The Old New England Homestead On The Hill

https://digitalcommons.library.umaine.edu/mmb-vp/3330/thumbnail.jp

The Old New England Homestead On The Hill

https://digitalcommons.library.umaine.edu/mmb-vp/3331/thumbnail.jp

NF-κB: blending metabolism, immunity, and inflammation

The procurement and management of nutrients and ability to fight infections are fundamental requirements for survival. These defense responses are bioenergetically costly, requiring the immune system to balance protection against pathogens with the need to maintain metabolic homeostasis. NF-κB transcription factors are central regulators of immunity and inflammation. Over the last two decades, these factors have emerged as a pivotal node coordinating the immune and metabolic systems in physiology and the etiopathogenesis of major threats to human health, including cancer, autoimmunity, chronic inflammation, and others. In this review, we discuss recent advances in understanding how NF-κB-dependent metabolic programs control inflammation, metabolism, and immunity and how improved knowledge of them may lead to better diagnostics and therapeutics for widespread human diseases

Unlocking the NF-κB Conundrum: Embracing Complexity to Achieve Specificity.

Transcription factors of the nuclear factor κB (NF-κB) family are central coordinating regulators of the host defence responses to stress, injury and infection. Aberrant NF-κB activation also contributes to the pathogenesis of some of the most common current threats to global human health, including chronic inflammatory diseases, autoimmune disorders, diabetes, vascular diseases and the majority of cancers. Accordingly, the NF-κB pathway is widely considered an attractive therapeutic target in a broad range of malignant and non-malignant diseases. Yet, despite the aggressive efforts by the pharmaceutical industry to develop a specific NF-κB inhibitor, none has been clinically approved, due to the dose-limiting toxicities associated with the global suppression of NF-κB. In this review, we summarise the main strategies historically adopted to therapeutically target the NF-κB pathway with an emphasis on oncology, and some of the emerging strategies and newer agents being developed to pharmacologically inhibit this pathway

Rewired lipid metabolism as an actionable vulnerability of aggressive colorectal carcinoma

Cancer cells reprogram lipid metabolism to fuel cell division, adaptation to stress, and metastatic dissemination. NF-κB transcription factors control this mechanism in aggressive Consensus Molecular Subtype (CMS)4 of colorectal carcinoma (CRC) via triacylglycerol (TAG) lipase, carboxylesterase 1 (CES1), thereby linking obesity-associated inflammation with metabolic adaptation and cytoprotection from lipid-induced toxicity. Our findings identify a potential therapeutic route to treat patients with metastasis-prone CRC and provide an example for targeting core tumor subtype-based vulnerabilities in cancers beyond CR

Engaging Farmers, Culinary Schools, and Communities in Value-Added Production to Strengthen Local Food Systems

Value-added products can generate farm income and improve community food access, yet lack of available kitchen infrastructure and labor can limit farm production capacity. This project explored how community-based culinary schools might fill the gap. A unique “product share” model was identified and piloted, meeting the collective needs of farmers, a culinary school, and urban consumers. By researching farmer crop availability and business model preferences, and aligning value-added production with community food preferences, we demonstrate a successful pilot indicative that similar initiatives can be replicated in other metropolitan areas, with potential to engage cross-disciplinary extension professionals

An algorithm based on OmniView technology to reconstruct sagittal and coronal planes of the fetal brain from volume datasets acquired by three-dimensional ultrasound

To describe a novel algorithm, based on the new display technology 'OmniView', developed to visualize diagnostic sagittal and coronal planes of the fetal brain from volumes obtained by three-dimensional (3D) ultrasonography

Vision-enhanced Peg-in-Hole for automotive body parts using semantic image segmentation and object detection

Artificial Intelligence (AI) is an enabling technology in the context of Industry 4.0. In particular, the automotive sector is among those who can benefit most of the use of AI in conjunction with advanced vision techniques. The scope of this work is to integrate deep learning algorithms in an industrial scenario involving a robotic Peg-in-Hole task. More in detail, we focus on a scenario where a human operator manually positions a carbon fiber automotive part in the workspace of a 7 Degrees of Freedom (DOF) manipulator. To cope with the uncertainty on the relative position between the robot and the workpiece, we adopt a three stage strategy. The first stage concerns the Three-Dimensional (3D) reconstruction of the workpiece using a registration algorithm based on the Iterative Closest Point (ICP) paradigm. Such a procedure is integrated with a semantic image segmentation neural network, which is in charge of removing the background of the scene to improve the registration. The adoption of such network allows to reduce the registration time of about 28.8%. In the second stage, the reconstructed surface is compared with a Computer Aided Design (CAD) model of the workpiece to locate the holes and their axes. In this stage, the adoption of a Convolutional Neural Network (CNN) allows to improve the holes’ position estimation of about 57.3%. The third stage concerns the insertion of the peg by implementing a search phase to handle the remaining estimation errors. Also in this case, the use of the CNN reduces the search phase duration of about 71.3%. Quantitative experiments, including a comparison with a previous approach without both the segmentation network and the CNN, have been conducted in a realistic scenario. The results show the effectiveness of the proposed approach and how the integration of AI techniques improves the success rate from 84.5% to 99.0%

Pilot Scale Evaluation of Wild Saccharomyces cerevisiae Strains in Aglianico

In winemaking, the influence of Saccharomyces cerevisiae strains on the aromatic components of wine is well recognized on a laboratory scale, but few studies deal with the comparison of numerous strains on a pilot scale fermentation. In this scenario, the present work aimed to validate the fermentative behavior of seven wild S. cerevisiae strains on pilot-scale fermentations to evaluate their impact on the aromatic profiles of the resulting wines. The strains, isolated from grapes of different Italian regional varieties, were tested in pilot-scale fermentation trials performed in the cellar in 1 hL of Aglianico grape must. Then, wines were analyzed for their microbiological cell loads, main chemical parameters of enological interest (ethanol, total sugars, fructose, glucose, total and volatile acidity, malic and lactic acids) and volatile aroma profiles by GC/MS/SPME. Seventy-six volatile compounds belonging to six different classes (esters, alcohols, terpenes, aldehydes, acids, and ketones) were identified. The seven strains showed different trends and significant differences, and for each class of compounds, high-producing and low-producing strains were found. Since the present work was performed at a pilot-scale level, mimicking as much as possible real working conditions, the results obtained can be considered as a validation of the screened S. cerevisiae strains and a strategy to discriminate in real closed conditions strains able to impart desired wine sensory features