249 research outputs found
Physical, physiological demands and movement profiles of elite men’s field hockey games
The aim of this study was to investigate physical demands, physiological demands, and movement profiles of different positions across four quarters in professional men’s field hockey games. Eighteen professional male field hockey players participated in the study, and data were collected in eleven official matches. Players wore global positioning system units and heart rate monitors to collect physical, physiological, and movement profile data. Defenders had significantly higher absolute total distance covered, player load, acceleration and deceleration count, and forward-backward initial movement analysis (IMA) count, but lower high speed running distance, compared with midfielders and forwards (p<.05). However, when using relative metrics (normalised by playing time), defenders had the lowest physical and physiological outputs, and forwards had the highest (p<.05). Total distance covered per minute, high-speed running distance per minute, player load per minute, acceleration and deceleration count per minute, and repeated high-intensity efforts per minute were all significantly higher in quarter 1 than in other three quarters (p<.05). The percentages of linear running and non-linear dynamic movement duration decreased quarter by quarter. Modified training impulse per minute reached its peak in quarter 2 (p<.05). It was concluded that defenders had the highest volume in terms of the game demands due to their high playing minutes; however, they had the lowest relative volume compared with the other two positions. Forwards had the highest linear running intensity, while midfielders were required to perform more multi-directional, non-linear movements. Quarter 1 was the most active quarter and players became fatigued in quarter 2. IMA counts were not sensitive to fatigue compared to movement profile and modified training impulse variables
De novo synthesis of trans-10, cis-12 conjugated linoleic acid in oleaginous yeast Yarrowia Lipolytica
<p>Abstract</p> <p>Background</p> <p>Conjugated linoleic acid (CLA) has many well-documented beneficial physiological effects. Due to the insufficient natural supply of CLA and low specificity of chemically produced CLA, an effective and isomer-specific production process is required for medicinal and nutritional purposes.</p> <p>Results</p> <p>The linoleic acid isomerase gene from <it>Propionibacterium acnes</it> was expressed in <it>Yarrowia lipolytica</it> Polh. Codon usage optimization of the PAI and multi-copy integration significantly improved the expression level of PAI in <it>Y. lipolytica.</it> The percentage of <it>trans</it>-10, <it>cis</it>-12 CLA was six times higher in yeast carrying the codon-optimized gene than in yeast carrying the native gene. In combination with multi-copy integration, the production yield was raised to approximately 30-fold. The amount of <it>trans</it>-10, <it>cis</it>-12 CLA reached 5.9% of total fatty acid yield in transformed <it>Y. lipolytica</it>.</p> <p>Conclusions</p> <p>This is the first report of production of <it>trans</it>-10, <it>cis</it>-12 CLA by the oleaginous yeast <it>Y. lipolytica</it>, using glucose as the sole carbon source through expression of linoleic acid isomerase from <it>Propionibacterium acnes</it>.</p
A Diffusion Model for Event Skeleton Generation
Event skeleton generation, aiming to induce an event schema skeleton graph
with abstracted event nodes and their temporal relations from a set of event
instance graphs, is a critical step in the temporal complex event schema
induction task. Existing methods effectively address this task from a graph
generation perspective but suffer from noise-sensitive and error accumulation,
e.g., the inability to correct errors while generating schema. We, therefore,
propose a novel Diffusion Event Graph Model~(DEGM) to address these issues. Our
DEGM is the first workable diffusion model for event skeleton generation, where
the embedding and rounding techniques with a custom edge-based loss are
introduced to transform a discrete event graph into learnable latent
representation. Furthermore, we propose a denoising training process to
maintain the model's robustness. Consequently, DEGM derives the final schema,
where error correction is guaranteed by iteratively refining the latent
representation during the schema generation process. Experimental results on
three IED bombing datasets demonstrate that our DEGM achieves better results
than other state-of-the-art baselines. Our code and data are available at
https://github.com/zhufq00/EventSkeletonGeneration
Bacterial conjugated linoleic acid production and their applications
peer-reviewedConjugated linoleic acid (CLA) has been shown to exert various potential physiological properties including anti-carcinogenic, anti-obesity, anti-cardiovascular and anti-diabetic activities, and consequently has been considered as a promising food supplement. Bacterial biosynthesis of CLA is an attractive approach for commercial production due to its high isomer-selectivity and convenient purification process. Many bacterial species have been reported to convert free linoleic acid (LA) to CLA, hitherto only the precise CLA-producing mechanisms in Propionibacterium acnes and Lactobacillus plantarum have been illustrated completely, prompting the development of recombinant technology used in CLA production. The purpose of the article is to review the bacterial CLA producers as well as the recent progress on describing the mechanism of microbial CLA-production. Furthermore, the advances and potential in the heterologous expression of CLA genetic determinants will be presented
How ChatGPT is Solving Vulnerability Management Problem
Recently, ChatGPT has attracted great attention from the code analysis
domain. Prior works show that ChatGPT has the capabilities of processing
foundational code analysis tasks, such as abstract syntax tree generation,
which indicates the potential of using ChatGPT to comprehend code syntax and
static behaviors. However, it is unclear whether ChatGPT can complete more
complicated real-world vulnerability management tasks, such as the prediction
of security relevance and patch correctness, which require an all-encompassing
understanding of various aspects, including code syntax, program semantics, and
related manual comments.
In this paper, we explore ChatGPT's capabilities on 6 tasks involving the
complete vulnerability management process with a large-scale dataset containing
78,445 samples. For each task, we compare ChatGPT against SOTA approaches,
investigate the impact of different prompts, and explore the difficulties. The
results suggest promising potential in leveraging ChatGPT to assist
vulnerability management. One notable example is ChatGPT's proficiency in tasks
like generating titles for software bug reports. Furthermore, our findings
reveal the difficulties encountered by ChatGPT and shed light on promising
future directions. For instance, directly providing random demonstration
examples in the prompt cannot consistently guarantee good performance in
vulnerability management. By contrast, leveraging ChatGPT in a self-heuristic
way -- extracting expertise from demonstration examples itself and integrating
the extracted expertise in the prompt is a promising research direction.
Besides, ChatGPT may misunderstand and misuse the information in the prompt.
Consequently, effectively guiding ChatGPT to focus on helpful information
rather than the irrelevant content is still an open problem
Natural Language Interfaces for Tabular Data Querying and Visualization: A Survey
The emergence of natural language processing has revolutionized the way users
interact with tabular data, enabling a shift from traditional query languages
and manual plotting to more intuitive, language-based interfaces. The rise of
large language models (LLMs) such as ChatGPT and its successors has further
advanced this field, opening new avenues for natural language processing
techniques. This survey presents a comprehensive overview of natural language
interfaces for tabular data querying and visualization, which allow users to
interact with data using natural language queries. We introduce the fundamental
concepts and techniques underlying these interfaces with a particular emphasis
on semantic parsing, the key technology facilitating the translation from
natural language to SQL queries or data visualization commands. We then delve
into the recent advancements in Text-to-SQL and Text-to-Vis problems from the
perspectives of datasets, methodologies, metrics, and system designs. This
includes a deep dive into the influence of LLMs, highlighting their strengths,
limitations, and potential for future improvements. Through this survey, we aim
to provide a roadmap for researchers and practitioners interested in developing
and applying natural language interfaces for data interaction in the era of
large language models.Comment: 20 pages, 4 figures, 5 tables. Submitted to IEEE TKD
Mining bifidobacteria from the neonatal gastrointestinal tract for conjugated linolenic acid production
peer-reviewedConjugated linolenic acid (CLNA) is a family of isomers of linolenic acid with a number of healthassociated benefits, which has been attracting great interest. Microbial CLNA producers are
potentially an alternative source of CLNA for human nutrition. In present study, 16 neonate feces
were collected and used for Bifidobacteria isolation, from which 25 bifidobacteria isolates were
obtained. The bifidobacteria isolates were identified using 16s rDNA sequencing as Bifidobacterium
adolescentis, B. breve, B. longum and B. pseudocatenulatum. These isolates were further investigated
for their ability to produce CLNA using linolenic acid as substrate via GC-MS. The results showed
most of the isolates could convert free linolenic acid into c9,t11,c15-CLNA and t9,t11,c15-CLNA at
different levels. B. pseudocatenulatum was the most effective CLNA producer, which converted
86.91% of linolenic acid to c9,t11,c15-CLNA and 3.59% of to t9,t11,c15-CLNA isomer and the isolate
exhibited to accumulate CLNA during 72 h culturing in which most CLNA isomers were in the
supernatant fluid. The results indicated that utilization of this isolate for CLNA production will
eliminate the purification process.National Natural Science Foundation of Chin
Transcriptomic Profiles Reveal the Interactions of Cd/Zn in Dwarf Polish Wheat (Triticum polonicum L.) Roots
Wetting of bio-rejuvenator nanodroplets on bitumen: A molecular dynamics investigation
Wetting is the first step during the mix process between rejuvenator and bitumen, which is important for mix efficiency and performance recovery. The wetting of bio-rejuvenator nanodroplets on bitumen was investigated by molecular dynamics (MD) simulations in this research. The bitumen molecule model and bio-rejuvenator nanodroplets were firstly built, then bio-rejuvenator nanodroplets/bitumen interface wetting model were assembled and constructed. Different simulated temperatures were applied to reach equilibrium in the wetting process. Dynamic wetting phenomenon, contact angle of nanodroplets, dynamic movement of nanodroplets, interaction between nanodroplets and bitumen, and hysteresis of contact angle were characterized respectively. The results show that the bio-rejuvenator nanodroplets will first approach the bitumen quickly, and then slow down to an equilibrium state in the wetting process, which delayed 1 ns with energy equilibrium independently. Its contact angle would decrease crossing 90° with time, the equilibrium contact angle of which varies linearly with simulated temperature. The time of nanodroplets reaching partial wetting state decreased with the increments of temperature, but complete wetting state was hard to reach even if the temperature was 433 K. During the nanodroplets movement, contact linear velocity of precursor film and cosine of contact angle was linearly related after nanodroplets and bitumen had caught each other. It was also found that the increasing mix degree was supported by the combination of wetting and infiltration before 373 K and by wetting mainly after 373 K. Finally, the application of external force on bio-rejuvenator nanodroplets will cause hysteresis phenomenon and it can be weakened by higher temperature
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