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https://www.readbyqxmd.com/read/28727867/unintended-consequences-of-machine-learning-in-medicine
#1
Federico Cabitza, Raffaele Rasoini, Gian Franco Gensini
No abstract text is available yet for this article.
July 20, 2017: JAMA: the Journal of the American Medical Association
https://www.readbyqxmd.com/read/28727559/a-microfluidic-cytometer-for-complete-blood-count-with-a-3-2-megapixel-1-1-%C3%AE-m-pitch-super-resolution-image-sensor-in-65-nm-bsi-cmos
#2
Xu Liu, Xiwei Huang, Yu Jiang, Hang Xu, Jing Guo, Han Wei Hou, Mei Yan, Hao Yu
Based on a 3.2-Megapixel 1.1- μm-pitch super-resolution (SR) CMOS image sensor in a 65-nm backside-illumination process, a lens-free microfluidic cytometer for complete blood count (CBC) is demonstrated in this paper. Backside-illumination improves resolution and contrast at the device level with elimination of surface treatment when integrated with microfluidic channels. A single-frame machine-learning-based SR processing is further realized at system level for resolution correction with minimum hardware resources...
July 18, 2017: IEEE Transactions on Biomedical Circuits and Systems
https://www.readbyqxmd.com/read/28727549/learning-and-inferring-dark-matter-and-predicting-human-intents-and-trajectories-in-videos
#3
Dan Xie, Tianmin Shu, Sinisa Todorovic, Song-Chun Zhu
This paper presents a method for localizing functional objects and predicting human intents and trajectories in surveillance videos of public spaces, under no supervision in training. People in public spaces are expected to intentionally take shortest paths (subject to obstacles) toward certain objects (e.g. vending machine, picnic table, dumpster etc.) where they can satisfy certain needs (e.g., quench thirst). Since these objects are typically very small or heavily occluded, they cannot be inferred by their visual appearance but indirectly by their influence on people's trajectories...
July 19, 2017: IEEE Transactions on Pattern Analysis and Machine Intelligence
https://www.readbyqxmd.com/read/28727421/shallow-representation-learning-via-kernel-pca-improves-qsar-modelability
#4
Stefano E Rensi, Russ B Altman
Linear models offer a robust, flexible, and computationally efficient set of tools for modeling quantitative structure activity relationships (QSAR), but have been eclipsed in performance by non-linear methods. Support vector machines (SVMs) and neural networks are currently among the most popular and accurate QSAR methods because they learn new representations of the data that greatly improve modelability. In this work we use shallow representation learning to improve the accuracy of L1 regularized logistic regression (LASSO) and meet the performance of Tanimoto SVM...
July 20, 2017: Journal of Chemical Information and Modeling
https://www.readbyqxmd.com/read/28727228/actin-gamma-1-a-new-skin-cancer-pathogenic-gene-identified-by-the-biological-feature-based-classification
#5
Xinqian Dong, Yingsheng Han, Zhen Sun, Junlong Xu
Skin cancer is the most common form of cancer that accounting for at least 40% of cancer cases around the world. This study aimed to identify skin cancer-related biological features and predict skin cancer candidate genes by employing machine learning based on biological features of known skin cancer genes. The known skin cancer-related genes were fetched from database and encoded by the enrichment scores of gene ontology and pathways. The optimal features of the skin cancer related genes were selected with a series of feature selection methods, such as mRMR, IFS, and Random Forest algorithm...
July 20, 2017: Journal of Cellular Biochemistry
https://www.readbyqxmd.com/read/28727222/spatiotemporal-pattern-of-gross-primary-productivity-and-its-covariation-with-climate-in-china-over-the-last-thirty-years
#6
Yitong Yao, Xuhui Wang, Yue Li, Tao Wang, Miaogen Shen, Mingyuan Du, Honglin He, Yingnian Li, Weijun Luo, Mingguo Ma, Yaoming Ma, Yanhong Tang, Huimin Wang, Xianzhou Zhang, Yiping Zhang, Liang Zhao, Guangsheng Zhou, Shilong Piao
The uncertainties of China's GPP estimates by global data-oriented products and ecosystem models justify a development of high-resolution data-oriented GPP dataset over China. We applied a machine learning algorithm developing a new GPP dataset for China with 0.1° spatial resolution and monthly temporal frequency based on eddy flux measurements from 40 sites in China and surrounding countries, most of which have not been explored in previous global GPP datasets. According to our estimates, mean annual GPP over China is 6...
July 20, 2017: Global Change Biology
https://www.readbyqxmd.com/read/28726762/user-interaction-modeling-and-profile-extraction-in-interactive-systems-a-groupware-application-case-study
#7
Cristina Tîrnăucă, Rafael Duque, José L Montaña
A relevant goal in human-computer interaction is to produce applications that are easy to use and well-adjusted to their users' needs. To address this problem it is important to know how users interact with the system. This work constitutes a methodological contribution capable of identifying the context of use in which users perform interactions with a groupware application (synchronous or asynchronous) and provides, using machine learning techniques, generative models of how users behave. Additionally, these models are transformed into a text that describes in natural language the main characteristics of the interaction of the users with the system...
July 20, 2017: Sensors
https://www.readbyqxmd.com/read/28726751/an-adaptive-feature-learning-model-for-sequential-radar-high-resolution-range-profile-recognition
#8
Xuan Peng, Xunzhang Gao, Yifan Zhang, Xiang Li
This paper proposes a new feature learning method for the recognition of radar high resolution range profile (HRRP) sequences. HRRPs from a period of continuous changing aspect angles are jointly modeled and discriminated by a single model named the discriminative infinite restricted Boltzmann machine (Dis-iRBM). Compared with the commonly used hidden Markov model (HMM)-based recognition method for HRRP sequences, which requires efficient preprocessing of the HRRP signal, the proposed method is an end-to-end method of which the input is the raw HRRP sequence, and the output is the label of the target...
July 20, 2017: Sensors
https://www.readbyqxmd.com/read/28725174/altered-functional-connectivity-following-an-inflammatory-white-matter-injury-in-the-newborn-rat-a-high-spatial-and-temporal-resolution-intrinsic-optical-imaging-study
#9
Edgar Guevara, Wyston C Pierre, Camille Tessier, Luis Akakpo, Irène Londono, Frédéric Lesage, Gregory A Lodygensky
Very preterm newborns have an increased risk of developing an inflammatory cerebral white matter injury that may lead to severe neuro-cognitive impairment. In this study we performed functional connectivity (fc) analysis using resting-state optical imaging of intrinsic signals (rs-OIS) to assess the impact of inflammation on resting-state networks (RSN) in a pre-clinical model of perinatal inflammatory brain injury. Lipopolysaccharide (LPS) or saline injections were administered in postnatal day (P3) rat pups and optical imaging of intrinsic signals were obtained 3 weeks later...
2017: Frontiers in Neuroscience
https://www.readbyqxmd.com/read/28724886/modulation-of-rna-primer-formation-by-mn-ii-substituted-t7-dna-primase
#10
Stefan Ilic, Sabine R Akabayov, Roy Froimovici, Ron Meiry, Dan Vilenchik, Alfredo Hernandez, Haribabu Arthanari, Barak Akabayov
Lagging strand DNA synthesis by DNA polymerase requires RNA primers produced by DNA primase. The N-terminal primase domain of the gene 4 protein of phage T7 comprises a zinc-binding domain that recognizes a specific DNA sequence and an RNA polymerase domain that catalyzes RNA polymerization. Based on its crystal structure, the RNA polymerase domain contains two Mg(II) ions. Mn(II) substitution leads to elevated RNA primer synthesis by T7 DNA primase. NMR analysis revealed that upon binding Mn(II), T7 DNA primase undergoes conformational changes near the metal cofactor binding site that are not observed when the enzyme binds Mg(II)...
July 19, 2017: Scientific Reports
https://www.readbyqxmd.com/read/28723659/data-driven-analysis-of-immune-infiltrate-in-a-large-cohort-of-breast-cancer-and-its-association-with-disease-progression-er-activity-and-genomic-complexity
#11
Ruth Dannenfelser, Marianne Nome, Andliena Tahiri, Josie Ursini-Siegel, Hans Kristian Moen Vollan, Vilde D Haakensen, Åslaug Helland, Bjørn Naume, Carlos Caldas, Anne-Lise Børresen-Dale, Vessela N Kristensen, Olga G Troyanskaya
The tumor microenvironment is now widely recognized for its role in tumor progression, treatment response, and clinical outcome. The intratumoral immunological landscape, in particular, has been shown to exert both pro-tumorigenic and anti-tumorigenic effects. Identifying immunologically active or silent tumors may be an important indication for administration of therapy, and detecting early infiltration patterns may uncover factors that contribute to early risk. Thus far, direct detailed studies of the cell composition of tumor infiltration have been limited; with some studies giving approximate quantifications using immunohistochemistry and other small studies obtaining detailed measurements by isolating cells from excised tumors and sorting them using flow cytometry...
July 7, 2017: Oncotarget
https://www.readbyqxmd.com/read/28722399/bias-free-chemically-diverse-test-sets-from-machine-learning
#12
Ellen Swann, Michael Fernandez, Michelle L Coote, Amanda S Barnard
Current benchmarking methods in quantum chemistry rely on databases that are built using a chemist's intuition. It is not fully understood how diverse or representative these databases truly are. Multivariate statistical techniques like archetypal analysis and K-means clustering have previously been used to summarise large sets of nanoparticles however molecules are more diverse and not as easily characterised by descriptors. In this work we compare three sets of descriptors based on the one-, two- and three-dimensional structure of a molecule...
July 19, 2017: ACS Combinatorial Science
https://www.readbyqxmd.com/read/28720874/knowledge-transfer-learning-for-prediction-of-matrix-metalloprotease-substrate-cleavage-sites
#13
Yanan Wang, Jiangning Song, Tatiana T Marquez-Lago, André Leier, Chen Li, Trevor Lithgow, Geoffrey I Webb, Hong-Bin Shen
Matrix Metalloproteases (MMPs) are an important family of proteases that play crucial roles in key cellular and disease processes. Therefore, MMPs constitute important targets for drug design, development and delivery. Advanced proteomic technologies have identified type-specific target substrates; however, the complete repertoire of MMP substrates remains uncharacterized. Indeed, computational prediction of substrate-cleavage sites associated with MMPs is a challenging problem. This holds especially true when considering MMPs with few experimentally verified cleavage sites, such as for MMP-2, -3, -7, and -8...
July 18, 2017: Scientific Reports
https://www.readbyqxmd.com/read/28720869/rapid-bayesian-optimisation-for-synthesis-of-short-polymer-fiber-materials
#14
Cheng Li, David Rubín de Celis Leal, Santu Rana, Sunil Gupta, Alessandra Sutti, Stewart Greenhill, Teo Slezak, Murray Height, Svetha Venkatesh
The discovery of processes for the synthesis of new materials involves many decisions about process design, operation, and material properties. Experimentation is crucial but as complexity increases, exploration of variables can become impractical using traditional combinatorial approaches. We describe an iterative method which uses machine learning to optimise process development, incorporating multiple qualitative and quantitative objectives. We demonstrate the method with a novel fluid processing platform for synthesis of short polymer fibers, and show how the synthesis process can be efficiently directed to achieve material and process objectives...
July 18, 2017: Scientific Reports
https://www.readbyqxmd.com/read/28720796/eeg-machine-learning-for-accurate-detection-of-cholinergic-intervention-and-alzheimer-s-disease
#15
Sonja Simpraga, Ricardo Alvarez-Jimenez, Huibert D Mansvelder, Joop M A van Gerven, Geert Jan Groeneveld, Simon-Shlomo Poil, Klaus Linkenkaer-Hansen
Monitoring effects of disease or therapeutic intervention on brain function is increasingly important for clinical trials, albeit hampered by inter-individual variability and subtle effects. Here, we apply complementary biomarker algorithms to electroencephalography (EEG) recordings to capture the brain's multi-faceted signature of disease or pharmacological intervention and use machine learning to improve classification performance. Using data from healthy subjects receiving scopolamine we developed an index of the muscarinic acetylcholine receptor antagonist (mAChR) consisting of 14 EEG biomarkers...
July 18, 2017: Scientific Reports
https://www.readbyqxmd.com/read/28720710/application-of-response-surface-methods-to-determine-conditions-for-optimal-genomic-prediction
#16
Réka Howard, Alicia L Carriquiry, William D Beavis
An epistatic genetic architecture can have a significant impact on prediction accuracies of genomic prediction (GP) methods. Machine learning methods predict traits composed of epistatic genetic architectures more accurately than statistical methods based on additive mixed linear models. The differences between these types of GP methods suggest a diagnostic for revealing genetic architectures underlying traits of interest. In addition to genetic architecture, the performance of GP methods may be influenced by the sample size of the training population, the number of QTL, and the proportion of phenotypic variability due to genotypic variability (heritability)...
July 18, 2017: G3: Genes—Genomes—Genetics
https://www.readbyqxmd.com/read/28719743/machine-learning-on-samdi-mass-spectrometry-signal-to-noise-ratio-improves-peptide-array-designs
#17
Albert Yan Xue, Lindsey C Szymczak, Milan Mrksich, Neda Bagheri
Emerging peptide array technologies are able to profile molecular activities within cell lysates. However, the structural diversity of peptides leads to inherent differences in peptide signal to noise ratios (S/N). These complex effects can lead to potentially unrepresentative signal intensities and can bias subsequent analyses. Within mass spectrometry-based peptide technologies, the relation between a peptide's amino acid sequence and S/N remains largely non-quantitative. To address this challenge, we present a method to quantify and analyze mass spectrometry S/N of two peptide arrays, and we use this analysis to portray quality of data and to design future arrays for SAMDI mass spectrometry...
July 18, 2017: Analytical Chemistry
https://www.readbyqxmd.com/read/28719206/direct-quantum-dynamics-using-grid-based-wavefunction-propagation-and-machine-learned-potential-energy-surfaces
#18
Gareth W Richings, Scott Habershon
We describe a method for performing nuclear quantum dynamics calculations using standard, grid-based algorithms, including the multi configurational time-dependent Hartree (MCTDH) method, where the potential energy surface (PES) is calculated ``on-the-fly''. The method of Gaussian process regression (GPR) is used to construct a global representation of the PES using values of the energy at points distributed in molecular configuration space during the course of the wavepacket propagation. We demonstrate this direct dynamics approach for both an analytical PES function describing 3-dimensional proton transfer dynamics in malonaldehyde, and for 2- and 6-dimensional quantum dynamics simulations of proton transfer in salicylaldimine...
July 18, 2017: Journal of Chemical Theory and Computation
https://www.readbyqxmd.com/read/28719054/evolutionary-cell-biology-of-proteins-from-protists-to-humans-and-plants
#19
REVIEW
Helmut Plattner
During evolution, the cell as a fine-tuned machine had to undergo permanent adjustments to match changes in its environment, while "closed for repair work" was not possible. Evolution from protists (protozoa and unicellular algae) to multicellular organisms may have occurred in basically two lineages, Unikonta and Bikonta, culminating in mammals and angiosperms (flowering plants), respectively. Unicellular models for unikont evolution are myxamoebae (Dictyostelium) and increasingly also choanoflagellates, whereas, for bikonts, ciliates are preferred models...
July 18, 2017: Journal of Eukaryotic Microbiology
https://www.readbyqxmd.com/read/28718848/label-free-high-throughput-holographic-screening-and-enumeration-of-tumor-cells-in-blood
#20
Dhananjay Kumar Singh, Caroline C Ahrens, Wei Li, Siva A Vanapalli
We introduce inline digital holographic microscopy (in-line DHM) as a label-free technique for detecting tumor cells in blood. The optimized DHM platform fingerprints every cell flowing through a microchannel at 10 000 cells per second, based on three features - size, maximum intensity and mean intensity. To identify tumor cells in a background of blood cells, we developed robust gating criteria using machine-learning approaches. We established classifiers from the features extracted from 100 000-cell training sets consisting of red blood cells, peripheral blood mononuclear cells and tumor cell lines...
July 18, 2017: Lab on a Chip
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