ANN-Based Thermal Load Prediction Approach for Advanced Controls in Building Energy Systems

The Artificial Neural Network (ANN) technology has been used in various areas. In the building industry, however, ANN is relatively less utilized due to its complexity and uncertain benefits of its application along with the costs associated with its development. This paper introduces ANN regarding its applicability and potential benefits in building operations, especially for energy savings. Thermal loads calculations are most widely used for the operation of building energy systems. An ANN model was developed to predict a large office building's cooling loads. The EnergyPlus simulation program was used to generate thermal loads data and the Python program to develop an ANN model. The initial ANN model predicted a case study building's cooling loads within the CVRMSE value of 7.3% initially, and later 6.8% after optimization, which is within the tolerance range of 3Q% recommended by the ASHRAE Guideline 14. This study showed the potential benefit of energy savings that can be achieved by utilizing the ANN model for accurately predicting the cooling loads

The dynamic transcriptional and translational landscape of the model antibiotic producer Streptomyces coelicolor A3(2)

Individual Streptomyces species have the genetic potential to produce a diverse array of natural products of commercial, medical and veterinary interest. However, these products are often not detectable under laboratory culture conditions. To harness their full biosynthetic potential, it is important to develop a detailed understanding of the regulatory networks that orchestrate their metabolism. Here we integrate nucleotide resolution genome-scale measurements of the transcriptome and translatome of Streptomyces coelicolor, the model antibiotic-producing actinomycete. Our systematic study determines 3,570 transcription start sites and identifies 230 small RNAs and a considerable proportion (∼21%) of leaderless mRNAs; this enables deduction of genome-wide promoter architecture. Ribosome profiling reveals that the translation efficiency of secondary metabolic genes is negatively correlated with transcription and that several key antibiotic regulatory genes are translationally induced at transition growth phase. These findings might facilitate the design of new approaches to antibiotic discovery and development

Efficacy of fermented grain using Bacillus coagulans in reducing visceral fat among people with obesity: a randomized controlled trial

BackgroundObesity is a socioeconomic problem, and visceral obesity, in particular, is related to cardiovascular diseases or metabolic syndrome. Fermented grains and various microorganisms are known to help with anti-obesity effects and weight management. Studies on the relationship between Bacillus coagulans and anti-obesity effects are not well known, and studies on the application of fermented grains and microorganisms to the human body are also insufficient.ObjectivesThis study aimed to evaluate the efficacy of Curezyme–LAC, an ingredient mixed with six-grain types fermented by B. coagulans, in reducing fat mass in adults with obesity.MethodsIn this randomized double-blinded placebo-controlled study, 100 participants [aged 40–65 years; body mass index (BMI) ≥ 25 to ≤ 33 kg/m2) were randomly allocated to two groups: 4 g/day Curezyme–LAC administered as a granulated powder or placebo (steamed grain powder mixture).ResultsAfter 12 weeks, visceral adipose tissue decreased significantly in the Curezyme–LAC group compared with that in the placebo group (mean ± standard error, SE of −9.3 cm2 ± 5.1) vs. (6.8 cm2 ± 3.4; p = 0.008). Compared to the placebo group, the Curezyme–LAC group also showed significant reductions in total fat mass (−0.43 ± 0.24 kg vs. 0.31 ± 0.19 kg, p = 0.011), body weight (−0.4 ± 0.3 kg vs. 0.3 ± 0.2 kg, p = 0.021), BMI (−0.14 ± 0.12 vs. 0.10 ± 0.07, p = 0.028), and waist circumference (−0.6 ± 0.2 cm vs. −0.1 ± 0.2 cm, p = 0.018) without a change in dietary intake and physical activity.ConclusionCurezyme–LAC supplementation for 12 weeks may benefit individuals with obesity by reducing visceral fat mass

HLA and Disease Associations in Koreans

The human leukocyte antigen (HLA), the major histocompatibility complex (MHC) in humans has been known to reside on chromosome 6 and encodes cell-surface antigen-presenting proteins and many other proteins related to immune system function. The HLA is highly polymorphic and the most genetically variable coding loci in humans. In addition to a critical role in transplantation medicine, HLA and disease associations have been widely studied across the populations world-wide and are found to be important in prediction of disease susceptibility, resistance and of evolutionary maintenance of genetic diversity. Because recently developed molecular based HLA typing has several advantages like improved specimen stability and increased resolution of HLA types, the association between HLA alleles and a given disease could be more accurately quantified. Here, in this review, we have collected HLA association data on some autoimmune diseases, infectious diseases, cancers, drug responsiveness and other diseases with unknown etiology in Koreans and attempt to summarize some remarkable HLA alleles related with specific diseases

Engineered biosynthesis of milbemycins in the avermectin high-producing strain Streptomyces avermitilis

Additional file 3 : Figure S2. HPLC analysis of milbemycins produced from S. avermitilis mutant strains and authentic standard milbemycins

Analysis of significant protein abundance from multiple reaction-monitoring data

Background Discovering reliable protein biomarkers is one of the most important issues in biomedical research. The ELISA is a traditional technique for accurate quantitation of well-known proteins. Recently, the multiple reaction-monitoring (MRM) mass spectrometry has been proposed for quantifying newly discovered protein and has become a popular alternative to ELISA. For the MRM data analysis, linear mixed modeling (LMM) has been used to analyze MRM data. MSstats is one of the most widely used tools for MRM data analysis that is based on the LMMs. However, LMMs often provide various significance results, depending on model specification. Sometimes it would be difficult to specify a correct LMM method for the analysis of MRM data. Here, we propose a new logistic regression-based method for Significance Analysis of Multiple Reaction Monitoring (LR-SAM). Results Through simulation studies, we demonstrate that LMM methods may not preserve type I error, thus yielding high false- positive errors, depending on how random effects are specified. Our simulation study also shows that the LR-SAM approach performs similarly well as LMM approaches, in most cases. However, LR-SAM performs better than the LMMs, particularly when the effects sizes of peptides from the same protein are heterogeneous. Our proposed method was applied to MRM data for identification of proteins associated with clinical responses of treatment of 115 hepatocellular carcinoma (HCC) patients with the tyrosine kinase inhibitor sorafenib. Of 124 candidate proteins, LMM approaches provided 6 results varying in significance, while LR-SAM, by contrast, yielded 18 significant results that were quite reproducibly consistent. Conclusion As exemplified by an application to HCC data set, LR-SAM more effectively identified proteins associated with clinical responses of treatment than LMM did.This research was supported by a grant of the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare, Republic of Korea (grant number: HI16C2037, HI15C2165). Publication of this article was sponsored by HI16C2037 grant

Welfare Genome Project: A Participatory Korean Personal Genome Project With Free Health Check-Up and Genetic Report Followed by Counseling.

The Welfare Genome Project (WGP) provided 1,000 healthy Korean volunteers with detailed genetic and health reports to test the social perception of integrating personal genetic and healthcare data at a large-scale. WGP was launched in 2016 in the Ulsan Metropolitan City as the first large-scale genome project with public participation in Korea. The project produced a set of genetic materials, genotype information, clinical data, and lifestyle survey answers from participants aged 20-96. As compensation, the participants received a free general health check-up on 110 clinical traits, accompanied by a genetic report of their genotypes followed by genetic counseling. In a follow-up survey, 91.0% of the participants indicated that their genetic reports motivated them to improve their health. Overall, WGP expanded not only the general awareness of genomics, DNA sequencing technologies, bioinformatics, and bioethics regulations among all the parties involved, but also the general public's understanding of how genome projects can indirectly benefit their health and lifestyle management. WGP established a data construction framework for not only scientific research but also the welfare of participants. In the future, the WGP framework can help lay the groundwork for a new personalized healthcare system that is seamlessly integrated with existing public medical infrastructure