as templates for the biogenesis of PIWI-interacting RNAs (piRNAs) in some mosquito species and cell lines, raising the possibility that EVEs may function as a source of immunological memory in these organisms. However, whether EVEs are capable of acting as templates for piRNA production in other arthropod species is unknown. Here we used publically available genome assemblies and small RNA sequencing datasets to characterize the repertoire and function of EVEs across 48 arthropod genomes. We found that EVEs are widespread in arthropod genomes and primarily correspond to unclassified ssRNA viruses and viruses belonging to the Rhabdoviridae and Parvoviridae families. Additionally, EVEs were enriched in piRNA clusters in a majority of species and we found that production of primary piRNAs from EVEs is common, particularly for EVEs located within piRNA clusters. While we found evidence suggesting that piRNAs mapping to a number of EVEs are produced via the ping-pong cycle, potentially pointing towards a role for EVE-derived piRNAs during viral infection, limited nucleotide identity between currently described viruses and EVEs identified here likely limits the extent to which this process plays a role during infection with known viruses.
New human chromosomal safe harbor sites for genome engineering with CRISPR/Cas9, TAL effector and homing endonucleases
Authors: Pellenz Stefan , Phelps Michael , Tang Weiliang , Hovde Blake T. , Sinit Ryan B. , Fu Wenqing , Li Hui , Chen Eleanor , Monnat Raymond J. 
Published: August 2018

AbstractSafe Harbor Sites (SHS) are genomic locations where new genes or genetic elements can be introduced without disrupting the expression or regulation of adjacent genes. We have identified 35 potential new human SHS in order to substantially expand SHS options beyond the three widely used canonical human SHS,AAVS1, CCR5andhROSA26. All 35 potential new human SHS and the three canonical sites were assessed for SHS potential using 9 different criteria weighted to emphasize safety that were broader and more genomics-based than previous efforts to assess SHS potential. We then systematically compared and rank-ordered our 35 new sites and the widely used humanAAVS1, hROSA26andCCR5sites, then experimentally validated a subset of the highly ranked new SHS together versus the canonicalAAVS1site. These characterizations includedin vitroandin vivocleavage-sensitivity tests; the assessment of population-level sequence variants that might confound SHS targeting or use for genome engineering; homology–dependent and –independent, SHS-targeted transgene integration in different human cell lines; and comparative transgene integration efficiencies at two new SHS versus the canonicalAAVS1site. Stable expression and function of new SHS-integrated transgenes were demonstrated for transgene-encoded fluorescent proteins, selection cassettes and Cas9 variants including a transcription transactivator protein that were shown to drive large deletions in aPAX3/FOXO1fusion oncogene and induce expression of theMYF5gene that is normally silent in human rhabdomyosarcoma cells. We also developed a SHS genome engineering ‘toolkit’ to enable facile use of the most extensively characterized of our new human SHS located on chromosome 4p. We anticipate our newly identified human SHS, located on 16 chromosomes including both arms of the human X chromosome, will be useful in enabling a wide range of basic and more clinically-oriented human gene editing and engineering.
Loan delinquency and macroeconomic conditions
Authors: Abaidoo Rexford 
Published: August 2018

Purpose
The purpose of this paper is to examine how specific macroeconomic indicators and conditions impact short- and long-run loan delinquency rates among US commercial banks under various economic episodes.

Design/methodology/approach
The study employs an autoregressive distributed lag framework (ARDL) and error correction model in its examination of how loan delinquency rates are impacted by specific macroeconomic variables and conditions.

Findings
This study finds that in both the short and long run, a percentage growth in macroeconomic indicators, such as industrial productivity and private domestic investments, reduces loan delinquency rates among commercial banks, given all things being equal. Additionally, this study also finds that adverse macroeconomic conditions, such as inflation, economic policy uncertainty and volatility, associated with specific macroeconomic variables, such as investment growth, etc., tend to worsen loan delinquency rates. Empirical results further suggest that among the various macroeconomic conditions examined, inflationary pressures tend to have the most significant heightening impact on loan delinquency rates among commercial banks.

Originality/value
The uniqueness of this study, compared to similar studies found in the literature, has to do with its verification of potential association between loan delinquency rates and specific hitherto unexamined macroeconomic conditions. Compared to similar studies on loan delinquency, this study collectively examines how conditions of uncertainty, volatility and expectations of macroeconomic conditions shape loan delinquency rates among commercial banks.
Food without sun: price and life-saving potential
Authors: Denkenberger David , Pearce Joshua , Taylor Andrew Ray , Black Ryan 
Published: August 2018

Purpose
The purpose of this study is to estimate the price and life-saving potential of alternate foods. The sun could be blocked by asteroid impact, supervolcanic eruption or nuclear winter caused by burning of cities during a nuclear war. The primary problem in these scenarios is loss of food production. Previous work has shown that alternate foods not dependent on sunlight, such as bacteria grown on natural gas and cellulose turned into sugar enzymatically, could feed everyone in these catastrophes, and preparation for these foods would save lives in a manner that is highly cost-effective.

Design/methodology/approach
This study estimates the price of alternate foods during a catastrophe in line with global trade and information sharing, but factors such as migration, loans, aid or conflict are not taken into consideration.

Findings
Without alternate foods, for a five-year winter, only approximately 10 per cent of the population would survive. The price of dry food would rise to approximately $100/kg, and the expenditure on this food would be approximately $100tn. If alternate foods were $8/kg, the surviving global population increases to approximately 70 per cent, saving >4billion lives.

Research limitations/implications
A nongovernmental mechanism for coordinating the investments of rich people may be possible. Identifying companies whose interests align with alternate food preparations may save lives at a negative cost.

Practical implications
The probability of loss of civilization and its impact on future generations would be lower in this scenario, and the total expenditure on food would be halved.

Originality/value
Preparation for alternate foods is a good investment even for wealthy people who would survive without alternate foods.
The last possible resort: Latin American migrants’ rapport with Spanish healthcare
Authors: Lamarque Muriel , Moro-Gutiérrez Lourdes 
Published: August 2018

Purpose
The purpose of this paper is to analyze the relationship between Latin American migrants and the Spanish healthcare system, from the users’ perspective, in order to examine the socio-cultural determinants for access and attention, as well as the possible difficulties faced when attending institutional facilities.

Design/methodology/approach
This work is based on qualitative ethnographic research with the Latin American community established in the region of Castile and León, Spain. Analysis was established through a thematic approach and illustrated with the testimonies of participants involved, to inspect personal meanings and interpretations, as well as specific practices regarding healthcare.

Findings
The encounter between foreigners and health services is frequently expressed with negative emotions, such as feelings of fear facing a system that is perceived as designed “for others.” For many Latin American immigrants in Spain, institutional healthcare is not considered their first choice for treating illness. This has been related to economic, bureaucratic, ideological and emotional factors.

Originality/value
Most of the existing literature about immigrant healthcare in Spain has focused on sanitary conditions or patterns of service use from an institutional perspective and a quantitative approach. This paper highlights the perceptions of users to enrich comprehension of the rapport between migrants and medical care.
Variables affecting positive faculty-student relationships in an Ecuadorian university
Authors: Cabezas Christian Benjamin , Acurio Carlos Vidal , Merlyn Marie-France , Orbe Cristina Elizabeth , Riera Wilma Leonila 
Published: August 2018

Purpose
The purpose of this paper is to identify the main variables that affect the establishment of a good faculty-student pedagogical relationship in representative samples from a main university in Ecuador.

Design/methodology/approach
In the first phase of this exploratory mixed methods study, focus groups were conducted to identify the variables of interest, and in a second phase, these variables were rated in relevance by applying the “pile-sort” method.

Findings
Results showed that for students, the variable that most affects the establishment of a good relationship with their faculty is the “faculty’s knowledge,” while the variable that showed the least effect is the “number of students in the classroom.” On the other hand, faculty members responded that the variables that most affect the establishment of a good pedagogical relationship are “empathy with students,” “vocation” and “faculty’s knowledge,” while they considered that the least relevant variables were “context” variables such as “the number of students in the classroom” and “the physical conditions of the classroom.”

Practical implications
These results provide relevant insights into the importance that students place on the theoretical resources that faculty members show as a foundation for establishing positive relationships. In the same way, the relevance that faculty members place on the elements “empathy,” “vocation” and “knowledge” as key variables needed to establish positive interactions.

Originality/value
Previous research had underlined the importance that positive faculty-students relationships have on achieving learning goals. However, the variables that would affect the establishment of these relationships were not clearly recognized.
Office space: Australian advertising agencies in the twentieth century
Authors: Crawford Robert 
Published: August 2018

Purpose
This paper aims to examine the evolution of the advertising agency and its offices in Australia over the course of the twentieth century. Historical accounts of advertising have paid scant attention to agencies’ attempts to organise and manage their offices, as well as the impact that these efforts has had on the work undertaken by agency staff.

Design/methodology/approach
This study draws on reports in the advertising industry press, as well as oral history testimony to examine the agencies’ changing layout and interior design. It identifies three distinct periods, which reveal the impact of modernist and post-industrialist ideas on the organisation and functions of the advertising agency’s offices and, indeed, their impact on the agency’s outputs.

Findings
This examination of the office space within the agency setting not only offers a new perspective of the advertising agency business as a whole but also demonstrates the importance of material culture for historians working across management, business and marketing fields.

Originality/value
The originality of this study lies in its use of material culture and space as a tool for examining management history and understanding its impact on everyday work practices. By charting the changes reflected in advertising agency office spaces, this study also offers a unique overview of the ways that management practices have historically interacted with business work spaces.