A dynamic simulation framework for biopharmaceutical capacity management

In biopharmaceutical manufacturing there have been significant increases in drug complexity, risk of clinical failure, regulatory pressures and demand. Compounded with the rise in competition and pressures of maintaining high profit margins this means that manufacturers have to produce more efficient and lower capital intensive processes. More are opting to use simulation tools to perform such revisions and to experiment with various process alternatives, activities which would be time consuming and expensive to carry out within the real system. A review of existing models created for different biopharmaceutical activities using the Extend® (ImagineThat!, CA) platform led to the development of a standard framework to guide the design and construct of a more efficient model. The premise of the framework was that any ‘good’ model should meet five requirement specifications: 1) Intuitive to the user, 2) Short Run-Time, 3) Short Development Time, 4) Relevant and has Ease of Data Input/Output, and 5) Maximised Reusability and Sustainability. Three different case studies were used to test the framework, two biotechnology manufacturing and one fill/finish, with each adding a new layer of understanding and depth to the standard due to the challenges faced. These Included procedures and constraints related to complex resource allocation, multi-product scheduling and complex ‘lookahead’ logic for scheduling activities such as buffer makeup and difficulties surrounding data availability. Subsequently, in order to review the relevance of the models, various analyses were carried out including schedule optimisation, debottlenecking and Monte Carlo simulations, using various data representation tools to deterministically and stochastically answer the different questions within each case study scope. The work in this thesis demonstrated the benefits of using the developed standard as an aid to building decision-making tools for biopharmaceutical manufacturing capacity management, so as to increase the quality and efficiency of decision making to produce less capital intensive processes

KLF9 and JNK3 Interact to Suppress Axon Regeneration in the Adult CNS

Neurons in the adult mammalian CNS decrease in intrinsic axon growth capacity during development in concert with changes in Krüppel-like transcription factors (KLFs). KLFs regulate axon growth in CNS neurons including retinal ganglion cells (RGCs). Here, we found that knock-down of KLF9, an axon growth suppressor that is normally upregulated 250-fold in RGC development, promotes long-distance optic nerve regeneration in adult rats of both sexes. We identified a novel binding partner, MAPK10/JNK3 kinase, and found that JNK3 (c-Jun N-terminal kinase 3) is critical for KLF9\u27s axon-growth-suppressive activity. Interfering with a JNK3-binding domain or mutating two newly discovered serine phosphorylation acceptor sites, Ser106 and Ser110, effectively abolished KLF9\u27s neurite growth suppression in vitro and promoted axon regeneration in vivo. These findings demonstrate a novel, physiologic role for the interaction of KLF9 and JNK3 in regenerative failure in the optic nerve and suggest new therapeutic strategies to promote axon regeneration in the adult CNS

Effect of temperature, relative humidity and aphid developmental stage on the efficacy of the mycoinsecticide Mycotal® against Myzus persicae

The green peach aphid, Myzus persicae, is a major pest worldwide. An examination of the impact of temperature, relative humidity and developmental stages of M. persicae on the efficacy of the whitefly mycoinsecticide Mycotal®, based on Lecanicillium muscarium and the effects of infection on aphid fecundity was evaluated under controlled conditions. Although this fungus can be grown at a broad range of temperatures (15-30oC), the optimum temperature for control of M. persicae ranged between 20 and 30oC. L. muscarium had high efficacy as a microbial control against M. persicae between 55 and 90% relative humidity. Total mortality of aphids treated with different spore dosages of L. muscarium varied according to the developmental stage: adults, fourth and third instar nymphs of proved more susceptible than first instar nymphs. Although the fungus did not affect the rate of nymph production, the reproductive period of aphids significantly decreased with increasing the spore dosage. Thus, total fecundity of treated aphids was 22.6 ± 1.1 and 31.6 ± 2.4 offspring per adult at the medium (644 ± viable spore/mm2) and low (330 ± 40 viable spore/mm2) dosages, compared with 45.7 ± 4.3 offspring per untreated aphid. The results suggest that L. muscarium has the potential as a biological control agent of M. persicae. However, fungal infection appears to have no sub-lethal effects on the fitness of the host’s progeny

The PERSIANN family of global satellite precipitation data: a review and evaluation of products

Over the past 2 decades, a wide range of studies have incorporated Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) products. Currently, PERSIANN offers several precipitation products based on different algorithms available at various spatial and temporal scales, namely PERSIANN, PERSIANN-CCS, and PERSIANN-CDR. The goal of this article is to first provide an overview of the available PERSIANN precipitation retrieval algorithms and their differences. Secondly, we offer an evaluation of the available operational products over the contiguous US (CONUS) at different spatial and temporal scales using Climate Prediction Center (CPC) unified gauge-based analysis as a benchmark. Due to limitations of the baseline dataset (CPC), daily scale is the finest temporal scale used for the evaluation over CONUS. Additionally, we provide a comparison of the available products at a quasi-global scale. Finally, we highlight the strengths and limitations of the PERSIANN products and briefly discuss expected future developments.</p

Assessment of three long-term gridded climate products for hydro-climatic simulations in tropical river basins

10.3390/w9030229Water (Switzerland)9322

Interpretable machine learning models for classifying low back pain status using functional physiological variables.

PURPOSE:To evaluate the predictive performance of statistical models which distinguishes different low back pain (LBP) sub-types and healthy controls, using as input predictors the time-varying signals of electromyographic and kinematic variables, collected during low-load lifting. METHODS:Motion capture with electromyography (EMG) assessment was performed on 49 participants [healthy control (con) = 16, remission LBP (rmLBP) = 16, current LBP (LBP) = 17], whilst performing a low-load lifting task, to extract a total of 40 predictors (kinematic and electromyographic variables). Three statistical models were developed using functional data boosting (FDboost), for binary classification of LBP statuses (model 1: con vs. LBP; model 2: con vs. rmLBP; model 3: rmLBP vs. LBP). After removing collinear predictors (i.e. a correlation of > 0.7 with other predictors) and inclusion of the covariate sex, 31 predictors were included for fitting model 1, 31 predictors for model 2, and 32 predictors for model 3. RESULTS:Seven EMG predictors were selected in model 1 (area under the receiver operator curve [AUC] of 90.4%), nine predictors in model 2 (AUC of 91.2%), and seven predictors in model 3 (AUC of 96.7%). The most influential predictor was the biceps femoris muscle (peak [Formula: see text]  = 0.047) in model 1, the deltoid muscle (peak [Formula: see text] =  0.052) in model 2, and the iliocostalis muscle (peak [Formula: see text] =  0.16) in model 3. CONCLUSION:The ability to transform time-varying physiological differences into clinical differences could be used in future prospective prognostic research to identify the dominant movement impairments that drive the increased risk. These slides can be retrieved under Electronic Supplementary Material

KIR gene content diversity in four Iranian populations

Killer cell immunoglobulin-like receptors (KIR) regulate natural killer cell response against infection and malignancy. KIR genes are variable in the number and type, thereby discriminating individuals and populations. Herein, we analyzed the KIR gene content diversity in four native populations of Iran. The KIR genomic diversity was comparable between Bakhtiari and Persian and displayed a balance of A and B KIR haplotypes, a trend reported in Caucasian and African populations. The KIR gene content profiles of Arab and Azeri were comparable and displayed a preponderance of B haplotypes, a scenario reported in the natives of America, India, and Australia. A majority of the B haplotype carriers of Azeri and Arab had a centromeric gene-cluster (KIR2DS2-2DL2-2DS3-2DL5). Remarkably, this cluster was totally absent from the American natives but occurred at highest frequencies in the natives of India and Australia in combination with another gene cluster at the telomeric region (KIR3DS1-2DL5-2DS5-2DS1). Therefore, despite having similar frequencies of B haplotypes, the occurrence of B haplotype-specific KIR genes, such as 2DL2, 2DL5, 3DS1, 2DS1, 2DS2, 2DS3, and 2DS5 in Azeri and Arab were substantially different from the natives of America, India, and Australia. In conclusion, each Iranian population exhibits distinct KIR gene content diversity, and the Indo-European KIR genetic signatures of the Iranians concur with geographic proximity, linguistic affinity, and human migrations

Slot Die Coated Triple Halide Perovskites for Efficient and Scalable Perovskite Silicon Tandem Solar Cells

Wide bandgap halide perovskite materials show promising potential to pair with silicon bottom cells. To date, most efficient wide bandgap perovskites layers are fabricated by spin coating, which is difficult to scale up. Here, we report on slot die coating for an efficient, 1.68 eV wide bandgap triple halide 3halide perovskite absorber, Cs0.22FA0.78 Pb I0.85Br0.15 3 5 mol MAPbCl3. A suitable solvent system is designed specifically for the slot die coating technique. We demonstrate that our fabrication route is suitable for tandem solar cells without phase segregation. The slot die coated wet halide perovskite is dried by a nitrogen N2 knife with high reproducibility and avoiding antisolvents. We explore varying annealing conditions and identify parameters allowing crystallization of the perovskite film into large grains reducing charge collection losses and enabling higher current density. At 150 C, an optimized trade off between crystallization and the PbI2 aggregates on the film s top surface is found. Thus, we improve the cell stability and performance of both single junction cells and tandems. Combining the 3halide top cells with a 120 amp; 956;m thin saw damage etched commercial Czochralski industrial wafer, a 2 terminal monolithic tandem solar cell with a PCE of 25.2 on a 1 cm2 active area is demonstrated with fully scalable processe