Attractive Area Research Articles

Intracellular Mechanism of Gastric Acid Secretion: What is the True Switch?

In 1985, I was accepted as postdoc by Professor Forte of UC Berkeley. He discovered H+,K+-ATPase and established the membrane recycling theory as the activation mechanism for acid secretion using whole animals. H+,K+-ATPase is an enzyme that exchanges H+ with K+. In resting state, it locates on the tubulovesicles and the pump does not work because the membrane lacks K+ permeability. Upon stimulation, the tubulovesicles fuse to the apical membrane and acquire K+ permeability, turning the pump on. The main route was known to be protein kinase A (PKA), but its specific targets remained unknown. Right after I joined Forte's lab, I was fortunate enough to reproduce the above mechanism in vitro, and I discovered proteins of molecular weight 120 kDa and 80 kDa that were specifically phosphorylated in stimulated parietal cells. After I returned to Japan, the former was cloned and named as parchorin, which is one of the chloride intracellular channels. Although no progress was made on ezrin, I found out the importance of PIP2 and Arf6 using permeabilized gland models. After I left parietal cell research, the link between ezrin and Arf6 was revealed. PKA phosphorylates S66 of ezrin and also MST4. The former changes the N-terminal structure of ezrin to bind syntaxin3, and the latter phosphorylates ACAP4, an Arf6-GAP, to accelerate binding to ezrin. Subsequently, H+,K+-ATPase, SNAREs, ezrin, and Arf6-GAP are aligned on the apical membrane. However, there are still many unsolved questions and the intracellular mechanism of parietal cells remains an attractive research area.

Multimodal Correlation-Aware Fusion Framework for Enhanced Machinery Health Prognosis With Unlabeled and Low-Quality Data Exploitation.

Accurate machinery health prognosis, also known as remaining useful life (RUL) prediction, is critical for preventing catastrophic accidents and implementing predictive maintenance strategies. This makes it a highly attractive research area. Many existing studies have been developed on unimodal data, yet such data can only provide a restricted perspective and incomplete health state monitoring. Some researchers seek to address this issue from a multimodal standpoint. While promising, these methods still have certain shortcomings: 1) the imbalance for unlabeled and low-quality data compared to well-labeled data is not considered, causing their potential underexploited; 2) information richness during fusion is insufficient, discarding many valuable original and subtle health state cues, and they fail to timely tackle unexpected online anomalies; and 3) correlations and complementary information across modalities are neglected. To address these challenges, a multimodal correlation-aware fusion framework is proposed for machinery health prognosis. The framework adopts a pretrain-finetune paradigm with two parts. The first part achieves effective exploitation of the unlabeled and low-quality multimodal data pieces. The second part, through degradation pattern recognition, enables the framework to bridge the gap between scarce multimodal labeled data and accurate RUL prediction. A real industrial multimodal dataset of milling cutters is applied to demonstrate the proposed framework. Results from a series of ablation experiments and comparisons with state-of-the-art prediction methods indicate the effectiveness of each key component within the framework and its overall superiority. The framework shows promise in adapting to more downstream industrial tasks, providing accurate and reliable insights from limited data resources.

Synthesis of high-entropy germanides and investigation of their formation process.

High-entropy alloys and compounds have emerged as an attractive research area in part because of their distinctive solid-solution structure and multi-element compositions that provide near-limitless tailorability. A diverse array of reports describing high-entropy compounds, including carbides, nitrides, sulfides, oxides, fluorides, silicides, and borides, has resulted. Strikingly, exploration of high-entropy germanides (HEGs) has remained relatively limited. In this study, we present a detailed investigation into the synthesis of HEGs, specifically AuAgCuPdPtGe and FeCoNiCrVGe, via a rapid thermal annealing. The structural, compositional, and morphological characteristics of the synthesized HEGs were assessed using laboratory X-ray diffraction (XRD), Fourier-transform infrared spectroscopy (FTIR), X-ray photoelectron spectroscopy (XPS), and transmission electron microscopy (TEM). Complementing these post-synthesis analyses, we interrogated the formation and growth mechanisms using in situ heating XRD and TEM and determined that HEG formation involved initial decomposition of germanane (GeNSs) during the annealing, followed by gradual grain growth via atom diffusion at temperatures below 600 °C, and finally a rapid grain growth process at elevated temperatures.

Semisupervised Subspace Learning With Adaptive Pairwise Graph Embedding.

Graph-based semisupervised learning can explore the graph topology information behind the samples, becoming one of the most attractive research areas in machine learning in recent years. Nevertheless, existing graph-based methods also suffer from two shortcomings. On the one hand, the existing methods generate graphs in the original high-dimensional space, which are easily disturbed by noisy and redundancy features, resulting in low-quality constructed graphs that cannot accurately portray the relationships between data. On the other hand, most of the existing models are based on the Gaussian assumption, which cannot capture the local submanifold structure information of the data, thus reducing the discriminativeness of the learned low-dimensional representations. This article proposes a semisupervised subspace learning with adaptive pairwise graph embedding (APGE), which first builds a k1 -nearest neighbor graph on the labeled data to learn local discriminant embeddings for exploring the intrinsic structure of the non-Gaussian labeled data, i.e., the submanifold structure. Then, a k2 -nearest neighbor graph is constructed on all samples and mapped to GE learning to adaptively explore the global structure of all samples. Clustering unlabeled data and its corresponding labeled neighbors into the same submanifold, sharing the same label information, improves embedded data's discriminative ability. And the adaptive neighborhood learning method is used to learn the graph structure in the continuously optimized subspace to ensure that the optimal graph matrix and projection matrix are finally learned, which has strong robustness. Meanwhile, the rank constraint is added to the Laplacian matrix of the similarity matrix of all samples so that the connected components in the obtained similarity matrix are precisely equal to the number of classes in the sample, which makes the structure of the graph clearer and the relationship between the near-neighbor sample points more explicit. Finally, multiple experiments on several synthetic and real-world datasets show that the method performs well in exploring local structure and classification tasks.

ОСОБЕННОСТИ СИНХРОНИЗАЦИИ ДИНАМИКИ ЧИСЛЕННОСТИ В СИСТЕМЕ ТРЕХ МИГРАЦИОННО СВЯЗАННЫХ ПОПУЛЯЦИЙ

The article is devoted to the study of oscillations (cycles) synchronization in a system of three populations coupled by migration in a circle. We consider a discrete time model of population dynamics, which is a system of three identical dissipatively connected logistic maps. One-dimensional bifurcation diagrams (trees) were constructed, supplemented with the capture index of the population numbers cycles phases at adjacent sites. We conducted numerical experiments that show phase multistability – the cycles coexistence with different phases. Using qualitative methods for studying dynamic systems, a complete phase portrait of the model is constructed, showing that in the phase space there are several periodic points corresponding to elements of synchronous (in-phase) and asynchronous (out-of-phase) cycles. The 2- and 3-cycle stability conditions are investigated. It is shown that these two cycles are represented by three options: 1) a fully synchronous regime, when the abundances in the three populations coincide at any point in time; 2) a partially synchronous regime, when abundances coincide only for two populations, 3) a non-synchronous (out-of-phase) regime, when all three numbers have different values. For a 2-cycle, the third regime is unstable, possible as part of a long transition process. We found that for the 3-cycle, besides to the synchronous and partially synchronous regime, it is possible a stable out-of-phase behavior of three populations. It is shown that stable and unstable periodic points lie on certain surfaces (invariant manifolds), which separate the areas of attraction for regimes with different degrees of phase synchronization.

Extensive analysis of the applicability range of the linear kinetic approaches in the case of the pressure driven gas mixture flows

The study of gas mixture flows through micro- and macro-channels remains a very attractive area for theorists and experimentalists worldwide, mainly due to their great practical applicability in several aspects of science and industry. The present work includes a comparative study between the linear (McCormack model) and the nonlinear (DSMC method) kinetic theories of binary gas mixture flows through channels over a wide range of the involved parameters. The results show that the McCormack model is a reliable kinetic model for predicting the gas mixture flow behavior. Specific criteria with respect to the applicability range of the linear kinetic theory of short and long capillaries are proposed. The analysis shows that the separation phenomenon remains strong even in the case of small pressure drops. The applicability range of the linear kinetic theory for binary gas mixture flows driven by large pressure drops is obtained to be smaller compared to that of single gases but still wide enough covering a wide range of pressure ratios. Furthermore, the present work shows that the long capillary theory remains a very powerful tool for studying the gas mixture flow behavior under weak and strong nonequilibrium conditions. This work provides a kinetic database of the linear kinetic data (as supplementary material), which until today is still missing from the literature, and as it is shown throughout this work, it can be used far beyond the restrictions defined by the linear kinetic theory.

Research on System Development of Spatial Clustering in Tourism Recommendation

Abstract This paper investigates the application of exploring spatial clustering techniques in travel recommendation systems to improve recommendation accuracy and user satisfaction. A system that can provide personalized travel information has been designed by analyzing the spatial data and behavioral patterns of travelers. With this approach, the travel experience can be optimized by considering geographic location, user preferences, and tourist attraction characteristics. User data and attraction characteristics are analyzed in this paper to obtain accurate travel recommendations. Combining the traveler’s interest matrix and the attraction area heat algorithm is used to optimize the recommendation process. This recommender system reduces the root mean square error (RMSE) and mean absolute error (MAE) by 0.487% and 21-60%, respectively, compared with the traditional algorithm in the travel recommendation error analysis, and improves the accuracy rate to 96.33%, with a significant increase in the recall rate. The spatial clustering cluster analysis demonstrates that the chosen number of clusters can effectively enhance the clustering quality and maximize the recommendation results. By effectively utilizing spatial data of users and attractions, the system can provide personalized travel recommendations and improve user experience with high accuracy and recall, demonstrating better recommendation performance than traditional methods.

Lewis acid-catalyzed (3 + 2) annulation of bicyclobutanes with ynamides: access to 2-amino-bicyclo[2.1.1]hexenes.

Strain-release driven annulations with bicyclo[1.1.0]butanes (BCBs) have become an attractive area of research for the synthesis of bioisosteric bicyclohexane derivatives, which play a vital role in drug discovery. Interestingly, the utilization of the inherent strain in BCBs for the synthesis of functionalized amino-bicyclo[2.1.1]hexenes, which may spatially mimic substituted benzenes and anilines, has received only scant attention. Herein, we report the Sc(OTf)3-catalyzed (3 + 2) annulation of BCBs with ynamides for the facile synthesis of 2-amino-bicyclo[2.1.1]hexenes in one step under mild conditions. The reaction likely proceeds via nucleophilic addition facilitated by the nitrogen lone pair from the alkynyl group of the ynamides to the unsubstituted side of the BCBs, followed by the annulation of the resulting enolate with the keteniminium species. For the first time, the C-C triple bond of ynamides was utilized as the coupling partner for BCBs, resulting in products adorned with a functionalizable amino group and an integrated strained alkene moiety.

Effect of talent management practices on talent retention in higher educational institutions of Nepal

This paper concerns answering how talents are managed in the dimensions of attracting, developing, and motivating, as well as examining the effect of these dimensions on retaining talents. Empirical shreds of evidence have indicated the positive and significant effects of various talent management practices on different aspects of performance. The absence of studies on talent management practices focusing on attracting, developing, and motivating talents and analyzing their effect on retaining them prompted this study. The study uses descriptive and causal-comparative research designs to describe and analyze the causal relations among the study variables. Quantitative data is collected from 132 respondents using a questionnaire survey developed based on intensive reviews. The findings of the descriptive analysis indicate moderate efforts given by the institutions managing talents. The correlation result shows positive and significant associations between talent retention and talent attraction and development, as well as talent motivation. The findings from multiple regression analysis showed a positive and significant effect of talent attraction and development on talent retention. However, talent motivation showed no significant effect on talent retention. The findings may have practical uses for administrators to manage talents by evaluating their actions, strategies, and policies to retain talented employees and deal with a surplus or shortfall of a talented workforce. The findings provide new insight into the field of study of talent management and retention. Particularly studies focusing on the areas of attraction, development, motivation, and retention of talents in diverse organizational settings.

Sustainable Tourism from Architectural Perspective: A Literature Review

This article discusses the main concept of sustainable tourism to comprehend the terminology on definition, aspects, and main concept. Furthermore, the discussion continues how architecture plays an important part in achieving the sustainability of tourist attraction areas, and the practical ways that have been done from an architectural perspective. The discussion on the architecture subject is divided into 2 parts: the heritage buildings and new buildings, and how the architectural decision in those areas plays a part in supporting sustainable tourism. In heritage building tourism, it is important to ensure the building is in good condition physically and functional. Adaptive reuse is mainly being used to revitalize heritage buildings. In new buildings, several ways can be done to support sustainability in tourism and it's usually by designing an environmentally low negative impact building's design. If these strategies are applied in building design, it is possible to develop a tourism area that emphasizes the concept of sustainability.

Deteksi Dini Loyalitas: Pelajaran Empiris dari Daerah Obyek Wisata Lawang Sewu Semarang

The research aims to analyze the impact of service quality and satisfaction on tourist loyalty in the Lawang Sewu Tourism Area, Semarang. The sample taken in this research was carried out by a population of tourists who went on tour in the Lawang Sewu Tourism Object Area, Semarang, where the number is unknown. The sample was taken using purposive sampling and amounted to 100 people. The analysis method is multiple linear regression. The analysis method uses linear regression multiple regression. The results of the research show that service quality has an impact on tourist loyalty, the service quality provided by the management of the Lawang Sewu Tourist Attraction Area, Semarang City, with operational service speed so that the better the service quality, the more loyalty will result. Satisfaction has an impact on tourist loyalty. Satisfaction is given by the management of the Lawang Sewu Tourism Object Area, Semarang City, which is very comfortable, the better tourist satisfaction will increase tourist loyalty.

HOW DOES THE GEOPOLITICAL POSITION OF DJIBOUTI INFLUENCE ITS RELATIONS WITH GREAT POWERS?

Djibouti gained independence in 1977 after being ruled by France for nearly a century. However, Djibouti remained under French influence for a long time due to both the military agreement with France and the economic aid provided by this country. Moreover, it had to act more distant when establishing relations with other countries. However, under the leadership of Omar Guelleh, who came to power in 1999, a new process was entered in foreign policy with the adoption of the principle of multilateralism in diplomatic relations. Djibouti, which has become a center of attraction for great powers such as the USA, China and Russia, as well as France, has started to cooperate with these countries in different fields. This study points out that Djibouti’s geopolitical position is the main factor that determines its relations with these great powers and prompts these countries to cooperate with Djibouti. It is also observed that Djibouti has turned its geopolitical position into a tool of its development strategy.

Machine Learning in Finance: A Case Study on Forecasting Google's Stock Price

Stock price prediction remains an attractive and essential area in financial markets, with researchers constantly working hard to improve existing models or develop new ones to achieve more accurate predictions. Foreseeing the future direction of stock prices allowing people to plan and formulate effective investment strategies. However, predicting stock prices remains a difficult challenge due to many uncontrollable factors. Traditional forecasting methods rely primarily on economic data analysis and formulation. However, these traditional methods often provide limited information and forecast accuracy due to market uncertainty. Many business organizations and individual investors started to utilize the programmed approaches to improve the accuracy of stock price predictions as machine learning and deep learning capabilities continue to advance. Through an actual case study, this essay examines the innovative use of machine learning methods for researching in the field of predicting stock prices. In the case study, an LSTM model is built to find the transforming trend of the stock price, while Googles stock price is collected to use as the dataset for training the model. The article finally conducts a comparative study on stock price prediction based on LSTM is conducted to clarify its working progress and accuracy of the outcome.

Analysis of the Influence of Human Development Index and Infrastructure Development on Improving Economic Inequality in Papua Province (Case Study of Eight Districts la Pago Customary territory)

Puspose: This study aims to analyze the effect of human development index and Infrastructure development (road length, health facilities, education facilities, housing facilities, lighting facilities, clean water facilities, per capita income) on improving economic inequality as measured by general public for the period 2013-2022. Method: Secondary data is needed to provide an explanation or answer to the research question. The data analysis model used in this study uses a panel data model with a linear regression approach (OLS). Result and Conclusion: The results showed that HDI (X1) had a positive effect on the gini ratio (Y), with a value of Prob. = 0,0374 < 0,05. Road length (X2) has a positive effect on the gini ratio (Y), with a value of Prob. = 0,0034 < 0,05. Health facilities (X3) have a positive effect on the gini ratio (Y), with a value of Prob. = 0,0079 < 0,05 . Educational facilities (X4) negatively affect the gini ratio (Y), with a value of Prob. = 0,1261 > 0,05. Housing facilities (X5) have a positive effect on the gini ratio (Y), with a value of Prob. = 0,0239 < 0,05. Electric lighting facilities (X6) have a positive effect on the gini ratio (Y), with a value of Prob. = 0,0017 < 0,05. Clean water facilities (X7) have a positive effect on the gini ratio (Y), with a value of Prob. = 0,0131 < 0,05. GRDP per capita (X8) has a positive effect on the gini ratio (Y), with a value of Prob. = 0,0349 < 0,05. Implication of Research: Based on the results of the analysis above, the districts/cities included in Hierarchy I are Jayawijaya Regency and Pegunungan Bintang Regency. In Hierarchy II there are Nduga Regency and Yalimo Regency. In Hierarchy III there are Yahukimo Regency, Lani Jaya Regency, Central Mamberamo Regency and Tolikara Regency. Originality/Value: This shows that Jayawijaya Regency and Pegunungan Bintang Regency are attractive areas for residents to carry out activities in the region due to the availability of various economic, social and government facilities. People who come to the area can get their various needs in close proximity. People will come there for shopping, schooling, and more. This is an attraction for the area to be visited, thus increasing the volume of transactions.

Changes in the attraction area and network structure of recreation flows in urban green, blue and grey spaces under the impact of the COVID-19 pandemic

Despite increasing attention to the impact of the COVID-19 pandemic on outdoor recreation, there is limited knowledge about its effects on visitor travel across cities for recreation and changes in the recreation response of blue, green and grey spaces within cities. This study examines changes in the attraction area of inter-city recreation flows within Wuhan, China, before and after the COVID-19 pandemic. The results showed a significant decrease in average attraction distance from 861.74 km to 787.77 km after the pandemic, while attraction directions remained stable. Intra-city recreation routes tend to be simplified, with average length decreasing from 40.86 km to 30.52 km and node number of recreation routes reducing from 7.02 to 5.00. However, the proportion of blue nodes in recreation routes increased significantly from 17.45 % to 23.25 %, while the proportion of grey nodes decreased noticeably from 57.36 % to 52.75 %. The proportion of green nodes did not show a significant change, but when the number of nodes exceeded 6.18, the proportion of green nodes slightly decreased below pre-pandemic levels. With the increase in the length of recreation routes, the proportion of grey nodes gradually converged to around 42 % from around 54 % before the pandemic. The total proportion of agglomeration and diffusion nodes increased from 13.71 % to 20.01 %, while the balanced nodes decreased from 8.87 % to 6.93 % after the pandemic. Notably, 35.71 % of the functional nodes before the pandemic remained their original functional type after the pandemic, but 7.14 % of functional nodes converted to weak function. These findings can assist tourism departments and scenic spot managers in developing strategies that balance pandemic control and recreation needs.

Critical overview on the use of hydrophobic (deep) eutectic solvents for the extraction of organic pollutants in complex matrices

During the last decades, many efforts have been devoted to the adaptation of sample preparation techniques and methods to the principles of Green Analytical Chemistry. Among them, this article review focusses on those aimed to green the solvents involved in sample treatment. Research in this field started in the late 1990s with the synthesis of room temperature ionic liquids, which were later replaced by the deep eutectic solvents (DESs). During the last years, a subclass of DESs, the so-called hydrophobic deep eutectic solvents (HDESs) have attracted attention. HDESs have contributed to circumventing some of the limitations of early-synthesised hydrophilic DESs regarding the cost of raw materials, the simplicity of synthesis, and the biocompatibility and, apparently, the biodegradability of the mixtures. In addition, these mixtures allowed the treatment of aqueous samples and the extraction of non-polar analytes.This article discusses fundamental aspects regarding the nomenclature used concerning HDESs, summarises the main physicochemical properties of these mixtures, and through discussion of key application studies, describes current progress in the use of these green solvents for the extraction of trace organic contaminants from a variety of matrices. Remaining gaps and possible lines of future development in this emerging, active and attractive research area are also identified and critically discussed.

Density Functional Theory Study of Cu6 Nanoclusters as a Phenylalanine Detector.

Research on amino acids is an attractive area because of their application in metabolism, cancer treatment, growth, and repair of body tissue, and RNA and DNA syntheses. Twenty amino acids are primarily responsible for protein synthesis. In our study, we used a Cu6 nanocluster as an amino acid detector. For the investigation, we adsorbed amino acids on the Cu6 nanocluster and studied their UV-visible spectra. It is observed that all of the Cu6-amino acid complexes have peaks at near 380 nm wavelength except the Cu-phenylalanine complex, where two UV-visible peaks are found at wavelengths 351 nm (excitation energy 3.49 eV) and 403 nm (excitation energy 3.02 eV), respectively, which originated from the HOMO - 2 to LUMO (28%) and HOMO - 1 to LUMO (38%) transitions. Due to this unique transition, the Cu6 nanocluster can be used for the detection of the phenylalanine amino acid out of the 20 amino acids.

Designing a new microchannel to collect microparticles using dielectrophoretic forces: Numerical and experimental investigation

Dielectrophoresis (DEP) is an effective technique for manipulating particles in microfluidic devices. The DEP force depends on the frequency and square gradient of the electric field, as well as the fluid and particle dielectric properties. An efficient system for manipulating particles can be designed by adjusting these factors. This study aims to develop an efficient microsystem for particle trapping using dual-frequency DEP force. The microfluidic system is divided into two parts of focusing and attracting. The negative DEP (nDEP) force in the focusing part concentrates particles near the microchannel axis. The positive DEP (pDEP) force in the attractive area then absorbs particles into the internal chamber via electrodes. In general, the main advantage of the present design is the maximum trapping of incoming particles (with a trapping rate of over 95%) regardless of their initial location. In this study, numerical modeling was first done in three dimensions to sort and trap the microparticles. Then, a microchip was designed, built, and tested in a laboratory to validate the results and confirm the microfluidic system behavior. Finally, a parametric study was conducted to figure out the best voltage range of the electric fields in the microfluidic system.

CONSERVATION AND TOURISM DEVELOPMENT OF KENADSA HERITAGE IN THE SAHARA OF ALGERIA

In the context of the economic crisis, Algeria develops, in recent decades, new strategies oriented towards the tourism sector in search of new areas of attractiveness. It contributes to the definition and staging of the tourist image of its vast territory, in particular its large part of the Sahara [1]. This fraction of the country full of wealth and values characterizes an apparent contrast between its great tourist possibilities and the weakness of their development: an unavoidable geographical part but unfortunately unappreciated and very little invested. In this perspective, this article proposes the redevelopment of the values of Kenadsa, an example of the Saharan city, by constitution an images of a tourist destination based on its promotional activities and visible landscapes. He determines to what extent the image communicated can contribute to the challenges of the attractiveness of the city and wonders how tourism will be able to register their sustainably to participate in the contemporary transformation of its heritage? The method used inspires by a contemporary approach to tourism development, mobilizing historical documents, speeches by individuals and committed architectural creations. And as the interventions on a historic building reveal as much the identity relation of the society with its history as that with the contemporary world, the methodology then combines the analysis of the speeches, as well as the analysis of the architecture and its representation.

Evaluation of antimicrobial and antioxidant activity of zinc oxide nanoparticles biosynthesized with Ziziphus spina-christi leaf extracts

Background: Due to their simplicity, eco-friendliness, availability and non-toxicity, the greener fabrication of metal and metal oxide nanoparticles has been a highly attractive research area over the last decade. Aim: This study aimed to assess the antioxidant and antimicrobial activities of the green synthesized zinc oxide nanoparticles (ZnO-NPs) using an aqueous leaf extract of Ziziphus spina-christi. Method: The antioxidant property of ZnO-NPs was analyzed by the α, α-diphenyl-β-picrylhydrazyl (DPPH) and hydrogen peroxide (H2O2). Additionally, the diffusion agar method assessed the antimicrobial activities against bacteria and fungi. Results: ZnO-NPs synthesized by Z. spina-christi had shown promising H2O2 and DPPH free radical scavenging actions compared to vitamin C. The ZnO-NPs exhibited significant antibacterial activity against the tested bacteria with various susceptibility as a concentration-dependent effect. The largest zone of inhibition for Staphylococcus aureus (S. aureus) was observed (36 ± 2 mm) compared to Escherichia coli (E. coli) (15 ± 2 mm) by the same concentration of ZnO-NPs. The ZnO-NPs showed remarkable antifungal activity against Aspergillus niger. Conclusion: It can be concluded that, ZnO-NP have been imposed as suitable antimicrobial agent being able to combat both S. aureus and E. coli in vitro.