ENHANCING BUSINESS PERFORMANCE OF CV. JATI MAKMUR PASURUAN USING E-BROCHURE MARKETING STRATEGY

This report provides an analysis and evaluation of CV Jati Makmur in entering the new market. CV. Jati Makmur is a plywood industry operating more than 15 years in producing woodworking, aiming to grasp the international market for expansion. There are various analysis methods included in the observation. These methods of analysis include Segmenting, Targeting, and Positioning. To conquer the market share in China and Japan as well to increase economic competitiveness several recommended strategies in incorporated. This strategy involves pricing strategy distribution and promotion strategy. In addition, this report also observes the fact that the analysis contains some limitations as well as risks. These risks include external factors such as Government, Competition, and Technology that will affect the future development of CV. Jati Makmur. This report finds the prospects for CV. Jati Makmur in its current position is positive. The major area of weakness requires further investigation and remedial action by management

Formation of one-part-mixing geopolymers and geopolymer ceramics from geopolymer powder

publisher: Elsevier articletitle: Formation of one-part-mixing geopolymers and geopolymer ceramics from geopolymer powder journaltitle: Construction and Building Materials articlelink: http://dx.doi.org/10.1016/j.conbuildmat.2017.08.110 content_type: article copyright: © 2017 Elsevier Ltd. All rights reserved

Small but crucial : the novel small heat shock protein Hsp21 mediates stress adaptation and virulence in Candida albicans

Peer reviewedPublisher PD

Broad-line region in NGC 4151 monitored by two decades of reverberation mapping campaigns. I. Evolution of structure and kinematics

We report the results of long-term reverberation mapping (RM) campaigns of the nearby active galactic nuclei (AGN) NGC 4151, spanning from 1994 to 2022, based on archived observations of the FAST Spectrograph Publicly Archived Programs and our new observations with the 2.3m telescope at the Wyoming Infrared Observatory. We reduce and calibrate all the spectra in a consistent way, and derive light curves of the broad H$\beta$ line and 5100\,{\AA} continuum. Continuum light curves are also constructed using public archival photometric data to increase sampling cadences. We subtract the host galaxy contamination using {\it HST} imaging to correct fluxes of the calibrated light curves. Utilizing the long-term archival photometric data, we complete the absolute flux-calibration of the AGN continuum. We find that the H$\beta$ time delays are correlated with the 5100\,{\AA} luminosities as $\tau_{\rm H\beta}\propto L_{5100}^{0.46\pm0.16}$. This is remarkably consistent with Bentz et al. (2013)'s global size-luminosity relationship of AGNs. Moreover, the data sets for five of the seasons allow us to obtain the velocity-resolved delays of the H$\beta$ line, showing diverse structures (outflows, inflows and disks). Combining our results with previous independent measurements, we find the measured dynamics of the H$\beta$ broad-line region (BLR) are possibly related to the long-term trend of the luminosity. There is also a possible additional $\sim$1.86 years time lag between the variation in BLR radius and luminosity. These results suggest that dynamical changes in the BLR may be driven by the effects of radiation pressure.Comment: Accepted for publication in MNRAS; comments welcome

A Human Depression Circuit Derived From Focal Brain Lesions

Background: Focal brain lesions can lend insight into the causal neuroanatomical substrate of depression in the human brain. However, studies of lesion location have led to inconsistent results.Methods: Five independent datasets with different lesion etiologies and measures of postlesion depression were collated (N = 461). Each 3-dimensional lesion location was mapped to a common brain atlas. We used voxel lesion symptom mapping to test for associations between depression and lesion locations. Next, we computed the network of regions functionally connected to each lesion location using a large normative connectome dataset (N = 1000). We used these lesion network maps to test for associations between depression and connected brain circuits. Reproducibility was assessed using a rigorous leave-one-dataset-out validation. Finally, we tested whether lesion locations associated with depression fell within the same circuit as brain stimulation sites that were effective for improving poststroke depression.Results: Lesion locations associated with depression were highly heterogeneous, and no single brain region was consistently implicated. However, these same lesion locations mapped to a connected brain circuit, centered on the left dorsolateral prefrontal cortex. Results were robust to leave-one-dataset-out cross-validation. Finally, our depression circuit derived from brain lesions aligned with brain stimulation sites that were effective for improving poststroke depression.Conclusions: Lesion locations associated with depression fail to map to a specific brain region but do map to a specific brain circuit. This circuit may have prognostic utility in identifying patients at risk for poststroke depression and therapeutic utility in refining brain stimulation targets.</p

LDLR Expression and Localization Are Altered in Mouse and Human Cell Culture Models of Alzheimer's Disease

Alzheimer's disease (AD) is a chronic neurodegenerative disorder and the most common form of dementia. The major molecular risk factor for late-onset AD is expression of the ε-4 allele of apolipoprotein E (apoE), the major cholesterol transporter in the brain. The low-density lipoprotein receptor (LDLR) has the highest affinity for apoE and plays an important role in brain cholesterol metabolism.Using RT-PCR and western blotting techniques we found that over-expression of APP caused increases in both LDLR mRNA and protein levels in APP transfected H4 neuroglioma cells compared to H4 controls. Furthermore, immunohistochemical experiments showed aberrant localization of LDLR in H4-APP neuroglioma cells, Aβ-treated primary neurons, and in the PSAPP transgenic mouse model of AD. Finally, immunofluorescent staining of LDLR and of γ- and α-tubulin showed a change in LDLR localization preferentially away from the plasma membrane that was paralleled by and likely the result of a disruption of the microtubule-organizing center and associated microtubule network.These data suggest that increased APP expression and Aβ exposure alters microtubule function, leading to reduced transport of LDLR to the plasma membrane. Consequent deleterious effects on apoE uptake and function will have implications for AD pathogenesis and/or progression

Pathogenic copy number variants and SCN1A mutations in patients with intellectual disability and childhood-onset epilepsy

Background Copy number variants (CNVs) have been linked to neurodevelopmental disorders such as intellectual disability (ID), autism, epilepsy and psychiatric disease. There are few studies of CNVs in patients with both ID and epilepsy. Methods We evaluated the range of rare CNVs found in 80 Welsh patients with ID or developmental delay (DD), and childhood-onset epilepsy. We performed molecular cytogenetic testing by single nucleotide polymorphism array or microarray-based comparative genome hybridisation. Results 8.8 % (7/80) of the patients had at least one rare CNVs that was considered to be pathogenic or likely pathogenic. The CNVs involved known disease genes (EHMT1, MBD5 and SCN1A) and imbalances in genomic regions associated with neurodevelopmental disorders (16p11.2, 16p13.11 and 2q13). Prompted by the observation of two deletions disrupting SCN1A we undertook further testing of this gene in selected patients. This led to the identification of four pathogenic SCN1A mutations in our cohort. Conclusions We identified five rare de novo deletions and confirmed the clinical utility of array analysis in patients with ID/DD and childhood-onset epilepsy. This report adds to our clinical understanding of these rare genomic disorders and highlights SCN1A mutations as a cause of ID and epilepsy, which can easily be overlooked in adults

Using machine learning to identify important predictors of COVID-19 infection prevention behaviors during the early phase of the pandemic

Before vaccines for coronavirus disease 2019 (COVID-19) became available, a set of infection-prevention behaviors constituted the primary means to mitigate the virus spread. Our study aimed to identify important predictors of this set of behaviors. Whereas social and health psychological theories suggest a limited set of predictors, machine-learning analyses can identify correlates from a larger pool of candidate predictors. We used random forests to rank 115 candidate correlates of infection-prevention behavior in 56,072 participants across 28 countries, administered in March to May 2020. The machine-learning model predicted 52% of the variance in infection-prevention behavior in a separate test sample—exceeding the performance of psychological models of health behavior. Results indicated the two most important predictors related to individual-level injunctive norms. Illustrating how data-driven methods can complement theory, some of the most important predictors were not derived from theories of health behavior—and some theoretically derived predictors were relatively unimportant