Climate change is one of the processes that have already overstepped the safe planetary boundaries, together with the rate of biodiversity loss and human interference with the nitrogen and phosphorus cycles. The three processes are related to agriculture and, as such, to both food safety and food security, and ultimately to human health. Adaptation to climate change is a difficult breeding objective because of its complexity, its unpredictability, and its location specificity. However, one strategy exists, which is based on a more dynamic use of agrobiodiversity in agriculture through the cultivation of evolutionary populations. In this review, we show how the translation into agricultural practice of nearly 100 years of research on evolutionary populations and mixtures is able to address the complexity of climate change while stabilizing yield, decreasing the use of most agrochemicals, thus reducing emissions and producing healthy food.In this perspective, we highlight the role of surface heterogeneity in electrosynthesis reactions. Heterogeneities may come in the form of distinct crystallographic facets, boundaries between facets or grains, or point defects. We approach this topic from a foundation of surface science, where signatures from model systems provide understanding of observations on more complex and higher-surface-area materials. In parallel, probe-based techniques can inform directly on spatial variation across electrode surfaces. We call attention to the role spectroscopy can play in understanding the impact of these heterogeneities in electrocatalyst activity and selectivity, particularly where these surface features have effects extending into the electrolyte double layer.Sensors are a key component of the Internet of Things (IoTs) to collect information of environments or objects. Considering the tremendous number and complex working conditions of sensors, multifunction and self-powered feathers are two basic requirements. Nanogenerators are a kind of devices based on the triboelectric, piezoelectric, or pyroelectric effects to harvest ambient energy and then converting to electricity. selleck The hybridized nanogenerators that combined multiple effects in one device have great potential in multifunctional self-powered sensors because of the unique superiority such as generating electrical signals directly, responding to diverse stimuli, etc. This review aims at introducing the latest advancements of hybridized nanogenerators for multifunctional self-powered sensing. Firstly, the principles and sensor prototypes based on TENG are summarized. To avoid signal interference and energy insufficiently, the multifunctional self-powered sensors based on hybridized nanogenerators are reviewed. At last, the challenges and future development of multifunctional self-powered sensors have prospected.Memristive devices share remarkable similarities to biological synapses, dendrites, and neurons at both the physical mechanism level and unit functionality level, making the memristive approach to neuromorphic computing a promising technology for future artificial intelligence. However, these similarities do not directly transfer to the success of efficient computation without device and algorithm co-designs and optimizations. Contemporary deep learning algorithms demand the memristive artificial synapses to ideally possess analog weighting and linear weight-update behavior, requiring substantial device-level and circuit-level optimization. Such co-design and optimization have been the main focus of memristive neuromorphic engineering, which often abandons the "non-ideal" behaviors of memristive devices, although many of them resemble what have been observed in biological components. Novel brain-inspired algorithms are being proposed to utilize such behaviors as unique features to further enhance the efficiency and intelligence of neuromorphic computing, which calls for collaborations among electrical engineers, computing scientists, and neuroscientists.Explosion of gene therapy approaches for treating rare monogenic and common liver disorders created an urgent need for disease models able to replicate human liver cellular environment. Available models lack 3D liver structure or are unable to survive in long-term culture. We aimed to generate and test a 3D culture system that allows long-term maintenance of human liver cell characteristics. The in vitro whole-organ "Bioreactor grown Artificial Liver Model" (BALM) employs a custom-designed bioreactor for long-term 3D culture of human induced pluripotent stem cells-derived hepatocyte-like cells (hiHEPs) in a mouse decellularized liver scaffold. Adeno-associated viral (AAV) and lentiviral (LV) vectors were introduced by intravascular injection. Substantial AAV and LV transgene expression in the BALM-grown hiHEPs was detected. Measurement of secreted proteins in the media allowed non-invasive monitoring of the system. We demonstrated that humanized whole-organ BALM is a valuable tool to generate pre-clinical data for investigational medicinal products.Aberrant activation of Wnt signaling triggered by mutations in either Adenomatous Polyposis Coli (APC) or CTNNB1 (β-catenin) is a hallmark of colorectal cancers (CRC). As part of a program to develop epigenetic regulators for cancer therapy, we developed carboxamide-substituted benzhydryl amines (CBAs) bearing either aryl or heteroaryl groups that selectively targeted histone lysine demethylases (KDMs) and functioned as inhibitors of the Wnt pathway. A biotinylated variant of N-((5-chloro-8-hydroxyquinolin-7-yl) (4-(diethylamino)phenyl)-methyl)butyramide (CBA-1) identified KDM3A as a binding partner. KDM3A is a Jumonji (JmjC) domain-containing demethylase that is significantly upregulated in CRC. KDM3A regulates the demethylation of histone H3's lysine 9 (H3K9Me2), a repressive marker for transcription. Inhibiting KDM3 increased H3K9Me2 levels, repressed Wnt target genes, and curtailed in vitro CRC cell proliferation. CBA-1 also exhibited in vivo inhibition of Wnt signaling in a zebrafish model without displaying in vivo toxicity.An incoherent feedforward loop (IFFL) is a network motif known for its ability to accelerate responses and generate pulses. It remains an open question to understand the behavior of IFFLs in contexts with high levels of retroactivity, where an upstream transcription factor binds to numerous downstream binding sites. Here we study the behavior of IFFLs by simulating and comparing ODE models with different levels of retroactivity. We find that increasing retroactivity in an IFFL can increase, decrease, or keep the network's response time and pulse amplitude constant. This suggests that increasing retroactivity, traditionally considered an impediment to designing robust synthetic systems, could be exploited to improve the performance of IFFLs. In contrast, we find that increasing retroactivity in a negative autoregulated circuit can only slow the response. The ability of an IFFL to flexibly handle retroactivity may have contributed to its significant abundance in both bacterial and eukaryotic regulatory networks.