Miracle-dz/UniFAR-FTUs huggingface dataset:
Inducible Resistance to β-Lactams in Oxacillin-Susceptible mecA1-Positive Staphylococcus sciuri Isolated From Retail Pork

Most isolated strains of Staphylococcus sciuri contain mecA1, the evolutionary origin of mecA, but are sensitive to β-lactams (OS-MRSS, oxacillin-susceptible mecA1-positive S. sciuri). In order to improve the efficacy of antibiotic treatment, it is important to clarify whether the resistance of OS-MRSS to β-lactams is an inducible phenotype. In this study, three OS-MRSS strains with oxacillin MIC = 1 μg/ml were isolated from 29 retail pork samples. The resistance of OS-MRSS to β-lactams (MIC > 256 μg/ml) was found to be induced by oxacillin, and the induced resistance was observed to remain stable within a certain period of time. Interestingly, the induced β-lactam resistance was not caused by mecA1, heterogeneous resistance, or any genetic mutation, but mainly due to increased wall teichoic acid (WTA) synthesis that thickened the cell wall. The induced strains also showed slower growth rate, as well as decreased adhesion ability and biofilm thickness. These phenotypes were found to be achieved through altered gene expression in associated pathways, such as the citrate cycle and pentose phosphate pathway. The results challenge the traditional antibiotic sensitivity test. In the presence of β-lactam antibiotics, OS-MRSS that was initially sensitive to β-lactams was observed to gradually develop β-lactam resistance in several days. This often-neglected phenomenon in antibiotic sensitivity tests requires further research attention.
Antibody- Based Immunotherapy Combined With Antimycotic Drug TMP- SMX to Treat Infection With Paracoccidioides brasiliensis

Monoclonal antibodies (mAbs) are promising alternatives to treat infectious diseases, especially given their potential for applications in combination therapies with antimicrobial drugs to enhance the antifungal efficacy. Protection mediated by mAbs used to treat experimental paracoccidioidomycosis (PCM) has been demonstrated previously. Our aim in the present work was to characterize a monoclonal antibody (mAbF1.4) raised against a cell wall glycoconjugate fraction of Paracoccidioides spp. and to analyze its efficacy combined with trimethoprim-sulfamethoxazole (TMP/SMX) as treatment for experimental PCM. We demonstrated that the epitope recognized by mAbF1.4 is consistent with branched glucose residues present on a cell wall β-glucan polymer. In vitro, mAbF1.4 increased the phagocytic capacity and nitric oxide concentration induced by the macrophage cell line J774.1A, and this resulted in a significant reduction in the viability of the opsonophagocytized yeasts. In vivo, we detected a significant reduction in pulmonary fungal burdens of mice treated with mAbF1.4 in association with TMP/SMX, which correlated with increased pulmonary concentrations (determined by ELISA) of IFN- γ, TNF-α, IL-10 and IL-17. In parallel, we observed a decrease in IL-4, suggesting that the treatment was associated with a mixed Th1-Th17 type immune response. Histopathology of lung segments from mice receiving the combination therapy showed a significant reduction in granulomas, which were well-defined, and improved maintenance of lung architecture. These findings demonstrate that mAbF1.4 + TMP/SMX therapy is a promising approach to combat PCM as well as decrease disease sequelae and highlights the potential benefits of immune mediators in PCM combined immunotherapy.
Functional and Structural Alteration of Default Mode, Executive Control, and Salience Networks in Alcohol Use Disorder

Alcohol use disorder (AUD) has been related to aberrant functional connectivity (FC) in the salience network (SN), executive control network (ECN), and default mode network (DMN). However, there is a lack of comprehensive and simultaneous examination of these networks in patients with AUD and of their relation to potential anatomical changes. We aimed to comprehensively examine the alteration in FC in the three networks in AUD patients, and the correlation of the alteration with anatomical/structural changes (volume) in the neural areas implicated in these networks, by applying voxel-based morphometry (VBM) and region of interest-to-region of interest connectivity analysis simultaneously. In all, 22 patients with AUD and 22 healthy adults participated in the study and underwent T1 magnetic resonance imaging. Patients with AUD showed increased FCs within the DMN and SN networks, especially in terms of connectivity of the frontal areas and bilateral hippocampi. They also showed decreased FCs in the ECN. In addition, there was significant volume reduction in these areas (frontal areas and hippocampus). The increased FCs within the frontal areas or bilateral hippocampi showed a negative correlation with gray matter volume of these areas in AUD patients. Our findings add to the empirical evidence that the frontal lobe and hippocampi are critical areas that are vulnerable to functional and structural changes due to AUD.
Fabrication of Antimicrobial Multilayered Nanofibrous Scaffolds-Loaded Drug via Electrospinning for Biomedical Application

Nanofibers prepared by biobased materials are widely used in the field of biomedicine, owing to outstanding biocompatibility, biodegradable characters, and excellent mechanical behavior. Herein, we fabricated multilayered nanofibrous scaffolds in order to improve the performance of drug delivery. The composite layer-by-layer scaffolds were incorporated by hydrophobic poly(l-lactic acid) (PLA): polycaprolactone (PCL) and hydrophilic poly(vinyl alcohol) (PVA) nanofibers via multilayer electrospinning. Morphological and structural characteristics of the developed scaffolds measured by scanning electron microscopy (SEM), and transmission electron microscopy (TEM) confirmed smooth and uniform fibers ranging in nanometer scale. The differences in contact angles and Fourier transform infrared spectrum (FTIR) between single-layered PVA nanofibers and multilayered scaffolds verified the existence of PLA: PCL surface. In vitro biodegradable and drug release analysis depicted multilayered scaffolds had good biodegradability and potential for medical application. Due to the model drug incorporation, scaffolds exhibited good antibacterial activity against Escherichia coli and Staphylococcus aureus by the zone of inhibition test. These results revealed that the multilayered scaffolds were proved to be desirable antibacterial materials for biomedical application.
Towards Semantically-Rich Spatial Network Representation Learning via Automated Feature Topic Pairing

Automated characterization of spatial data is a kind of critical geographical intelligence. As an emerging technique for characterization, spatial Representation Learning (SRL) uses deep neural networks (DNNs) to learn non-linear embedded features of spatial data for characterization. However, SRL extracts features by internal layers of DNNs, and thus suffers from lacking semantic labels. Texts of spatial entities, on the other hand, provide semantic understanding of latent feature labels, but is insensible to deep SRL models. How can we teach a SRL model to discover appropriate topic labels in texts and pair learned features with the labels? This paper formulates a new problem: feature-topic pairing, and proposes a novel Particle Swarm Optimization (PSO) based deep learning framework. Specifically, we formulate the feature-topic pairing problem into an automated alignment task between 1) a latent embedding feature space and 2) a textual semantic topic space. We decompose the alignment of the two spaces into: 1) point-wise alignment, denoting the correlation between a topic distribution and an embedding vector; 2) pair-wise alignment, denoting the consistency between a feature-feature similarity matrix and a topic-topic similarity matrix. We design a PSO based solver to simultaneously select an optimal set of topics and learn corresponding features based on the selected topics. We develop a closed loop algorithm to iterate between 1) minimizing losses of representation reconstruction and feature-topic alignment and 2) searching the best topics. Finally, we present extensive experiments to demonstrate the enhanced performance of our method.
Using Neural Networks to Control Glut in the Active Logic Machine

In this paper, we address the problem of inferential glut – specifically the problem that symbolic reasoners tend to produce vastly more deductive information than they can tractably process. As a concrete example, let us imagine an agent which is searching for a specific book. Entering a study, it encounters, among other things, a bookshelf, a lamp, and a table. If the agent has a knowledge base with a large number of facts about lamps and tables, it may spend an inordinate amount of resources reasoning about lamps and tables, and such irrelevant reasoning can prevent it from drawing conclusions that are helpful for finding the book. In fact, such swamping can also arise immediately and devastatingly even without distractor entities, as discussed below. Of course, the agent could also have special information guiding the reasoning in ways appropriate to the specific task (e.g., books tend to be on shelves). But how is this special information obtained? Here we address this as a learning issue and report on preliminary results from our approach. As an example, consider a round library with one continuous circular shelf along its circumference, where each book B has exactly one "left" neighbor left(B) and another “right” neighbor right(B), where these function symbols simply refer to those two neighbors of B. This might be encoded by the following rules K for some book a:
Gel formulation and evaluation of antimicrobial activity of turmeric (Curcuma longa L.) from Aceh, Indonesia against Aggregatibacter actinomycetemcomitans

Aim: Periodontitis is a chronic disease of dental tissue caused by a bacterial infection, for instance, Aggregatibacter actinomycetemcomitans is caused by dominant bacteria. Currently, there are many cases regarding bacterial resistance to some antibiotics, so an alternative search for new antibiotics from herbal plants is needed. Turmeric (Curcuma longa Linn.) is a plant that is known to contain bioactive compounds that can function as antimicrobials. So far, research on turmeric extract in the form of a gel formula used as a herbal medicine for periodontitis in Indonesia has not been reported. The aim of this research was to study the oral gel formula of the indigenous turmeric extract from Aceh, Indonesia and to evaluate its antimicrobial efficacy against A. actinomycetemcomitans. Materials and Methods: This research was designed to test the effectiveness of C. longa gel in vitro in inhibiting the growth of A. actinomycetemcomitan. Turmeric rhizome was extracted using the maceration method with 96% ethanol; then, the extract was formulated into a gel with concentrations of 0%, 25%, 50%, 75%, and 100%. The curcuma gel was then tested for antimicrobials against A. actinomycetemcomitans bacteria. Results: The in vitro antimicrobial test showed that a 50% concentration of turmeric rhizome extracts gel was effective in inhibiting the growth of A. actinomycetemcomitans with an inhibition zone of 10.8 mm, which is classified as a strong inhibition zone. Conclusion: The efficacy of the oral gel formula containing C. longa extracts as a natural antimicrobial agent has been confirmed, and the possibility of applying it as periodontitis herbal medicine has been suggested.
Dietary Risk with Other Risk Factors of Breast Cancer

Breast cancer is one of the most common and feared cancers. The incidence of breast cancer is persistently on the rise due to urbanization and lifestyle changes. Breast cancer cannot be prevented fully but can be effectively treated and controlled if risk factors are determined accurately. Detection of breast cancer at an early stage along with the advancement in treatment options has provided a greater chance of survival. Objectives: The objective of the study was (a) To determine the most common risk factors of breast cancer in women and (b) to identify the risk ratio of dietary risk factors among breast cancer patients. Methodology: A hospital-based case–control study was conducted at a tertiary care center in coastal Karnataka, India. Results: Total 240 participants were included in the study, 120 cases and 120 controls, who were matched by ± 2-year age range. All the study participants were between 34 and 70 years of age group; the occurrence of breast cancer was found more among females within the normal range of body mass index and with a history of breast cancer among first-degree relatives. A statistically significant association was found with consumption of red meat, fatty food consumption, and bad dietary habit. Conclusion: High fat-low fiber diet is the most important risk factor for breast Cancer.
Postoperative infective complications following percutaneous nephrolithotomy

Introduction: Percutaneous nephrolithotomy (PCNL) is recommended as the first choice of therapeutic strategy for patients with renal stones larger than 2 cm. It is reported that up to one-third of patients might have some perioperative complications, especially fever and urinary tract infections, which constitutes about 21%–39.8% of all the complications. Primary and Secondary: The primary aim of the study was to study about the proportion of patients getting post-operative infective complications following PCNL. The secondary aim was to study the patient, stone and procedure related risk factors associated with the infective complications. Settings and Design: This is an institution-based observational study. Materials and Methods: All patients who underwent PCNL in the Department of Urology, Medical College, Thiruvananthapuram, during 3 years from September 2016– to August 2019, were included in the study. In this study, the demographic factors and factors related to the patient, stone, and the procedure were collected and analyzed. Statistical Analysis Used: Data analysis was performed using SPSS version 22.0. Results: During the 3-year period, a total of 201 patients with renal stones were treated with PCNL in our hospital. Of this 190 cases were taken for analysis. The mean age of patients was 47.7 years, 148 (77.9%) were male, 42 (22.1%) were female, The final outcomes evaluated were episodes of fever, documented urinary tract infection (UTI), pyelonephritis, and sepsis. Thirty-six (18.9%) patients had fever, of which 21 (11.1%) had UTI, 6 (3.1%) had pyelonephritis and 5 (2.6%) developed sepsis. Conclusions: Post-PCNL complications are more commonly found in patients with history of preoperative UTI, previous history of renal surgeries, large stone burden, operative procedure more than 90 min, and presence of residual calculi.
A Mathematical Modeling Approach for Targeted Radionuclide and Chimeric Antigen Receptor T Cell Combination Therapy

Simple Summary Targeted radionuclide therapy (TRT) and immunotherapy, an example being chimeric antigen receptor T cells (CAR-Ts), represent two potent means of eradicating systemic cancers. Although each one as a monotherapy might have a limited effect, the potency can be increased with a combination of the two therapies. The complications involved in the dosing and scheduling of these therapies make the mathematical modeling of these therapies a suitable solution for designing combination treatment approaches. Here, we investigate a mathematical model for TRT and CAR-T cell combination therapies. Through an analysis of the mathematical model, we find that the tumor proliferation rate is the most important factor affecting the scheduling of TRT and CAR-T cell treatments with faster proliferating tumors requiring a shorter interval between the two therapies. Abstract Targeted radionuclide therapy (TRT) has recently seen a surge in popularity with the use of radionuclides conjugated to small molecules and antibodies. Similarly, immunotherapy also has shown promising results, an example being chimeric antigen receptor T cell (CAR-T) therapy in hematologic malignancies. Moreover, TRT and CAR-T therapies possess unique features that require special consideration when determining how to dose as well as the timing and sequence of combination treatments including the distribution of the TRT dose in the body, the decay rate of the radionuclide, and the proliferation and persistence of the CAR-T cells. These characteristics complicate the additive or synergistic effects of combination therapies and warrant a mathematical treatment that includes these dynamics in relation to the proliferation and clearance rates of the target tumor cells. Here, we combine two previously published mathematical models to explore the effects of dose, timing, and sequencing of TRT and CAR-T cell-based therapies in a multiple myeloma setting. We find that, for a fixed TRT and CAR-T cell dose, the tumor proliferation rate is the most important parameter in determining the best timing of TRT and CAR-T therapies.
THE EFFECT OF GROWTH REGULATORS AND PRESERVATIVE ON SENESCENCE OF CUT ORIENTAL

The quality of cut lily flowers during its postharvest life is important both for growers and customers. It is determined by two factors: longevity of flowers and a general appearance of the entire cut stem during its vase life. The aim of this work was to evaluate the effects of growth regulators (gibberellic acid and benzyladenine) and the preservative (200 mg·dm 8HQC+2%S) on quality of cut oriental lily ‘Helvetia,’ a white blooming cultivar very popular on the Polish market. After cutting the flowers were treated for 20 h with growth regulators (GA3 or BA, 500 mg·dm) and then placed into distilled water. Non pulsed flowers were kept in a preservative solution composed of 200 mg·dm 8HQC and 2% sucrose. Untreated flowers held in distilled water served as a control. During the senescence of cut lily flowers the soluble protein and free proline contents were determined. As the major problem in the postharvest handling of lilies is leaf yellowing, the effects of postharvest treatments on quality of leaves and their chlorophyll contents were also studied. The preservative increased vase life of cut lily flowers while gibberellic acid improved the quality of leaves. In leaves treated with GA3 the chlorophyll level was 25% higher than in leaves on stems placed into distilled water directly after harvest. The level of soluble protein in petals dropped while free proline accumulated during flower senescence. Flowers treated with GA3 and those placed into 8HQC + 2%S solution showed a delayed protein degradation: the concentrations of soluble proteins after 12 day of vase life was over 3-fold higher than in control flowers. Also the proline accumulation was delayed in flowers on stems placed in the preservative solution, however, no effect of GA3 on the proline level was observed. The soluble protein level correlated with a flower position on the stem being lower in lower flowers as compared to the upper ones.
Sources, Channels and Strategies of Disinformation in the 2020 US Election: Social Networks, Traditional Media and Political Candidates

The dissemination of fake news during the conduct of an electoral campaign can significantly distort the process by which voters form their opinion on candidates and decide their vote. Cases of disinformation have been happening since the rise of social networks and the last presidential election held in 2020 in the United States was not an exception. The present research aims at analyzing the ways in which political disinformation is generated by different types of sources (social networks users, the media and political candidates) through various channels for communication (social and traditional media). Quantitative and qalitative methods were used to analyze a sample of news published during the election and verified by the most important fact-checking organizations in the United States and Europe. The results indicate that users of social networks spread false information on equal terms with presidential candidates, although the channel preferred to spread misleading messages was social networks in 67.4% of cases. The candidates relied on the use of classic disinformation strategies through traditional media, although the greatest degree of disinformation occurred when conspiratorial hoaxes were circulated through social networks.
Aardvark: An Asynchronous Authenticated Dictionary with Applications to Account-based Cryptocurrencies

We design Aardvark, a novel authenticated dictionary with short proofs of correctness for lookups and modifications. Our design reduces storage requirements for transaction validation in cryptocurrencies by outsourcing data from validators to untrusted servers, which supply proofs of correctness of this data as needed. In this setting, short proofs are particularly important because proofs are distributed to many validators, and the transmission of long proofs can easily dominate costs. A proof for a piece of data in an authenticated dictionary may change whenever any (even unrelated) data changes. This presents a problem for concurrent issuance of cryptocurrency transactions, as proofs become stale. To solve this problem, Aardvark employs a versioning mechanism to safely accept stale proofs for a limited time. On a dictionary with 100 million keys, operation proof sizes are about 1KB in a Merkle Tree versus 100–200B in Aardvark. Our evaluation shows that a 32-core validator processes 1492– 2941 operations per second, saving about 800 × in storage costs relative to maintaining the entire state.
Mn(II) Oxidation Is Catalyzed by Heme Peroxidases in â•œ<italic toggle='yes'>Aurantimonas manganoxydans</italic>â•š Strain SI85-9A1 and <italic toggle='yes'>Erythrobacter</italic> sp. Strain SD-21

Division of Environmental and Biomolecular Systems, Oregon Health & Science University, 20000 NW Walker Road, Beaverton, Oregon 97006; Department of Biological Sciences, California State University, Fullerton, P.O. Box 6850, Fullerton, California 92834-6850; and Biomolecular Mass Spectrometry Facility, Department of Chemistry and Biochemistry, University of California San Diego, 9500 Gilman Drive, La Jolla, California 92093-0378
A nanoscale perspective on solid oxide and semiconductor membrane fuel cells: materials and technology

Fuel cells could play an important role in the ongoing energy transition by providing clean and efficient energy conversion. Although the solid oxide fuel cell (SOFC) technology is a potential alternative for large-scale applications, its commercialization is limited by its electrolyte materials and has not yet been realized. Progress on new functional semiconductor-ionic materials (SIMs) and the fundamentals of SOFCs will provide new paths for their research and development. Herein, we discuss the nanoscale electrochemistry phenomena of SIMs in the context of new concepts for advanced SOFCs. A traditional SOFC consists of a three-layer anode/electrolyte/cathode structure, where the physically separated electrolyte layer is indispensable for ion transport to support the redox reaction and prevent the occurrence of short circuiting. A novel nano-SOFC concept Page 2 of Zhu et al. Energy Mater 2021;1:100002 https://dx.doi. org/10.20517/energymater.2021. 03 13 is proposed to replace the traditional electrolyte by a SIM or semiconductor membrane and it can deliver superior performance, even at a lower temperature range (< 500 °C). The scientific basis and prospects of this new technological approach are presented and discussed.