Rotational-Linear Attack: A New Framework of Cryptanalysis on ARX ciphers with Applications to Chaskey

In this paper, we formulate a new framework of cryptanalysis called rotational-linear attack on ARX ciphers. We firstly build an efficient distinguisher for the cipher $E$ consisted of the rotational attack and the linear attack together with some intermediate variables. Then a key recovery technique is introduced with which we can recover some bits of the last whitening key in the related-key scenario. To decrease data complexity of our attack, we also apply a new method, called bit flipping, in the rotational cryptanalysis for the first time and the effective partitioning technique to the key-recovery part. Applying the new framework of attack to the MAC algorithm Chaskey, we build a full-round distinguisher over it. Besides, we have recovered $21$ bits of information of the key in the related-key scenario, for keys belonging to a large weak-key class based on 6-round distinguisher. The data complexity is $2^{38.8}$ and the time complexity is $2^{46.8}$. Before our work, the rotational distinguisher can only be used to reveal key information by checking weak-key conditions. This is the first time it is applied in a last-rounds key-recovery attack. We build a 17-round rotational-linear distinguisher for ChaCha permutation as an improvement compared to single rotational cryptanalysis over it

Visual question answering model for fruit tree disease decision-making based on multimodal deep learning

Visual Question Answering (VQA) about diseases is an essential feature of intelligent management in smart agriculture. Currently, research on fruit tree diseases using deep learning mainly uses single-source data information, such as visible images or spectral data, yielding classification and identification results that cannot be directly used in practical agricultural decision-making. In this study, a VQA model for fruit tree diseases based on multimodal feature fusion was designed. Fusing images and Q&A knowledge of disease management, the model obtains the decision-making answer by querying questions about fruit tree disease images to find relevant disease image regions. The main contributions of this study were as follows: (1) a multimodal bilinear factorized pooling model using Tucker decomposition was proposed to fuse the image features with question features: (2) a deep modular co-attention architecture was explored to simultaneously learn the image and question attention to obtain richer graphical features and interactivity. The experiments showed that the proposed unified model combining the bilinear model and co-attentive learning in a new network architecture obtained 86.36% accuracy in decision-making under the condition of limited data (8,450 images and 4,560k Q&A pairs of data), outperforming existing multimodal methods. The data augmentation is adopted on the training set to avoid overfitting. Ten runs of 10-fold cross-validation are used to report the unbiased performance. The proposed multimodal fusion model achieved friendly interaction and fine-grained identification and decision-making performance. Thus, the model can be widely deployed in intelligent agriculture

POST-IVUS: A perceptual organisation-aware selective transformer framework for intravascular ultrasound segmentation

Intravascular ultrasound (IVUS) is recommended in guiding coronary intervention. The segmentation of coronary lumen and external elastic membrane (EEM) borders in IVUS images is a key step, but the manual process is time-consuming and error-prone, and suffers from inter-observer variability. In this paper, we propose a novel perceptual organisation-aware selective transformer framework that can achieve accurate and robust segmentation of the vessel walls in IVUS images. In this framework, temporal context-based feature encoders extract efficient motion features of vessels. Then, a perceptual organisation-aware selective transformer module is proposed to extract accurate boundary information, supervised by a dedicated boundary loss. The obtained EEM and lumen segmentation results will be fused in a temporal constraining and fusion module, to determine the most likely correct boundaries with robustness to morphology. Our proposed methods are extensively evaluated in non-selected IVUS sequences, including normal, bifurcated, and calcified vessels with shadow artifacts. The results show that the proposed methods outperform the state-of-the-art, with a Jaccard measure of 0.92 for lumen and 0.94 for EEM on the IVUS 2011 open challenge dataset. This work has been integrated into a software QCU-CMS2 to automatically segment IVUS images in a user-friendly environment

Decoding the absolute stoichiometric composition and structural plasticity of α-carboxysomes

Carboxysomes are anabolic bacterial microcompartments that play an essential role in carbon fixation in cyanobacteria and some chemoautotrophs. This self-assembling organelle encapsulates the key CO 2 -fixing enzymes, Rubisco, and carbonic anhydrase using a polyhedral protein shell that is constructed by hundreds of shell protein paralogs. The α-carboxysome from the chemoautotroph Halothiobacillus neapolitanus serves as a model system in fundamental studies and synthetic engineering of carboxysomes. Here we adopt a QconCAT-based quantitative mass spectrometry to determine the absolute stoichiometric composition of native α-carboxysomes from H. neapolitanus . We further performed an in-depth comparison of the protein stoichiometry of native and recombinant α-carboxysomes heterologously generated in Escherichia coli to evaluate the structural variability and remodeling of α-carboxysomes. Our results provide insight into the molecular principles that mediate carboxysome assembly, which may aid in rational design and reprogramming of carboxysomes in new contexts for biotechnological applications

Engineering α-carboxysomes into plant chloroplasts to support autotrophic photosynthesis

The growth in world population, climate change, and resource scarcity necessitate a sustainable increase in crop productivity. Photosynthesis in major crops is limited by the inefficiency of the key CO2-fixing enzyme Rubisco, owing to its low carboxylation rate and poor ability to discriminate between CO2 and O2. In cyanobacteria and proteobacteria, carboxysomes function as the central CO2-fixing organelles that elevate CO2 levels around encapsulated Rubisco to enhance carboxylation. There is growing interest in engineering carboxysomes into crop chloroplasts as a potential route for improving photosynthesis and crop yields. Here, we generate morphologically correct carboxysomes in tobacco chloroplasts by transforming nine carboxysome genetic components derived from a proteobacterium. The chloroplast-expressed carboxysomes display a structural and functional integrity comparable to native carboxysomes and support autotrophic growth and photosynthesis of the transplastomic plants at elevated CO2. Our study provides proof-of-concept for a route to engineering fully functional CO2-fixing modules and entire CO2-concentrating mechanisms into chloroplasts to improve crop photosynthesis and productivity

Producing fast and active Rubisco in tobacco to enhance photosynthesis

Ribulose-1,5-bisphosphate carboxylase/oxygenase (Rubisco) performs most of the carbon fixation on Earth. However, plant Rubisco is an intrinsically inefficient enzyme given its low carboxylation rate, representing a major limitation to photosynthesis. Replacing endogenous plant Rubisco with a faster Rubisco is anticipated to enhance crop photosynthesis and productivity. However, the requirement of chaperones for Rubisco expression and assembly has obstructed the efficient production of functional foreign Rubisco in chloroplasts. Here, we report the engineering of a Form 1A Rubisco from the proteobacterium Halothiobacillus neapolitanus in Escherichia coli and tobacco (Nicotiana tabacum) chloroplasts without any cognate chaperones. The native tobacco gene encoding Rubisco large subunit was genetically replaced with H. neapolitanus Rubisco (HnRubisco) large and small subunit genes. We show that HnRubisco subunits can form functional L8S8 hexadecamers in tobacco chloroplasts at high efficiency, accounting for ∼40% of the wild-type tobacco Rubisco content. The chloroplast-expressed HnRubisco displayed a ∼2-fold greater carboxylation rate and supported a similar autotrophic growth rate of transgenic plants to that of wild type in air supplemented with 1% CO2. This study represents a step towards the engineering of a fast and highly active Rubisco in chloroplasts to improve crop photosynthesis and growth

Boosting Electrocatalytic Nitrate-to-Ammonia Conversion via Plasma Enhanced CuCo Alloy–Substrate Interaction

Electrocatalytic conversion of widely distributed nitrate from industrial wastewater into value-added ammonia was proposed as an attractive and sustainable alternative to harvesting green ammonia. Herein, CuCo alloys were facilely synthesized for nitrate conversion, while nonthermal Ar-plasma was employed to enhance the adhesion strength between the electrocatalyst and substrate interface via regulating the surface hydrophobicity and roughness. Based on Ar-plasma treatment, a high ammonia yield rate (5129.29 μg cm-2 h-1) was achieved using Cu30Co70 electrocatalyst -0.47 V vs RHE, while nearly 100% of Faradaic efficiency was achieved using Cu50Co50 electrocatalyst at -0.27 V vs RHE (reversible hydrogen electrode). Validated by in situ spectroscopy and density functional theory calculations, the high activity of the CuCo alloy was derived from the regulation of Co to weaken the strong adsorption capacity of Cu and the shift of the d-band center to lower the energy barrier, while Ar-plasma modification promoted the formation of *NO to boost nitrate conversion