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

Bulletin (1942-1943)

https://red.mnstate.edu/bulletins/1023/thumbnail.jp

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

Microcrystalline Dolomite in a Middle Permian Volcanic Lake: Insights on Primary Dolomite Formation in a Non-Evaporitic Environment

Lacustrine dolomite nucleation commonly occurs in modern and Neogene evaporitic alkaline lakes. As a result, ancient lacustrine microcrystalline dolomite has been conventionally interpreted to be formed in evaporitic environments. This study, however, suggests a non-evaporitic origin of dolomite precipitated in a volcanic–hydrothermal lake, where hydrothermal and volcanic processes interacted. The dolomite occurs in lacustrine fine-grained sedimentary rocks in the middle Permian Lucaogou Formation in the Santanghu intracontinental rift basin, north-west China. Dolostones are composed mainly of nano-sized to micron-sized dolomite with a euhedral to subhedral shape and a low degree of cation ordering, and are interlaminated and intercalated with tuffaceous shale. Non-dolomite minerals, including quartz, alkaline feldspars, smectite and magnesite mix with the dolomite in various proportions. The 87Sr/86Sr ratios (0.704528 to 0.705372, average = 0.705004) and δ26Mg values (−0.89 to −0.24‰, average = −0.55‰) of dolostones are similar to those of mantle rocks, indicating that the precipitates mainly originated from fluids that migrated upward from the mantle and were subject to water–rock reactions at a great depth. The δ18O values (−3.1 to −22.7‰, average = −14.0‰) of the dolostones indicate hydrothermal influence. The trace and rare earth element concentrations suggest a saline, anoxic and volcanic–hydrothermally-influenced subaqueous environment. In this subaqueous environment of Lucaogou lake, locally high temperatures and a supply of abundant Mg2+ from a deep source induced by volcanic–hydrothermal activity formed favourable chemical conditions for direct precipitation of primary dolomite. This study\u27s findings deepen the understanding of the origin and processes of lacustrine primary dolomite formation and provide an alternative possibility for environmental interpretations of ancient dolostones

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)

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