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One way of categorization relies on the nature of the data to be analyzed, thus, leading to the categories of supervised, unsupervised, and reinforcement methods Figure 7. Supervised machine learning is a classification or regression approach that builds a predictive model based on labeled reference data [ 45 ].
Several research groups focusing on imaging analysis apply supervised machine learning to retrospective datasets [ 7 , 10 , 43 ]. Three main types of machine learning based on the nature of the input data or environment: supervised, unsupervised and reinforcement learning and their main properties by the means of data, fitness, and model.
Unsupervised machine learning operates with unlabeled data, hence, it can be characterized as a clustering approach [ 44 ]. Research groups focusing on exploratory analysis of their imaging dataset without a ground truth, utilize unsupervised machine learning methodologies [ 46 , 48 — 50 ].
Whenever, an action is taken and the environment state changes, a reward or punishment is issued back to the agent. The goal is to build a set of actions that maximize the reward and minimize the punishment regardless of the current environment stage.
Reinforcement learning can be utilized in case a given environment is very complex, prone to changes, or—generally speaking—is of unknown nature [ 51 ]. To date, reinforcement learning is underrepresented in the field of medical imaging with only limited applications [ 46 , 52 ].
Another type of categorizing ML methods follows the nature of the feature extraction and analysis of the data. In this case two main groups can be identified, such as shallow learning methods building on engineered or handcrafted features [ 8 , 53 ] and deep learning DL methods building on automatically comprehended, multi-layer representation of the data [ 54 ]. Several related works utilize machine learning built on engineered features [ 55 — 59 ].
These approaches typically employ feature selection [ 60 ] in the form of feature redundancy reduction [ 61 ] or feature ranking [ 8 ]. Deep learning is reported to generally outperform shallow learning ML approaches, as it is able to decompose and analyse the data on different levels of information complexity [ 54 , 62 ]. Nevertheless, the true potential of DL is seen only in view of data having a complex, hierarchical structure.
This is a challenging requirement to fulfill, and, thus, DL to date is underrepresented in the context of tumor characterization and hybrid imaging [ 62 , 63 ]. Despite the wide range of ML approaches available today, there is no unique ML method which generally outperforms all others [ 67 ].
Therefore, testing of multiple ML approaches is encouraged to identify the most suitable for a particular evaluation [ 68 ]. Table 2 provides an overview of the most common ML algorithms applied in medical science. Overview of machine learning algorithms and example uses in medical imaging.
High-Performance and Cloud Computing Machine learning evaluation of Medical Big Data requires high-performance computational resources [ 50 ]. As the amount of data increases exponentially, progressively complex computational architectures are needed for the storage, processing, and analysis of the data [ 98 ].
Distributed systems, such as the Hadoop ecosystem are potential solutions to deal with Medical Big Data [ 99 , ]. The HDFS is a fault-tolerant distributed file system designed to run on low-cost hardware, which is suitable for medical image data applications [ ].
Hadoop has been applied to address numerous tasks in medical imaging, such as parameter optimization for lung texture segmentation, content-based medical image indexing, and 3D directional wavelet analysis for solid texture classification [ 99 , , ]. Next to Apache Hadoop, several distributed high-performance computing platforms are available as well [ 99 , ].
Helminths and the microbiota: parts of the hygiene hypothesis
One example is the open-source Apache Spark [ , ], which has a better ability of computing compared to Hadoop [ , ]. It has been shown that Spark is up to 20 times faster than Hadoop for iterative applications, it accelerates a real-world data analytics report by a factor of 40 and it can be used interactively to scan a 1 TB dataset within a few seconds [ ].
These characteristics enable Spark to serve as an efficient tool for medical imaging data analysis tasks, such as the computation of voxel-wise local clustering coefficients of fMRI data [ ].
To date, major industry leaders provide cloud storage with the combination of cloud ML engines to address the need of Big Data evaluation [ 15 , ]. These systems are potentially ideal frameworks for large-scale hybrid imaging data evaluation [ , ].
An example service is the Google Cloud Platform used by both academic research institutions and by a variety of healthcare companies. Similar to the Google Cloud Platform, site provides a suite of services called the site Web Services including cloud computing and machine learning tools. The site Elastic Compute Cloud, for example, is an Infrastructure as a Service which offers the possibility to rent virtual computers.
It is being used to develop a technology for supporting radiologists to identify abnormalities in medical images across different modalities as well as for providing a blood flow imaging solution that enables doctors to render MRI scans in multi-dimensional models and better diagnose patients for cardiovascular diseases [ , ] 1. Another package for cloud-based services is Azure provided by Microsoft. Azure has been employed for medical image classification using algorithms, such as support vector machines and k-nearest neighbors [ , ].
A few machine learning examples for medical imaging analysis utilizing Azure are covered in Criminisi [ ]. Data Handling Data Preservation and Reproduction Beyond technical considerations of evaluating large-scale medical data, persistent storage is a challenge for various medical institutions. To date, hospitals that generate and collect medical data are also responsible for archiving the data [ ].
The process is further defined in the triangle of legal obligations, practical considerations as well as financial resources. The time period a hospital needs to archive patient data varies with the country, however, mandatory preservation periods are generally between 10 and years [ ]. However, these storage periods are much longer than the estimated storage durability of memory technologies available today Table 3. Since PET raw data aka list mode files is considerably large, it is frequently not archived at all [ ].
This prevents scientists from retrospectively optimizing image quality by new image reconstruction approaches and eventually to standardize protocols for accurate, population-wide evaluations. Since the required data preservation for both routine and research purposes is not feasible by conventional tools, there is an urgent need to shift focus toward more persistent solutions.
As an example, cloud storage and evaluation approaches [ , ] can support convenient data sharing and repeatability of published results across various research groups. Estimated lifespan of some media storage technologies according to Morgan [ ]. Data Sharing Clinical research is an essential building block for the concept of efficient patient management.
Research studies are generally complex and the resulting data are valuable, not only to the principal investigator but to society as a whole [ , ].
Nonetheless, many researchers remain reluctant to share their data with an expert audience [ , ] beyond describing them as part of peer-reviewed publications. In contrast, sharing research data in a structured and tangible way has been shown to yield benefits for both the principal investigators and other experts in the field who may re-use the data with alternative evaluation approaches to extract new information that may subsequently benefit patient management [ ].
However, the quality of public supplementary material collections of published studies is variable, and frequently the re-use of these data is not possible [ ]. In the light of medical imaging and hybrid imaging in particular, a wide range of imaging data from different fields is already available for researchers worldwide Table 4. Some of these databases are dedicated to the collection and sharing of very heterogeneous data from different modalities, various diseases, and different body regions.
Selection of online medical image sources. TCIA is an open-access repository, funded by the National Cancer Institute, containing several million medical images from various cancer types 2. The data is partitioned into collections based on characteristics, such as cancer type or affected region in common.
Apart from supplying users with massive amounts of high quality data, it also provides an application programming interface API for automated data access. Nevertheless, it mainly focuses on the training of radiologists and provides no automated data access 3 Open-i is a service hosted by the National Library of Medicine NLM [ ].
Nevertheless, not all images which can be retrieved using open-i are free to use. Another major source for in vivo medical images is the National Biomedical Imaging Archive NBIA which includes clinical and genetic data associated to the images [ ].
In addition to these more general data repositories, there are many specialized data sources for medical images. In addition to neuroimaging data, it also includes clinical and biomarker information. Another specialized data base is the medical image repository of the Johns Hopkins Medical Institute [ ].
It includes MRI images of human, mouse and monkey brain images. Further data sources for medical images can be found in Table 4. Joint Data Exploration To date, in vivo disease characterization with hybrid imaging data—especially in the light of oncological applications—is performed mainly by analyzing engineered features [ 63 , ]. Radiomics In general, any radiomics-based exploration of imaging data requires object delineation, followed by feature extraction and evaluation [ , , ]. Extracted features cover the range of first to higher order, wavelet, Laplacian, and fractal features [ 6 , 8 , , ].
Biochem Biophys Res Commun. EMBO J. Analysis of the sites phosphorylated by two-dimensional peptide mapping. Biochem J. Protein Seq Data Anal. Mol Cell Biol. Eur J Biochem. Mol Cell Endocrinol.
A brute force approach based on "substrate-trapping" mutants. Epub Oct Epub Nov Epub Jul Epub Dec J Clin Invest. Epub Aug Zoolog Sci. Nat Rev Mol Cell Biol. Polymer-based ATPSs are showing promise for a wide range of commercially valuable molecules, however their versatility also complicates its adoption. The principles governing high-value molecule partitioning has been investigated Willauer et al.
One other factor that prohibits the attractiveness of polymer-polymer ATPSs is the cost of high molecular weight polymers at industrial scale Torres-Acosta et al.
Such polymer-salt ATPSs were demonstrated to have high yield and scalability from bench to industrial scale Hart et al. Ionic Liquid ATPS Ionic liquids have similar tunable properties such as hydrophobicity, polarity, solvation, and phase separation Greaves et al. IL-ATPSs provide improvements in separation performance, selectivity, and broader design opportunities for a diverse range of molecules, including small molecules and biomolecules.
IL-ATPSs also possess lower molecular weight and viscosity which enables faster separations making them industrially attractive.
We note that the separation of cells, which is frequently performed in drug discovery experiments, and an obvious area for improvement through use of IL-ATPSs is not possible or demonstrated with IL-ATPSs, to the best of our awareness, likely due to osmotic pressure effects. While this review provides a pharmaceutical focus, the reader is encouraged to reference other IL-ATPS reviews that informatively summarize properties for partitioning Li et al. A review of various roles of ILs in pharmaceutical applications is also recommended Huddleston et al.
The IL-rich environment, often the upper phase, is relatively more hydrophobic thus attracting hydrophobic molecules. Phase separation is thus understood to be solvophobic and driven by electrostatic attractions and hydrophobic interactions.
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Several different systems have been discovered, characterized, and investigated for their potential applications in the pharmaceutical industry. IL-ATPSs can be tuned to improve solubility and thus extraction efficiency and partitioning properties which in turn improves speed and completion of phase separation. Generally, the lower the salt concentration required, the stronger the phase separation ability of the ILs from aqueous solutions of salts.
Other systems such as cholinium based carboxylate ILs oxalate, malonate, succinate, L-malate, fumarate, glutarate, and citrate , and pyridinium based -ILs are described with PEG and as the second phase former Passos et al. The behavior was inconsistent on changing the IL anion. Phosphonium-based ILs have the advantage of being less dense than water and less viscous than imidazolium based ILs, thus prospectively facilitating the separation of the aqueous waste stream of biomolecule separations in production scale equipment Louros et al.
The ability to form IL-ATPS was related to hydrophobicity of the amino acid anion such that phase formation occurs with increasing hydrophobicity of the anion. Formate, acetate, propionate, lactate, chloride, and bromide salts of functionalized guanidinium ILs were shown to be effective at protein partitioning Ding et al. Antioxidant anions butanoate, propanoate, acetate, lactate, glycolate, bitartrate, dihydrogen phosphate, chloride, dihydrogen citrate, gallate, syringate, vanillate, and caffeate of cholinium ILs were effective in separating immunoglobulin G Ramalho et al.
Improvements in the extraction performance for a hydrophobic molecule, tryptophan was demonstrated in citrate-based IL-ATPSs for use in continuous extraction of amino acids.
The incorporation of surfactants decreased the viscosities of both phases and flipped the IL-rich phase to the bottom phase as a result of density changes Wei et al. The molecular mechanisms that govern the ability of salt ions to induce IL-ATPS formation was studied using a wide range of salts with diverse combinations of cations and anions, with [bmim][CF3SO3] a hydrophilic ionic liquid Shahriari et al.
The ions' trend to salt-out the IL into an ATPS follows the Hofmeister series and demonstrates the molar entropy of salt ion hydration as the driving force. Additives Addition of a third component to create an IL-ATPS is another tactic used to enhance separation, presumably by increasing the solvophobic nature of the systems.
Addition of CO2 into aqueous solutions of ILs and amines was shown to be effective in recovering ILs from aqueous mixtures Xiong et al. Interestingly, these systems also modified densities such that the IL-rich phase is at the bottom.
Increased salt concentration and decreased IL concentration were observed to phase separate faster and have a higher efficiency of electrokinetic demixing due to a salting-out effect, whereas electric field had no impact on protein distribution.
In addition, pH-dependent reversible partitioning of IL-ATPS systems at ambient conditions offers dual functionality as a catalytic medium and separation extraction Ferreira et al. IL Extractions Small Molecule Separations Several types of small molecules such as antioxidants, flavonoids, alkaloids, sulfonamides, functionalized amino acids, antibiotics such as tetracycline, oxytetracycline, cephalexin, and chloramphenicol effectively partition and extract in IL-ATPSs Li et al.
In general, small molecules partition into the IL-rich upper phase. These systems are advantageous as a sample pre-treatment technique offering reduction of steps, materials and volatiles, and a sample concentration method Basheer et al.
IL-ATPSs have broad application in selective separation and extraction of small molecules and biomolecules through simple modifications of the IL structure and composition along with parameters that affect the extraction efficiency, such as the salt concentration, pH and extraction temperature, to appropriately modulate the IL-rich phase.Helminth-derived immunomodulators: can understanding the worm produce the pill?
In a remarkable study, supplementation of dietary fiber to mice led to increased concentrations of circulating short chain fatty acids SCFA , as a result of metabolizing the fibers by the gut microbiota In a study of healthy celiac disease patients infected with N. However, a biological response was elicited to N.
Unravelling the effects of the environment and host genotype on the gut microbiome. This concept appears to be particularly promising in the field of cancer research [ 8 , 9 ], where diagnosis of tumors is typically performed with ex vivo approaches, such as biopsy.
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