Number Of Arms Research Articles

Optimal analysis for bandit learning in matching markets with serial dictatorship

The problem of two-sided matching markets is well-studied in computer science and economics, owing to its diverse applications across numerous domains. Since market participants are usually uncertain about their preferences in various online matching platforms, an emerging line of research is dedicated to the online setting where one-side participants (players) learn their unknown preferences through multiple rounds of interactions with the other side (arms). Sankararaman et al. [23] provide an Ω(Nlog⁡(T)Δ2+Klog⁡(T)Δ) regret lower bound for this problem under serial dictatorship assumption, where N is the number of players, K(≥N) is the number of arms, Δ is the minimum reward gap across players and arms, and T is the time horizon. Serial dictatorship assumes arms have the same preferences, which is common in reality when one side participants have a unified evaluation standard. Recently, the work of Kong and Li [10] proposes the ET-GS algorithm and achieves an O(Klog⁡(T)Δ2) regret upper bound, which is the best upper bound attained so far. Nonetheless, a gap between the lower and upper bounds, ranging from N to K, persists. It remains unclear whether the lower bound or the upper bound needs to be improved. In this paper, we propose a multi-level successive selection algorithm that obtains an O(Nlog⁡(T)Δ2+Klog⁡(T)Δ) regret bound when the market satisfies serial dictatorship. To the best of our knowledge, we are the first to propose an algorithm that matches the lower bound in the problem of matching markets with bandits.

Grand Design versus Multiarmed Spiral Galaxies: Dependence on Galaxy Structure

We developed an algorithm to use Galaxy Zoo 3D spiral arm masks produced by citizen scientist volunteers to semiautomatically classify spiral galaxies as either multiarmed or grand design spirals. Our final sample consists of 299 multiarmed and 245 grand design galaxies. On average, the grand design galaxies have smaller stellar masses than the multiarmed galaxies. For a given stellar mass, the grand design galaxies have larger concentrations, earlier Hubble types, smaller half-light radii, and larger central surface mass densities than the multiarmed galaxies. Lower-mass galaxies of both arm classes have later Hubble types and lower concentrations than higher-mass galaxies. In our sample, a higher fraction of grand design galaxies have classical bulges rather than pseudo-bulges, compared to multiarmed galaxies. These results are consistent with theoretical models and simulations, which suggest that dense classical bulges support the development and/or longevity of two-armed spiral patterns. Similar specific star formation rates (sSFRs) are found in multiarmed and grand design galaxies with similar stellar masses and concentrations. This implies that the sSFRs in spiral galaxies are a function of concentration and stellar mass, but independent of the number of spiral arms. Our classifications are consistent with arm counts from the Galaxy Zoo 2 project and published m = 3 Fourier amplitudes.

Abstract IA015: Identifying and targeting synthetic lethalities of aneuploid (cancer) cells

Abstract Aneuploidy, an imbalanced number of chromosomes or chromosome arms, is a genetic hallmark of cancer cells, yet aneuploidy remains a biological enigma and a missed opportunity for cancer therapy. Aneuploid cells must cope with several types of cellular stresses, potentially creating synthetic lethalities that can be used to target aneuploid cancer cells. Here, I will describe our efforts to identify synthetic lethalities of the aneuploid state (in contrast to synthetic lethalities of specific recurrent aneuploidies. Specifically, I will focus on three recent unpublished studies, where we report that: (1) Aneuploid cells are preferentially sensitive to perturbation of the spindle assembly checkpoint (SAC) and its regulator KIF18A; the expression and activity of CDC20 determine the sensitivity to SAC inhibition. (2) Aneuploid cells depend on MAPK signaling for overcoming aneuploidy-induced DNA damage; targeting MAPK signaling can sensitize aneuploid cells to DNA damage inducing agents and to PARP inhibitors. (3) Aneuploid cells depend on RNA and protein degradation mechanisms to attenuate the cellular consequence of extra chromosomes; this renders aneuploid cells more sensitive to inhibition of nonsense-mediate decay, miRNA-mediated gene silencing, and the proteasome complex. Citation Format: Uri Ben-David. Identifying and targeting synthetic lethalities of aneuploid (cancer) cells [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Expanding and Translating Cancer Synthetic Vulnerabilities; 2024 Jun 10-13; Montreal, Quebec, Canada. Philadelphia (PA): AACR; Mol Cancer Ther 2024;23(6 Suppl):Abstract nr IA015.

Abstract B003: Identification of novel genes that regulate aneuploidy tolerance by attenuating aneuploidy-induced stresses

Abstract Aneuploidy, an imbalanced number of chromosomes or chromosome arms, is the most common genetic aberration in cancer and is associated with a worse prognosis and advanced disease. However, in non-transformed cells, aneuploidy has a substantial fitness cost, leading to cell cycle arrest and cell death. This fitness cost is the result of several cellular stresses associated with aneuploidy, including proteotoxic, metabolic, mitotic and replication stress. Therefore, for aneuploidy to become beneficial, tolerance mechanisms that enable cancer cells to cope with aneuploidy-induced cellular stresses must come into play. Such mechanisms may uncover novel synthetic lethalities of aneuploid cancer cells. To this end, we systematically dissected the cellular mechanisms that facilitate aneuploidy tolerance. We combined large-scale analyses of gene expression, as well as genetic and pharmacological dependency datasets from human tumors, cancer cell lines and non-transformed cells, to identify genes and pathways associated with aneuploidy tolerance. We identified 55 genes associated (with high confidence) with aneuploidy tolerance. Gene set enrichment analysis (GSEA) revealed an enrichment for ribosome biogenesis and translation, DNA repair, replication stress and the p53 pathway. Interestingly, these pathways were all previously implicated with the aforementioned aneuploidy-induced cellular stresses. We plan to conduct a dependency mini-screen to validate the top ‘hits’ from our comprehensive bioinformatic analysis, and to explore the role of validated candidates in conferring aneuploidy tolerance. One of the top ‘hits’ of our exploratory analysis was a novel gene with an unclear cellular function. We experimentally confirmed that this gene was over-expressed in aneuploid cells, across several isogenic systems of matched diploid-aneuploid cell lines. Aneuploidy induction using MPS1 inhibitors led to its significant upregulation, demonstrating that its increased expression is a general feature of aneuploid cells. Mechanistically, we found that the novel gene had a role in regulating chromosome segregation, as its knockdown increased the rate of mitotic aberrations – as well as the sensitivity to mitotic checkpoint inhibition – resulting in increased chromosomal instability. We now study the molecular basis for the effect of this novel gene on aneuploidy tolerance. Specifically, we knocked it down in aneuploid cells, and over-expressed it in diploid cells, and we currently explore how these manipulations affect the above-mentioned aneuploidy tolerance pathways, namely replication stress, ribosome biogenesis, DNA damage repair and tp53 activation. In summary, we revealed a novel gene that is a key regulator of aneuploidy tolerance in cancer cells, likely due to its direct involvement in pathways required for the cellular coping with aneuploidy-induced stresses. Citation Format: Yonatan Eliezer, Adi V Tarrab, Tal Ben-Yishay, Hajime Okada, Tom Winkler, Uri Ben-David. Identification of novel genes that regulate aneuploidy tolerance by attenuating aneuploidy-induced stresses [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Expanding and Translating Cancer Synthetic Vulnerabilities; 2024 Jun 10-13; Montreal, Quebec, Canada. Philadelphia (PA): AACR; Mol Cancer Ther 2024;23(6 Suppl):Abstract nr B003.

Multi-arm multi-stage (MAMS) randomised selection designs: impact of treatment selection rules on the operating characteristics

BackgroundMulti-arm multi-stage (MAMS) randomised trial designs have been proposed to evaluate multiple research questions in the confirmatory setting. In designs with several interventions, such as the 8-arm 3-stage ROSSINI-2 trial for preventing surgical wound infection, there are likely to be strict limits on the number of individuals that can be recruited or the funds available to support the protocol. These limitations may mean that not all research treatments can continue to accrue the required sample size for the definitive analysis of the primary outcome measure at the final stage. In these cases, an additional treatment selection rule can be applied at the early stages of the trial to restrict the maximum number of research arms that can progress to the subsequent stage(s).This article provides guidelines on how to implement treatment selection within the MAMS framework. It explores the impact of treatment selection rules, interim lack-of-benefit stopping boundaries and the timing of treatment selection on the operating characteristics of the MAMS selection design.MethodsWe outline the steps to design a MAMS selection trial. Extensive simulation studies are used to explore the maximum/expected sample sizes, familywise type I error rate (FWER), and overall power of the design under both binding and non-binding interim stopping boundaries for lack-of-benefit.ResultsPre-specification of a treatment selection rule reduces the maximum sample size by approximately 25% in our simulations. The familywise type I error rate of a MAMS selection design is smaller than that of the standard MAMS design with similar design specifications without the additional treatment selection rule. In designs with strict selection rules - for example, when only one research arm is selected from 7 arms - the final stage significance levels can be relaxed for the primary analyses to ensure that the overall type I error for the trial is not underspent. When conducting treatment selection from several treatment arms, it is important to select a large enough subset of research arms (that is, more than one research arm) at early stages to maintain the overall power at the pre-specified level.ConclusionsMulti-arm multi-stage selection designs gain efficiency over the standard MAMS design by reducing the overall sample size. Diligent pre-specification of the treatment selection rule, final stage significance level and interim stopping boundaries for lack-of-benefit are key to controlling the operating characteristics of a MAMS selection design. We provide guidance on these design features to ensure control of the operating characteristics.

From Facile One-Pot Synthesis of Semi-Degradable Amphiphilic Miktoarm Polymers to Unique Degradation Properties.

We report a one-pot synthesis of well-defined A5B and A8B miktoarm star-shaped polymers where N,N-dimethylaminoethyl methacrylate (DMAEMA) and various cyclic esters such as ε-caprolactone (ε-CL), lactide (LA) and glycolide (GA) were used for the synthesis. Miktopolymers were obtained by simultaneously carrying out atom transfer radical polymerization (ATRP) of DMAEMA, ring-opening polymerization (ROP) of cyclic esters, and click reaction between the azide group in gluconamide-based (GLBr5-Az) or lactonamide-based (GLBr8-Az) ATRP initiators and 4-pentyn-1-ol. The relatively low dispersity indices of the obtained miktoarm stars (Đ = 1.2-1.6) indicate that control over the polymerization processes was sustained despite almost complete monomers conversions (83-99%). The presence of salts from phosphate-buffered saline (PBS) in polymer solutions affects the phase transition, increasing cloud point temperatures (TCP) values. The critical aggregation concentration (CAC) values increased with a decreasing number of average molecular weights of the hydrophobic fraction. Hydrolytic degradation studies revealed that the highest reduction of molecular weight was observed for polymers with PCL and PLGCL arm. The influence of the composition on the miktopolymers hydrophilicity was investigated via water contact angle (WCA) measurement. Thermogravimetric analysis (TGA) disclosed that the number of arms and their composition in the miktopolymer affects its weight loss under the influence of temperature.

Elongational rheology of 2, 3 and 4 polymer stars connected by linear backbone chains

We consider the elongational rheology of model polystyrene topologies with 2, 3 and 4 stars, which are connected by one (2-star or “Pom-Pom”), two (3-star) and three (4-star) linear backbone chains. The number of arms of each star varies from qa = 3 to 24, the molecular weight of the arms from Mw,a = 25 kg/mol to 300 kg/mol, and the backbone chains from Mw,b = 100 kg/mol to 382 kg/mol. If the length of the arm is shorter than the length of the backbone, i.e. Mw,a < Mw,b, and despite the vastly different topologies considered, the elongational stress growth coefficient can be modeled by the Hierarchical Multi-mode Molecular Stress Function (HMMSF) model, based exclusively on the linear-viscoelastic characterization and a single nonlinear parameter, the dilution modulus. If the length of the arms of the stars is similar or longer than the length of the backbone chain (Mw,a ≥ Mw,b) connecting two stars, the impact of the backbone chain on the rheology vanishes and the elongational stress growth coefficient is dominated by the star topology showing similar features of the elongational stress growth coefficient as those of linear polymers.Graphical

Recursively Generated Spirals

Summary When the angles are changed in the usual algorithm to generate the Koch curve, a wide variety of images may be produced, some of which possess rotational symmetry. One family of such images consists of simple spiral-like images. While there are only two segments in each spiral arm, the number of segments that must be drawn to render a complete image of the spiral grows exponentially with the number of arms in the spiral.

Abstract PO1-08-12: Genomic Characterization of the Carolina Breast Cancer Study

Abstract Background: Breast cancer is a heterogeneous disease defined by distinct subtypes, mutational profiles, and genomic characteristics. Previous analyses in The Cancer Genome Atlas (TCGA) have been instrumental in understanding the genomic landscape of breast cancer, including identification of key driver mutations, mutational signatures, and distinguishing features of breast cancer subtypes. However, TCGA may not be representative of other population-based breast cancer cohorts, particularly those of earlier stage and including a more diverse set of patients. Our aim was to characterize the genomic landscape of breast cancer in the Carolina Breast Cancer Study (CBCS), a cohort which oversamples Black and young women. Methods: We used targeted sequencing to profile somatic alterations in 1,175 genes from 275 formalin fixed paraffin embedded primary breast tumors in CBCS, 52% of which came from Black women, 41% from women under age 50, and 29% of which were Basal-like based on bulk RNA profiling. We also evaluated TP53 mutation as a source of breast cancer heterogeneity, comparing the type and location of TP53 mutations in each cohort. We assessed chromosome arm-level gains and losses using GISTIC and defined genomic instability as the total number of aneuploid chromosome arms. Intratumoral heterogeneity was assessed with PyClone-VI and defined as the total number and Shannon’s Diversity Index of tumor-specific subclones. For genes mutated in more than 5% of samples and chromosomal aberrations significantly enriched in GISTIC, we estimated the prevalence of alterations and compared to previous distributions in 981 TCGA tumors. Results: Seven genes [TP53 (N=101, 37%), PIK3CA (N=76, 28%), GATA3 (N=32, 12%), CDH1 (N=31, 11%), MAP3K1 (N=22, 8%), KMT2C (N=20, 7%), and CBFB (N =19, 7%)] were mutated in more than 5% of CBCS tumors; all but CBFB which were also highly prevalent in TCGA. While most TP53 hotspot mutations observed in CBCS were previously reported in TCGA, CBCS had twice as many Y220C mutations, one-third fewer R175H mutations, and a non-significantly higher proportion of nonsense mutations than TCGA (20% vs 12%, p = 0.05). Hotspot mutation prevalence in other Luminal-associated driver genes (e.g. PIK3CA, GATA3, CDH1) did not differ by dataset. CBCS tumors showed a higher degree of genomic instability (8 arms vs. 5 arms, p &amp;lt; 0.001) and subclonal diversity than TCGA (48% vs 12% comprised of two or more subclones, p &amp;lt; 0.05). Both datasets showed significant amplifications of 1q and 8q, and deletions of 13q and 17p (where BRCA2 and TP53, respectively, are located), with CBCS having non-significantly higher prevalence of each of these changes. Conclusions: While comparisons should be interpreted in light of technical and population differences between TCGA and CBCS, the overall results show that the suite of commonly altered genes and chromosome arms were highly consistent between TCGA and the diverse CBCS cohort. CBCS tumors tended to display higher genomic instability, intratumoral heterogeneity, and number and diversity of TP53 mutations. This may reflect distinctions in tumor evolutionary state between CBCS and TCGA tumors, and/or could reflect differences in the study populations. Results underscore the importance of considering population characteristics – particularly stage and race – in large-scale genomic contexts, and highlight the importance of diverse cohorts in genomic research. Citation Format: Sarah Van Alsten, Ebonee Butler, Benjamin Calhoun, Michael Love, Charles Perou, Katherine A Hoadley, Melissa Troester. Genomic Characterization of the Carolina Breast Cancer Study [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO1-08-12.

Optimal sample size division in two-stage seamless designs.

Inferentially seamless 2/3 designs are increasingly popular in clinical trials. It is important to understand their relative advantages compared with separate phase 2 and phase 3 trials, and to understand the consequences of design choices such as the proportion of patients included in the phase 2 portion of the design. Extending previous work in this area, we perform a simulation study across multiple numbers of arms and efficacy response curves. We consider a design space crossing the choice of a separate versus seamless design with the choice of allocating 0%-100% of available patients in phase 2, with the remainder in phase 3. The seamless designs achieve greater power than their separate trial counterparts. Importantly, the optimal seamless design is more robust than the optimal separate program, meaning that one range of values for the proportion of patients used in phase 2 (30%-50% of the total phase 2/3 sample size) is nearly optimal for a wide range of response scenarios. In contrast, a percentage of patients used in phase 2 for separate trials may be optimal for some alternative scenarios but decidedly inferior for other alternative scenarios. When operationally and scientifically viable, seamless trials provide superior performance compared with separate phase 2 and phase 3 trials. The results also provide guidance for the implementation of these trials in practice.

Enhancing UCB-tuned and Asymptotically Optimal UCB Algorithms through Weighted Average Techniques in Multi-Armed Bandit Scenarios

This paper delves into the complexities of the Multi-Armed Bandit (MAB) problem, a fundamental concept in reinforcement learning and probability theory, with a focus on its application in recommendation systems and dynamic fields such as dynamic pricing and investment. It begins by shedding light on the essential paradox at the heart of the MAB problem – the balance between exploration and exploitation within limited parameters. The study primarily centers on Upper Confidence Bound (UCB) policies, especially UCB-tuned and Asymptotically Optimal UCB, noted for their adept balance between exploration and utilization. The novel contribution of this research is the enhancement of these UCB policies via an innovative weighted average method, leading to the development of WA-UCB-tuned and WA Asymptotically Optimal UCB algorithms. The research rigorously compares these optimized iterations with traditional UCB1, UCB-tuned, and Asymptotically Optimal UCB across varied MAB models featuring different numbers of arms. This study provides an exhaustive introduction to the MAB problem and pertinent UCB policies, the methodology behind the weighted average optimization, extensive experimental analysis, and comprehensive evaluations of the findings. The results showcase marked improvements in algorithmic performance, suggesting significant advancements in the domain of recommendation systems and other applications of the MAB problem.

Accelerated Sequence Design of Star Block Copolymers: An Unbiased Exploration Strategy via Fusion of Molecular Dynamics Simulations and Machine Learning.

Star block copolymers (s-BCPs) have potential applications as novel surfactants or amphiphiles for emulsification, compatibilization, chemical transformations, and separations. s-BCPs have chain architectures where three or more linear diblock copolymer arms comprised of two chemically distinct linear polymers, e.g., solvophobic and solvophilic chains, are covalently joined at one point. The chemical composition of each of the subunit polymer chains comprising the arms, their molecular weights, and the number of arms can be varied to tailor the surface and interfacial activity of these architecturally unique molecules. This makes identification of the optimal s-BCP design nontrivial as the total number of plausible s-BCP architectures is experimentally or computationally intractable. In this work, we use molecular dynamics (MD) simulations coupled with a reinforcement learning-based Monte Carlo tree search (MCTS) to identify s-BCP designs that minimize the interfacial tension between polar and nonpolar solvents. We first validate the MCTS approach for the design of small- and medium-sized s-BCPs and then use it to efficiently identify sequences of copolymer blocks for large-sized s-BCPs. The structural origins of interfacial tension in these systems are also identified by using the configurations obtained from MD simulations. Chemical insights into the arrangement of copolymer blocks that promote lower interfacial tension were mined using machine learning (ML) techniques. Overall, this work provides an efficient approach to solve design problems via fusion of simulations and ML and provides important groundwork for future experimental investigation of s-BCPs for various applications.

Abstract LB249: Multi-omics cancer risk assessment of Barrett’s esophagus with DNA methylation sequencing from endoscopic brush samples

Abstract Introduction: Barrett’s esophagus (BE) confers increased cancer risk. Endoscopic surveillance is recommended to distinguish non-dysplastic BE (NDBE), low grade dysplasia (LGD), high grade dysplasia (HGD), or adenocarcinoma (EAC) for endoscopic intervention. However, this paradigm is limited by sampling error, leading to missed/interval EAC. Herein, we aimed to classify these samples more accurately by performing whole genome methylation sequencing (WGMS) and utilizing multiple genomic features on endoscopic brush samples. Materials and methods: Endoscopic brush samples from patients with NDBE (n=18), HGD (18), EAC (18), and normal controls (18) were collected and underwent WGMS (discovery dataset). Sequence reads were processed through internal pipeline to extract CpG methylation and read level methylation ratio (alpha value). Copy number aberration (CNA) detection was performed using both CNVkit and ichorCNA. Differential methylated regions (DMRs) were identified through comparing EAC/HGD with NDBE and normal samples. Targeted methylation sequencing for selected DMRs and shallow WGMS were performed on EAC (41), HGD (26), LGD (23), NDBE (42), and normal controls (37) as validation. Ratio of the reads with alpha value&amp;gt;0.6 for each DMR was used for further analysis. Detected CNA segments were summarized into chromosome arm loss or gain (aneuploidy score or AS representing the number of chromosome arms gained or lost in a sample) and trimmed median absolute deviation (tMAD). Classification models were developed using the discovery set and the best performing one was applied to the validation dataset. Results: Genome-wide DNA methylation largely segregated different groups of samples as diagnosed; however, some unexpected group crossings were observed. Top 200 DMRs were identified between EAC/HGD and NDBE and normal samples. CNA changes were widespread in EAC, very common in HGD but rare in NDBE (17/18 EACs,14/18 HGDs, 5/18 NDBEs with AS&amp;gt;0). With tMAD &amp;gt; 0.015, 16/18 EACs, 8/18 HGDs but none of 18 NDBEs passed the threshold. EAC or HGD samples without detectable CNAs had very low “tumor” fraction as determined by methylation-based cell type deconvolution or ichorCNA. Similar results were found in the validation dataset. A multi-omics (AS, tMAD, estimated tumor fraction, and DMR methylation) model could predict EAC, HGD, or NDBE with 0.81 accuracy through cross validation (10X) and 0.73 in the independent dataset. This increased to 0.98 and 0.90 in separating high risk EAC/HGD from NDBE, respectively. Conclusion: Multi-omics data obtained from single WGMS allow for a comprehensive analysis of BE samples with varying dysplasia levels. Using classification modeling, these characteristics can be effectively used to accurately identify and distinguish between EAC and HGD from NDBE, suggesting their utility as adjuncts to endoscopic surveillance biopsies. Citation Format: Zhifu Sun, Caroline L. Matchett, Seth W. Slettedahl, William R. Taylor, Panwen Wang, Calise Berger, Caryn E. Anderson, Melissa A. Passe, Ramona Lansing, Collin E. Chalmers, Patrick H. Foote, Jeanette E. Eckel Passow, Doug W. Mahoney, John B. Kisiel, Prasad G. Iyer. Multi-omics cancer risk assessment of Barrett’s esophagus with DNA methylation sequencing from endoscopic brush samples [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(7_Suppl):Abstract nr LB249.

Abstract NG02: Distinct genomic and immunologic tumor evolution in germline TP53-driven breast cancers

Abstract NG02: Distinct genomic and immunologic tumor evolution in germline TP53-driven breast cancers

Closely Packed Core-Shell Micelle Structures of Double Hydrophilic Miktoarm Star Copolymers at the Air-Water Interface.

The aggregation behavior of amphiphilic block copolymers at the air-water interface has been extensively studied, but less attention was given to that of star copolymers. In this work, we studied the interfacial aggregation behavior of two double hydrophilic pH- and temperature-responsive miktoarm star copolymers of poly[di(ethylene glycol) methyl ether methacrylate]-poly[2-(dimethylamino)ethyl methacrylate] (PDEGMA3-PDMAEMA3 and PDEGMA4-PDMAEMA7, the subscripts denote arm numbers) with different molecular weights. The effects of subphase pH and temperature on the monolayer isotherms and hysteresis curves of the two star copolymers and the morphologies of their Langmuir-Blodgett (LB) films were studied by the Langmuir film balance technique and atomic force microscopy, respectively. At the air-water interface, the two star copolymers tend to form closely packed micelles. These micelles exhibit a core-shell structure, where the small hydrophobic core consists of cross-linker of ethylene glycol dimethacrylate (EGDMA) and the carbon backbones of PDEGMA and PDMAEMA arms and the short hydrophilic shell is composed of di(ethylene glycol) and tertiary amine side groups. With increasing subphase pH, the surface pressure versus molecular area isotherms shift toward larger mean molecular areas as a result of the enhanced interface adsorption of nonprotonated tertiary amine groups. The isotherm shift of PDEGMA3-PDMAEMA3 monolayers is primarily attributed to high density of tertiary amine groups in the shells, while that of PDEGMA4-PDMAEMA7 is mainly attributed to high density of di(ethylene glycol) groups in the shells. The hysteresis degrees in the monolayers of the two copolymers under alkaline and neutral conditions are greater than those under acidic conditions due to the decreased protonation degree of the tertiary amine groups. At 10 °C, the mobility of the shells is poor and the isotherms are located on the right. Above the lower critical solution temperature, di(ethylene glycol) groups contract, which causes a slight shift of the isotherms toward smaller mean molecular areas.

Influence of the number of end-chain amine-functionalized arms of star-shaped functionalized SBR

Star-shaped SBRs with alkoxy silyl groups in the center of the molecule and amine groups at the polymer chain ends were prepared. The number of arms were varied, and its influence on the silica/silane-filled compound properties was investigated. The polymer–filler interactions and the filler–filler interactions were evaluated from the viscoelastic properties. The flocculation, tensile properties and tanδ at 60 °C, an indicator of rolling resistance, were investigated as well. The polymer–filler interactions were increased in the SBR with a higher number of arms due to the higher amount of amine groups in these SBRs. The Payne effect was slightly increased with a higher number of arms. This indicates a deteriorated micro-dispersion of the silica caused by too many interactions generated by the higher amount of amine groups in SBR. These high interactions due to the amine groups lead to higher tensile moduli. The combination of the higher polymer–filler interactions and the higher Payne effect in SBR with a high number of arms results in the same tanδ compared to the SBR with a low number of arms.

Thompson Sampling for Real-Valued Combinatorial Pure Exploration of Multi-Armed Bandit

We study the real-valued combinatorial pure exploration of the multi-armed bandit (R-CPE-MAB) problem. In R-CPE-MAB, a player is given stochastic arms, and the reward of each arm follows an unknown distribution. In each time step, a player pulls a single arm and observes its reward. The player's goal is to identify the optimal action from a finite-sized real-valued action set with as few arm pulls as possible. Previous methods in the R-CPE-MAB require enumerating all of the feasible actions of the combinatorial optimization problem one is considering. In general, since the size of the action set grows exponentially large with respect to the number of arms, this is almost practically impossible when the number of arms is large. We introduce an algorithm named the Generalized Thompson Sampling Explore (GenTS-Explore) algorithm, which is the first algorithm that can work even when the size of the action set is exponentially large with respect to the number of arms. We also introduce a novel problem-dependent sample complexity lower bound of the R-CPE-MAB problem, and show that the GenTS-Explore algorithm achieves the optimal sample complexity up to a problem-dependent constant factor.

Multiobjective Lipschitz Bandits under Lexicographic Ordering

This paper studies the multiobjective bandit problem under lexicographic ordering, wherein the learner aims to simultaneously maximize ? objectives hierarchically. The only existing algorithm for this problem considers the multi-armed bandit model, and its regret bound is O((KT)^(2/3)) under a metric called priority-based regret. However, this bound is suboptimal, as the lower bound for single objective multi-armed bandits is Omega(KlogT). Moreover, this bound becomes vacuous when the arm number K is infinite. To address these limitations, we investigate the multiobjective Lipschitz bandit model, which allows for an infinite arm set. Utilizing a newly designed multi-stage decision-making strategy, we develop an improved algorithm that achieves a general regret bound of O(T^((d_z^i+1)/(d_z^i+2))) for the i-th objective, where d_z^i is the zooming dimension for the i-th objective, with i in {1,2,...,m}. This bound matches the lower bound of the single objective Lipschitz bandit problem in terms of T, indicating that our algorithm is almost optimal. Numerical experiments confirm the effectiveness of our algorithm.

Abstract 2052: Screening novel format antibodies to design bispecific ADCs that address target heterogeneity

Abstract Addressing inter-patient and intra-tumoral target heterogeneity is a challenge for antibody drug conjugates (ADCs). The most common approach to mitigate intra-tumoral ADC target heterogeneity is to employ a bystander active payload. Once the ADC is internalized and metabolized, the payload can diffuse into tumor cells, independent of target expression. This strategy has proven effective, as evidenced by all but one of the eleven FDA-approved ADCs incorporating a bystander active payload. Nevertheless, it is important to note that in most cases there is clear evidence of an expression-response relationship, and the bystander approach does not specifically address inter-patient heterogeneity in target expression. Bispecific ADCs that can target two different tumor associated antigens (TAAs) are a promising approach to overcoming challenges associated with spatial and temporal target heterogeneity. A traditional bispecific ADC design employs a bivalent IgG where one paratope interacts with target A, and the other paratope interacts with target B. This design ensures that the molecule is maximally active when both targets A and B are present. However, this enhanced specificity approach has limitations, as it requires cellular co-expression of both targets to be effective. In contrast, a novel-format bispecific ADC targeting two different TAAs independently could increase the addressable patient population relative to a monospecific ADC. For example, a bispecific ADC with the potential for both independent and dual targeting of folate receptor alpha (FRα) and NaPi2b, established targets in ovarian cancers, could significantly expand the number of patients who could benefit relative to an ADC against either target alone. Here we describe a novel approach to the design and screening of a FRα x NaPi2b bispecific ADC library with the aim of targeting tumors that express either FRα, NaPi2b, or both targets. A library of 48 bispecific molecules was designed, employing multiple paratopes and variable antibody formats. The library comprised three valency bins, 1+1, 2+1, and 2+2, corresponding to the number of binding arms to each target. Bispecific antibodies were generated from 31 half antibodies containing Azymetric™ Het_Fc mutations. Complementary half-antibodies were combined and oxidized to generate interchain disulfide bonds, with bispecific antibody purity in the range of 90%. Antibodies and their corresponding ADCs, prepared through direct conjugation to a cytotoxic payload, were evaluated on a panel of FRα-expressing and Napi2b-expressing cancer cell lines. Key ADC parameters, including binding, internalization, and in vitro cytotoxicity, highlighted paratope combinations, valencies, and geometric formats that offer the unique potential to overcome target heterogeneity in ovarian cancer. Citation Format: Stuart D. Barnscher, Dunja Urosev, Kevin Yin, Andrea Hernández Rojas, Sam Lawn, Vincent Fung, Jodi Wong, Araba Sagoe-Wagner, Lemlem Degefie, Ali Livernois, Catrina Kim, Paul A. Moore, Jamie R. Rich. Screening novel format antibodies to design bispecific ADCs that address target heterogeneity [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 2052.

Optimal allocation strategies in platform trials with continuous endpoints.

Platform trials are randomized clinical trials that allow simultaneous comparison of multiple interventions, usually against a common control. Arms to test experimental interventions may enter and leave the platform over time. This implies that the number of experimental intervention arms in the trial may change as the trial progresses. Determining optimal allocation rates to allocate patients to the treatment and control arms in platform trials is challenging because the optimal allocation depends on the number of arms in the platform and the latter typically varies over time. In addition, the optimal allocation depends on the analysis strategy used and the optimality criteria considered. In this article, we derive optimal treatment allocation rates for platform trials with shared controls, assuming that a stratified estimation and a testing procedure based on a regression model are used to adjust for time trends. We consider both, analysis using concurrent controls only as well as analysis methods using concurrent and non-concurrent controls and assume that the total sample size is fixed. The objective function to be minimized is the maximum of the variances of the effect estimators. We show that the optimal solution depends on the entry time of the arms in the trial and, in general, does not correspond to the square root of allocation rule used in classical multi-arm trials. We illustrate the optimal allocation and evaluate the power and type 1 error rate compared to trials using one-to-one and square root of allocations by means of a case study.