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We developed genetically-encoded fluorescent sensors based on Förster Resonance Energy Transfer to monitor phosphatidic acid (PA) fluctuations in the plasma membrane using Spo20 as PA-binding motif. Basal PA levels and phospholipase D activity varied in different cell types. In addition, stimuli that activate PA phosphatases, leading to lower PA levels, increased lamellipodia and filopodia formation. Lower PA levels were observed in the leading edge than in the trailing edge of migrating HeLa cells. In MSC80 and OLN93 cells, which are stable cell lines derived from Schwann cells and oligodendrocytes, respectively, a higher ratio of diacylglycerol to PA levels was demonstrated in the membrane processes involved in myelination, compared to the cell body. We propose that the PA sensors reported here are valuable tools to unveil the role of PA in a variety of intracellular signaling pathways.

Background There is a clearly increasing trend of wildfires that become catastrophic in some countries such as the United States, Australia, Russia, Portugal, Greece, and Spain. Fuel is one of the key components that influences fire behavior and its effects. Assessing the fuel load and distribution of its components in the landscape provides effective fire management treatments in terms of fire prevention campaigns on a scientific basis. This study aims to evaluate the litter, duff, and herb fuels in highly flammable coniferous forest types in Ukrainian Polissia. To estimate relationships between forestry variables that reflect the characteristics of the pine stand (DBH, height of the stand, age, relative density, stock of the plantation etc.) and the load of litter, duff, and herb fuels (CWD, FWD, litter, live grass, etc.), correlation analysis was used. To analyze difference between groups of sampling plots that have different forests, we use generalized linear mixed models including random effects of sampling plot type. Cluster analysis was performed using k-means partitioning method and Calinski-Harabasz criterion. To assess the significance of individual variables on which the variation of forest fuel depends, the random forest algorithm was used; for variable selection, we used two parameters: the percent increase in mean squared error and the Gini impurity index. Results The research revealed that in the pine forest stands, the stock of litter and duff varies from 15.5 (15 years) to 140 ton/ha (139 years). When modeling, the humidity level of the forest site (soil) significantly affects the dynamics of forest fuel accumulation. In fresh types of forest-growing conditions, the forest litter stock increases to the age of 80–90 years; then, it strongly decreases, while in wet forest types, continuous forest fuel stock accumulation is established during the entire growth period. Moreover, the results showed that the forest fuel load was influenced by the soil fertility. The stock of live and dead herbaceous fuel in fresh and wet conditions is not statistically different, and soil moisture has not had a significant impact. Fine woody debris stocks were more dependent on stand productivity and practically does not depend on the soil fertility index, site moisture content, and its age and ranged from 0.4 to 1.9 t/ha (1 h), from 0.1 to 2.2 t/ha (10 h), and from 0 to 1.6 t/ha (100 h). Conclusions The obtained results enabled to develop mathematical models for estimating litter and duff stocks in the Polissia forest stands based on stand characteristic and the soil humidity level. Moreover, the results will serve as basis to develop local forest fuel models as well as to determine potential fire hazards and a fire behavior modeling process in coniferous forests of that region. These models constitute the basis for the national set of fuel model development for each nature zone of Ukraine.

The measurement of roadside wood stacks in the forest still plays an important role in many forestry operations. Traditional manual measuring methods can be laborious, inaccurate and error-prone. Therefore, the issue is whether 2.5D or 3D optical remote sensing measuring methods provide more precise or detailed results and advantages in further data processing. This study examined and partly developed nine different manual, photo-optical, iPad®-LiDAR and handheld laser scanner-LiDAR-based wood stack measurement methods. Forty-seven wood stacks, ranging from 8.9 to 209.3 m3 (totalling approximately 2700 m3), were measured and compared using these nine methods. All the methods give volume estimations, and none can be seen to give the real or true wood stack gross volume. Surprisingly, the results varied significantly within and between the individual methods, with up to a 9% mean relative deviation. The relative deviation is strongly dependent on the size of the wood stack. The 3D measurement methods using iPad® RGB and LiDAR recorded lower timber volumes than the other methods, in contrast to the method based on samples taken with handheld laser scanner-LiDAR, which overestimated the volume. Generally, optical- and laser-based surveying techniques could be more widely applied in measuring wood stacks in the future. However, such automatic wood stack gross volume determination approaches still face some challenges, regarding accuracy in the case of the 2.5D methods and the lack of automatisation in the case of 3D methods. Consequently, further research is required in the near future.

Protein fitness prediction plays a crucial role in the advancement of protein engineering endeavours. However, the combinatorial complexity of the protein sequence space and the limited availability of assay-labelled data hinder the efficient optimization of protein properties. Data-driven strategies utilizing machine learning methods have emerged as a promising solution, yet their dependence on labelled training datasets poses a significant obstacle. To overcome this challenge, in this work, we explore various ways of introducing the latent information present in evolutionarily related sequences (homologous sequences) into the training process. To do so, we establish several strategies based on semi-supervised learning (unsupervised pre-processing and wrapper methods) and perform a comprehensive comparison using 19 datasets containing protein-fitness pairs. Our findings reveal that using the information present in the homologous sequences can improve the performance of the models, especially when the number of available labelled sequences is considerably low. Specifically, the combination of a sequence encoding method based on Direct Coupling Analysis (DCA), with MERGE (a hybrid regression framework that combines evolutionary information with supervised learning) and an SVM regressor, outperforms other encodings (PAM250, UniRep, eUniRep) and other semi-supervised wrapper methods (Tri-Training Regressor, Co-Training Regressor). In summary, the demonstrated performance gains of this strategy mark a substantial leap towards more robust and reliable predictive models for protein engineering tasks. This advancement holds the potential to streamline the design and optimisation of proteins for diverse applications in biotechnology and therapeutics. Scientific Contribution We explore several semi-supervised learning strategies capable of including the homologous sequences (unlabelled) to the protein of interest in the training process. Among them, we present two new methods to exploit the information in the homologous sequences: i) a new generalised version of MERGE capable of employing any regressor as a base estimator; ii) the Tri-Training Regressor method, an adaptation of the Tri-Training method for regression problems. We find that the information inherent in the homologous sequences has the ability to improve the predictive capacity of models when the number of available sequences is scarce, especially when using the DCA encoding together with MERGE and an SVM regressor.

Neurogranin (Ng) is a calmodulin (CaM)-binding protein that is phosphorylated by protein kinase C (PKC) and is highly enriched in the dendrites and spines of telencephalic neurons. It is proposed to be involved in regulating CaM availability in the post-synaptic environment to modulate the efficiency of excitatory synaptic transmission. There is a close relationship between Ng and cognitive performance; its expression peaks in the forebrain coinciding with maximum synaptogenic activity, and it is reduced in several conditions of impaired cognition. We studied the expression of Ng in cultured hippocampal neurons and found that both protein and mRNA levels were about 10% of that found in the adult hippocampus. Long-term blockade of NMDA receptors substantially decreased Ng expression. On the other hand, treatments that enhanced synaptic activity such as long-term bicuculline treatment or co-culture with glial cells or cholesterol increased Ng expression. Chemical long-term potentiation (cLTP) induced an initial drop of Ng, with a minimum after 15 min followed by a slow recovery during the next 2–4 h. This effect was most evident in the synaptosome-enriched fraction, thus suggesting local synthesis in dendrites. Lentiviral expression of Ng led to increased density of both excitatory and inhibitory synapses in the second and third weeks of culture. These results indicate that Ng expression is regulated by synaptic activity and that Ng promotes the synaptogenesis process. Given its relationship with cognitive function, we propose targeting of Ng expression as a promising strategy to prevent or alleviate the cognitive deficits associated with aging and neuropathological conditions.

Recognizing transcription start sites is key to gene identification. Several approaches have been employed in related problems such as detecting translation initiation sites or promoters, many of the most recent ones based on machine learning. Deep learning methods have been proven to be exceptionally effective for this task, but their use in transcription start site identification has not yet been explored in depth. Also, the very few existing works do not compare their methods to support vector machines (SVMs), the most established technique in this area of study, nor provide the curated dataset used in the study. The reduced amount of published papers in this specific problem could be explained by this lack of datasets. Given that both support vector machines and deep neural networks have been applied in related problems with remarkable results, we compared their performance in transcription start site predictions, concluding that SVMs are computationally much slower, and deep learning methods, specially long short-term memory neural networks (LSTMs), are best suited to work with sequences than SVMs. For such a purpose, we used the reference human genome GRCh38. Additionally, we studied two different aspects related to data processing: the proper way to generate training examples and the imbalanced nature of the data. Furthermore, the generalization performance of the models studied was also tested using the mouse genome, where the LSTM neural network stood out from the rest of the algorithms. To sum up, this article provides an analysis of the best architecture choices in transcription start site identification, as well as a method to generate transcription start site datasets including negative instances on any species available in Ensembl. We found that deep learning methods are better suited than SVMs to solve this problem, being more efficient and better adapted to long sequences and large amounts of data. We also create a transcription start site (TSS) dataset large enough to be used in deep learning experiments.

Background: There is evidence that early admission to the palliative care (PC) program in adult cancer patients improves symptoms management, reduces unplanned hospital admissions, minimizes aggressive cancer treatments, and enables patients to make decisions about their end-of-life (EOL) care. Objectives: This retrospective cohort study aimed to determine whether late admission to a PC program is associated with aggressive treatment at the EOL in adult patients with oncological diseases from their admission until death. Design/Methods: The study evaluated the aggressiveness in EOL management in patients with advanced stage oncological diseases who died between 2017 and 2019. The study population was divided into two groups based on the time of admission to the PC program. Aggressiveness at the EOL was measured using five criteria: treatment, hospital admission and duration, emergency department care, and/or intensive care unit utilization. Results: The study found a significant difference in the rate of aggressive EOL treatments between late admission to PC care and early admission [adjusted EOL 79.6% versus 70.4%; relative risk (RR): 1.98, 90% CI: 1.08–3.59, p: 0.061]; In the analysis of secondary variables, a significant association was observed between early admission to PC and the suspension of active treatments at the EOL, leading to a decrease in aggressiveness (77% versus 55.8%; RR: 1.38, 95% CI: 1.14–1.67, p: 0.004). Conclusion: Our findings suggest that early referral to PC services is associated with less aggressive treatment at the EOL, including suspension of active treatments.

This work presents a study of chromatic and luminance stimuli in low-, medium-, and high-frequency stimulation to evoke steady-state visual evoked potential (SSVEP) in the behind-the-ears area. Twelve healthy subjects participated in this study. The electroencephalogram (EEG) was measured on occipital (Oz) and left and right temporal (TP9 and TP10) areas. The SSVEP was evaluated in terms of amplitude, signal-to-noise ratio (SNR), and detection accuracy using power spectral density analysis (PSDA), canonical correlation analysis (CCA), and temporally local multivariate synchronization index (TMSI) methods. It was found that stimuli based on suitable color and luminance elicited stronger SSVEP in the behind-the-ears area, and that the response of the SSVEP was related to the flickering frequency and the color of the stimuli. Thus, green-red stimulus elicited the highest SSVEP in medium-frequency range, and green-blue stimulus elicited the highest SSVEP in high-frequency range, reaching detection accuracy rates higher than 80%. These findings will aid in the development of more comfortable, accurate and stable BCIs with electrodes positioned on the behind-the-ears (hairless) areas.

The appearance of the Acheulean is one of the hallmarks of human evolution. It represents the emergence of a complex behavior, expressed in the recurrent manufacture of large-sized tools, with standardized forms, implying more advance forethought and planning by hominins than those required by the precedent Oldowan technology. The earliest known evidence of this technology dates back to c . 1.7 Ma. and is limited to two sites (Kokiselei [Kenya] and Konso [Ethiopia]), both of which lack functionally-associated fauna. The functionality of these earliest Acheulean assemblages remains unknown. Here we present the discovery of another early Acheulean site also dating to c . 1.7 Ma from Olduvai Gorge. This site provides evidence of the earliest steps in developing the Acheulean technology and is the oldest Acheulean site in which stone tools occur spatially and functionally associated with the exploitation of fauna. Simple and elaborate large-cutting tools (LCT) and bifacial handaxes co-exist at FLK West, showing that complex cognition was present from the earliest stages of the Acheulean. Here we provide a detailed technological study and evidence of the use of these tools on the butchery and consumption of fauna, probably by early Homo erectus sensu lato .

Unmanned Aerial-Underwater Vehicles (UAUVs) arise as a new kind of unmanned system capable of performing equally well in multiple mediums and seamlessly transitioning between them. This work focuses in the modeling and trajectory tracking control of a special class of air-underwater vehicle with full torque actuation and a single thrust force directed along the vehicle’s vertical axis. In particular, a singularity-free representation is required in order to orient the vehicle in any direction, which becomes critical underwater in order to direct the thrust force in the direction of motion and effectively overcome the increased drag and buoyancy forces. A quaternion based representation is used for this purpose. A hierarchical controller is proposed, where trajectory tracking is accomplished by a Proportional-Integral-Derivative (PID) controller with compensation of the restoring forces. The outer trajectory tracking control loop provides the thrust force and desired orientation. The latter is fed to the inner attitude control loop, where a nonlinear quaternion feedback is employed. A gain scheduling strategy is used to deal with the drastic change in medium density during transitions. The proposed scheme is studied through numerical simulations, while real time experiments validate the good performance of the system.