The Court of Justice of the European Union Clarifies When Human Embryonic Stem Cells Can Be Patented

On 18 December 2014, in International Stem Cell Corporation v. Comptroller General of Patents, Designs and Trade Marks (C-364/13), the Court of Justice of the European Union (CJEU) delivered an important decision regarding the scope of the exclusion from patentability on morality-related grounds under Article 6(2) of the EU Biotech Directive. The Court made an important distinction between embryonic stem cell technologies based on fertilised human ovum and those based on unfertilised human ovum stimulated by parthenogenesis. The CJEU held, in particular, that a human ovum: (i) who is unfertilized and (ii) whose division and further development has been stimulated by parthenogenesis, is not a human embryo under Article 6(2)(c) of the Biotech Directive, if it in itself has not the inherent capacity of developing into a human being, this matter to be ascertained by the national court in the light of current scientific knowledge. By doing so, the Court clarified its previous ruling in Brüstle (October 2011)

A Comprehensive Review on Time Sensitive Networks with a Special Focus on Its Applicability to Industrial Smart and Distributed Measurement Systems

The groundbreaking transformations triggered by the Industry 4.0 paradigm have dramati-cally reshaped the requirements for control and communication systems within the factory systems of the future. The aforementioned technological revolution strongly affects industrial smart and distributed measurement systems as well, pointing to ever more integrated and intelligent equipment devoted to derive accurate measurements. Moreover, as factory automation uses ever wider and complex smart distributed measurement systems, the well-known Internet of Things (IoT) paradigm finds its viability also in the industrial context, namely Industrial IoT (IIoT). In this context, communication networks and protocols play a key role, directly impacting on the measurement accuracy, causality, reliability and safety. The requirements coming both from Industry 4.0 and the IIoT, such as the coexistence of time-sensitive and best effort traffic, the need for enhanced horizontal and vertical integration, and interoperability between Information Technology (IT) and Operational Technology (OT), fostered the development of enhanced communication subsystems. Indeed, established tech-nologies, such as Ethernet and Wi-Fi, widespread in the consumer and office fields, are intrinsically non-deterministic and unable to support critical traffic. In the last years, the IEEE 802.1 Working Group defined an extensive set of standards, comprehensively known as Time Sensitive Networking (TSN), aiming at reshaping the Ethernet standard to support for time-, mission-and safety-critical traffic. In this paper, a comprehensive overview of the TSN Working Group standardization activity is provided, while contextualizing TSN within the complex existing industrial technological panorama, particularly focusing on industrial distributed measurement systems. In particular, this paper has to be considered a technical review of the most important features of TSN, while underlining its applicability to the measurement field. Furthermore, the adoption of TSN within the Wi-Fi technology is addressed in the last part of the survey, since wireless communication represents an appealing opportunity in the industrial measurement context. In this respect, a test case is presented, to point out the need for wirelessly connected sensors networks. In particular, by reviewing some literature contributions it has been possible to show how wireless technologies offer the flexibility necessary to support advanced mobile IIoT applications

A learning model for battery lifetime prediction of LoRa sensors in additive manufacturing

Today, an innovative leap for wireless sensor networks, leading to the realization of novel and intelligent industrial measurement systems, is represented by the requirements arising from the Industry 4.0 and Industrial Internet of Things (IIoT) paradigms. In fact, unprecedented challenges to measurement capabilities are being faced, with the ever-increasing need to collect reliable yet accurate data from mobile, battery-powered nodes over potentially large areas. Therefore, optimizing energy consumption and predicting battery life are key issues that need to be accurately addressed in such IoT-based measurement systems. This is the case for the additive manufacturing application considered in this work, where smart battery-powered sensors embedded in manufactured artifacts need to reliably transmit their measured data to better control production and final use, despite being physically inaccessible. A Low Power Wide Area Network (LPWAN), and in particular LoRaWAN (Long Range WAN), represents a promising solution to ensure sensor connectivity in the aforementioned scenario, being optimized to minimize energy consumption while guaranteeing long-range operation and low-cost deployment. In the presented application, LoRa equipped sensors are embedded in artifacts to monitor a set of meaningful parameters throughout their lifetime. In this context, once the sensors are embedded, they are inaccessible, and their only power source is the originally installed battery. Therefore, in this paper, the battery lifetime prediction and estimation problems are thoroughly investigated. For this purpose, an innovative model based on an Artificial Neural Network (ANN) is proposed, developed starting from the discharge curve of lithium-thionyl chloride batteries used in the additive manufacturing application. The results of experimental campaigns carried out on real sensors were compared with those of the model and used to tune it appropriately. The results obtained are encouraging and pave the way for interesting future developments

Red blood cell precursor mass as an independent determinant of serum erythropoietin level.

Serum erythropoietin (sEpo) concentration is primarily related to the rate of renal production and, under the stimulus of hypoxia, increases exponentially as hemoglobin (Hb) decreases. Additional factors, however, appear to influence sEpo, and in this work, we performed studies to evaluate the role of the red blood cell precursor mass. We first compared the relationship of sEpo with Hb in patients with low versus high erythroid activity. The first group included 27 patients with erythroid aplasia or hypoplasia having serum transferrin receptor (sTfR) levels 10 mg/L (erythroid activity > 2 times normal). There was no difference between the two groups with respect to Hb (8.3 +/- 1.6 v 8.0 +/- 1.3 g/dL, P > .05), but sEpo levels were notably higher in patients with low erythroid activity (1,601 +/- 1,542 v 235 +/- 143 mU/mL, P < . 001). In fact, multivariate analysis of variance (ANOVA) showed that, at any given Hb level, sEpo was higher in patients with low erythroid activity (P < .0001). Twenty patients undergoing allogeneic or autologous bone marrow transplantation (BMT) were then investigated. A marked increase in sEpo was seen in all cases at the time of marrow aplasia, disproportionately high when compared with the small decrease in Hb level. Sequential studies were also performed in five patients with iron deficiency anemia undergoing intravenous (IV) iron therapy. Within 24 to 72 hours after starting iron treatment, marked decreases in sEpo (up to one log magnitude) were found before any change in Hb level. Similar observations were made in patients with megaloblastic anemia and in a case of pure red blood cell aplasia. These findings point to an inverse relationship between red blood cell precursor mass and sEpo: at any given Hb level, the higher the number of red blood cell precursors, the lower the sEpo concentration. The most likely explanation for this is that sEpo levels are regulated not only by the rate of renal production, but also by the rate of utilization by erythroid cells

Differential Epigenetic Changes in the Dorsal Hippocampus of Male and Female SAMP8 Mice: A Preliminary Study

Alzheimer’s disease (AD) is the most common age-related neurodegenerative disease characterized by memory loss and cognitive impairment. The causes of the disease are not well understood, as it involves a complex interaction between genetic, environmental, and epigenetic factors. SAMP8 mice have been proposed as a model for studying late-onset AD, since they show age-related learning and memory deficits as well as several features of AD pathogenesis. Epigenetic changes have been described in SAMP8 mice, although sex differences have never been evaluated. Here we used western blot and qPCR analyses to investigate whether epigenetic markers are differentially altered in the dorsal hippocampus, a region important for the regulation of learning and memory, of 9-month-old male and female SAMP8 mice. We found that H3Ac was selectively reduced in male SAMP8 mice compared to male SAMR1 control mice, but not in female mice, whereas H3K27me3 was reduced overall in SAMP8 mice. Moreover, the levels of HDAC2 and JmjD3 were increased, whereas the levels of HDAC4 and Dnmt3a were reduced in SAMP8 mice compared to SAMR1. In addition, levels of HDAC1 were reduced, whereas Utx and Jmjd3 were selectively increased in females compared to males. Although our results are preliminary, they suggest that epigenetic mechanisms in the dorsal hippocampus are differentially regulated in male and female SAMP8 mice

An IoT Measurement System Based on LoRaWAN for Additive Manufacturing

The Industrial Internet of Things (IIoT) paradigm represents a significant leap forward for sensor networks, potentially enabling wide-area and innovative measurement systems. In this scenario, smart sensors might be equipped with novel low-power and long range communication technologies to realize a so-called low-power wide-area network (LPWAN). One of the most popular representative cases is the LoRaWAN (Long Range WAN) network, where nodes are based on the widespread LoRa physical layer, generally optimized to minimize energy consumption, while guaranteeing long-range coverage and low-cost deployment. Additive manufacturing is a further pillar of the IIoT paradigm, and advanced measurement capabilities may be required to monitor significant parameters during the production of artifacts, as well as to evaluate environmental indicators in the deployment site. To this end, this study addresses some specific LoRa-based smart sensors embedded within artifacts during the early stage of the production phase, as well as their behavior once they have been deployed in the final location. An experimental evaluation was carried out considering two different LoRa end-nodes, namely, the Microchip RN2483 LoRa Mote and the Tinovi PM-IO-5-SM LoRaWAN IO Module. The final goal of this research was to assess the effectiveness of the LoRa-based sensor network design, both in terms of suitability for the aforementioned application and, specifically, in terms of energy consumption and long-range operation capabilities. Energy optimization, battery life prediction, and connectivity range evaluation are key aspects in this application context, since, once the sensors are embedded into artifacts, they will no longer be accessible

Driver Drowsiness Detection: A Machine Learning Approach on Skin Conductance

The majority of car accidents worldwide are caused by drowsy drivers. Therefore, it is important to be able to detect when a driver is starting to feel drowsy in order to warn them before a serious accident occurs. Sometimes, drivers are not aware of their own drowsiness, but changes in their body signals can indicate that they are getting tired. Previous studies have used large and intrusive sensor systems that can be worn by the driver or placed in the vehicle to collect information about the driver’s physical status from a variety of signals that are either physiological or vehicle-related. This study focuses on the use of a single wrist device that is comfortable for the driver to wear and appropriate signal processing to detect drowsiness by analyzing only the physiological skin conductance (SC) signal. To determine whether the driver is drowsy, the study tests three ensemble algorithms and finds that the Boosting algorithm is the most effective in detecting drowsiness with an accuracy of 89.4%. The results of this study show that it is possible to identify when a driver is drowsy using only signals from the skin on the wrist, and this encourages further research to develop a real-time warning system for early detection of drowsiness

Reciprocal interference between the NRF2 and LPS signaling pathways on the immune-metabolic phenotype of peritoneal macrophages

The metabolic and immune adaptation to extracellular signals allows macrophages to carry out specialized functions involved in immune protection and tissue homeostasis. Nuclear factor erythroid 2-related factor 2 (NRF2) is a transcription factor that coordinates cell redox and metabolic responses to stressors. However, the individual and concomitant activation of NRF2 and inflammatory pathways have been poorly investigated in isolated macrophages. We here took advantage of reporter mice for the transcriptional activities of NRF2 and nuclear factor-kB (NF\u3baB), a key transcription factor in inflammation, and observe a persisting reciprocal interference in the response of peritoneal macrophages to the respective activators, tert-Butylhydroquinone (tBHQ) and lipopolysaccharide (LPS). When analyzed separately by gene expression studies, these pathways trigger macrophage-specific metabolic and proliferative target genes that are associated with tBHQ-induced pentose phosphate pathway (PPP) with no proliferative response, and with opposite effects observed with LPS. Importantly, the simultaneous administration of tBHQ&nbsp;+&nbsp;LPS alters the effects of each individual pathway in a target gene-specific manner. In fact, this co-treatment potentiates the effects of tBHQ on the antioxidant enzyme, HMOX1, and the antibacterial enzyme, IRG1, respectively; moreover, the combined treatment reduces tBHQ activity on the glycolytic enzymes, TALDO1 and TKT, and decreases LPS effects on the metabolic enzyme IDH1, the proliferation-related proteins KI67 and PPAT, and the inflammatory cytokines IL-1\u3b2, IL-6, and TNF\u3b1. Altogether, our results show that the activation of NRF2 redirects the metabolic, immune, and proliferative response of peritoneal macrophages to inflammatory signals, with relevant consequences for the pharmacological treatment of diseases that are associated with unopposed inflammatory responses