Dekolorisasi Fotokatalitik Air Limbah Tekstil Mengandung Zat Warna Azo Acid Red 4 Menggunakan Mikropartikel Tio2 Dan Zno

Dalam penelitian ini dilakukan evaluasi terhadap proses dekolorisasi fotokatalitik air limbah tekstil artifisialmengandung zat warna azo Acid Red 4 (AR4) dengan menggunakan katalis mikropartikel TiO2 dan ZnO. Tujuanpenelitian ini adalah untuk menentukan proses fotokatalitik optimum dengan menganalisis pengaruh variabel antaralain: pH, konsentrasi awal zat warna, dosis katalis, kombinasi katalis, dan temperatur awal air limbah. Evaluasi terhadapefisiensi dan laju dekolorisasi dilakukan melalui pengukuran absorbansi menggunakan spektrofotometer.Proses dekolorisasi fotokatalitik AR4 ditemukan berlangsung efektif pada kondisi optimum: pH 11, konsentrasiawal zat warna 10 mg/L dan dosis katalis 0,5 g/L baik untuk mikropartikel TiO2 maupun ZnO. Setelah waktu irradiasiselama 2 jam, proses dengan mikropartikel ZnO mampu mencapai efisiensi dekolorisasi lebih baik (89,9%)dibanding mikropartikel TiO2 (86,9%). Berdasarkan kinetika reaksi pseudo orde pertama, dekolorisasi fotokatalitikmenggunakan mikropartikel ZnO memperlihatkan laju lebih cepat (k'= 0,022 menit-1) dibandingkan denganmikropartikel TiO2 (k'= 0,018 menit-1). Kombinasi kedua jenis katalis menyebabkan laju dekolorisasi menjadi lebihlambat (k'= 0,015 menit-1) dibandingkan penggunaan katalis secara individual. Temperatur awal air limbah yanglebih tinggi ditemukan menyebabkan penurunan efisiensi dekolorisasi fotokatalitik

Immobilisasi Mikropartikel Tio2 Dan Pengaruh Anion Garam Pada Dekolorisasi Fotokatalitik Zat Warna Azo Reactive Black 5

Dalam studi ini dilakukan penelitian mengenai pengaruh immobilisasi katalis mikropartikel TiO2 dan anion garam terhadap dekolorisasi fotokatalitik air limbah tekstil mengandung zat warna azo Reactive Black 5 (RB5).Immobilisasi katalis mikropartikel TiO2dibuat di atas media pelat akrilat dan pengamatan terhadap aktivitas dekolorisasi fotokatalitiknya dilakukan pada fotoreaktor skala laboratorium. Pengaruh anion garam dievaluasi dengan mengamati efek inhibisi anion garam terhadap proses dekolorisasi fotokatalitik. Dekolorisasi fotokatalitik RB5 dengan katalis tersuspensi ditemukan optimal pada kondisi basa (pH 11), konsentrasi warna rendah (10 mg/L) dan konsentrasi katalis TiO2 1,0 g/L. Katalis mikropartikel TiO2 terimmobilisasi menunjukkan performa dekolorisasi fotokatalitik lebih rendah dibandingkan katalis tersuspensi pada konsentrasi TiO2 tersuspensi optimum, namun pada konsentrasi katalis lebih tinggi menunjukkan performa yang lebih baik. Anion garam ditemukan dapat memberikan efek inhibisi terhadap performa dekolorisasi fotokatalitik dengan indikasi penurunan konstanta laju dekolorisasi (k') seiring peningkatan konsentrasi garam baik pada katalis tersuspensi maupun terimmobilisasi

Critical Networks Exhibit Maximal Information Diversity in Structure-Dynamics Relationships

Network structure strongly constrains the range of dynamic behaviors available to a complex system. These system dynamics can be classified based on their response to perturbations over time into two distinct regimes, ordered or chaotic, separated by a critical phase transition. Numerous studies have shown that the most complex dynamics arise near the critical regime. Here we use an information theoretic approach to study structure-dynamics relationships within a unified framework and how that these relationships are most diverse in the critical regime

Evaluation of methods for detection of fluorescence labeled subcellular objects in microscope images

<p>Abstract</p> <p>Background</p> <p>Several algorithms have been proposed for detecting fluorescently labeled subcellular objects in microscope images. Many of these algorithms have been designed for specific tasks and validated with limited image data. But despite the potential of using extensive comparisons between algorithms to provide useful information to guide method selection and thus more accurate results, relatively few studies have been performed.</p> <p>Results</p> <p>To better understand algorithm performance under different conditions, we have carried out a comparative study including eleven spot detection or segmentation algorithms from various application fields. We used microscope images from well plate experiments with a human osteosarcoma cell line and frames from image stacks of yeast cells in different focal planes. These experimentally derived images permit a comparison of method performance in realistic situations where the number of objects varies within image set. We also used simulated microscope images in order to compare the methods and validate them against a ground truth reference result. Our study finds major differences in the performance of different algorithms, in terms of both object counts and segmentation accuracies.</p> <p>Conclusions</p> <p>These results suggest that the selection of detection algorithms for image based screens should be done carefully and take into account different conditions, such as the possibility of acquiring empty images or images with very few spots. Our inclusion of methods that have not been used before in this context broadens the set of available detection methods and compares them against the current state-of-the-art methods for subcellular particle detection.</p

Robust regression for periodicity detection in non-uniformly sampled time-course gene expression data

<p>Abstract</p> <p>Background</p> <p>In practice many biological time series measurements, including gene microarrays, are conducted at time points that seem to be interesting in the biologist's opinion and not necessarily at fixed time intervals. In many circumstances we are interested in finding targets that are expressed periodically. To tackle the problems of uneven sampling and unknown type of noise in periodicity detection, we propose to use robust regression.</p> <p>Methods</p> <p>The aim of this paper is to develop a general framework for robust periodicity detection and review and rank different approaches by means of simulations. We also show the results for some real measurement data.</p> <p>Results</p> <p>The simulation results clearly show that when the sampling of time series gets more and more uneven, the methods that assume even sampling become unusable. We find that M-estimation provides a good compromise between robustness and computational efficiency.</p> <p>Conclusion</p> <p>Since uneven sampling occurs often in biological measurements, the robust methods developed in this paper are expected to have many uses. The regression based formulation of the periodicity detection problem easily adapts to non-uniform sampling. Using robust regression helps to reject inconsistently behaving data points.</p> <p>Availability</p> <p>The implementations are currently available for Matlab and will be made available for the users of R as well. More information can be found in the web-supplement <abbrgrp><abbr bid="B1">1</abbr></abbrgrp>.</p

RAGE and Modulation of Ischemic Injury in the Diabetic Myocardium

OBJECTIVE—Subjects with diabetes experience an increased risk of myocardial infarction and cardiac failure compared with nondiabetic age-matched individuals. The receptor for advanced glycation end products (RAGE) is upregulated in diabetic tissues. In this study, we tested the hypothesis that RAGE affected ischemia/reperfusion (I/R) injury in the diabetic myocardium. In diabetic rat hearts, expression of RAGE and its ligands was enhanced and localized particularly to both endothelial cells and mononuclear phagocytes

Reconstruction and Validation of RefRec: A Global Model for the Yeast Molecular Interaction Network

Molecular interaction networks establish all cell biological processes. The networks are under intensive research that is facilitated by new high-throughput measurement techniques for the detection, quantification, and characterization of molecules and their physical interactions. For the common model organism yeast Saccharomyces cerevisiae, public databases store a significant part of the accumulated information and, on the way to better understanding of the cellular processes, there is a need to integrate this information into a consistent reconstruction of the molecular interaction network. This work presents and validates RefRec, the most comprehensive molecular interaction network reconstruction currently available for yeast. The reconstruction integrates protein synthesis pathways, a metabolic network, and a protein-protein interaction network from major biological databases. The core of the reconstruction is based on a reference object approach in which genes, transcripts, and proteins are identified using their primary sequences. This enables their unambiguous identification and non-redundant integration. The obtained total number of different molecular species and their connecting interactions is ∼67,000. In order to demonstrate the capacity of RefRec for functional predictions, it was used for simulating the gene knockout damage propagation in the molecular interaction network in ∼590,000 experimentally validated mutant strains. Based on the simulation results, a statistical classifier was subsequently able to correctly predict the viability of most of the strains. The results also showed that the usage of different types of molecular species in the reconstruction is important for accurate phenotype prediction. In general, the findings demonstrate the benefits of global reconstructions of molecular interaction networks. With all the molecular species and their physical interactions explicitly modeled, our reconstruction is able to serve as a valuable resource in additional analyses involving objects from multiple molecular -omes. For that purpose, RefRec is freely available in the Systems Biology Markup Language format

Effects of Transcriptional Pausing on Gene Expression Dynamics

Stochasticity in gene expression affects many cellular processes and is a source of phenotypic diversity between genetically identical individuals. Events in elongation, particularly RNA polymerase pausing, are a source of this noise. Since the rate and duration of pausing are sequence-dependent, this regulatory mechanism of transcriptional dynamics is evolvable. The dependency of pause propensity on regulatory molecules makes pausing a response mechanism to external stress. Using a delayed stochastic model of bacterial transcription at the single nucleotide level that includes the promoter open complex formation, pausing, arrest, misincorporation and editing, pyrophosphorolysis, and premature termination, we investigate how RNA polymerase pausing affects a gene's transcriptional dynamics and gene networks. We show that pauses' duration and rate of occurrence affect the bursting in RNA production, transcriptional and translational noise, and the transient to reach mean RNA and protein levels. In a genetic repressilator, increasing the pausing rate and the duration of pausing events increases the period length but does not affect the robustness of the periodicity. We conclude that RNA polymerase pausing might be an important evolvable feature of genetic networks