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During the process of reprogramming to induced pluripotent stem (iPS) cells, somatic cells switch from oxidative to glycolytic metabolism, a transition associated with profound mitochondrial reorganization. Neither the importance of mitochondrial remodelling for cell reprogramming, nor the molecular mechanisms controlling this process are well understood. Here, we show that an early wave of mitochondrial fragmentation occurs upon expression of reprogramming factors. Reprogramming-induced mitochondrial fission is associated with a minor decrease in mitochondrial mass but not with mitophagy. The pro-fission factor Drp1 is phosphorylated early in reprogramming, and its knockdown and inhibition impairs both mitochondrial fragmentation and generation of iPS cell colonies. Drp1 phosphorylation depends on Erk activation in early reprogramming, which occurs, at least in part, due to downregulation of the MAP kinase phosphatase Dusp6. Taken together, our data indicate that mitochondrial fission controlled by an Erk-Drp1 axis constitutes an early and necessary step in the reprogramming process to pluripotency. Reprogramming of somatic cells is a stepwise process where cells must overcome several barriers before reaching the pluripotent state. Here the authors show that mitochondrial fission in response to ERK1/2 signalling is an important early step during reprogramming to pluripotency.

At present, the domotization of homes and public buildings is becoming increasingly popular. Domotization is most commonly applied to the field of energy management, since it gives the possibility of managing the consumption of the devices connected to the electric network, the way in which the users interact with these devices, as well as other external factors that influence consumption. In buildings, Heating, Ventilation and Air Conditioning (HVAC) systems have the highest consumption rates. The systems proposed so far have not succeeded in optimizing the energy consumption associated with a HVAC system because they do not monitor all the variables involved in electricity consumption. For this reason, this article presents an agent approach that benefits from the advantages provided by a Multi-Agent architecture (MAS) deployed in a Cloud environment with a wireless sensor network (WSN) in order to achieve energy savings. The agents of the MAS learn social behavior thanks to the collection of data and the use of an artificial neural network (ANN). The proposed system has been assessed in an office building achieving an average energy savings of 41% in the experimental group offices.

This paper shows coastal evolution along the Andalusia Region (Spain) and the impacts on it of coastal structures. The study area was divided into 47 units to calculate the erosion/accretion/stability (or evolution) rates by using the DSAS extension of ArcGIS software. Evolution rates were divided into different classes from “Very high accretion” to “Very high erosion”. As a result, 9 units recorded accretion, 19 stability and 19 erosion. Further, 17 units presented a positive balance and 28 units a negative one, showing a negative net balance of 29,738.4 m2/year corresponding to the loss of 1784.30 km2 of beach surface in the 1956–2016 period. The distribution of evolution areas along the studied coast was carried out by means of the “R” project for statistical computing. The analysis evidenced the impact of rigid structures: accretion was essentially observed up-drift of ports and groins and in correspondence of protection structures, especially of breakwaters. Erosion classes were observed down-drift of ports and groins and in correspondence of revetments/seawalls, and at largest river deltas, and “stability” was observed at pocket beaches and coastal areas locally stabilized by protection structures. Last, results were used to determine the distribution of swash- and drift-aligned coastal sectors and main direction of sedimentary transport.

The global evolution of the Internet is experiencing a notable and inevitable change towards a convergent scenario known as the Internet of Things (IoT), where a large number of devices with heterogeneous characteristics and requirements have to be interconnected to serve different verticals, such as smart cities, intelligent transportation systems, smart grids, (ITS) or e-health [...]

Stevia (Stevia rebaudiana Bertoni) is gaining attention due to its sweetening power. The stevia crop is still relatively unknown in Europe, and very little is known about its nutrient requirements. Therefore, agronomic studies are needed.  Field trials were carried out in inland Spain in 2014 and 2015, in order to evaluate the yield, quality, and nutrient requirements of stevia according to planting density and harvest regime, under a continental Mediterranean climate. In 2014, the dynamics of growth, quality and accumulation of nutrients were studied during the vegetative period according to three planting densities (5.0, 7.5, and 10.0 plants m-2). No significant influence was found. The steviol glycosides concentration decreased sharply at flowering, while the leaf and steviol glycosides yields continued to increase for another 30 days or more. Yield, quality and nutrient uptake were studied during 2015 according to the three same planting densities, and three harvest regimes (one, two, and three cuts per year; all before flowering). Both factors had significant influence on most of the studied parameters. The best quality and the highest yield were not obtained under the same crop management. The highest yield was achieved with 10 plants m-2 and with 2 cuts per year, achieving around 6000 kg ha-1 of dry leaf and 650 kg ha-1 of steviol glycosides. The best quality was achieved with one cut just before flowering (12.2 % of steviol glycosides, 0.35 of Reb A to Stev ratio). The average uptakes of N, P, K, Ca, Mg and S were 35.6, 4.8, 59.9, 14.2, 5.2, and 1.6 kg t-1 of leaf.

Physical aggression is a serious and widespread problem in society, affecting people worldwide. It impacts nearly every aspect of life. While some studies explore the root causes of violent behavior, others focus on urban planning in high-crime areas. Real-time violence detection, powered by artificial intelligence, offers a direct and efficient solution, reducing the need for extensive human supervision and saving lives. This paper is a continuation of a systematic mapping study and its objective is to provide a comprehensive and up-to-date review of AI-based video violence detection, specifically in physical assaults. Regarding violence detection, the following have been grouped and categorized from the review of the selected papers: 21 challenges that remain to be solved, 28 datasets that have been created in recent years, 21 keyframe extraction methods, 16 types of algorithm inputs, as well as a wide variety of algorithm combinations and their corresponding accuracy results. Given the lack of recent reviews dealing with the detection of violence in video, this study is considered necessary and relevant.

The growing demand in food and non-food industries for camelina oil is driving the interest of farmers and contractors in investing in such feedstock. Nonetheless, the cost, performance and critical aspects related to the harvesting stage are still not properly investigated. In the present study, an ad-hoc test was performed in Spain in order to fulfill this gap. The results support the hypothesis to harvest camelina seeds with the same combine harvester used for cereal harvesting without further investment. Theoretical field capacity (TFC), effective field capacity (EFC), material capacity (MC), and field efficiency (FE) were 4.34 ha h−1, 4.22 ha h−1, 4.66 Mg h−1 FM, and 97.24%, respectively. The harvesting cost was estimated in 48.51 € ha−1. Approximately, the seed loss of 0.057 ± 0.028 Mg ha−1 FM was due to the impact of the combine harvester header and dehiscence of pods, whilst 0.036 ± 0.006 Mg ha−1 FM of seeds were lost due to inefficiency of the threshing system of the combine harvester. Adjustment of the working speed of the combine and the rotation speed of the reel may help to reduce such loss.

Defect in the SMN1 gene causes spinal muscular atrophy (SMA), which shows loss of motor neurons, muscle weakness and atrophy. While current treatment strategies, including small molecules or viral vectors, have shown promise in improving motor function and survival, achieving a definitive and long-term correction of SMA’s endogenous mutations and phenotypes remains highly challenging. We have previously developed a CRISPR-Cas9 based homology-independent targeted integration (HITI) strategy, enabling unidirectional DNA knock-in in both dividing and non-dividing cells in vivo. In this study, we demonstrated its utility by correcting an SMA mutation in mice. When combined with Smn1 cDNA supplementation, it exhibited long-term therapeutic benefits in SMA mice. Our observations may provide new avenues for the long-term and efficient treatment of inherited diseases. Spinal muscular atrophy (SMA) is caused by a defect in the SMN1 gene, leading to muscle weakness and motor neuron loss. Here, the authors show that combining CRISPR-Cas9 genome editing with gene supplementation improves survival and motor function in SMA mice, highlighting a potential new treatment avenue.

Partial reprogramming by expression of reprogramming factors (Oct4, Sox2, Klf4 and c-Myc) for short periods of time restores a youthful epigenetic signature to aging cells and extends the life span of a premature aging mouse model. However, the effects of longer-term partial reprogramming in physiologically aging wild-type mice are unknown. Here, we performed various long-term partial reprogramming regimens, including different onset timings, during physiological aging. Long-term partial reprogramming lead to rejuvenating effects in different tissues, such as the kidney and skin, and at the organismal level; duration of the treatment determined the extent of the beneficial effects. The rejuvenating effects were associated with a reversion of the epigenetic clock and metabolic and transcriptomic changes, including reduced expression of genes involved in the inflammation, senescence and stress response pathways. Overall, our observations indicate that partial reprogramming protocols can be designed to be safe and effective in preventing age-related physiological changes. We further conclude that longer-term partial reprogramming regimens are more effective in delaying aging phenotypes than short-term reprogramming.

Probabilistic Boolean Networks can capture the dynamics of complex biological systems as well as other non-biological systems, such as manufacturing systems and smart grids. In this proof-of-concept manuscript, we propose a Probabilistic Boolean Network architecture with a learning process that significantly improves the prediction of the occurrence of faults and failures in smart-grid systems. This idea was tested in a Probabilistic Boolean Network model of the WSCC nine-bus system that incorporates Intelligent Power Routers on every bus. The model learned the equality and negation functions in the different experiments performed. We take advantage of the complex properties of Probabilistic Boolean Networks to use them as a positive feedback adaptive learning tool and to illustrate that these networks could have a more general use than previously thought. This multi-layered PBN architecture provides a significant improvement in terms of performance for fault detection, within a positive-feedback network structure that is more tolerant of noise than other techniques.