Isolation of antimicrobial compounds from aniseed and techno-economic feasibility report for industrial-scale application

This work evaluates the antimicrobial activity of the fractionated aqueous extract obtained from the aerial parts of aniseed. Fifteen fractions were collected by column chromatography using ethanol and water as mobile phases. All fractions were tested as potential antimicrobial agents against three common food-spoilage microorganisms: a yeast (Saccharomyces cerevisiae), a mold (Aspergillus awamori) and a bacterium (Lactobacillus delbrueckii subsp. bulgaricus). Fraction 2 exhibited the highest antimicrobial activity in both types of susceptibility test, and therefore, it was further studied for the identification of bioactive compounds. GC-MS analysis of Fraction 2 revealed the presence of threo-Anethole glycol. Based on laboratory-scale experiments, a process flowsheet for potential industrial-scale application is proposed combined with a techno-economic feasibility report. Fraction 2 with the detected compound threo-Anethole glycol showed promising results encouraging the attempts for industrial-scale application of antimicrobial compounds produced from aniseed.     

Development of Deep Learning Models for Predicting the Effects of Exposure to Engineered Nanomaterials on Daphnia magna

This study presents the results of applying deep learning methodologies within the ecotoxicology field, with the objective of training predictive models that can support hazard assessment and eventually the design of safer engineered nanomaterials (ENMs). A workflow applying two different deep learning architectures on microscopic images of Daphnia magna is proposed that can automatically detect possible malformations, such as effects on the length of the tail, and the overall size, and uncommon lipid concentrations and lipid deposit shapes, which are due to direct or parental exposure to ENMs. Next, classification models assign specific objects (heart, abdomen/claw) to classes that depend on lipid densities and compare the results with controls. The models are statistically validated in terms of their prediction accuracy on external D. magna images and illustrate that deep learning technologies can be useful in the nanoinformatics field, because they can automate time‐consuming manual procedures, accelerate the investigation of adverse effects of ENMs, and facilitate the process of designing safer nanostructures. It may even be possible in the future to predict impacts on subsequent generations from images of parental exposure, reducing the time and cost involved in long‐term reproductive toxicity assays over multiple generations.     

Optimized placement of virtualized resources for 5G services exploiting live migration

This paper focuses on a Centralized Radio Access Network solution adopting the concept of resource disaggregation. In this context, it proposes a heuristic suitable to optimally assign Base Band Unit processing functions in softwarized Radio Access Networks to different servers, taking into consideration their processing requirements with the aim to minimize the overall energy consumption. It also proposes the adoption of live migration of virtualized resources, in order to dynamically reallocate these functions to different servers that better match the continuously changing characteristics of 5G services, for increased energy efficiency purposes. The benefits associated with live migration are quantified through a series of experiments. Our results show a reduction of the number of switched-on servers through live migration that leads to a notable improvement in terms of resource and energy efficiency.

     

High Responsivity and Response Speed Single‐Layer Mixed‐Cation Lead Mixed‐Halide Perovskite Photodetectors Based on Nanogap Electrodes Manufactured on Large‐Area Rigid and Flexible Substrates

Mixed‐cation lead mixed‐halide perovskites are employed as the photoactive material in single‐layer solution‐processed photodetectors fabricated with coplanar asymmetric nanogap Al–Au and indium tin oxide–Al electrodes. The nanogap electrodes, bearing an interelectrode distance of ≈10 nm, are patterned via adhesion lithography, a simple, low‐cost, and high‐throughput technique. Different electrode shapes and sizes are demonstrated on glass and flexible plastic substrates, effectively engineering the device architecture, and, along with perovskite film and material optimization, paving the way toward devices with tunable operational characteristics. The optimized coplanar nanogap junction perovskite photodetectors show responsivities up to 33 A W^?1, specific detectivity on the order of 10¹¹ Jones, and response times below 260 ns, while retaining a low dark current (0.3 nA) under ?2 V reverse bias. These values outperform the vast majority of perovskite photodetectors reported so far, while avoiding the complicated fabrication steps involved in conventional multilayer device structures. This work highlights the promising potential of the proposed asymmetric nanogap electrode architecture for application in the field of flexible optoelectronics.     

The Diagnostic Potential of Amyloidogenic Proteins

Neurodegenerative disorders are a highly prevalent class of diseases, whose pathological mechanisms start before the appearance of any clear symptoms. This fact has prompted scientists to search for biomarkers that could aid early treatment. These currently incurable pathologies share the presence of aberrant aggregates called amyloids in the nervous system, which are composed of specific proteins. In this review, we discuss how these proteins, their conformations and modifications could be exploited as biomarkers for diagnostic purposes. We focus on proteins that are associated with the most prevalent neurodegenerative disorders, including Alzheimer’s and Parkinson’s diseases, amyotrophic lateral sclerosis, and frontotemporal dementia. We also describe current challenges in detection, the most recent techniques with diagnostic potentials and possible future developments in diagnosis.     

Pepsin Promotes Activation of Epidermal Growth Factor Receptor and Downstream Oncogenic Pathways,at Slightly Acidic and Neutral pH,in Exposed Hypopharyngeal Cells

Pepsin refluxate is considered a risk factor for laryngopharyngeal carcinogenesis. Non-acidic pepsin was previously linked to an inflammatory and tumorigenic effect on laryngopharyngeal cells in vitro. Yet there is no clear evidence of the pepsin-effect on a specific oncogenic pathway and the importance of pH in this process. We hypothesized that less acidic pepsin triggers the activation of a specific oncogenic factor and related-signalling pathway. To explore the pepsin-effect in vitro, we performed intermittent exposure of 15 min, once per day, for a 5-day period, of human hypopharyngeal primary cells (HCs) to pepsin (1 mg/mL), at a weakly acidic pH of 5.0, a slightly acidic pH of 6.0, and a neutral pH of 7.0. We have documented that the extracellular environment at pH 6.0, and particularly pH 7.0, vs. pH 5.0, promotes the pepsin-effect on HCs, causing increased internalized pepsin and cell viability, a pronounced activation of EGFR accompanied by NF-κB and STAT3 activation, and a significant upregulation of EGFR, AKT1, mTOR, IL1β, TNF-α, RELA(p65), BCL-2, IL6 and STAT3. We herein provide new evidence of the pepsin-effect on oncogenic EGFR activation and its related-signaling pathway at neutral and slightly acidic pH in HCs, opening a window to further explore the prevention and therapeutic approach of laryngopharyngeal reflux disease.     

High-throughput biomarker segmentation on ovarian cancer tissue microarrays via hierarchical normalized cuts

We present a system for accurately quantifying the presence and extent of stain on account of a vascular biomarker on tissue microarrays. We demonstrate our flexible, robust, accurate, and high-throughput minimally supervised segmentation algorithm, termed hierarchical normalized cuts (HNCuts) for the specific problem of quantifying extent of vascular staining on ovarian cancer tissue microarrays. The high-throughput aspect of HNCut is driven by the use of a hierarchically represented data structure that allows us to merge two powerful image segmentation algorithms-a frequency weighted mean shift and the normalized cuts algorithm. HNCuts rapidly traverses a hierarchical pyramid, generated from the input image at various color resolutions, enabling the rapid analysis of large images (e.g., a 1500 × 1500 sized image under 6 s on a standard 2.8-GHz desktop PC). HNCut is easily generalizable to other problem domains and only requires specification of a few representative pixels (swatch) from the object of interest in order to segment the target class. Across ten runs, the HNCut algorithm was found to have average true positive, false positive, and false negative rates (on a per pixel basis) of 82%, 34%, and 18%, in terms of overlap, when evaluated with respect to a pathologist annotated ground truth of the target region of interest. By comparison, a popular supervised classifier (probabilistic boosting trees) was only able to marginally improve on the true positive and false negative rates (84% and 14%) at the expense of a higher false positive rate (73%), with an additional computation time of 62% compared to HNCut. We also compared our scheme against a k-means clustering approach, which both the HNCut and PBT schemes were able to outperform. Our success in accurately quantifying the extent of vascular stain on ovarian cancer TMAs suggests that HNCut could be a very powerful tool in digital pathology and bioinformatics applications where it could be used to facilitate computer-assisted prognostic predictions of disease outcome.     

NBTI-Aware Data Allocation Strategies for Scratchpad Based Embedded Systems

The push to embed reliable and low-power memories architectures into modern systems-on-chip is driving the EDA community to develop new design techniques and circuit solutions that can concurrently optimize aging effects due to Negative Bias Temperature Instability (NBTI), and static power consumption due to leakage mechanisms. While recent works have shown how conventional leakage optimization techniques can help mitigate NBTI-induced aging effects on cache memories, in this paper we focus specifically on scratchpad memory (SPM) and present novel software approaches as a means of alleviating the NBTI-induced aging effects. In particular, we demonstrate how intelligent software directed data allocation strategies can extend the lifetime of partitioned SPMs by means of distributing the idleness across the memory sub-banks.     

Alkylphenols and phthalates in bottled waters

The aim of this study was to investigate the occurrence of endocrine disrupting compounds (EDCs) in bottled waters. The examined compounds were bisphenol A (BPA), nonylphenol (NP), tert-octylphenol (tOP), dimethyl phthalate (DMP), diethyl phthalate (DEP), di-n-butyl phthalate (DBP), butyl benzyl phthalate (BBP), di(2-ethylhexyl)phthalate (DEHP) and di(n-octyl)phthalate (DNOP). The presence of EDCs in bottled waters under poor storage conditions was also investigated after exposure outdoors under realistic conditions for 15 and 30 days. EDCs were recovered after liquid-liquid extraction and determined by employing Gas Chromatography-Mass Spectrometry. Most of these compounds were detected in bottled water from different brands purchased from local market. Storage at outdoor conditions had no significant effect on the concentrations of the examined compounds. Only BPA occurred at higher concentrations in polycarbonate containers exhibited an increasing trend during exposure. The estimated exposure to EDCs via consumption of drinking water was very low.     

Light microscopy image de-noising using optimized LPA-ICI filter

Microscopic images are often corrupted by noise, where Poisson noise is one of the major types that can damage them. The local polynomial approximation (LPA) filter supported by the intersection confidence interval (ICI) rule was considered as an efficient filter for image de-noising. However, this filter depends on several parameters that affect its performance. In order to determine the optimal parameters, the present study employed the classic LPA-ICI (C-LPA-ICI) filter supported by optimization algorithms, namely the genetic algorithm (GA) and the particle swarm optimization (PSO) in the context of light microscopy imaging systems. Nevertheless, inclusion of the optimization algorithms increased the computational time. A novel automatic technique entitled “Standard Optimized LPA-ICI” (SO-LPA-ICI) is proposed. In this context, the average of the optimized ICI parameters was calculated, which obtained from both LPA-ICI-based GA (G-LPA-ICI) and LPA-ICI-based PSO (P-LPA-ICI). Thus, the proposed SO-LPA-ICI is included the optimal ICI parameters without optimization iterations. This procedure is proposed to speed up the optimized filter. A pool of 50 rats’ renal microscopic images is involved to test the proposed approach. A comparative study was conducted to compare the effectiveness of the four methods, namely C-LPA-ICI, G-LPA-ICI, P-LPA-ICI, and the SO-LPA-ICI for de-noising in the presence of Poisson noise. The experimental results established the outstanding performance of the SO-LPA-ICI in terms of the PSNR, MAE, and MSSIM with 28.27, 7.65, and 0.93 values, respectively. In addition, the proposed approach achieved fast de-noising compared to the G-LPA-ICI and the P-LPA-ICI.