Cloning and characterization of a developmentally regulated sea urchin cDNA encoding glutamine synthetase

A 2935-bp cDNA clone encoding glutamine synthetase (GS) was isolated from a cDNA library prepared from four-blastomere Paracentrotus lividus sea urchin embryos. The sequence consists of a 75-bp 5' untranslated region (5'-UTR) followed by a 1095-bp coding region corresponding to a 365-amino-acid (aa) protein, a 1747-bp 3'-UTR and a terminal 18-bp poly(A) tail. The encoded protein shows about 66% identical residues, as compared with human and lobster class-II GS. The sequence contains the Mn(2+)-binding aa and the highly conserved aa regions observed in other GS. Northern blot analyses show that the GS mRNA is present in the sea urchin egg and is developmentally regulated in the embryo.     

Influence of the contact model and support characteristics on the partition constant for kinetically controlled synthesis of peptides

The influence of the physical characteristics of the support and the contact model on the partition constant (p) in kinetically controlled peptide synthesis was studied. Large values for p were obtained using a small pore size carrier and a recirculation reactor. However, with a larger pore size carrier and the same contact model, p values were lower, the latter conditions were more favorable for product synthesis.     

Morphology and phase transition of high melt temperature crystallized poly(vinylidene fluoride)

When PVDF is crystallized at temperatures above 155°C it presents a multiform morphology composed of ringed, non ringed and mixed spherulites. Infrared spectroscopy showed that the ringed spherulites are formed exclusively by the phase when crystallization takes place at temperatures below 155°C. Higher temperatures induce a solid-state phase transformation in these structures, increasing the amount of phase with crystallization time. The rate at which this transformation takes place increases with crystallization temperature. The non ringed spherulites, only formed at crystallization temperatures above 155°C, consist predominantly of the phase, crystallized from the melt, with small phase inclusions. The melt process of the different spherulites, observed by optical microscopy and calorimetric measurements (DSC) showed that the melt temperature of the phase, originated from the phase transition, is 8°C higher than that crystallized directly from the melt. Optical micrographs of samples heated up to 186°C and quickly cooled allowed visualization of the ringed spherulite regions which underwent the phase transformation at different crystallization times and temperatures.     

On a selection and scheduling problem in automatic storage and retrieval warehouses

Warehousing is one of the main components of the supply chain and its optimisation is crucial to achieve global efficiency. Warehouse operations involve receiving, shipping, storing and order picking, among other things, and the coordinated optimisation of all these different operations is highly complex. This paper examines a real selection and scheduling problem that arises in an automatic storage/retrieval warehouse system involving the scheduling of forklift pickup operations. The objective is to minimise the total loading time of the vehicles performing transportation, while respecting their departure due dates. This complex problem is approached via a two-phase decomposition method, combining both exact and heuristic procedures. The performance of the proposed solution method is evaluated using extensive computational results from several scenarios from a real case study using data from a real mattress warehouse.     

Receiver-side nonlinearities mitigation using an extended iterative decision-based technique

This work presents an iterative receiver cancellation technique for mitigating the inband distortion introduced by a nonlinear wideband transmitter power amplifier (PA). The proposed decision-based technique employs a Wiener-Hammerstein model that accounts for the nonlinear transfer function and memory of the PA as well as for the wireless propagation channel. As such, the mitigation technique can be seen as a generalization of existing iterative decision-based techniques assuming memoryless PA nonlinearities. For successful distortion mitigation, the iterative technique requires an estimate of the nonlinear model that characterizes the PA. We propose to perform this model identification at the receiver, embedded in an iterative decision-based scheme, avoiding the nonideal analog-to-digital feedback loop associated with transmitter-based model identification. A stochastic algorithm is proposed for the model identification providing the necessary PA model parameters required for symbol detection. In addition, we analyze the convergence properties of the proposed technique. Simulation results confirm that the proposed mitigation technique provides distortion cancellation at almost the same level to the case of perfect knowledge of the PA model. These results enable us to employ power amplifiers with more relaxed linearity requirement, moving the operation point to a region with improved power efficiency while reducing the system overall degradation.     

Low-Cost Identifiers for Ubiquitous Computing

Any device we want to connect to a global network, e.g. Internet, should have a unique global identifier. However, the size of this identifier can be an unacceptable overhead for devices with limited resources (sensors, toys, disposable devices, micro-robots, etc.), because conventional protocols use full addresses to transmit, process, and store the data required for routing. The usual solution for such devices is to limit the address space to 1 or 2 bytes, but this sacrifices the global unicity of the identifiers. The proposal presented in this article enables devices with limited resources to use reduced addresses that globally identify hosts. We propose the use of abbreviated addresses for routing. We have developed a new protocol named ADSR that takes advantage of these new addresses. This protocol is a modified version of DSR based on the use of abbreviated addresses. The abbreviation procedure can lead to two different nodes having the same address, which we will term collision. ADSR allows rather than avoids collisions. The foundations of this protocol, and some results of an implementation are also presented in this article.     

Effects of Mediterranean dehesa management on epiphytic lichens

Spanish holm oak (Quercus ilex subsp. ballota) open woodlands (dehesas) maintain a high diversity of plants and animals compared to other forested Mediterranean habits, but little is known about the responses of epiphytic lichens to different management regimes that are applied to this woodland type. The present study was carried out in central-southern Spain and included four management regimes: agriculture, grazing of sheep, grassland grazed by wild ungulates (deer), and abandoned dehesas covered by shrubs. Total species richness and cover exhibited considerable variation among management regimes. Both parameters tended to decrease with the intensity of management, abandoned dehesas maintaining a higher number of species than more intensively managed habitats. Lichen composition also significantly differed among the four regimes. Nitrophytic species were clearly associated with more intensive management regimes (farming or livestock management), whereas non-nitrophytic species favored abandoned dehesas.     

Intelligent and behavioral-based detection of malware in IoT spectrum sensors

The number of Cyber-Physical Systems (CPS) available in industrial environments is growing mainly due to the evolution of the Internet-of-Things (IoT) paradigm. In such a context, radio frequency spectrum sensing in industrial scenarios is one of the most interesting applications of CPS due to the scarcity of the spectrum. Despite the benefits of operational platforms, IoT spectrum sensors are vulnerable to heterogeneous malware. The usage of behavioral fingerprinting and machine learning has shown merit in detecting cyberattacks. Still, there exist challenges in terms of (i) designing, deploying, and evaluating ML-based fingerprinting solutions able to detect malware attacks affecting real IoT spectrum sensors, (ii) analyzing the suitability of kernel events to create stable and precise fingerprints of spectrum sensors, and (iii) detecting recent malware samples affecting real IoT spectrum sensors of crowdsensing platforms. Thus, this work presents a detection framework that applies device behavioral fingerprinting and machine learning to detect anomalies and classify different botnets, rootkits, backdoors, ransomware and cryptojackers affecting real IoT spectrum sensors. Kernel events from CPU, memory, network, file system, scheduler, drivers, and random number generation have been analyzed, selected, and monitored to create device behavioral fingerprints. During testing, an IoT spectrum sensor of the ElectroSense platform has been infected with ten recent malware samples (two botnets, three rootkits, three backdoors, one ransomware, and one cryptojacker) to measure the detection performance of the framework in two different network configurations. Both supervised and semi-supervised approaches provided promising results when detecting and classifying malicious behaviors from the eight previous malware and seven normal behaviors. In particular, the framework obtained 0.88–0.90 true positive rate when detecting the previous malicious behaviors as unseen or zero-day attacks and 0.94–0.96 F1-score when classifying them.