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Comparative Genre Analysis of Research Abstracts: Philippine Versus International Colloquia

• William D. Magday, Jr. Nueva Vizcaya State University
• Marcielyne A. Razalan Nueva Vizcaya State University
• Kristine V. Uhuad Nueva Vizcaya State University
• Roxanne Elaine J. Concepcion Nueva Vizcaya State University
• Liriolyn B. Pacursa Nueva Vizcaya State University
• Frelita O. Bartolome Nueva Vizcaya State University

Abstract is the gateway to whether the intended audience reads the rest of the research article or not. This is one of the many reasons why, across disciplines and cultures, the rhetorical structure of research article abstracts has been explored. Taking a different approach, this comparative genre-based study, which employed a hybrid method, investigated the rhetorical structure of research abstracts of non-native English-speaking undergraduate students in Philippine and international research colloquia. A corpus of 116 research abstracts, 58 from each group, in the field of education was purposively selected and explored using Hyland’s model of five-move abstracts. Categories of move occurrence along with the frequencies were utilized to compare the move use of the two groups. Findings indicated that the three moves which are purpose, method, and product frequently occurred in local and international writers’ research conference abstracts. The number of words was also recorded through frequency and average. The local conference abstracts were lengthier than the international ones. The findings can have some pedagogical implications for the academic writing professors and assist novice writers or undergraduate students, primarily those in the field of teacher education in organizing their abstracts for the international audiences.

Author Biographies

William d. magday, jr., nueva vizcaya state university.

Graduate School, College of Teacher Education

Marcielyne A. Razalan, Nueva Vizcaya State University

Kristine v. uhuad, nueva vizcaya state university, roxanne elaine j. concepcion, nueva vizcaya state university, liriolyn b. pacursa, nueva vizcaya state university, frelita o. bartolome, nueva vizcaya state university.

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Philippine E-Journals

Home ⇛ malay ⇛ vol. 23 no. 2 (2011), kasaysayang pasalita: ang kulturang filipino at karanasan ng mga filipinong mananaliksik sa larangang pasalita = oral history: the filipino culture and the experiences of filipino oral historians nancy kimuell-gabriel.

Nancy Kimuell-gabriel

Discipline: Culture , Philippine Culture , Oral History

Dokumentasyon ito ng karanasan ng mga Filipinong mananaliksik na nagsasagawa o gumagamit ng metodo ng kasaysayang pasalita (KP). Ibinabahagi ng pag-aaral ang mga naranasang kultura ng mga Filipino sa proseso ng pakikisalamuha at pakikipanayaman ng mga Filipinong oral historian kasabay ng pagsesentro ng mga suliranin at kahirapan ng mga Filipinong istoryador sa metodong ito. Pinapayaman nito ang mga literatura tungkol sa metodolohiya ng kasaysayang pasalita na nakabatay pa mismo sa karanasan ng mga Filipino

Ilan sa mga nakitang kultura sa pag-aaral na ito ay ang sumusunod: 1) napakahalaga sa mga Filipino ng tiwala at pakikipagkapuwa; 2) kailangan ng pamamaraan ng pagkapa, pagmamasid at pakiramdam, pagkuha ng loob, pagbubukas ng loob, at pakikipalagayang-loob; 3) maselang paksa ang sex at kasarian; 4) kailangang marunong sa wikang Filipino at/o Tagalog at magkaroon ng kababayan/kadua/kabalen/kabanua sa panayam; 5) may gawing nagpaparinig, nangangantiyaw, nagbibigay-konsuwelo, at pasalubong; 6) nagpapakain, nagpapainom ng malamig, nagpapabaon, at nagpapahatid ang mga maykaya; 7) may kulturang “amuyong,” usisero, at kuyog ang mga maralita. Masasabi ring mapagbigay at mapagtiwala ng mga Filipino nang hindi nakasalalay sa pirma o nakasulat na dokumento.

This is a documentation of Filipino researchers’ experiences regarding their use of oral history methodology. The study imparts aspects of the Filipino culture which oral historians experienced during the actual conduct of the research while at the same time identifies the difficulties and problems encountered in the oral history method. The paper enriches the oral history literature by way of contributing the actual experiences of Filipino practitioners.

Filipino cultural aspects presented in this research are the following: 1) the paramount value of trust and the sense of other (kapuwa-tao) among the Pinoys; 2) the need to employ Filipino research methods such as getting the feel first (pagkapa), observation (pagmamasid) sensing or ascertaining (nakikiramdam), confidence-building (pagkuha ng loob), opening of one’s self (pagbubukas ng loob), and acceptance of the other (pakikipagpalagayang-loob); 3) sex is a delicate issue ; 4) knowledge of Filipino, Tagalog or other Philippine languages is desired as well as the presence of townmate, neighbor, relative, friend, or acquaintance during the interview; 5) those from the lower classes have the attitude of hinting (nagpaparinig,) teasing and heckling (nangangantiyaw), asking for consolation (nanghihingi ng-konswelo), and favors or souvenirs from travels (pasalubong); 6) those from the upper classes usually serve refreshments, give something as provision—usually food (nagpapabaon), send you off (naghahatid); 7) the culture of being inquisitive (usisero), uncalled for butting-in (amuyong), and swarming ( kuyog o sama-sama, kampihan) are evident also with the lower classes. It can also be said that Filipinos are generous and trusting and the sense of goodness (bait ng loob) and word of honor (isang–salita) are more important than a signature.

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AI Is Everybody’s Business

This briefing presents three principles to guide business leaders when making AI investments: invest in practices that build capabilities required for AI, involve all your people in your AI journey, and focus on realizing value from your AI projects. The principles are supported by the MIT CISR data monetization research, and the briefing illustrates them using examples from the Australia Taxation Office and CarMax. The three principles apply to any kind of AI, defined as technology that performs human-like cognitive tasks; subsequent briefings will present management advice distinct to machine learning and generative tools, respectively.

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Author Barb Wixom reads this research briefing as part of our audio edition of the series. Follow the series on SoundCloud.

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Today, everybody across the organization is hungry to know more about AI. What is it good for? Should I trust it? Will it take my job? Business leaders are investing in massive training programs, partnering with promising vendors and consultants, and collaborating with peers to identify ways to benefit from AI and avoid the risk of AI missteps. They are trying to understand how to manage AI responsibly and at scale.

Our book Data Is Everybody’s Business: The Fundamentals of Data Monetization describes how organizations make money using their data.[foot]Barbara H. Wixom, Cynthia M. Beath, and Leslie Owens, Data Is Everybody's Business: The Fundamentals of Data Monetization , (Cambridge: The MIT Press, 2023), https://mitpress.mit.edu/9780262048217/data-is-everybodys-business/ .[/foot] We wrote the book to clarify what data monetization is (the conversion of data into financial returns) and how to do it (by using data to improve work, wrap products and experiences, and sell informational solutions). AI technology’s role in this is to help data monetization project teams use data in ways that humans cannot, usually because of big complexity or scope or required speed. In our data monetization research, we have regularly seen leaders use AI effectively to realize extraordinary business goals. In this briefing, we explain how such leaders achieve big AI wins and maximize financial returns.

Using AI in Data Monetization

AI refers to the ability of machines to perform human-like cognitive tasks.[foot]See Hind Benbya, Thomas H. Davenport, and Stella Pachidi, “Special Issue Editorial: Artificial Intelligence in Organizations: Current State and Future Opportunities , ” MIS Quarterly Executive 19, no. 4 (December 2020), https://aisel.aisnet.org/misqe/vol19/iss4/4 .[/foot] Since 2019, MIT CISR researchers have been studying deployed data monetization initiatives that rely on machine learning and predictive algorithms, commonly referred to as predictive AI.[foot]This research draws on a Q1 to Q2 2019 asynchronous discussion about AI-related challenges with fifty-three data executives from the MIT CISR Data Research Advisory Board; more than one hundred structured interviews with AI professionals regarding fifty-two AI projects from Q3 2019 to Q2 2020; and ten AI project narratives published by MIT CISR between 2020 and 2023.[/foot] Such initiatives use large data repositories to recognize patterns across time, draw inferences, and predict outcomes and future trends. For example, the Australian Taxation Office (ATO) used machine learning, neural nets, and decision trees to understand citizen tax-filing behaviors and produce respectful nudges that helped citizens abide by Australia’s work-related expense policies. In 2018, the nudging resulted in AUD$113 million in changed claim amounts.[foot]I. A. Someh, B. H. Wixom, and R. W. Gregory, “The Australian Taxation Office: Creating Value with Advanced Analytics,” MIT CISR Working Paper No. 447, November 2020, https://cisr.mit.edu/publication/MIT_CISRwp447_ATOAdvancedAnalytics_SomehWixomGregory .[/foot]

In 2023, we began exploring data monetization initiatives that rely on generative AI.[foot]This research draws on two asynchronous generative AI discussions (Q3 2023, N=35; Q1 2024, N=34) regarding investments and capabilities and roles and skills, respectively, with data executives from the MIT CISR Data Research Advisory Board. It also draws on in-progress case studies with large organizations in the publishing, building materials, and equipment manufacturing industries.[/foot] This type of AI analyzes vast amounts of text or image data to discern patterns in them. Using these patterns, generative AI can create new text, software code, images, or videos, usually in response to user prompts. Organizations are now beginning to openly discuss data monetization initiative deployments that include generative AI technologies. For example, used vehicle retailer CarMax reported using OpenAI’s ChatGPT chatbot to help aggregate customer reviews and other car information from multiple data sets to create helpful, easy-to-read summaries about individual used cars for its online shoppers. At any point in time, CarMax has on average 50,000 cars on its website, so to produce such content without AI the company would require hundreds of content writers and years of time; using ChatGPT, the company’s content team can generate summaries in hours.[foot]Paula Rooney, “CarMax drives business value with GPT-3.5,” CIO , May 5, 2023, https://www.cio.com/article/475487/carmax-drives-business-value-with-gpt-3-5.html ; Hayete Gallot and Shamim Mohammad, “Taking the car-buying experience to the max with AI,” January 2, 2024, in Pivotal with Hayete Gallot, produced by Larj Media, podcast, MP3 audio, https://podcasts.apple.com/us/podcast/taking-the-car-buying-experience-to-the-max-with-ai/id1667013760?i=1000640365455 .[/foot]

Big advancements in machine learning, generative tools, and other AI technologies inspire big investments when leaders believe the technologies can help satisfy pent-up demand for solutions that previously seemed out of reach. However, there is a lot to learn about novel technologies before we can properly manage them. In this year’s MIT CISR research, we are studying predictive and generative AI from several angles. This briefing is the first in a series; in future briefings we will present management advice specific to machine learning and generative tools. For now, we present three principles supported by our data monetization research to guide business leaders when making AI investments of any kind: invest in practices that build capabilities required for AI, involve all your people in your AI journey, and focus on realizing value from your AI projects.

Principle 1: Invest in Practices That Build Capabilities Required for AI

Succeeding with AI depends on having deep data science skills that help teams successfully build and validate effective models. In fact, organizations need deep data science skills even when the models they are using are embedded in tools and partner solutions, including to evaluate their risks; only then can their teams make informed decisions about how to incorporate AI effectively into work practices. We worry that some leaders view buying AI products from providers as an opportunity to use AI without deep data science skills; we do not advise this.

But deep data science skills are not enough. Leaders often hire new talent and offer AI literacy training without making adequate investments in building complementary skills that are just as important. Our research shows that an organization’s progress in AI is dependent on having not only an advanced data science capability, but on having equally advanced capabilities in data management, data platform, acceptable data use, and customer understanding.[foot]In the June 2022 MIT CISR research briefing, we described why and how organizations build the five advanced data monetization capabilities for AI. See B. H. Wixom, I. A. Someh, and C. M. Beath, “Building Advanced Data Monetization Capabilities for the AI-Powered Organization,” MIT CISR Research Briefing, Vol. XXII, No. 6, June 2022, https://cisr.mit.edu/publication/2022_0601_AdvancedAICapabilities_WixomSomehBeath .[/foot] Think about it. Without the ability to curate data (an advanced data management capability), teams cannot effectively incorporate a diverse set of features into their models. Without the ability to oversee the legality and ethics of partners’ data use (an advanced acceptable data use capability), teams cannot responsibly deploy AI solutions into production.

It’s no surprise that ATO’s AI journey evolved in conjunction with the organization’s Smarter Data Program, which ATO established to build world-class data analytics capabilities, and that CarMax emphasizes that its governance, talent, and other data investments have been core to its generative AI progress.

Capabilities come mainly from learning by doing, so they are shaped by new practices in the form of training programs, policies, processes, or tools. As organizations undertake more and more sophisticated practices, their capabilities get more robust. Do invest in AI training—but also invest in practices that will boost the organization’s ability to manage data (such as adopting a data cataloging tool), make data accessible cost effectively (such as adopting cloud policies), improve data governance (such as establishing an ethical oversight committee), and solidify your customer understanding (such as mapping customer journeys). In particular, adopt policies and processes that will improve your data governance, so that data is only used in AI initiatives in ways that are consonant with your organization's values and its regulatory environment.

Principle 2: Involve All Your People in Your AI Journey

Data monetization initiatives require a variety of stakeholders—people doing the work, developing products, and offering solutions—to inform project requirements and to ensure the adoption and confident use of new data tools and behaviors.[foot]Ida Someh, Barbara Wixom, Michael Davern, and Graeme Shanks, “Configuring Relationships between Analytics and Business Domain Groups for Knowledge Integration, ” Journal of the Association for Information Systems 24, no. 2 (2023): 592-618, https://cisr.mit.edu/publication/configuring-relationships-between-analytics-and-business-domain-groups-knowledge .[/foot] With AI, involving a variety of stakeholders in initiatives helps non-data scientists become knowledgeable about what AI can and cannot do, how long it takes to deliver certain kinds of functionality, and what AI solutions cost. This, in turn, helps organizations in building trustworthy models, an important AI capability we call AI explanation (AIX).[foot]Ida Someh, Barbara H. Wixom, Cynthia M. Beath, and Angela Zutavern, “Building an Artificial Intelligence Explanation Capability,” MIS Quarterly Executive 21, no. 2 (2022), https://cisr.mit.edu/publication/building-artificial-intelligence-explanation-capability .[/foot]

For example, at ATO, data scientists educated business colleagues on the mechanics and results of models they created. Business colleagues provided feedback on the logic used in the models and helped to fine-tune them, and this interaction helped everyone understand how the AI made decisions. The data scientists provided their model results to ATO auditors, who also served as a feedback loop to the data scientists for improving the model. The data scientists regularly reported on initiative progress to senior management, regulators, and other stakeholders, which ensured that the AI team was proactively creating positive benefits without neglecting negative external factors that might surface.

Given the consumerization of generative AI tools, we believe that pervasive worker involvement in ideating, building, refining, using, and testing AI models and tools will become even more crucial to deploying fruitful AI projects—and building trust that AI will do the right thing in the right way at the right time.

Principle 3: Focus on Realizing Value From Your AI Projects

AI is costly—just add up your organization’s expenses in tools, talent, and training. AI needs to pay off, yet some organizations become distracted with endless experimentation. Others get caught up in finding the sweet spot of the technology, ignoring the sweet spot of their business model. For example, it is easy to become enamored of using generative AI to improve worker productivity, rolling out tools for employees to write better emails and capture what happened in meetings. But unless those activities materially impact how your organization makes money, there likely are better ways to spend your time and money.

Leaders with data monetization experience will make sure their AI projects realize value in the form of increased revenues or reduced expenses by backing initiatives that are clearly aligned with real challenges and opportunities. That is step one. In our research, the leaders that realize value from their data monetization initiatives measure and track their outcomes, especially their financial outcomes, and they hold someone accountable for achieving the desired financial returns. At CarMax, a cross-functional team owned the mission to provide better website information for used car shoppers, a mission important to the company’s sales goals. Starting with sales goals in mind, the team experimented with and then chose a generative AI solution that would enhance the shopper experience and increase sales.

Figure 1: Three Principles for Getting Value from AI Investments

The three principles are based on the following concepts from MIT CISR data research: 1. Data liquidity: the ease of data asset recombination and reuse 2. Data democracy: an organization that empowers employees in the access and use of data 3. Data monetization: the generation of financial returns from data assets

Managing AI Using a Data Monetization Mindset

AI has and always will play a big role in data monetization. It’s not a matter of whether to incorporate AI, but a matter of how to best use it. To figure this out, quantify the outcomes of some of your organization’s recent AI projects. How much money has the organization realized from them? If the answer disappoints, then make sure the AI technology value proposition is a fit for your organization’s most important goals. Then assign accountability for ensuring that AI technology is applied in use cases that impact your income statements. If the AI technology is not a fit for your organization, then don’t be distracted by media reports of the AI du jour.

Understanding your AI technology investments can be hard if your organization is using AI tools that are bundled in software you purchase or are built for you by a consultant. To set yourself up for success, ask your partners to be transparent with you about the quality of data they used to train their AI models and the data practices they relied on. Do their answers persuade you that their tools are trustworthy? Is it obvious that your partner is using data compliantly and is safeguarding the model from producing bad or undesired outcomes? If so, make sure this good news is shared with the people in your organization and those your organization serves. If not, rethink whether to break with your partner and find another way to incorporate the AI technology into your organization, such as by hiring people to build it in-house.

To paraphrase our book’s conclusion: When people actively engage in data monetization initiatives using AI , they learn, and they help their organization learn. Their engagement creates momentum that initiates a virtuous cycle in which people’s engagement leads to better data and more bottom-line value, which in turn leads to new ideas and more engagement, which further improves data and delivers more value, and so on. Imagine this happening across your organization as all people everywhere make it their business to find ways to use AI to monetize data.

This is why AI, like data, is everybody’s business.

© 2024 MIT Center for Information Systems Research, Wixom and Beath. MIT CISR Research Briefings are published monthly to update the center’s member organizations on current research projects.

Related Publications

Talking Points

Ai, like data, is everybody's business.

Working Paper: Vignette

The australian taxation office: creating value with advanced analytics.

Research Briefing

Building advanced data monetization capabilities for the ai-powered organization.

Building AI Explanation Capability for the AI-Powered Organization

What is Data Monetization?

About the researchers.

Barbara H. Wixom, Principal Research Scientist, MIT Center for Information Systems Research (CISR)

Cynthia M. Beath, Professor Emerita, University of Texas and Academic Research Fellow, MIT CISR

Mit center for information systems research (cisr).

Founded in 1974 and grounded in MIT's tradition of combining academic knowledge and practical purpose, MIT CISR helps executives meet the challenge of leading increasingly digital and data-driven organizations. We work directly with digital leaders, executives, and boards to develop our insights. Our consortium forms a global community that comprises more than seventy-five organizations.

MIT CISR Associate Members

MIT CISR wishes to thank all of our associate members for their support and contributions.

MIT CISR's Mission Expand

MIT CISR helps executives meet the challenge of leading increasingly digital and data-driven organizations. We provide insights on how organizations effectively realize value from approaches such as digital business transformation, data monetization, business ecosystems, and the digital workplace. Founded in 1974 and grounded in MIT’s tradition of combining academic knowledge and practical purpose, we work directly with digital leaders, executives, and boards to develop our insights. Our consortium forms a global community that comprises more than seventy-five organizations.

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Optimizing one-axis twists for variational Bayesian quantum metrology

Tyler g. thurtell and akimasa miyake, phys. rev. research 6 , 023179 – published 16 may 2024.

• No Citing Articles
• INTRODUCTION
• BACKGROUND FOR QUANTUM METROLOGY
• ARBITRARY-AXIS TWIST ANSATZES
• TENSOR NETWORK SIMULATION FOR NOISY…
• ACKNOWLEDGMENTS

Quantum metrology and sensing seek advantages in estimating an unknown parameter of some quantum state or channel, using entanglement such as spin squeezing produced by one-axis twists or other quantum resources. In particular, qubit phase estimation, or rotation sensing, appears as a ubiquitous problem with applications to electric field sensing, magnetometry, atomic clocks, and gyroscopes. By adopting the Bayesian formalism to the phase estimation problem to account for limited initial knowledge about the value of the phase, we formulate variational metrology and treat the state preparation (or encoding) and measurement (or decoding) procedures as parameterized quantum circuits. It is important to understand how effective various parametrized protocols are as well as how robust they are to the effects of complex noise such as spatially correlated noise. First, we propose a family of parametrized encoding and decoding protocols called arbitrary-axis twist ansatzes, and show that it can lead to a substantial reduction in the number of one-axis twists needed to achieve a target estimation error. Furthermore, we demonstrate that the estimation error associated with these strategies decreases with system size in a faster manner than classical (or no-twist) protocols, even in the less-explored regimes where the prior information is limited. Last, using a polynomial-sized tensor network algorithm, we numerically analyze practical variational metrology beyond the symmetric subspace of a collective spin and find that quantum advantage persists for the arbitrary-axis twist ansatzes with a few one-axis twists and smaller total twisting angles for practically relevant noise levels.

• Received 13 February 2023
• Accepted 15 April 2024

DOI: https://doi.org/10.1103/PhysRevResearch.6.023179

Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.

Published by the American Physical Society

Physics Subject Headings (PhySH)

• Research Areas

Authors & Affiliations

• Department of Physics and Astronomy, Center for Quantum Information and Control, University of New Mexico, Albuquerque, New Mexico 87106, USA
• * [email protected]
• † [email protected]

Article Text

Vol. 6, Iss. 2 — May - July 2024

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• Quantum Physics

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A schematic representation of a general quantum metrology experiment. Time advances from left to right.

Illustration of the general form of (a) encoding and (b) decoding layers used in the parity symmetric ansatzes [ 34, 57 ]. In this diagram, operations further to the left occur first.

Illustration of the general form of the unitaries that are appended to the (a) encoding and (b) decoding circuits to increase the number of one-axis twists once at least one is in use in the arbitrary-axis twist ansatzes. In this diagram, time proceeds from left to right.

(a) The optimized root Bayesian mean squared error Δ φ / δ φ of various low depth arbitrary-axis twist ansatzes plotted vs the prior standard deviation δ φ . (b) Plot of Δ φ / δ φ vs δ φ for up to five decoding twists for 0.5 ≲ δ φ ≲ 0.9 . In (c) and (d), respectively, the variances and squared biases of the estimators associated with AAT 1 1 , AAT 2 1 , and AAT 1 2 are plotted vs φ at δ φ ≈ 0.74 . In all plots, N = 30 .

Plots of the shifted Bayesian mean squared error Δ φ ̃ 2 vs system size N for prior standard deviations of (a)  δ φ = 10 − 3 and (b)  δ φ = 0.7 . The markers on the solid curves correspond to the performance of the strategies the dashed lines are the fitted curves. In (a), the optimal, AAT 2 1 , twist-untwist (TuT) strategies are all fit to curves with exponents ν ≈ 2.0 , the AAT 0 1 is fit to a curve with ν ≈ 1.7 , and the classical strategy is fit to a curve with ν ≈ 1.0 . In (b), the optimal strategy is again fit to a curve with ν ≈ 2.0 , the AAT 1 1 strategy is fit to a curve with ν ≈ 0.73 , and the classical strategy is fit to a curve with ν ≈ 0.45 .

Plot of the root Bayesian mean squared error Δ φ / δ φ vs prior variance δ φ for the optimized ansatzes PAR 2 2 , PAR 6 2 , AAT 1 1 , and AAT 4 1 in the 0.5 ≲ δ φ ≲ 0.9 regime for N = 30 . AAT 1 1 performs better than PAR 2 2 and AAT 4 1 performs about as well as PAR 6 2 . We note that the rightmost PAR 6 2 point is trapped in a local minima in this numerical setting, but can be resolved by a different initialization, for instance. See Appendixes pp1 and pp3 for details on how the optimization was performed and values of the Bayesian mean squared error that can be obtained with this ansatz and prior variance via other optimization methods.

Reduction of the root Bayesian mean-squared error Δ φ / δ φ for the optimized AAT 1 1 ansatz under a correlated dephasing channel of Eq. ( 24 ) during a free evolution when N = 30 and δ φ ≈ 0.74 . The correlated noise is characterized in terms of the variance of r j at each site c 1 and the covariances at adjacent sites c 2 . The curves of constant Δ φ / δ φ indicate that correlated dephasing is not much worse than uncorrelated, and anticorrelated dephasing is more favorable than uncorrelated.

The root Bayesian mean squared error Δ φ / δ φ for (a), (b)  AAT 1 1 , AAT 2 1 , AAT 3 1 , AAT 4 1 , AAT 5 1 , AAT 6 1 ; (c), (d)  PAR 2 2 , PAR 4 2 , and PAR 6 2 , and the classically optimal strategy under noisy one-axis twists vs the noise strength when N = 30 and δ φ ≈ 0.74 . In (a) and (c), the noise model is dephasing noise with noise strength p and in (b) and (d) the noise model is amplitude damping noise with noise strength γ . The ansatzes with deeper decoding circuits are advantageous only when the noise strength p or γ is sufficiently small.

Illustration of the algorithm used to find the optimal strategies. First, the expectation values of J z and J z 2 are estimated at a sufficient number of different values of φ to estimate Δ φ . Then the estimate of Δ φ / δ φ and possibly estimates of the derivatives of this function are used to choose new values of the circuit parameters θ to consider.

Plot of the total twisting angle of the optimal AAT n De 1 strategies vs the decoding depth for various values of δ φ . Note that there is a clear relation between the two quantities and in the case that δ φ ≈ 0.74 the relationship is nearly linear for the depths considered here.

Plot of the root Bayesian mean-squared error Δ φ / δ φ vs prior variance δ φ resulting from the Nelder-Mead sequential quadratic programming approach described in the text for various hyperparameters for (a)  PAR 6 2 and (b)  AAT 3 1 . The blue crosses are the results for initializing all circuit parameters to zero, setting ε = 10 − 8 , and using only 25 points in the numerical integration. The orange circles are the same as the blue crosses but with 500 points used in the numerical integration. The green triangles are the same as the orange circles but with ε = 10 − 13 . The red diamonds use ε = 10 − 8 but sequential initialization. Finally, the purple triangles are the result of using the hyperparameters described in Appendix  pp1 . As they are usually the best, the numerical setting of the purple triangles are displayed throughout the main text

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Journal of Materials Chemistry C

Functionalised al(iii) metal organic frameworks for fluorescence sensing of nitroaromatic vapours.

The employment of fluorescence sensors provides a platform for rapid and efficient in-field detection of nitroaromatic compounds and is gaining increasing research ground. Herein, we report the synthesis and characterisation of three new fluorescent Al(ΙΙΙ) MOFs, structurally analogous to MIL–53, with the assigned formula {Al(OH)(bdc) 1-n (L–1) n }·xsolv (bdc 2- = terephthalate; L–1 = 2–((benzyl)amino)-terephthalate). L–1 is a strongly fluorescent dicarboxylic ligand with a pendant π–electron rich aromatic group suitable for donor-acceptor interactions with electron–deficient nitroaromatic guests. Our MOFs show strong fluorescence quenching upon exposure to vapours of nitrobenzene, 1,3-dinitrobenzene, 4-nitrotoluene, 2,4-dinitrotoluene. Additionally, we prepare and study MOF-polymer composites in the form of thin films that are strongly quenched in the presence of nitrobenzene vapours.

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A. Haj-Yahya, D. Kouskouki, A. G. Margellou, E. K. Andreou, G. S. Armatas and T. Lazarides, J. Mater. Chem. C , 2024, Accepted Manuscript , DOI: 10.1039/D4TC00862F

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ORIGINAL RESEARCH article

Pharmacologic inhibition of dipeptidyl peptidase 1 (cathepsin c) does not block in vitro granzyme-mediated target cell killing by cd8 t or nk cells.

• 1 Peter MacCallum Cancer Centre, Melbourne, Australia
• 2 Insmed Inc., Bridgewater, New Jersey, United States

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Recently developed small molecule inhibitors of the lysosomal protease dipeptidyl peptidase 1 (DPP1), also known as cathepsin C (CatC), can suppress suppurative inflammation in vivo by blocking the processing of zymogenic (pro-) forms of the neutrophil serine proteases (NSPs), including neutrophil elastase, proteinase 3, and cathepsin G. However, DPP1 also plays an important role in activating granzyme serine proteases expressed by cytotoxic T lymphocytes (CTL) and natural killer (NK) cells. Therefore, it is critical to determine whether inhibition of DPP1 can also cause 'offtarget' suppression of CTL/NK cell mediated killing of virus-infected or malignant cells. Here, we demonstrated that the processing of human granzymes A and B, transitioning from a zymogen to an active protease, is not solely dependent on DPP1. As a result, the killing of target cells by primary human CD8+ T cells, NK cells, and gene-engineered anti-CD19 CAR T cells was not blocked in vitro, even after prior exposure to high concentrations of the reversible DPP1 inhibitor, brensocatib.Consistent with this, the turnover of model granzyme A/B peptide substrates in human CTL/NK cell lysates was not significantly reduced by brensocatib. In contrast, pre-incubation with brensocatib almost entirely abolished (>90%) both the cytotoxic activity of mouse CD8+ T cells against cognate target cells and granzyme substate turnover. Overall, our finding that the effects of DPP1 inhibition on human cytotoxic lymphocytes are attenuated in comparison to the mouse indicate that granzyme processing/activation pathways differ between mice and humans. Moreover, our in vitro data suggest that human subjects treated with reversible DPP1 inhibitors, such as brensocatib, are will be unlikely to experience any appreciable deficit in CTL/NK cell-mediated immunity.

Keywords: DPP1, granzyme, Apoptosis, serine protease, T cell, Lysosome, protease inhibitor, Granule exocytosis

Received: 06 Mar 2024; Accepted: 15 May 2024.

Copyright: © 2024 Trapani, Sutton, Watt, Akhlaghi, Cipolla, Perkins, Chen, Lasala, Mcdonald, Beavis, Munoz, Hodel, Noori and Voskoboinik. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Joseph A. Trapani, Peter MacCallum Cancer Centre, Melbourne, Australia

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

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Bariatric Surgery Reduces Breast Cancer Incidence in a Prospective Trial

• 1 Division of Breast Surgery, Department of Surgery, Robert H. Lurie Comprehensive Cancer Center, Feinberg School of Medicine, Northwestern University, Chicago, Illinois
• Original Investigation Breast Cancer Risk After Bariatric Surgery and Influence of Insulin Levels Felipe M. Kristensson, MD; Johanna C. Andersson-Assarsson, PhD; Markku Peltonen, PhD; Peter Jacobson, MD, PhD; Sofie Ahlin, MD, PhD; Per-Arne Svensson, PhD; Kajsa Sjöholm, PhD; Lena M. S. Carlsson, MD, PhD; Magdalena Taube, PhD JAMA Surgery

In this issue of JAMA Surgery , Kristensson et al 1 build on their previous research using participants from the Swedish Obese Subjects Study (SOS), a prospective, controlled trial comparing bariatric surgery to usual care. The SOS investigators initially reported a decrease in cancer incidence after bariatric surgery in women, but not in men. 2 Subsequently, in the cohort of women participants, they reported that bariatric surgery reduced the incidence of female-specific cancers, particularly in those women who had hyperinsulinemia at baseline. 3 In this current study, women were followed up for a median of 23.9 years after bariatric surgery or usual care. The authors found a significantly lower incidence of breast cancer in the surgery group compared to the usual care group in premenopausal women and in women with elevated median insulin levels and insulin resistance at the time of enrollment. 1 This study is the first prospective study with long-term follow-up to report an association between bariatric surgery and a reduction in breast cancer incidence. While cancer incidence was not a prespecified end point and the SOS study was not randomized, a significant strength of the study is that both the surgery and usual care arm patients were eligible for bariatric surgery, which allowed for a more unbiased comparison of the 2 groups.

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Kulkarni SA , Sterbling HM. Bariatric Surgery Reduces Breast Cancer Incidence in a Prospective Trial. JAMA Surg. Published online May 15, 2024. doi:10.1001/jamasurg.2024.1158

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Should our future food be genetically engineered?

Genetically modified crops can help cut carbon emissions, research shows — but they still face major hurdles.

The Philippines Department of Agriculture has a vision: to become the first country to allow the commercial production of golden rice , a 20-year-old genetically modified crop that could prevent hundreds of thousands of cases of childhood blindness around the world.

But the country’s appeals court came to a very different decision last month. The court banned cultivating the crop, named for the yellow color that comes from the addition of Vitamin A , as well as a genetically modified eggplant.

“This decision is a monumental win for Filipino farmers and Filipino people who have for decades stood up against genetically modified (GM) crops,” Wilhelmina Pelegrina, a Southeast Asia campaigner for Greenpeace, an advocacy group that has opposed genetically modified crops for decades, said in a statement .

While genetically modified crops may still provoke fear and uncertainty, some scientists argue that not only can they help to alleviate human health concerns, but they might also be able to help fight climate change. And as new tools like CRISPR , which can make targeted cuts in DNA, gain traction, genetic food engineering could be on the cusp of a quantum leap.

“It’s all political,” Stuart Smyth, a professor of agricultural and resource economics at the University of Saskatchewan, said of the Philippines decision. “It’s not based on science.”

Genetically modified crops are ones that have had genetic material inserted from another species of organism. For example, the first genetically modified food product — a tomato introduced to the public in 1994 as the “Flavr Savr” — had two genes added. One conferred antibiotic resistance, and another gave the tomato a longer shelf life. (The company manufacturing the Flavr Savr, Calgene, had to cease production in 1997 because of rising costs.)

Today, there are only a few genetically modified crops in production, but those that exist are widely grown. In the United States, 94 percent of all soybeans, 96 percent of all cotton and 92 percent of all corn was genetically modified as of 2020, according to the Food and Drug Administration . These crops became popular because of their ability to withstand glyphosate, a key ingredient in the herbicide known as “Roundup.” Other countries that grow genetically modified crops widely include Canada, Brazil and India.

No major scientific research has found that genetically modified crops cause health problems in humans. In a 400-plus-page report published in 2016, the National Academies of Science found that “no substantiated evidence that foods from GE [genetically engineered] crops were less safe than foods from non-GE crops.” The report urged analysis of such foods by the traits that they include, rather than how they were created.

Yet engineered crops remain unpopular. According to a Pew Research Center poll from 2020, 38 percent of Americans believe genetically modified crops are unsafe, compared with 27 percent who believe they are safe. Thanks to a law passed by Congress in 2016, foods in the United States are required to be labeled as bioengineered if they involved genetic engineering beyond what could be accomplished with conventional breeding techniques. One analysis showed that consumers are willing to pay 20 percent more to avoid GM foods.

At the same time, a small but growing body of research has argued that GM foods could play a significant role in cutting carbon emissions. In a study published last year, researchers at the University of Bonn in Germany and the Berkeley, Calif.-based Breakthrough Institute found that widespread use of these crops in Europe could cut the agricultural sector’s emissions by 7.5 percent.

Another study found that the use of GM crops globally saves around 23 million metric tons of carbon dioxide every year — equal to removing around half of all vehicles from roads in the United Kingdom.

There are two primary ways genetically engineered crops could cut carbon emissions.

First, they can be more productive, creating higher yields for farmers and allowing them to grow more food on less land. One global analysis found that GM crops on average lead to a 22 percent increase in yields. At the same time, one-third of all emissions from agriculture are from deforestation and the destruction of other natural areas — as farmers expand and grow more crops, they cut down trees that are storing CO2 in their trunks and leaves.

If farmers can grow their crops on less land, less forest is converted into farmland, allowing trees and landscapes to store more carbon. “That decrease in deforestation is the big reason why yield increases cut emissions,” said Emma Kovak, a senior food and agriculture analyst for the Breakthrough Institute.

Other scientists say crops with herbicide resistance can require less tilling. “Every time soil is tilled, it releases carbon back into the atmosphere,” Smyth said. Herbicide-resistant corn, for example, can endure being sprayed by weed-killing agents, preventing farmers from having to till the land to remove weeds.

But the environmental community is split. Some activists say focusing on climate change obscures the real problem with genetically modified crops: the role of big corporations in controlling food production.

“We see GMOs as a tool of the major corporations that already have a stranglehold on our food system,” said Amanda Starbuck, research director at Food and Water Watch. Many genetically modified crops, Starbuck says, go toward feeding animals for meat production — and improvements in yield won’t change the fact that humans need to move away from eating so much meat. “We need to move to significantly reduce that consumption,” she added.

Research into alleviating climate change with genetically modified crops has just begun. “On a scale of one to 100, I’d say it’s single digits,” Smyth said. Scientists say they need more analysis of how GM crops change land use and carbon sequestration, and studies that take place over longer periods of time.

But even in areas where the science is relatively settled, genetically engineered foods have struggled to gain acceptance. Golden rice was developed in 1999 by a Swiss scientist; it was intended to combat the estimated 250,000 to 500,000 children every year who go blind from Vitamin A deficiency. More than two decades later, however, the crop has not entered widespread cultivation, thanks in part to regulatory battles in Asia and resistance from environmentalists.

In its decision to ban genetically modified crops, the appeals court cited a Philippines legal principle granting the right to a healthy environment.

For opponents of genetically engineered crops, that is a victory; for some scientists, it is a missed opportunity. “It’s sad that something someone developed in the 1980s to solve a problem — a really bad problem, children going blind — is still relevant,” Kovak said.

And while the battle lines around genetically modified crops have been set for decades, new technologies may shake things up. Gene-editing tools like CRISPR allow scientists to make tweaks, deletions or changes in a genome without inserting genes from another species. Researchers are already working on gene-edited crops that could speed up photosynthesis and increase crop yields.

Changing a genome without adding a component from another species could be more palatable to consumers — but some environmental groups believe it is just a way to rebrand the same type of work.

“Industry could say, ‘Well, it’s not GMO. It’s gene-edited,’” Starbuck said. “It’s just another smokescreen.”

The shift could also complicate existing regulations, which have been tied to older definitions of genetic modification.

“It’s frustrating,” Smyth said. “We need to make all of these changes to cut carbon emissions. But how are we supposed to meet the Paris accord with one hand tied behind our back?”

More on climate change

Understanding our climate: Global warming is a real phenomenon , and weather disasters are undeniably linked to it . As temperatures rise, heat waves are more often sweeping the globe — and parts of the world are becoming too hot to survive .

What can be done? The Post is tracking a variety of climate solutions , as well as the Biden administration’s actions on environmental issues . It can feel overwhelming facing the impacts of climate change, but there are ways to cope with climate anxiety .

Inventive solutions: Some people have built off-the-grid homes from trash to stand up to a changing climate. As seas rise, others are exploring how to harness marine energy .

What about your role in climate change? Our climate coach Michael J. Coren is answering questions about environmental choices in our everyday lives. Submit yours here. You can also sign up for our Climate Coach newsletter .