Toxoplasma gondii cathepsin proteases are undeveloped prominent vaccine antigens against toxoplasmosis

BACKGROUND: Toxoplasma gondii, an obligate intracellular apicomplexan parasite, infects a wide range of warm-blooded animals including humans. T. gondii expresses five members of the C1 family of cysteine proteases, including cathepsin B-like (TgCPB) and cathepsin L-like (TgCPL) proteins. TgCPB is involved in ROP protein maturation and parasite invasion, whereas TgCPL contributes to proteolytic maturation of proTgM2AP and proTgMIC3. TgCPL is also associated with the residual body in the parasitophorous vacuole after cell division has occurred. Both of these proteases are potential therapeutic targets in T. gondii. The aim of this study was to investigate TgCPB and TgCPL for their potential as DNA vaccines against T. gondii. METHODS: Using bioinformatics approaches, we analyzed TgCPB and TgCPL proteins and identified several linear-B cell epitopes and potential Th-cell epitopes in them. Based on these results, we assembled two single-gene constructs (TgCPB and TgCPL) and a multi-gene construct (pTgCPB/TgCPL) with which to immunize BALB/c mice and test their effectiveness as DNA vaccines. RESULTS: TgCPB and TgCPL vaccines elicited strong humoral and cellular immune responses in mice, both of which were Th-1 cell mediated. In addition, all of the vaccines protected the mice against infection with virulent T. gondii RH tachyzoites, with the multi-gene vaccine (pTgCPB/TgCPL) providing the highest level of protection. CONCLUSIONS: T. gondii CPB and CPL proteases are strong candidates for development as novel DNA vaccines

LMSanitator: Defending Prompt-Tuning Against Task-Agnostic Backdoors

Prompt-tuning has emerged as an attractive paradigm for deploying large-scale language models due to its strong downstream task performance and efficient multitask serving ability. Despite its wide adoption, we empirically show that prompt-tuning is vulnerable to downstream task-agnostic backdoors, which reside in the pretrained models and can affect arbitrary downstream tasks. The state-of-the-art backdoor detection approaches cannot defend against task-agnostic backdoors since they hardly converge in reversing the backdoor triggers. To address this issue, we propose LMSanitator, a novel approach for detecting and removing task-agnostic backdoors on Transformer models. Instead of directly inverting the triggers, LMSanitator aims to invert the predefined attack vectors (pretrained models' output when the input is embedded with triggers) of the task-agnostic backdoors, which achieves much better convergence performance and backdoor detection accuracy. LMSanitator further leverages prompt-tuning's property of freezing the pretrained model to perform accurate and fast output monitoring and input purging during the inference phase. Extensive experiments on multiple language models and NLP tasks illustrate the effectiveness of LMSanitator. For instance, LMSanitator achieves 92.8% backdoor detection accuracy on 960 models and decreases the attack success rate to less than 1% in most scenarios.Comment: To Appear in the Network and Distributed System Security (NDSS) Symposium 2024, 26 February - 1 March 2024, San Diego, CA, USA; typos correcte

Intelligent Exploration for User Interface Modules of Mobile App with Collective Learning

A mobile app interface usually consists of a set of user interface modules. How to properly design these user interface modules is vital to achieving user satisfaction for a mobile app. However, there are few methods to determine design variables for user interface modules except for relying on the judgment of designers. Usually, a laborious post-processing step is necessary to verify the key change of each design variable. Therefore, there is a only very limited amount of design solutions that can be tested. It is timeconsuming and almost impossible to figure out the best design solutions as there are many modules. To this end, we introduce FEELER, a framework to fast and intelligently explore design solutions of user interface modules with a collective machine learning approach. FEELER can help designers quantitatively measure the preference score of different design solutions, aiming to facilitate the designers to conveniently and quickly adjust user interface module. We conducted extensive experimental evaluations on two real-life datasets to demonstrate its applicability in real-life cases of user interface module design in the Baidu App, which is one of the most popular mobile apps in China.Comment: 10 pages, accepted as a full paper in KDD 202

A genome-wide association study identifies a genomic region for the polycerate phenotype in sheep (Ovis aries)

201

A combination network of CNN and transformer for interference identification

Communication interference identification is critical in electronic countermeasures. However, existed methods based on deep learning, such as convolutional neural networks (CNNs) and transformer, seldom take both local characteristics and global feature information of the signal into account. Motivated by the local convolution property of CNNs and the attention mechanism of transformer, we designed a novel network that combines both architectures, which make better use of both local and global characteristics of the signals. Additionally, recognizing the challenge of distinguishing contextual semantics within the one-dimensional signal data used in this study, we advocate the use of CNNs in place of word embedding, aligning more closely with the intrinsic features of the signal data. Furthermore, to capture the time-frequency characteristics of the signals, we integrate the proposed network with a cross-attention mechanism, facilitating the fusion of temporal and spectral domain feature information through multiple cross-attention computational layers. This innovation obviates the need for specialized time-frequency analysis. Experimental results demonstrate that our approach significantly improves recognition accuracy compared to existing methods, highlighting its efficacy in addressing the challenge of communication interference identification in electronic warfare

NLRP3 Inflammasome Activation-Mediated Pyroptosis Aggravates Myocardial Ischemia/Reperfusion Injury in Diabetic Rats

The reactive oxygen species- (ROS-) induced nod-like receptor protein-3 (NLRP3) inflammasome triggers sterile inflammatory responses and pyroptosis, which is a proinflammatory form of programmed cell death initiated by the activation of inflammatory caspases. NLRP3 inflammasome activation plays an important role in myocardial ischemia/reperfusion (MI/R) injury. Our present study investigated whether diabetes aggravated MI/R injury through NLRP3 inflammasome-mediated pyroptosis. Type 1 diabetic rat model was established by intraperitoneal injection of streptozotocin (60 mg/kg). MI/R was induced by ligating the left anterior descending artery (LAD) for 30 minutes followed by 2 h reperfusion. H9C2 cardiomyocytes were exposed to high glucose (HG, 30 mM) conditions and hypoxia/reoxygenation (H/R) stimulation. The myocardial infarct size, CK-MB, and LDH release in the diabetic rats subjected to MI/R were significantly higher than those in the nondiabetic rats, accompanied with increased NLRP3 inflammasome activation and increased pyroptosis. Inhibition of inflammasome activation with BAY11-7082 significantly decreased the MI/R injury. In vitro studies showed similar effects, as BAY11-7082 or the ROS scavenger N-acetylcysteine, attenuated HG and H/R-induced H9C2 cell injury. In conclusion, hyperglycaemia-induced NLRP3 inflammasome activation may be a ROS-dependent process in pyroptotic cell death, and NLRP3 inflammasome-induced pyroptosis aggravates MI/R injury in diabetic rats

Bis(3,5-dimethyl­pyrazole)[N-salicyl­idene-β-alaninato(2–)]copper(II) dihydrate

In the title compound, [Cu(C10H10NO3)(C5H8N2)2]·2H2O, the CuII atom is coordinated by three N atoms and two O atoms in a distorted square-pyramidal geometry. The crystal packing is stabilized by inter­molecular O—H⋯O and N—H⋯O hydrogen bonds