Data (assemblies and outputs) accompanying the submission titled "Herbaria provide a valuable resource for obtaining informative mRNA".

Data (assemblies and outputs) accompanying the submission titled "Herbaria provide a valuable resource for obtaining informative mRNA".

The response of pixelated silicon sensors to incident charged particles (pions) was simulated using kinematic properties derived from fitted CMS Run‑2 tracks. Silvaco/Synopsis TCAD and a time-sliced version of PixelAV were used to simulate the initial electron-hole pairs produced in the active material of the pixel detector and subsequently calculate the induced signals. The pixel detector is taken to be a 21x13 array of pixels situated along a barrel layer (radius=30 mm) that is parallel to the beamline and centered around the interaction point. The detector is immersed in a 3.8T magnetic field parallel to the sensor X coordinate. Multiple sensor geometries, with varying pixel X-pitch, Y-pitch, and thickness (along Z), were studied in the smartpixels co-design program. Endcap sensor modules of the CMS detector that are perpendicular to the beamline and at around 250 mm from the nominal interaction point were also simulated for these geometries. The schematic diagrams (SP_coordinates.pdf and y-local_coordinate.pdf) illustrate some relevant geometric parameters of the simulation framework.In addition, four further datasets were produced: one for the baseline sensor (50x12.5x100 um^3) and the CMS Phase 2 sensor design (100x25x150 um^3), each simulated at two radiation levels in the barrel region, corresponding to integrated luminosities of 370 and 1100 1/fb. These irradiation levels map to fluences of 3.3 x 10^15 and 1.0 x 10^16 neq/cm^2 (1 MeV neutron equivalent), respectively. All datasets mentioned above consist of a flat pT dataset and a physical pT dataset variant. The flat pT corresponds to tracks that have a uniform distribution in transverse momentum in the 0 to 5 GeV range. The physical pT corresponds to tracks with a pT distribution similar to what is observed in CMS minimum-bias data.The following tables summarize the datasets produced in this study, along with the folder names to locate the associated data:Sl. No.Sensor namePixel length [um]Pixel width [um]Pixel thickness [um]Bias voltage [V]1S150101001002S25012.51001003S350151001004S450201001005S550251001006S6100251001007S710025150175 Sl. No.Detector regionSimulated sensorsAngle between E and B (deg)Irradiation level [1/fb]Dataset name1BarrelS1-S79002s_LxWxT (physical pT)2BarrelS1-S79003s_LxWxT (flat pT)3EndcapS1-S718006s_LxWxT (physical pT)4EndcapS1-S718007s_LxWxT (flat pT)5BarrelS1, S790370, 11008s_LxWxT_Xfb (physical pT)6BarrelS1, S790370, 11009s_LxWxT_Xfb (flat pT) Under the dataset name, L, W, and T refer to the length, width, and thickness of the sensor in consideration. If the thickness value is not present in the name, please consider a default 100 microns thickness. For datasets 8s and 9s, the suffix 'Xfb' corresponds to X level of irradiation in units of 1/fb.Truth properties of each particle are saved in the labels files, which include columns x-entry, y-entry, z-entry, n_x, n_y, n_z, number_eh_pairs, y-local, pt, cotAlpha, cotBeta, y-midplane, x-midplane. The particle impact point on the sensor surface is described by (x-entry, y-entry), and the impact point at the sensor mid-plane (i.e., at Z=sensor thickness/2) of the sensor is (x-midplane, y-midplane). These impact points are in units of microns. The track direction is a unit vector given by (n_x, n_y, n_z). The number of electron-hole pairs created in the active silicon volume is number_eh_pairs. The y distance in mm between the center of the 21x13 pixel array and the center of a flat module is denoted by y-local (see included diagram). As illustrated in SP_coordinates.pdf, please note the differences between the barrel and endcap module coordinate systems. The particle transverse momentum in GeV is stored in the pt variable, with the sign indicating the sign of the particle charge. The angle of incidence in the x-z plane is described by cotAlpha, and the angle of incidence in the y-z plane (the bending plane of the magnetic field) is described by cotBeta.The deposited charge per pixel per time slice is saved in the recon3D files. Reshaping each line as (20,13,21) gives the three-dimensional cluster in (time, y, x). Each slice in time corresponds to a window of 200 ps.

The response of pixelated silicon sensors to incident charged particles (pions) was simulated using kinematic properties derived from fitted CMS Run‑2 tracks. Silvaco/Synopsis TCAD and a time-sliced version of PixelAV were used to simulate the initial electron-hole pairs produced in the active material of the pixel detector and subsequently calculate the induced signals. The pixel detector is taken to be a 21x13 array of pixels situated along a barrel layer (radius=30 mm) that is parallel to the beamline and centered around the interaction point. The detector is immersed in a 3.8T magnetic field parallel to the sensor X coordinate. Multiple sensor geometries, with varying pixel X-pitch, Y-pitch, and thickness (along Z), were studied in the smartpixels co-design program. Endcap sensor modules of the CMS detector that are perpendicular to the beamline and at around 250 mm from the nominal interaction point were also simulated for these geometries. The schematic diagrams (SP_coordinates.pdf and y-local_coordinate.pdf) illustrate some relevant geometric parameters of the simulation framework.In addition, four further datasets were produced: one for the baseline sensor (50x12.5x100 um^3) and the CMS Phase 2 sensor design (100x25x150 um^3), each simulated at two radiation levels in the barrel region, corresponding to integrated luminosities of 370 and 1100 1/fb. These irradiation levels map to fluences of 3.3 x 10^15 and 1.0 x 10^16 neq/cm^2 (1 MeV neutron equivalent), respectively. All datasets mentioned above consist of a flat pT dataset and a physical pT dataset variant. The flat pT corresponds to tracks that have a uniform distribution in transverse momentum in the 0 to 5 GeV range. The physical pT corresponds to tracks with a pT distribution similar to what is observed in CMS minimum-bias data.The following tables summarize the datasets produced in this study, along with the folder names to locate the associated data:Sl. No.Sensor namePixel length [um]Pixel width [um]Pixel thickness [um]Bias voltage [V]1S150101001002S25012.51001003S350151001004S450201001005S550251001006S6100251001007S710025150175 Sl. No.Detector regionSimulated sensorsAngle between E and B (deg)Irradiation level [1/fb]Dataset name1BarrelS1-S79002s_LxWxT (physical pT)2BarrelS1-S79003s_LxWxT (flat pT)3EndcapS1-S718006s_LxWxT (physical pT)4EndcapS1-S718007s_LxWxT (flat pT)5BarrelS1, S790370, 11008s_LxWxT_Xfb (physical pT)6BarrelS1, S790370, 11009s_LxWxT_Xfb (flat pT) Under the dataset name, L, W, and T refer to the length, width, and thickness of the sensor in consideration. If the thickness value is not present in the name, please consider a default 100 microns thickness. For datasets 8s and 9s, the suffix 'Xfb' corresponds to X level of irradiation in units of 1/fb.Truth properties of each particle are saved in the labels files, which include columns x-entry, y-entry, z-entry, n_x, n_y, n_z, number_eh_pairs, y-local, pt, cotAlpha, cotBeta, y-midplane, x-midplane. The particle impact point on the sensor surface is described by (x-entry, y-entry), and the impact point at the sensor mid-plane (i.e., at Z=sensor thickness/2) of the sensor is (x-midplane, y-midplane). These impact points are in units of microns. The track direction is a unit vector given by (n_x, n_y, n_z). The number of electron-hole pairs created in the active silicon volume is number_eh_pairs. The y distance in mm between the center of the 21x13 pixel array and the center of a flat module is denoted by y-local (see included diagram). As illustrated in SP_coordinates.pdf, please note the differences between the barrel and endcap module coordinate systems. The particle transverse momentum in GeV is stored in the pt variable, with the sign indicating the sign of the particle charge. The angle of incidence in the x-z plane is described by cotAlpha, and the angle of incidence in the y-z plane (the bending plane of the magnetic field) is described by cotBeta.The deposited charge per pixel per time slice is saved in the recon3D files. Reshaping each line as (20,13,21) gives the three-dimensional cluster in (time, y, x). Each slice in time corresponds to a window of 200 ps.

WebViews are a prevalent method of embedding web-based content in Android apps. While they offer functionality similar to that of browsers and execute in an isolated context, apps can directly interfere with WebViews by dynamically injecting JavaScript code at runtime. While prior work has extensively analyzed apps' Java code, existing frameworks have limited visibility of the JavaScript code being executed inside WebViews. Consequently, there is limited understanding of the behaviors and characteristics of the scripts executed within WebViews, and whether privacy violations occur. To address this gap, we propose WebViewTracer, a framework designed to dynamically analyze the execution of JavaScript code within WebViews at runtime. Our system combines within-WebView JavaScript execution traces with Java method-call information to also capture the information exchange occurring between Java SDKs and web scripts. We leverage WebViewTracer to perform the first large-scale, dynamic analysis of privacy-violating behaviors inside WebViews, on a dataset of 10K Android apps. We detect almost 4,600 apps that load WebViews, and find that over 69% of them inject sensitive and tracking-related information that is typically inaccessible to JavaScript code into WebViews. This includes identifiers like the Advertising ID and Android build ID. Crucially, 90% of those apps use web-based APIs to exfiltrate this information to third-party servers. We also uncover concrete evidence of common web fingerprinting techniques being used by JavaScript code inside of WebViews, which can supplement their tracking information. We observe that the dynamic properties of WebViews are being actively leveraged for sensitive information diffusion across multiple actors in the mobile tracking ecosystem, demonstrating the privacy risks posed by Android WebViews. By shedding light on these ongoing privacy violations, our study seeks to prompt additional scrutiny from platform stakeholders on the use of embedded web technologies and highlights the need for additional safeguard. This dataset contains the execution traces of all apps successfully executed in our dataset.

The health risks of climate change need to be identified to inform the prioritization of adaptation efforts. This is particularly true within low- and middle-income countries (LMICs) with limited resources, heterogenous climates, and varying degrees of social vulnerability. In Kenya, diarrheal disease is one of the leading causes of death and identifying risk factors of diarrheal disease is critical. This research aims to characterize factors associated with a high risk of diarrheal disease in western Kenya by developing a risk index based on the Intergovernmental Panel on Climate Change (IPCC) risk framework.  We developed a conceptual model of risk factors based on prior research with risk factors grouped into the four components of the IPCC risk framework: hazard, exposure, and vulnerability (which is comprised of sensitivity and adaptive capacity). We obtained 30 data elements corresponding to the four components for 99 sub-counties in 14 western Kenya counties. We conducted principal component analysis (PCA) to develop a risk index for diarrheal disease. Our risk index aligns with epidemiological literature, including precipitation, temperature, water sanitation and hygiene (WASH), sensitive populations, education, poverty, and health facilities. Within counties, we found that the risk varied substantially, and a geographic cluster of high-risk sub-counties was identified. Our findings should be useful for policymakers and health officials in Kenya to prioritize efforts to prepare communities for health impacts of climate change. The process may be useful for standardizing approaches to assessing the risk of climate-sensitive health outcomes.

No description available

No description available

Premise: Evolutionary theory predicts polymorphism should be rare; however, variation in floral color is common, often attributed to drift, plasticity, or variable selection. Examining floral color polymorphism both within contact zones and across a species’ range can reveal mechanisms maintaining this variation. Here, we used a multistep approach to investigate spatially heterogeneous variation in floral bract color in Castilleja coccinea. Methods: We compared frequencies of color morphs, floral morphology, fitness, and genetic structure in regional populations and a common garden. Next, we examined habitat differences, including plant communities and edaphic factors, as potential drivers of variation. Lastly, we leveraged herbarium and iNaturalist occurrence data to investigate whether patterns were consistent at the landscape scale. Key results: Bract color in C. coccinea is genetically heritable, with yellow dominant over red, and is under selection. Populations are predominantly monomorphic, with color distance showing no correlation to genetic or geographic distance, despite significant genetic isolation-by-distance. Yellow morphs are associated with open wetlands, while red morphs occur at drier sites associated with nearby tree cover. Red morphs demonstrated lower fitness in a common garden, suggesting tradeoffs associated with pleiotropic effects of adaptation to drier soil conditions. Conclusions: Differences in floral color between morphs are consistent with diversification associated with a shift in ecological niche. We identified variation in edaphic and habitat conditions as probable drivers of divergence in floral color. Additionally, variation in other floral traits suggests a combined role of pollinators and habitat differences acting in concert to maintain distinct floral color morphs.

We tested whether the leg condition (missing legs or not) of an arachnid species (Prionostemma sp.2) affects roosting location within the group by experimentally inducing the formation of aggregations overnight. Autotomized individuals roosted more frequently in the aggregation center than intact individuals. Additionally, this pattern was observed only for aggregations of more than 13 individuals.