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Analysis of interspecies adherence of oral bacteria using a membrane binding assay coupled with polymerase chain reaction-denaturing gradient gel electrophoresis profiling.
Information on co-adherence of different oral bacterial species is important for understanding interspecies interactions within oral microbial community. Current knowledge on this topic is heavily based on pariwise coaggregation of known, cultivable species. In this study, we employed a membrane binding assay coupled with polymerase chain reaction-denaturing gradient gel electrophoresis (PCR-DGGE) to systematically analyze the co-adherence profiles of oral bacterial species, and achieved a more profound knowledge beyond pairwise coaggregation. Two oral bacterial species were selected to serve as "bait": Fusobacterium nucleatum (F. nucleatum) whose ability to adhere to a multitude of oral bacterial species has been extensively studied for pairwise interactions and Streptococcus mutans (S. mutans) whose interacting partners are largely unknown. To enable screening of interacting partner species within bacterial mixtures, cells of the "bait" oral bacterium were immobilized on nitrocellulose membranes which were washed and blocked to prevent unspecific binding. The "prey" bacterial mixtures (including known species or natural saliva samples) were added, unbound cells were washed off after the incubation period and the remaining cells were eluted using 0.2 mol x L(-1) glycine. Genomic DNA was extracted, subjected to 16S rRNA PCR amplification and separation of the resulting PCR products by DGGE. Selected bands were recovered from the gel, sequenced and identified via Nucleotide BLAST searches against different databases. While few bacterial species bound to S. mutans, consistent with previous findings F. nucleatum adhered to a variety of bacterial species including uncultivable and uncharacterized ones. This new approach can more effectively analyze the co-adherence profiles of oral bacteria, and could facilitate the systematic study of interbacterial binding of oral microbial species
Novel MSVPWM to Reduce the Inductor Current Ripple for Z-Source Inverter in Electric Vehicle Applications
Energy Wall for Exascale Supercomputing
"Sustainable development" is one of the major issues in the 21st century. Thus the notions of green computing, green development and so on show up one after another. As the large-scale parallel computing systems develop rapidly, energy consumption of such systems is becoming very huge, especially system performance reaches Petascale (10^15 Flops) or even Exascale (10^18 Flops). The huge energy consumption increases the system temperature, which seriously undermines the stability and reliability, and limits the growth of system size. The effects of energy consumption on scalability become a growing concern. Against the background, this paper proposes the concept of "Energy Wall" to highlight the significance of achieving scalable performance in peta/exascale supercomputing by taking energy consumption into account. We quantify the effect of energy consumption on scalability by building the energy-efficiency speedup model, which integrates computing performance and system energy. We define the energy wall quantitatively, and provide the theorem on the existence of the energy wall, and categorize the large-scale parallel computers according to the energy consumption. In the context of several representative types of HPC applications, we analyze and extrapolate the existence of the energy wall considering three kinds of topologies, 3D-Torus, binary n-cube and Fat tree which provides insights on how to mitigate the energy wall effect in system design and through hardware/software optimization in peta/exascale supercomputing
A Privacy-Preserving Hybrid Federated Learning Framework for Financial Crime Detection
The recent decade witnessed a surge of increase in financial crimes across
the public and private sectors, with an average cost of scams of $102m to
financial institutions in 2022. Developing a mechanism for battling financial
crimes is an impending task that requires in-depth collaboration from multiple
institutions, and yet such collaboration imposed significant technical
challenges due to the privacy and security requirements of distributed
financial data. For example, consider the modern payment network systems, which
can generate millions of transactions per day across a large number of global
institutions. Training a detection model of fraudulent transactions requires
not only secured transactions but also the private account activities of those
involved in each transaction from corresponding bank systems. The distributed
nature of both samples and features prevents most existing learning systems
from being directly adopted to handle the data mining task. In this paper, we
collectively address these challenges by proposing a hybrid federated learning
system that offers secure and privacy-aware learning and inference for
financial crime detection. We conduct extensive empirical studies to evaluate
the proposed framework's detection performance and privacy-protection
capability, evaluating its robustness against common malicious attacks of
collaborative learning. We release our source code at
https://github.com/illidanlab/HyFL .Comment: PETs prize challenge versio
Quantum tricriticality of incommensurate phase induced by quantum domain walls in frustrated Ising magnetism
Incommensurability plays a critical role not only in the strongly correlated
systems such as frustrated quantum magnets. Meanwhile, the origin of such
exotic order can be theoretically understood in the framework of quantum domain
wall excitations. Here, we study the extended anisotropic transverse Ising
model in the triangular lattice. Using the large scale quantum Monte Carlo
simulations, we find that spatial anisotropy can stabilize the incommensurate
phase out of the commensurate clock phase. Both the structure factor and the
domain wall density exhibit the linear relationship between incommensurate
order wave vector and number of domain walls, which is reminiscent of hole
density in under-doped cuprate superconductors. Different from an indirect clue
of the domain wall's existence, we find direct evidence from the features in
the excitation spectrum. On the other hand, when introducing the next nearest
neighbor interaction, we observed a novel quantum tricritical point related to
the incommensurate phase. After carefully analyzing the energy of the ground
state with different domain wall numbers, we conclude this tricriticality is
non-trivial because it is caused by effective long-range inter-domain wall
interactions with two competing terms
. At last, we focus on the clock
phase, which is highly related to recently discovered material TmMgGaO, and
find that the so-called "roton" mode results from the merging of domain wall
mode and vortex-antivortex mode which breaks the local constraint of spin
configuration.Comment: 10 pages, 10 figures, comments are welcome and more information at
http://cqutp.org/users/xfzhang
OccuQuest: Mitigating Occupational Bias for Inclusive Large Language Models
The emergence of large language models (LLMs) has revolutionized natural
language processing tasks. However, existing instruction-tuning datasets suffer
from occupational bias: the majority of data relates to only a few occupations,
which hampers the instruction-tuned LLMs to generate helpful responses to
professional queries from practitioners in specific fields. To mitigate this
issue and promote occupation-inclusive LLMs, we create an instruction-tuning
dataset named \emph{OccuQuest}, which contains 110,000+ prompt-completion pairs
and 30,000+ dialogues covering over 1,000 occupations in 26 occupational
categories. We systematically request ChatGPT, organizing queries
hierarchically based on Occupation, Responsibility, Topic, and Question, to
ensure a comprehensive coverage of occupational specialty inquiries. By
comparing with three commonly used datasets (Dolly, ShareGPT, and WizardLM), we
observe that OccuQuest exhibits a more balanced distribution across
occupations. Furthermore, we assemble three test sets for comprehensive
evaluation, an occu-test set covering 25 occupational categories, an estate set
focusing on real estate, and an occu-quora set containing real-world questions
from Quora. We then fine-tune LLaMA on OccuQuest to obtain OccuLLaMA, which
significantly outperforms state-of-the-art LLaMA variants (Vicuna, Tulu, and
WizardLM) on professional questions in GPT-4 and human evaluations. Notably, on
the occu-quora set, OccuLLaMA reaches a high win rate of 86.4\% against
WizardLM
Proteomic differences between developmental stages of Toxoplasma gondii revealed by iTRAQ-based quantitative proteomics
Toxoplasma gondii has a complex two-host life-cycle between intermediate host and definitive host. Understanding proteomic variations across the life-cycle stages of T. gondii may improve the understanding of molecular adaption mechanism of T. gondii across life-cycle stages, and should have implications for the development of new treatment and prevention interventions against T. gondii infection. Here, we utilized LC–MS/MS coupled with iTRAQ labeling technology to identify differentially expressed proteins (DEPs) specific to tachyzoite (T), bradyzoites-containing cyst (C) and sporulated oocyst (O) stages of the cyst-forming T. gondii Prugniuad (Pru) strain. A total of 6285 proteins were identified in the three developmental stages of T. gondii. Our analysis also revealed 875, 656, and 538 DEPs in O vs. T, T vs. C, and C vs. O, respectively. The up- and down-regulated proteins were analyzed by Gene Ontology enrichment, KEGG pathway and STRING analyses. Some virulence-related factors and ribosomal proteins exhibited distinct expression patterns across the life-cycle stages. The virulence factors expressed in sporulated oocysts and the number of up-regulated virulence factors in the cyst stage were about twice as many as in tachyzoites. Of the 79 ribosomal proteins identified in T. gondii, the number of up-regulated ribosomal proteins was 33 and 46 in sporulated oocysts and cysts, respectively, compared with tachyzoites. These results support the hypothesis that oocyst and cystic stages are able to adapt to adverse environmental conditions and selection pressures induced by the host’s immune response, respectively. These findings have important implications for understanding of the developmental biology of T. gondii, which may facilitate the discovery of novel therapeutic targets to better control toxoplasmosis
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