Category: 3. Business

• Carlsbad location is company’s third US-based radioligand therapy (RLT) manufacturing site
• New site expands manufacturing footprint to meet future demand; ensures continued on-time delivery rate of >99.9% to patients across western US, Alaska and Hawaii
• First to have delivered at scale, Novartis fortifies leadership in RLT innovation and infrastructure with California facility, additional plans for Florida and Texas

Basel, November 10, 2025 – Novartis, a leading global innovative medicines company, today announced the opening of a new 10,000-square-foot radioligand therapy (RLT) manufacturing facility in Carlsbad, California. This state-of-the-art site represents a key milestone in the company’s previously announced $23 billion investment in US infrastructure over the next five years.

The opening of the Carlsbad manufacturing facility allows Novartis to seamlessly meet future demand for RLT, adding additional capacity and augmenting the company’s world-class supply chain capabilities. The Carlsbad facility has been filed with the FDA as an additional US point of supply, and commercial manufacturing may begin once approval is granted.

RLTs are a form of precision medicine that combines a tumor-targeting molecule (ligand) with a therapeutic radioisotope, enabling the delivery of radiation to the tumor with the goal of limiting damage to the surrounding cells. Because each RLT dose is custom-made and time-sensitive, with a radioactive half-life measured in hours, proximity to treatment centers and transit hubs helps ensure patients receive their treatment when and where they need it.

“At Novartis, we tackle the toughest challenges in medicine by doing what’s never been done before for patients,” said Vas Narasimhan, CEO of Novartis. “Radioligand therapy is a breakthrough we’ve unlocked at scale, made possible by reimagining how innovation reaches patients. As the global leader in RLT for more than seven years, we’ve advanced this technology with a deep belief in its power to transform cancer care. The opening of our Carlsbad facility underscores our strong commitment to the US and dedication to bringing this pioneering treatment to patients across the country.”

Novartis is the only pharmaceutical company with a dedicated commercial RLT portfolio, and the Carlsbad facility is its third US RLT manufacturing site, reinforcing its global leadership in radioligand therapies with unmatched expertise in development, production, and delivery to patients worldwide. The Carlsbad facility is purpose-built to manufacture the company’s FDA-approved RLTs with capacity for future expansion.

“We commend Novartis for supporting our broader mission of bringing manufacturing capacity in the United States,” said FDA Commissioner Marty Makary, M.D., M.P.H.. “Our unique partnership approach is working.”

“Novartis is transforming the future of cancer care—and it’s happening right here in Carlsbad,” said Carlsbad City Council Member Melanie Burkholder. “This new advanced RLT production facility is a major milestone for our region, strengthening California’s position as a hub for life sciences innovation. It will bring exciting new opportunities for our community, including more engineering and manufacturing jobs. I’m proud our local community will be part of the future of cancer care.”

In addition to the Carlsbad opening, Novartis has announced multiple construction initiatives and future plans in the US, including:

• Two additional RLT manufacturing facilities in Florida and Texas.
• Expansion of existing sites in Durham, North Carolina, Indianapolis, Indiana, and Millburn, New Jersey.
• Establishing its second global R&D hub in the US with a new state-of-the-art biomedical research innovation facility in San Diego, California.

These investments, enabled by a pro-innovation policy and regulatory environment in the US, reflect Novartis’ broad commitment to the market and building its infrastructure. Novartis expects to invest nearly $50 billion in its US operations over the next five years, including the $23 billion announced earlier this year, underscoring its long-term commitment to strengthening the US healthcare ecosystem.

Novartis and Radioligand Therapy (RLT)

Novartis is reimagining cancer care with RLT for patients with advanced cancers. By harnessing the power of targeted radiation and applying it to advanced cancers, RLT is designed to deliver treatment directly to target cells anywhere in the body^3,4.

Novartis is actively investigating the application of RLTs across cancer types and settings, with one of the deepest and most advanced pipelines in the industry, with trials in prostate cancer, breast, colon, lung, brain, pancreatic and other cancers. Novartis has established global expertise, with specialized supply chain and manufacturing capabilities across its network of RLT production sites around the world.

Disclaimer

This press release contains forward-looking statements within the meaning of the United States Private Securities Litigation Reform Act of 1995. Forward-looking statements can generally be identified by words such as “potential,” “can,” “will,” “plan,” “may,” “could,” “would,” “expect,” “anticipate,” “look forward,” “believe,” “committed,” “investigational,” “pipeline,” “launch,” or similar terms, or by express or implied discussions regarding potential marketing approvals, new indications or labeling for the investigational or approved products described in this press release, or regarding potential future revenues from such products. You should not place undue reliance on these statements. Such forward-looking statements are based on our current beliefs and expectations regarding future events, and are subject to significant known and unknown risks and uncertainties. Should one or more of these risks or uncertainties materialize, or should underlying assumptions prove incorrect, actual results may vary materially from those set forth in the forward-looking statements. There can be no guarantee that the investigational or approved products described in this press release will be submitted or approved for sale or for any additional indications or labeling in any market, or at any particular time. Nor can there be any guarantee that such products will be commercially successful in the future. In particular, our expectations regarding such products could be affected by, among other things, the uncertainties inherent in research and development, including clinical trial results and additional analysis of existing clinical data; regulatory actions or delays or government regulation generally; global trends toward health care cost containment, including government, payor and general public pricing and reimbursement pressures and requirements for increased pricing transparency; our ability to obtain or maintain proprietary intellectual property protection; the particular prescribing preferences of physicians and patients; general political, economic and business conditions, including the effects of and efforts to mitigate pandemic diseases; safety, quality, data integrity or manufacturing issues; potential or actual data security and data privacy breaches, or disruptions of our information technology systems, and other risks and factors referred to in Novartis AG’s current Form 20-F on file with the US Securities and Exchange Commission. Novartis is providing the information in this press release as of this date and does not undertake any obligation to update any forward-looking statements contained in this press release as a result of new information, future events or otherwise.

About Novartis
Novartis is an innovative medicines company. Every day, we work to reimagine medicine to improve and extend people’s lives so that patients, healthcare professionals and societies are empowered in the face of serious disease. Our medicines reach nearly 300 million people worldwide.

Reimagine medicine with us: Visit us at https://www.novartis.com and connect with us on LinkedIn, Facebook, X/Twitter and Instagram.

# # #

FORT LAUDERDALE, Fla., Nov. 10, 2025 /PRNewswire/ — Princess Cruises has announced Hampton Water Rosé as the latest addition to the Love Line Premium Liquors Collection. Known for its vibrant flavors and refined craftsmanship, the Hampton Water portfolio will offer Princess guests a still and sparkling rosé for toasting special moments at sea.

Crafted in partnership with Jesse Bongiovi and his dad, Jon Bon Jovi, along with world-renowned winemaker, Gérard Bertrand, Hampton Water Rosé has received critical acclaim for its quality and lively fresh wine. The wines are available fleetwide and included in Princess Premier and Princess Plus  beverage packages.

“Our Love Line Collection celebrates the spirit of connection through premium, celebrity-crafted libations that elevate the onboard experience,” said Sami Kohen, Princess Cruises Vice President of Food and Beverage. “Hampton Water Rosé is more than a wine – it’s a reflection of the lifestyle our guests embrace: vibrant, celebratory, and inspired by the stars they love.”

Princess Cruises Love Line Premium Liquors Collection features a selection of thoughtfully curated wines and spirits, offering both alcohol and non-alcoholic options with a diverse lineup of celebrity-crafted beverages:

• Pantalones Organic Tequila by Camila and Matthew McConaughey
• Meili Vodka by Jason Momoa and Blaine Halvorson
• Sláinte Irish Whiskey by Liev Schreiber
• Archer Roose co-owned by Elizabeth Banks
• Seven Daughters Moscato by Taraji P. Henson
• Melorosa Sauvignon Blanc and Red Blend co-founded by Jason Aldean, Kasi Wicks and Chuck Wicks
• Love Prosecco by Romero Britto
• Zero Alcohol Sparkling Rosé by Kylie Minogue
• Betty Booze by Blake Lively

“Hampton Water is about bringing people together over great conversation and even better wine,” said Jesse Bongiovi. “To have it featured aboard Princess Cruises – a place where countless memories and connections are made – is an incredible opportunity to share our rosé with people who truly value celebration and togetherness.”

The addition of Hampton Water Rosé builds on Princess Cruises’ reputation for delivering exceptional culinary and beverage experiences, ensuring guests have access to innovative and exclusive offerings during their voyage.

For further details about the Love Line Premium Liquors Collection, visit www.princess.com.

Additional information about Princess Cruises is available through a professional travel advisor, by calling 1-800-Princess (1-800-774-6237) or by visiting www.princess.com.

*Princess’ Love Line non-alcohol beverages may contain up to 0.5% alcohol by volume (ABV). These beverages are classified as non-alcoholic under U.S. regulations but may contain trace amounts of alcohol.

About Hampton Water
Jesse and his dad, Jon Bon Jovi, shared a vision to disrupt the wine category with a brand that is unlike all others. The father-son duo created the Hampton Water Wine brand concept, bringing on famed French winemaker, Gérard Bertrand. Launching in 2018 with Hampton Water Rosé, the brand quickly rose above the ranks to be more than just another celebrity brand. It is a family business that has earned four years of 90-point ratings from Wine Spectator, 91 points from Wine Enthusiast and Decanter, and was recognized as an Impact Hot Prospect brand two years in a row. With an incredibly engaged social media presence of over 625,000 followers, Hampton Water is making waves by taking a modern digital approach in an often-traditional category. Seeing such success with the still rosé, Hampton Water is proud to have expanded their brand portfolio with a sparkling rosé in 2024: Hampton Water Bubbly. The brand is creating loyal brand advocates, surpassing their category, and delivering double-digit volume growth year over year.

More information on the company can be found at www.hamptonwaterwine.com, www.facebook.com/hamptonwater, TikTok: @HamptonWater, Instagram: @HamptonWater, and X: @HamptonWater. Sip responsibly.         

About Princess Cruises
Princess Cruises is The Love Boat, the world’s most iconic cruise brand that delivers dream vacations to millions of guests every year in the most sought-after destinations on the largest ships that offer elite service personalization and simplicity customary of small, yacht-class ships. Well-appointed staterooms, world class dining, grand performances, award-winning casinos and entertainment, luxurious spas, imaginative experiences and boundless activities blend with exclusive Princess MedallionClass service to create meaningful connections and unforgettable moments in the most incredible settings in the world – the Caribbean, Alaska, Panama Canal, Mexican Riviera, Europe, South America, Australia/New Zealand, the South Pacific, Hawaii, Asia, Canada/New England, Antarctica, and World Cruises. Sun Princess, the brand’s new, next-level Love Boat named Condé Nast Traveler’s Mega Ship of the Year, introduces the groundbreaking Sphere Class platform and will be joined by sister ship, Star Princess, in Fall 2025. The company is part of Carnival Corporation & plc (NYSE/LSE:CCL; NYSE:CUK). 

SOURCE Princess Cruises

Scoping

A scoping exercise was carried out to assess the feasibility of a synthesis of MR studies with respect to survival. A query of the Guerra et al. [9] database indicated the number of mark–recapture studies with or without known-age releases, which is presented in Table 1. Though known-age studies are a minority, 51 were available with potential information. The set of ages-at-release in any study was usually small: single-age release, more occasionally two or three. A prime requirement was that MR data were available that could be put into the form of a capture history matrix or m-array (these data structures are explained further in the section hereafter). Scoping found that publications never reported the capture history matrix and rarely the full m-array but often reported m-array information directly in a tabular form or indirectly in graphical form. Single-release experiments were much more common and would usually yield information for a single-row m-array. Recapture was usually carried out with similar apparatus and effort from occasion to occasion. Mosquitoes were usually killed on recapture; experiments with re-release of recaptured mosquitoes were very unusual. The exercise concluded that there were no strong obstacles to a pooled analysis of studies making use of an age-dependent CJS model, and the assumption of time-independent capture probability was defensible.

Data

The ‘capture history’ of the cohort(s) can be put in matrix form. This mark–recapture matrix forms a summary of the set of individual capture histories in the experiment, of which there are ({2}^{T}-1) possibilities where T is the number of recapture occasions. Under the assumptions of mark–recapture, an even more concise summary is provided by the ‘reduced m-array’ (henceforth ‘m-array’), which counts the numbers of mosquitoes released at i and next caught at j, without regard to the capture history prior to i or subsequent to j. The m-array is the usual form of reporting recapture data in publications.

When only a single release is carried out, the data reported can be structured as a single-row m-array, and this is the most common format. In some experiments, the release and/or recapture occasions are irregularly spaced.

An example m-array is shown here from Takagi et al. [14], in which three cohorts of 4-day old marked mosquitoes were released on three successive days (days 22, 23 and 24):

The first to penultimate columns show a set of counts ({m}_{ij}) of animals released at occasion i and subsequently caught at occasion j. The final column shows the numbers released but not re-caught again over the course of the experiment. In this example, the final column was calculated from the supplied numbers released and numbers re-caught. No recaptures were attempted on days 32 and 35, so the columns are empty.

A much fuller expression of the study information contains m-arrays by age (the ‘full’ or ‘generalised’ m-array, McRea and Morgan [13]). This structure is a set of counts with ({m}_{ij}left(aright)) denoting the number of individuals which, when released on day i at age a days, are next captured at j. Accompanying it is an array ({R}_{i}left(aright)), which is the number of individuals released on day i of age a days. The generalised m-array is almost never reported directly but could in principle be surmised from the study report. A reduced m-array for an experiment with cohorts of known age can be expressed as a generalised m-array.

In the example above, the cohorts released were all of the same age on each occasion (multiple release, single age). An alternative is to release multiple ages simultaneously (single release, multiple ages), for which a full m-array data structure is required. Harrington et al. [15] simultaneously released ‘young’ and ‘old’ cohorts (3 and 13 days old, respectively) in Puerto Rico. The data can be formed to give the following R-array.

and generalised m-array:

Finally, different ages may be released at each occasion (single or multiple release and multiple age). For example, Eldridge and Reeves [16] released cohorts of ages 5, 1 and 2 on days 1, 4 and 7, respectively.

The MR and release data were extracted from the original studies and then entered into R as arrays.

A study may report more than one MR experiment, with releases separated in time and space. For example, Reisen et al. [17] reported separate experiments carried out in different months. When release cohorts overlap, for our study, a multiple-release m-array was formed where possible. Recaptures from separate releases were sometimes accumulated in the source publication in such a way that they could not be disaggregated and had to be treated as a single-release experiment, e.g. in Ref. [18].

Other aggregation or conditioning factors include experimental site, sex, mosquito species and age. For example, Reisen et al. [17] released and recaptured male and female cohorts of Cu. tarsalis and Cu. quinquefasciatus. However, the recapture data were only reported fully for Cu. tarsalis females. Table 2 presents a brief summary of study and dataset-level characteristics.

Searching

The references identified by Guerra et al. [9] were supplemented by a much smaller set of references collected ad hoc by the author (n = 26), and combined with a Web of Science search from 2014 to date [title terms: mosquito AND (surviv* OR longevity OR mortality)] to create the complete reference set, n = 188.

A flow diagram of the search and selection process is provided in Additional File 1: Supplementary Fig. S1 (Appendix S2). Studies in which a survival-related experimental negative intervention (e.g. genetic modification) was apparent from the title were excluded. Studies in which age of release was unknown were excluded. For example, Takken et al. [19] captured mosquitoes from houses with aspirators prior to marking, so the ages of these adults at release were not known. Studies without useable MR information were excluded. For example, the authors of Marini et al. [20] reported recapture numbers in aggregated form, e.g. in the first experiment as 2–5, 6–9 and 10–21 day totals; these data could not be put into the usual m-array form by recapture day, and the study was therefore excluded.

None of the selected datasets were exclusively concerned with males, and the majority contained female-specific MR information, so the analysis followed Ref. [9] in confining results to females. Similarly, the vast majority of datasets were in the three genera Anopheles, Aedes and Culex; other genera were excluded.

Studies were then filtered according to conditions established by simulation (details below). For example, Takagi et al. [14] released three laboratory-reared cohorts 4–5 days after emergence. Criteria established on the basis of simulation results excluded this single-age study because of the older age of the mosquito cohorts and the inadequate size (< 500) of the release cohorts (296, 161 and 249).

After exclusions, there were 73 MR datasets with ages known at release and, from these, 30 datasets of female mosquitoes with suitable MR information and experimental characteristics. The references supplying the final datasets are listed in Additional File 2.

Analysis

After the selection of studies described above, analysis is carried out in two stages. In the first stage, the parameters of a mark recapture model are estimated for each selected study, which include survival and capture parameters. The capture parameters have a modelling function, but the survival parameters are of primary interest. Study-specific capture probabilities are ascribed to each study, allowing study characteristics (experimental design, local conditions, etc.) to influence the data. For example, a study in which recapture uses baited recapture is allowed a different (probably higher) recapture probability to one that does not. In this way, important heterogeneity is modelled. In the second stage, the EL and its variance are estimated from the survival parameters, and this outcome is analysed by conventional meta-analysis.

In the first stage, each study is analysed using the CJS model. In our analysis, the Weibull survival curve determines the values of the discrete survival parameters in the CJS model, so the parameters in the likelihood are reduced from a potentially large set of discrete survival parameters to the small set of Weibull parameters that they map to. A summary of symbols used is presented in Table 3.

Analysis uses the age-specific CJS likelihood equation [13, p. 74] written here as:

where ({R}_{i}left(aright)) and ({m}_{ij}left(aright)) are data arrays with examples given in the Data section, and for a mosquito of age a when released at occasion i, ({nu }_{ij}left(aright)) is the probability of next recapture at j, and ({chi }_{i}left(aright)) is the probability, of not being caught afterwards, so (chi_{i} left( a right) = 1 – mathop sum limits_{j} nu_{ij} left( a right).)

This is a multinomial likelihood and, leaving age aside for the purposes of explanation, includes:

• the probability of no recapture (({chi }_{i})) raised to the power of the numbers not recaptured (({R}_{i}-sum {m}_{ij})); hence, the first term, and

• the probabilities of recapture (({nu }_{ij})) raised to the power of the number of recaptures (({m}_{ij})); hence, the second term.

The parameters in the current analysis are relatively simple compared with the general form: p is the probability of capture on any recapture occasion, which is assumed time-independent, and (underset{_}{phi }) is a vector of probabilities, with element (phi left[kright]) the probability of surviving from age k to k + 1.

Then,

Conventionally, discrete survival probabilities (({phi }_{k})) are used in MR analyses (see Ref. [13]). The analysis for age-dependence when interest lies in discrete age classes is set out by Pollock et al. [21], as is common in some fields (e.g. birds: immature and mature). Analysis with many age classes requires many parameters and associated limits on precision. Parametric age-dependence on a continuum has been additionally utilised in this paper because it provides a more compact parameterisation and potentially increased precision. The parameter vector for an individual study under the discrete survival formulation (with a time-independent capture model) is (left(p,{phi }_{1},…{phi }_{k}…right)), whereas under the compact parameterisation it is (for the Weibull survival model) (underset{_}{theta }=left(p,alpha ,eta right)).

A Weibull survival model is assumed with shape and scale parameters (alpha) and (eta). There are several textbook parameterisations of the Weibull, and the one adopted here corresponds to that coded in R. Note that the symbol for the Weibull scale parameter ((sigma)) in the R parameterisation is replaced in this paper with (eta) because (sigma) is also commonly used for measures of dispersion. The Weibull distribution is fairly flexible though monotonic, and it includes the exponential as a special case when (alpha =1).

For the Weibull, the continuous survival function is:

The conditional survival over a time step is (Sleft(k+1right)/Sleft(kright)) [22, p. 31], so the equation:

connects the continuous survival model with the discrete apparent survival of the CJS, in which (phi left(kright)) represents the probability of an animal alive at age (k) surviving to (k+1).

Weibull parameters are restricted to (alpha >0) and (eta >0). These constraints were implemented by numerical fitting with the Nelder–Mead method on the transformed variables (text{log}left(alpha right)) and (text{log}left(eta right)).

The EL of a mosquito is given by (int Sleft(tright)text{d}t) and has an analytic solution for the Weibull model for known parameter values. To incorporate the parameter uncertainty in estimates of (alpha) and (eta), further analysis is required. The calculation in this paper of the variance of the conditional mean of the EL under a Weibull model is described in Additional File 1: Appendix S3.

Meta-analysis was carried out using the metafor package in R. The meta-analysis on expected lifetimes used a log transformation for this positive-value outcome, with inverse-variance weighting. The variance of the log-transformed EL was approximated using the ‘delta method’, that is:

The pooled estimate from the meta-analysis used a random-effects model to account for heterogeneity, which incorporates extra ‘between-study’ variation in the estimates.

Each study receives its own (constant) capture probability, which means there are as many capture parameters as studies; however, it is the survival parameters that are of primary interest and the capture parameters serve a modelling function only. In the analysis with genus as a moderator, there is an average for each group (e.g. for genus Anopheles) shared by those studies.

Three sensitivity analyses were carried out with alternative constraints:

1. 1.

   For the overall model, with 0 < p < 0.05 and (alpha ge) 1. The analysis asserts increasing or constant mortality with age, and rules out capture probabilities > 0.05, which may be implausible.

2. 2.

   For the genus-specific model, 0 < p < 0.05 and (alpha) >0.1. The boundary on low values of (alpha) is a practical step to help avoid numerical difficulties, as discussed elsewhere.

3. 3.

   For the genus-specific model, a meta-analysis excluding any studies with (alpha <1), where simulations showed estimation, produced a high root mean square error (RSME) (Additional File 1: Appendix S4).

Simulations

Simulations of known-age MR experiments were carried out using known parameter values and known age, with time-independent capture probability and age-dependent survival. Four Weibull-derived survival models were used to generate simulated data, one exponential ((alpha =1)), two with larger shoulders and increasing mortality with age ((alpha >1)) and one where mortality fell with age ((alpha <1)). These survival curves are shown in Additional File 1: Supplementary Fig. S2 along with their parameter values. The capture probability was set to 0.01 throughout, and there were 1000 runs in each scenario. When summarising scenarios, simulated data were trimmed where (widehat{alpha }) > 30 or (widehat{eta })> 30. The proportion of simulations with these outliers was 0.13.

In broad terms, the simulations showed that bias and variance reduces with more recapture occasions and larger cohort sizes, with younger release ages, and with more releases. The following inclusion criteria were adopted, when mosquitoes are released at known age, to give broadly accurate estimates (details below): releases at young age (le 3), a sufficient number of recapture occasions (ge 8) and

1. 1.

   With single release, cohort size (Rge 1000)

2. 2.

   With multiple releases, (Rge 500)

The heuristic reasoning for allowing smaller cohort sizes for multiple release experiments (500 versus 1000 for single release) is that the resulting loss of efficiency from the smaller cohorts is somewhat balanced by further releases made within the same experiment. Studies that did not meet the inclusion criteria were excluded from the meta-analysis (see Appendix S2).

The statistical performance of the main outcome of interest in the present study (text{EL}), along with results for (alpha) and (eta), is summarised in Appendix S4. Under the inclusion criteria, it can be seen that the bias of (widehat{text{EL}}) is low (magnitude (lesssim) 0.5). Furthermore the RMSE of EL is rather smaller than the RMSE of (alpha) when (alpha gtrsim 2) (scenarios a and b). However, when (alpha <1) (scenario d), the RMSE of (widehat{text{EL}}) is large. The main conclusion of the simulations is that the bias of (widehat{text{EL}}) may be reduced to low-moderate levels by the inclusion criteria but that the RMSE of (widehat{text{EL}}) is especially high when (alpha <1). This is the region where Weibull variables are inherently most variable, and any estimates can be very imprecise.

NEW YORK, Nov. 10, 2025 /PRNewswire/ — The Bank of New York Mellon Corporation (“BNY”) (NYSE: BK), a global financial services company, today announced that Robin Vince, Chief Executive Officer, will speak at the Goldman Sachs Financial Services Conference in New York at 10:00 a.m. ET on Wednesday, December 10, 2025. The discussion may include forward-looking statements and other material information.

A live webcast of the audio portion of the conference will be available on the BNY website (www.bny.com/investorrelations). An archived version of the audio portion will be available on the BNY website approximately 24 hours after the live webcast and will remain available until January 9, 2026. 

About BNY

BNY is a global financial services company that helps make money work for the world – managing it, moving it and keeping it safe. For more than 240 years BNY has partnered alongside clients, putting its expertise and platforms to work to help them achieve their ambitions. Today BNY helps over 90% of Fortune 100 companies and nearly all the top 100 banks globally access the money they need. BNY supports governments in funding local projects and works with over 90% of the top 100 pension plans to safeguard investments for millions of individuals, and so much more. As of September 30, 2025, BNY oversees $57.8 trillion in assets under custody and/or administration and $2.1 trillion in assets under management.

BNY is the corporate brand of The Bank of New York Mellon Corporation (NYSE: BK). Headquartered in New York City, BNY has been named among Fortune’s World’s Most Admired Companies and Fast Company’s Best Workplaces for Innovators. Additional information is available on www.bny.com.  Follow on LinkedIn or visit the BNY Newsroom for the latest company news.

Contacts:

Investors
Marius Merz
+1 212 298 1480
marius.merz@bny.com

Media
Anneliese Diedrichs
+1 646 468 6026
anneliese.diedrichs@bny.com

NEW YORK, Nov. 10, 2025 /PRNewswire/ — Kyndryl (NYSE: KD), a leading provider of mission-critical enterprise technology services, today announced the expansion of its nearly two-decade collaboration with Dow (NYSE: DOW), a global leader in materials science. Through this expanded engagement, Kyndryl will collaborate with Dow to modernize infrastructure applications leveraging AI and automation to boost operational agility and accelerate innovation across Dow’s technology stack.

“Dow’s IT mission is to empower our teams with modern, intelligent solutions that drive productivity and innovation across our global operations,” said Chris Koniecny, Enterprise Applications & Technology IT Director at Dow. “By partnering with Kyndryl to modernize our application landscape and infuse AI and automation, we are taking a bold step forward in our digital transformation journey. This collaboration has also delivered measurable cost savings for Dow. Kyndryl’s expertise in providing end-to-end modernization and applications services across the enterprise and commitment make them the ideal choice for this next phase.”

“We are proud to expand our collaboration with Dow,” said Gretchen Tinnerman, Managing Partner at Kyndryl. “By applying our advanced capabilities in application management, AI and automation, we are helping Dow drive enterprise-wide innovation, enhance efficiency and support its applications in becoming modern, resilient and future-ready.”

Over the two decades, the collaboration has enabled Dow to modernize and enhance operational efficiency across its global operations. Kyndryl also manages and provides Kyndryl Consult services to Dow’s IT infrastructure including cloud, network, digital workplace, and security and resiliency services.

About Kyndryl

Kyndryl (NYSE: KD) is a leading provider of mission-critical enterprise technology services offering advisory, implementation and managed service capabilities to thousands of customers in more than 60 countries. As the world’s largest IT infrastructure services provider, the company designs, builds, manages and modernizes the complex information systems that the world depends on every day. For more information, visit www.kyndryl.com.

Kyndryl press contact 
[email protected]

SOURCE Kyndryl

NEW YORK, Nov. 10, 2025 /PRNewswire/ — S&P Global (NYSE: SPGI) announced today that its Board of Directors has approved the addition of Mr. Robert Moritz to the Board, effective March 1, 2026.

Mr. Moritz has more than four decades of global leadership experience specifically in audit and assurance in the financial services, banking sectors and capital markets. Most recently, Mr. Moritz served as global Chairman of PricewaterhouseCoopers LLC (PwC) where he led the company’s global leadership teams – setting strategy and elevating PwC’s brand among its clients and stakeholders.

Mr. Moritz is currently a member of Walmart’s Board of Directors, where he sits on the retailer’s audit and technology and e-commerce committees, and Northern Trust Corporation, as a member of the audit and human capital and compensation committees. In addition, he holds several not-for-profit Board seats, including at SUNY-Oswego College Foundation, his alma mater.

“We’re thrilled to welcome Bob to our Board,” said Martina L. Cheung, President and CEO, S&P Global. “He brings extensive global experience and unique perspectives on the opportunities and risks facing global companies in today’s fast-paced environment.”  

“We’re delighted that Bob will be joining our Board,” said Ian P. Livingston, Non-Executive Chairman of the Board of S&P Global. “His experience as a global financial services industry leader will be extremely valuable in helping S&P Global to manage the opportunities and challenges that lie ahead and the evolving needs of our clients.”

“It’s a privilege to serve on the Board of a company that has built a trusted reputation in global markets anchored on integrity and independence,” said Bob Moritz. “I’m excited to collaborate with my fellow Board members, supporting Martina and S&P Global’s leaders as the company moves into its next phase of growth.”

Mr. Moritz will serve on the S&P Global Board’s Audit and Nominating and Corporate Governance committees.

About S&P Global

S&P Global (NYSE: SPGI) provides Essential Intelligence. We enable governments, businesses and individuals with the right data, expertise and connected technology so that they can make decisions with conviction. From helping our customers assess new investments to guiding them through sustainability and energy transition across supply chains, we unlock new opportunities, solve challenges and Accelerate Progress for the world.

We are widely sought after by many of the world’s leading organizations to provide credit ratings, benchmarks, analytics and workflow solutions in the global capital, commodity, and automotive markets. With every one of our offerings, we help the world’s leading organizations plan for tomorrow and today. For more information, visit www.spglobal.com.

Investor Contact:

Mark Grant 
Senior Vice President, Investor Relations and Treasurer 
 +1 347-640-1521
[email protected]

Media Contact:

April Kabahar 
Global Head of Corporate Communications 
 +1 917-796-3121 
[email protected]

SOURCE S&P Global

Study population and dataset

The CHARLS is a prospective national cohort study that enrolled 17,708 participants in 2011, and three waves of follow-up were conducted in 2013, 2015, and 2018. Participants were randomly selected using a probability-proportional-to-size (PPS) technique and a four-stage random sample method. The workgroup selected 150 counties in 28 provinces. Administrative villages in rural areas and neighborhoods in urban areas were the primary sampling units (PSUs). Three PSUs within each county-level unit were selected using PPS sampling. Detailed information on the methodology and cohort profile has been reported previously [11].

We collected data on participants enrolled in the baseline investigation who attended all three follow-up investigations and aged above 60 years old. Those with a history of cancer were excluded because the progression and treatment of cancer affect multiple organs and systems throughout the body; this aspect could have introduced substantial bias into our study. During the data cleaning process, we identified participants with implausible data regarding body weight and height, specifically heights less than 100 cm or weights less than 20 kg. These values were considered erroneous owing to likely data entry mistakes; therefore, participants with an abnormal body mass index (BMI) (BMI < 10 or BMI > 60) in the baseline investigation were excluded.

Altitude data acquisition

The participants’ place of residence was determined by their community ID. The latitude and longitude were acquired through amap api (https://restapi.amap.comv3/geocode), and the local altitude was acquired based on the latitude and longitude using geodata (version 0.5-8) [12] packages and the raster package (version 3.6–23) [13]. Participants were stratified into a low-altitude or high-altitude group based on a criterion of 1500 m, which was used in previous studies [14].

Variable collection

The primary outcome was hip fractures reported by the participants, which was defined by their answer to the question, “Have you fractured your hip since the last interview?” Participants choosing “yes” were considered to have experienced a hip fracture, and the time point the investigation occurred was recorded as the time the event happened.

Individual income, marital status, medical history, sex, BMI, smoking status, alcohol consumption, and age were selected as covariates for propensity score matching (PSM). Individual income was acquired from harmonized CHARLS data and evenly divided into five groups. Educational status was retrieved from the answer to the question: “What is the highest level of education completed?” and re-coded into preschool, primary, secondary, and higher education. Marital status was retrieved from the answer to the question: “What is your marital status?” and re-coded as unmarried, married, widowed, divorced (or long-term separation). History of stroke, cardiovascular dysfunction, falls, hip fractures, chronic lung diseases, and arthritis were retrieved from the answer to the question: “Have you been diagnosed with any of the following by a doctor?” The presence of specific diseases was determined based on whether the participant selected “Yes” to the following conditions: (1) “Chronic lung diseases, such as chronic bronchitis and emphysema (excluding tumors or cancer),” (2) “Heart attack, coronary heart disease, angina, congestive heart failure, or other heart problems,” (3) “Stroke,” and (4) “Arthritis or rheumatism.” BMI was calculated using the following formula: BMI (kg/m^[2]) = body weight (kg)/body height (m)².

Propensity score matching

Individual income, marital status, medical history, sex, BMI, smoking status, alcohol consumption, and age were selected for PSM. A general linear model was used to calculate the distance, and the nearest method was used for matching with a matching ratio of 1:8 (high altitude: low altitude). After matching, a balance test was performed to evaluate the imbalance between the two groups in the matched data. The MatchIt package [15] was used for matching, and the Cobalt package [16] was used for the balanced test and plotting.

Statistical analysis

Categorical variables are shown as counts (percentages, %), and continuous variables are shown as mean ± SD. To compare the differences in baseline data between the two groups, chi-square and t-tests were used for categorical and continuous variables, respectively. Kaplan–Meier survival analysis and Cox regression were used to compare the differences between the two groups regarding fall and hip fracture risks.

Subgroup analysis was performed, and the participants were divided into subgroups according to sex, smoking history, and overweight status. The effects of high-altitude exposure on fall and hip fracture risks in different subgroups were evaluated using Cox regression analysis.

To test the robustness of our results, we performed different sensitivity analyses as follows: (1) We randomly sampled 80% and 90% of all participants, respectively, and replicated the previously described analysis workflow. (2) Using optimal matching as the PSM method and a matching ratio of 1:8, we replicated the previously described analysis workflow. (3) Cox regression was performed with and without adjustments for covariates without matching the participants.

All analyses were performed using R (version 4.3.1). The survival analysis was performed using the survival package (version 3.5-5) [17] and visualized using the Survminer package (version 0.4.9) [18]. The comparison of baseline characteristics and the generation of tables was performed using the gtsummary package (version 1.7.2) [19].