The PRAME-AS knockout cells showed a decrease in migration, proliferation, stemness, and viability. by Zahra Hosseininia (22265113)

Top k-NN clusters of metabolites decreased at 4 hours. by Zarazuela Zolkipli-Cunningham (5234186)

Top k-NN cluster of metabolites decreased at 30 minutes. by Zarazuela Zolkipli-Cunningham (5234186)

Change in support for AI-enabled autonomous systems from 2018 to 2020. by Michael C. Horowitz (5883152)

Table 1_Association between the atherogenic index of plasma and risk of large-artery atherosclerotic ischemic stroke.xlsx by Wen Zhong (425650)

“…</p>Results<p>Individuals with consistently high levels of AIP were at increased risk of developing LAA, and this risk increased progressively with increasing levels of AIP. …”

GSK343 decreased tumor stemness. by Laura V. Bownes (10276762)

“…Treatment with GSK343 significantly decreased tumorsphere formation by both the COA3 (A) and COA6 (B) cells, representing a decrease in stem cell-like phenotype. …”

Data Sheet 1_Conversational AI agent for precision oncology: AI-HOPE-WNT integrates clinical and genomic data to investigate WNT pathway dysregulation in colorectal cancer.docx by Ei-Wen Yang (149486)

“…</p>Methods<p>AI-HOPE-WNT employs a modular architecture combining large language models (LLMs), a natural language-to-code engine, and a backend statistical workflow interfaced with harmonized data from cBioPortal. …”

Table_1_Association Between CST3 Gene Polymorphisms and Large-Artery Atherosclerotic Stroke.PDF by Yarong Ding (10952170)

“…Per C allele of rs13038305 also showed an increased level of low-density lipoprotein cholesterol (LDL-C), β (95% CI) was 1.335 (1.008–1.250), p = 0.044. …”

Flow charts showing the performance for all the relevant pathologies (a) in all age groups, (b) 7–14 years, and (c) 2–6 years. by Prerana Agarwal (21781515)

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Novel agent DMAMCL suppresses osteosarcoma growth and decreases the stemness of osteosarcoma stem cell by Gen Ba (6440999)

Data Sheet 1_Knowledge, use and perceptions of artificial intelligence Chatbots among Italian physiotherapists: an online cross-sectional survey.pdf by Manuela Deodato (11135016)

Data Sheet 2_Knowledge, use and perceptions of artificial intelligence Chatbots among Italian physiotherapists: an online cross-sectional survey.pdf by Manuela Deodato (11135016)

NN analysis to assign the importance of variables. by Sulmaz Ghahramani (3387635)

Data_Sheet_1_Application of a nomogram model for the prediction of 90-day poor outcomes following mechanical thrombectomy in patients with acute anterior circulation large-vessel o... by Xia Li (14984)

“…Moreover, five variables, namely, age (odds ratio [OR]: 1.049, 95% CI [1.016–1.083]; p = 0.003), glucose level (OR: 1.163, 95% CI [1.038–1.303]; p = 0.009), baseline National Institute of Health Stroke Scale (NIHSS) score (OR: 1.066, 95% CI [0.995–1.142]; p = 0.069), unsuccessful recanalization (defined as a TICI grade of 0 to 2a) (OR: 3.730, 95% CI [1.688–8.245]; p = 0.001), and early neurological deterioration (END, defined as an increase of ≥4 points between the baseline NIHSS score and the NIHSS score at 24 h after MT) (OR: 3.383, 95% CI [1.411–8.106]; p = 0.006), were included in the nomogram to predict the potential risk of poor outcomes at 90 days following MT in LVO patients, with a C-index of 0.763 (0.693–0.832) in the training set and 0.804 (0.719–0.889) in the validation set.…”

Figure 2 -CyrtarachneSurfaceSpreading Comparison.ai from The moth specialist spider <i>Cyrtarachne akirai</i> uses prey scales to increase adhesion. by Candido Diaz (1477900)

“…<i>Larinioides cornutus</i> glue droplets failed to penetrate the moth scales, their force of adhesion thus limited by the strength of attachment of scales to the cuticle. The large size and low viscosity of <i>C. akirai</i> glue droplets function together to use the three-dimensional topography of the moth's scales against itself via capillary forces. …”

Brief Report: Increased technology use associated with lower A1C in a large pediatric clinical population by G. Todd Alonso (9568724)

Mining Solid-State Electrolytes from Metal–Organic Framework Databases through Large Language Models and Representation Clustering by Jinglang Zhang (20989405)

“…However, their development is hindered by their inherent complexity and the absence of design guidelines. Large language models (LLMs) and machine learning, as emerging artificial intelligence (AI) technologies, can significantly accelerate the development of MOF SSEs by analyzing data and identifying potential materials. …”

For 1,000 random y(0). by Xi Chen (35903)

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For the Karate network. by Xi Chen (35903)

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Graphical illustration of Case 2 from Theorem 1 (i.e. <i>p</i> < 0 and <i>β</i> ≥ −1/<i>p</i>). by Xi Chen (35903)

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