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The acquisition of seismic exploration data in remote locations presents several logistical and economic criticalities. The irregular distribution of sources and/or receivers facilitates seismic acquisition operations in these areas. A convenient approach is to deploy nodal receivers on a regular grid and to use sources only in accessible locations, creating an irregular source–receiver layout. It is essential to evaluate, adapt, and verify processing workflows, specifically for near-surface velocity model estimation using surface-wave analysis, when working with these types of datasets. In this study, we applied three surface-wave techniques (i.e., wavelength–depth (W/D) method, laterally constrained inversion (LCI), and surface-wave tomography (SWT)) to a large-scale 3D dataset obtained from a hard-rock site using the irregular source–receiver acquisition method. The methods were fine-tuned for the data obtained from hard-rock sites, which typically exhibit a low signal-to-noise ratio. The wavelength–depth method is a data transformation method that is based on a relationship between skin depth and surface-wave wavelength and provides both S- and P-wave velocity (Vs and Vp) models. We used Poisson's ratios estimated through the wavelength–depth method to constrain the laterally constrained inversion and surface-wave tomography and to retrieve both Vs and Vp also from these methods. The pseudo-3D Vs and Vp models were obtained down to 140 m depth over an area of approximately 900 × 1500 m2. The estimated models from the methods matched the geological information available for the site. A difference of less than 6 % was observed between the estimated Vs models from the three methods, whereas this value was 7.1 % for the retrieved Vp models. The methods were critically compared in terms of resolution and efficiency, which provides valuable insights into the potential of surface-wave analysis for estimating near-surface models at hard-rock sites.

While more universities are including IT accessibility in their computer science programs for undergraduate and graduate students, there is little accessibility training available for K-12 teachers. We created an intervention through which postsecondary students had opportunities to experience five computer games with a simulated impairment (color-blindness, auditory impairments, physical disabilities, blindness, or low-vision); first they played the game that was inaccessibly designed and then they played a version that was accessibly designed. The activity ended with a discussion of accessible design techniques. We tested the intervention with 18 teachers who were students in a university web development course that was part of their computer science training. Results show that teachers were very receptive to including accessibility topics in their future classrooms and thought the intervention was an effective method for teaching high school, middle school, and elementary school students about accessible design.

Stem cell based replacement therapies could provide a treatment for Parkinson’s disease. Here the authors outline a retinoic acid-based approach for robust derivation of dopamine neurons from stem cells that restore motor deficits in parkinsonian rats.

BackgroundThe transition from active treatment to long-term cancer survivorship leaves the needs of many cancer survivors unaddressed as they struggle with physical, cognitive, psychological, and social consequences of cancer and its treatment. The lack of guidance after treatment has forced cancer survivors to manage long-term effects on their own, which has an impact on their overall health, quality of life, and social participation. Mobile health (mHealth) interventions can be used to promote self-management and evidence-informed education. ObjectiveThis study aims to design an mHealth app for cancer survivors with disabilities that will offer interventions to improve their quality of life and increase their self-efficacy to manage cancer as a chronic condition. MethodsWe organized 3 co-design workshops with cancer survivors (n=5). These workshops included persona development based on data from 25 interviews with cancer survivors with disabilities; prototype ideation, where we sketched ideas for the prototype; and prototype development, where participants critiqued, and suggested improvements for, the wireframes. ResultsThese workshops helped us to define the challenges that cancer survivors with disabilities face as well as important considerations when designing an mHealth app for cancer survivors with disabilities, such as the need for including flexibility, engagement, socialization, and a minimalistic design. We also outline guidelines for other researchers to follow when planning their own co-design workshops, which include allowing more time for discussion among participants, having small participant groups, keeping workshops engaging and inclusive, and letting participants dream big. ConclusionsUsing a co-design process aided us in developing a prototype of an mHealth app for cancer survivors with disabilities as well as a list of guidelines that other researchers can use to develop their own co-design workshops and design their app. Furthermore, working together with cancer survivors ensured that the design team had a deeper sense of empathy toward the target users and kept the focus on our ultimate goal: creating something that cancer survivors would want to use and benefit from. Future work will include usability testing of a high-fidelity prototype based on the results of these workshops.

What happens to cells on engrafting into the brain in animal models to treat Parkinson’s disease is unclear. Here, the authors use scRNA-seq to examine ventral midbrain (VM)-patterned human embryonic stem cells after functional maturation in a pre-clinical rat model for Parkinson’s disease and identify perivascular-like cells.

New precipitation (P) datasets are released regularly, following innovations in weather forecasting models, satellite retrieval methods, and multi-source merging techniques. Using the conterminous US as a case study, we evaluated the performance of 26 gridded (sub-)daily P datasets to obtain insight into the merit of these innovations. The evaluation was performed at a daily timescale for the period 2008–2017 using the Kling–Gupta efficiency (KGE), a performance metric combining correlation, bias, and variability. As a reference, we used the high-resolution (4 km) Stage-IV gauge-radar P dataset. Among the three KGE components, the P datasets performed worst overall in terms of correlation (related to event identification). In terms of improving KGE scores for these datasets, improved P totals (affecting the bias score) and improved distribution of P intensity (affecting the variability score) are of secondary importance. Among the 11 gauge-corrected P datasets, the best overall performance was obtained by MSWEP V2.2, underscoring the importance of applying daily gauge corrections and accounting for gauge reporting times. Several uncorrected P datasets outperformed gauge-corrected ones. Among the 15 uncorrected P datasets, the best performance was obtained by the ERA5-HRES fourth-generation reanalysis, reflecting the significant advances in earth system modeling during the last decade. The (re)analyses generally performed better in winter than in summer, while the opposite was the case for the satellite-based datasets. IMERGHH V05 performed substantially better than TMPA-3B42RT V7, attributable to the many improvements implemented in the IMERG satellite P retrieval algorithm. IMERGHH V05 outperformed ERA5-HRES in regions dominated by convective storms, while the opposite was observed in regions of complex terrain. The ERA5-EDA ensemble average exhibited higher correlations than the ERA5-HRES deterministic run, highlighting the value of ensemble modeling. The WRF regional convection-permitting climate model showed considerably more accurate P totals over the mountainous west and performed best among the uncorrected datasets in terms of variability, suggesting there is merit in using high-resolution models to obtain climatological P statistics. Our findings provide some guidance to choose the most suitable P dataset for a particular application.

Understanding how transplanted cells interact with the host nervous system will be important for cell based neural regeneration approaches. Here, the authors study the sensory fate of neural progenitor cell grafts transplanted to the injured spinal cord, and show that host axons retain the ability to distinguish appropriate and inappropriate graft targets.

Neural progenitor cells grafted to sites of spinal cord injury have supported electrophysiological and functional recovery in several studies. Mechanisms associated with graft-related improvements in outcome appear dependent on functional synaptic integration of graft and host systems, although the extent and diversity of synaptic integration of grafts with hosts are unknown. Using transgenic mouse spinal neural progenitor cell grafts expressing the TVA and G-protein components of the modified rabies virus system, we initiated monosynaptic tracing strictly from graft neurons placed in sites of cervical spinal cord injury. We find that graft neurons receive synaptic inputs from virtually every known host system that normally innervates the spinal cord, including numerous cortical, brainstem, spinal cord, and dorsal root ganglia inputs. Thus, implanted neural progenitor cells receive an extensive range of host neural inputs to the injury site, potentially enabling functional restoration across multiple systems.

Summary: Cell replacement is currently being explored as a therapeutic approach for neurodegenerative disease. Using stem cells as a source, transplantable progenitors can now be generated under conditions compliant with clinical application in patients. In this study, we elucidate factors controlling target-appropriate innervation and circuitry integration of human embryonic stem cell (hESC)-derived grafts after transplantation to the adult brain. We show that cell-intrinsic factors determine graft-derived axonal innervation, whereas synaptic inputs from host neurons primarily reflect the graft location. Furthermore, we provide evidence that hESC-derived dopaminergic grafts transplanted in a long-term preclinical rat model of Parkinson’s disease (PD) receive synaptic input from subtypes of host cortical, striatal, and pallidal neurons that are known to regulate the function of endogenous nigral dopamine neurons. This refined understanding of how graft neurons integrate with host circuitry will be important for the design of clinical stem-cell-based replacement therapies for PD, as well as for other neurodegenerative diseases. : Adler et al. graft hESC-derived dopaminergic progenitors into a rat model of Parkinson’s disease. They find grafts correctly innervate host targets and receive appropriate synaptic input after intranigral and intrastriatal placement. Furthermore, the same host neurons projecting toward endogenous dopamine neurons are found to also connect to the grafts. Keywords: Parkinson’s disease, cell transplantation, stem cells, grafting, brain repair, monosynaptic tracing, cell replacement therapy

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

In the title compound, C20H16Cl2N2O2, the indazole ring system is approximately planar [maximum deviation = 0.033 (1) Å], its mean plane is oriented at dihedral angles of 25.04 (4) and 5.10 (4)° to the furan and benzene rings, respectively. In the crystal, pairs of C—Hind...Obo (ind = indazole and bo = benzyloxy) hydrogen bonds link the molecules into centrosymmetric dimers with graph-set motif R22(12). Weak C—H...π interactions is also observed. Aromatic π–π stacking between the benzene and the pyrazole rings from neighbouring molecules [centroid–centroid distance = 3.8894 (7) Å] further consolidates the crystal packing.

Study of hydro-climatology at a range of temporal scales is important in understanding and ultimately mitigating the potential severe impacts of hydrological extreme events such as floods and droughts. Using daily in-situ data over the last two decades combined with the recently available multiple-years satellite remote sensing data, we analyzed and simulated, with a distributed hydrologic model, the hydro-climatology in Nzoia, one of the major contributing sub-basins of Lake Victoria in the East African highlands. The basin, with a semi arid climate, has no sustained base flow contribution to Lake Victoria. The short spell of high discharge showed that rain is the prime cause of floods in the basin. There is only a marginal increase in annual mean discharge over the last 21 years. The 2-, 5- and 10- year peak discharges, for the entire study period showed that more years since the mid 1990's have had high peak discharges despite having relatively less annual rain. The study also presents the hydrologic model calibration and validation results over the Nzoia basin. The spatiotemporal variability of the water cycle components were quantified using a hydrologic model, with in-situ and multi-satellite remote sensing datasets. The model is calibrated using daily observed discharge data for the period between 1985 and 1999, for which model performance is estimated with a Nash Sutcliffe Efficiency (NSCE) of 0.87 and 0.23% bias. The model validation showed an error metrics with NSCE of 0.65 and 1.04% bias. Moreover, the hydrologic capability of satellite precipitation (TRMM-3B42 V6) is evaluated. In terms of reconstruction of the water cycle components the spatial distribution and time series of modeling results for precipitation and runoff showed considerable agreement with the monthly model runoff estimates and gauge observations. Runoff values responded to precipitation events that occurred across the catchment during the wet season from March to early June. The spatially distributed model inputs, states, and outputs, were found to be useful for understanding the hydrologic behavior at the catchment scale. The monthly peak runoff is observed in the months of April, May and November. The analysis revealed a linear relationship between rainfall and runoff for both wet and dry seasons. Satellite precipitation forcing data showed the potential to be used not only for the investigation of water balance but also for addressing issues pertaining to sustainability of the resources at the catchment scale.