Assembly of higher-order SMN oligomers is essential for metazoan viability and requires an exposed structural motif present in the YG zipper dimer
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99 GENERAL AND MISCELLANEOUS↗
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A small molecule antagonist of SMN disrupts the interaction between SMN and RNAP II
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Liu, Yanli↗
Tuning of electronic properties in highly lattice-mismatched epitaxial SmN
Here, we establish the relationship between native N vacancies, introduced through varying growth parameters, and the structural and transport properties in SmN thin films grown via molecular beam epitaxy grown on MgO(001). The varying levels of N vacancies introduced by varying the ratio of Sm to N atoms during deposition creates excess carriers that radically transform the electrical behavior of the film, over a range of five orders of magnitude, from highly resistive to highly conductive, and unlocking a phase transition evidenced by the presence of a ferromagnetic feature in resistivity. X-ray photoelectron spectroscopy results show that this effect is much less pronounced when varying the available nitrogen species. These samples retain a highly crystal quality despite being grown on a substrate with a lattice mismatch of 20%, alleviating the strain by forming a highly strained SmO oxide layer. The integration between SmN and several transition metal nitride compounds has the potential to unlock new architectures for Josephson junction devices.
36 - MATERIALS SCIENCE↗
Materials Data on SmN by Materials Project
SmN is Halite, Rock Salt structured and crystallizes in the cubic Fm-3m space group. The structure is three-dimensional. Sm3+ is bonded to six equivalent N3- atoms to form a mixture of edge and corner-sharing SmN6 octahedra. The corner-sharing octahedral tilt angles are 0°. All Sm–N bond lengths are 2.54 Å. N3- is bonded to six equivalent Sm3+ atoms to form a mixture of edge and corner-sharing NSm6 octahedra. The corner-sharing octahedral tilt angles are 0°.
36 MATERIALS SCIENCE↗
Experimental equation of state of 11 lanthanide nitrides (NdN to LuN) and pressure induced phase transitions in NdN, SmN, EuN, and GdN
Through an extensive data analysis of powder X-ray diffraction data obtained at pressures up to at least 78 GPa, we report the experimental equations of state for all lanthanide nitrides between NdN and LuN, excluding the radioactive Pm. By fitting the obtained unit cell volumes as a function of pressure with the third order Birch–Murnaghan equation of state, we find that the bulk modulus increases with an increasing lanthanide number from K 0 = 146(12) GPa for NdN to 182(7) GPa in EuN. Hereafter, the bulk modulus reaches a plateau for the rest of the series except for TmN which has a lower bulk modulus. We find that the first derivative of the bulk modulus is around 4 for all compounds except TbN, which displays a significantly different compression behavior. In addition, we find a B1 to B10 pressure-induced phase transition in NdN, SmN, EuN, and GdN at increasingly higher pressures. In fact, we observe that the onset pressure of the phase transition increases linearly with Ln atomic number. From TbN and onwards, we do not observe any sign of a B1 to B10 transition indicating that the transition pressure exceeds the studied pressure range. Therefore, we believe that, for the heavier lanthanides, the linear relationship between the onset pressure and the lanthanide number does not hold and even higher pressures are needed to observe the transition. This coherent study of the series of lanthanide nitrides offers a unique opportunity for benchmark studies of computational methods applied to compounds with 4f electrons.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Risk Factors and Trends for HPV-Associated Subsequent Malignant Neoplasms among Adolescent and Young Adult Cancer Survivors
Subsequent malignant neoplasms (SMN; new cancers that arise after an original diagnosis) contribute to premature mortality among adolescent and young adult (AYA) cancer survivors. Because of the high population prevalence of human papillomavirus (HPV) infection, we identify demographic and clinical risk factors for HPV-associated SMNs (HPV-SMN) among AYA cancer survivors in the SEER-9 registries diagnosed from 1976 to 2015. Outcomes included any HPV-SMN, oropharyngeal-SMN, and cervical-SMN. Follow-up started 2 months after their original diagnosis. Standardized incidence ratios (SIR) compared risk between AYA survivors and general population. Age-period-cohort (APC) models examined trends over time. Fine and Gray's models identified therapy effects controlling for cancer and demographic confounders. Of 374,408 survivors, 1,369 had an HPV-SMN, occurring on average 5 years after first cancer. Compared with the general population, AYA survivors had 70% increased risk for any HPV-SMN [95% confidence interval (CI), 1.61–1.79] and 117% for oropharyngeal-SMN (95% CI, 2.00–2.35); cervical-SMN risk was generally lower in survivors (SIR, 0.85; 95% CI, 0.76–0.95), but Hispanic AYA survivors had a 8.4 significant increase in cervical-SMN (SIR, 1.46; 95% CI, 1.01–2.06). AYAs first diagnosed with Kaposi sarcoma, leukemia, Hodgkin, and non-Hodgkin lymphoma had increased HPV-SMN risks compared with the general population. Oropharyngeal-SMN incidence declined over time in APC models. Chemotherapy and radiation were associated with any HPV-SMN among survivors with first HPV-related cancers, but not associated among survivors whose first cancers were not HPV-related. HPV-SMN in AYA survivors are driven by oropharyngeal cancers despite temporal declines in oropharyngeal-SMN. Hispanic survivors are at risk for cervical-SMN relative to the general population. Encouraging HPV vaccination and cervical and oral cancer screenings may reduce HPV-SMN burden among AYA survivors.
60 APPLIED LIFE SCIENCES↗
Structure–activity relationship-based chemical classification of highly imbalanced Tox21 datasets
Abstract The specificity of toxicant-target biomolecule interactions lends to the very imbalanced nature of many toxicity datasets, causing poor performance in Structure–Activity Relationship (SAR)-based chemical classification. Undersampling and oversampling are representative techniques for handling such an imbalance challenge. However, removing inactive chemical compound instances from the majority class using an undersampling technique can result in information loss, whereas increasing active toxicant instances in the minority class by interpolation tends to introduce artificial minority instances that often cross into the majority class space, giving rise to class overlapping and a higher false prediction rate. In this study, in order to improve the prediction accuracy of imbalanced learning, we employed SMOTEENN, a combination of Synthetic Minority Over-sampling Technique (SMOTE) and Edited Nearest Neighbor (ENN) algorithms, to oversample the minority class by creating synthetic samples, followed by cleaning the mislabeled instances. We chose the highly imbalanced Tox21 dataset, which consisted of 12 in vitro bioassays for > 10,000 chemicals that were distributed unevenly between binary classes. With Random Forest (RF) as the base classifier and bagging as the ensemble strategy, we applied four hybrid learning methods, i.e., RF without imbalance handling (RF), RF with Random Undersampling (RUS), RF with SMOTE (SMO), and RF with SMOTEENN (SMN). The performance of the four learning methods was compared using nine evaluation metrics, among which F 1 score, Matthews correlation coefficient and Brier score provided a more consistent assessment of the overall performance across the 12 datasets. The Friedman’s aligned ranks test and the subsequent Bergmann-Hommel post hoc test showed that SMN significantly outperformed the other three methods. We also found that a strong negative correlation existed between the prediction accuracy and the imbalance ratio (IR), which is defined as the number of inactive compounds divided by the number of active compounds. SMN became less effective when IR exceeded a certain threshold (e.g., > 28). The ability to separate the few active compounds from the vast amounts of inactive ones is of great importance in computational toxicology. This work demonstrates that the performance of SAR-based, imbalanced chemical toxicity classification can be significantly improved through the use of data rebalancing.
Idakwo, Gabriel↗
Design and Characterization of ALD-Based Overcoats for Supported Metal Nanoparticle Catalysts
Supported metal nanoparticle (SMN) catalysts are enormously crucial for many catalytic applications. However, catalyst deactivation, caused by sintering and coke formation, is a ubiquitous problem that significantly undermines catalytic processing economics. The application of material overcoating onto supported metal nanoparticles by atomic layer deposition (ALD) offers the solution to inhibit catalyst deactivation. Herein, we discuss examples in which ALD has been used to stabilize SMN catalysts in gaseous and aqueous-phase reactions. We highlight the ALD tools and methods in coating high-surface-area catalysts. Besides, various techniques are introduced to understand better how to characterize the overcoating layer and the catalyst itself. Finally, the outlook for new techniques to characterize the ALD overcoated catalytic materials is discussed.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
GdN/SmN superlattices; influence of a Zeeman/exchange conflict
GdN and SmN are two of the lanthanide nitrides, most of which are intrinsic ferromagnetic semiconductors. Superlattices comprising the pair offer a unique opportunity to investigate heterojunctions that feature simultaneous conductivity and magnetic interface influences. In this work, we report an investigation of these influences, using magnetisation and X-ray magnetic circular dichroism for magnetic effects, and magnetoresistance and Hall effect studies of their electrical conductance. Magnetic data show clear signatures of a conflicting Zeeman vs. interfacial exchange and the magnetic disruption that results, while resistivity and Hall measurements show conduction in both GdN and SmN.
36 MATERIALS SCIENCE↗
Synthesis of Samarium Nitride Thin Films on Magnesium Oxide (001) Substrates Using Molecular Beam Epitaxy
Rare-earth nitrides are an exciting family of materials with a wide variety of properties desirable for new physics and applications in spintronics and superconducting devices. Among them, samarium nitride is an interesting compound reported to have ferromagnetic behavior coupled with the potential existence of p-wave superconductivity. Synthesis of high-quality thin films is essential in order to manifest these behaviors and understand the impact that vacancies, structural distortions, and doping can have on these properties. In this study, we report the synthesis of samarium nitride monocrystalline thin films on magnesium oxide (001) substrates with a chromium nitride capping layer using molecular beam epitaxy (MBE). We observed a high-quality monocrystalline SmN film with matching orientation to the substrate, then optimized the growth temperature. Despite the initial 2 nm of growth showing formation of a potential samarium oxide layer, the subsequent layers showed high-quality SmN, with semiconducting behavior revealed by an increase in resistivity with decreasing temperature. These promising results highlight the importance of studying diverse heteroepitaxial schemes and open the door for integration of rare-earth nitrides and transition metal nitrides for future spintronic devices.
36 MATERIALS SCIENCE↗
Sensory stimulation for upper limb amputations modulates adaptability of cortical large-scale systems and combination of somatosensory and visual inputs
Abstract Touch-like phantom limb sensations can be elicited through targeted transcutaneous electrical nerve stimulation (tTENS) in individuals with upper limb amputation. The corresponding impact of sensory stimulation on cortical activity remains an open question. Brain network research shows that sensorimotor cortical activity is supported by dynamic changes in functional connections between relevant brain regions. These groups of interconnected regions are functional modules whose architecture enables specialized function and related neural processing supporting individual task needs. Using electroencephalographic (EEG) signals to analyze modular functional connectivity, we investigated changes in the modular architecture of cortical large-scale systems when participants with upper limb amputations performed phantom hand movements before, during, and after they received tTENS. We discovered that tTENS substantially decreased the flexibility of the default mode network (DMN). Furthermore, we found increased interconnectivity (measured by a graph theoretic integration metric) between the DMN, the somatomotor network (SMN) and the visual network (VN) in the individual with extensive tTENS experience. While for individuals with less tTENS experience, we found increased integration between DMN and the attention network. Our results provide insights into how sensory stimulation promotes cortical processing of combined somatosensory and visual inputs and help develop future tools to evaluate sensory combination for individuals with amputations.
60 APPLIED LIFE SCIENCES↗
Soil Water Retention and Hydraulic Conductivity Data and Model at Pump House in East River Watershed, Colorado 2019-2024
This data package includes soil water retention and hydraulic conductivity data and model fitting results from measurements of ex-situ soil samples and in-situ soil sensors near Pump House at Mount Crested Butte in the East River Watershed. Soil water retention curves (SWRC) characterize soil water content as a function of soil water potential. SWRC depends on soil texture and pore structure and can be used to describe the constraints on biogeochemical processes in terms of soil water availability. In this data package, the sample identification follows the format ER-X-Y, where ER refers to East River, X is the location identifier, and Y is the depth identifier at the same X (shallow Y=1). Specifically, ER-PHS, ER-LMC, ER-LMF, and ER-SMN are associated with ecohydrology sites under the East-Taylor Watershed Community Observatory Sites directory, and ER-RBTn (upslope n=1) are sampling transects during the 2019 Rootball Campaign. The sample and location information can be found in metadata.csv. Sampling and Measurements Each sample falls into one of the three sampling methods – (1) intact cores, (2) repacked samples, or (3) soil sensors – and one of the two measurement methods – (a) laboratory or (b) in-situ. Both intact cores and repacked samples were measured using the laboratory methods, which include measurements of soil water potential (HYPROP & WP4C, METER), saturated (KSAT, METER) and unsaturated hydraulic conductivity (HYPROP). The in-situ method uses a pair of co-located soil sensors to measure volumetric water content (TEROS12, METER) and soil water potential (TEROS21, METER), and the hydraulic conductivity was not measured. In comparison, the laboratory methods progress from full saturation to dry conditions, and the in-situ method includes both dry-to-wet and wet-to-dry cycles. The sampling and measurement methods for each sample can be found in metadata.csv, and more information about the measurements is detailed in the Methods section below. Models Retention and hydraulic conductivity data were fitted with four van-Genuchten-type models (specified by “model_name” column in the files): (1) traditional constrained van Genuchten model (“vG_constrained”), (2) traditional unconstrained van Genuchten model (“vG_unconstrained”), (3) PDI-variant of the constrained van Genuchten model (“vG_constrained_PDI”), and (4) PDI-variant of the unconstrained van Genuchten model (“vG_unconstrained_PDI”). The difference between the constrained (1: n) and the unconstrained (2: n, m) van Genuchten models is the number of pore-size distribution parameters in the model equations, giving the unconstrained model more degrees of freedom when fitting the data. Between the traditional and the PDI-variant models, model fitting differs the most at the dry end of the measurements. The traditional models allow infinite suction at the residual water content (water content does not drop below residual water content), and the PDI-variant models enforce a soil water potential value of pF=6.8 (~ -630 MPa) at oven-dryness (water content reaches 0). The inclusion of the van-Genuchten-type models is due to their common application. If other retention models are required, users can access the data in data.csv for further data fitting. More information about the models can be found in the Methods section below. Fitting Tasks The model fitting can be categorized into three levels of tasks (specified by “fitting_task” column in the files). Level 1 (“fit_retention”) only includes retention data fitting (the only level available for the in-situ method). Level 2 (“fit_retention_conductivity”) includes both retention and hydraulic conductivity data fitting, and the saturated hydraulic conductivity (Ks, a parameter of the hydraulic conductivity functions) is fixed by the measurements from KSAT. Level 3 (“fit_retention_conductivity_Ks”) also includes both retention and hydraulic conductivity data fitting, but Ks is a fitted parameter without the constraints from KSAT measurements. Among the same retention models (e.g. vG_constrained models of the same sample), level 1 should produce the best retention data fitting. Level 2 should have the highest misfit of the retention and hydraulic conductivity data, because the retention and hydraulic conductivity functions share common model parameters, and the unsaturated hydraulic conductivity (HYPROP) data fitting is subject to Ks measured independently by KSAT. Level 3 should have mid-level misfits of the retention and hydraulic conductivity data. While level 3 fits the hydraulic conductivity data better than level 2, the fitted Ks value might be unreasonable due to the lack of constraints at the wet end of the measurements. General recommendation when using this data package: (1) Choice of sampling methods: Intact cores and in-situ soil sensors could be prioritized because these sampling methods are less destructive. While the repacked samples were packed to the target bulk density (estimated post-sampling, when sample volume was known), these samples had altered pore structures. Nevertheless, intact cores might suffer from sample gaps that would lead to overestimation of Ks (sample gaps can be inferred from the “soil_sample_volume” column in metadata.csv when the value is < 249). In-situ method also has higher uncertainty in characterizing the wet end of the SWRC because of sensor limitations and the difficulty in reaching full saturation under natural conditions. (2) Choice of fitting tasks: When only retention data is needed, level 1 (“fit_retention”) should be prioritized. When both retention and hydraulic conductivity data are needed, level 2 (“fit_retention_conductivity”) could be prioritized. (3) Choice of models: This could depend on what the downstream models call for. If no specific model is required, model misfit could be used as a ranking criterion. Model misfit values in terms of RMSE can be found in model_parameters.csv. The following files are included in this data package: (1) metadata.csv – This file includes the general information of each sample, including location (description, geocoordinates, elevation), sampling and measurements details (method, depth, time or period, volume, instruments), and soil physical properties (bulk density, saturated hydraulic conductivity, only applicable to physical soil samples). (2) data.csv – This file includes soil water potential, volumetric water content, and unsaturated hydraulic conductivity data of each sample. Column “instrument” specifies the instrument (HYPROP, WP4C, or TEROS) used to perform the measurements. (3) model_fit.csv – This file includes soil water potential, volumetric water content, and unsaturated hydraulic conductivity fitted from the four models and three fitting tasks. Column “model_name” specifies the retention model used, and “fitting_task” specifies the level of data fitting. Missing values indicate that the variable does not apply to that fitting task. (4) model_parameters.csv – This file includes the fitted model parameters, model misfits, and conventional water content thresholds (field capacity and wilting point) from the four models and three fitting tasks. Column “model_name” specifies the retention model used, and “fitting_task” specifies the level of data fitting. Missing values indicate that the parameter does not apply to that model and/or that fitting task. (5) data_Ks.csv – This file includes the saturated hydraulic conductivity measurements from KSAT. (6) /figure/*.png – This folder includes three quick visualizations of the data, retention model fitting results and misfits, and hydraulic conductivity model fitting results, misfits, and parameters. The model fitting results are separated by samples and fitting tasks and colored by models. Zoom-in required. (7) /hyprop/*.bdhx – This folder includes proprietary hyprop files that require the free Labros SoilView-Analysis (METER) to open. Users can explore data fitting using other retention models (i.e. Brooks-Corey, Fredlund-Xing, Kosugi, bimodal models). Be aware that Ks value is pre-entered under “Fitting tab, Conductivity functions parameters” for level 2 fitting. If the value is lost, please refer to metadata.csv under “Ks” column. (8) Six file-level metadata that summarize file, header, column, and variable information of all files. This work was supported by the Watershed Function Science Focus Area at Lawrence Berkeley National Laboratory funded by the US Department of Energy, Office of Science, Biological and Environmental Research under Contract No. DE-AC02-05CH11231.
EARTH SCIENCE > LAND SURFACE > SOILS↗