Case Western Reserve University researchers report rise in global fungal drug-resistant infections

Researchers issue call to action to address and prevent growing problem

CASE WESTERN RESERVE UNIVERSITY

CLEVELAND—A global wave of infections caused by fungi growing drug-resistant has the medical community issuing precautions on how to protect yourself.

Skin contact with microorganisms found in soil or on hard surfaces, such as common shower facilities, or exposure to infected pets, can result in fungal infections known as dermatomycoses. Rashes, itching, burning and skin irritation are among the symptoms. 

Epidemiological data published in Microbial Cell indicates that a rise in severe fungal infections has resulted in over 150 million cases annually and almost 1.7 million fatalities globally.

In a recent study published in Pathogens and Immunity, Thomas McCormick and Mahmoud Ghannoum, professors of dermatology at the Case Western Reserve University School of Medicine and affiliated with University Hospitals Cleveland Medical Center, explain how rising antifungal resistance is worsening the problem of invasive fungal infections.

“This is not just an issue that affects individual patients,” McCormick said. “The World Health Organization has recognized it as a widespread threat that has the potential to impact entire healthcare systems if left unchecked.”

Based on their findings, the researchers issued precautions and a “call to action” for the medical community to help protect people from multidrug-resistant fungi—starting with awareness and education.

“Healthcare providers must prioritize the use of diagnostic tests when faced with an unknown fungal infection,” Ghannoum said. “Early detection can make all the difference in improving patient outcomes.”

Patients treated with medications to protect the immune system after cancer and transplant procedures are more vulnerable to fungal infections—making them especially more vulnerable to infections from drug-resistant fungi, the researchers said. 

The emergence of multidrug-resistant fungal species, such as Candida auris and Trichophyton indotineae, is especially troubling and requires urgent attention, they reported. 

In a study recently published in Emerging Infectious Diseases, Ghannoum’s research team and the Centers for Disease Control and Prevention (CDC), detailed a case that demonstrated Trichophyton indotineae, in addition to becoming drug-resistant, was also sexually transmissible.  

To address the growing health concern, McCormick and Ghannoum suggest several measures:

• Increased awareness and education: Raising awareness in the general healthcare setting to obtain a more accurate understanding of the rise of antifungal-resistant infections.
• Diagnostic Testing:  Routine use of diagnostic tests can guide appropriate treatment strategies.
• Antifungal Susceptibility Testing (AST): Improving insurance reimbursement rates for AST and increasing the number of qualified laboratories with the capacity to perform these tests.
• Call to Action: Addressing the emerging challenge of antifungal resistance involves concerted efforts from healthcare professionals, researchers, policymakers and the pharmaceutical industry to develop and implement strategies for managing and preventing antifungal resistance.

“The ultimate goal of these measures,” Ghannoum said, “is to improve the quality of patient care by ensuring effective treatment and preventing further escalation of the problem.” 

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About Case Western Reserve University

Case Western Reserve University is one of the country's leading private research institutions. Located in Cleveland, we offer a unique combination of forward-thinking educational opportunities in an inspiring cultural setting. Our leading-edge faculty engage in teaching and research in a collaborative, hands-on environment. Our nationally recognized programs include arts and sciences, dental medicine, engineering, law, management, medicine, nursing, and social work. About 6,200 undergraduate and 6,100 graduate students comprise our student body. Visit case.edu to see how Case Western Reserve thinks beyond the possible.

About University Hospitals / Cleveland, Ohio
Founded in 1866, University Hospitals serves the needs of patients through an integrated network of 21 hospitals (including five joint ventures), more than 50 health centers and outpatient facilities, and over 200 physician offices in 16 counties throughout northern Ohio. The system’s flagship quaternary care, academic medical center, University Hospitals Cleveland Medical Center, is affiliated with Case Western Reserve University School of Medicine, Northeast Ohio Medical University, Oxford University, the Technion Israel Institute of Technology and National Taiwan University College of Medicine. The main campus also includes the UH Rainbow Babies & Children's Hospital, ranked among the top children’s hospitals in the nation; UH MacDonald Women's Hospital, Ohio's only hospital for women; and UH Seidman Cancer Center, part of the NCI-designated Case Comprehensive Cancer Center. UH is home to some of the most prestigious clinical and research programs in the nation, with more than 3,000 active clinical trials and research studies underway. UH Cleveland Medical Center is perennially among the highest performers in national ranking surveys, including “America’s Best Hospitals” from U.S. News & World Report. UH is also home to 19 Clinical Care Delivery and Research Institutes. UH is one of the largest employers in Northeast Ohio with more than 30,000 employees. Follow UH on LinkedIn, Facebook and Twitter. For more information, visit UHhospitals.org.

 

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JOURNAL

Pathogens and Immunity

DOI

10.20411/pai.v8i2.656 

ARTICLE TITLE

Time to Think Antifungal Resistance

Newly identified yeast could prevent fungal infections by outcompeting rivals, study suggests

ROCKEFELLER UNIVERSITY PRESS

 

IMAGE: 

C. ALBICANS SPREADS TO THE KIDNEYS OF IMMUNOSUPPRESSED MICE (LEFT), BUT INVASIVE CANDIDIASIS IS MITIGATED BY EXPOSURE TO K. WEIZMANNII (RIGHT).

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CREDIT: © 2024 SEKERESOVA KRALOVA ET AL. ORIGINALLY PUBLISHED IN <EM>JOURNAL OF EXPERIMENTAL MEDICINE</EM>. HTTPS://DOI.ORG/10.1084/JEM.20231686

Researchers at the Weizmann Institute of Science in Israel have identified a yeast that could be used to prevent invasive candidiasis, a major cause of death in hospitalized and immunocompromised patients. The study, to be published March 18 in the Journal of Experimental Medicine (JEM), shows that the novel yeast lives harmlessly in the intestines of mice and humans and can displace the yeast responsible for candidiasis, Candida albicans.

Millions of microbial species live within or on the human body, many of them being harmless or even beneficial to human health. The microscopic yeast C. albicans is commonly found in the intestines and other mucosal surfaces of the body and is usually benign, though occasionally it may overgrow and cause superficial infections commonly known as thrush. Under certain circumstances, however, the yeast may penetrate the intestinal barrier and systemically infect the blood or internal organs. This dangerous condition, known as invasive candidiasis, is commonly seen in healthcare environments, particularly in immunocompromised patients, with mortality rates of up to 25%.

While studying yeast infections in laboratory mice, Steffen Jung and colleagues at the Weizmann Institute discovered that some of their mice carried a novel species of yeast that prevented the animals from being infected with C. albicans. The new species, which the researchers named Kazachstania weizmannii, is closely related to yeast associated with sourdough production and appears to live innocuously in the intestines of mice, even when the animals are immunosuppressed.

The researchers found that K. weizmannii can outcompete C. albicans for its place within the gut, reducing the population of C. albicans in mouse intestines. Moreover, while C. albicans can cross the intestinal barrier and spread to other organs in immunosuppressed mice, the presence of K. weizmannii in the animals’ drinking water significantly delayed the onset of invasive candidiasis.

Notably, Jung and colleagues also identified K. weizmannii and other, similar species in human gut samples. Their preliminary data suggest that the presence of K. weizmannii was mutually exclusive with the presence of Candida species, suggesting that the two species might also compete with each other in human intestines.

“By virtue of its ability to successfully compete with C. albicans in the murine gut, K. weizmannii lowered the C. albicans burden and mitigated candidiasis development in immunosuppressed animals,” Jung says. “This competition between Kazachstania and Candida species could have potential therapeutic value for the management of C. albicans–mediated diseases.”

 

 

Sekeresova Kralova et al. 2024. J. Exp. Med. https://rupress.org/jem/article-lookup/doi/10.1084/jem.20231686?PR

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About Journal of Experimental Medicine

Journal of Experimental Medicine (JEM) publishes peer-reviewed research on immunology, cancer biology, stem cell biology, microbial pathogenesis, vascular biology, and neurobiology. All editorial decisions on research manuscripts are made through collaborative consultation between professional scientific editors and the academic editorial board. Established in 1896, JEM is published by Rockefeller University Press, a department of The Rockefeller University in New York. For more information, visit jem.org.

Visit our Newsroom, and sign up for a weekly preview of articles to be published. Embargoed media alerts are for journalists only.

Follow JEM on Twitter at @JExpMed and @RockUPress.

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JOURNAL

Journal of Experimental Medicine

DOI

10.1084/jem.20231686 

METHOD OF RESEARCH

Experimental study

SUBJECT OF RESEARCH

Animals

ARTICLE TITLE

Competitive fungal commensalism mitigates candidiasis pathology

ARTICLE PUBLICATION DATE

18-Mar-2024

COI STATEMENT

C. Donic and S. Jung reported a patent to PCT/IL2023/050470 pending (Weizmann Institute of Science). No other disclosures were reported.

AS ABOVE, SO BELOW

Harnessing computational intelligence for 3D modeling of maize canopies

NANJING AGRICULTURAL UNIVERSITY THE ACADEMY OF SCIENCE

 

IMAGE: 

THE COMPLETE METHODOLOGICAL PROCESS.

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CREDIT: PLANT PHENOMICS

Understanding the structure of crop canopies is essential for optimizing crop production as it significantly influences resource utilization efficiency, yield and stress resistance. While research has integrated canopy studies into various agricultural practices, the construction of  accurate 3D models remains challenging due to complex spatial distributions and technological limitations. Current methods struggle to capture detailed morphological data due to issues such as resolution and cost. To address these issues, there is an emerging interest in applying Computational Intelligence (CI) techniques. These techniques have shown promise in various agricultural applications but haven’t  yet been  explored for constructing  3D models of maize canopies.

In March 2024, Plant Phenomics published a research article entitled by “Three-dimensional modelling of maize canopies based on computational intelligence”. This research aims to integrate CI into 3D plant canopy modeling, particularly focusing on overcoming the challenges of internal occlusion and resource competition among densely planted crops.

The study presents a computational intelligence-based 3D modeling method for maize canopies, focusing on visualizing and validating the structure of maize canopies across different planting densities and varieties. Using this method, 3D models for the JNK728 and JK968 maize varieties were constructed at densities of 3, 6, and 9×10^4 plants per hectare. The mothed demonstrated the method's ability to capture the effects of planting density on canopy structure, including increased shading and adjustments in leaf azimuth angles to optimize light capture. The  models were validated and showed significant improvements in simulating the distribution of leaf azimuth angles, The R2 values indicated a high degree of consistency with measured data, especially after optimization through a reflective approach.

 The study also validated the models' accuracy in representing canopy coverage, showing a correlation with actual canopy conditions and highlighting the models' limitations in capturing elements like fallen leaves and weeds. The distribution of leaf azimuth angles close to 90° increases with planting density, suggesting an adaptive response of maize leaves to environmental stress by aligning more perpendicular to the row direction. This trend was further validated through the construction of 3D models across a gradient of planting densities.

The computational process, though time-intensive, highlights the efficiency and potential of computational intelligence in 3D canopy modeling. The iterative optimization of sunlit leaf area ratios and the intelligent adjustment of 3D phytomers' azimuth angles reflect the application of swarm intelligence principles to crop canopy modeling. The study highlights the significance of  precise crop  canopy modeling to comprehend plant competition for light resources. It suggests further enhancements and future work to improve the models' accuracy and practicality by considering a broader range of environmental factors and incorporating more detailed phenotypic and growth information.

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References

Authors

Yandong  Wu1,2† ,  Weiliang  Wen2,3,4† ,  Shenghao  Gu2,3,  Guanmin  Huang2,3,4,  Chuanyu Wang2,3, Xianju Lu2,3,4, Pengliang Xiao1,2, Xinyu Guo2,3*, Linsheng Huang1*

†  These authors contributed equally to the article.

Affiliations

1National Engineering Research Center for Agro-Ecological Big Data Analysis & Application, Anhui University, Hefei 230601, China.

2Information  Technology  Research  Center,  Beijing  Academy  of  Agriculture  and  Forestry  Sciences,  Beijing  100097, China.

3Beijing Key Lab of Digital Plant, National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China.

4Nongxin Science & Technology (Beijing) Co., Ltd, Beijing 100097, China

About Linsheng Huang

He is currently a Professor and the Deputy Director of the National Engineering Research Center for Agro-Ecological Big Data Analysis and Application, Anhui University. His research interests include quantitative remote sensing applications in crop diseases and insect pests.

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JOURNAL

Plant Phenomics

DOI

10.34133/plantphenomics.0160 

METHOD OF RESEARCH

Experimental study

SUBJECT OF RESEARCH

Not applicable

ARTICLE TITLE

Three-dimensional modelling of maize canopies based on computational intelligence

Maize genes control little helpers in the soil

An international team of researchers discovers how microbes boost root growth

UNIVERSITY OF BONN

 

IMAGE: 

PROF. DR. FRANK HOCHHOLDINGER (LEFT) AND DR. PENG YU (RIGHT) FROM THE CHAIR OF CROP FUNCTIONAL GENOMICS AT THE UNIVERSITY OF BONN 

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CREDIT: PHOTO: VOLKER LANNERT/UNIVERSITY OF BONN

Tiny organisms such as bacteria and fungi help to promote the health and function of plant roots. It is commonly assumed that the composition of these microbes is dependent on the properties of the soil. However, an international team of researchers led by the University of Bonn has now discovered when studying different local varieties of maize that the genetic makeup of the plants also helps to influence which microorganisms cluster around the roots. The results, which have now been published in the prestigious journal Nature Plants, could help to breed future varieties of maize that are better suited to drought and limited nutrients.

In order to grow properly, plants take in water and nutrients through their roots. But they have the assistance of some tiny helpers: A layer of bacteria and fungi, just a few millimeters thick, can be found directly around the roots. “These microorganisms are essential for the health and fitness of the plants,” says Dr. Peng Yu, head of the junior research group “Root Functional Biology” at the Institute of Crop Science and Resource Conservation (INRES) at the University of Bonn. The microbes help with the absorption of water and nutrients and protect the plants against harmful organisms – similar to how the “microbiome” in the intestines of humans helps to determine whether we become ill or stay healthy.

The traditional view is that the composition of the microbiome – the totality of all microorganisms – is mainly determined by the properties of the soil. This includes things such as the type of soil and whether it is more acidic or alkaline. However, an international team of researchers led by the University of Bonn has now demonstrated in maize plants that the genetic makeup of the host plants has a significant influence on the composition of the root microbes.

“Our study also showed that the microbiome around the roots has a crucial influence on how resilient the maize plants are when faced with stressful conditions such as a nutrient deficits or lack of water,” says Prof. Dr. Frank Hochholdinger from the Crop Functional Genomics department in INRES at the University of Bonn. In view of global climate change and the limited supply of the nutrient phosphorous, resilience of these plants to drought and a lack of nutrients could play an even greater role in the future.

Adapting regional varieties of maize to environmental conditions

The various varieties of maize have very different genetic composition. Regional varieties have adapted themselves to very different environmental conditions depending on whether they are cultivated, for example, in cooler highland or the warmer lowland areas of South America. “The fact that farmers have continued to select those varieties of maize suited to the local climate over many centuries has led to very different genotypes that we were able to utilize for our study,” says Dr. Yu, who is head of an Emmy Noether junior research group funded by the German Research Foundation and also a member of the PhenoRob Cluster of Excellence and the transdisciplinary research area “Sustainable Futures” at the University of Bonn.

In cooperation with scientists from Southwest University in Chongqing (China), the researchers studied a total of 129 different varieties of maize. Some of these were cultivated under “normal” conditions while others experienced deficiencies in phosphorus, nitrogen, or water. Additionally, the team sequenced the DNA of the microbes from 3168 samples taken from the layer found directly around the roots that is just a few millimeters thick.

The role played by the genetic makeup of the roots became apparent in those plants grown under stressful conditions. Interestingly, the lack of nutrients and water had a significant influence on the composition of the microbes. Furthermore, the team discovered important characteristic differences in the microbiome between different varieties of maize under the same stressful conditions. “We were able to prove that certain maize genes are able to interact with certain bacteria,” says Dr. Yu to explain on the most important results. Using data on the growth conditions at the place of origin of a certain variety of maize and on its genetic composition, the researchers were even able to predict which key organisms would be found in the microbiome around the roots.

The bacterium Massilia promotes the growth of lateral roots

The results for bacteria of the genus Massilia especially stood out: “It was very noticeable that very few specimens of this microbe were found when there was a sufficient supply of nitrogen,” says Prof. Dr. Gabriel Schaaf from the Ecophysiology of Plant Nutrition department at INRES and member of the PhenoRob Cluster of Excellence. If there was a lack of nitrogen, however, lots of Massilia could be found clustering around the roots. The team then inoculated maize roots with this bacterium. The plants grew a lot more lateral roots as a result and were therefore able to significantly improve their uptake of nutrients and water.

But how do maize plants manage to harness the tiny Massilia bacterium for this type of root growth? Following further studies, the researchers discovered that the roots actually attracted the Massilia bacteria using flavones. This substance is one of many secondary metabolites in the plant and stimulates the growth of lateral roots with the aid of the bacteria. “However, this was dependent on whether the maize plant had a microtubule-binding gene,” says Dr. Peng Yu. If this gene was missing, the plant did not produce more lateral roots.

The varieties of maize with the missing gene come from a huge database of maize mutations that has been set up by the researchers headed by Dr. Caroline Marcon at INRES. This database helps researchers explain the functions of maize genes.

Maize varieties better adapted to drought and a lack of nutrients

The international team of researchers hopes that they will also be able to predict yield in the medium term. “We are carrying out basic research,” says Hochholdinger. “However, these results could act as the basis for cultivation of maize varieties better suited to drought and a lack of phosphorous by using genome and microbiome data.”

Participating institutes and funding:

Alongside various departments in the Institute of Crop Science and Resource Conservation (INRES) at the University of Bonn, the following institutions also participated in the research: Southwest University Chongqing (China), Leibniz Institute of Plant Genetics and Crop Plant Research, Pennsylvania State University (USA), Institute of Natural Resources and Agrobiology of Seville (Spain), University of Hohenheim, University of Nebraska-Lincoln (USA), Julius Kühn Institute in Braunschweig, Ghent University (Belgium), Center for Plant Systems Biology in Ghent, University of Amsterdam (Netherlands) and the Department of Food Microbiology at the University of Bonn. The study was funded by, amongst others, the German Research Foundation (DFG), including funds from the PhenoRob Cluster of Excellence.

Publication: Xiaoming He, Danning Wang, Yong Jiang, Meng Li, Manuel Delgado-Baquerizo, Chloee McLaughlin, Caroline Marcon, Li Guo, Marcel Baer, Yudelsy A.T. Moya, Nicolaus von Wirén, Marion Deichmann, Gabriel Schaaf, Hans-Peter Piepho, Zhikai Yang, Jinliang Yang, Bunlong Yim, Kornelia Smalla, Sofie Goormachtig, Franciska T. de Vries, Hubert Hüging, Mareike Baer, Ruairidh J. H. Sawers, Jochen C. Reif, Frank Hochholdinger, Xinping Chen, Peng Yu: Heritable microbiome variation is correlated with source environment in locally adapted maize varieties, Nature Plants, DOI: 10.1038/s41477-024-01654-7; Internet: https://www.nature.com/articles/s41477-024-01654-7

Video: https://youtu.be/VaYnX-ph9gg

Contact:

Dr. Peng Yu
Institute of Crop Science and Resource Conservation (INRES)
Crop Functional Genomics
University of Bonn
Tel. +49 228 73-60532
E-mail: yupeng@uni-bonn.de

Prof. Dr. Frank Hochholdinger
INRES – Crop Functional Genomics
University of Bonn
Tel. +49 228 73-60334 or -60331
E-mail: hochhold@uni-bonn.de

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JOURNAL

Nature Plants

DOI

10.1038/s41477-024-01654-7 

METHOD OF RESEARCH

Experimental study

SUBJECT OF RESEARCH

Not applicable

ARTICLE TITLE

Heritable microbiome variation is correlated with source environment in locally adapted maize varieties

ARTICLE PUBLICATION DATE

21-Mar-2024

 

Cogeneration of innovative audio-visual content: A new challenge for computing art

BEIJING ZHONGKE JOURNAL PUBLISING CO. LTD.

 

IMAGE: 

SUMMARY OF AI-BASED VISUAL ART GENERATION  

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CREDIT: BEIJING ZHONGKE JOURNAL PUBLISING CO. LTD.

Walter Benjamin came up with aura and authenticity in The Work of Art in the Age of Mechanical Reproduction in 1936 to describe the value of original artworks created by artists instead of mechanical copies. He wanted to defend artificiality and support traditional fine arts. Not singly but in pairs, Mitchell’s core concern was about the work of art in the age of biocybernetic reproduction in What Do Pictures Want?: The Lives and Loves of Images (2005) followed Benjamin’s train of thought. He especially mentioned the first cloned sheep, Dolly, and regarded it as a living image. Mitchell is one of the most representative and influential theorists in the arts and human sciences. He developed a series of concepts to explain the value of biocybernetic artworks. However, based on carbon, artists in this field are limited by the development of biotechnology, and their creation is sometimes repressed instead of released. Some disappointing cooperation between biologists and artists seems to popularize scientific knowledge by entertaining visual culture and show a lack of critical thinking. Therefore, researchers need to reflect on what artists can do with advanced technology.

 

Humans have faced new challenges in the era of the metaverse. Humans have not only mechanical copies of artworks or biocybernetic replicas but also avatars of humans themselves. This paper proposes the concept of artificial intelligent (AI) art, thus summarizing the main features of artworks produced by artificial intelligent technology, such as extended reality (XR, the combination of VR/AR/MR), cyber-physical system (CPS), cloud computing, and blockchain. The cooperation between AI technical staff and artists is more intimate than that between biologists and artists. AI technology discharges artists from laborious work, which was primarily accused by Marx et al., and encourages them to realize their full potential in art. As a result, AI is like a capable partner in the team who always understands the artist in time and does intense work to bring the artist’s romantic conceiving into reality.

 

With bright prospects for development, AI technology plays essential roles in design, creation, and exhibition in art circles. The concept of AI art may be easily confused with computer art. It is important to note that AI art is more advanced than computer art and can cover more perceptual requirements, including optical and acoustical requirements. AI art usually presents a fusion of the human senses. Art appreciators obtain visual, aural, and tactual feelings simultaneously. In other words, AI technology provides a rich audio-visual feast for modern art exhibitions.

 

The development plan for AI art is still in its infancy. There are some concerns in society about the general applicability of AI technology. Here is an interesting question that has always been mentioned. Do AI dream of electric sheep? In 1968, Philip K. Dick first put this question forward in his science fiction: Do Androids dream of electric sheep? The inspiration for the films Blade Runner and Blade Runner 2049. After discussing AI ethical issues, this title has become the core question that represents the fear that AI would replace humans. Such fears soon spread to the humanities. Some intellectuals believe there should be limits to AI technology. However, if one has sufficient knowledge of AI technology, one will find such fears laughable. People’s fear is nothing more than a rejection of the unknown. Cutting-edge AI technology still needs to reach the emotional level of humans. The urgent need to work on developing and applying AI technology remains as strong as possible.

 

AI technology is currently used in the art field for technique classification, style migration, interactive design, manufacturing, and cultural industry, and so on. AI art has produced AI-generated poetry, VR painting, digital media art, AI voiceovers, and smart electrical appliances. These examples show the solid creative power of AI art. However, some artists are not happy with it. In 1972, the German artist Joseph Beuys gave a speech at Documenta in Kassel, presenting the idea that “everyone is an artist.” His views have caused an uproar. In those days, it was nothing more than an imagination. After all, not everyone was skilled in creating art. With the development of AI art, this idea seems to be becoming a reality. AI is powerful enough to allow anyone to become an artist. It should be noted that the creative ability of AI is not endless. It comes from humans who have talents in creating art. The development of AI art is, therefore, not incompatible with the training of artists. In contrast, the spread of AI art enables artists to do what they do best. In this way, AI art development and traditional art innovation can hold a win-win situation.

 

To be on target, a paper by Prof. Gao Feng from Peking University focuses on AI-generated video and AI-generated audio. Audio-visual ability is often thought of as a composite human sensory ability. Their combination has rapidly improved the production efficiency of industries such as movies, short videos, and games. The summary of AI visual and auditory technology and the presentation of existing results can help practitioners in the industry determine art industry trends in the future.

 

Audio-visual art generation can be divided into visual art generation and auditory art generation. Section 2 of this paper provides a comprehensive overview of the datasets and methods in the two fields. Visual art generation part: first, researchers introduce ten classic image datasets; then, based on three tasks of AI painting, style transfer and text-to-image translation, researchers summarize the classic models in the field of visual content generation; finally, researchers show typical systems and products for it. Auditory art generation part: they use the form of sound expression as an indicator, specifically listing eight classic music datasets in the field of auditory art generation; then, regarding the model structure as standard, the music generation methods are divided into two categories, general model, and composite model. Researchers outline nine classical frameworks for music generation and identify related models and products.

 

There are two types of evaluation methods for algorithm performance: objective evaluation and subjective evaluation. Objective evaluation applies several metrics based on mathematical theory, which is quantitative, efficient, and widely used, but it is not suitable for content that requires subjective feelings. Subjective evaluation usually needs to design experiments, and observers evaluate the results of the algorithm, which is time-consuming, laborious, and difficult to quantify. Nevertheless, subjective evaluation is consistent with subjective feelings. In the field of art generation, subjective evaluation plays an important role in evaluating the creativity of the model. In Section 3, researchers provide an overview of measuring the quality of generated results from objective and subjective perspectives.

 

Section 4 introduces proposed materials and mechanism. Cogeneration of audio-visual content is a multimodal task and requires approaches to fuse information from different sources, including image, video, audio, text, etc. By weighing the strengths and limitations of various audio-visual art generation algorithms, researchers develop and propose a joint generation mechanism for generating digital audio-visual art works using multiple types of algorithms. The system is divided into a visual art generation module and an auditory art generation module. The former is responsible for generating dynamic video content of a specified style, and the latter generates the corresponding video soundtrack through the text features associated with the video. In Section 4.1, researchers introduce two datasets constructed for audio-visual joint tasks. In Sections 4.2 and 4.3, they demonstrate the visual art generation module and the auditory art generation module, respectively.

 

This paper has summarized the results of the technical development of audio-visual art generation. The technology of audio-visual art generation has a wide range of applications. It can be used at home to make entertainment more diverse. It can also be used in public places. For example, it can increase the attractiveness of commercial promotion and art exhibits. A study proposed a new museum archiving system that applies AI technology to the service of art institutions, including museums. Studies such as this show widespread interest in AI-based computing art, which can facilitate people’s daily lives and empower the development of cultural industries. Furthermore, visual art generation and auditory art generation will revolutionize the way art is produced and increase its productivity. However, this inevitably poses some challenging issues. Traditional artists have shown great anxiety about the development of computing art. They fear that computers will soon replace their jobs. This concern is not unwarranted. AI is increasingly replacing manual labor. There are two aspects involved in Section 5. On the one hand, researchers need to clarify whether computing art qualifies to replace artificial art. On the other hand, researchers need to know whether computing art instead of artificial art is more beneficial to the well-being of society. In summary, the main challenges of AI-based computing art can be summarized as the artificial and intelligent aspects of computing art.

 

This paper has provided a comprehensive survey on audio-visual content generation. Researchers hope that this review will help people better understand the research field of audio-visual art and the development tendency of AI-based generation.

 

See the article:

Cogeneration of Innovative Audio-visual Content: A New Challenge for Computing Art

http://doi.org/10.1007/s11633-023-1453-5

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JOURNAL

Machine Intelligence Research

DOI

10.1007/s11633-023-1453-5 

ARTICLE TITLE

Cogeneration of Innovative Audio-visual Content: A New Challenge for Computing Art

 

Aston University research center to focus on using AI to improve lives

ASTON UNIVERSITY

 

IMAGE: 

PROFESSOR ANIKÓ EKÁRT AND 'PEPPER' THE ROBOT

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CREDIT: ASTON UNIVERSITY

•   

 New centre specifically focuses on using AI to improve society
•    Current research is designed to improve transport, health and industry
•    “There have been a lot of reports focusing on the negative use of AI...this is why the centre is so       important now.”

Aston University researchers have marked the opening of a new centre which focuses on harnessing artificial intelligence (AI) to improve people’s lives.

The Aston Centre for Artificial Intelligence Research and Application (ACAIRA) has been set up to become a West Midlands hub for the use of AI to benefit of society. 

Following its official opening, the academics leading it are looking to work with organisations and the public. Director Professor Anikó Ekárt said: “There have been a lot of reports focusing on the negative use of AI and subsequent fear of AI. This is why the centre is so important now, as we aim to achieve trustworthy, ethical and sustainable AI solutions for the future, by co-designing them with stakeholders.”

Deputy director Dr Ulysses Bernardet added: “We work with local, national and international institutions from academia, industry, and the public sector, expanding Aston University’s external reach in AI research and application. 

“ACAIRA will benefit our students enormously by training them to become the next generation of AI practitioners and researchers equipped for future challenges.”

The centre is already involved in various projects that use AI to solve some of society’s challenges.

A collaboration with Legrand Care aims to extend and improve independent living conditions for older people by using AI to analyse data collected through home sensors which detect decline in wellbeing. This allows care professionals to change and improve individuals’ support plans whenever needed. 

A project with engineering firm Lanemark aims to reduce the carbon footprint of industrial gas burners by exploring new, more sustainable fuel mixes. 

Other projects include work with asbestos consultancy Thames Laboratories which will lead to reduced costs, emissions, enhanced productivity and improved resident satisfaction in social housing repairs and a partnership with transport safety consultancy Agilysis to produce an air quality prediction tool which uses live data to improve transport planning decisions.  

The centre is part of the University’s College of Engineering and Physical Sciences and its official launch took place on the University campus on 29 February. The event included a talk by the chair of West Midlands AI and Future Tech Forum, Dr Chris Meah. He introduced the vision for AI within the West Midlands and the importance of bringing together academics, industry and the public.

Current research in sectors such as traffic management, social robotics, bioinformatics, health, and virtual humans was highlighted, followed by industry talks from companies Smart Transport Hub, Majestic, DRPG and Proximity Data Centres. 

The centre’s academics work closely with West Midlands AI and Future Tech Forum and host the regular BrumAI Meetup.

Artificial intelligence to reconstruct particle paths leading to new physics

THE HENRYK NIEWODNICZANSKI INSTITUTE OF NUCLEAR PHYSICS POLISH ACADEMY OF SCIENCES

 

IMAGE: 

THE PRINCIPLE OF RECONSTRUCTING THE TRACKS OF SECONDARY PARTICLES BASED ON HITS RECORDED DURING COLLISIONS INSIDE THE MUONE DETECTOR. SUBSEQUENT TARGETS ARE MARKED IN GOLD, AND SILICON DETECTOR LAYERS ARE MARKED IN BLUE.

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CREDIT: SOURCE: IFJ PAN

Cracow, 20 March 2024

Artificial intelligence to reconstruct particle paths leading to new physics

Particles colliding in accelerators produce numerous cascades of secondary particles. The electronics processing the signals avalanching in from the detectors then have a fraction of a second in which to assess whether an event is of sufficient interest to save it for later analysis. In the near future, this demanding task may be carried out using algorithms based on AI, the development of which involves scientists from the Institute of Nuclear Physics of the PAS.

Electronics has never had an easy life in nuclear physics. There is so much data coming in from the LHC, the most powerful accelerator in the world, that recording it all has never been an option. The systems that process the wave of signals coming from the detectors therefore specialise in... forgetting – they reconstruct the tracks of secondary particles in a fraction of a second and assess whether the collision just observed can be ignored or whether it is worth saving for further analysis. However, the current methods of reconstructing particle tracks will soon no longer suffice.

Research presented in Computer Science by scientists from the Institute of Nuclear Physics of the Polish Academy of Sciences (IFJ PAN) in Cracow, Poland, suggests that tools built using artificial intelligence could be an effective alternative to current methods for the rapid reconstruction of particle tracks. Their debut could occur in the next two to three years, probably in the MUonE experiment which supports the search for new physics.

In modern high-energy physics experiments, particles diverging from the collision point pass through successive layers of the detector, depositing a little energy in each. In practice, this means that if the detector consists of ten layers and the secondary particle passes through all of them, its path has to be reconstructed on the basis of ten points. The task is only seemingly simple.

“There is usually a magnetic field inside the detectors. Charged particles move in it along curved lines and this is also how the detector elements activated by them, which in our jargon we call hits, will be located with respect to each other,” explains Prof. Marcin Kucharczyk, (IFJ PAN) and immediately adds: “In reality, the so-called occupancy of the detector, i.e. the number of hits per detector element, may be very high, which causes many problems when trying to reconstruct the tracks of particles correctly. In particular, the reconstruction of tracks that are close to each other is quite a problem.”

Experiments designed to find new physics will collide particles at higher energies than before, meaning that more secondary particles will be created in each collision. The luminosity of the beams will also have to be higher, which in turn will increase the number of collisions per unit time. Under such conditions, classical methods of reconstructing particle tracks can no longer cope. Artificial intelligence, which excels where certain universal patterns need to be recognised quickly, can come to the rescue.

“The artificial intelligence we have designed is a deep-type neural network. It consists of an input layer made up of 20 neurons, four hidden layers of 1,000 neurons each and an output layer with eight neurons. All the neurons of each layer are connected to all the neurons of the neighbouring layer. Altogether, the network has two million configuration parameters, the values of which are set during the learning process,” describes Dr Milosz Zdybal (IFJ PAN).

The deep neural network thus prepared was trained using 40,000 simulated particle collisions, supplemented with artificially generated noise. During the testing phase, only hit information was fed into the network. As these were derived from computer simulations, the original trajectories of the responsible particles were known exactly and could be compared with the reconstructions provided by the artificial intelligence. On this basis, the artificial intelligence learned to correctly reconstruct the particle tracks.

“In our paper, we show that the deep neural network trained on a properly prepared database is able to reconstruct secondary particle tracks as accurately as classical algorithms. This is a result of great importance for the development of detection techniques. Whilst training a deep neural network is a lengthy and computationally demanding process, a trained network reacts instantly. Since it does this also with satisfactory precision, we can think optimistically about using it in the case of real collisions,” stresses Prof. Kucharczyk.

The closest experiment in which the artificial intelligence from IFJ PAN would have a chance to prove itself is MUonE (MUon ON Electron elastic scattering). This examines an interesting discrepancy between the measured values of a certain physical quantity to do with muons (particles that are about 200 times more massive equivalents of the electron) and predictions of the Standard Model (that is, the model used to describe the world of elementary particles). Measurements carried out at the American accelerator centre Fermilab show that the so-called anomalous magnetic moment of muons differs from the predictions of the Standard Model with a certainty of up to 4.2 standard deviations (referred as sigma). Meanwhile, it is accepted in physics that a significance above 5 sigma, corresponding to a certainty of 99.99995%, is a value deemed acceptable to announce a discovery.

The significance of the discrepancy indicating new physics could be significantly increased if the precision of the Standard Model's predictions could be improved. However, in order to better determine the anomalous magnetic moment of the muon with its help, it would be necessary to know a more precise value of the parameter known as the hadronic correction. Unfortunately, a mathematical calculation of this parameter is not possible. At this point, the role of the MUonE experiment becomes clear. In it, scientists intend to study the scattering of muons on electrons of atoms with low atomic number, such as carbon or beryllium. The results will allow a more precise determination of certain physical parameters that directly depend on the hadronic correction. If everything goes according to the physicists' plans, the hadronic correction determined in this way will increase the confidence in measuring the discrepancy between the theoretical and measured value of the muon's anomalous magnetic moment by up to 7 sigma – and the existence of hitherto unknown physics may become a reality.

The MUonE experiment is to start at Europe's CERN nuclear facility as early as next year, but the target phase has been planned for 2027, which is probably when the Cracow physicists will have the opportunity to see if the artificial intelligence they have created will do its job in reconstructing particle tracks. Confirmation of its effectiveness in the conditions of a real experiment could mark the beginning of a new era in particle detection techniques.

The work of the team of physicists from the IFJ PAN was funded by a grant from the Polish National Science Centre.

The Henryk Niewodniczański Institute of Nuclear Physics (IFJ PAN) is currently one of the largest research institutes of the Polish Academy of Sciences. A wide range of research carried out at IFJ PAN covers basic and applied studies, from particle physics and astrophysics, through hadron physics, high-, medium-, and low-energy nuclear physics, condensed matter physics (including materials engineering), to various applications of nuclear physics in interdisciplinary research, covering medical physics, dosimetry, radiation and environmental biology, environmental protection, and other related disciplines. The average yearly publication output of IFJ PAN includes over 600 scientific papers in high-impact international journals. Each year the Institute hosts about 20 international and national scientific conferences. One of the most important facilities of the Institute is the Cyclotron Centre Bronowice (CCB), which is an infrastructure unique in Central Europe, serving as a clinical and research centre in the field of medical and nuclear physics. In addition, IFJ PAN runs four accredited research and measurement laboratories. IFJ PAN is a member of the Marian Smoluchowski Kraków Research Consortium: “Matter-Energy-Future”, which in the years 2012-2017 enjoyed the status of the Leading National Research Centre (KNOW) in physics. In 2017, the European Commission granted the Institute the HR Excellence in Research award. As a result of the categorization of the Ministry of Education and Science, the Institute has been classified into the A+ category (the highest scientific category in Poland) in the field of physical sciences.

SCIENTIFIC PUBLICATIONS:

“Machine Learning based Event Reconstruction for the MUonE Experiment”

M. Zdybał, M. Kucharczyk, M. Wolter

Computer Science 25(1) (2024) 25-46

DOI: 10.7494/csci.2024.25.1.5690

 

LINKS:

http://www.ifj.edu.pl/

The website of the Institute of Nuclear Physics, Polish Academy of Sciences.

http://press.ifj.edu.pl/

Press releases of the Institute of Nuclear Physics, Polish Academy of Sciences.

 

IMAGES:

IFJ240320b_fot01s.jpg                                 

HR: http://press.ifj.edu.pl/news/2024/03/20/IFJ240320b_fot01.jpg

The principle of reconstructing the tracks of secondary particles based on hits recorded during collisions inside the MUonE detector. Subsequent targets are marked in gold, and silicon detector layers are marked in blue. (Source: IFJ PAN)

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JOURNAL

Computer Science

DOI

10.7494/csci.2024.25.1.5690 

ARTICLE TITLE

Machine Learning based Event Reconstruction for the MUonE Experiment