bluuebunny/crossref_metadata_2025 huggingface dataset:
Lose Weight to Donate: Development of a Program to Optimize Potential Donors With Hepatic Steatosis or Obesity for Living Liver Donation
Authors: Rose John T. , Vargas Paola , Seay Tara , Pesch Arthur J. , Williams Tessa , Sites Anita , Henry Zachary , Northup Patrick G. , Pelletier Shawn J. , Oberholzer Jose , Argo Curtis K. , Goldaracena Nicolas
Published: May 2021

Background.

Living donor liver transplantation offers an attractive option to reduce the waitlist mortality. However, in recent years, the rising prevalence of obesity and nonalcoholic fatty liver disease has posed a serious threat to the donor pool while simultaneously increasing demand for liver transplant. To our knowledge, there have been no major published studies in the United States documenting a diet and exercise intervention to expand the living donor pool. Hereby, we established a pilot program called “Lose Weight to Donate” and present our initial experience.

Methods.

Our center instituted a remotely monitored diet and exercise pilot program to increase eligibility for living liver donation. Potential donors with any of the following were included: body mass index >30 kg/m2, hepatic steatosis >5% on screening MRI, or isolated hypertension.

Results.

Over 19 mo, 7 individuals enrolled in the program of remote monitoring for at least 6–8 wk. Initial and follow-up abdominal MRI was performed in 5 of these individuals to assess steatosis, anatomy, and volume. Initial steatosis was highly variable (fat signal fraction range, 8%–26%). Follow-up MRI fat signal fraction values and hepatic volume all decreased to varying degrees. Ultimately, 2 of 7 individuals donated, whereas a third was approved, but the intended recipient was transplanted in the interim.

Conclusions.

These results indicate the feasibility of a remotely monitored program to expand donation in light of the rising incidence of hepatic steatosis and obesity.
Deep Covid - Covid Diagnosis Using Deep Neural Networks and Transfer Learning
Authors: Sagar Abhinav
Published: May 2021

Coronavirus is a global emergency as of May 2021. If not acted upon by drugs at the right time, coronavirus may result in the death of individuals. Hence early diagnosis is very important along the progress of the disease. This paper focuses on coronavirus detection using x-ray images, for automating the diagnosis pipeline using convolutional neural networks and transfer learning. This could be deployed in places where radiologists are not easily available in order to detect the disease at very early stages. In this study we propose our deep learning architecture for the classification task, which is trained with modified images, through multiple steps of preprocessing. Our classification method uses convolutional neural networks and transfer learning architecture for classifying the images. Our findings yield an accuracy value of 91.03%, precision of 89.76 %, recall value of 96.67% and F1 score of 93.09%.
Umbilical Cord Blood DNA Methylation in Children Who Later Develop Type 1 Diabetes
Authors: Laajala Essi , Ullah Ubaid , Grönroos Toni , Rasool Omid , Halla-aho Viivi , Konki Mikko , Kattelus Roosa , Mykkänen Juha , Nurmio Mirja , Vähä-Mäkilä Mari , Kallionpää Henna , Lietzén Niina , Ghimire Bishwa R. , Laiho Asta , Hyöty Heikki , Elo Laura L. , Ilonen Jorma , Knip Mikael , Lund Riikka J. , Orešič Matej , Veijola Riitta , Lähdesmäki Harri , Toppari Jorma , Lahesmaa Riitta
Published: May 2021

Distinct DNA methylation patterns have recently been observed to precede type 1 diabetes in whole blood collected from young children. Our aim was to determine, whether perinatal DNA methylation could be associated with later progression to type 1 diabetes. Reduced representation bisulfite sequencing (RRBS) analysis was performed on umbilical cord blood samples collected within the Type 1 Diabetes Prediction and Prevention (DIPP) study. Children later diagnosed with type 1 diabetes and/or testing positive for multiple islet autoantibodies (N=43) were compared to control individuals (N=79), who remained autoantibody-negative throughout the DIPP follow-up until 15 years of age. Potential confounding factors related to the pregnancy and the mother were included in the analysis. No differences in the cord blood methylation patterns were observed between these cases and controls.
Automatic Electrocardiogram Detection of Suspected Hypertrophic Cardiomyopathy: Application to Wearable Heart Monitors
Authors: Denis Max , Bachoro Mulatu , Gebreslassie Winta , Oladunni Timothy
Published: May 2021

In this work, an automatic detection algorithm for hypertrophic cardiomyopathy (HCM) is presented. Of particular interest is the algorithm’s ability to differentiate HCM subjects and healthy volunteers from a single lead ECG dataset. Suspected HCM subjects are identified by the primary clinical abnormality associated with HCM, left ventricular hypertrophy (LVH). In total, n=43 human subjects ECG datasets are investigated: n=21 healthy volunteers and n=22 left ventricular hypertrophy (LVH) patients. Significant differences of p-value 0.01 and 0.04 were found for the respective ECG parameters, S-wave amplitude and ST-segment, when differentiating between the LVH patients and healthy human volunteers.
A Geospatial Analysis of salmonellosis and its association with socioeconomic status in Texas
Authors: Gourishankar Anand
Published: May 2021

Objective

The study’s objective was to find the association between salmonellosis and socioeconomic status (SES) in hot spot areas and statewide counties.

Design

A retrospective cohort study.

Setting

The data was recorded regarding salmonellosis in 2017 from the Texas surveillance database. It included assessment of hot spot analysis and SES association with salmonellosis at the county level.

Participants

Patients with salmonellosis of all age groups in Texas.

Results

There were a total of 5113 salmonelloses from 254 counties with an unadjusted crude rate of 18 per 100,000 Person-year. Nine SES risk factors in the hot spot counties were as follows: low values of the severe housing problem, unemployment, African American, and high values of college education, social association rate, fast food/full-service restaurant use, Hispanic, and senior low access-to-store (P < 0.05). A 12% difference existed between local health departments in hot (25%) and cold spot (37%) counties (χ2 [1, n = 108] = 0.5, P = 0.81). Statewide independent risk factors were severe housing problem (IRR = 1.1; CI:1.05-1.14), social association rate (IRR = 0.89; CI:0.87-0.92), college education (IRR = 1.05; CI: 1.04-1.07), and non-Hispanic senior local access-to-store (IRR = 1.98; CI: 1.26-3.11). The severe housing problem predicted zero occurrences of infection in a county (OR = 0.51; CI: 0.28-0.95).

Conclusions

Disparity exists in salmonellosis and socioeconomic status. Attention to unmet needs will decrease salmonellosis. Severe housing problem is a notable risk.
Biologic Therapies for the Treatment of Post Ileal Pouch Anal Anastomosis Surgery Chronic Inflammatory Disorders: Systematic Review and Meta-analysis
Authors: Shehab Mohammad , Alrashed Fatema , Charabaty Aline , Bessissow Talat
Published: May 2021

Background

Chronic inflammatory disorders after ileal pouch-anal anastomosis (IPAA) surgery are common. These include chronic pouchitis, Crohn’s disease of the pouch, prepouch ileitis, and rectal cuff inflammation. The aim of this study was to evaluate the efficacy of biologic therapies in treating these disorders.

Method

Systematic review of all published studies from inception to April 1st, 2021 was performed to investigate the efficacy of biologic therapies for post IPAA chronic inflammatory disorders. The primary outcome was the efficacy of biologic therapies in achieving complete response or remission. A subgroup analysis was performed for each disorder separately.

Results

A total of 24 studies were identified including 682 patients. Using a random effect model, the overall pooled efficacy of biologic therapies in achieving complete response or remission in patients with post IPAA surgery chronic inflammatory disorders was 60% (95% Confidence Interval (CI), 56 - 64). Specifically, the pooled efficacy of ustekinumab was 75% (95% CI, 64 - 85, p = 0.007), whereas the efficacy of vedolizumab was 60% (95% CI, 52 - 68, p = 0.172). In addition, the efficacy infliximab and adalimumab were 59% (95% CI, 53 - 64, p <0.01) and 51% (95% CI, 42 - 60, p = 0.452) respectively.

Conclusion

Ustekinumab, infliximab, vedolizumab and adalimumab are effective in achieving complete response or remission in post IPAA surgery chronic inflammatory disorders. There is also a clear trend toward higher efficacy in patients with Crohn’s disease of the pouch compared to chronic pouchitis. More studies are needed to determine the efficacy of biologics in cuffitis.

Chronic inflammatory disorders after ileal pouch-anal anastomosis surgery are common. These include chronic pouchitis, Crohn’s disease of the pouch, prepouch ileitis, and rectal cuff inflammation. This study aimed to evaluate the efficacy of biologic therapies in achieving complete response or remission in these disorders.
Symptoms of common mental disorders and adherence to antiretroviral therapy among adults living with HIV in rural Zimbabwe: a cross-sectional study
Authors: Haas Andreas D , Kunzekwenyika Cordelia , Hossmann Stefanie , Manzero Josphat , van Dijk Janneke H , Manhibi Ronald , Verhey Ruth , Limacher Andreas , von Groote Per , Manda Ethel , Hobbins Michael , Chibanda Dixon , Egger Matthias ,   for IeDEA Southern Africa
Published: May 2021

Objectives

To examine the proportion of people living with HIV who screen positive for common mental disorders (CMD) and the associations between CMD and self-reported adherence to antiretroviral therapy (ART).

Setting

Sixteen government-funded health facilities in the rural Bikita district of Zimbabwe.

Design

Cross-sectional survey.

Participants

HIV-positive non-pregnant adults, aged 18 years or older, who lived in Bikita district and had received ART for at least six months.

Outcome measures

The primary outcome was the proportion of participants screening positive for CMD defined as a Shona Symptoms Questionnaire (SSQ-14) score of 9 or greater. Secondary outcomes were the proportion of participants reporting suicidal ideation, perceptual symptoms, and suboptimal ART adherence and adjusted prevalence ratios (aPR) for factors associated with CMD, suicidal ideation, perceptual symptoms, and suboptimal ART adherence.

Results

Out of 3,480 adults, 18.8% (95% confidence interval [CI] 14.8-23.7) screened positive for CMD, 2.7% (95% CI 1.5-4.7) reported suicidal ideations, and 1.5% (95% 0.9-2.6) reported perceptual symptoms. Positive CMD screens were more common in women (adjusted prevalence ratio [aPR] 1.67, 95% CI 1.19-2.35) than in men and were more common in adults aged 40-49 years (aPR 1.47 95% CI 1.16-1.85) or aged 50-59 years (20.3%; aPR 1.51 95% CI 1.05-2.17) than in those 60 years or older. Positive CMD screen was associated with suboptimal adherence (aPR 1.53; 95% CI 1.37-1.70).

Conclusions

A substantial proportion of people living with HIV in rural Zimbabwe are affected by CMD. There is a need to integrate mental health services and HIV programs in rural Zimbabwe.

Strengths and limitations of this study

Inclusion of a large sample of people living with HIV recruited at 16 government-funded primary and secondary care facilities in a rural district of Zimbabwe.

Use of a locally developed screening tool that showed good psychometric properties for detecting common mental disorders in Zimbabwe in HIV-positive urban populations.

The screening tool was not validated for the rural setting, and the cutoff score was selected based on data from the urban setting.

Adherence to antiretroviral therapy was self-reported.
ENRICHing Medical Imaging Training Sets Enables More Efficient Machine Learning
Authors: Chinn Erin , Arora Rohit , Arnaout Ramy , Arnaout Rima
Published: May 2021

Objective

Deep learning (DL) has been applied in proofs of concept across biomedical imaging, including across modalities and medical specialties1–17. Labeled data is critical to training and testing DL models, but human expert labelers are limited. In addition, DL traditionally requires copious training data, which is computationally expensive to process and iterate over. Consequently, it is useful to prioritize using those images that are most likely to improve a model’s performance, a practice known as instance selection. The challenge is determining how best to prioritize. It is natural to prefer straightforward, robust, quantitative metrics as the basis for prioritization for instance selection. However, in current practice such metrics are not tailored to, and almost never used for, image datasets.

Methods

To address this problem, we introduce ENRICH—EliminateNoise andRedundancy for ImagingChallenges—a customizable method that prioritizes images based on how much diversity each image adds to the training set.

Results

First, we show that medical datasets are special in that in general each image adds less diversity than in non-medical datasets. Next, we demonstrate that ENRICH achieves nearly maximal performance on classification and segmentation tasks on several medical image datasets using only a fraction of the available images and outperforms random image selection, the negative control. Finally, we show that ENRICH can also be used to identify errors and outliers in imaging datasets.

Conclusion

ENRICH is a simple, computationally efficient method for prioritizing images for expert labeling and use in DL.
The Telomere Length Landscape of Prostate Cancer
Authors: Livingstone Julie , Shiah Yu-Jia , Yamaguchi Takafumi N. , Heisler Lawrence E. , Huang Vincent , Lesurf Robert , Gebo Tsumugi , Carlin Benjamin , Eng Stefan , Drysdale Erik , Green Jeffrey , van der Kwast Theodorus , Bristow Robert G. , Fraser Michael , Boutros Paul C.
Published: May 2021

Replicative immortality is a hallmark of cancer, and can be achieved through telomere lengthening and maintenance. We report telomere lengths (TLs) of 392 localized prostate cancer tumours and characterize their relationship to genomic, transcriptomic and proteomic features. Shorter tumour TLs were associated with elevated genomic instability, including single-nucleotide variants, indels and structural variants. Genes involved in cell proliferation and signaling were correlated with tumour TL at all levels of the central dogma. TL was also associated with multiple clinical features of a tumour. Longer TLs in non-tumour samples were associated with a lower rate of biochemical relapse after definitive local therapy. Our analysis integrates multi-omics data to illuminate the relationship of specific genomic alterations in a tumour and TL in prostate cancer. Although the role of telomere length in cancer has been well studied, its association to genomic features is less well known. We describe the multi-level integration of telomere length, genomics, transcriptomics and proteomics in localized prostate cancer. Patient Summary We examined the association between telomere length and multiple omics-level data in prostate cancer. We observed that traditional telomere mutations are rare in prostate cancer and that telomere length is associated with multiple measure of genomic instability.
A Phenomenological Analysis of COVID-19
Authors: Macchiavelli A. O. , Martí G. V. , Martí J. G.
Published: May 2021

A phenomenological analysis of the time evolution of some COVID-19 data in terms of a Fermi-Dirac function is presented. In spite of its simplicity, the approach appears to describe the data well and allows to correlate the information in a universal plot in terms of non-dimensional or reduced variables Nr = N (t)/Nmax, and tr = t/ΔT, with N (t) being the total number of cases as a function of time, Nmax the number of total infected cases, and ΔT the diffuseness of the Fermi/Dirac function associated with the rate of infection. The analysis of the reported data for the first outbreak in some selected countries and the results are presented and discussed. The approach is also applicable to subsequent waves. Support of our framework is provided by the SIS limit of the SIR model, and simulations carried out with the SEICRD extension.
A note on decoding elicited and self-generated inner speech
Authors: Jones Oiwi Parker , Voets Natalie L.
Published: May 2021

A recent result shows that inner speech can, with proper care, be decoded to the same high-level of accuracy as articulated speech. This relies, however, on neural data obtained while subjects performelicitedtasks, such as covert reading and repeating, whereas a neural speech prosthetic will require the decoding of inner speech that isself-generated. Prior work has, moreover, emphasised differences between these two kinds of inner speech, raising the question of how well a decoder optimised for one will generalise to the other. In this study, we trained phoneme-level decoders on an atypically large, elicited inner speech dataset, previously acquired using 7T fMRI in a single subject. We then acquired a second self-generated inner speech dataset in the same subject. Although the decoders were trained exclusively on neural recordings obtained during elicited inner speech, they predicted unseen phonemes accurately in both elicited and selfgenerated test conditions, illustrating the viability ofzero-shot task transfer. This has significant practical importance for the development of a neural speech prosthetic, as labelled data is far easier to acquire at scale for elicited than for self-generated inner speech. Indeed, elicited tasks may be the only option for acquiring labelled data in critical patient populations who cannot control their vocal articulators.
Correlated Drug Action as a Baseline Model for Combination Therapy in Patient Cohorts and Cell Cultures
Authors: Arun Adith S. , Kim Sung-Cheol , Ahsen Mehmet Eren , Stolovitzky Gustavo
Published: May 2021

Identifying and characterizing the effect of combination therapies is of paramount importance in various diseases, including cancer. Various competing null models have been proposed to serve as baselines against which to compare the effect of drug combinations. In this work, we introduce Correlated Drug Action (CDA), a baseline model for the study of drug combinations in both cell cultures and in patient populations. CDA assumes that the efficacy of pairs of drugs to be used in a combination may be correlated, that is, if the efficacy of a drug in a given patient or cell is high (low), then the efficacy of the other drug may also be high (low) in the same patient or cell. Our model can be used in the temporal domain (temporal CDA or tCDA) to explain survival curves in patient populations, and in the dose domain (dose CDA or dCDA), to explain dose-response curves in cell cultures. At the level of clinical trials, we demonstrate tCDA’s utility in identifying possibly synergistic combinations and cases where the combination can be explained in terms of the monotherapies. At the level of cells in culture, dCDA generalizes null models such as Bliss independence, the Highest Single Agent model, the dose equivalence principle, and is consistent with what should be expected in sham combinations. We demonstrate the applicability of dCDA in assessing combinations in experimental MCF7 cell-line data by introducing a new metric, the Excess over CDA (EOCDA).
On the intersection between data quality and dynamical modelling of large-scale fMRI signals
Authors: Aquino Kevin M. , Fulcher Ben , Oldham Stuart , Parkes Linden , Gollo Leonardo , Deco Gustavo , Fornito Alex
Published: May 2021

Large-scale dynamics of the brain are routinely modelled using systems of nonlinear dynamical equations that describe the evolution of population-level activity, with distinct neural populations often coupled according to an empirically measured structural connection matrix. This modelling approach has been used to generate insights into the neural underpinnings of spontaneous brain dynamics, as recorded with techniques such as resting state functional MRI (fMRI). In fMRI, researchers have many degrees of freedom in the way that they can process the data and recent evidence indicates that the choice of pre-processing steps can have a major effect on empirical estimates of functional connectivity. However, the potential influence of such variations on modelling results are seldom considered. Here we show, using three popular whole-brain dynamical models, that different choices during fMRI preprocessing can dramatically affect model fits and interpretations of findings. Critically, we show that the ability of these models to accurately capture patterns in fMRI dynamics is mostly driven by the degree to which they fit global signals rather than interesting sources of coordinated neural dynamics. We show that widespread deflections can arise from simple global synchronisation. We introduce a simple two-parameter model that captures these fluctuations and which performs just as well as more complex, multi-parameter biophysical models. From our combined analyses of data and simulations, we describe benchmarks to evaluate model fit and validity. Although most models are not resilient to denoising, we show that relaxing the approximation of homogeneous neural populations by more explicitly modelling inter-regional effective connectivity can improve model accuracy at the expense of increased model complexity. Our results suggest that many complex biophysical models may be fitting relatively trivial properties of the data, and underscore a need for tighter integration between data quality assurance and model development.
Business as Un-usual: Access to mental health and primary care services for people with severe mental illness during the COVID-19 restrictions
Authors: Newbronner Elizabeth , Spanakis Panagiotis , Wadman Ruth , Crossland Suzanne , Heron Paul , Johnston Gordon , Walker Lauren , Gilbody Simon , Peckham Emily
Published: May 2021

Aims

To explore: how satisfied people with severe mental illness (SMI) are with the support received during the pandemic; understand any difficulties encountered when accessing both mental health and primary care services; consider ways to mitigate these difficulties; and assess the perceived need for future support from mental health services.

Materials and Methods

A representative sample was drawn from a large transdiagnostic clinical cohort of people with SMI, which was recruited between April 2016 and March 2020. The sample was re-surveyed a few months after the beginning of the restrictions. Descriptive frequency statistics were used to analyse the quantitative data. The free text responses were analysed thematically.

Results

367 participants responded to the survey. Two thirds were receiving support from mental health services with the rest supported in primary care or self-managing. A quarter thought they would need more mental health support in the coming year. Half had needed to used community mental health services during the pandemic and the majority had been able to get support. A minority reported that their mental health had deteriorated but they had either not got the supported they wanted or had not sought help. The biggest service change was the reduction in face-to-face appointments and increasing use of phone and video call support. Nearly half of those using mental health services found this change acceptable or even preferred it; acceptability was influenced by several factors. Participants were more likely to be satisfied with support received when seen in person.

Discussion

Although most participants were satisfied with the mental health support they had received, a minority were not. This, couple with findings on future need for mental health support has implications for post pandemic demand on services. Remote care has brought benefits but also risks that it could increase inequalities in access to services.
Dengue transmission risk in a changing climate: Bangladesh could experience a longer dengue fever season in the future
Authors: Paul Kishor K , Macadam Ian , Green Donna , Regan David G , Gray Richard T
Published: May 2021

Our changing climate is already affecting the transmission of vector borne diseases such as dengue fever. This issue presents a significant public health concern for some nations, such as Bangladesh, which already experience regular seasonal outbreaks of dengue fever under present day conditions. To provide guidance for proactive public health planning to potentially mitigate future infections, we explore the impact of climate change on dengue infections by calculating the change in vectorial capacity of Aedes aegypti mosquito at a seasonal level for all regions in Bangladesh under two scenarios for future atmospheric greenhouse gas concentrations. For each of the four climate models used, and for both scenarios, our analysis reveals that the annual vectorial capacity remains at a level that would enable potential dengue epidemic transmission in all regions in Bangladesh. We found a slight decline in vectorial capacity in half of the regions examined during the last two decades of the 21st Century for the lower-concentration scenario, with a pronounced decline in vectorial capacity in all geographic regions beginning in 2060 for the higher-concentration scenario. The likely reason is that in many regions, warming is leading to sub-optimal mosquito breeding temperatures. However, seasonal differences in vectorial capacity dissipates as the climate warms, to the point that there is almost no observable seasonality for the higher-concentration scenario during the last two decades of this century. This finding suggests there is the potential for the dengue season to extend all year, with outbreaks occurring at any time. Consequently, disease surveillance and control activities would need to be geographically and temporally adapted to mitigate dengue epidemic risk in response to climate change.
Integrative functional analysis uncovers metabolic differences between Candida species
Authors: Begum Neelu , Lee Sunjae , Pellon Aize , Nasab Shervin Sadeghi , Nieslen Jens , Uhlen Mathias , Moyes David , Shoaie Saeed
Published: May 2021

Candida species are a dominant constituent of the human mycobiome and a better understanding of their metabolism from a fungal perspective can provide key insights into their ability to cause pathogenesis. Here, we have developed the BioFung database – a fungal specific tool for functional annotation using the KEGG database that provides an efficient method for annotation of protein-encoding gene. Analysis of carbohydrate-active enzyme (CAZymes) and BioFung, uncovered core and accessory features across Candida species demonstrating plasticity, adaptation to the environment and acquired features. Integerative functional analysis revealed that all Candida species can employ amino acid metabolism. However, metabolomics revealed that only a specific cluster of species (AGAu species - C. albicans, C. glabrata and C. auris) utilised amino acid metabolism. We identified critical metabolic pathways in the AGAu clusters with biomarkers and anti-fungal target potential in the CAZyme profile, polyamine, choline and fatty acid biosynthesis pathways. This study, combining genomic analysis, metabolomics and gene expression validation, highlights the metabolic diversity within AGAu species that underlies their remarkable ability to dominate the mycobiome and cause disease.
Language barriers in global bird conservation
Authors: Negret Pablo Jose , Atkinson Scott C. , Woodworth Bradley K. , Tor Marina Corella , Allan James R. , Fuller Richard A. , Amano Tatsuya
Published: May 2021

Multiple languages being spoken within a species’ distribution can impede communication among conservation stakeholders, the compilation of scientific information, and the development of effective conservation actions. Here, we investigate the number of official languages spoken within the distributions of 10,863 bird species to identify which ones might be particularly affected by consequences of language barriers. We show that 1587 species have 10 languages or more spoken within their distributions. Threatened, migratory and wide-ranging species have especially many languages spoken within their distribution. Particularly high numbers of species with many languages within their distribution are found in Eastern Europe, Russia and central and western Asia. Global conservation efforts would benefit from implementing guidelines to overcome language barriers, especially in regions with high species and language diversity.
Mechanics of inactive swelling and bursting of porate pollen grains
Authors: Božič Anže , Šiber Antonio
Published: May 2021

The mechanical structure of pollen grains, typically characterized by soft apertures in an otherwise stiff exine shell, guides their response to changes in the humidity of the environment. These changes can lead both to infolding but also to excessive swelling and even bursting of pollen grains. We use an elastic model to explore the mechanics of pollen grain swelling and the role that soft, circular apertures (pores) play in this process. We identify and explore a mechanical weakness of the pores, which are prone to a rapid inflation once the grain swells to a critical extent. This transition leads to the bursting of the grain and the release of its content. Our results shed light on the inactive part of the mechanical response of pollen grains to hydration once they land on a stigma as well as on bursting of airborne pollen grains during rapid changes in air humidity.
Modeling the effects of Aedes aegypti’s larval environment on adult body mass at emergence
Authors: Walker Melody , Chandrasegaran Karthikeyan , Vinauger Clément , Robert Michael A , Childs Lauren M
Published: May 2021

Mosquitoes vector harmful pathogens that infect millions of people every year, and developing approaches to effectively control mosquitoes is a topic of great interest. However, the success of many control measures is highly dependent upon ecological, physiological, and life history traits of the mosquito species. The behavior of mosquitoes and their potential to vector pathogens can also be impacted by these traits. One trait of interest is mosquito body mass, which depends upon many factors associated with the environment in which juvenile mosquitoes develop. Our experiments examined the impact of larval density on the body mass of Aedes aegypti mosquitoes, which are important vectors of dengue, Zika, yellow fever, and other pathogens. To investigate the interactions between the larval environment and mosquito body mass, we built a discrete time mathematical model that incorporates body mass, larval density, and food availability and fit the model to our experimental data. We considered three categories of model complexity informed by data, and selected the best model within each category using Akaike’s Information Criterion. We found that the larval environment is an important determinant of the body mass of mosquitoes upon emergence. Furthermore, we found that larval density has greater impact on body mass of adults at emergence than on development time, and that inclusion of density dependence in the survival of female aquatic stages in models is important. We discuss the implications of our results for the control of Aedes mosquitoes and on their potential to spread disease.

Author summary

In this work we examined how the environment in which young mosquitoes develop affects their adult body size as measured by adult body mass. Adult size has potential impacts on mosquito behavior and the ability of mosquitoes to transmit disease. We used a combination of experimental work and mathematical modeling to determine important factors affecting adult mosquito body size. In our model, we incorporated potentially interacting aspects of the mosquito life cycle and traits that affect mosquito growth as juveniles. These aspects include body mass, density of the population, and level of available resource. We compared different models to determine the one that best describes the data. As mass at emergence is linked to the success of adult mosquitoes to produce offspring and to their ability transmit pathogens, we discuss how important influences on development and survival of young mosquitoes affect mosquito control and disease spread.
Modelling, Analysis, and Optimization of Three-Dimensional Restricted Visual Field Metric-Free Swarms
Authors: Li Qing , Zhang Lingwei , Jia Yongnan , Lu Tianzhao
Published: May 2021

Models of collective behaviour have been proved helpful in revealing what mechanism may underlie characteristics of a flock of birds, a school of fish, and a swarm of herds. Recently, the metric-free model gradually occupies a dominant position in the research field of collective intelligence. Most of these models endow every single individual with the ability of a global visual field, which can offer each particle sufficient external information. In this paper, we mainly focus on whether the global visual field is necessary to form a consistent and cohesive group or not. Inspired by the biological characteristic of starlings, we develop a three-dimensional restricted visual field metric-free(RVFMF) model based on Pearce and Turner’s previous work. We further investigate several vital factors governing the convergent consistency of the RVFMF model with the assistance of extensive numerical simulations. According to the simulation results, we conclude that the best view angle of each particle in a swarm increases with the expansion of the population size. Besides, the best view angle gradually becomes stable around 155 degrees when the population size is larger than 1000. We also offer quantitative analysis data to prove that a flock of birds could obtain better consistency under optimal restricted visual field than under global visual field.
Pathway analysis in metabolomics: pitfalls and best practice for the use of over-representation analysis
Authors: Wieder Cecilia , Frainay Clément , Poupin Nathalie , Rodríguez-Mier Pablo , Vinson Florence , Cooke Juliette , Lai Rachel PJ , Bundy Jacob G , Jourdan Fabien , Ebbels Timothy
Published: May 2021

Over-representation analysis (ORA) is one of the commonest pathway analysis approaches used for the functional interpretation of metabolomics datasets. Despite the widespread use of ORA in metabolomics, the community lacks guidelines detailing its best-practice use. Many factors have a pronounced impact on the results, but to date their effects have received little systematic attention in the field. We developed in-silico simulations using five publicly available datasets and illustrated that changes in parameters, such as the background set, differential metabolite selection methods, and pathway database choice, could all lead to profoundly different ORA results. The use of a non-assay-specific background set, for example, resulted in large numbers of false-positive pathways. Pathway database choice, evaluated using three of the most popular metabolic pathway databases: KEGG, Reactome, and BioCyc, led to vastly different results in both the number and function of significantly enriched pathways. Metabolomics data specific factors, such as reliability of compound identification and assay chemical bias also impacted ORA results. Simulated metabolite misidentification rates as low as 4% resulted in both gain of false-positive pathways and loss of truly significant pathways across all datasets. Our results have several practical implications for ORA users, as well as those using alternative pathway analysis methods. We offer a set of recommendations for the use of ORA in metabolomics, alongside a set of minimal reporting guidelines, as a first step towards the standardisation of pathway analysis in metabolomics.

Author summary

Metabolomics is a rapidly growing field of study involving the profiling of small molecules within an organism. It allows researchers to understand the effects of biological status (such as health or disease) on cellular biochemistry, and has wide-ranging applications, from biomarker discovery and personalised medicine in healthcare to crop protection and food security in agriculture. Pathway analysis helps to understand which biological pathways, representing collections of molecules performing a particular function, are involved in response to a disease phenotype, or drug treatment, for example. Over-representation analysis (ORA) is perhaps the most common pathway analysis method used in the metabolomics community. However, ORA can give drastically different results depending on the input data and parameters used. In this work, we have established the effects of these factors on ORA results using computational simulations applied to five real-world datasets. Based on our results, we offer the research community a set of best-practice recommendations applicable not only to ORA but also to other pathway analysis methods to help ensure the reliability and reproducibility of results.
Quantification of long-term doxorubicin response dynamics in breast cancer cell lines to direct treatment schedules
Authors: Howard Grant , Jost Tyler A. , Yankeelov Thomas E. , Brock Amy
Published: May 2021

While acquired chemoresistance is recognized as a key challenge to treating many types of cancer, the dynamics with which drug sensitivity changes after exposure are poorly characterized. Most chemotherapeutic regimens call for repeated dosing at regular intervals, and if drug sensitivity changes on a similar time scale then the treatment interval could be optimized to improve treatment performance. Theoretical work suggests that such optimal schedules exist, but experimental confirmation has been obstructed by the difficulty of deconvolving the simultaneous processes of death, adaptation, and regrowth taking place in cancer cell populations. Here we present work characterizing dynamic changes in sensitivity to the chemotherapeutic doxorubicin in three breast cancer cell lines subjected to treatment schedules varying in concentration, interval between pulse treatments, and number of sequential pulse treatments. Cell populations are monitored longitudinally through automated imaging for 600-800 hours, and this data is used to calibrate a family of cancer growth models derived from the bi-exponential model which characterizes resistant and sensitive subpopulations. We identify a model incorporating both a period of growth arrest in surviving cells and a delay in the death of chemosensitive cells which outperforms the original bi-exponential growth model in Akaike Information Criterion based model selection, and use the calibrated model to quantify the performance of each drug schedule. We find that the inter-treatment interval is a key variable in determining the performance of sequential dosing schedules and identify an optimal retreatment time for each cell line which extends regrowth time by 40%-106%, demonstrating that the time scale of changes in chemosensitivity following doxorubicin exposure allows optimization of drug scheduling by varying this inter-treatment interval.
RNA secondary structure prediction with Convolutional Neural Networks
Authors: Booy Mehdi Saman , Ilin Alexander , Orponen Pekka
Published: May 2021

Predicting the secondary, i.e. base-pairing structure of a folded RNA strand is an important problem in synthetic and computational biology. First-principle algorithmic approaches to this task are challenging because existing models of the folding process are inaccurate, and even if a perfect model existed, finding an optimal solution would be in general NP-complete. In this paper, we propose a simple, yet extremely effective data-driven approach. We represent RNA sequences in the form of three-dimensional tensors in which we encode possible relations between all pairs of bases in a given sequence. We then use a convolutional neural network to predict a two-dimensional map which represents the correct pairings between the bases. Our model achieves significant accuracy improvements over existing methods on two standard datasets. Our experiments show excellent performance of the model across a wide range of sequence lengths and RNA families. We also observe considerable improvements in predicting complex pseudoknotted RNA structures, as compared to previous approaches.

Author summary

Structure prediction for RNA sequences is a computationally difficult task that is of increasing importance in applications such as medical diagnostics and drug design; this is because the structure of a folded RNA strand to a large extent defines its function. An open RNA strand can fold to many different structures of varying thermal stability, and the goal of structure prediction is to determine a most stable one among these. There are two main difficulties to this task. Firstly, a given RNA sequence can fold into an enormous number of alternative structures, and a computational search for a most stable one in this huge space can be very demanding. The search can however be facilitated by using heuristics that take into account some underlying principles of the folding process. Here is where machine learning methods come into play: they are suitable for discovering patterns in data, and can thus predict features of the desired structure based on previously learned patterns. Secondly, there do not yet exist fully satisfactory coarse-grained models for the most popular metric for stability, the free energy of the folded structure. Although in principle a minimum free energy (MFE) structure should be a good candidate for a most stable one, MFE structures determined according to current energy models do not match experimental data on native RNA conformations very well. We show how to use an artificial neural network design to predict the structure for a given RNA sequence with high accuracy only by learning from samples whose native structures have been experimentally characterized, independent of any stability metric or energy model.
Genetically engineered coral: A mixed-methods analysis of initial public opinion
Authors: Hobman Elizabeth V. , Mankad Aditi , Carter Lucy , Ruttley Chantale ,   Aditi Mankad, Lucy Carter, Chantale Ruttley
Published: May 2021

Rising sea surface water temperatures is contributing to coral degradation in the Great Barrier Reef. Synthetic biology technologies offer the potential to enhance coral resilience to higher water temperatures. To explore what the public think of genetically engineered coral, qualitative responses to an open-ended question in a survey of 1,148 of the Australian public were analysed. More respondents supported the technology (59%) than did not (11%). However, a considerable proportion indicated moderate or neutral support (29%). Participants commented about the (moral) right to interfere with nature and uncertainty regarding the consequences of implementing the technology. Participants also mentioned the need to take responsibility and act to save the reef, as well as the benefits likely to result from implementing the technology. Other themes included a desire for further testing and proof, more information, and tight regulation and controls when introducing the technology.
A panel of single nucleotide polymorphism (SNP) markers identifies potential duplicates in cassava (Manihot esculentaCrantz) varieties from Côte d’Ivoire
Authors: Yéo Edwige F. , Amoakon William J-L. , Pita Justin S. , Mutuku J. Musembi , N’zué Boni , Kouassi Modeste K. , Yao Nasser , Otron Daniel H. , Shah Trushar , Vanié-Leabo Linda P. L. , Kpahé Kanh M. H. , Sié Raoul , Sorho Fatogoma , Koné Daouda , N’guetta Simon-Pierre A. , Kouassi Nazaire K. , Ferguson Morag E.
Published: May 2021

Accurate identification of varieties is paramount to optimizing efficiencies in the management and conservation of genetic resources. A relatively inexpensive, rapid methodology is required to identify putative duplicates from any collection, when morphological traits give insufficient discrimination. Here we select a panel of 36 SNPs, visualized using the Kompetitive Allele-specific PCR (KASP) system. We used a panel of 95 cassava genotypes from Côte d’Ivoire to identify varieties that are not duplicates and few potential duplicates which could be put forward for further verification. The genetic variability and population structure of the germplasm is also described. 36 SNPs were polymorphic across the panel of 95 varieties with polymorphic information contents ranging from 0.23 to 0.37. Using these SNPs, we were able to identify 66 unique genotypes from the panel of 95 genotypes, discriminate three sets of known duplicates and identify 11 sets of unknown putative duplicates which can be subjected to further verification using higher density genotyping. As expected in an outcrossing species, both expected heterozygosity (0.46) and observed heterozygosity (0.48) were high with an analysis of molecular variance (AMOVA) indicating that the majority of variation was within individuals. Three statistical approaches i.e., hierarchical ascending clustering, Bayesian analysis and discriminant analysis of principal components were used and all revealed low genetic differentiation between sub-populations, a conclusion that was supported by the low value of the fixation index (0.05). This panel of SNPs can be used to enhance cost-effectiveness and efficiency of germplasm conservation and enhance quality control at various stages in the breeding process through varietal tracking.
The Impact Of Long-term Grazing Intensity On Functional Groups Richness, Biomass, And Species Diversity In an Inner Mongolian Steppe Grassland
Authors: Zainelabdeen Yousif Mohamed , Ahmed Ahmed Ibrahim , Yan Ruirui , Xin Xiaoping , Juan Cao , Olaide Jimoh Saheed
Published: May 2021

Livestock grazing is one of the major land uses, causing changes in the plant community's structure and grasslands composition. We assessed the effect of grazing intensity on aboveground biomass, species richness, and plant functional group (PFG) diversity in a temperature meadow steppe in Hulunbuir in northern China, involving 78 plant species from eight functional groups. Four grazing intensity classes were characterized, including light, moderate, heavy, and no grazing, based on stocking rates of 0.23, 0.46, 0.92, and 0.00 animal units per hectare. Our results show that the richness of short species, including perennial short grass, perennial short grass, and legume increased under light to moderate grazing, while no effect of grazing was observed on the richness of shrubs. With increasing grazing intensity, the aboveground biomass of perennial tall grasses and perennial tall forbs decreased significantly, while that of annual/biennial plant functional groups increased. The community diversity and evenness of annual/biennial plants increased significantly with grazing intensity. We concluded that heavy grazing has negative impacts on plant functional group richness and aboveground biomass.