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DIII-D experiments demonstrate simultaneous stability measurements and control of resistive wall modes (RWMs) with toroidal mode numbers n=1 and n=2. RWMs with n>1 are sometimes observed on DIII-D following the successful feedback stabilization of the n=1 mode, motivating the development of multi-n control. A new optimal multi-mode feedback algorithm based on the VALEN physics code has been implemented on the DIII-D tokamak using a real-time GPU installed directly in the DIII-D plasma control system (PCS). In addition to stabilizing RWMs, the feedback can control the stable plasma error field response, enabling compensation of the typically unaddressed DIII-D n=2 error field component. Experiments recently demonstrated this algorithm’s ability to simultaneously control n=1 and n=2 perturbed fields for the first time in a tokamak, using reactor relevant external coils. Control was maintained for hundreds of wall-times above the n=1 no-wall pressure limit and approaching the n=1 and n=2 ideal-wall limit. Multi-mode feedback also improved the control of the ELM-driven n=1 and n=2 fields which further validates the feedback performance. Furthermore, a rotating non-zero target was set for the feedback, allowing stability to be assessed by monitoring the rotating plasma response while maintaining control. This novel technique can be viewed as a closed-loop extension of active MHD spectroscopy, which has been used to validate stability models through comparisons of the plasma response to applied, open-loop perturbations. The closed-loop response measurements are consistent with open-loop MHD spectroscopy data over a range of βN approaching the n=1 ideal-wall limit, demonstrating the potential of this technique as a useful tool for measuring stability while maintaining control even as the marginal stability point is approached. These plasma response measurements were then fit to produce both VALEN and single-mode stability models. These models allow for important plasma stability information to be determined and have been shown to agree with experimentally observed RWM growth rates. This improved understanding and control of the n=1 and n=2 RWM will allow for more robust operation above the n=2 no-wall limit.

In this paper we present a new thin-wall eddy current modeling code, ThinCurr, for studying inductively-coupled currents in 3D conducting structures -- with primary application focused on the interaction between currents flowing in coils, plasma, and conducting structures of magnetically-confined plasma devices. The code utilizes a boundary finite element method on an unstructured, triangular grid to accurately capture device structures. The new code, part of the broader Open FUSION Toolkit, is open-source and designed for ease of use without sacrificing capability and speed through a combination of Python, Fortran, and C/C++ components. Scalability to large models is enabled through use of hierarchical off-diagonal low-rank compression of the inductance matrix, which is otherwise dense. Ease of handling large models of complicated geometry is further supported by automatic determination of supplemental elements through a greedy homology approach. A detailed description of the numerical methods of the code and verification of the implementation of those methods using cross-code comparisons against the VALEN code and Ansys commercial analysis software is shown.

In this paper we present a new thin-wall eddy current modeling code, ThinCurr, for studying inductively-coupled currents in 3D conducting structures -- with primary application focused on the interaction between currents flowing in coils, plasma, and conducting structures of magnetically-confined plasma devices. The code utilizes a boundary finite element method on an unstructured, triangular grid to accurately capture device structures. The new code, part of the broader Open FUSION Toolkit, is open-source and designed for ease of use without sacrificing capability and speed through a combination of Python, Fortran, and C/C++ components. Scalability to large models is enabled through use of hierarchical off-diagonal low-rank compression of the inductance matrix, which is otherwise dense. Ease of handling large models of complicated geometry is further supported by automatic determination of supplemental elements through a greedy homology approach. A detailed description of the numerical methods of the code and verification of the implementation of those methods using cross-code comparisons against the VALEN code and Ansys commercial analysis software is shown.

Abstract Background Several studies have highlighted the role of host–microbiome interactions in the pathogenesis of inflammatory bowel disease (IBD), resulting in an increasing amount of data mainly focusing on Western patients. Because of the increasing prevalence of IBD in newly industrialized countries such as those in Asia, the Middle East, and South America, there is mounting interest in elucidating the gut microbiota of these populations. We present a comprehensive analysis of several IBD-related biomarkers and gut microbiota profiles and functions of a unique population of patients with IBD and healthy patients from Kazan (Republic of Tatarstan, Russia). Methods Blood and fecal IBD biomarkers, serum cytokines, and fecal short-chain fatty acid (SCFA) content were profiled. Finally, fecal microbiota composition was analyzed by 16S and whole-genome shotgun sequencing. Results Fecal microbiota whole-genome sequencing confirmed the presence of classic IBD dysbiotic features at the phylum level, with increased abundance of Proteobacteria, Actinobacteria, and Fusobacteria and decreased abundance of Firmicutes, Bacteroidetes, and Verrucomicrobia. At the genus level, the abundance of both fermentative (SCFA-producing and hydrogen (H2)-releasing) and hydrogenotrophic (H2-consuming) microbes was affected in patients with IBD. This imbalance was confirmed by the decreased abundance of SCFA species in the feces of patients with IBD and the change in anaerobic index, which mirrors the redox status of the intestine. Conclusions Our analyses highlighted how IBD-related dysbiotic microbiota—which are generally mainly linked to SCFA imbalance—may affect other important metabolic pathways, such as H2 metabolism, that are critical for host physiology and disease development.

Select patients with metastatic clear-cell renal-cell carcinoma can be treated without systemic therapy, yet few studies have explored this population. We investigated the efficacy of metastasis-directed therapy without systemic therapy in oligometastatic clear-cell renal-cell carincoma. This investigator-initiated single-arm, phase 2 trial enrolled patients aged 18 years or older with an Eastern Cooperative Oncology Group performance status of 0–2, histologically confirmed clear-cell renal-cell carcinoma, and one to five metastases. Patients remained off systemic therapy and underwent metastasis-directed therapy to all disease sites, with additional metastasis-directed therapy for limited progression. Co-primary endpoints were progression-free survival based on Response Evaluation Criteria in Solid Tumours version 1.1 (RECIST 1.1) in the per-protocol population (patients who received radiation to at least one metastatic lesion during their initial local treatment) and systemic therapy-free survival in the intention-to-treat population. Progression-free survival was defined as the interval from enrolment to the first instance of disease progression, according to RECIST 1.1, or clinical progression, or death from any cause. Systemic therapy-free survival was defined as time from enrolment to initiation of systemic therapy or death from clear-cell renal-cell carcinoma. A prespecified 24-month median systemic therapy-free survival was the threshold for success. Safety was analysed in the per-protocol population. This trial is registered with ClinicalTrials.gov, NCT03575611, and is closed to new patient enrolment. Between July 13, 2018, and May 2, 2023, 121 patients were enrolled and included in the intention-to-treat population, of whom 120 received at least one round of definitive radiotherapy and were included in the per-protocol and safety populations. Median follow-up time for the 121 enrolled patients was 36·3 months (IQR 26·5–51·1). Median progression-free survival was 17·7 months (95% CI 14·9–22·4), and median systemic therapy-free survival time was 34·0 months (28·3–54·1). The median and lower bound of 95% CI of the median systemic therapy-free survival time exceeded the prespecified 24-month target. Eight (7%) of 120 patients had grade 3–4 adverse events at least possibly attributable to metastasis-directed therapy. The most common grade 3 event was pain near the treatment site (four events). The single grade 4 event was hyperglycaemia. There were no treatment-related deaths. Select patients with oligometastatic disease can be managed with serial metastasis-directed therapy with prolonged time off systemic therapy, favourable progression-free survival, and limited adverse events. Cancer Prevention and Research Institute of Texas, US National Cancer Institute, and Myriad Genetics.

Identification of partial sweeps, which include both hard and soft sweeps that have not currently reached fixation, provides crucial information about ongoing evolutionary responses. To this end, we introduce partialS/HIC, a deep learning method to discover selective sweeps from population genomic data. partialS/HIC uses a convolutional neural network for image processing, which is trained with a large suite of summary statistics derived from coalescent simulations incorporating population-specific history, to distinguish between completed versus partial sweeps, hard versus soft sweeps, and regions directly affected by selection versus those merely linked to nearby selective sweeps. We perform several simulation experiments under various demographic scenarios to demonstrate partialS/HIC’s performance, which exhibits excellent resolution for detecting partial sweeps. We also apply our classifier to whole genomes from eight mosquito populations sampled across sub-Saharan Africa by the Anopheles gambiae 1000 Genomes Consortium, elucidating both continent-wide patterns as well as sweeps unique to specific geographic regions. These populations have experienced intense insecticide exposure over the past two decades, and we observe a strong overrepresentation of sweeps at insecticide resistance loci. Our analysis thus provides a list of candidate adaptive loci that may be relevant to mosquito control efforts. More broadly, our supervised machine learning approach introduces a method to distinguish between completed and partial sweeps, as well as between hard and soft sweeps, under a variety of demographic scenarios. As whole-genome data rapidly accumulate for a greater diversity of organisms, partialS/HIC addresses an increasing demand for useful selection scan tools that can track in-progress evolutionary dynamics.

For a number of reasons, the NIH and the biomedical research community are facing a period of fiscal constraint after pronounced growth. In these difficult times, it is important that we all speak from the facts and work together to do a better job of explaining the importance of the nation's investment in biomedical research.

We, the directors of the 27 NIH institutes and centers, wanted to respond to the points made by Andrew Marks in his recent editorial. While we appreciate that the scientific community has concerns, the current initiatives and directions of the NIH have been developed through planning processes that reflect openness and continued constituency input, all aimed at assessing scientific opportunities and addressing public health needs.