Miracle-dz/UniFAR-FTUs huggingface dataset:
Interfacial fluid transport is a key to hydrogel bioadhesion

Significance Bioadhesive hydrogels capable of sticking to living tissues are of utmost interest for better-integrated implants and less invasive surgical techniques. Nevertheless, adhesion is most difficult to achieve and control on the wet surface of internal tissues. This work demonstrates that fluid transport across the tissue−hydrogel interface governs the creation of adhesion. Through ex vivo experiments and a simple model, we capture how the onset of adhesion depends on a competition between draining and wetting of the interface. This coupling between adhesion and interfacial transport provides a potent key to improve the accuracy of adhesion tests and to enhance the bioadhesive performance of hydrogels. We illustrate this potential in vivo by using superabsorbent hydrogel membranes to make strong liver adhesives. Attaching hydrogels to soft internal tissues is a key to the development of a number of biomedical devices. Nevertheless, the wet nature of hydrogels and tissues renders this adhesion most difficult to achieve and control. Here, we show that the transport of fluids across hydrogel−tissue interfaces plays a central role in adhesion. Using ex vivo peeling experiments on porcine liver, we characterized the adhesion between model hydrogel membranes and the liver capsule and parenchyma. By varying the contact time, the tissue hydration, and the swelling ratio of the hydrogel membrane, a transition between two peeling regimes is found: a lubricated regime where a liquid layer wets the interface, yielding low adhesion energies (0.1 J/m2 to 1 J/m2), and an adhesive regime with a solid binding between hydrogel and tissues and higher adhesion energies (1 J/m2 to 10 J/m2). We show that this transition corresponds to a draining of the interface inducing a local dehydration of the tissues, which become intrinsically adhesive. A simple model taking into account the microanatomy of tissues captures the transition for both the liver capsule and parenchyma. In vivo experiments demonstrate that this effect still holds on actively hydrated tissues like the liver capsule and show that adhesion can be strongly enhanced when using superabsorbent hydrogel meshes. These results shed light on the design of predictive bioadhesion tests as well as on the development of improved bioadhesive strategies exploiting interfacial fluid transport.
Improved uptake and therapeutic intervention of curcumin via designing binary lipid nanoparticulate formulation for oral delivery in inflammatory bowel disorder

Abstract This study was focussed on development of curcumin loaded solid binary lipid nanoparticles (C-SBLNs) to ameliorate stability, uptake and therapeutic potential of curcumin during inflammatory bowel disease (IBD). C-SBLNs with nano-size range (210.56 ± 41.22 nm) and high entrapment efficiency (83.12 ± 6.57%) were prepared by solvent emulsification evaporation method using binary lipids i.e. stearic acid and tristearin after optimizing various formulation and process variables. Physicochemical characterization of C-SBLNs by ATR-FTIR confirmed drug entrapment whereas thermal and pXRD study corroborated loss of crystallinity of drug into C-SBLNs. Lyophilized C-SBLNs were found to be spherical shaped with good gastrointestinal stability and prolonged drug release up to 24 h. Optimized C-SBLNs formulation displayed significantly enhanced cellular uptake and localization in inflamed tissues during IBD. Oral administration of C-SBLNs in DSS induced colitis model revealed significant reduction in leucocyte infiltration, oxidative stress, pro-inflammatory cytokine (TNF-α) secretion and maintenance of colonic structure similar to healthy animal group compared to curcumin. Thus, in vitro and preclinical findings of study clearly confirmed that C-SBLNs could be a stable and efficacious alternative platform for curcumin delivery with strong competence in IBD chemotherapy. Graphical Abstract
Particle Swarm Optimization: Development of a General-Purpose Optimizer

Traditional methods present a very restrictive range of applications, mainly limited by the features of the function to be optimized and of the constraint functions. In contrast, evolutionary algorithms present almost no restriction to the features of these functions, although the most appropriate constraint-handling technique is still an open question. The particle swarm optimization (PSO) method is sometimes viewed as another evolutionary algorithm because of their many similarities, despite not being inspired by the same metaphor. Namely, they evolve a population of individuals taking into consideration previous experiences and using stochastic operators to introduce new responses. The advantages of evolutionary algorithms with respect to traditional methods have been greatly discussed in the literature for decades. While all such advantages are valid when comparing the PSO paradigm to traditional methods, its main advantages with respect to evolutionary algorithms consist of its noticeably lower computational cost and easier implementation. In fact, the plain version can be programmed in a few lines of code, involving no operator design and few parameters to be tuned. This paper deals with three important aspects of the method: the influence of the parameters' tuning on the behaviour of the system; the design of stopping criteria so that the reliability of the solution found can be somehow estimated and computational cost can be saved; and the development of appropriate techniques to handle constraints, given that the original method is designed for unconstrained optimization problems.
Collective Reconstructive Embeddings for Cross-Modal Hashing

In this paper, we study the problem of cross-modal retrieval by hashing-based approximate nearest neighbor search techniques. Most existing cross-modal hashing works mainly address the issue of multi-modal integration complexity using the same mapping and similarity calculation for data from different media types. Nonetheless, this may cause information loss during the mapping process due to overlooking the specifics of each individual modality. In this paper, we propose a simple yet effective cross-modal hashing approach, termed collective reconstructive embeddings (CRE), which can simultaneously solve the heterogeneity and integration complexity of multi-modal data. To address the heterogeneity challenge, we propose to process heterogeneous types of data using different modality-specific models. Specifically, we model textual data with cosine similarity-based reconstructive embedding to alleviate the data sparsity to the greatest extent, while for image data, we utilize the Euclidean distance to characterize the relationships of the projected hash codes. Meanwhile, we unify the projections of text and image to the Hamming space into a common reconstructive embedding through rigid mathematical reformulation, which not only reduces the optimization complexity significantly but also facilitates the inter-modal similarity preservation among different modalities. We further incorporate the code balance and uncorrelation criteria into the problem and devise an efficient iterative algorithm for optimization. Comprehensive experiments on four widely used multimodal benchmarks show that the proposed CRE can achieve a superior performance compared with the state of the art on several challenging cross-modal tasks.
Novel Antibacterial Peptides Isolated from the Maillard Reaction Products of Half-Fin Anchovy (Setipinna taty) Hydrolysates/Glucose and Their Mode of Action in Escherichia Coli

The Maillard reaction products (MRPs) of half-fin anchovy hydrolysates and glucose, named as HAHp(9.0)-G MRPs, were fractionated by size exclusion chromatography into three major fractions (F1–F3). F2, which demonstrated the strongest antibacterial activity against Escherichia coli (E. coli) and showed self-production of hydrogen peroxide (H2O2), was extracted by solid phase extraction. The hydrophobic extract of F2 was further isolated by reverse phase-high performance liquid chromatography into sub-fractions HE-F2-1 and HE-F2-2. Nine peptides were identified from HE-F2-1, and two peptides from HE-F2-2 using liquid chromatography-electrospray ionization/multi-stage mass spectrometry. Three peptides, FEDQLR (HGM-Hp1), ALERTF (HGM-Hp2), and RHPEYAVSVLLR (HGM-Hp3), with net charges of −1, 0, and +1, respectively, were synthesized. The minimal inhibitory concentration of these synthetic peptides was 2 mg/mL against E. coli. Once incubated with logarithmic growth phase of E. coli, HGM-Hp1 and HGM-Hp2 induced significant increases of both extracellular and intracellular H2O2 formation. However, HGM-Hp3 only dramatically enhanced intracellular H2O2 production in E. coli. The increased potassium ions in E. coli suspension after addition of HGM-Hp1 or HGM-Hp2 indicated the destruction of cell integrity via irreversible membrane damage. It is the first report of hydrolysates MRPs-derived peptides that might perform the antibacterial activity via inducing intracellular H2O2 production.
New artifacts – new practices: Putting mobile literacies into focus

The transformative relationship between people, culture and artifacts is vital for understanding digital literacies. This argument is approached through introducing the concept of mobile literacies which is indivisibly related to usage of mobile phones in cultural practices. Case studies concerning mobile phone usage in Bangladesh show that SIM-switching and missed calls have developed into powerful artifacts in people’s lives through facilitating social needs and providing certain norms of communication. The article thus illustrates central socio-cultural aspects that we find important to consider in understanding digital literacy.
Advance Care Planning in the Nursing Home Setting: A Practice Improvement Evaluation

Abstract This study evaluated a practice improvement initiative conducted over a 6 month period in 15 Canadian nursing homes. Goals of the initiative included: (1) use the Plan-Do-Study-Act (PDSA) model to improve advance care planning (ACP) within the sample of nursing homes; (2) investigate whether improved ACP practice resulted in a change in residents’ hospital use and ACP preferences for home-based care; (3) engage participating facilities in regular data collection to inform the initiative and provide a basis for reflection about ACP practice and; (4) foster a team-based participatory care culture. The initiative entailed two cycles of learning sessions followed by implementation of ACP practice improvement projects in the facilities using a PDSA approach by participating clinicians (e.g., physicians, social workers, nurses). Clinicians reported significantly increased confidence in many dimensions of ACP activities. Rates of hospital use and resident preference for home-based care did not change significantly. The initiative established routine data collection of outcomes to inform practice change, and successfully engaged physicians and non-physician clinicians to work together to improve ACP practices. Results suggest recurrent PDSA cycles that engage a ‘critical mass’ of clinicians may be warranted to reinforce the standardization of ACP in practice.
A Method for Direct Monitoring of Atorvastatin Adherence in Cardiovascular Disease Prevention: Quantification of the Total Exposure to Parent Drug and Major Metabolites Using 2-Channel Chromatography and Tandem Mass Spectrometry

Background: Low adherence to statin therapy remains a public health concern associated with poor prognosis in cardiovascular disease patients. A feasible method for statin adherence monitoring in clinical practice has yet to be developed. In this article, we describe a novel method designed for the direct monitoring of atorvastatin adherence based on the sum of parent drug and major metabolites in blood samples. Methods: Acid and lactone forms of atorvastatin, 2-OH-atorvastatin, and 4-OH-atorvastatin were assayed. Plasma proteins were precipitated with an acidified mixture of methanol, acetonitrile, and aqueous zinc sulfate, and the supernatant was analyzed with 2-channel reversed-phase chromatography coupled to tandem mass spectrometry. Assay validation was performed according to the guidelines provided by the European Medicines Agency and the US Food and Drug Administration. Results: The effective run time was 1 minute and 45 seconds per sample. Mean accuracy ranged from 92% to 110%, and coefficients of variation were ⩽8.1% over the measurement ranges for individual compounds. The sum of acids and corresponding lactones was stable in clinical plasma samples kept at ambient temperature for up to 6 days after blood sampling (mean sum within 96.6%–101% of baseline). Conclusions A fast and reliable assay for the quantification of atorvastatin and its 5 major metabolites in clinical blood samples is reported. Limitations of preanalytical stability were solved using the sum of the acid and lactone forms. The assay is feasible for implementation in clinical practice, and the sum of parent drug and metabolites may be used for direct monitoring of atorvastatin adherence.
Exploring the Sensitivity of Choropleths under Attribute Uncertainty

The choropleth map is an essential tool for spatial data analysis. However, the underlying attribute values of a spatial unit greatly influence the statistical analyses and map classification procedures when generating a choropleth map. If the attribute values incorporate a range of uncertainty, a critical task is determining how much the uncertainty impacts both the map visualization and the statistical analysis. In this paper, we present a visual analytics system that enhances our understanding of the impact of attribute uncertainty on data visualization and statistical analyses of these data. Our system consists of a parallel coordinates-based uncertainty specification view, an impact river and impact matrix visualization for region-based and simulation-based analysis, and a dual-choropleth map and t-SNE plot for visualizing the changes in classification and spatial autocorrelation over the range of uncertainty in the attribute values. We demonstrate our system through three use cases illustrating the impact of attribute uncertainty in geographic analysis.
The Dynamics of Deformed Wing Virus Concentration and Host Defensive Gene Expression after Varroa Mite Parasitism in Honey Bees, Apis mellifera

The synergistic interactions between the ectoparasitic mite Varroa destructor and Deformed wing virus (DWV) lead to the reduction in lifespan of the European honey bee Apis mellifera and often have been implicated in colony losses worldwide. However, to date, the underlying processes and mechanisms that form the multipartite interaction between the bee, mite, and virus have not been fully explained. To gain a better understanding of honey bees’ defense response to Varroa mite infestation and DWV infection, the DWV titers and transcription profiles of genes originating from RNAi, immunity, wound response, and homeostatic signaling pathways were monitored over a period of eight days. With respect to DWV, we observed low viral titers at early timepoints that coincided with high levels of Toll pathway transcription factor Dorsal, and its downstream immune effector molecules Hymenoptaecin, Apidaecin, Abaecin, and Defensin 1. However, we observed a striking increase in viral titers beginning after two days that coincided with a decrease in Dorsal levels and its corresponding immune effector molecules, and the small ubiquitin-like modifier (SUMO) ligase repressor of Dorsal, PIAS3. We observed a similar expression pattern for genes expressing transcripts for the RNA interference (Dicer/Argonaute), wound/homeostatic (Janus Kinase), and tissue growth (Map kinase/Wnt) pathways. Our results demonstrate that on a whole, honey bees are able to mount an immediate, albeit, temporally limited, immune and homeostatic response to Varroa and DWV infections, after which downregulation of these pathways leaves the bee vulnerable to expansive viral replication. The critical insights into the defense response upon Varroa and DWV challenges generated in this study may serve as a solid base for future research on the development of effective and efficient disease management strategies in honey bees.
The Pathway to Intelligence: Using Stimuli‐Responsive Materials as Building Blocks for Constructing Smart and Functional Systems

Systems that are intelligent have the ability to sense their surroundings, analyze, and respond accordingly. In nature, many biological systems are considered intelligent (e.g., humans, animals, and cells). For man‐made systems, artificial intelligence is achieved by massively sophisticated electronic machines (e.g., computers and robots operated by advanced algorithms). On the other hand, freestanding materials (i.e., not tethered to a power supply) are usually passive and static. Hence, herein, the question is asked: can materials be fabricated so that they are intelligent? One promising approach is to use stimuli‐responsive materials; these “smart” materials use the energy supplied by a stimulus available from the surrounding for performing a corresponding action. After decades of research, many interesting stimuli‐responsive materials that can sense and perform smart functions have been developed. Classes of functions discussed include practical functions (e.g., targeting and motion), regulatory functions (e.g., self‐regulation and amplification), and analytical processing functions (e.g., memory and computing). The pathway toward creating truly intelligent materials can involve incorporating a combination of these different types of functions into a single integrated system by using stimuli‐responsive materials as the basic building blocks.
The Individual and Combined Effects of the Cyanotoxins, Anatoxin-a and Microcystin-LR, on the Growth, Toxin Production, and Nitrogen Fixation of Prokaryotic and Eukaryotic Algae

Globally, eutrophication and warming of aquatic ecosystems has increased the frequency and intensity of cyanobacterial blooms and their associated toxins, with the simultaneous detection of multiple cyanotoxins often occurring. Despite the co-occurrence of cyanotoxins such as microcystins and anatoxin-a (ATX) in water bodies, their effects on phytoplankton communities are poorly understood. The individual and combined effects of microcystin-LR (MC-LR) and ATX on the cyanobacteria Microcystis spp. , and Anabaena variabilis (a.k.a. Trichormus variabilis), and the chlorophyte, Selenastrum capricornutum were investigated in the present study. Cell density, chlorophyll-a content, and the maximum quantum efficiency of photosystem II (Fv/Fm) of Microcystis cells were generally lowered after exposure to ATX or MC-LR, while the combined treatment with MC-LR and ATX synergistically reduced the chlorophyll-a concentration of Microcystis strain LE-3. Intracellular levels of microcystin in Microcystis LE-3 significantly increased following exposure to MC-LR + ATX. The maximum quantum efficiency of photosystem II of Anabaena strain UTEX B377 declined during exposure to the cyanotoxins. Nitrogen fixation by Anabaena UTEX B377 was significantly inhibited by exposure to ATX, but was unaffected by MC-LR. In contrast, the combination of both cyanotoxins (MC-LR + ATX) caused a synergistic increase in the growth of S. capricornutum. While the toxins caused an increase in the activity of enzymes that scavenge reactive oxygen species in cyanobacteria, enzyme activity was unchanged or decreased in S. capricornutum. Collectively this study demonstrates that MC-LR and ATX can selectively promote and inhibit the growth and performance of green algae and cyanobacteria, respectively, and that the combined effect of these cyanotoxins was often more intense than their individual effects on some strains. This suggests that the release of multiple cyanotoxins in aquatic ecosystems, following the collapse of blooms, may influence the succession of plankton communities.
EFR-Mediated Innate Immune Response in Arabidopsis thaliana is a Useful Tool for Identification of Novel ERQC Modulators

Plants offer a simpler and cheaper alternative to mammalian animal models for the study of endoplasmic reticulum glycoprotein folding quality control (ERQC). In particular, the Arabidopsis thaliana (At) innate immune response to bacterial peptides provides an easy means of assaying ERQC function in vivo. A number of mutants that are useful to study ERQC in planta have been described in the literature, but only for a subset of these mutants the innate immune response to bacterial elicitors has been measured beyond monitoring plant weight and some physio-pathological parameters related to the plant immune response. In order to probe deeper into the role of ERQC in the plant immune response, we monitored expression levels of the Phosphate-induced 1 (PHI-1) and reticulin-oxidase homologue (RET-OX) genes in the At ER α-Glu II rsw3 and the At UGGT uggt1-1 mutant plants, in response to bacterial peptides elf18 and flg22. The elf18 response was impaired in the rsw3 but not completely abrogated in the uggt1-1 mutant plants, raising the possibility that the latter enzyme is partly dispensable for EF-Tu receptor (EFR) signaling. In the rsw3 mutant, seedling growth was impaired only by concomitant application of the At ER α-Glu II NB-DNJ inhibitor at concentrations above 500 nM, compatibly with residual activity in this mutant. The study highlights the need for extending plant innate immune response studies to assays sampling EFR signaling at the molecular level.
CMTN-SP: A Novel Coverage-Control Algorithm for Moving-Target Nodes Based on Sensing Probability Model in Sensor Networks

The non-consecutive coverage problem for the target nodes in Sensor Networks could lead to the coverage blind area and a large amount of redundant data, which causes the bottleneck phenomenon for the communication link. A novel Coverage Control Algorithm for Moving Target Nodes Based on Sensing Probability Model (CMTN-SP) is proposed in this work. Firstly, according to the probability theory, we derive the calculation method for the expectation of the coverage quality with multiple joint nodes, which aims to reduce the coverage blind area and improving network coverage rate. Secondly, we employ the dynamic transferring mechanism of the nodes to re-optimize the deployment of the nodes, which alleviates the rapid exhaustion of the proper network energy. Finally, it is verified via the results of the simulation that the network coverage quality could not only be improved by the proposed algorithm, but the proposed algorithm could also effectively curb the rapid exhaustion of the node energy.
Convergent synaptic inputs to layer 1 cells of mouse cortex

We used whole cell recordings from slice preparations of mouse cortex to identify various inputs to neurons of layer 1. Two sensory cortical areas were targeted: a primary somatosensory area, namely, the barrel cortex of S1, and a higher order visual area, namely, V2M. Results were similar from both areas. By activating local inputs using photostimulation with caged glutamate, we also identified glutamatergic (and possibly GABAergic) inputs from all lower layers plus GABAergic inputs from nearby layer 1 neurons. However, the patterns of such inputs to layer 1 neurons showed great variation among cells. In separate experiments, we found that electrical stimulation of axons running parallel to the cortical surface in layer 1 also evoked a variety of convergent input types to layer 1 neurons, including glutamatergic “drivers” and “modulators” plus classic modulatory inputs, including serotonergic, nicotinic, α‐ and β‐adrenergic, from subcortical sites. Given that these layer 1 cells significantly affect the responses of other cortical neurons, especially via affecting the apical dendrites of pyramidal cells so important to cortical functioning, their role in cortical processing is significant. We believe that the data presented here lead to better understanding of the functioning of layer 1 neurons in their role of influencing cortical processing.