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Postharvest environmentally and human-friendly pre-treatments to minimize carrot waste in the supply chain caused by physiological disorders and fungi
پیش تصفیه های دوستانه محیط زیست و انسانی پس از برداشت برای به حداقل رساندن ضایعات هویج در زنجیره تأمین ناشی از اختلالات فیزیولوژیکی و قارچ ها-2021 Background: Carrot is one of the most important horticultural crops, with an annual worldwide production exceeding 40 million tonnes. Carrots are sold either fresh intact or fresh-cut as minimally processed vegetables (MPV). In the postharvest supply chain, physiological disorders, fungal decay, and their combinations reduce the quality of fresh intact and MPV carrots. MPV carrots are more susceptible to quality changes than fresh intact carrots due to a higher loss of protective epidermis, greater number of wounded cells, and increased respiration rates. Scope and approach: The current review summarizes different environmentally and human-friendly treatments applied in the postharvest supply chain to minimize the adverse effects of handling and storage on physiological disorders and fungal decay. Key findings and conclusions: Bitterness, white blush, and browning are the most critical physiological disorders of fresh and MPV carrots. Bitterness can be prevented by storing carrots in well-ventilated rooms without ethylene- producing fruit and vegetables, while white blush and browning can be controlled by the application of heat treatment, ultraviolet (UV)-irradiation, hydrogen sulfide (H2S), and edible films. Sclerotinia sclerotiorum, Botrytis cinerea, Alternaria radicina, and Berkeleyomyces spp. (formerly Thielaviopsis spp.) are important fungi causing carrot postharvest losses and waste. Fungal decay of carrots can be controlled by selecting healthy carrots and applying natural compounds, ozone (O3), heat treatment, UV-irradiation, inorganic salt, and/or biocontrol agents, and their combinations. However, a successful combination of different sustainable treatment methods requires treatment compatibility, and -omics techniques may reveal the best combinations of sustainable treatment methods. Keywords: Daucus carota | Horticulture | Supply chain | Ozone | UV-Irradiation | Heat treatment |
مقاله انگلیسی |
2 |
Unsupervised classification of multi-omics data during cardiac remodeling using deep learning
طبقه بندی بدون نظارت شده داده های چند omics در طی بازسازی قلب با استفاده از یادگیری عمیق-2019 Integration of multi-omics in cardiovascular diseases (CVDs) presents high potentials for translational discoveries.
By analyzing abundance levels of heterogeneous molecules over time, we may uncover biological
interactions and networks that were previously unidentifiable. However, to effectively perform integrative
analysis of temporal multi-omics, computational methods must account for the heterogeneity and complexity in
the data. To this end, we performed unsupervised classification of proteins and metabolites in mice during
cardiac remodeling using two innovative deep learning (DL) approaches. First, long short-term memory (LSTM)-
based variational autoencoder (LSTM-VAE) was trained on time-series numeric data. The low-dimensional
embeddings extracted from LSTM-VAE were then used for clustering. Second, deep convolutional embedded
clustering (DCEC) was applied on images of temporal trends. Instead of a two-step procedure, DCEC performes a
joint optimization for image reconstruction and cluster assignment. Additionally, we performed K-means clustering,
partitioning around medoids (PAM), and hierarchical clustering. Pathway enrichment analysis using the
Reactome knowledgebase demonstrated that DL methods yielded higher numbers of significant biological
pathways than conventional clustering algorithms. In particular, DCEC resulted in the highest number of enriched
pathways, suggesting the strength of its unified framework based on visual similarities. Overall, unsupervised
DL is shown to be a promising analytical approach for integrative analysis of temporal multi-omics. Keywords: Cardiovascular | Clustering | Multi-omics Time-series | Unsupervised deep learning | Integrative analysis |
مقاله انگلیسی |
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An Evaluation of Machine Learning Approaches for the Prediction of Essential Genes in Eukaryotes Using Protein Sequence-Derived Features
ارزیابی رویکردهای یادگیری ماشینی برای پیش بینی ژنهای ضروری در یوکاریوتها با استفاده از ویژگیهای حاصل از توالی پروتئین-2019 The availability of whole-genome sequences and associated multi-omics data sets, combined with advances in
gene knockout and knockdown methods, has enabled large-scale annotation and exploration of gene and protein
functions in eukaryotes. Knowing which genes are essential for the survival of eukaryotic organisms is paramount
for an understanding of the basic mechanisms of life, and could assist in identifying intervention targets
in eukaryotic pathogens and cancer. Here, we studied essential gene orthologs among selected species of eukaryotes,
and then employed a systematic machine-learning approach, using protein sequence-derived features and
selection procedures, to investigate essential gene predictions within and among species. We showed that the
numbers of essential gene orthologs comprise small fractions when comparedwith the total number of orthologs
among the eukaryotic species studied. In addition, we demonstrated that machine-learning models trainedwith
subsets of essentiality-related data performed better than random guessing of gene essentiality for a particular
species. Consistent with our gene ortholog analysis, the predictions of essential genes among multiple (including
distantly-related) species is possible, yet challenging, suggesting that most essential genes are unique to a species.
The presentwork provides a foundation for the expansion of genome-wide essentiality investigations in eukaryotes
using machine learning approaches. Keywords: Machine-learning | Essential genes | Essentiality prediction | Eukaryotes |
مقاله انگلیسی |
4 |
Using machine learning to explain the heterogeneity of schizophrenia: Realizing the promise and avoiding the hype
استفاده از یادگیری ماشین برای توضیح ناهمگونی اسکیزوفرنی: تحقق وعده و پرهیز از اعتیاد به مواد مخدر-2019 Despite extensive research and prodigious advances in neuroscience, our comprehension of the nature of schizophrenia
remains rudimentary.Our failure to make progress is attributed to the extremeheterogeneity of this condition,
enormous complexity of the human brain, limitations of extant research paradigms, and inadequacy of
traditional statistical methods to integrate or interpret increasingly large amounts of multidimensional information
relevant to unravelling brain function. Fortunately, the rapidly developing science of machine learning appears
to provide tools capable of addressing each of these impediments. Enthusiasm about the potential of
machine learning methods to break the current impasse is reflected in the steep increase in the number of scientific
publication about the application of machine learning to the study of schizophrenia. Machine learning approaches
are, however, poorly understood by schizophrenia researchers and clinicians alike. In this paper, we
provide a simple description of the nature and techniques of machine learning and their application to the
study of schizophrenia.We then summarize its potential and constraints with illustrations fromsix studies of machine
learning in schizophrenia and address some common misconceptions about machine learning.Wesuggest
some guidelines for researchers, readers, science editors and reviewers of the burgeoning machine learning literature
in schizophrenia. In order to realize its enormous promise,we suggest the need for the disciplined application
of machine learning methods to the study of schizophrenia with a clear recognition of its capability and
challenges accompanied by a concurrent effort to improve machine learning literacy among neuroscientists
and mental health professionals. Keywords: Machine learning | Schizophrenia | Methods | Research | Neuroscience | Computational psychiatry | -omics | Big data | Hype | Promise |
مقاله انگلیسی |
5 |
پلت فرم eTRIKS: مفهوم و بهره برداری از پلت فرم مبتنی بر ابر بسیار مقیاس پذیر برای تحقیق و توسعه برنامه های کاربردی-2018 We describe the genesis, design and evolution of a computing platform designed and built to improve the success
rate of biomedical translational research. The eTRIKS project platform was developed with the aim of building a
platform that can securely host heterogeneous types of data and provide an optimal environment to run tranS
MART analytical applications. Many types of data can now be hosted, including multi-OMICS data, preclinical
laboratory data and clinical information, including longitudinal data sets. During the last two years, the platform
has matured into a robust translational research knowledge management system that is able to host other data
mining applications and support the development of new analytical tools.
Keywords: Computing ، Cloud ، eTRIKS ، tranSMART ، Hosting ، Analysis ، Security ، Translational research ، Authentication ، Platform ، Storage ، Web application ، Knowledge management |
مقاله انگلیسی |
6 |
Exploiting the natural product potential of fungi with integrated -omics and synthetic biology approaches
بهره برداری از پتانسیل محصول طبیعی قارچ با رویکردهای یکپارچه ی omics و زیست شناسی مصنوعی-2017 Fungi are rich, underexploited reservoirs for natural products that may serve as medicines, commodity
chemicals, insecticides, pesticides and other valuable chemicals. Moreover, the biochemistry of natural
product formation may be repurposed with emerging synthetic biology tools to make valuable non-
natural compounds such as biofuels. However, the pathways that lead to these products are poorly
understood and frequently inactive under lab conditions making discovery challenging. Recent advances
in –omics approaches and synthetic biology tools provide powerful new methods to elucidate and tap
this wealth of novel chemistry. In this review, we describe cutting-edge approaches to activate and
characterize natural product formation, and discuss the potential of established and emerging fungal
systems for natural product discovery
Keywords: fungi | natural products | synthetic biology | integrated omics |
مقاله انگلیسی |
7 |
Exploiting the natural product potential of fungi with integrated -omics and synthetic biology approaches
Exploiting the natural product potential of fungi with integrated -omics and synthetic biology approaches-2017 Fungi are rich, underexploited reservoirs for natural products that may serve as medicines, commodity
chemicals, insecticides, pesticides and other valuable chemicals. Moreover, the biochemistry of natural
product formation may be repurposed with emerging synthetic biology tools to make valuable non
natural compounds such as biofuels. However, the pathways that lead to these products are poorly
understood and frequently inactive under lab conditions making discovery challenging. Recent advances
in –omics approaches and synthetic biology tools provide powerful new methods to elucidate and tap
this wealth of novel chemistry. In this review, we describe cutting-edge approaches to activate and
characterize natural product formation, and discuss the potential of established and emerging fungal
systems for natural product discovery.
keywords: fungi| natural products| synthetic biology| integrated omics 19 |
مقاله انگلیسی |
8 |
Mini-encyclopaedia of the wound healing - Opportunities for integrating multi-omic approaches into medical practice
دایره المعارف زخم - امکان ادغام رویکردهای چندگانه در عمل پزشکی-2017 Wound healing is a highly complex life-important repair process triggered by plenty of local and/or systemic organ
and tissue damaging events, such as an acute surgical invasion, accidental organ and tissue damages, acute and
chronic diseases, aggressive local and systemic therapeutic approaches (e.g. irradiation and systemic chemothera
py). Individual health condition determines over the quality of wound healing. Impaired wound healing, in turn,
may lead, for example, to post-surgical complications frequently observed in elderly, chronic ulcers in diabetic pa
tients, hindered and ineffective pain management, etc. However, these well-acknowledged examples are just the tip
of the iceberg. The entire spectrum of potential consequences is much broader. Therefore, all the aspects of wound
healing need to receive a dedicated attention of many specialised medical fields and healthcare as a whole. In con
trast, there is still strongly limited knowledge collected regarding the molecular and cellular mechanisms underly
ing the physiological versus impaired wound healing. The contents of this article might be of great importance for
multi-professional considerations as well as for the experts working in specific fields such as clinical proteomics,
general practice, laboratory medicine, surgery including plastic surgery and aesthetic medicine, gerontology,
psychology, diabetology, endocrinology, oncology, cardiovascular disease, radiology, and healthcare economy.
Significance: The contents of this article are strongly motivated by the particular value of wound healing quality for
medical care and might be of great importance for multi-professional considerations and experts working in
specialised fields: predictive and preventive medicine, general practitioners, laboratory medicine, surgery including
plastic surgery and aesthetic medicine, gerontology, psychology, diabetology, endocrinology, oncology, cardiovascu
lar disease, radiology, and healthcare economy. The article is aiming at both educational and scientific purposes: on
one side it summarises comprehensive information available regarding wound healing mechanisms and molecular
pathways involved. On the other side the article provides highly innovative hypotheses for multi-professional con
siderations relevant for several research fields which may potentially advance medical services in the close future
such as clinical proteomics and multi-omics.
Keywords: Wound healing | Predictive preventive personalised medicine | General practitioners | Laboratory medicine | Surgery | Aesthetic medicine | Psychology | Gerontology | Diabetology | Endocrinology | Oncology | Cardiovascular disease | Radiology | Healthcare economy |
مقاله انگلیسی |
9 |
Using systems biology approaches to elucidate cause and effect in host-microbiome interactions
رویکرد استفاده از سیستم های بیولوژی برای تشخیص عللی تاثیر ان در تعاملات میکرو بیوم - میزبان -2017 The human microbiome is a diverse and complex ecosystem
integral for healthy human development. Recent advances in
next-generation sequencing technology have paved the way
for a ‘multi-omics’ era of microbiome research, uncovering
associations between microbial dysbiosis and disease. Our
ability to harness the full potential of these ‘multi-omics’ data
sets are currently constrained by several technical, analytical,
computational and bioinformatics factors. However, it may be
possible to overcome such limitations through the use of novel
systems biology thinking and approaches, to integrate and
analyse these large ‘multi-omics’ datasets. Thus, the question
arises - can systems biology approaches pave the way to a
new era in microbiome research; determining underlying
mechanisms in health and disease, and identifying key mi
crobial interactions and causalities?
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مقاله انگلیسی |
10 |
Unlocking the potential of plant phenotyping data through integration and data-driven approaches
باز کردن پتانسیل داده های فن آوری های گیاهی از طریق یکپارچه سازی و رویکردهای مبتنی بر داده ها-2017 Plant phenotyping has emerged as a comprehensive field of
research as the result of significant advancements in the
application of imaging sensors for high-throughput data
collection. The flip side is the risk of drowning in the massive
amounts of data generated by automated phenotyping sys
tems. Currently, the major challenge lies in data management,
on the level of data annotation and proper metadata collection,
and in progressing towards synergism across data collection
and analyses. Progress in data analyses includes efforts to
wards the integration of phenotypic and -omics data resources
for bridging the phenotype–genotype gap and obtaining in
depth insights into fundamental plant processes.
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مقاله انگلیسی |