Bioactive dairy ingredients for food and non-food applications

Lactobacilli and bifidobacteria are most commonly encountered in the dairy industries, either existing naturally in milk or inoculated as starters in fermented dairy products. Recent research suggests that fermented dairy products are a cocktail of bioactive ingredients. The objective of our study was to evaluate the bioactivity of cell wall fractions of Lactobacillus and Bifidobacterium grown in reconstituted skimmed milk, and the possibility of intra- and extracellular extracts of these bacteria for applications in foods and beyond. Intracellular and extracellular extracts of Lactobacillus and Bifidobacterium showed inhibitory activities against food and dermal pathogens. All strains were able to produce inhibitors, such as organic acids, antimicrobial peptides, diacetyl, and hydrogen peroxide. Most strains showed higher production of extracellular than intracellular inhibitors (P<0.05). Meanwhile, all strains were able to produce hyaluronic acid, lipoteichoic acid, peptidoglycan, neutral sphingomyelinase and acid sphingomyelinase at concentrations applicable for cosmeceutical application. Findings from our study demonstrated that inhibitors and bioactives from lactobacilli and bifidobacteria have the potential to be developed into formulations for food and non-food applications

HUBUNGAN ANTARA MUTU JASA PELAYANAN KESEHATAN DENGAN KEPUASAN PASIEN DI PUSKESMAS BENGKOL

Mutu Pelayanan Kesehatan yaitu keseluruhan pelayanan kesehatan  sesuai standar yang diberikan oleh instansi kesehatan kepada masyrakat atau pasien dengan tetap mengevaluasi pelayanan kesehatan yang diberikan agar dapat memenuhi setiap kebutuhan dan keinginan pasien. Kepuasan pasien adalah perasaan yang dirasakan seseorang setelah melakukan penilaian pelayanan kesehatan yang diterimanya dari petugas kesehatan sesuai dengan harapan atau tidak sesuai dengan harapan. Penelitian ini menggunakan jenis penelitian survei analitik dengan pendekatan cross sectional study. Tempat penelitian dilakukan di Puskesmas Bengkol, Kecamatan Mapanget. Waktu penelitian dilaksanakan pada bulan Juni – Oktober 2019. Jumlah sampel sebanyak 100 responden. Teknik pengambilan sampel menggunakan Rumus Slovin. Pengumpulan data diperoleh lewat wawancara menggunakan kuesioner  dan profil Puskesmas Bengkol. Analisis data dalam penelitian ini adalah analisis univariate dan analisis bivariate menggunakan uji statistic Fisher Exact Test pada tingkat kemaknaan 95% (α = 0,05).  Hasil Penelitian menunjukkan H1 diterima artinya ada hubungan antara mutu jasa pelayanan kesehatan dengan kepuasan pasien di Puskesmas Bengkol dengan nilai p= 0,000 (p value <0,05), dimana nilai mutu jasa pelayanan kesehatan yang baik sebesar  91%  dan nilai kepuasan pasien yang menjawab puas sebanyak 89%. Saran bagi Puskesmas Bengkol untuk tetap menjaga mutu pelayanan yang ada dan memperhatikan keluhan pengunjung tentang lahan parkir dan pengarah suara di ruang pendaftaran. Kata Kunci :  Mutu Jasa Pelayanan Kesehatan , Kepuasan pasien ABSTRACTThe quality of healthcare service is the overall health service in accordance with the standards provided by the health institution to the community or patients by still evaluating the health services provided in order to meet every need and Patient Wishes. Patient satisfaction is the feeling of a person after conducting the assessment of the health care services he received from the health care officer in accordance with expectations or not in accordance with expectations. The study used a type of analytical survey research with a cross sectional study approach. The sererach place is done in the village Puskesmas in Mapanget Bengkol. Time of research conducted in June – October 2019. Sample count of 100 respondents. Sampling techniques using formula Slovin. Collection of data obtained through interviews using questionnainers and profiles of Bengkol. The analysis of data in this research is the univariate analysis and bivariate analysis using the Fisher Exact Test statistic at a rate of 95% (a= 0,05). Results show H1 accepted means there is a relationship between the quality of healthcare services with the satisfaction of patients in a Bengkol Puskesmas with a value of p= 0.000  (p value of <0.05), where the quality value of good health services is 91% and the satisfaction value of patients who answered satisfied as much as 89%. Advice for the health center of Bengkol to maintain the quality of existing service and pay attention to visitor complaints about parking area and voice director in the registration room. Keywords : Quality of healthcare Services, Patient satisfaction

A hybrid, auto-adaptive, and rule-based multi-agent approach using evolutionary algorithms for improved searching

Selecting the most appropriate heuristic for solving a specific problem is not easy, for many reasons. This article focuses on one of these reasons: traditionally, the solution search process has operated in a given manner regardless of the specific problem being solved, and the process has been the same regardless of the size, complexity and domain of the problem. To cope with this situation, search processes should mould the search into areas of the search space that are meaningful for the problem. This article builds on previous work in the development of a multi-agent paradigm using techniques derived from knowledge discovery (data-mining techniques) on databases of so-far visited solutions. The aim is to improve the search mechanisms, increase computational efficiency and use rules to enrich the formulation of optimization problems, while reducing the search space and catering to realistic problems.Izquierdo Sebastián, J.; Montalvo Arango, I.; Campbell, E.; Pérez García, R. 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Multi-objective optimization of water supply network rehabilitation with non-dominated sorting Genetic Algorithm-II. Journal of Zhejiang University-SCIENCE A, 9(3), 391-400. doi:10.1631/jzus.a071448Johns, M. B., Keedwell, E., & Savic, D. (2014). Adaptive locally constrained genetic algorithm for least-cost water distribution network design. Journal of Hydroinformatics, 16(2), 288-301. doi:10.2166/hydro.2013.218Jourdan, L., Corne, D., Savic, D., & Walters, G. (2005). Preliminary Investigation of the ‘Learnable Evolution Model’ for Faster/Better Multiobjective Water Systems Design. Evolutionary Multi-Criterion Optimization, 841-855. doi:10.1007/978-3-540-31880-4_58Kamwa, I., Samantaray, S. R., & Joos, G. (2009). Development of Rule-Based Classifiers for Rapid Stability Assessment of Wide-Area Post-Disturbance Records. IEEE Transactions on Power Systems, 24(1), 258-270. doi:10.1109/tpwrs.2008.2009430Kang, D., & Lansey, K. (2012). Revisiting Optimal Water-Distribution System Design: Issues and a Heuristic Hierarchical Approach. Journal of Water Resources Planning and Management, 138(3), 208-217. doi:10.1061/(asce)wr.1943-5452.0000165Keedwell, E., & Khu, S.-T. (2005). A hybrid genetic algorithm for the design of water distribution networks. Engineering Applications of Artificial Intelligence, 18(4), 461-472. doi:10.1016/j.engappai.2004.10.001Kehl, V., & Ulm, K. (2006). Responder identification in clinical trials with censored data. Computational Statistics & Data Analysis, 50(5), 1338-1355. doi:10.1016/j.csda.2004.11.015Liu, X., Minin, V., Huang, Y., Seligson, D. B., & Horvath, S. (2004). Statistical Methods for Analyzing Tissue Microarray Data. Journal of Biopharmaceutical Statistics, 14(3), 671-685. doi:10.1081/bip-200025657Marchi, A., Dandy, G., Wilkins, A., & Rohrlach, H. (2014). Methodology for Comparing Evolutionary Algorithms for Optimization of Water Distribution Systems. Journal of Water Resources Planning and Management, 140(1), 22-31. doi:10.1061/(asce)wr.1943-5452.0000321Martínez-Rodríguez, J. B., Montalvo, I., Izquierdo, J., & Pérez-García, R. (2011). Reliability and Tolerance Comparison in Water Supply Networks. Water Resources Management, 25(5), 1437-1448. doi:10.1007/s11269-010-9753-2McClymont, K., Keedwell, E., Savić, D., & Randall-Smith, M. (2013). A general multi-objective hyper-heuristic for water distribution network design with discolouration risk. Journal of Hydroinformatics, 15(3), 700-716. doi:10.2166/hydro.2012.022McClymont, K., Keedwell, E. C., Savić, D., & Randall-Smith, M. (2014). Automated construction of evolutionary algorithm operators for the bi-objective water distribution network design problem using a genetic programming based hyper-heuristic approach. Journal of Hydroinformatics, 16(2), 302-318. doi:10.2166/hydro.2013.226Michalski, R. S. (2000). Machine Learning, 38(1/2), 9-40. doi:10.1023/a:1007677805582Montalvo, I., Izquierdo, J., Pérez-García, R., & Herrera, M. (2014). Water Distribution System Computer-Aided Design by Agent Swarm Optimization. Computer-Aided Civil and Infrastructure Engineering, 29(6), 433-448. doi:10.1111/mice.12062Montalvo, I., Izquierdo, J., Schwarze, S., & Pérez-García, R. (2010). Multi-objective particle swarm optimization applied to water distribution systems design: An approach with human interaction. Mathematical and Computer Modelling, 52(7-8), 1219-1227. doi:10.1016/j.mcm.2010.02.017Nguyen, V. V., Hartmann, D., & König, M. (2012). A distributed agent-based approach for simulation-based optimization. Advanced Engineering Informatics, 26(4), 814-832. doi:10.1016/j.aei.2012.06.001Nicklow, J., Reed, P., Savic, D., Dessalegne, T., Harrell, L., … Chan-Hilton, A. (2010). State of the Art for Genetic Algorithms and Beyond in Water Resources Planning and Management. Journal of Water Resources Planning and Management, 136(4), 412-432. doi:10.1061/(asce)wr.1943-5452.0000053Onwubolu, G. C., & Babu, B. V. (2004). New Optimization Techniques in Engineering. Studies in Fuzziness and Soft Computing. doi:10.1007/978-3-540-39930-8Pelikan, M., Goldberg, D. E., & Lobo, F. G. (2002). Computational Optimization and Applications, 21(1), 5-20. doi:10.1023/a:1013500812258Reed, P. M., Hadka, D., Herman, J. D., Kasprzyk, J. R., & Kollat, J. B. (2013). Evolutionary multiobjective optimization in water resources: The past, present, and future. Advances in Water Resources, 51, 438-456. doi:10.1016/j.advwatres.2012.01.005Shang, W., Zhao, S., & Shen, Y. (2009). A flexible tolerance genetic algorithm for optimal problems with nonlinear equality constraints. Advanced Engineering Informatics, 23(3), 253-264. doi:10.1016/j.aei.2008.09.001Vrugt, J. A., & Robinson, B. A. (2007). Improved evolutionary optimization from genetically adaptive multimethod search. 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R., & Zecchin, A. C. (2014). Coupled Binary Linear Programming–Differential Evolution Algorithm Approach for Water Distribution System Optimization. Journal of Water Resources Planning and Management, 140(5), 585-597. doi:10.1061/(asce)wr.1943-5452.000036

Predicting Construction Litigation Outcome Using Particle Swarm Optimization

Construction claims are normally affected by a large number of complex and interrelated factors. It is highly desirable for the parties to a dispute to know with some certainty how the case would be resolved if it were taken to court. The use of artificial neural networks can be a cost-effective technique to help to predict the outcome of construction claims, on the basis of characteristics of cases and the corresponding past court decisions. In this paper, a particle swarm optimization model is adopted to train perceptrons. The approach is demonstrated to be feasible and effective by predicting the outcome of construction claims in Hong Kong in the last 10 years. The results show faster and more accurate results than its counterparts of a benching back-propagation neural network and that the PSO-based network are able to give a successful prediction rate of up to 80%. With this, the parties would be more prudent in pursuing litigation and hence the number of disputes could be reduced significantly.Department of Civil and Environmental EngineeringAuthor name used in this publication: Kwokwing ChauSeries: Lecture notes in computer scienc

Influenza A virus causes maternal and fetal pathology via innate and adaptive vascular inflammation in mice

Influenza A virus (IAV) infection during pregnancy causes severe maternal and perinatal complications, despite a lack of vertical transmission of IAV across the placenta. Here, we demonstrate a significant alteration in the maternal vascular landscape that underpins the maternal and downstream fetal pathology to IAV infection in mice. In IAV infection of nonpregnant mice, the local lung inflammatory response was contained to the lungs and was self-resolving, whereas in pregnant mice, virus dissemination to major maternal blood vessels, including the aorta, resulted in a peripheral "vascular storm," with elevated proinflammatory and antiviral mediators and the influx of Ly6Clow and Ly6Chigh monocytes, plus neutrophils and T cells. This vascular storm was associated with elevated levels of the adhesion molecules ICAM and VCAM and the pattern-recognition receptors TLR7 and TLR9 in the vascular wall, resulting in profound vascular dysfunction. The sequalae of this IAV-driven vascular storm included placental growth retardation and intrauterine growth restriction, evidence of placental and fetal brain hypoxia, and increased circulating cell free fetal DNA and soluble Flt1. In contrast, IAV infection in nonpregnant mice caused no obvious alterations in endothelial function or vascular inflammation. Therefore, IAV infection during pregnancy drives a significant systemic vascular alteration in pregnant dams, which likely suppresses critical blood flow to the placenta and fetus. This study in mice provides a fundamental mechanistic insight and a paradigm into how an immune response to a respiratory virus, such as IAV, is likely to specifically drive maternal and fetal pathologies during pregnancy.Stella Liong, Osezua Oseghale, Eunice E. To, Kurt Brassington, Jonathan R. Erlich, Raymond Luong ... et al

A novel multiplex assay combining autoantibodies plus PSA has potential implications for classification of prostate cancer from non-malignant cases

<p>Abstract</p> <p>Background</p> <p>The lack of sufficient specificity and sensitivity among conventional cancer biomarkers, such as prostate specific antigen (PSA) for prostate cancer has been widely recognized after several decades of clinical implications. Autoantibodies (autoAb) among others are being extensively investigated as potential substitute markers, but remain elusive. One major obstacle is the lack of a sensitive and multiplex approach for quantifying autoAb against a large panel of clinically relevant tumor-associated antigens (TAA).</p> <p>Methods</p> <p>To circumvent preparation of phage lysates and purification of recombinant proteins, we identified B cell epitopes from a number of previously defined prostate cancer-associated antigens (PCAA). Peptide epitopes from cancer/testis antigen NY-ESO-1, XAGE-1b, SSX-2,4, as well as prostate cancer overexpressed antigen AMACR, p90 autoantigen, and LEDGF were then conjugated with seroMAP microspheres to allow multiplex measurement of autoAb present in serum samples. Moreover, simultaneous quantification of autoAb plus total PSA was achieved in one reaction, and termed the "A+PSA" assay.</p> <p>Results</p> <p>Peptide epitopes from the above 6 PCAA were identified and confirmed that autoAb against these peptide epitopes reacted specifically with the full-length protein. A pilot study was conducted with the A+PSA assay using pre-surgery sera from 131 biopsy-confirmed prostate cancer patients and 121 benign prostatic hyperplasia and/or prostatitis patients. A logistic regression-based A+PSA index was found to enhance sensitivities and specificities over PSA alone in distinguishing prostate cancer from nonmalignant cases. The A+PSA index also reduced false positive rate and improved the area under a receiver operating characteristic curve.</p> <p>Conclusions</p> <p>The A+PSA assay represents a novel platform that integrates autoAb signatures with a conventional cancer biomarker, which may aid in the diagnosis and prognosis of prostate cancer and others.</p

Blood-Based Biomarkers of Aggressive Prostate Cancer

Purpose: Prostate cancer is a bimodal disease with aggressive and indolent forms. Current prostate-specific-antigen testing and digital rectal examination screening provide ambiguous results leading to both under-and over-treatment. Accurate, consistent diagnosis is crucial to risk-stratify patients and facilitate clinical decision making as to treatment versus active surveillance. Diagnosis is currently achieved by needle biopsy, a painful procedure. Thus, there is a clinical need for a minimally-invasive test to determine prostate cancer aggressiveness. A blood sample to predict Gleason score, which is known to reflect aggressiveness of the cancer, could serve as such a test. Materials and Methods: Blood mRNA was isolated from North American and Malaysian prostate cancer patients/controls. Microarray analysis was conducted utilizing the Affymetrix U133 plus 2·0 platform. Expression profiles from 255 patients/controls generated 85 candidate biomarkers. Following quantitative real-time PCR (qRT-PCR) analysis, ten disease-associated biomarkers remained for paired statistical analysis and normalization. Results: Microarray analysis was conducted to identify 85 genes differentially expressed between aggressive prostate cancer (Gleason score ≥8) and controls. Expression of these genes was qRT-PCR verified. Statistical analysis yielded a final seven-gene panel evaluated as six gene-ratio duplexes. This molecular signature predicted as aggressive (ie, Gleason score ≥8) 55% of G6 samples, 49% of G7(3+4), 79% of G7(4+3) and 83% of G8-10, while rejecting 98% of controls. Conclusion: In this study, we have developed a novel, blood-based biomarker panel which can be used as the basis of a simple blood test to identify men with aggressive prostate cancer and thereby reduce the overdiagnosis and overtreatment that currently results from diagnosis using PSA alone. We discuss possible clinical uses of the panel to identify men more likely to benefit from biopsy and immediate therapy versus those more suited to an “active surveillance” strategy