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LONDON, Oct 13 (Reuters) – Oil prices rose on Monday after hitting five-month lows in the previous session, as investors focused on potential talks between the presidents of the United States and China that could ease trade tensions between the world’s two largest economies.

Brent crude futures rose $1.08, or 1.7%, to $63.81 a barrel by 1056 GMT. U.S. West Texas Intermediate crude was at $60.03 a barrel, up $1.13, or 1.92%. Both contracts lost around 4% on Friday to settle at their lowest since May.

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“Last week’s price meltdown was largely on the back of ceasefire in Gaza and return of U.S.-China trade volatility ahead of the November 10 trade truce deadline,” DBS energy analyst Suvro Sarkar said.

The selloff in markets now looked to be capped by Washington and Beijing’s willingness to negotiate, he said, adding the near-term outlook hinged on the eventual outcome of the trade talks.

An expected meeting between Trump and Chinese President Xi Jinping later this month was in doubt after Trump said on Friday there was no reason to meet his counterpart. U.S. Trade Representative Jamison Greer said on Sunday that a meeting could still happen in South Korea on the sidelines of the Asia-Pacific Economic Cooperation forum.

Oil prices tumbled in March and April at the height of trade tensions between the two countries.

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The dollar is trading towards the top of its trading range, buoyed by the lack of US data and some challenges faced by the euro and the yen. Yet US consumers remain fearful of their employment prospects, and we doubt the Fed will stray from its path toward two more rate cuts this year. Lower US hedging costs and seasonal trends suggest recent $ strength won’t last

Clinicopathological data

Overall, 54 patients with SGC were included in this study. The most frequent entity was salivary duct carcinoma (SDC; 43.6%), followed by acinic cell carcinoma (ACC; 24.1%), mucoepidermoid carcinoma (MEC; 22.2%), and secretory carcinoma (Sec; 11.1%). The mean age across the whole cohort was 57.7 (± 17.5) years, and 46.3% of all patients were female. Most patients (57.7%) had T1/2 tumors. Among SDC, 81.1% were androgen receptor (AR)-positive, and 54.5% showed nuclear AR positivity in > 70% of tumor cells (AR_high), whereas 45.5% showed nuclear AR positivity in ≤ 70% of tumor cells (AR_low). Furthermore, 36.4% of the SDC were positive for human epidermal growth factor receptor 2 (HER2; score 3 + or HER2-amplified). The primary therapy was surgery in 98.1% of cases, and chemoradiation in 1.9% of cases. Neck dissection was performed in 74.1% of the cases, and 57.4% of the patients received adjuvant (chemo-)radiation therapy. Detailed data for the specific entities are presented in Table 1.

With a median follow-up of 63.5 months for the entire cohort, SDC patients showed the most unfavorable five-year OS (50.6%), followed by MEC (83.3%), ACC (83.9%), and Sec patients (100.0%). Five-year RFS rates were 30.7% for SDC, 69.2% for ACC, 83.3% for MEC, and 66.7% for Sec. Moreover, five-year RFP was 39.9% for SDC, 69.2% for ACC, 91.7% for MEC, and 66.7% for Sec. Lastly, five-year DCR was 47.0% for SDC, 69.2% for ACC, 100.0% for MEC, and 83.3% for Sec.

A 13-marker IMC panel identifies distinct immune cell and CAF subsets in nonMYO SGC

We used tissue from SGC primaries and metastases from 54 patients with SGC to create a tissue microarray (TMA). Tissue sections from TMA were stained with a cocktail of 13 metal-conjugated antibodies. Ablation of the tissue slices produced single-channel images that were used to generate multichannel images. After segmentation, the cells were categorized into cellular subsets. The cellular frequencies and results from the spatial analyses were then correlated with clinical parameters and patient outcomes (Fig. 1A).

After the thorough exclusion of non-tumor-bearing TMA cores, we analyzed 199 SGC TMA cores from 54 SGC, including 188 primaries and 11 metastases from SDC, ACC, MEC, and Sec. A median cell count of 3,019 (ACC), 2,274 (Sec), 2,104 (MEC), and 2,029 (SDC) per 1 mm² ROI was noted. Using a stepwise Gaussian mixture model of cytokeratin expression and SOM clustering, we measured the expression of 13 different markers in 408,939 cells, (Fig. 1B and C). First, we separated tumor cells (CKAE1/3⁺; n = 266,172) from endothelial cells (CD31⁺, CKAE1/3⁻; n = 1,349), immune cells (CKAE1/3⁻, CD45⁺, or CD138⁺; n = 50,432), and CAFs (CKAE1/3/CD45/CD31/CD138⁻; n = 90,986). Immune cells were then clustered into plasma cells (CD138⁺), CD8⁺ T cells, CD4⁺ T cells, and weakly CD4-expressing cells, which are most likely monocytes or other myeloid-derived cells (other CD4⁺cells; [5, 12, 19]). CD4⁺ T cells were subclassified as CD4⁺CD74⁺ T cells, which most likely correspond to effector Tregs [20, 21], proliferating CD4⁺ T cells (Ki67⁺), and other CD4⁺ T cells. Immune cells solely expressing CD45 were classified as “other immune cells.” To classify CAFs, we adopted an IMC-validated classification scheme proposed by Cords et al. [5, 25], separating mCAFs (SMA⁺Collagen1⁺), Collagen CAFs (SMA-Collagen1⁺), SMA-CAFs (SMA⁺Collagen1⁻), dCAFs (Ki67⁺, Vimentin⁺), and apCAFs (CD74⁺, Vimentin⁺). We also included CD73, IDO, and CD34 to detect rCAFs, ifnCAF/IDO CAFs, and iCAFs, respectively. However, the CD73 and IDO antibodies used did not produce a specific IMC signal. We could not discriminate iCAFs, since all CD34⁺ cells co-expressed CD31, a specific vascular marker, which was used to identify endothelia. Two subsets of cells did not display a specific marker profile: cells without expression of any of the markers used were designated as tumor cells, as they were located in tumor cell patches. Regarding the TME-centered focus of this work, we reasoned that this conservative approach would not impact TME-cell classification and thus minimize the chance of false-positive results. Cells with sole expression of Vimentin were classified as “other CAFs” with Vimentin expression (Fig. 1D). Figure 1E displays representative ROIs of the included tumor entities as multiplex images and the corresponding cellular maps after segmentation and cell phenotyping.

SGC entities are characterized by different TME cell frequencies

When pooling all ROIs, the cell type distribution across SGC tumor types displayed marked differences with a prominent immune cell compartment in ACC (Supplementary Figure S1C and D). No prominent differences in cell category contributions were noted when comparing primaries and metastases or peripheral and central tumor regions (Supplementary Figure S1E and F).

To account for differing numbers of ROIs per patient due to the exclusion of non-tumor-bearing and torn TMA cores, we calculated the median cell frequency per patient and compared the distribution of cells across the different SGC entities. We found a significantly varying abundance of immune cell subsets across tumor types (Fig. 2A), which was largely due to significantly elevated levels of CD4⁺ T cells (p = 0.03), CD8⁺ T cells (p = 0.04), other CD4⁺ cells (p = 0.03), and unclassified immune cells (p = 0.04) in ACC. This resulted in a significantly higher frequency of overall immune cells in ACC (p = 0.04; Fig. 2B and C).

Comparison of median cell frequencies per patient across tumor subtypes (A) Immune cell subsets across SGC entities. B Immune cell subsets in ACC vs. other carcinoma types. C Representative core immune marker expression (top row) and selected immune cell subsets in SDC versus ACC. D Frequencies of CAF subtypes across SGC entities. E Examples of the spectrum of core CAF marker expression (top row) and cell categories (bottom row) in SDC. F Frequency of tumor-infiltrating CAFs dependent on AR expression in SDC. G Frequency of tumor-infiltrating apCAFs in ACC versus other SGC subtypes. H Representative images of CD74⁺ expression in apCAFs in ACC (top row) and corresponding cell categories after segmentation (bottom row). 100-micron scale bars

Although the overall distribution of CAFs did not significantly vary in SGC types (Fig. 2D), we detected the highest mean frequency of mCAFs in SDC, a CAF subset that has been associated with ECM production. However, in direct comparison with other entities, this elevation was not significant (Supplementary Figure S2A), probably because of the broad spectrum of mCAF frequencies within SDC (Fig. 2D and E).

We also analyzed the frequency of tumor-infiltrating CAFs using tumor patch detection (Supplementary Figure S2B). As AR and HER2 are frequently expressed in SDC and are molecular targets for systemic therapy approaches [26], we examined the association between AR/HER2 expression and CAF frequency. We were able to show that the number of tumor-infiltrating CAFs was significantly elevated in AR_high SDC compared to AR_low tumors (p = 0.049; Fig. 2F). No other significant cellular TME alterations were noted when SDC was subgrouped based on AR (Supplementary Figure S2C-E) and HER2 status (Supplementary Figure S2F-H). In contrast, in direct comparison to other tumor types, ACC displayed elevated levels of tumor-infiltrating apCAFs (p = 0.003; Fig. 2G and H, Supplementary Figure S2I). However, the frequency of these cells was generally very low. No significant differences in tumor-infiltrating immune cells were noted across SGC tumor types (Supplementary Figure S2J).

Spatial interaction analyses reveal a co-localisation of mCAFs with intratumoral vasculature

The spatial composition of the TME is an integral feature of tumor biology and influences the response to immunotherapy and patient outcomes [27]. Therefore, we leveraged the spatial information preserved in the dataset.

After computing a spatial interaction graph (examples in Supplementary Figure S3A), we clustered cells based on their 20 nearest neighbors into 9 cellular neighborhoods (CN; Fig. 3A and B). We found that the tumor cell-rich neighborhoods were largely devoid of CAFs and immune cells (CN1 and CN3). In contrast, CD4⁺ T cells, proliferating CD4⁺ T cells, other CD4⁺ cells, and CD8⁺ T cells formed a distinct lymphocyte-rich cluster (CN9, Fig. 3C). Another immune-related CN mainly consisted of CD4⁺CD74⁺ T-cells, apCAFs, and dCAFs.

Spatial analysis of the TME of SGC and correlation with ECM protein modules. A Exemplary ROIs with cells colored by cellular neighborhood (CN). B Scaled cell abundance per CN. C Representative ROI rich in cells that mainly contribute to CN9 (ACC) and CN8 (MEC). D Frequency of CN across SCG types. E Spatial interaction of cell types colored by the number of ROIs with significant co-localization of two cell types. A red color indicates more ROIs with significant interaction while blue tiles indicate more ROIs with significant avoidance of two cell types. F Correlation of cell types with ECM module eigengenes which were published previously. Symbols within the tiles indicate the test significance (* p < 0.05, ns not significant). 100-micron scale bars

However, as the contributing cell types, the latter CN occurred at very low frequencies (CN2, Supplementary Figure S3B). SMA CAF and mCAFs contributed to two distinct CN with partial overlap (CN 6 and 8, respectively; Supplementary Figure S3C). Interestingly, the matrix-associated CAF-rich (mCAF-rich) CN8 displayed a strong contribution to endothelia (Fig. 3C). No specific marker for vCAFs, another cell type that was found to be associated with endothelia by Cords et al., was available in this panel. However, in contrast to mCAFs, very low expression of SMA and Collagen1 was reported in vCAFs [12], allowing for discrimination between the two cell types.

Although we did not observe significant differences in CN frequencies across SGC types, immune cell-rich CN was distinctively enriched in ACC. In addition, in SDC, we noted higher frequencies of the mCAF-rich CN8, but not of CNs 5, 6, and 7, which have high contributions of Collagen CAFs and SMA CAFs (Fig. 3D, Supplementary Figure S3C and D).

The CN analyses were complemented by a more direct spatial interaction analysis, as proposed by Schapiro et al. [22] (Fig. 3E). This approach compares the spatial interactions of cell type pairs with a null distribution in each ROI. The number of ROIs with significant positive (red) and negative (blue) interactions can then be summarized and graphically displayed. This methodically different analysis largely validated the aforementioned results, indicating potential interactions among immune cells and between mCAFs and endothelia as well as SMA CAFs. Again, mutual exclusion of TME and tumor cells was noted.

In summary, we describe distinct spatial cellular TME neighborhoods in SGC that largely show a mutually exclusive predominance of tumor cells, immune cells, and CAFs. However, mCAFs and Collagen CAFs tend to localize in proximity to the tumor vasculature.

CAFs and particularly mCAFs are associated with a distinct ECM profile

Recently, we dissected the ECM of SGC using an unbiased proteomic approach and discovered three protein clusters (“ECM modules”) that explain ECM differences across SGC tumor types via module Eigengenes (i.e., the resulting vector of the module’s proteomic signature) [15]. Using gene set enrichment analysis, these modules were biologically annotated and found to be enriched for classic CAF-associated (CAF-module), basement membrane-associated (BM-module), and peripheral blood-associated (Hem-module) biological terms. The most significant members of the latter include coagulation-related factors such as kallikrein, kininogen, prothrombin, plasminogen, and angiotensin. Since CAFs are considered the main source of ECM in carcinomas, we leveraged an overlap of 40 patients between that and the present SGC cohort and correlated the module Eigengenes with the cell type frequencies (Fig. 3F), thereby establishing a connection between the ECM and the cellular TME. As anticipated, we observed a strong positive correlation between CAFs with a more classic myofibroblastic phenotype (mCAF, Collagen CAF, SMA CAF) and the CAF protein module. However, only the correlation between mCAFs and overall CAFs was significant. In contrast, all immune cell subsets, except for plasma cells, were negatively correlated with the CAF module. Unexpectedly, very similar associations were found for the Hem-module, including an anti-correlation with endothelia and all immune cell subsets except plasma cells. We previously reasoned that this module consists of blood-related factors that are deposited within the ECM. However, the present data indicate that this does not imply exaggerated vasculature or deposition of cellular components of the peripheral blood. The BM module was mainly expressed in SGC with myoepithelial differentiation (e.g., adenoid cystic carcinomas), which were not analyzed in the present study.

Together, these data clearly link SGC mCAFs, Collagen CAFs, and SMA CAFs to an increase in classical CAF-associated ECM proteins and provide evidence that the ECM components of the CAF and the Hem module participate in an immune-exclusive TME.

Metastasis-associated signatures are enriched in tumor niches with a co-localization of mCAFs and endothelia and the interaction may be mediated by specific gene signatures

A significant positive correlation between mCAF-like and pericyte as well as endothelia signatures found in all three ST SDC data sets additionally supported the previously identified spatial co-localization of mCAFs and intratumoral vasculature (Fig. 5A and B; Supplementary Figure S5A and S5B).

A marked enrichment of metastasis-associated signatures such as “ANASTASSIOU_MULTICANCER_INVASIVENESS_SIGNATURE”, “GILDEA_METASTASIS”, “ROZANOV_MMP14_TARGETS_UP”, and “HEBERT_MATRISOME_TNBC_LUNG_METASTASIS” among others in mCAF^highendothelia^high and a simultaneous depletion of those signatures in mCAF^lowendothelia^low ST spots (Fig. 5C) supported our previous clinically-based findings indicating that the spatial interaction of mCAFs and endothelia may be associated with metastasis. Notably, enrichment of metastasis-associated signatures was markedly higher in mCAF^highendothelia^high than in mCAF^highendothelia^low spots. In line with these findings, the invasiveness suppressing signature “RODRIGUES_DCC_TARGETS_UP” [28] was markedly depleted in mCAF^highendothelia^high tumor niches. Alternative cut-offs for spot classification were evaluated and led to similar results, demonstrating the robustness of this approach (Supplementary Figure S6 A and B).

DE analysis followed by GSEA for mCAF^highendothelia^high versus all other spots was performed to identify which factors may mediate the interaction between mCAFs and endothelia and therefore may be associated with metastasis of SDC (Fig. 5D-F and Supplementary Fig. 5 C). Notably, beside several ECM-/fibroblast-/collagen-related signatures, the “metalloendopeptidase activity” signature (enrichment score = 0.59; q-value < 0.001) was among the top-enriched signatures and, accordingly, the pro-metastatic matrix metalloproteinases [29,30,31] (MMP-28 (avg_log2FC = 2.10; p-adj. < 0.001), MMP-9 (avg_log2FC = 2.01; p-adj. < 0.001), MMP-11 (avg_log2FC = 1.69; p-adj. < 0.001), MMP-14 (avg_log2FC = 1.68; p-adj. < 0.001), and MMP-2 (avg_log2FC = 1.64; p-adj. < 0.001) were identified as strongly upregulated genes. More importantly, the “platelet-derived growth factor binding” (enrichment score = 0.84; q-value < 0.001) signature showed the second highest enrichment score among all signatures. Further, the “insulin-like growth factor binding” (enrichment score = 0.67; q-value = 0.007) signature ranked among the top-enriched signatures within mCAF^highendothelia^high tumor niches. Accordingly, platelet-derived growth factor receptor β (PDGFRβ; avg_log2FC = 2.07; p-adj. < 0.001) and PDGFRα (avg_log2FC = 1.67; p-adj. < 0.001) as well as insulin-like growth factor family member 1 (IGFL1; avg_log2FC = 1.84; p-adj. < 0.001) were strongly upregulated genes within mCAF^highendothelia^high niches (Supplementary Table S1).

A higher frequency of mCAFs is an independent prognostic factor for recurrence and distant metastasis in SDC

Cox regression analysis and log-rank test were performed to identify a potential influence of TME on the prognosis of SDC. The results of the univariate Cox regression model showed that a higher frequency of mCAFs was a prognostic factor for a higher rate of distant metastasis in SDC (p = 0.02; HR (95%CI) = 10.99 (1.37–88.16); Fig. 4A). Regarding RFP, a higher frequency of mCAFs (p = 0.02; HR (95%CI) = 6.23 (1.34–28.93)) and a higher frequency of other CD4⁺ cells (p = 0.04; HR (95%CI) = 3.82 (1.07–13.69)) were prognostic factors for a higher probability of recurrence in the univariate Cox regression model (Fig. 4B). Multivariate Cox regression revealed a higher frequency of mCAFs as an independent prognostic factor for a higher probability of recurrence (p = 0.032; HR (95%CI) = 7.81 (1.19–51.3); Fig. 4C). The five-year DCR was 75.0% for patients with a low frequency of mCAFs and 24.2% for those with a high frequency of mCAFs (p = 0.0049; Fig. 4D). The five-year RFP was 68.2% for patients with a low frequency and 18.2% for those with a high frequency of mCAFs (p = 0.0077; Fig. 4E). A high frequency of CN8 (mCAF-rich) was marginally non-significant as a prognostic factor for a higher probability of recurrence (p = 0.06; HR (95%CI) = 4.54 (0.94–22.01); Supplementary Fig. 4 A) and distant metastasis (p = 0.07; HR (95%CI) = 3.46 (0.91–13.10); Supplementary Fig. 4B) in the Cox regression model. Nevertheless, the five-year DCR was significantly lower in patients with a high frequency (27.7%) than in those with a low frequency of CN8 (68.2%; p = 0.04; Fig. 4F). Further, the five-year RFP was 20.4% for patients with a high frequency and 61.4% for those with a low frequency of CN8 (p = 0.052; Fig. 4G). The association between higher mCAF frequencies and a decreased DCR as well as RFP was consistent across AR subgroups (Supplementary Fig. 4 C and D). Finally, a significantly higher frequency of mCAFs was found in patients with distant metastatic disease than in those without (p = 0.048). Sex did neither show significance as prognostic factor for DCR (p = 0.23; HR (95%CI) = 0.28 (0.03–2.25), nor for RFP (p = 0.36; HR (95%CI) = 0.48 (0.10–2.28). When including survival data, a higher frequency of mCAFs was not a prognostic factor for OS (p = 0.90; HR (95%CI) = 1.10 (0.34–3.63) or RFS (p = 0.16; HR (95%CI) = 2.10 (0.74–5.91) in univariate cox regression. Furthermore, the five-year OS did not differ significantly between patients with a high frequency of mCAFs (45.5%) and those with a low mCAF frequency (58.3%; p = 0.87). There was no difference in the five-year RFS between patients with a high mCAF frequency (18.2%) and those with a low mCAF frequency (43.7%; p = 0.15). Notably, patients with low mCAF frequencies were markedly older (mean age = 69.17 years) than those with high mCAF frequencies (mean age = 63.64 years).

Association of cell types with distant control rate (DCR), recurrence-free probability (RFP), and frequency of distant metastasis in salivary duct carcinoma (SDC) patients. A Univariate cox proportional hazards model for DCR for cell types and T stage, stratified by median proportion or as negative vs. positive in case median equals zero. N stage was excluded due to complete separation, resulting in an unbounded 95% confidence interval for its odds ratio. B Univariate cox proportional hazards model for RFP for cell types and T stage, stratified by median proportion or as negative vs. positive in case median equals zero. N stage was excluded due to complete separation, resulting in an unbounded 95% confidence interval for its odds ratio. C Multivariate cox proportional hazards model for RFP for mCAFs, AR status (AR_high > 70% and AR_low ≤ 70 of tumor cells positive for AR), HER2 status, T stage, age, and other CD4⁺ cells. N stage was excluded due to complete separation, resulting in an unbounded 95% confidence interval for its odds ratio. D Kaplan–Meier plot with log-rank test for DCR comparing patients stratified as high and low based on the median proportion of mCAFs. E Kaplan–Meier plot with log-rank test for RFP comparing patients stratified as high and low based on the median proportion of mCAFs. F Kaplan–Meier plot with log-rank test for DCR comparing patients stratified as high and low based on the median proportion of CN8. G Kaplan–Meier plot with log-rank test for RFP comparing patients stratified as high and low based on the median proportion of CN8. H Boxplot displaying the proportion of mCAFs samples from patients with vs. without distant metastasis

When testing these results for validity within a previously published independent cohort with RNA-seq data from n = 67 SDC cases [23], we found that patients with low scores of the mCAF-like signature (mCAF_high) had a significantly lower RFP (median RFP = 10.2 months; two-year survival = 23.5%) than mCAF_low patients (median RFP = 21.6 months; two-year-survival = 46.4%; p = 0.0294; Fig. 5G). Accordingly, mCAF_high patients had a significantly lower RFS (median RFS = 10.2 months, two-year survival = 23.5%) than mCAF_low patients (median RFS = 21.6 months; two-year-survival = 46.4%; p = 0.049; Fig. 5H). RFS was also less favorable in mCAF_high compared to mCAF_low patients when using mCAF-like signatures with different cut-offs (Supplementary Fig. 5 D-G).

Validation analyses with RNA-seq and Spatial Transcriptomics (ST) data. A Spearman correlation of the mCAF-like module score with the pericytes and the B) endothelial module score, leveraging ST data of three SDC specimens. C Expression of metastasis-, IL-6- and VEGF-associated module scores in mCAF^highendothelia^high ST-spots. IL-6- and VEGF-related signatures were not significantly enriched. D Differential expression analysis contrasting mCAF^highendothelia^high ST-spots with all other ST-spots. The top10 overexpressed genes are highlighted. E Gene set enrichment analysis of DE-results depicted in C) using “Cellular Component” and (F) “Molecular Functions” GO terms. G and H) Kaplan–Meier plots and log-rank tests depicting the prognostic impact of the transcriptomic mCAF-like signature (top20% mCAF marker) after median dichotomization of 67 samples through estimation of the recurrence-free probability (G) and recurrence-free survival (H); LN = lymph node; met. = metastasis

No prognostic tests were performed for entities other than SDC because of the low absolute number of events within those entities.

In commemoration of Hangeul Day and the 80th anniversary of Korea’s liberation, Samsung partnered with the Korean Cultural Center New York (KCCNY) to launch a public art initiative aimed at promoting the beauty of Hangeul, the Korean alphabet. A mobile Hangeul Truck served as the centerpiece of the experience, merging cutting-edge Galaxy AI technology with the creative vision of Korean-born installation artist, Ik-Joong Kang.

Traveling Artwork

The Hangeul Truck transformed a classic Airstream trailer into a vibrant work of art. Wrapped in Kang’s signature Hangeul Cubes, the truck became a traveling exhibition, making stops at six major universities in the United States this fall. Each stop offered visitors the opportunity to immerse themselves in the cultural and artistic spirit of Hangeul while experiencing Samsung’s latest AI-powered innovations.

Co-creating With Galaxy AI

At the heart of the Hangeul Truck experience was a message station where students and visitors could type personal reflections or write notes to their future selves. Galaxy AI instantly translated these words into Korean and the messages were then displayed on a central LED screen, transforming individual voices into a piece of living artwork. By weaving personal expression into the artistry of Hangeul, participants co-created a shared cultural canvas that bridged languages, ideas and generations.

Self-Expression, Creativity and Discovery in the Spirit of Hangeul

In addition to engaging with the interactive message wall, guests were invited to explore a series of playful experiences inspired by Korean culture and Galaxy innovation. A selfie kiosk designed with artist Ik-Joong Kang offered creative frames for photos, while erasable Hangeul tattoos allowed visitors to take a piece of the experience with them.

“The Hangeul Truck is where the past meets the present — where Hangeul, created by King Sejong centuries ago, now breathes with the dreams of today’s young generation,” said Kang. “What made this project meaningful was its openness — art not confined to an exhibition hall, but moving freely through various cities and campuses.”

The Hangeul Truck tour ran from late September through early October, concluding on Hangeul Day (October 9) with a celebration hosted at Times Square in New York City. By uniting art, culture and AI-powered innovation, Samsung and KCCNY gave a new generation the opportunity to discover the timeless beauty of Hangeul. It also showed how technology can help connect us all.

News

10/10/25

Challenging fossil fuel talks continue ahead of Cop 30

Challenging fossil fuel talks continue ahead of Cop 30

Cop host Brazil embodies the nuances of depending on fossil fuels while shifting
to cleaner energy, writes Caroline Varin Edinburgh, 10 October (Argus) — Talks
about phasing out fossil fuels have gained momentum since the UN Cop 28 summit
in 2023, but translating pledges into policy remains difficult — especially for
economies reliant on oil and gas revenues. Nearly 200 countries agreed to a call
“to transition away from fossil fuels in energy systems, in a just, orderly and
equitable manner” by 2050 at Cop 28 in Dubai almost two years ago. The climate
summit was not the only forum at which countries agreed to do so. Similar
agreements were taken at G7 level and last year in the form of a UN pact by
heads of states and governments . Brazil’s Cop 30 director Ana Toni urges
parties to accelerate implementation. “A central element of this transition is
ensuring the availability of affordable and reliable low-carbon energy sources
to gradually and securely replace fossil fuels,” she told Argus. The discussions
are taking place. Beyond Oil and Gas Alliance (Boga) head of secretariat Sian
Bradley has recorded a clear shift since the Cop 28 call. “There is no
high-level political space where this is not raised as a topic, and that was not
the world we were in pre-Cop 28,” she told Argus. Boga, an international
alliance launched at Cop 26 in Glasgow, spearheaded by Denmark and Costa Rica,
is working to facilitate a managed phase-out of oil and gas production. The
initiative works alongside oil producers to understand their priorities and runs
a $20mn fund to assist developed countries in transition planning. So far, it is
supporting five countries — Nigeria, Brazil, Kenya, Colombia and Barbados. The
fund also seeks to support developing countries looking to reflect the Dubai
agreement in their nationally determined contributions (NDCs). Despite the
increase in visibility, the transition away from fossil fuels faces resistance
in the form of softening commitments, pressure from the US — the second-largest
greenhouse gas emitter, the largest consumer of oil, and which has left the
Paris agreement — and oil and gas firms’ strategy shifts . Last year in Baku, at
a Cop 29 that was dominated by difficult climate finance negotiations, parties
failed to agree on how to advance the implementation of the global stocktake
(GST), which features the call to transition away from fossil fuels. Fossil
fuels did not get a mention in the final unapproved draft of the discussions’
conclusions, although the draft did reaffirm the role of transitional fuels —
most likely natural gas — in the energy transition. Recently, a report led by
the Stockholm Environment Institute found that planned oil production is 31pc
above levels consistent with a 1.5°C pathway in 2030, and 260pc above by 2050.
For gas, production plans are 92pc above a level consistent with 1.5°C and 230pc
higher by 2050. The Paris agreement aims to keep the global rise in temperature
to “well below” 2°C above pre-industrial levels, while pursuing efforts to limit
warming to 1.5°C. Transition cramp Discussions are becoming more nuanced,
according to Boga’s Bradley. “Major oil and gas producers are not going to be
standing on a stage announcing they have halted licensing yet, but they are
asking questions behind closed doors,” she says. For Cop 30’s Toni, consumer
countries’ hesitancy in coming forward with plans stems “from the fear of being
left without reliable energy”, while producers question moving ahead while
global demand remains high, especially when these resources are vital to finance
their economies and social priorities. “Brazil itself reflects this dual
reality,” Toni acknowledges. “We are a global leader in clean energy — with
nearly 90pc of our electricity and 45pc of our energy coming from renewables —
and in sustainable fuels. At the same time, we remain an oil producer. That is
the reality of a world in transition.” Strategies for how the transition will
unfold are still lagging, Toni says. “The economic dimensions of the transition,
including improving transparency on fossil fuel subsidies, need to be addressed
to move forward, while protecting energy affordability and broader economic
stability,” she says. The fossil fuel discussions are not going away at Cop,
non-profit WRI’s director of international climate action, David Waskow, told
Argus , but the question of economic diversification — which is coming up in
various negotiation tracks, including the Just Transition Work Programme — must
be given more attention. This is an issue that Boga has worked on in the years
since its creation. The organisation has been running closed-door dialogues and
forums, with “most of the major oil and gas producers” and consumers, the IMF
and IEA, to work out what the transition means for long-term policy planning,
Bradley says. Writing on the wall “We have been asked to work with the group on
developing illustrative pathways to guide the transition, depending on costs of
production and other country factors,” Bradley says. Global net zero scenarios,
such as the IEA’s, exist, but some producers lack country-level pathways to
understand the timeframe for their transition, and whether they will be
producing in 15-20 years. “The writing is increasingly on the wall for the
higher-cost and the more vulnerable oil and gas producers, and they know that
they have to begin engaging with this issue in a meaningful way through their
energy and finance ministries,” she says. “The discussion is taking root in a
much more structural way.” The GST, an exercise to assess progress under the
Paris agreement, is also meant to inform new NDCs — the climate plans out to
2035 that countries are due to submit by Cop 30. “We are encouraged that over
100 parties, representing two-thirds of global emissions, have already submitted
their new NDCs or announced they would do so — and the majority of them have
pledges related to energy transition,” Cop 30’s Toni said. But major oil and gas
producers that have submitted plans, apart from the UK, are not yet talking
about the transition away from fossil fuels from a production point of view,
only from an emissions reduction perspective. Some fossil fuel producers that
are part of the G20 group — including India, Saudi Arabia, Indonesia and South
Africa — have yet to release new plans. Producers that think they will be in the
oil and gas market for longer will be slower to come forward, Bradley says. But
she points to notable shifts. Nigeria, while saying it will continue to develop
resources, recognises “the economic dilemma of the transition”, with fossil
fuels leaving the economy “highly vulnerable to price shocks and climate risks”.
It also talks about stranded assets and the need to protect workers and expand
non-oil sectors. Having this in the NDC of a major oil producer in the last
round of climate plans would have been unthinkable, she says. Fossil fuel talks
could also receive a boost at Cop 30 in the so-called action agenda. The
Brazilian Cop 30 presidency decided to use the Dubai conclusions as the spine of
its summit’s action agenda, and leverage existing initiatives — many including
governments — to bolster implementation. Each item under the GST will have its
own discussion group, and although it is not yet clear what the outcome of this
process may be, the action agenda has been positively received. “There has never
been a structured space for the transition away from fossil fuel in the action
agenda,” Bradley says. Boga is also part of the fossil fuel action group. “We
have now a presidency setting out the agenda and saying that [the transition
away from fossil fuels] is a fundamental part of the UNFCCC process.” Oil
production forecast scenarios wpa p3 legend.pdf Send comments and request more
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By Tim Hepher and Joanna Plucinska

PARIS/LONDON (Reuters) -Global airlines face more than $11 billion in extra costs from supply chain disruption this year, a leading industry group said on Monday, in a report likely to rekindle debate over competition in the $250-billion aerospace industry.

The study by the International Air Transport Association, produced with consultants Oliver Wyman, marks the first attempt to quantify the impact of a five-year supply chain crisis that has driven up fares and led to flight cancellations.

IATA Director General Willie Walsh said he was surprised by the extent of the findings and told Reuters there may be grounds to revisit whether airlines are being subjected to anti-competitive practices by suppliers, after dropping a previous complaint in 2018.

“Even if you halve the number, it’s still a massive drag on the industry,” Walsh said in an interview.

REPORT DETAILS COST OF BOTTLENECKS

Researchers found the largest impact stems from $4.2 billion in extra fuel as airlines keep older planes in service.

Additional maintenance is expected to cost $3.1 billion, while leasing engines to replace those stuck in queues for maintenance adds another $2.6 billion.

Holding more spare parts to cushion delays is projected to cost airlines $1.4 billion.

Planemakers and their suppliers have waded through a mire of setbacks, from shortages of labour, materials and parts to mounting delays at repair shops, particularly for engines.

There is also a growing tug of war with the defence industry for capacity as governments increase military spending.

“There’s now going to be continuing competition for the limited supply that is there,” Walsh said, adding that supply chains would be an issue for the rest of the decade.

He questioned the influence suppliers exert over parts pricing and called for “additional competition in the aftermarket, which clearly has seen significant consolidation.”

PROFIT GAP

IATA has previously called for greater competition in maintenance, including improved access to independent parts known as PMA.

In 2016, it filed a complaint with the European Union against CFM International but withdrew it two years later after the engine maker agreed to maintain an open and competitive market.

A similar agreement was reached with Rolls-Royce in 2021.

Walsh said there were no plans to launch any new challenge, but did not rule it out.

“We have been evaluating it, but we’d have to do a lot more work,” he said, noting that airlines have confidential agreements, so digging deeper involves teams of lawyers.

“It’s a complex piece of work, but I think there could be merit in us looking at that again.”

He pointed to the gap between airline operating margins, forecast at 6.7% this year, and margins of some engine makers and suppliers in the mid-20s as a source of concern.

“How is it that they can make such massive margins from an industry that makes margins that are wafer-thin? It just doesn’t add up,” Walsh said.

Engine makers argue they are entitled to adequate returns given the risks involved in developing new technologies and offering insurance-style contracts to cover repair costs.

Airlines are expected to spend $120 billion on repair and maintenance this year, rising to $150 billion by 2030, IATA said.

Walsh softened his tone towards Airbus and Boeing, saying they were becoming more transparent about jet delays. In June, he accused planemakers of “failing badly”.

(Reporting by Tim Hepher, Editing by Louise Heavens)