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Root-knot nematodes (RKN) represent one of the most damaging plant-parasitic nematode genera worldwide. RNAi-mediated suppression of essential nematode genes provides a novel biotechnological strategy for the development of sustainable pest-control methods. Here, we used a Host Induced Gene Silencing (HIGS) approach by stacking dsRNA sequences into a T-DNA construct to target three essential RKN genes: cysteine protease (Mi-cpl), isocitrate lyase (Mi-icl), and splicing factor (Mi-sf), called dsMinc1, driven by the pUceS8.3 constitutive soybean promoter. Transgenic dsMinc1-T4 plants infected with Meloidogyne incognita showed a significant reduction in gall formation (57–64%) and egg masses production (58–67%), as well as in the estimated reproduction factor (60–78%), compared with the susceptible non-transgenic cultivar. Galls of the RNAi lines are smaller than the wild-type (WT) plants, whose root systems exhibited multiple well-developed root swellings. Transcript levels of the three RKN-targeted genes decreased 13- to 40-fold in nematodes from transgenic cotton galls, compared with those from control WT galls. Finally, the development of non-feeding males in transgenic plants was 2–6 times higher than in WT plants, indicating a stressful environment for nematode development after RKN gene silencing. Data strongly support that HIGS of essential RKN genes is an effective strategy to improve cotton plant tolerance. This study presents the first application of dsRNA sequences to target multiple genes to promote M. incognita tolerance in cotton without phenotypic penalty in transgenic plants.

(S)-Norcoclaurine is synthesized in vivo through a metabolic pathway that ends with (S)-norcoclaurine synthase (NCS). The former constitutes the scaffold for the biosynthesis of all benzylisoquinoline alkaloids (BIAs), including many drugs such as the opiates morphine and codeine and the semi-synthetic opioids oxycodone, hydrocodone, and hydromorphone. Unfortunately, the only source of complex BIAs is the opium poppy, leaving the drug supply dependent on poppy crops. Therefore, the bioproduction of (S)-norcoclaurine in heterologous hosts, such as bacteria or yeast, is an intense area of research nowadays. The efficiency of (S)-norcoclaurine biosynthesis is strongly dependent on the catalytic efficiency of NCS. Therefore, we identified vital NCS rate-enhancing mutations through the rational transition-state macrodipole stabilization method at the Quantum Mechanics/Molecular Mechanics (QM/MM) level. The results are a step forward for obtaining NCS variants able to biosynthesize (S)-norcoclaurine on a large scale.

Over the last decade, ocean sunfish movements have been monitored worldwide using various satellite tracking methods. This study reports the near-real time monitoring of finescale (< 10 m) behaviour of sunfish. The study was conducted in southern Portugal in May 2014 and involved satellite tags and underwater and surface robotic vehicles to measure both the movements and the contextual environment of the fish. A total of four individuals were tracked using custom-made GPS satellite tags providing geolocation estimates of fine-scale resolution. These accurate positions further informed sunfish areas of restricted search (ARS), which were directly correlated to steep thermal frontal zones. Simultaneously, and for two different occasions, an Autonomous Underwater Vehicle (AUV) videorecorded the path of the tracked fish and detected buoyant particles in the water column. Importantly, the densities of these particles were also directly correlated to steep thermal gradients. Thus, both sunfish foraging behaviour (ARS) and possibly prey densities, were found to be influenced by analogous environmental conditions. In addition, the dynamic structure of the water transited by the tracked individuals was described by a Lagrangian modelling approach. The model informed the distribution of zooplankton in the region, both horizontally and in the water column, and the resultant simulated densities positively correlated with sunfish ARS behaviour estimator (r(s) = 0.184, p < 0.001). The model also revealed that tracked fish opportunistically displace with respect to subsurface current flow. Thus, we show how physical forcing and current structure provide a rationale for a predator's finescale behaviour observed over a two weeks in May 2014.

Owing to their distinct physical and chemical properties, inorganic nanoparticles (NPs) have shown promising results in preclinical cancer therapy, but designing and engineering them for effective therapeutic purposes remains a challenge. Although a comprehensive database of inorganic NP research is not currently available, it is crucial for developing effective cancer therapies. In this context, machine learning (ML) has emerged as a transformative tool, but its adaptation to nanomedicine is hindered by inexistent or small datasets. Here we assembled a large database of inorganic NPs, comprising experimental datasets from 745 preclinical studies in cancer nanomedicine. Using descriptive statistics and explainable ML models we mined this database to gain knowledge of inorganic NP design patterns and inform future NP research for cancer treatment. Our analyses suggest that NP shape and therapy type are prominent features in determining in vivo efficacy, measured as a percentage of tumour reduction. Moreover, our database provides a large-scale open-access resource for discriminative ML that the broader nanotechnology community can utilize. Our work blueprints data mining for translational cancer research and offers evidence for standardizing NP reporting to accelerate and de-risk inorganic NP-based drug delivery, which may help to improve patient outcomes in clinical settings. This analysis leverages a large-scale literature review, text mining, statistics and machine learning to identify trends, shortcomings and future opportunities in developing and deploying inorganic nanoparticles for cancer diagnosis and therapy.

FCT (Fundação para a Ciência e Tecnologia, Portugal) and FEDER (European Fund for Regional Development)-COMPETEQREN-EU for financial support to the research centers CQ/UM (UID/QUI/00686/2021), and CBMA (Ref. UIDB/04050/2020), as well as a PhD grant to J. C. Ferreira (SFRH/BD/133207/2017 and COVID/BD/151978/2021). The NMR spectrometer Bruker Avance III 400 (part of the National NMR Network) was financed by FCT and FEDER.

Double-walled oxide nanotube structures are interesting for a wide range of applications, from photocatalysis to drug delivery. In this work, a progressive oxidation method to fabricate double-walled nanotube structures is reported in detail. The approach is based on the electrodeposition of metallic iron nanowires, in porous alumina templates, followed by a selective chemical etching, nanoscale Kirkendall effect, a fast oxidation and out-diffusion of the metallic core structure during thermal annealing. To validate the formation mechanism of such core-shell structure, chemical composition and atomic structure were assessed. The resulting hematite nanotubes have a high degree of uniformity, along several microns, and a nanoscopic double-walled structure.

The implementation of renewable energy sources is rapidly growing in the electrical sector. This is a major step for civilization since it will reduce the carbon footprint and ensure a sustainable future. Nevertheless, these sources of energy are far from perfect and require complementary technologies to ensure dispatchable energy and this requires storage. In the last few decades, redox flow batteries (RFB) have been revealed to be an interesting alternative for this application, mainly due to their versatility and scalability. This technology has been the focus of intense research and great advances in the last decade. This review aims to summarize the most relevant advances achieved in the last few years, i.e., from 2015 until the middle of 2021. A synopsis of the different types of RFB technology will be conducted. Particular attention will be given to vanadium redox flow batteries (VRFB), the most mature RFB technology, but also to the emerging most promising chemistries. An in-depth review will be performed regarding the main innovations, materials, and designs. The main drawbacks and future perspectives for this technology will also be addressed.

Despite technological advances, two-dimensional electrophoresis (2DE) of biological fluids, such as vitreous, remains a major challenge. In this study, artificial neural network was applied to optimize the recovery of vitreous proteins and its detection by 2DE analysis through the combination of several solubilizing agents (CHAPS, Genapol, DTT, IPG buffer), temperature, and total voltage. The highest protein recovery (94.9% ± 4.5) was achieved using 4% (w/v) CHAPS, 0.1% (v/v) Genapol, 20 mM DTT, and 2% (v/v) IPG buffer. Two iterations were required to achieve an optimized response (580 spots) using 4% (w/v) CHAPS, 0.2% (v/v) Genapol, 60 mM DTT, and 0.5% (v/v) IPG buffer at 35 kVh and 25 °C, representing a 2.4-fold improvement over the standard initial conditions of the experimental design. The analysis of depleted vitreous using the optimized protocol resulted in an additional 1.3-fold increment in protein detection over the optimal output, with an average of 761 spots detected in vitreous from different vitreoretinopathies. Our results clearly indicate the importance of combining the appropriate amount of solubilizing agents with a suitable control of the temperature and voltage to obtain high-quality gels. The high-throughput of this model provides an effective starting point for the optimization of 2DE protocols. This experimental design can be adapted to other types of matrices.

Resin Transfer Molding (RTM) is a widely used composite manufacturing process where liquid resin is injected into a closed mold filled with a fibrous preform. By applying this process, large pieces with complex shapes can be produced on an industrial scale, presenting excellent properties and quality. A true physical phenomenon occurring in the RTM process, especially when using vegetable fibers, is related to the absorption of resin by the fiber during the infiltration process. The real effect is related to the slowdown in the advance of the fluid flow front, increasing the mold filling time. This phenomenon is little explored in the literature, especially for non-isothermal conditions. In this sense, this paper does a numerical study of the liquid injection process in a closed and heated mold. The proposed mathematical modeling considers the radial, three-dimensional, and transient flow, variable injection pressure, and fluid viscosity, including the effect of liquid fluid absorption by the reinforcement (fiber). Simulations were carried out using Computational Fluid Dynamic tools. The numerical results of the filling time were compared with experimental results, and a good approximation was obtained. Further, the pressure, temperature, velocity, and volumetric fraction fields, as well as the transient history of the fluid front position and injection fluid volumetric flow rate, are presented and analyzed.

Stress is a well-established trigger for a number of neuropsychiatric disorders, as it alters both structure and function of several brain regions and its networks. Herein, we conduct a longitudinal neuroimaging study to assess how a chronic unpredictable stress protocol impacts the structure of the rat brain and its functional connectome in both high and low responders to stress. Our results reveal the changes that stress triggers in the brain, with structural atrophy affecting key regions such as the prelimbic, cingulate, insular and retrosplenial, somatosensory, motor, auditory and perirhinal/entorhinal cortices, the hippocampus, the dorsomedial striatum, nucleus accumbens, the septum, the bed nucleus of the stria terminalis, the thalamus and several brain stem nuclei. These structural changes are associated with increasing functional connectivity within a network composed by these regions. Moreover, using a clustering based on endocrine and behavioural outcomes, animals were classified as high and low responders to stress. We reveal that susceptible animals (high responders) develop local atrophy of the ventral tegmental area and an increase in functional connectivity between this area and the thalamus, further spreading to other areas that link the cognitive system with the fight-or-flight system. Through a longitudinal approach we were able to establish two distinct patterns, with functional changes occurring during the exposure to stress, but with an inflection point after the first week of stress when more prominent changes were seen. Finally, our study revealed differences in functional connectivity in a brainstem–limbic network that distinguishes resistant and susceptible responders before any exposure to stress, providing the first potential imaging-based predictive biomarkers of an individual’s resilience/vulnerability to stressful conditions.