A High-Throughput Screening System for Populus Wood-Associated Transcription Factors and Its Application to Lignin Regulation

Wood formation of trees is a complex and costly developmental process, whose regulatory network is involved in the protein-protein and protein-DNA interactions. To detect such interactions in wood development, we developed a high-throughput screening system with 517 Gal4-AD-wood-associated transcription factors (TFs) library from Populus alba × P. glandulosa cv “84K.” This system can be used for screening the upstream regulators and interacting proteins of targets by mating-based yeast-one hybrid (Y1H) and yeast-two-hybrid (Y2H) method, respectively. Multiple regulatory modules of lignin biosynthesis were identified based on this Populus system. Five TFs interacted with the 500-bp promoter fragment of PHENYLALANINE AMMONIA-LYASE 2 (PAL2), the first rate-limiting enzyme gene in the lignin biosynthesis pathway, and 10 TFs interacted with PaMYB4/LTF1, a key regulator of lignin biosynthesis. Some of these interactions were further validated by EMSA and BiFC assays. The TF-PaPAL2 promoter interaction and TF-PaMYB4 interaction revealed a complex mechanism governing the regulation of lignin synthesis in wood cells. Our high-throughput Y1H/Y2H screening system may be an efficient tool for studying regulatory network of wood formation in tree species.

Fuzzy-Based Screening System for Determination of Enhanced Oil Recovery (EOR) Method in Reservoir

The petroleum industry is developing technology to increase oil recovery in reservoirs. One of the technologies used is Enhanced Oil Recovery (EOR). Selecting an EOR method for a specific reservoir condition is one of the most challenging tasks for a reservoir engineer. This study tries to build a fuzzy logic-based screening system to determine the EOR method. It created the system intending to be able to assist in selecting and determining the appropriate EOR method used in the field. There are nine input criteria used to screen the EOR criteria, namely: API Gravity, Oil Saturation, Formation Type, Net Thickness, Viscosity, Permeability, Temperature, Porosity, Depth criteria. The output criteria generated from the calculation of the EOR screening criteria are 14 outputs, namely: CO2 MF Miscible Flooding, CO2 IMMF Immiscible Flooding, HC MF Miscible Flooding, HC IMMF Immiscible Flooding, N2 MF Miscible Flooding, N2 IMMF Immiscible Flooding, WAG MF Miscible Flooding , HC+WAG IMMF Immiscible Flooding, Polymer, ASP, Combustion, Steam, Hot Water, Microbial. In this system, 512 rules are generated to produce 14 different outputs of the EOR method, with Mamdani's Fuzzy Inference reasoning. This fuzzy-based screening system has an accuracy rate of 80.95%, so this system is suitable to be used to assist reservoir engineers in determining the appropriate EOR method to be used according to the conditions in the reservoir. The sensitivity level of the system only reaches 53.1%, while the specificity level reaches 94%.

Validation of an Automated Screening System for Diabetic Retinopathy Operating under Real Clinical Conditions

Background: Retinopathy is the most common microvascular complication of diabetes mellitus. It is the leading cause of blindness among working-aged people in developed countries. The use of telemedicine in the screening system has enabled the application of large-scale population-based programs for early retinopathy detection in diabetic patients. However, the need to support ophthalmologists with other trained personnel remains a barrier to broadening its implementation. Methods: Automatic diagnosis of diabetic retinopathy was carried out through the analysis of retinal photographs using the 2iRetinex software. We compared the categorical diagnoses of absence/presence of retinopathy issued by family physicians (PCP) with the same categories provided by the algorithm (ALG). The agreed diagnosis of three specialist ophthalmologists is used as the reference standard (OPH). Results. There were 653 of 3520 patients diagnosed with diabetic retinopathy (DR). Diabetic retinopathy threatening to vision (STDR) was found in 82 patients (2.3%). Diagnostic sensitivity for STDR was 94% (ALG) and 95% (PCP). No patient with proliferating or severe DR was misdiagnosed in both strategies. The k-value of the agreement between the ALG and OPH was 0.5462, while between PCP and OPH was 0.5251 (p = 0.4291). Conclusions: The diagnostic capacity of 2iRetinex operating under normal clinical conditions is comparable to screening physicians.

PP119 Innovative Screening System For COVID-19 Using Application Of Artificial Intelligence For Telemedicine

IntroductionArtificial intelligence (AI) and innovative technology offer opportunities for enhanced health care during the COVID-19 pandemic. Populations living in low-income countries do not have access to reverse transcription polymerase chain reaction (RT-PCR) testing for COVID-19 and, thus, depend on the scarce resources of their health system. In this context, an automated screening system for COVID-19 based on AI for a telemedicine platform could be directed towards alleviating the current lack of trained radiologists who can interpret computed tomography images at countryside hospitals.MethodsThis descriptive study was carried out in Paraguay by the Telemedicine Unit of the Ministry of Public Health and Social Welfare in collaboration with the Department of Biomedical Engineering and Imaging of the Health Science Research Institute and the University of the Basque Country. The utility of the screening system for COVID-19 was analyzed by dividing the results from two tailored AI systems implemented in 14 public hospitals into four likelihood levels for COVID-19.ResultsBetween March and October 2020, 911 COVID-19 diagnoses were performed in 14 regional hospitals (62.6% were men and 37.4% were women). The average age of the patients diagnosed with COVID-19 was 50.7 years; 59.1% were aged 19 to 59 years. The two AI systems used have different background information for detecting COVID-19. The most common findings were severe pneumonia and bilateral pneumonia with pleural effusions. The role of computed tomography was to find lesions and evaluate the effects of treatment. The sensitivity of AI for detecting COVID-19 was 93%.ConclusionsAI technology could help in developing a screening system for COVID-19 and other respiratory pathologies. It could speed up medical imaging diagnosis at regional hospitals for patients with suspected infection during the COVID-19 pandemic and rationalize scarce RT-PCR and specialized human resources in low-income countries. These results must be contextualized with the local or regional epidemiological profile before widespread implementation.