154 research outputs found
Facile synthesis of coreāshell porous FeO@carbon microspheres with high lithium storage performance
Coreāshell porous Fe3O4@C (CP-Fe3O4@C) microspheres were synthesized using an environmentally viable hydrothermal method. Carbonization can reduce Fe2O3 and provide a conductive coating simultaneously. CP-Fe3O4@C microspheres as an active material for Lithium-ion batteries demonstrate pseudocapacity for improved rate performance. With a distinct nanostructure and pseudocapacitive effect, the CP-Fe3O4@C microspheres show excellent electrochemical performance ( at after 200 cycles). Capacity measurements of CP-Fe3O4@C microspheres suggest near 90% pseudocapacitance at relatively low scan rates ()
Facile synthesis of coreāshell porous FeO@carbon microspheres with high lithium storage performance
Coreāshell porous Fe3O4@C (CP-Fe3O4@C) microspheres were synthesized using an environmentally viable hydrothermal method. Carbonization can reduce Fe2O3 and provide a conductive coating simultaneously. CP-Fe3O4@C microspheres as an active material for Lithium-ion batteries demonstrate pseudocapacity for improved rate performance. With a distinct nanostructure and pseudocapacitive effect, the CP-Fe3O4@C microspheres show excellent electrochemical performance ( at after 200 cycles). Capacity measurements of CP-Fe3O4@C microspheres suggest near 90% pseudocapacitance at relatively low scan rates ()
Language Agents for Detecting Implicit Stereotypes in Text-to-image Models at Scale
The recent surge in the research of diffusion models has accelerated the
adoption of text-to-image models in various Artificial Intelligence Generated
Content (AIGC) commercial products. While these exceptional AIGC products are
gaining increasing recognition and sparking enthusiasm among consumers, the
questions regarding whether, when, and how these models might unintentionally
reinforce existing societal stereotypes remain largely unaddressed. Motivated
by recent advancements in language agents, here we introduce a novel agent
architecture tailored for stereotype detection in text-to-image models. This
versatile agent architecture is capable of accommodating free-form detection
tasks and can autonomously invoke various tools to facilitate the entire
process, from generating corresponding instructions and images, to detecting
stereotypes. We build the stereotype-relevant benchmark based on multiple
open-text datasets, and apply this architecture to commercial products and
popular open source text-to-image models. We find that these models often
display serious stereotypes when it comes to certain prompts about personal
characteristics, social cultural context and crime-related aspects. In summary,
these empirical findings underscore the pervasive existence of stereotypes
across social dimensions, including gender, race, and religion, which not only
validate the effectiveness of our proposed approach, but also emphasize the
critical necessity of addressing potential ethical risks in the burgeoning
realm of AIGC. As AIGC continues its rapid expansion trajectory, with new
models and plugins emerging daily in staggering numbers, the challenge lies in
the timely detection and mitigation of potential biases within these models
Oral microbiota of periodontal health and disease and their changes after nonsurgical periodontal therapy
This study examined the microbial diversity and community assembly of oral microbiota in periodontal health and disease and after nonsurgical periodontal treatment. The V4 region of 16S rRNA gene from DNA of 238 saliva and subgingival samples of 21 healthy and 48 diseased subjects was amplified and sequenced. Among 1979 OTUs identified, 28 were overabundant in diseased plaque. Six of these taxa were also overabundant in diseased saliva. Twelve OTUs were overabundant in healthy plaque. There was a trend for disease-associated taxa to decrease and health-associated taxa to increase after treatment with notable variations among individual sites. Network analysis revealed modularity of the microbial communities and identified several health- and disease-specific modules. Ecological drift was a major factor that governed community turnovers in both plaque and saliva. Dispersal limitation and homogeneous selection affected the community assembly in plaque, with the additional contribution of homogenizing dispersal for plaque within individuals. Homogeneous selection and dispersal limitation played important roles, respectively, in healthy saliva and diseased pre-treatment saliva between individuals. Our results revealed distinctions in both taxa and assembly processes of oral microbiota between periodontal health and disease. Furthermore, the community assembly analysis has identified potentially effective approaches for managing periodontitis
Attention Paper: How Generative AI Reshapes Digital Shadow Industry?
The rapid development of digital economy has led to the emergence of various
black and shadow internet industries, which pose potential risks that can be
identified and managed through digital risk management (DRM) that uses
different techniques such as machine learning and deep learning. The evolution
of DRM architecture has been driven by changes in data forms. However, the
development of AI-generated content (AIGC) technology, such as ChatGPT and
Stable Diffusion, has given black and shadow industries powerful tools to
personalize data and generate realistic images and conversations for fraudulent
activities. This poses a challenge for DRM systems to control risks from the
source of data generation and to respond quickly to the fast-changing risk
environment. This paper aims to provide a technical analysis of the challenges
and opportunities of AIGC from upstream, midstream, and downstream paths of
black/shadow industries and suggest future directions for improving existing
risk control systems. The paper will explore the new black and shadow
techniques triggered by generative AI technology and provide insights for
building the next-generation DRM system
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Functional Gene Array-Based Ultrasensitive and Quantitative Detection of Microbial Populations in Complex Communities.
While functional gene arrays (FGAs) have greatly expanded our understanding of complex microbial systems, specificity, sensitivity, and quantitation challenges remain. We developed a new generation of FGA, GeoChip 5.0, using the Agilent platform. Two formats were created, a smaller format (GeoChip 5.0S), primarily covering carbon-, nitrogen-, sulfur-, and phosphorus-cycling genes and others providing ecological services, and a larger format (GeoChip 5.0M) containing the functional categories involved in biogeochemical cycling of C, N, S, and P and various metals, stress response, microbial defense, electron transport, plant growth promotion, virulence, gyrB, and fungus-, protozoan-, and virus-specific genes. GeoChip 5.0M contains 161,961 oligonucleotide probes covering >365,000 genes of 1,447 gene families from broad, functionally divergent taxonomic groups, including bacteria (2,721 genera), archaea (101 genera), fungi (297 genera), protists (219 genera), and viruses (167 genera), mainly phages. Computational and experimental evaluation indicated that designed probes were highly specific and could detect as little as 0.05āng of pure culture DNAs within a background of 1āĪ¼g community DNA (equivalent to 0.005% of the population). Additionally, strong quantitative linear relationships were observed between signal intensity and amount of pure genomic (ā¼99% of probes detected; r > 0.9) or soil (ā¼97%; r > 0.9) DNAs. Application of the GeoChip to a contaminated groundwater microbial community indicated that environmental contaminants (primarily heavy metals) had significant impacts on the biodiversity of the communities. This is the most comprehensive FGA to date, capable of directly linking microbial genes/populations to ecosystem functions.IMPORTANCE The rapid development of metagenomic technologies, including microarrays, over the past decade has greatly expanded our understanding of complex microbial systems. However, because of the ever-expanding number of novel microbial sequences discovered each year, developing a microarray that is representative of real microbial communities, is specific and sensitive, and provides quantitative information remains a challenge. The newly developed GeoChip 5.0 is the most comprehensive microarray available to date for examining the functional capabilities of microbial communities important to biogeochemistry, ecology, environmental sciences, and human health. The GeoChip 5 is highly specific, sensitive, and quantitative based on both computational and experimental assays. Use of the array on a contaminated groundwater sample provided novel insights on the impacts of environmental contaminants on groundwater microbial communities
Effect of mobile health reminders on tuberculosis treatment outcomes in Shanghai, China: A prospective cohort study
BackgroundPoor adherence increases the risk of unfavorable outcomes for tuberculosis (TB) patients. Mobile health (mHealth) reminders become promising approaches to support TB patientsā treatment. But their effects on TB treatment outcomes remain controversial. In this prospective cohort study, we evaluated the effect of the reminder application (app) and the smart pillbox on TB treatment outcomes compared with the standard care in Shanghai, China.MethodsWe recruited new pulmonary TB (PTB) patients diagnosed between April and November 2019 who were aged 18 or above, treated with the first-line regimen (2HREZ/4HR), and registered at Songjiang CDC (Shanghai). All eligible patients were invited to choose the standard care, the reminder app, or the smart pillbox to support their treatment. Cox proportional hazard model was fitted to assess the effect of mHealth reminders on treatment success.Results260 of 324 eligible patients enrolled with 88 using standard care, 82 the reminder app, and 90 the smart pillbox, followed for a total of 77,430ādays. 175 (67.3%) participants were male. The median age was 32 (interquartile range [IQR] 25 to 50) years. A total of 44,785 doses were scheduled for 172 patients in the mHealth reminder groups during the study period. 44,604 (99.6%) doses were taken with 39,280 (87.7%) monitored by the mHealth reminders. A significant time-dependent downward linear trend was observed in the monthly proportion of dose intake (p <ā0.001). 247 (95%) patients were successfully treated. The median treatment duration of successfully treated patients in the standard care group was 360 (IQR 283ā369) days, significantly longer than those in the reminder app group (296, IQR 204ā365, days) and the smart pillbox group (280, IQR 198ā365, days) (both p <ā0.01). Using the reminder app and the smart pillbox was associated with 1.58 times and 1.63 times increase in the possibility of treatment success compared with the standard care, respectively (both p <ā0.01).ConclusionThe reminder app and the smart pillbox interventions were acceptable and improved the treatment outcomes compared with the standard care under the programmatic setting in Shanghai, China. More high-level evidence is expected to confirm the effect of mHealth reminders on TB treatment outcomes
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Biogeographic patterns of soil diazotrophic communities across six forests in North America.
Soil diazotrophs play important roles in ecosystem functioning by converting atmospheric N2 into biologically available ammonium. However, the diversity and distribution of soil diazotrophic communities in different forests and whether they follow biogeographic patterns similar to macroorganisms still remain unclear. By sequencing nifH gene amplicons, we surveyed the diversity, structure and biogeographic patterns of soil diazotrophic communities across six North American forests (126 nested samples). Our results showed that each forest harboured markedly different soil diazotrophic communities and that these communities followed traditional biogeographic patterns similar to plant and animal communities, including the taxa-area relationship (TAR) and latitudinal diversity gradient. Significantly higher community diversity and lower microbial spatial turnover rates (i.e. z-values) were found for rainforests (~0.06) than temperate forests (~0.1). The gradient pattern of TARs and community diversity was strongly correlated (r(2) > 0.5) with latitude, annual mean temperature, plant species richness and precipitation, and weakly correlated (r(2) < 0.25) with pH and soil moisture. This study suggests that even microbial subcommunities (e.g. soil diazotrophs) follow general biogeographic patterns (e.g. TAR, latitudinal diversity gradient), and indicates that the metabolic theory of ecology and habitat heterogeneity may be the major underlying ecological mechanisms shaping the biogeographic patterns of soil diazotrophic communities
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