Decoding the dopamine transporter imaging for the differential diagnosis of parkinsonism using deep learning.

PURPOSE This work attempts to decode the discriminative information in dopamine transporter (DAT) imaging using deep learning for the differential diagnosis of parkinsonism. METHODS This study involved 1017 subjects who underwent DAT PET imaging ([11C]CFT) including 43 healthy subjects and 974 parkinsonian patients with idiopathic Parkinson's disease (IPD), multiple system atrophy (MSA) or progressive supranuclear palsy (PSP). We developed a 3D deep convolutional neural network to learn distinguishable DAT features for the differential diagnosis of parkinsonism. A full-gradient saliency map approach was employed to investigate the functional basis related to the decision mechanism of the network. Furthermore, deep-learning-guided radiomics features and quantitative analysis were compared with their conventional counterparts to further interpret the performance of deep learning. RESULTS The proposed network achieved area under the curve of 0.953 (sensitivity 87.7%, specificity 93.2%), 0.948 (sensitivity 93.7%, specificity 97.5%), and 0.900 (sensitivity 81.5%, specificity 93.7%) in the cross-validation, together with sensitivity of 90.7%, 84.1%, 78.6% and specificity of 88.4%, 97.5% 93.3% in the blind test for the differential diagnosis of IPD, MSA and PSP, respectively. The saliency map demonstrated the most contributed areas determining the diagnosis located at parkinsonism-related regions, e.g., putamen, caudate and midbrain. The deep-learning-guided binding ratios showed significant differences among IPD, MSA and PSP groups (P < 0.001), while the conventional putamen and caudate binding ratios had no significant difference between IPD and MSA (P = 0.24 and P = 0.30). Furthermore, compared to conventional radiomics features, there existed average above 78.1% more deep-learning-guided radiomics features that had significant differences among IPD, MSA and PSP. CONCLUSION This study suggested the developed deep neural network can decode in-depth information from DAT and showed potential to assist the differential diagnosis of parkinsonism. The functional regions supporting the diagnosis decision were generally consistent with known parkinsonian pathology but provided more specific guidance for feature selection and quantitative analysis

Radiogenomics analysis reveals the associations of dynamic contrast-enhanced–MRI features with gene expression characteristics, PAM50 subtypes, and prognosis of breast cancer

BackgroundTo investigate reliable associations between dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) features and gene expression characteristics in breast cancer (BC) and to develop and validate classifiers for predicting PAM50 subtypes and prognosis from DCE-MRI non-invasively.MethodsTwo radiogenomics cohorts with paired DCE-MRI and RNA-sequencing (RNA-seq) data were collected from local and public databases and divided into discovery (n = 174) and validation cohorts (n = 72). Six external datasets (n = 1,443) were used for prognostic validation. Spatial–temporal features of DCE-MRI were extracted, normalized properly, and associated with gene expression to identify the imaging features that can indicate subtypes and prognosis.ResultsExpression of genes including RBP4, MYBL2, and LINC00993 correlated significantly with DCE-MRI features (q-value &lt; 0.05). Importantly, genes in the cell cycle pathway exhibited a significant association with imaging features (p-value &lt; 0.001). With eight imaging-associated genes (CHEK1, TTK, CDC45, BUB1B, PLK1, E2F1, CDC20, and CDC25A), we developed a radiogenomics prognostic signature that can distinguish BC outcomes in multiple datasets well. High expression of the signature indicated a poor prognosis (p-values &lt; 0.01). Based on DCE-MRI features, we established classifiers to predict BC clinical receptors, PAM50 subtypes, and prognostic gene sets. The imaging-based machine learning classifiers performed well in the independent dataset (areas under the receiver operating characteristic curve (AUCs) of 0.8361, 0.809, 0.7742, and 0.7277 for estrogen receptor (ER), human epidermal growth factor receptor 2 (HER2)-enriched, basal-like, and obtained radiogenomics signature). Furthermore, we developed a prognostic model directly using DCE-MRI features (p-value &lt; 0.0001).ConclusionsOur results identified the DCE-MRI features that are robust and associated with the gene expression in BC and displayed the possibility of using the features to predict clinical receptors and PAM50 subtypes and to indicate BC prognosis

Streptococcal Toxic Shock Syndrome Caused by Streptococcus suis Serotype 2

BACKGROUND: Streptococcus suis serotype 2 ( S. suis 2, SS2) is a major zoonotic pathogen that causes only sporadic cases of meningitis and sepsis in humans. Most if not all cases of Streptococcal toxic shock syndrome (STSS) that have been well-documented to date were associated with the non-SS2 group A streptococcus (GAS). However, a recent large-scale outbreak of SS2 in Sichuan Province, China, appeared to be caused by more invasive deep-tissue infection with STSS, characterized by acute high fever, vascular collapse, hypotension, shock, and multiple organ failure. METHODS AND FINDINGS: We investigated this outbreak of SS2 infections in both human and pigs, which took place from July to August, 2005, through clinical observation and laboratory experiments. Clinical and pathological characterization of the human patients revealed the hallmarks of typical STSS, which to date had only been associated with GAS infection. Retrospectively, we found that this outbreak was very similar to an earlier outbreak in Jiangsu Province, China, in 1998. We isolated and analyzed 37 bacterial strains from human specimens and eight from pig specimens of the recent outbreak, as well as three human isolates and two pig isolates from the 1998 outbreak we had kept in our laboratory. The bacterial isolates were examined using light microscopy observation, pig infection experiments, multiplex-PCR assay, as well as restriction fragment length polymorphisms (RFLP) and multiple sequence alignment analyses. Multiple lines of evidence confirmed that highly virulent strains of SS2 were the causative agents of both outbreaks. CONCLUSIONS: We report, to our knowledge for the first time, two outbreaks of STSS caused by SS2, a non-GAS streptococcus. The 2005 outbreak was associated with 38 deaths out of 204 documented human cases; the 1998 outbreak with 14 deaths out of 25 reported human cases. Most of the fatal cases were characterized by STSS; some of them by meningitis or severe septicemia. The molecular mechanisms underlying these human STSS outbreaks in human beings remain unclear and an objective for further study

A proteome-wide protein interaction map for Campylobacter jejuni

'Systematic identification of protein interactions for the bacterium Campylobacter jejuni using high-throughput yeast two-hybrid screens detected interactions for 80% of the organism's proteins

Overview of Theory and Model of Tropical Agricultural Circular Economy in Hainan Province

This paper gives a general overview of rationale of tropical agricultural circular economy. On me basis of ecological economic theory, circular economy is a new model of economic development which aims at realizing reducing of resources use, reuse of products and recycling of wastes, conforming to the principle of 4R. It is to make economic system melt into me process of cycle of material of natural ecological system harmoniously. This paper introduces the advantages of developing tropical agriculture in Hainan Province, the main problems existing in the development and mo main models of sustainable tropical agricultural development in Hainan Province at present. 9 typical models of tropical agriculture circular economy in Hainan Province are listed in particular, including compound ecological project of forestry and animal husbandry, three-dimensional planting model of plantation, multilayered structure of eucalyptuses, sustainable agriculture model of sightseeing, the model of using animal manure. precision tropical agriculture model. clean production model of tropical agriculture. deep processing model of tropical agriculture products. and the model of ecological agriculture industrialization. Corresponding countermeasures and suggestions-are put forward to promote development of tropical agriculture circular economy in Hainan Province as follows: take actions that suit local circumstances, exert advantage and exploit potential; give full play to advantage of integration of production, teaching and research, and promote agriculture by applying scientific and technological advances; the whole staff participate and strengthen construction of soft environment and hard environment

Overview of Theory and Model of Tropical Agricultural Circular Economy in Hainan Province

This paper gives a general overview of rationale of tropical agricultural circular economy. On me basis of ecological economic theory, circular economy is a new model of economic development which aims at realizing reducing of resources use, reuse of products and recycling of wastes, conforming to the principle of 4R. It is to make economic system melt into me process of cycle of material of natural ecological system harmoniously. This paper introduces the advantages of developing tropical agriculture in Hainan Province, the main problems existing in the development and mo main models of sustainable tropical agricultural development in Hainan Province at present. 9 typical models of tropical agriculture circular economy in Hainan Province are listed in particular, including compound ecological project of forestry and animal husbandry, three-dimensional planting model of plantation, multilayered structure of eucalyptuses, sustainable agriculture model of sightseeing, the model of using animal manure. precision tropical agriculture model. clean production model of tropical agriculture. deep processing model of tropical agriculture products. and the model of ecological agriculture industrialization. Corresponding countermeasures and suggestions-are put forward to promote development of tropical agriculture circular economy in Hainan Province as follows: take actions that suit local circumstances, exert advantage and exploit potential; give full play to advantage of integration of production, teaching and research, and promote agriculture by applying scientific and technological advances; the whole staff participate and strengthen construction of soft environment and hard environment.Circular economy, Tropical agriculture, Sustainable development, Hainan, China, Agribusiness,

Sorption of Atrazine in Tropical Soil by Biochar Prepared from Cassava Waste

Biochar (BC) is a carbonaceous and porous product generated from the incomplete combustion of biomass and has been recognized as an efficient adsorbent. This study evaluated the ability of BC to sorb atrazine pesticide in tropical soil, and explored potential environmental values of BC on mitigating organic micro-pollutants. BC was produced from cassava waste via pyrolyzation under oxygen-limiting conditions at 350, 550, and 750 °C (MS350, MS550, and MS750, respectively). Three biochars were characterized and investigated as sorbents for the removal atrazine from tropical soil. BC pyrolyzed at higher temperatures more quickly reached equilibrium. The pseudo-second-order model perfectly simulated the sorption kinetics for atrazine with the coefficients R2 above 0.996, and the sorption amount at equilibrium (qe) was 0.016 mg/g for MS350, 0.025 mg/g for MS550 and 0.050 mg/g for MS750. The isotherms of MS350 displayed relatively linear behavior, whereas the sorption of atrazine on MS550 and MS750 followed a nonlinear isotherm. The sorption data were well described by the Freundlich model with logKF of 0.476 for MS350, 0.771 for MS550, 1.865 for MS750. A thermodynamic study indicated that the sorption of atrazine in BC-added soil was a spontaneous and endothermic process and was primarily controlled by physisorption. In addition, lower pH was conducive to the sorption of atrazine in BC-added soil