PURPOSE This first-in-human, dose-escalation and dose-expansion study evaluated the safety, tolerability, and antitumor activity of datopotamab deruxtecan (Dato-DXd), a novel trophoblast cell-surface antigen 2 (TROP2)–directed antibody-drug conjugate in solid tumors, including advanced non–small-cell lung cancer (NSCLC). PATIENTS AND METHODS Adults with locally advanced/metastatic NSCLC received 0.27-10 mg/kg Dato-DXd once every 3 weeks during escalation or 4, 6, or 8 mg/kg Dato-DXd once every 3 weeks during expansion. Primary end points were safety and tolerability. Secondary end points included objective response rate (ORR), survival, and pharmacokinetics. RESULTS Two hundred ten patients received Dato-DXd, including 180 in the 4-8 mg/kg dose-expansion cohorts. This population had a median of three prior lines of therapy. The maximum tolerated dose was 8 mg/kg once every 3 weeks; the recommended dose for further development was 6 mg/kg once every 3 weeks. In patients receiving 6 mg/kg (n = 50), median duration on study, including follow-up, and median exposure were 13.3 and 3.5 months, respectively. The most frequent any-grade treatment-emergent adverse events (TEAEs) were nausea (64%), stomatitis (60%), and alopecia (42%). Grade ≥3 TEAEs and treatment-related AEs occurred in 54% and 26% of patients, respectively. Interstitial lung disease adjudicated as drug-related (two grade 2 and one grade 4) occurred in three of 50 patients (6%). The ORR was 26% (95% CI, 14.6 to 40.3), and median duration of response was 10.5 months; median progression-free survival and overall survival were 6.9 months (95% CI, 2.7 to 8.8 months) and 11.4 months (95% CI, 7.1 to 20.6 months), respectively. Responses occurred regardless of TROP2 expression. CONCLUSION Promising antitumor activity and a manageable safety profile were seen with Dato-DXd in heavily pretreated patients with advanced NSCLC. Further investigation as first-line combination therapy in advanced NSCLC and as monotherapy in the second-line setting and beyond is ongoing.
This work presents a motion retargeting approach for legged robots, aimed at transferring the dynamic and agile movements to robots from source motions. In particular, we guide the imitation learning procedures by transferring motions from source to target, effectively bridging the morphological disparities while ensuring the physical feasibility of the target system. In the first stage, we focus on motion retargeting at the kinematic level by generating kinematically feasible whole-body motions from keypoint trajectories. Following this, we refine the motion at the dynamic level by adjusting it in the temporal domain while adhering to physical constraints. This process facilitates policy training via reinforcement learning, enabling precise and robust motion tracking. We demonstrate that our approach successfully transforms noisy motion sources, such as hand-held camera videos, into robot-specific motions that align with the morphology and physical properties of the target robots. Moreover, we demonstrate terrain-aware motion retargeting to perform BackFlip on top of a box. We successfully deployed these skills to four robots with different dimensions and physical properties in the real world through hardware experiments.
Abstract We study higher uniformity properties of the Möbius function $\mu $ , the von Mangoldt function $\Lambda $ , and the divisor functions $d_k$ on short intervals $(X,X+H]$ with $X^{\theta +\varepsilon } \leq H \leq X^{1-\varepsilon }$ for a fixed constant $0 \leq \theta < 1$ and any $\varepsilon>0$ . More precisely, letting $\Lambda ^\sharp $ and $d_k^\sharp $ be suitable approximants of $\Lambda $ and $d_k$ and $\mu ^\sharp = 0$ , we show for instance that, for any nilsequence $F(g(n)\Gamma )$ , we have $$\begin{align*}\sum_{X < n \leq X+H} (f(n)-f^\sharp(n)) F(g(n) \Gamma) \ll H \log^{-A} X \end{align*}$$ when $\theta = 5/8$ and $f \in \{\Lambda , \mu , d_k\}$ or $\theta = 1/3$ and $f = d_2$ . As a consequence, we show that the short interval Gowers norms $\|f-f^\sharp \|_{U^s(X,X+H]}$ are also asymptotically small for any fixed s for these choices of $f,\theta $ . As applications, we prove an asymptotic formula for the number of solutions to linear equations in primes in short intervals and show that multiple ergodic averages along primes in short intervals converge in $L^2$ . Our innovations include the use of multiparameter nilsequence equidistribution theorems to control type $II$ sums and an elementary decomposition of the neighborhood of a hyperbola into arithmetic progressions to control type $I_2$ sums.
BACKGROUND:Scientific research relies on well-structured, standardized data; however, much of it is stored in formats such as free-text lab notebooks, nonstandardized spreadsheets, or data repositories. This lack of structure challenges interoperability, making data integration, validation, and reuse difficult. FINDINGS:LinkML (Linked Data Modeling Language) is an open framework that simplifies the process of authoring, validating, and sharing data. LinkML can describe a range of data structures, from flat, list-based models to complex, interrelated, and normalized models that utilize polymorphism and compound inheritance. It offers an approachable syntax that is not tied to any one technical architecture and can be integrated seamlessly with many existing frameworks. The LinkML syntax provides a standard way to describe schemas, classes, and relationships, allowing modelers to build well-defined, stable, and optionally ontology-aligned data structures. Once defined, LinkML schemas may be imported into other LinkML schemas. These key features make LinkML an accessible platform for interdisciplinary collaboration and a reliable way to define and share data semantics. CONCLUSIONS:LinkML helps reduce heterogeneity, complexity, and the proliferation of single-use data models while simultaneously enabling compliance with FAIR (Findable, Accessible, Interoperable, and Reusable) data standards. LinkML has seen increasing adoption in various fields, including biology, chemistry, biomedicine, microbiome research, finance, electrical engineering, transportation, and commercial software development. In short, LinkML makes implicit models explicitly computable and allows data to be standardized at their origin. LinkML documentation and code are available at https://linkml.io/.
Hypoglycemia and impaired metabolic transition are frequently observed in neonates during the first 24–48 h after birth [1, 2]. Severe (< 36 mg/dL or 2 mmol/L) and recurrent (3 or more episodes) hypoglycemia can cause neurological injury and developmental delays. The ambiguity regarding a threshold blood glucose concentration remains due to differing values proposed by various professional organizations. This poses a challenge in diagnosing neonatal hypoglycemia in addition to using a single blood glucose value, which in itself is not entirely reflective of various key molecular processes uncovered by in vitro or pre-clinical studies. The symptoms of hypoglycemia can also be present in conditions other than hypoglycemia, e.g., sepsis and polycythemia, and in many cases, hypoglycemia is clinically unrecognized. Therefore, early screening of at-risk and otherwise healthy-appearing neonates is essential. Continuous glucose monitoring and early interventions such as glucose gel, breast and formula feeding, and intravenous glucose administration are utilized to prevent long-term neurological impairments. However, the safe limits of serum glucose that will prevent neuroglycopenia and neural injury are elusive. The impact of early screening and available therapies on neurodevelopmental outcomes remains uncertain due to the absence of a robust clinical design and combining all causes of neonatal hypoglycemia without making further distinctions from other conditions. This review highlights the controversies in definitions and the most recent information on long-term neurodevelopmental outcomes that may impact the early management of NH. Conclusion: Optimizing the definitions and treatment of neonatal dysglycemia is crucial for preventing hypoglycemia-related brain injury. Continuous glucose monitoring technology in neonates offers a promising approach for real-time screening and early intervention.
