Search

The ACR Incidental Findings Committee (IFC) presents recommendations for managing adnexal masses incidentally detected on CT and MRI. These recommendations represent an update of those provided in our previous JACR 2013 white paper. The Adnexal Subcommittee, which included six radiologists with subspecialty expertise in abdominal imaging or ultrasound and one gynecologist, developed this algorithm. The recommendations draw from published evidence and expert opinion and were finalized by iterative consensus. Algorithm branches successively categorize adnexal masses based on patient characteristics (eg, pre- versus postmenopausal) and imaging features. They terminate with a management recommendation. The algorithm addresses most, but not all, pathologies and clinical scenarios. Our goal is to improve quality of care by providing guidance on how to manage incidentally detected adnexal masses.

Background Brugada syndrome is a rare inherited arrhythmic syndrome with a coved type 1 ST-segment elevation on ECG and an increased risk of sudden death. Many studies have evaluated risk stratification performance based on ECG-derived parameters. However, since historical Brugada patient cohorts included mostly paper ECGs, most studies have been based on manual ECG parameter measurements. We hypothesized that it would be possible to run automated algorithm-based analysis of paper ECGs. We aimed: 1) to validate the digitization process for paper ECGs in Brugada patients; and 2) to quantify the acute class I antiarrhythmic drug effect on relevant ECG parameters in Brugada syndrome. Methods A total of 176 patients (30% female, 43 ± 13 years old) with induced type 1 Brugada syndrome ECG were included in the study. All of the patients had paper ECGs before and during class I antiarrhythmic drug challenge. Twenty patients also had a digital ECG, in whom printouts were used to validate the digitization process. Paper ECGs were scanned and then digitized using ECGScan software, version 3.4.0 (AMPS, LLC, New York, NY, USA) to obtain FDA HL7 XML format ECGs. Measurements were automatically performed using the Bravo (AMPS, LLC, New York, NY, USA) and Glasgow algorithms. Results ECG parameters obtained from digital and digitized ECGs were closely correlated (r = 0.96 ± 0.07, R2 = 0.93 ± 0.12). Class I antiarrhythmic drugs significantly increased the global QRS duration (from 113 ± 20 to 138 ± 23, p Conclusions Automated algorithm-based measurements of depolarization and repolarization parameters from digitized paper ECGs are reliable and could quantify the acute effects of class 1 antiarrhythmic drug challenge in Brugada patients. Our results support using computerized automated algorithm-based analyses from digitized paper ECGs to establish risk stratification decision trees in Brugada syndrome.

The ACR Committee on Incidental Findings presents recommendations for managing liver lesions that are incidentally detected on CT. These recommendations represent an update from the liver component of the ACR 2010 white paper on managing incidental findings in the pancreas, adrenal glands, kidneys, and liver. The Liver Subcommittee-which included five abdominal radiologists, one hepatologist, and one hepatobiliary surgeon-developed this algorithm. The recommendations draw from published evidence and expert opinion and were finalized by informal iterative consensus. Algorithm branches categorize liver lesions on the basis of patient characteristics and imaging features. They terminate with an assessment of benignity or a specific follow-up recommendation. The algorithm addresses most, but not all, pathologies and clinical scenarios. The goal is to improve the quality of care by providing guidance on how to manage incidentally detected liver lesions.

Highlights • In this position paper, we provide a collection of views on the role of AI in the COVID-19 pandemic, from the clinical needs to the design of AI-based systems, to the translation of the developed tools to the clinic. We highlight key factors in designing system solutions - per specific task; as well as design issues in managing the disease on the national level. We focus on three specific use-cases for which AI systems can be built: from the early disease detection, the management of the disease in a hospital setting, and building patient-specific predictive models that require the combination of imaging with additional clinical features. Infrastructure considerations and population modeling in two European countries will be described. This pandemic has made the practical and scientific challenges of making AI solutions very explicit. A discussion concludes this paper, with a list of challenges facing the community in the AI road ahead., In this position paper, we provide a collection of views on the role of AI in the COVID-19 pandemic, from clinical requirements to the design of AI-based systems, to the translation of the developed tools to the clinic. We highlight key factors in designing system solutions - per specific task; as well as design issues in managing the disease at the national level. We focus on three specific use-cases for which AI systems can be built: early disease detection, management in a hospital setting, and building patient-specific predictive models that require the combination of imaging with additional clinical data. Infrastructure considerations and population modeling in two European countries will be described. This pandemic has made the practical and scientific challenges of making AI solutions very explicit. A discussion concludes this paper, with a list of challenges facing the community in the AI road ahead.

Objective Most preventable adverse drug events and medication errors occur during medication ordering. Medication order entry and clinical decision support are available on paper or as computerized systems. In this post-hoc analysis we investigated frequency and clinical impact of blood glucose (BG) documentation- and user-related calculation errors as well as workflow deviations in diabetes management. We aimed to compare a paper-based protocol to a computerized medication management system combined with clinical workflow and decision support. Methods Seventy-nine hospitalized patients with type 2 diabetes mellitus were treated with an algorithm driven basal-bolus insulin regimen. BG measurements, which were the basis for insulin dose calculations, were manually entered either into the paper-based workflow protocol (PaperG: 37 patients) or into GlucoTab ® —a mobile tablet PC based system (CompG: 42 patients). We used BG values from the laboratory information system as a reference. A workflow simulator was used to determine user calculation errors as well as workflow deviations and to estimate the effect of errors on insulin doses. The clinical impact of insulin dosing errors and workflow deviations on hypo- and hyperglycemia was investigated. Results The BG documentation error rate was similar for PaperG (4.9%) and CompG group (4.0%). In PaperG group, 11.1% of manual insulin dose calculations were erroneous and the odds ratio (OR) of a hypoglycemic event following an insulin dosing error was 3.1 (95% CI: 1.4–6.8). The number of BG values influenced by insulin dosing errors was eightfold higher than in the CompG group. In the CompG group, workflow deviations occurred in 5.0% of the tasks which led to an increased likelihood of hyperglycemia, OR 2.2 (95% CI: 1.1–4.6). Discussion Manual insulin dose calculations were the major source of error and had a particularly strong influence on hypoglycemia. By using GlucoTab ® , user calculation errors were entirely excluded. The immediate availability and automated handling of BG values from medical devices directly at the point of care has a high potential to reduce errors. Computerized systems facilitate the safe use of more complex insulin dosing algorithms without compromising usability. In CompG group, missed or delayed tasks had a significant effect on hyperglycemia, while in PaperG group insufficient precision of documentation times limited analysis. The use of old BG measurements was clinically less relevant. Conclusion Insulin dosing errors and workflow deviations led to measurable changes in clinical outcome. Diabetes management systems including decision support should address nurses as well as physicians in a computerized way. Our analysis shows that such systems reduce the frequency of errors and therefore decrease the probability of hypo- and hyperglycemia.

The assessment of pesticide residue levels demands fast, low cost and easy-to-use procedures which are not found in conventional methods. In this work, SERS substrates based on the deposition of gold nanoparticles (GNPs) on common office paper were prepared using a wax printer. These substrates combined with Data Driven Soft Independent Modelling of Class Analogies (DD-SIMCA), a one-class classifier algorithm, were used for detection of pesticide residues in water extracts of mango peels. Paper-based substrates made sample collection easier compared with conventional SERS methods, since few microliters of the pesticide aqueous extract from fruit peels needed to be deposited onto the substrate. Moreover, one-class classifiers dismiss the need for quantification or calibration curves. Classification of a fruit with residue levels in accordance to regulatory bodies' limits is based on a mathematical threshold. Just as in an authentication problem, all the possibilities for a given analysed fruit are now restricted to agreeing or not agreeing with current regulations. The performance of the one-class model was demonstrated by detecting thiabendazole (TBZ) residues at various mango samples, with all results being confirmed by HPLC-DAD analysis. The final model could distinguish samples with TBZ levels above the ones allowed by the Brazilian Health Regulatory Agency with 94% of selectivity and 92% of sensitivity, even in the presence of other pesticides.

Images can be helpful for eliciting data in the form of responses from participants. Sometimes photographs can help participants speak about issues, events, thoughts and emotions which they find difficult to talk about. This paper discusses how photos can be used and how the data collected through their use in a discussion can be analysed using Correspondence Analysis (CA). Although CA is considered a quantitative tool for analysing data, this paper describes how it can be used with qualitative data.

Efficient and safe detection of Bacillus anthracis spores (BAS) is a challenging task especially in bio-terror scenarios where the agent is concealed. We provide a proof-of-concept for the identification of concealed BAS inside mail envelopes using short-wave infrared hyperspectral imaging (SWIR-HSI). The spores and two other benign materials are identified according to their typical absorption spectrum. The identification process is based on the removal of the envelope signal using a new automatic new algorithm. This method may serve as a fast screening tool prior to using classical bioanalytical techniques.

The use of continuous intravenous regular human insulin (RHI) infusion is often necessary to achieve glycemic control in critically ill patients. Because insulin is a high-risk medication owing to the potential for severe hypoglycemia, it is imperative that insulin infusion algorithms are designed to be safe, effective, and instructionally clear. The safety and efficacy of our intravenous RHI infusion algorithm protocol has been previously established (Nutrition 2008;24:536-45); however, the protocol violations by nursing personnel were not examined. The objective of this study was to assess nursing adherence to our RHI infusion algorithm.Continuous RHI infusion algorithm violations were retrospectively evaluated in adult patients admitted to a trauma intensive care unit who received concurrent continuous enteral and/or parenteral nutrition therapy and our algorithm for at least 3 d. Blood glucose (BG) monitoring was done every 1 to 2 h with the target BG at 70 to 149 mg/dL (3.9 to 8.3 mmol/L). Nursing adherence to the RHI infusion protocol was evaluated for each patient by comparing the adjustments in insulin infusion rates documented by the nursing personnel with the prescribed adjustments per our graduated continuous intravenous RHI infusion algorithm.A total of 4150 BG measurements necessitating the determination of the appropriate RHI dosage rate by nursing personnel in 40 patients occurred during the observational period. The target BG was achieved for a mean of 20 h/d and none of the patients had an episode of severe hypoglycemia (BG40 mg/dL or 2.2 mmol/L). The overall rate of algorithm violations was 12.1%. The algorithm violations accounted for a single episode of mild to moderate hypoglycemia (BG 40 to 60 mg/dL or 2.2 to 3.3 mmol/L) in 4 patients and 65 total episodes of hyperglycemia (BG ≥150 mg/dL or 8.3 mmol/L) in 18 patients.An adherence rate of nearly 90% is indicative of excellent nursing adherence compared with other published paper-based algorithms that examined protocol adherence. These data, combined with our previously published glycemic control data, indicate that this RHI infusion algorithm is an effective one for hyperglycemic trauma patients receiving continuous enteral and/or parenteral nutritional therapy.

Pediatric empyema can be managed with a variety of modalities, and the evidence for an ideal management strategy is limited. Early or simple effusions can be treated with antibiotics alone or with drainage when respiratory distress occurs. Once fibrinopurulent empyema has developed, therapy may involve either chest tube placement with instillation of fibrinolytics or video-assisted thoracoscopic surgery with pleural decortication. In late or fibrotic empyema, an assumption persists that the fibrotic peel must be managed by decortication that can be done either thoracoscopically or through a minithoracotomy incision. This position paper is coauthored by the New Technology Committee of the American Pediatric Surgery Association. The goal is to discuss the ongoing controversies and summarize, in an evidence-based manner, the various treatment options and to suggest a reasonable therapeutic algorithm for the care of children with empyema.

The plotting of data into graphs should be a mandatory step in all data analysis as part of a descriptive statistics exercise, since it gives the researcher an overview of the shape and nature of the data. Moreover, outlier values may be identified, which may be incorrect data, or true outliers, from which important findings (and publications) may arise. This exercise should always precede inferential statistics, when possible, and this paper in the Early Human Development WASP series provides some pointers for doing so in Microsoft Excel™.

An Electronic Portable Information Collection audio device (EPIC-Vox) has been developed to deliver questionnaires in spoken word format via headphones. Patients respond by pressing buttons on the device. The aims of this study were to determine limits of agreement between, and test-retest reliability of audio (A) and paper (P) versions of the Brief Fatigue Inventory (BFI). Two hundred sixty outpatients (204 male, mean age 55.7 years) attending a sleep disorders clinic were allocated to four groups using block randomization. All completed the BFI twice, separated by a one-minute distracter task. Half the patients completed paper and audio versions, then an evaluation questionnaire. The remainder completed either paper or audio versions to compare test-retest reliability. BFI global scores were analyzed using Bland-Altman methodology. Agreement between categorical fatigue severity scores was determined using Cohen's kappa. The mean (SD) difference between paper and audio scores was −0.04 (0.48). The limits of agreement (mean difference±2SD) were −0.93 to +1.00. Test-retest reliability of the paper BFI showed a mean (SD) difference of 0.17 (0.32) between first and second presentations (limits −0.46 to +0.81). For audio, the mean (SD) difference was 0.17 (0.48) (limits −0.79 to +1.14). For agreement between categorical scores, Cohen's kappa=0.73 for P and A, 0.67 (P at test and retest) and 0.87 (A at test and retest). Evaluation preferences (n=128): 36.7% audio; 18.0% paper; and 45.3% no preference. A total of 99.2% found EPIC-Vox "easy to use." These data demonstrate that the English audio version of the BFI provides an acceptable alternative to the paper questionnaire.

Game theory provides a solution to the problem of finding a set of optimal decision-making strategies in a group. However, people seldom play such optimal strategies and adjust their strategies based on their experience. Accordingly, many theories postulate a set of variables related to the probabilities of choosing various strategies and describe how such variables are dynamically updated. In reinforcement learning, these value functions are updated based on the outcome of the player's choice, whereas belief learning allows the value functions of all available choices to be updated according to the choices of other players. We investigated the nature of learning process in monkeys playing a competitive game with ternary choices, using a rock-paper-scissors game. During the baseline condition in which the computer selected its targets randomly, each animal displayed biases towards some targets. When the computer exploited the pattern of animal's choice sequence but not its reward history, the animal's choice was still systematically biased by the previous choice of the computer. This bias was reduced when the computer exploited both the choice and reward histories of the animal. Compared to simple models of reinforcement learning or belief learning, these adaptive processes were better described by a model that incorporated the features of both models. These results suggest that stochastic decision-making strategies in primates during social interactions might be adjusted according to both actual and hypothetical payoffs.

A new system by which paper-recorded EEG can be converted to computer treatable digital data was developed. It consists simply of an image scanner and a microcomputer. The performance of the system was evaluated and found to perform well in data reconstruction. The system makes it possible to apply various computer analyses to EEGs recorded on paper.

Artificial intelligence (AI) in GI endoscopy holds tremendous promise to augment clinical performance, establish better treatment plans, and improve patient outcomes. Although there are promising initial applications and preliminary clinical data for AI in gastroenterology, the field is still in a very early phase, with limited clinical use. The American Society for Gastrointestinal Endoscopy has convened an AI Task Force to develop guidance around clinical implementation, testing/validating algorithms, and building pathways for successful implementation of AI in GI endoscopy. This White Paper focuses on 3 areas: (1) priority use cases for development of AI algorithms in GI, both for specific clinical scenarios and for streamlining clinical workflows, quality reporting, and practice management; (2) data science priorities, including development of image libraries, and standardization of methods for storing, sharing, and annotating endoscopic images/video; and (3) research priorities, focusing on the importance of high-quality, prospective trials measuring clinically meaningful patient outcomes.

DNA damage is a common cellular feature seen in cancer and neurodegenerative disease, but fast and accurate methods for quantifying DNA damage are lacking. Comet assays are a biochemical tool to measure DNA damage based on the migration of broken DNA strands towards a positive electrode, which creates a quantifiable 'tail' behind the cell. However, a major limitation of this approach is the time needed for analysis of comets in the images with available open-source algorithms. The requirement for manual curation and the laborious pre- and post-processing steps can take hours to days. To overcome these limitations, we developed AutoComet, a new open-source algorithm for comet analysis that utilizes automated comet segmentation and quantification of tail parameters. AutoComet first segments and filters comets based on size and intensity and then filters out comets without a well-connected head and tail, which significantly increases segmentation accuracy. Because AutoComet is fully automated, it minimizes curator bias and is scalable, decreasing analysis time over ten-fold, to less than 3s per comet. AutoComet successfully detected statistically significant differences in tail parameters between cells with and without induced DNA damage, and was more comparable to the results of manual curation than other open-source software analysis programs. We conclude that the AutoComet algorithm provides a fast, unbiased and accurate method to quantify DNA damage that avoids the inherent limitations of manual curation and will significantly improve the ability to detect DNA damage.

The aim of this work was to study the application of genetic algorithm (GA) in modelling and optimization of cellulose production by Trichoderma reesei from pea hull. Enzyme activity of cellulase was determined using Filter Paper Activity (FPA) assay. Optimization of process parameters was performed using mathematical (MO) and genetic optimizers to obtain combination of variables for highest possible enzyme activity. GA generated a higher value of cellulase activity (0.353 U/mL) as compared to MO (0.302 U/mL). The values of independent variables in set (GA, MO) were: agitation speed (127, 120 rpm), %H2O2 concentration (10.36, 5.0), cultivation time (112, 91 h). The investigation highlights that GA could be used as a potential optimizer for processes involving waste utilization.

Numerous value-added chemicals can be produced using xylan as a feedstock. However, the product yields are limited by low xylan utilization efficiency, as well as by carbon flux competition between biomass production and biosynthesis. Herein, a dynamic consolidated bioprocessing strategy was developed, which coupled xylan utilization and yield optimization modules. Specifically, we achieved the efficient conversion of xylan to valuable chemicals in a fully consolidated manner by optimizing the expression level of xylanases and xylose transporter in the xylan utilization module. Moreover, a cell density-dependent, and Cre-triggered dynamic system that enabled the dynamic decoupling of biosynthesis and biomass production was constructed in the yield optimization module. The final shake flask-scale titers of xylonate, produced through an exogenous pathway, and shikimate, produced through an endogenous pathway, reached 16.85 and 3.2 g L−1, respectively. This study not only provides an efficient microbial platform for the utilization of xylan, but also opens up the possibility for the large-scale production of high value-added chemicals from renewable feedstocks.

Background Early detection of influenza activity followed by timely response is a critical component of preparedness for seasonal influenza epidemic and influenza pandemic. However, most relevant studies were conducted at the regional or national level with regular seasonal influenza trends. There are few feasible strategies to forecast influenza activity at the local level with irregular trends. Methods Multi-source electronic data, including historical percentage of influenza-like illness (ILI%), weather data, Baidu search index and Sina Weibo data of Chongqing, China, were collected and integrated into an innovative Self-adaptive AI Model (SAAIM), which was constructed by integrating Seasonal Autoregressive Integrated Moving Average model and XGBoost model using a self-adaptive weight adjustment mechanism. SAAIM was applied to ILI% forecast in Chongqing from 2017 to 2018, of which the performance was compared with three previously available models on forecasting. Findings ILI% showed an irregular seasonal trend from 2012 to 2018 in Chongqing. Compared with three reference models, SAAIM achieved the best performance on forecasting ILI% of Chongqing with the mean absolute percentage error (MAPE) of 11·9%, 7·5%, and 11·9% during the periods of the year 2014–2016, 2017, and 2018 respectively. Among the three categories of source data, historical influenza activity contributed the most to the forecast accuracy by decreasing the MAPE by 19·6%, 43·1%, and 11·1%, followed by weather information (MAPE reduced by 3·3%, 17·1%, and 2·2%), and Internet-related public sentiment data (MAPE reduced by 1·1%, 0·9%, and 1·3%). Interpretation Accurate influenza forecast in areas with irregular seasonal influenza trends can be made by SAAIM with multi-source electronic data.

Background Ear and mastoid disease can easily be treated by early detection and appropriate medical care. However, short of specialists and relatively low diagnostic accuracy calls for a new way of diagnostic strategy, in which deep learning may play a significant role. The current study presents a machine learning model to automatically diagnose ear disease using a large database of otoendoscopic images acquired in the clinical environment. Methods Total 10,544 otoendoscopic images were used to train nine public convolution-based deep neural networks to classify eardrum and external auditory canal features into six categories of ear diseases, covering most ear diseases (Normal, Attic retraction, Tympanic perforation, Otitis externa±myringitis, Tumor). After evaluating several optimization schemes, two best-performing models were selected to compose an ensemble classifier, by combining classification scores of each classifier. Findings According to accuracy and training time, transfer learning models based on Inception-V3 and ResNet101 were chosen and the ensemble classifier using the two models yielded a significant improvement over each model, the accuracy of which is in average 93·67% for the 5-folds cross-validation. Considering substantial data-size dependency of classifier performance in the transfer learning, evaluated in this study, the high accuracy in the current model is attributable to the large database. Interpretation The current study is unprecedented in terms of both disease diversity and diagnostic accuracy, which is compatible or even better than an average otolaryngologist. The classifier was trained with data in a various acquisition condition, which is suitable for the practical environment. This study shows the usefulness of utilizing a deep learning model in the early detection and treatment of ear disease in the clinical situation. Fund This research was supported by Brain Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science and ICT ( NRF-2017M3C7A1049051 ).

Background Fibrous cap thickness (FCT), best measured by intravascular optical coherence tomography (OCT), is the most important determinant of plaque rupture in the coronary arteries. Statin treatment increases FCT and thus reduces the likelihood of acute coronary events. However, substantial statin-related FCT increase occurs in only a subset of patients. Currently, there are no methods to predict which patients will benefit. We use transcriptomic data from a clinical trial of rosuvastatin to predict if a patient's FCT will increase in response to statin therapy. Methods FCT was measured using OCT in 69 patients at (1) baseline and (2) after 8–10 weeks of 40 mg rosuvastatin. Peripheral blood mononuclear cells were assayed via microarray. We constructed machine learning models with baseline gene expression data to predict change in FCT. Finally, we ascertained the biological functions of the most predictive transcriptomic markers. Findings Machine learning models were able to predict FCT responders using baseline gene expression with high fidelity (Classification AUC = 0.969 and 0.972). The first model (elastic net) using 73 genes had an accuracy of 92.8%, sensitivity of 94.1%, and specificity of 91.4%. The second model (KTSP) using 18 genes has an accuracy of 95.7%, sensitivity of 94.3%, and specificity of 97.1%. We found 58 enriched gene ontology terms, including many involved with immune cell function and cholesterol biometabolism. Interpretation In this pilot study, transcriptomic models could predict if FCT increased following 8–10 weeks of rosuvastatin. These findings may have significance for therapy selection and could supplement invasive imaging modalities.

Background Evaluating clinical outcome prior to concurrent chemoradiotherapy remains challenging for oesophageal squamous cell carcinoma (OSCC) as traditional prognostic markers are assessed at the completion of treatment. Herein, we investigated the potential of using sub-region radiomics as a novel tumour biomarker in predicting overall survival of OSCC patients treated by concurrent chemoradiotherapy. Methods Independent patient cohorts from two hospitals were included for training (n = 87) and validation (n = 46). Radiomics features were extracted from sub-regions clustered from patients' tumour regions using K-means method. The LASSO regression for ‘Cox’ method was used for feature selection. The survival prediction model was constructed based on the sub-region radiomics features using the Cox proportional hazards model. The clinical and biological significance of radiomics features were assessed by correlation analysis of clinical characteristics and copy number alterations(CNAs) in the validation dataset. Findings The overall survival prediction model combining with seven sub-regional radiomics features was constructed. The C-indexes of the proposed model were 0.729 (0.656–0.801, 95% CI) and 0.705 (0.628–0.782, 95%CI) in the training and validation cohorts, respectively. The 3-year survival receiver operating characteristic (ROC) curve showed an area under the ROC curve of 0.811 (0.670–0.952, 95%CI) in training and 0.805 (0.638–0.973, 95%CI) in validation. The correlation analysis showed a significant correlation between radiomics features and CNAs. Interpretation The proposed sub-regional radiomics model could predict the overall survival risk for patients with OSCC treated by definitive concurrent chemoradiotherapy. Fund This work was supported by the Zhejiang Provincial Foundation for Natural Sciences, National Natural Science Foundation of China.

BACKGROUND: Recurrent mutations in the promoter of the telomerase reverse transcriptase (TERT) gene (C228T and C250T) detected in tumours and cells shed into urine of urothelial cancer (UC) patients are putative biomarkers for UC detection and monitoring. However, the possibility of detecting these mutations in cell-free circulating DNA (cfDNA) in blood and urine, or DNA from urinary exfoliated cells (cellDNA) with a single-gene sensitive assay has never been tested in a case-control setting. METHODS: We developed a single-plex assay (UroMuTERT) for the detection of low-abundance TERT promoter mutations. We tested 93 primary and recurrent UC cases and 94 controls recruited in France (blood, urine samples and tumours for the cases), and 50 primary UC cases and 50 controls recruited in Portugal (urinary exfoliated cell samples). We compared our assay with urine cytology. FINDINGS: In the French series, C228T or C250T were detected in urinary cfDNA or cellDNA in 81 cases (87·1%; 95% CI 78·6–93·2), and five controls (Specificity 94·7%; 95%CI 88·0–98·3), with 98·6% (95% CI 92·5–99·96) concordance in matched tumours. Detection rate in plasma cfDNA among cases was 7·1%. The UroMuTERT sensitivity was (i) highest for urinary cfDNA and cellDNA combined, (ii) consistent across primary and recurrent cases, tumour stages and grades, (iii) higher for low-risk non-muscle invasive UC (86·1%) than urine cytology (23·0%) (P

Background Skin cancer (SC), especially melanoma, is a growing public health burden. Experimental studies have indicated a potential diagnostic role for deep learning (DL) algorithms in identifying SC at varying sensitivities. Previously, it was demonstrated that diagnostics by dermoscopy are improved by applying an additional sonification (data to sound waves conversion) layer on DL algorithms. The aim of the study was to determine the impact of image quality on accuracy of diagnosis by sonification employing a rudimentary skin magnifier with polarized light (SMP). Methods Dermoscopy images acquired by SMP were processed by a first deep learning algorithm and sonified. Audio output was further analyzed by a different secondary DL. Study criteria outcomes of SMP were specificity and sensitivity, which were further processed by a F2-score, i.e. applying a twice extra weight to sensitivity over positive predictive values. Findings Patients (n = 73) fulfilling inclusion criteria were referred to biopsy. SMP analysis metrics resulted in a receiver operator characteristic curve AUC's of 0.814 (95% CI, 0.798–0.831). SMP achieved a F2-score sensitivity of 91.7%, specificity of 41.8% and positive predictive value of 57.3%. Diagnosing the same set of patients' lesions by an advanced dermoscope resulted in a F2-score sensitivity of 89.5%, specificity of 57.8% and a positive predictive value of 59.9% (P=NS). Interpretation DL processing of dermoscopic images followed by sonification results in an accurate diagnostic output for SMP, implying that the quality of the dermoscope is not the major factor influencing DL diagnosis of skin cancer. Present system might assist all healthcare providers as a feasible computer-assisted detection system. Fund Bostel Technologies. Trial Registration clinicaltrials.gov Identifier: NCT03362138

Background Molecular subtyping of cancer aimed to predict patient overall survival (OS) and nominate drug targets for patient treatments is central to precision oncology. Owing to the rapid development of phosphoproteomics, we can now measure thousands of phosphoproteins in human cancer tissues. However, limited studies report how to analyse the complex phosphoproteomic data for cancer subtyping and to nominate druggable kinase candidates. Findings In this work, we reanalysed the phosphoproteomic data of high-grade serous ovarian cancer (HGSOC) from the Clinical Proteomic Tumour Analysis Consortium (CPTAC). Our analysis classified HGSOC into 5 major subtypes that were associated with different OS and appeared to be more accurate than that achieved with protein profiling. We provided a workflow to identify 29 kinases whose increased activities in tumours are associated with poor survival. The altered kinase signalling landscape of HGSOC included the PI3K/AKT/mTOR, cell cycle and MAP kinase signalling pathways. We also developed a “patient-specific” hierarchy of clinically actionable kinases and selected kinase inhibitors by considering kinase activation and kinase inhibitor selectivity. Interpretation Our study offered a global phosphoproteomics data analysis workflow to aid in cancer molecular subtyping, determining phosphorylation-based cancer hallmarks and facilitating nomination of kinase inhibition in cancer.

Background: Early diagnosis of skin cancer lesions by dermoscopy, the gold standard in dermatological imaging, calls for a diagnostic upscale. The aim of the study was to improve the accuracy of dermoscopic skin cancer diagnosis through use of novel deep learning (DL) algorithms. An additional sonification-derived diagnostic layer was added to the visual classification to increase sensitivity. Methods: Two parallel studies were conducted: a laboratory retrospective study (LABS, n = 482 biopsies) and a non-interventional prospective observational study (OBS, n = 63 biopsies). A training data set of biopsy-verified reports, normal and cancerous skin lesions (n = 3954), were used to develop a DL classifier exploring visual features (System A). The outputs of the classifier were sonified, i.e. data conversion into sound (System B). Derived sound files were analyzed by a second machine learning classifier, either as raw audio (LABS, OBS) or following conversion into spectrograms (LABS) and by image analysis and human heuristics (OBS). The OBS criteria outcomes were System A specificity and System B sensitivity as raw sounds, spectrogram areas or heuristics. Findings: LABS employed dermoscopies, half benign half malignant, and compared the accuracy of Systems A and B. System A algorithm resulted in a ROC AUC of 0.976 (95% CI, 0.965–0.987). Secondary machine learning analysis of raw sound, FFT and Spectrogram ROC curves resulted in AUC's of 0.931 (95% CI 0.881–0.981), 0.90 (95% CI 0.838–0.963) and 0.988 (CI 95% 0.973–1.001), respectively. OBS analysis of raw sound dermoscopies by the secondary machine learning resulted in a ROC AUC of 0.819 (95% CI, 0.7956 to 0.8406). OBS image analysis of AUC for spectrograms displayed a ROC AUC of 0.808 (CI 95% 0.6945 To 0.9208). By applying a heuristic analysis of Systems A and B a sensitivity of 86% and specificity of 91% were derived in the clinical study. Interpretation: Adding a second stage of processing, which includes a deep learning algorithm of sonification and heuristic inspection with machine learning, significantly improves diagnostic accuracy. A combined two-stage system is expected to assist clinical decisions and de-escalate the current trend of over-diagnosis of skin cancer lesions as pathological. Fund: Bostel Technologies.Trial Registration clinicaltrials.gov Identifier: NCT03362138 Keywords: Skin cancer, Deep learning, Sonification, Artificial intelligence, Dermoscopy, Melanoma, Telemedicine

Background Recently, innovative attempts have been made to identify moyamoya disease (MMD) by focusing on the morphological differences in the head of MMD patients. Following the recent revolution in the development of deep learning (DL) algorithms, we designed this study to determine whether DL can distinguish MMD in plain skull radiograph images. Methods Three hundred forty-five skull images were collected as an MMD-labeled dataset from patients aged 18 to 50 years with definite MMD. As a control-labeled data set, 408 skull images of trauma patients were selected by age and sex matching. Skull images were partitioned into training and test datasets at a 7:3 ratio using permutation. A total of six convolution layers were designed and trained. The accuracy and area under the receiver operating characteristic (AUROC) curve were evaluated as classifier performance. To identify areas of attention, gradient-weighted class activation mapping was applied. External validation was performed with a new dataset from another hospital. Findings For the institutional test set, the classifier predicted the true label with 84·1% accuracy. Sensitivity and specificity were both 0·84. AUROC was 0·91. MMD was predicted by attention to the lower face in most cases. Overall accuracy for external validation data set was 75·9%. Interpretation DL can distinguish MMD cases within specific ages from controls in plain skull radiograph images with considerable accuracy and AUROC. The viscerocranium may play a role in MMD-related skull features. Fund This work was supported by grant no. 18-2018-029 from the Seoul National University Bundang Hospital Research Fund.

An exergy equilibrium model was established to obtain the exergy efficiency under different conditions of compound bacteria pretreatment and anaerobic digestion (AD) of corn stalk. The Genetic Algorithm (GA) was applied to optimize the exergy efficiency of the combination process of the pretreatment and AD. The maximum exergy efficiency with the GA was 19.04%, corresponding to the optimal pretreatment parameters: pretreatment temperature 33.34℃, stalk particle size 0.50 mm, ventilation rate 0.88 L/min, pretreatment time 169.03 h. The optimal AD parameters were: digestion temperature 38.08℃ and stirring rate 48.04 r/min. The validation experiment exergy efficiency reached to 19.25%, which was 24.37% higher as compared to that of the non-pretreatment process. Under these optimal conditions, the energy consumption of the compound bacteria pretreatment and the time of the bio-methane production process were effectively reduced.

Pathological evaluation of tumor tissue is pivotal for diagnosis in cancer patients and automated image analysis approaches have great potential to increase precision of diagnosis and help reduce human error. In this study, we utilize several computational methods based on convolutional neural networks (CNN) and build a stand-alone pipeline to effectively classify different histopathology images across different types of cancer. In particular, we demonstrate the utility of our pipeline to discriminate between two subtypes of lung cancer, four biomarkers of bladder cancer, and five biomarkers of breast cancer. In addition, we apply our pipeline to discriminate among four immunohistochemistry (IHC) staining scores of bladder and breast cancers. Our classification pipeline includes a basic CNN architecture, Google's Inceptions with three training strategies, and an ensemble of two state-of-the-art algorithms, Inception and ResNet. Training strategies include training the last layer of Google's Inceptions, training the network from scratch, and fine-tunning the parameters for our data using two pre-trained version of Google's Inception architectures, Inception-V1 and Inception-V3. We demonstrate the power of deep learning approaches for identifying cancer subtypes, and the robustness of Google's Inceptions even in presence of extensive tumor heterogeneity. On average, our pipeline achieved accuracies of 100%, 92%, 95%, and 69% for discrimination of various cancer tissues, subtypes, biomarkers, and scores, respectively. Our pipeline and related documentation is freely available at https://github.com/ih-_lab/CNN_Smoothie., Highlights • This work presents the power of deep learning approaches for classifying pathology images in presence of extensive tumor heterogeneity. • A basic CNN architecture and several state-of-the-art image classification architectures are compared in an open source pipeline. • The pipeline is applicable to wide rang of histopathology images.

Background: To reduce the high incidence and mortality of gastric cancer (GC), we aimed to develop deep learning-based models to assist in predicting the diagnosis and overall survival (OS) of GC patients using pathological images. Methods: 2333 hematoxylin and eosin-stained pathological pictures of 1037 GC patients were collected from two cohorts to develop our algorithms, Renmin Hospital of Wuhan University (RHWU) and the Cancer Genome Atlas (TCGA). Additionally, we gained 175 digital pictures of 91 GC patients from National Human Genetic Resources Sharing Service Platform (NHGRP), served as the independent external validation set. Two models were developed using artificial intelligence (AI), one named GastroMIL for diagnosing GC, and the other named MIL-GC for predicting outcome of GC. Findings: The discriminatory power of GastroMIL achieved accuracy 0.920 in the external validation set, superior to that of the junior pathologist and comparable to that of expert pathologists. In the prognostic model, C-indices for survival prediction of internal and external validation sets were 0.671 and 0.657, respectively. Moreover, the risk score output by MIL-GC in the external validation set was proved to be a strong predictor of OS both in the univariate (HR = 2.414, P

Background Neutralizing mAbs can prevent communicable viral diseases. MK-1654 is a respiratory syncytial virus (RSV) F glycoprotein neutralizing monoclonal antibody (mAb) under development to prevent RSV infection in infants. Development and validation of methods to predict efficacious doses of neutralizing antibodies across patient populations exposed to a time-varying force of infection (i.e., seasonal variation) are necessary. Methods Five decades of clinical trial literature were leveraged to build a model-based meta-analysis (MBMA) describing the relationship between RSV serum neutralizing activity (SNA) and clinical endpoints. The MBMA was validated by backward translation to animal challenge experiments and forward translation to predict results of a recent RSV mAb trial. MBMA predictions were evaluated against a human trial of 70 participants who received either placebo or one of four dose-levels of MK-1654 and were challenged with RSV [NCT04086472]. The MBMA was used to perform clinical trial simulations and predict efficacy of MK-1654 in the infant target population. Findings The MBMA established a quantitative relationship between RSV SNA and clinical endpoints. This relationship was quantitatively consistent with animal model challenge experiments and results of a recently published clinical trial. Additionally, SNA elicited by increasing doses of MK-1654 in humans reduced RSV symptomatic infection rates with a quantitative relationship that approximated the MBMA. The MBMA indicated a high probability that a single dose of ≥ 75 mg of MK-1654 will result in prophylactic efficacy (> 75% for 5 months) in infants. Interpretation An MBMA approach can predict efficacy of neutralizing antibodies against RSV and potentially other respiratory pathogens.

Bioemulsifier and exopolysaccharides are industrially important biomolecules produced by microorganisms using green technology. They have applications in food, biomedical, pharmaceutical and cosmetic industries and hence high yield of both products becomes necessary. The current study showed that Brevibacillus borstelensis has a potential to produce bioemulsifier and exopolysaccharide simultaneously but yield of both products is limited. In this study, CCD-RSM has been used as experimental design to increase concentration of both products. Concentrations of glucose, monosodium glutamate, yeast extract and magnesium sulphate were process variables and concentrations of bioemulsifiers, exopolysaccharides and biomass were responses. 30 experimental runs were performed and the models from CCD were optimized by genetic algorithm and NSGA. The results from modelling and optimization techniques were compared along with validation of models. The predicted values from optimization techniques were better than experimental values. The study concluded that NSGA is most suitable to optimize multiple responses simultaneously when compared to RSM and genetic algorithm. The optimum conditions for production were 22 g/l glucose, 14 g/l monosodium glutamate, 6 g/l yeast extract and 0.6 g/l magnesium sulphate with maximum yield of 6.1, 17.6 and 2.8 g/l bioemulsifier, exopolysaccharide and biomass, respectively. Knowledge of optimum concentrations of carbon and nitrogen source will help to utilize industrial and agricultural wastes for production of both products. They have applications in environmental bioremediation by clearing oil spills. Bioemulsifiers also help in heavy metal removal from hazardous waste. Hence this will result in environmental bioremediation by utilization of wastes by employing products generated from wastes.

Background: Breast cancers can be divided into HER2-negative and HER2-positive subtypes according to different status of HER2 gene. Despite extensive studies connecting germline mutations with possible risk of HER2-negative breast cancer, the main category of breast cancer, it remains challenging to obtain accurate risk assessment and to understand the potential underlying mechanisms. Methods: We developed a novel framework named Damage Assessment of Genomic Mutations (DAGM), which projects rare coding mutations and gene expressions into Activity Profiles of Signalling Pathways (APSPs). Findings: We characterized and validated DAGM framework at multiple levels. Based on an input of germline rare coding mutations, we obtained the corresponding APSP spectrum to calculate the APSP risk score, which was capable of distinguish HER2-negative from HER2-positive cases. These findings were validated using breast cancer data from TCGA (AUC = 0.7). DAGM revealed that HER2 signalling pathway was up-regulated in germline of HER2-negative patients, and those with high APSP risk scores had exhibited immune suppression. These findings were validated using RNA sequencing, phosphoproteome analysis, and CyTOF. Moreover, using germline mutations, DAGM could evaluate the risk for HER2-negative breast cancer, not only in women carrying BRCA1/2 mutations, but also in those without known disease-associated mutations. Interpretation: The DAGM can facilitate the screening of subjects at high risk of HER2-negative breast cancer for primary prevention. This study also provides new insights into the potential mechanisms of developing HER2-negative breast cancer. The DAGM has the potential to be applied in the prevention, diagnosis, and treatment of HER2-negative breast cancer. Funding: This work was supported by the National Key Research and Development Program of China (grant no. 2018YFC0910406 and 2018AAA0103302 to CZ); the National Natural Science Foundation of China (grant no. 81202076 and 82072939 to MY, 81871513 to KW); the Guangzhou Science and Technology Program key projects (grant no. 2014J2200007 to MY, 202002030236 to KW); the National Key R&D Program of China (grant no. 2017YFC1309100 to CL); Shenzhen Science and Technology Planning Project (grant no. JCYJ20170817095211560 574 to YN); and the Natural Science Foundation of Guangdong Province (grant no. 2017A030313882 to KW and S2013010012048 to MY); Hefei National Laboratory for Physical Sciences at the Microscale (grant no. KF2020009 to GN); and RGC General Research Fund (grant no. 17114519 to YQS).

To develop a modified predictive model for severe COVID-19 in people infected with Sars-Cov-2. We developed the predictive model for severe patients of COVID-19 based on the clinical date from the Tumor Center of Union Hospital affiliated with Tongji Medical College, China. A total of 151 cases from Jan. 26 to Mar. 20, 2020, were included. Then we followed 5 steps to predict and evaluate the model: data preprocessing, data splitting, feature selection, model building, prevention of overfitting, and Evaluation, and combined with artificial neural network algorithms. We processed the results in the 5 steps. In feature selection, ALB showed a strong negative correlation (r = 0.771, P

Background Platelet Derived Growth Factor Receptor beta (PDGFRβ) has been associated to hepatic stellate cell activation and has been the target of multiple therapeutic studies. However, little is known concerning its use as a diagnostic agent. Methods Circulating PDGFRβ levels were analysed in a cohort of patients with liver fibrosis/cirrhosis due to chronic alcohol abuse, viral hepatitis, or non-alcoholic fatty liver disease (NAFLD). The diagnostic performance of PDGFRβ as individual blood parameter, or in combination with other metabolic factors was evaluated. Findings sPDGFRβ levels are progressively increased with increasing fibrosis stage and the largest difference was observed in patients with significant fibrosis, compared to no or mild fibrosis. The accuracy of sPDGFRβ-levels predicting fibrosis could be increased by combining it with albumin levels and platelet counts into a novel diagnostic algorithm, the PRTA-score, generating a predictive value superior to Fib-4, APRI, and AST/ALT. The sPDGFRβ levels and the PRTA-score are independent of liver disease aetiology, thus overcoming one of the major weaknesses of current non-invasive clinical and experimental scores. Finally, we confirmed the diagnostic value of sPDGFRβ levels and the PRTA-score in an independent patient cohort with NAFLD which was staged for fibrosis by liver biopsy. Interpretation The PRTA-score is an accurate tool for detecting significant liver fibrosis in a broad range of liver disease aetiologies. Fund Vrije Universiteit Brussel, the Institute for the Promotion of Innovation through Science and Technology in Flanders (IWT-Flanders) (HILIM-3D; SBO140045), and the Fund of Scientific Research Flanders (FWO).

Background Identifying which individuals will develop tuberculosis (TB) remains an unresolved problem due to few animal models and computational approaches that effectively address its heterogeneity. To meet these shortcomings, we show that Diversity Outbred (DO) mice reflect human-like genetic diversity and develop human-like lung granulomas when infected with Mycobacterium tuberculosis (M.tb) . Methods Following M.tb infection, a “supersusceptible” phenotype develops in approximately one-third of DO mice characterized by rapid morbidity and mortality within 8 weeks. These supersusceptible DO mice develop lung granulomas patterns akin to humans. This led us to utilize deep learning to identify supersusceptibility from hematoxylin & eosin (H&E) lung tissue sections utilizing only clinical outcomes (supersusceptible or not-supersusceptible) as labels. Findings The proposed machine learning model diagnosed supersusceptibility with high accuracy (91.50 ± 4.68%) compared to two expert pathologists using H&E stained lung sections (94.95% and 94.58%). Two non-experts used the imaging biomarker to diagnose supersusceptibility with high accuracy (88.25% and 87.95%) and agreement (96.00%). A board-certified veterinary pathologist (GB) examined the imaging biomarker and determined the model was making diagnostic decisions using a form of granuloma necrosis (karyorrhectic and pyknotic nuclear debris). This was corroborated by one other board-certified veterinary pathologist. Finally, the imaging biomarker was quantified, providing a novel means to convert visual patterns within granulomas to data suitable for statistical analyses. Implications Overall, our results have translatable implication to improve our understanding of TB and also to the broader field of computational pathology in which clinical outcomes alone can drive automatic identification of interpretable imaging biomarkers, knowledge discovery, and validation of existing clinical biomarkers. Funding National Institutes of Health and American Lung Association.

Background Diagnosis of rib fractures plays an important role in identifying trauma severity. However, quickly and precisely identifying the rib fractures in a large number of CT images with increasing number of patients is a tough task, which is also subject to the qualification of radiologist. We aim at a clinically applicable automatic system for rib fracture detection and segmentation from CT scans. Methods A total of 7,473 annotated traumatic rib fractures from 900 patients in a single center were enrolled into our dataset, named RibFrac Dataset, which were annotated with a human-in-the-loop labeling procedure. We developed a deep learning model, named FracNet, to detect and segment rib fractures. 720, 60 and 120 patients were randomly split as training cohort, tuning cohort and test cohort, respectively. Free-Response ROC (FROC) analysis was used to evaluate the sensitivity and false positives of the detection performance, and Intersection-over-Union (IoU) and Dice Coefficient (Dice) were used to evaluate the segmentation performance of predicted rib fractures. Observer studies, including independent human-only study and human-collaboration study, were used to benchmark the FracNet with human performance and evaluate its clinical applicability. A annotated subset of RibFrac Dataset, including 420 for training, 60 for tuning and 120 for test, as well as our code for model training and evaluation, was open to research community to facilitate both clinical and engineering research. Findings Our method achieved a detection sensitivity of 92.9% with 5.27 false positives per scan and a segmentation Dice of 71.5%on the test cohort. Human experts achieved much lower false positives per scan, while underperforming the deep neural networks in terms of detection sensitivities with longer time in diagnosis. With human-computer collobration, human experts achieved higher detection sensitivities than human-only or computer-only diagnosis. Interpretation The proposed FracNet provided increasing detection sensitivity of rib fractures with significantly decreased clinical time consumed, which established a clinically applicable method to assist the radiologist in clinical practice. Funding A full list of funding bodies that contributed to this study can be found in the Acknowledgements section. The funding sources played no role in the study design; collection, analysis, and interpretation of data; writing of the report; or decision to submit the article for publication .

In the organosolv pulping of the oil palm fronds, the influence of the operational variables of the pulping reactor (viz. cooking temperature and time, ethanol and NaOH concentration) on the properties of the resulting pulp (yield and kappa number) and paper sheets (tensile index and tear index) was investigated using a wavelet neural network model. The experimental results with error less than 0.0965 (in terms of MSE) were produced, and were then compared with those obtained from the response surface methodology. Performance assessment indicated that the neural network model possessed superior predictive ability than the polynomial model, since a very close agreement between the experimental and the predicted values was obtained.

Graphical abstract, The second osmotic virial coefficients (b2) of four proteins – lysozyme, recombinant human lactoferrin, concanavalin A and catalase were measured by self-interaction chromatography (SIC) in solutions of varying salt type, concentration and pH. Protein aggregate sizes based on the initial hydrodynamic radius of the protein solution species present were measured using dynamic light scattering, and the relationship between b2 and protein aggregate size was studied. A linear correlation was established between b2 values and protein aggregate hydrodynamic size for all proteins, and for almost all solution conditions. Aggregate sizes of 0. The observed b2 trends as a function of solution conditions were very much protein dependent, with notable trends including the existence of attractive interactions (negative b2 values) at low ionic strengths for catalase and concanavalin A, and the highly positive b2 values observed for lactoferrin over a wide range of solution conditions, reflecting lactoferrin’s innately high stability. It is concluded that the quantification of protein–protein interactions using SIC based b2 data is a potentially valuable screening tool for predicting protein aggregation propensity.

This study evaluated the biological removal of trichloroethylene (TCE) by Rhodococcus opacus using airlift bioreactor under continuous operation mode. The effect of inlet TCE concentration in the range 0.12–2.34 g m−3 on TCE removal has studied for 55 days. During the continuous bioreactor operation, a maximum of 96% TCE removal was obtained for low inlet TCE concentration, whereas the highest elimination capacity was 151.2 g m−3 h−1 for the TCE loading rate of 175.0 g m−3 h−1. The carbon dioxide (CO2) concentration profile from the airlift bioreactor revealed that the degraded TCE has primarily converted to CO2 with a fraction of organic carbon utilized for bacterial growth. The artificial neural network (ANN) based model was able to successfully predict the performance of the bioreactor system using the Levenberg-Marquardt (LM) back propagation algorithm, and optimized biological topology is 3:12:1. The prediction accuracy of the model was high as the experimental data were in good agreement (R2 = 0.9923) with the ANN predicted data. Overall, from the bioreactor experiments and its ANN modeling, the potential strength of R. opacus in TCE biodegradation is proved.

Motivation Global protein surface comparison (GPSC) studies have been limited compared to other research works on protein structure alignment/comparison due to lack of real applications associated with GPSC. However, the technology advances in cryo-electron tomography (CET) have made methods to identify proteins from their surface shapes extremely useful. Results In this study, we developed a new method called Farthest point sampling (FPS)-enhanced Triangulation-based Iterative-closest-Point (ICP) (FTIP) for GPSC. We applied it to protein classification using only surface shape information. Our method first extracts a set of feature points from protein surfaces using FPS and then uses a triangulation-based efficient ICP algorithm to align the feature points of the two proteins to be compared. Tested on a benchmark dataset with 2329 proteins using nearest-neighbor classification, FTIP outperformed the state-of-the-art method for GPSC based on 3D Zernike descriptors. Using real and simulated cryo-EM data, we show that FTIP could be applied in the future to address problems in protein identification in CET experiments. Availability and implementation Programs/scripts we developed/used in the study are available at http://ani.stat.fsu.edu/∼yuan/index.fld/FTIP.tar.bz2. Supplementary information Supplementary data are available at Bioinformatics online.

Ion-exchange (IEX) and Donnan dialysis (DD) are techniques which can selectively remove cations, limiting scaling in reverse osmosis (RO). If the RO concentrate could be recycled for regeneration of these pre-treatment techniques, RO recovery could be largely increased without the need for chemical addition or additional technologies. In this study, two different RO feed streams (treated industrial waste water and simple tap water) were tested in the envisioned IEX-RO and DD-RO hybrids including RO concentrate recycling. The efficiency of multivalent cation removal depends mainly on the ratio of monovalent to multivalent cations in the feed stream, influencing the ion-exchange efficiency in both IEX and DD. Since the mono-to-multivalent ratio was very high in the waste water, the RO recovery could potentially be increased to 92%. For the tap water, these high RO recoveries could only be reached by adding additional NaCl, because of the low initial monovalent to multivalent ratio in the feed. In both cases, the IEX-RO hybrid proved to be most cost-efficient, due to the high current cost of the membranes used in DD. The membrane cost would have to decrease from ±300 €/m² to 10-30 €/m² - comparable to current reverse osmosis membranes - to achieve a comparable cost. In conclusion, the recycling of RO concentrate to regenerate ion exchange pre-treatment techniques for RO is an interesting option to increase RO recovery without addition of chemicals, but only at high monovalent/multivalent cation-ratios in the feed stream.

Salt used to make brines for regeneration of ion exchange (IX) resins is the dominant economic and environmental liability of IX treatment systems for nitrate-contaminated drinking water sources. To reduce salt usage, the applicability and environmental benefits of using a catalytic reduction technology to treat nitrate in spent IX brines and enable their reuse for IX resin regeneration were evaluated. Hybrid IX/catalyst systems were designed and life cycle assessment of process consumables are used to set performance targets for the catalyst reactor. Nitrate reduction was measured in a typical spent brine (i.e., 5000 mg/L NO3(-) and 70,000 mg/L NaCl) using bimetallic Pd-In hydrogenation catalysts with variable Pd (0.2-2.5 wt%) and In (0.0125-0.25 wt%) loadings on pelletized activated carbon support (Pd-In/C). The highest activity of 50 mgNO3(-)/(min - g(Pd)) was obtained with a 0.5 wt%Pd-0.1 wt%In/C catalyst. Catalyst longevity was demonstrated by observing no decrease in catalyst activity over more than 60 days in a packed-bed reactor. Based on catalyst activity measured in batch and packed-bed reactors, environmental impacts of hybrid IX/catalyst systems were evaluated for both sequencing-batch and continuous-flow packed-bed reactor designs and environmental impacts of the sequencing-batch hybrid system were found to be 38-81% of those of conventional IX. Major environmental impact contributors other than salt consumption include Pd metal, hydrogen (electron donor), and carbon dioxide (pH buffer). Sensitivity of environmental impacts of the sequencing-batch hybrid reactor system to sulfate and bicarbonate anions indicate the hybrid system is more sustainable than conventional IX when influent water contains80 mg/L sulfate (at any bicarbonate level up to 100 mg/L) or20 mg/L bicarbonate (at any sulfate level up to 100 mg/L) assuming 15 brine reuse cycles. The study showed that hybrid IX/catalyst reactor systems have potential to reduce resource consumption and improve environmental impacts associated with treating nitrate-contaminated water sources.

(14)C analysis of flue gas by accelerator mass spectrometry (AMS) and liquid scintillation counting (LSC) were used to determine the biomass fraction of mixed waste at an operational energy-from-waste (EfW) plant. Results were converted to bioenergy (% total) using mathematical algorithms and assessed against existing industry methodologies which involve manual sorting and selective dissolution (SD) of feedstock. Simultaneous determinations using flue gas showed excellent agreement: 44.8 ± 2.7% for AMS and 44.6 ± 12.3% for LSC. Comparable bioenergy results were obtained using a feedstock manual sort procedure (41.4%), whilst a procedure based on selective dissolution of representative waste material is reported as 75.5% (no errors quoted). (14)C techniques present significant advantages in data acquisition, precision and reliability for both electricity generator and industry regulator.

In this study, the optimal conditions for biomass torrefaction were determined by comparing the gain of energy content to the weight loss of biomass from the final products. Torrefaction experiments were performed at temperatures ranging from 220 to 280 °C using 20-80 min reaction times. Polynomial regression models ranging from the 1st to the 3rd order were used to determine a relationship between the severity factor (SF) and calorific value or weight loss. The intersection of two regression models for calorific value and weight loss was determined and assumed to be the optimized SF. The optimized SFs on each biomass ranged from 6.056 to 6.372. Optimized torrefaction conditions were determined at various reaction times of 15, 30, and 60 min. The average optimized temperature was 248.55 °C in the studied biomass when torrefaction was performed for 60 min.

An experimental study was carried out to demonstrate and quantify the feasibility of antiscalant (AS) removal from brackish water RO concentrate of high gypsum scaling propensity via lime treatment prior to seeded gypsum precipitation. Based on studies with model solutions, it was shown that sufficient AS removal (up to ∼90%) from RO concentrate is feasible via a lime treatment step (at a dose significantly lower than that required for conventional lime softening) to enable effective subsequent seeded gypsum precipitation. This two-step chemically-enhanced seeded precipitation (CESP) treatment of primary RO concentrate is suitable as an intermediate concentrate demineralization (ICD) stage for high recovery desalting employing secondary RO desalination. Analysis of gypsum precipitation and lime treatment kinetic data suggests that, after adequate CaCO 3 precipitation has been induced for effective AS scavenging, CaSO 4 desupersaturation can be achieved via seeded gypsum precipitation without retardation due to seed poisoning by AS. Also, the lime dose required to prevent seed poisoning during subsequent gypsum desupersaturation via seeded gypsum precipitation can be adequately assessed with a precipitation kinetics model that considers AS seed poisoning based on a Langmuir adsorption isotherm. The degree of AS removal after lime treatment increased linearly with the logarithm of the single lime dose additions. Staged lime dosing (i.e., multiple lime additions), however, removed a higher degree of AS relative to an equivalent single lime dose addition since a higher driving force for CaCO 3 precipitation could be maintained over the course of the lime treatment period.

The use of the solar photo-Fenton process is proposed to degrade Paracetamol in water in order to form biodegradable reaction intermediates which can be finally removed with a downstream biological treatment. Firstly, biodegradability enhancement with photo-Fenton treatment time has been evaluated; the minimum mineralization level should be at least 18.6% where Paracetamol has been degraded and biodegradability efficiency is higher than 40%. 20 mg L −1 of Fe 2+ and 200 mg L −1 of H 2 O 2 were selected in a lab-scale study looking at Paracetamol's degradation rate and organic carbon mineralization rate. As a result of scaling up the process at a pilot plant, 157.5 mg L −1 of Paracetamol (∼1 mM) was treated in 25 min of photo-Fenton treatment achieving the desired biodegradability. A further economic evaluation shows how the proposed treatment strategy markedly increases plant efficiency, resulting in an 83.33% reduction in reagent cost and a 79.11% reduction in costs associated with reaction time. Total cost is reduced from 3.4502 €/m 3 to 0.7392 €/m 3 .

In MBR processes, sufficient aeration is necessary to maintain sustainable flux and to retard membrane fouling. Membrane permeability, sludge characteristics, nutrient removal and biomass growth at various air flow rates in the membrane and moving bed biofilm reactor (MBBR) compartments were studied in a pilot plant. The highest nitrogen and phosphorous removal rates were found at MBBR aeration rates of 151 and 85 L h−1 and a specific aeration demand per membrane area (SADm) of 1.2 and 0.4 m air 3 m − 2 h − 1 , respectively. A linear correlation was found between the amount of attached biofilm and the nutrient removal rate. The aeration rate in the MBBR compartment and SADm significantly influenced the sludge characteristics and membrane permeability. The optimum combination of the aeration rate in the MBBR compartment and SADm were 151 L h−1 and 0.8 – 1.2 m air 3 m membrane − 2 h − 1 , respectively.

An expanded unified model for the biomass fractions, soluble-organic fractions, and oxygen-uptake rates considering extracellular polymeric substances (EPS), intracellular storage products (X(STO)), and predators for activated sludge is used to study the impacts of predators on biomass components and oxygen uptake. The new model is applied to evaluate how predation affects the oxygen-uptake rate (OUR) and the different forms of biomass: active bacteria (X(H)), X(EPS), and X(STO), under dynamic feast-and-famine and continuous conditions. For the dynamic conditions of a sequencing batch reactor (SBR), eliminating predators from the model increases X(H) and X(EPS) fractions significantly, and this causes the substantial increases in OUR and MLVSS once the famine period begins. An analysis of how the OUR is distributed among the several respiration processes shows that the predation of X(H) is the most significant oxygen utilization rate process in the system under famine conditions of an SBR. Application of the model to simulate the long-term operation of an SBR indicates that predators reach their maximum fraction in the MLVSS (approximately 4% of MLVSS) at a solids retention time of about 13 days, but they are washed out at a solids retention time less than approximately 3 days. Simulation for a continuous system indicates that predators take more time (about 800 h) to reach steady state and reach their maximum fraction (approximately 5.5%) at an SRT of approximately 14 days. Comparison of SBR and continuous systems reveals that the predators have greater impact in the continuous system because the permanent near-famine condition accentuates predation processes.

High concentrations of heavy metal in livestock manures limit land application of their sludges. A practical and economical method of sludge treatment is important for converting the livestock sludge into soil conditioners or fertilizers. In this study, the effect of solid contents on the simultaneous aerobic digestion and metal leaching at thermophilic condition were investigated in a batch reactor. Different solid contents in the range of 0.5–4% (dry-w/v) were studied. The results showed that an increase of solid content decreased the pH reducing rate. It was the result of increase in buffering capacity and possible microbial inhibition at a higher solid content. Similar results were also found in the variations of ORP and sulfate concentrations during this process. In most cases, this biological process is able to solubilize 82–99% of heavy metals from the livestock sludge. It was found that the efficiency and rate of metal solubilization decreased with increasing solid contents. In addition, 54–80% of organic matter in the sludge was degraded after 28 days of reaction. A low sludge digestion efficiency was found at a high solid content. Moreover, the dewaterability of sludge was improved and the fertility (N, P and K) of sludge did not change significantly after this bioprocess.