The characteristics of potatoes differing in glycemic index

This thesis describes studies on seven potato cultivars with the objective of identifying a potato cultivar with a low glycemic index (GI), and to describe its tuber and starch properties. The potato cultivars were selected in consultation with potato breeders from Agrico Holland and sourced from growers in South Australia (The Mitolo Group) and Tasmania (Agronico) Australia and consisted of well established (Bintje, Desiree, Nicola, Russet Burbank) and newly introduced commercial cultivars (Carisma, Maiflower, Virginia Rose). The potato cultivars were tested for their GI according to International Standard Organisation (ISO) guidelines. In vitro enzymatic starch hydrolysis and chemical analyses were performed for each potato cultivar and correlations sought with the respective GI values. Different imaging techniques were used to study and compare cell structure and native starch granule morphology, and the effect of cooking on cell wall structure and starch gelatinization. Physicochemical and functional properties of starch from the seven potato cultivars were analyzed for amylose content, amylopectin chain length distribution, relative crystallinity, phosphorus content, granule size distribution, thermal properties and starch pasting profiles. Physicochemical, thermal and pasting properties of starch from the same cultivars of potatoes grown in the Netherlands under very different conditions were also examined

Study of Optical-Feedback Using an Integrated Laser-Modulator/Amplifier Device

We study optical-feedback effects using an integrated laser-modulator/amplifier. Our experiment and theory are agree well and provide interesting results of feedback effects on optical spectrum, spatial-hole burning, the photon density profile, and the microwave modulation

Preventing Advanced Persistent Threats in Complex Control Networks

An Advanced Persistent Threat (APT) is an emerging attack against Industrial Control and Automation Systems, that is executed over a long period of time and is difficult to detect. In this context, graph theory can be applied to model the interaction among nodes and the complex attacks affecting them, as well as to design recovery techniques that ensure the survivability of the network. Accordingly, we leverage a decision model to study how a set of hierarchically selected nodes can collaborate to detect an APT within the network, concerning the presence of changes in its topology. Moreover, we implement a response service based on redundant links that dynamically uses a secret sharing scheme and applies a flexible routing protocol depending on the severity of the attack. The ultimate goal is twofold: ensuring the reachability between nodes despite the changes and preventing the path followed by messages from being discovered.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Sentiment Analysis of Long-term Social Data during the COVID-19 Pandemic

The COVID-19 pandemic has bringing the “infodemic” in the social media worlds. Various social platforms play a significant role in instantly acquiring the latest updates of the pandemic. Social media such as Twitter and Facebook produce vast amounts of posts related to the virus, vaccines, economics, and politics. In order to figure out how public opinion and sentiments are expressed during the pandemic, this work analyzes the long-term social posts from social media and conducts sentiment analysis on tweets within 12 months. Our findings show the trend topics of long-term social communities during the pandemic and express people’s attitudes towards progress of major actions during the pandemic. We explore the main topics during the prolonged pandemic, including information surrounding economics, vaccines, and politics. Besides, we show the differences in gender-based attitudes and propose future research questions refer to the “infodemic”. We believe that our work contributes to attracting public attention to the “infodemic” of the social crisis

30 days wild: development and evaluation of a large-scale nature engagement campaign to improve well-being

There is a need to increase people’s engagement with and connection to nature, both for human well-being and the conservation of nature itself. In order to suggest ways for people to engage with nature and create a wider social context to normalise nature engagement, The Wildlife Trusts developed a mass engagement campaign, 30 Days Wild. The campaign asked people to engage with nature every day for a month. 12,400 people signed up for 30 Days Wild via an online sign-up with an estimated 18,500 taking part overall, resulting in an estimated 300,000 engagements with nature by participants. Samples of those taking part were found to have sustained increases in happiness, health, connection to nature and pro-nature behaviours. With the improvement in health being predicted by the improvement in happiness, this relationship was mediated by the change in connection to nature

Genomic Expansion of Magnetotactic Bacteria Reveals an Early Common Origin of Magnetotaxis with Lineage-specific Evolution

The origin and evolution of magnetoreception, which in diverse prokaryotes and protozoa is known as magnetotaxis and enables these microorganisms to detect Earth’s magnetic field for orientation and navigation, is not well understood in evolutionary biology. The only known prokaryotes capable of sensing the geomagnetic field are magnetotactic bacteria (MTB), motile microorganisms that biomineralize intracellular, membrane-bounded magnetic single-domain crystals of either magnetite (Fe3O4) or greigite (Fe3S4) called magnetosomes. Magnetosomes are responsible for magnetotaxis in MTB. Here we report the first large-scale metagenomic survey of MTB from both northern and southern hemispheres combined with 28 genomes from uncultivated MTB. These genomes expand greatly the coverage of MTB in the Proteobacteria, Nitrospirae, and Omnitrophica phyla, and provide the first genomic evidence of MTB belonging to the Zetaproteobacteria and “Candidatus Lambdaproteobacteria” classes. The gene content and organization of magnetosome gene clusters, which are physically grouped genes that encode proteins for magnetosome biosynthesis and organization, are more conserved within phylogenetically similar groups than between different taxonomic lineages. Moreover, the phylogenies of core magnetosome proteins form monophyletic clades. Together, these results suggest a common ancient origin of iron-based (Fe3O4 and Fe3S4) magnetotaxis in the domain Bacteria that underwent lineage-specific evolution, shedding new light on the origin and evolution of biomineralization and magnetotaxis, and expanding significantly the phylogenomic representation of MTB

Diversified Late Acceptance Search

The well-known Late Acceptance Hill Climbing (LAHC) search aims to overcome the main downside of traditional Hill Climbing (HC) search, which is often quickly trapped in a local optimum due to strictly accepting only non-worsening moves within each iteration. In contrast, LAHC also accepts worsening moves, by keeping a circular array of fitness values of previously visited solutions and comparing the fitness values of candidate solutions against the least recent element in the array. While this straightforward strategy has proven effective, there are nevertheless situations where LAHC can unfortunately behave in a similar manner to HC. For example, when a new local optimum is found, often the same fitness value is stored many times in the array. To address this shortcoming, we propose new acceptance and replacement strategies to take into account worsening, improving, and sideways movement scenarios with the aim to improve the diversity of values in the array. Compared to LAHC, the proposed Diversified Late Acceptance Search approach is shown to lead to better quality solutions that are obtained with a lower number of iterations on benchmark Travelling Salesman Problems and Quadratic Assignment Problems

Bridging the Mid-Infrared-to-Telecom Gap with Silicon Nanophotonic Spectral Translation

Expanding far beyond traditional applications in optical interconnects at telecommunications wavelengths, the silicon nanophotonic integrated circuit platform has recently proven its merits for working with mid-infrared (mid-IR) optical signals in the 2-8 {\mu}m range. Mid-IR integrated optical systems are capable of addressing applications including industrial process and environmental monitoring, threat detection, medical diagnostics, and free-space communication. Rapid progress has led to the demonstration of various silicon components designed for the on-chip processing of mid-IR signals, including waveguides, vertical grating couplers, microcavities, and electrooptic modulators. Even so, a notable obstacle to the continued advancement of chip-scale systems is imposed by the narrow-bandgap semiconductors, such as InSb and HgCdTe, traditionally used to convert mid-IR photons to electrical currents. The cryogenic or multi-stage thermo-electric cooling required to suppress dark current noise, exponentially dependent upon the ratio Eg/kT, can limit the development of small, low-power, and low-cost integrated optical systems for the mid-IR. However, if the mid-IR optical signal could be spectrally translated to shorter wavelengths, for example within the near-infrared telecom band, photodetectors using wider bandgap semiconductors such as InGaAs or Ge could be used to eliminate prohibitive cooling requirements. Moreover, telecom band detectors typically perform with higher detectivity and faster response times when compared with their mid-IR counterparts. Here we address these challenges with a silicon-integrated approach to spectral translation, by employing efficient four-wave mixing (FWM) and large optical parametric gain in silicon nanophotonic wires

Search algorithms as a framework for the optimization of drug combinations

Combination therapies are often needed for effective clinical outcomes in the management of complex diseases, but presently they are generally based on empirical clinical experience. Here we suggest a novel application of search algorithms, originally developed for digital communication, modified to optimize combinations of therapeutic interventions. In biological experiments measuring the restoration of the decline with age in heart function and exercise capacity in Drosophila melanogaster, we found that search algorithms correctly identified optimal combinations of four drugs with only one third of the tests performed in a fully factorial search. In experiments identifying combinations of three doses of up to six drugs for selective killing of human cancer cells, search algorithms resulted in a highly significant enrichment of selective combinations compared with random searches. In simulations using a network model of cell death, we found that the search algorithms identified the optimal combinations of 6-9 interventions in 80-90% of tests, compared with 15-30% for an equivalent random search. These findings suggest that modified search algorithms from information theory have the potential to enhance the discovery of novel therapeutic drug combinations. This report also helps to frame a biomedical problem that will benefit from an interdisciplinary effort and suggests a general strategy for its solution.Comment: 36 pages, 10 figures, revised versio

PGC-1α Deficiency Causes Multi-System Energy Metabolic Derangements: Muscle Dysfunction, Abnormal Weight Control and Hepatic Steatosis

The gene encoding the transcriptional coactivator peroxisome proliferator-activated receptor-γ coactivator-1α (PGC-1α) was targeted in mice. PGC-1α null (PGC-1α(−/−)) mice were viable. However, extensive phenotyping revealed multi-system abnormalities indicative of an abnormal energy metabolic phenotype. The postnatal growth of heart and slow-twitch skeletal muscle, organs with high mitochondrial energy demands, is blunted in PGC-1α(−/−) mice. With age, the PGC-1α(−/−) mice develop abnormally increased body fat, a phenotype that is more severe in females. Mitochondrial number and respiratory capacity is diminished in slow-twitch skeletal muscle of PGC-1α(−/−) mice, leading to reduced muscle performance and exercise capacity. PGC-1α(−/−) mice exhibit a modest diminution in cardiac function related largely to abnormal control of heart rate. The PGC-1α(−/−) mice were unable to maintain core body temperature following exposure to cold, consistent with an altered thermogenic response. Following short-term starvation, PGC-1α(−/−) mice develop hepatic steatosis due to a combination of reduced mitochondrial respiratory capacity and an increased expression of lipogenic genes. Surprisingly, PGC-1α(−/−) mice were less susceptible to diet-induced insulin resistance than wild-type controls. Lastly, vacuolar lesions were detected in the central nervous system of PGC-1α(−/−) mice. These results demonstrate that PGC-1α is necessary for appropriate adaptation to the metabolic and physiologic stressors of postnatal life