Matty\u27s First Hike: Hiking Spain\u27s northwest coast with a baby and a beagle

New parents venture along the coast of Galicia, from the Cliffs of Loiba to O Esteiro and back, with their son in a carrier

Antibody and protein engineering for cancer immunotherapy: advancing targeted and immune modulation therapies

Cancer remains one of the most challenging and deadly diseases worldwide due to the complexity of tumor biology and the ability of cancer cells to evade treatment. Despite advancements, cancer remains the second leading cause of death globally. Two major challenges are tumor heterogeneity and the immunosuppressive tumor microenvironment (TME). Tumor heterogeneity, driven by genetic and phenotypic changes, allows cancer cells to lose antigen expression, leading to resistance against single-target therapies. Targeting multiple tumor-specific antigens with antibody cocktails or modulating immune checkpoints that restore immune function in the TME could help overcome these challenges. In this work, we applied directed evolution in yeast and phage display systems to engineer antibodies from human antibody fragment libraries that are specific to neoepitopes in the mouse B16F10 melanoma and CT26 colon carcinoma models. Many of these antibodies showed binding to tumor cells, elicited effector functions, and inhibited tumor growth in mice. Additionally, we explored the engineering of an immune checkpoint blockade antibody for canine oncology that showed promising results in a canine safety trial. Furthermore, we engineered two anti-PD-L2 checkpoint blockade antibodies with distinct receptor-blocking properties to explore how PD-L2 interacts with its binding partners, PD-1 and RGMB, in the context of tumor immunology. The results of these studies highlight the feasibility of engineering patient-customized antibody therapies as a possible solution to overcoming tumor heterogeneity as well as offer immune checkpoint blockade strategies and insight for both human and veterinary oncology. Future work should focus on optimizing these therapies and evaluating their clinical potential in both human and canine cancer treatments

Soft Modular Robots: From Modular Tensegrity Structures to Bioinspired Sea Robots

The rapid advancement of robotics necessitates systems capable of adapting to complex, unstructured environments. Soft robots, with their flexibility and compliance, excel in delicate interactions, making them ideal for medical applications and search-and-rescue missions. Modular robots, on the other hand, offer reconfigurability, enabling diverse task-specific adaptations in dynamic settings. Despite their individual advantages, the integration of soft and modular robotics remains underexplored. This proposal aims to develop soft modular robots that combine the adaptability of soft robotics with the versatility of modularity. These systems will be capable of autonomously transitioning between locomotion, manipulation, and infrastructure assembly across land, water, and air. This research is motivated by the limitations of traditional industrial robots, which are confined to predefined, repetitive tasks in controlled settings. In contrast, disaster response, environmental monitoring, and infrastructure development demand robots capable of role transitions in unstructured environments. However, soft robots often lack the structural integrity for load-bearing tasks, while modular robots struggle with compliance for sensitive interactions. Integrating these paradigms introduces key challenges, including (i) balancing mechanical adaptability and load-bearing capacity through stiffness-tunable mechanisms, (ii) developing robust self-reconfigurable architectures, (iii) enabling autonomy via real-time sensing and planning, and (iv) optimizing configurations for task-specific adaptation using data-driven approaches. This proposal will explore four key directions: (1) tensegrity-inspired soft modular robots for deployable infrastructure such as shelters and bridges, (2) aquatic soft modular robots for trash collection, amphibious locomotion, and drone landing, (3) bioinspired soft modular robots, including dolphin-like designs for efficient fluid movement and (4) STEM education kits to make biomimetic soft modular robotics accessible for education and research. By addressing these challenges, this research aims to develop adaptive, multifunctional robotic systems capable of autonomous decision-making and real-time adaptation in complex environments

Appalachia Summer/Fall 2025: Complete Issue

Summer/Fall 2025 - Volume LXXVI, Number 2 - Issue #260. On the Ground and Screen: Stories of smartphones in wild places

Host–Guest Interactions of Metal–Organic Cage-Capped Gold Nanoparticles

Metal–organic cages (MOCs) are a class of porous materials made of metal ions and organic ligands. These self-assembled compounds have promising applications in catalysis, sensing, biomedicine, and energy storage due to their stability and tunability. Cages vary greatly in shape, internal cavity size, solubility, and net charge, enabling supramolecular binding to guest molecules with high affinity and selectivity. Our work focuses on tetrahedral MOCs constructed with zirconium-based nodes and phenyl-containing organic ligands. We modify gold nanoparticles (GNPs) with our cages to maximize selectivity and reactivity in hydrogenation reactions for industrial applications; cage porosity enables selectivity while maintaining high reactivity by providing access to the gold surface. In this study, we examined the effects of solvent and relative cage and guest size on host–guest behavior. Our objective was to identify a guest molecule with strong binding affinity for our cages. Through this, we could confirm the tetrahedral geometry of our MOCs. Further, we could use host–guest binding to block the cage internal cavity in hydrogenation control experiments. These experiments would prove that reactants must go through the pore during catalysis.https://digitalcommons.dartmouth.edu/wetterhahn_2025/1014/thumbnail.jp

The Role of the Orbitofrontal Cortex in Social Cognition: An Analysis of Betweenness Centrality and Functional Heterogeneity

Research into the role of the orbitofrontal cortex in social cognition has been dominated by lesion studies. While the OFC has been suggested to organize large neural networks and contribute to appropriate social behavior, this bias towards lesion studies fails to provide positive evidence of the specific function the OFC facilitates. This study utilizes iEEG high gamma data for an analysis of betweenness centrality in the OFC during a social task to bridge the gap in knowledge. The OFC is found to exhibit high centrality across the entirety of a social task as well as during the planning of communicative behavior in comparison to frontal control regions. Further exploration into the networks that the OFC organizes implicates a high frequency circuit connecting the OFC, middle temporal, and other frontal regions. These results provide insight into the network-level dynamics of the OFC and can be helpful in understanding the clinical implications of OFC loss of function.https://digitalcommons.dartmouth.edu/wetterhahn_2025/1006/thumbnail.jp

APPLICATIONS OF LANGUAGE-THEORETIC SECURITY TOWARDS SYSTEM SECURITY

Language-theoretic security (or LangSec) research lies at the intersection of computer security research and formal language theory. In addition to investigating novel approaches for secure input handling in software systems, LangSec research also investigates novel conceptions of software exploitability derived from insights of formal language theory. This thesis advances this line of research by presenting: (1) A survey of parser differential antipatterns (Chapter 3) (2) A formal grammar backed secure parser generation framework for microcontrollers (Chapter 4) (3) A toolkit for securing software module boundaries from crafted-input attacks (Chapter 5) ( 4) An analysis which demonstrates latent functionality in complex package management systems and its security implications (Chapter 8) (5) A framework for testing parser correctness via grammar-based input synthesis (Chapter 6) (6) A novel fuzzing method for discovering parser differentials (Chapter 7)

Understanding firn dynamics: modeling and microstructure from East Antarctica

Glacier ice is formed from the accumulation of snow and its compaction through a transitional material called firn. Firn dynamics are fundamentally influenced by climatic factors, such as temperature and snow accumulation rate, and so are crucial for understanding a number of cryospheric applications. For example, ice-sheet mass loss contributions to sea-level rise from repeat satellite-altimetry observations depend on calculating firn density and its evolution. Past atmospheric gases in ice core bubbles are often younger than the surrounding ice, and their exact age depends on how firn closes off interconnected pores to become impermeable. Models often estimate bulk properties, such as density, to predict firn characteristics for these applications; however, recent research efforts indicate that firn microstructure is integral for their construction. My thesis examines and integrates new and emerging modeling and experimental methods to study firn dynamics and microstructure, and specifically centers on two East Antarctic sites of different depositional histories: South Pole and Allan Hills. I used micro-computed tomography to investigate firn microstructure, and uniaxial compression to explore its evolution in a range of temperature and overburden conditions. I integrated these methods to build and explore the relevance of two-phase modeling and pore-network modeling, which are new model frameworks for firn dynamics. My major findings include: the large difference in material properties between air and ice in firn will reduce the effect of air diffusivity on compaction, negating the strengths of two-phase modeling (Chapter 1); uniaxial compression is an analog for natural compaction at South Pole, where we observe the gradual transition of mechanisms for compaction from stage 1 into stage 2 compaction and the increased effect of the evolution of SSA on the evolution of stress with temperature in the range of -10 deg C to -15 deg C (Chapter 2); spatial variability in wind speed, micro- and macro-topography, and their interrelated factors cause distinct microstructural differences at Allan Hills, such as existence of depth hoar, which has implications on permeability and likely the interpretation of stable water isotope ratios (Chapter 3); pore-network modeling is an effective way to simulate firn behavior and estimate bulk properties, such as permeability, that are reflections of microstructure (Chapter 4). These results offer insight into firn’s material response to microstructure, and overall contribute to better understanding its role in the cryosphere

Exploitation to Liberation: Care Worker Kinship in New York City

This project aims to identify the relationship between power and care in the home health industry and how who has the power (the worker or another entity) can drastically transform the embodied effect of care—either as overwhelming or emancipatory. Focusing on Home Health Aides (HHAs) in New York City, I apply geographies of care and labor geography to analyze how home health workers organize across disparate workplaces to regain their agency over their labor and care relationships. Through this research, I found that HHAs use kinships and care webs to mobilize and organize against care agency and patient surveillance and state failures. I conclude that HHAs depend on each other to mobilize using principles of inter-community solidarity and kinship to regain agency over their spatial productions. In this praxis of care, HHAs offer a unique insight into how care and power intersect, transforming the act of caring either into subjugation or liberation