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Abstract Plant roots dynamically respond to nitrogen availability by executing a signaling and transcriptional cascade resulting in altered plant growth that is optimized for nutrient uptake. The NIN-LIKE PROTEIN 7 (NLP7) transcription factor senses nitrogen and, along with its paralog NLP6, partially coordinates transcriptional responses. While the post-translational regulation of NLP6 and NLP7 is well established, their upstream transcriptional regulation remains understudied in Arabidopsis (Arabidopsis thaliana) and other plant species. Here, we dissected a known sub-circuit upstream of NLP6 and NLP7 in Arabidopsis, which was predicted to contain multiple multi-node feedforward loops suggestive of an optimized design principle of nitrogen transcriptional regulation. This sub-circuit comprises AUXIN RESPONSE FACTOR 18 (ARF18), ARF9, DEHYDRATION-RESPONSIVE ELEMENT-BINDING PROTEIN 26 (DREB26), Arabidopsis NAC-DOMAIN CONTAINING PROTEIN 32 (ANAC032), NLP6 and NLP7 and their regulation of NITRITE REDUCTASE 1 (NIR1). Conservation and divergence of this circuit and its influence on nitrogen-dependent root system architecture were similarly assessed in tomato (Solanum lycopersicum). The specific binding sites of these factors within their respective promoters and their putative cis-regulatory architectures were identified. The direct or indirect nature of these interactions was validated in planta. The resulting models were genetically validated in varying concentrations of available nitrate by measuring the transcriptional output of the network revealing rewiring of nitrogen regulation across distinct plant lineages. 

Phase transitions in halide perovskites triggered by external stimuli generate significantly different material properties, providing a great opportunity for broad applications. Here, we demonstrate an In-based, charge-ordered (In+/In3+) inorganic halide perovskite with the composition of Cs2In(I)In(III)Cl6 in which a pressure-driven semiconductor-to-metal phase transition exists. The single crystals, synthesized via a solid-state reaction method, crystallize in a distorted perovskite structure with space group I4/m with a = 17.2604(12) Å, c = 11.0113(16) Å if both the strong reflections and superstructures are considered. The supercell was further confirmed by rotation electron diffraction measurement. The pressure-induced semiconductor-to-metal phase transition was demonstrated by high-pressure Raman and absorbance spectroscopies and was consistent with theoretical modeling. This type of charge-ordered inorganic halide perovskite with a pressure-induced semiconductor-to-metal phase transition may inspire a range of potential applications. 

Abstract For hardware artificial intelligence, the central task is to design and develop artificial synapses with needed characteristics. Here, the design and experimental demonstration of a van der Waals (vdW) photo‐ferroelectric synapse are reported. In the photo‐ferroelectric synapse, the synaptic memory is extracted by reading the photocurrent, and written or edited by electrical pulses. The semiconducting vdW organic‐inorganic halide perovskite ((R)‐(–)‐1‐cyclohexylethylammonium)PbI₃(R‐CYHEAPbI₃) photo‐ferroelectric serves as the model photo‐ferroelectric channel. Here, the vdW organic layer provides ferroelectric dipole and the PbI₆octahedron is responsible for photon absorption and charge transport. The R‐CYHEAPbI₃photo‐ferroelectric synapse show a writing/reading dynamics with >200 synaptic states, close to 10³on/off ratio, and reasonable endurance and retention characteristics. With the experimentally measured weight dynamics (parallel reading through ferroelectric photovoltaic effect and writing by electrical pulses) of R‐CYHEAPbI₃synapses, the feasibility of using a crossbar circuit to implement classic training and inference of hand‐written digits is presented. An image recognition accuracy of up to 90% is obtained. The demonstration of such a vdW photo‐ferroelectric synapse opens a window in the design of advanced devices for artificial intelligence.