bluuebunny/crossref_metadata_2025_split huggingface dataset:
Characterization of 128 × 128 MM-PAD-2.1 ASIC: a fast framing hard x-ray detector with high dynamic range
Authors: Gadkari D. , Shanks K.S. , Hu H. , Philipp H.T. , Tate M.W. , Thom-Levy J. , Gruner S.M. 
Published: March 2022

Abstract
We characterize a new x-ray Mixed-Mode Pixel Array Detector
(MM-PAD-2.1) Application Specific Integrated Circuit (ASIC). Using
an integrating pixel front-end with dynamic charge removal
architecture, the MM-PAD-2.1 ASIC extends the maximum measurable
x-ray signal (in 20 keV photon units) to > 107
x-rays/pixel/frame while maintaining a low read noise across the
full dynamic range, all while imaging continuously at a frame rate
of up to 10 kHz. The in-pixel dynamic charge removal mechanism
prevents saturation of the input amplifier and proceeds in parallel
with signal integration to achieve deadtime-less measurements with
incident x-ray rates of > 1010 x-rays/pixel/s. The ASIC format
consists of 128 × 128 square pixels each 150 μm on a
side and is designed to be 3-side buttable so large arrays can be
effectively tiled. Here we use both laboratory x-ray sources and the
Cornell High Energy Synchrotron Source (CHESS) to characterize two
single ASIC prototype detectors for both low (single x-ray) and high
incident flux detection. In the first detector the ASIC was solder
bump-bonded to a 500 μm thick Si sensor for efficient
detection of x-rays below 20 keV, whereas the second detector used
a 750 μm thick CdTe sensor for x-rays above ∼ 20 keV.
Development of a CsI(Tl) scintillator based gamma probe for the identification of nuclear materials in unknown areas
Authors: Kim G. , Lee J. , Jeong S. , Kim M. 
Published: March 2022

Abstract
In case of nuclear accident or an act of terrorism, such as
planting a dirty bomb, the nuclear and radioactive materials could
be spread over a large area. The environmental radioactivity
information has to be obtained rather fast in order to secure the
safety of any activity in these potentially high-level radiation
contaminated areas. In addition, a lack of both social
infrastructure and environmental radiation information may be
expected in this area. Therefore, the sampling process must be
carried out in a timely and efficient manner. In this study, a probe
operated via a distribution method was developed to improve the
efficiency of the sampling process by identifying the location and
nuclides of leaked materials. For distribution type operation,
CsI(Tl) and SiPM were applied to the sensor part of the probe to
operate at low voltage operation and for durability. In addition,
the power consumption of the data acquisition module and the weight
of the probe were optimized for distribution through drones, battery
operation, and wireless communication. The energy resolution of this
probe was 10.84% at 662 keV and it showed adequate characteristics
in the spectrum of nuclear materials. In addition, the practical
operability of this probe was verified through determination of the
minimum detectable dose rate condition and the difference in the
measured values based on the landing orientation.
Global fossil carbon emissions rebound near pre-COVID-19 levels
Authors: Jackson R B , Friedlingstein P , Le Quéré C , Abernethy S , Andrew R M , Canadell J G , Ciais P , Davis S J , Deng Zhu , Liu Zhu , Korsbakken J I , Peters G P 
Published: March 2022

Abstract
Fossil CO2 emissions in 2021 grew an estimated 4.2% (3.5%–4.8%) to 36.2 billion metric tons compared with 2020, pushing global emissions back close to 2019 levels (36.7 Gt CO2).
Potential impacts of climate, land use and land cover changes on hydropower generation in West Africa: a review
Authors: Obahoundje Salomon , Diedhiou Arona 
Published: March 2022

AbstractThis study aims to review the existing literature on the past and future effects of climate, land use, and land cover changes on hydropower generation in West Africa (WA), based on listings in the Scopus and Google Scholar databases. This review shows that several African hydropower plants have experienced repeated power disruptions over the last three decades due to climate change and variability but it is less documented how increasing land use and land cover changes around the major dams have impacted the hydrological system and the hydropower generation. In the future, the risks of hydropower in WA may not be equally distributed within a country or region. Despite uncertainties in precipitation and on impacts on streamflow and water level in major basins, climate change is likely to reduce the available water over the range of 10%–20% (15%–40%) for the RCP4.5 (RCP8.5) scenario by 2050, which may considerably affect the water demand across all sectors, including hydropower. However, in the Kainji dam (Niger River basin), models project an increase in rainfall favorable to hydropower production for both RCP4.5 and RCP8.5. In contrast, within the Black Volta sub-basin, the intensification of land use is predicted to favor runoff and, consequently, an increase in the generation of Bui hydropower in the near future, even though models predict a rainfall decrease. This increase in land use for agriculture to feed a growing population has other adverse effects that need to be assessed, namely sedimentation and siltation, which are harmful to hydropower plants. Finally, the combined impact of climate and land use changes on the efficiency of hydroelectric infrastructure in WA is not well documented, while sustainable planning and investments in the hydropower sector require consideration of the nexus between climate, land use changes, and water.
Global markets and the commons: the role of imports in the US wild-caught shrimp market
Authors: Asche Frank , Oglend Atle , Smith Martin D 
Published: March 2022

Abstract
The commons literature focuses heavily on rules and the behavior of resource users but places less emphasis on the returns to individual effort. However, for most resource settings, market conditions and associated resource prices are key drivers of exploitation effort. In a globalized world, import competition can strongly influence the incentives for individual resource users, a topic largely unexplored in the commons literature. Import competition is especially salient for seafood, one of the most internationally traded food groups. We analyze the US shrimp market, which was once dominated by domestic catches but is now mostly supplied by imports. For domestic producers (users of the commons), lower revenues result, while US consumers eat more shrimp at lower prices. Globalization changed the sources of price risk and compensation that domestic producers face and altered incentives to exploit the commons. In a market dominated by domestic supply shocks, the price response to a shock moderates the effect on revenue and effort. In a market dominated by imports, domestic shocks are buffered by import adjustments, while price movements are determined by global shocks. Despite losses for the domestic fishery, globalization creates new incentives to coordinate effort and capture price premiums determined in the global market.
Mad/Utopian/Prehistoric: Black Powers of Speculation
Authors: Carrington André 
Published: March 2022

Abstract
Black life in the modern world makes incisive demands on the cultural politics of imaginative fiction as well as social movement rhetoric. Recent scholarship supplements the ongoing efflorescence of Afrofuturist art by reconstructing the history of speculative, utopian, and cautionary tales in Black writing. Isiah Lavender’s Afrofuturism Rising and Alex Zamalin’s Black Utopia join a growing body of intellectual history prepared for the task of historicizing fantastic Black aesthetics. Sami Schalk’s more contemporary intervention, Bodyminds Reimagined, demonstrates the integral value of Black women’s speculative fiction to rethinking the role of dis/ability—conspicuously including the radical difference signified by “madness”—in the panoply of power relations that stand to be transformed by Black radical thought in action.
deepNEC: a novel alignment-free tool for the identification and classification of nitrogen biochemical network-related enzymes using deep learning
Authors: Duhan Naveen , Norton Jeanette M , Kaundal Rakesh 
Published: March 2022

Abstract
Nitrogen is essential for life and its transformations are an important part of the global biogeochemical cycle. Being an essential nutrient, nitrogen exists in a range of oxidation states from +5 (nitrate) to −3 (ammonium and amino-nitrogen), and its oxidation and reduction reactions catalyzed by microbial enzymes determine its environmental fate. The functional annotation of the genes encoding the core nitrogen network enzymes has a broad range of applications in metagenomics, agriculture, wastewater treatment and industrial biotechnology. This study developed an alignment-free computational approach to determine the predicted nitrogen biochemical network-related enzymes from the sequence itself. We propose deepNEC, a novel end-to-end feature selection and classification model training approach for nitrogen biochemical network-related enzyme prediction. The algorithm was developed using Deep Learning, a class of machine learning algorithms that uses multiple layers to extract higher-level features from the raw input data. The derived protein sequence is used as an input, extracting sequential and convolutional features from raw encoded protein sequences based on classification rather than traditional alignment-based methods for enzyme prediction. Two large datasets of protein sequences, enzymes and non-enzymes were used to train the models with protein sequence features like amino acid composition, dipeptide composition (DPC), conformation transition and distribution, normalized Moreau–Broto (NMBroto), conjoint and quasi order, etc. The k-fold cross-validation and independent testing were performed to validate our model training. deepNEC uses a four-tier approach for prediction; in the first phase, it will predict a query sequence as enzyme or non-enzyme; in the second phase, it will further predict and classify enzymes into nitrogen biochemical network-related enzymes or non-nitrogen metabolism enzymes; in the third phase, it classifies predicted enzymes into nine nitrogen metabolism classes; and in the fourth phase, it predicts the enzyme commission number out of 20 classes for nitrogen metabolism. Among all, the DPC + NMBroto hybrid feature gave the best prediction performance (accuracy of 96.15% in k-fold training and 93.43% in independent testing) with an Matthews correlation coefficient (0.92 training and 0.87 independent testing) in phase I; phase II (accuracy of 99.71% in k-fold training and 98.30% in independent testing); phase III (overall accuracy of 99.03% in k-fold training and 98.98% in independent testing); phase IV (overall accuracy of 99.05% in k-fold training and 98.18% in independent testing), the DPC feature gave the best prediction performance. We have also implemented a homology-based method to remove false negatives. All the models have been implemented on a web server (prediction tool), which is freely available at http://bioinfo.usu.edu/deepNEC/.
Translating cell therapies for neurodegenerative diseases: Huntington’s disease as a model disorder
Authors: Rosser Anne E. , Busse Monica E. , Gray William P. , Badin Romina Aron , Perrier Anselme L. , Wheelock Vicki , Cozzi Emanuele , Martin Unai Perpiña , Salado-Manzano Cristina , Mills Laura J. , Drew Cheney , Goldman Steven A. , Canals Josep M. , Thompson Leslie M. 
Published: March 2022

AbstractThere has been substantial progress in the development of regenerative medicine strategies for CNS disorders over the last decade, with progression to early clinical studies for some conditions. However, there are multiple challenges along the translational pipeline, many of which are common across diseases and pertinent to multiple donor cell types. These include defining the point at which the preclinical data are sufficiently compelling to permit progression to the first clinical studies; scaling-up, characterization, quality control and validation of the cell product; design, validation and approval of the surgical device; and operative procedures for safe and effective delivery of cell product to the brain. Furthermore, clinical trials that incorporate principles of efficient design and disease-specific outcomes are urgently needed (particularly for those undertaken in rare diseases, where relatively small cohorts are an additional limiting factor), and all processes must be adaptable in a dynamic regulatory environment.Here we set out the challenges associated with the clinical translation of cell therapy, using Huntington’s disease as a specific example, and suggest potential strategies to address these challenges. Huntington’s disease presents a clear unmet need, but, importantly, it is an autosomal dominant condition with a readily available gene test, full genetic penetrance and a wide range of associated animal models, which together mean that it is a powerful condition in which to develop principles and test experimental therapeutics. We propose that solving these challenges in Huntington’s disease would provide a road map for many other neurological conditions. This white paper represents a consensus opinion emerging from a series of meetings of the international translational platforms Stem Cells for Huntington’s Disease and the European Huntington’s Disease Network Advanced Therapies Working Group, established to identify the challenges of cell therapy, share experience, develop guidance and highlight future directions, with the aim to expedite progress towards therapies for clinical benefit in Huntington’s disease.
A New Neural Distinguisher Considering Features Derived From Multiple Ciphertext Pairs
Authors: Chen Yi , Shen Yantian , Yu Hongbo , Yuan Sitong 
Published: March 2022

Abstract
Neural-aided cryptanalysis is a challenging topic, in which the neural distinguisher ($\mathcal{ND}$) is a core module. In this paper, we propose a new $\mathcal{ND}$ considering multiple ciphertext pairs simultaneously. Besides, multiple ciphertext pairs are constructed from different keys. The motivation is that the distinguishing accuracy can be improved by exploiting features derived from multiple ciphertext pairs. To verify this motivation, we have applied this new $\mathcal{ND}$ to five different ciphers. Experiments show that taking multiple ciphertext pairs as input indeed brings accuracy improvement. Then, we prove that our new $\mathcal{ND}$ applies to two different neural-aided key recovery attacks. Moreover, the accuracy improvement is helpful for reducing the data complexity of the neural-aided statistic attack. The code is available at https://github.com/AI-Lab-Y/ND_mc.
RNA transcription and degradation of Alu retrotransposons depends on sequence features and evolutionary history
Authors: Baar Till , Dümcke Sebastian , Gressel Saskia , Schwalb Björn , Dilthey Alexander , Cramer Patrick , Tresch Achim 
Published: March 2022

Abstract
Alu elements are one of the most successful groups of RNA retrotransposons and make up 11% of the human genome with over 1 million individual loci. They are linked to genetic defects, increases in sequence diversity, and influence transcriptional activity. Still, their RNA metabolism is poorly understood yet. It is even unclear whether Alu elements are mostly transcribed by RNA Polymerase II or III. We have conducted a transcription shutoff experiment by α-amanitin and metabolic RNA labeling by 4-thiouridine combined with RNA fragmentation (TT-seq) and RNA-seq to shed further light on the origin and life cycle of Alu transcripts. We find that Alu RNAs are more stable than previously thought and seem to originate in part from RNA Polymerase II activity, as previous reports suggest. Their expression however seems to be independent of the transcriptional activity of adjacent genes. Furthermore, we have developed a novel statistical test for detecting the expression of quantitative trait loci in Alu elements that relies on the de Bruijn graph representation of all Alu sequences. It controls for both statistical significance and biological relevance using a tuned k-mer representation, discovering influential sequence features missed by regular motif search. In addition, we discover several point mutations using a generalized linear model, and motifs of interest, which also match transcription factor-binding motifs.
Coupled body and surface wave sensitivity kernels for coda-wave interferometry in a three-dimensional scalar scattering medium
Authors: Barajas Andres , Margerin Ludovic , Campillo Michel 
Published: March 2022

SUMMARY
Analysis of long recordings of ambient seismic noise has shown to be effective for estimation of seismic responses between points located on the surface. This includes both the ballistic and the coda part of the waveforms. Passive image interferometry is used to analyse perturbations in the reconstructed coda, to detect and locate changes in the medium. This method has been shown to be effective in monitoring variations in seismic velocity produced by a wide range of phenomena. However, localization of the sources of these changes is still an open problem for a 3-D half-space, given the difficulties of integrating body and surface waves within the same framework. In this study, we approach this problem by developing the sensitivity kernels of a scalar model that integrates the body and surface scalar waves. First, we establish a parallel between the penetration depth of the surface waves for the elastic and scalar cases, which equips the latter with a natural scaling with frequency that is otherwise not included in the model. Next, using a variational approach, we quantify how a velocity perturbation in the medium affects the propagation velocity of the surface waves. Based on these results, we extend the sensitivity theory to include the body and surface waves as modes of propagation and detection, as restricted to a 1-D depth-dependent perturbation description, for simplicity. The obtained kernel can be expressed as the sum of a surface and a body waves sensitivity kernels, which are inter-dependent through a set of traveltime distributions. These distributions are estimated with Monte Carlo simulations based on the radiative transfer equations of the system, with the source and the receiver located in the same position at the surface. The sensitivity at depth is in good agreement with previous results based on full wavefield elastic simulations in 3-D inhomogeneous half-space. The temporal evolution of the body and surface waves sensitivity is quantified, as well as the contribution of all the possible modes of propagation and detection to each of these sensitivities. We show how the position of the source affects the sensitivity between the two types of waves. We find that the efficacy of energy conversion from surface to body waves is controlled by the ratio between the surface wave penetration depth and the mean free path, a feature that has not been reported in previous studies. This means that configurations that share this ratio have the same sensitivity as long as all the spatial and temporal variables (e.g. elapsed time, depth) are non-dimensionalized with the mean free path and the mean free time, respectively.
Absenteeism among family planning providers: a mixed-methods study in western Kenya
Authors: Tumlinson Katherine , Britton Laura E , Williams Caitlin R , Wambua Debborah Muthoki , Onyango Dickens Otieno 
Published: March 2022

Abstract
Public-sector healthcare providers are on the frontline of family planning service delivery in low- and middle-income countries like Kenya, yet research suggests public-sector providers are frequently absent. The current prevalence of absenteeism in Western Kenya, as well as the impact on family planning clients, is unknown. The objective of this paper is to quantify the prevalence of public-sector healthcare provider absenteeism in this region of Kenya, to describe the potential impact on family planning uptake and to source locally grounded solutions to provider absenteeism. We used multiple data collection methods including unannounced visits to a random sample of 60 public-sector healthcare facilities in Western Kenya, focus group discussions with current and former family planning users, key informant interviews (KIIs) with senior staff from healthcare facilities and both governmental and non-governmental organizations and journey mapping activities with current family planning providers and clients. We found healthcare providers were absent in nearly 60% of unannounced visits and, among those present, 19% were not working at the time of the visit. In 20% of unannounced visits, the facility had no providers present. Provider absenteeism took many forms including providers arriving late to work, taking an extended lunch break, not returning from lunch or being absent for the entire day. While 56% of provider absences resulted from sanctioned activities such as planned vacation, sick leave or off-site work responsibilities, nearly half of the absences were unsanctioned, meaning providers were reportedly running personal errands, intending to arrive later or no one at the facility could explain the absence. Key informants and focus group participants reported high provider absence is a substantial barrier to contraceptive use, but solutions for resolving this problem remain elusive. Identification and rigorous evaluation of interventions designed to redress provider absenteeism are needed.
Influencing and implementing mandatory alcohol pregnancy warning labels in Australia and New Zealand
Authors: Heenan Maddie , Shanthosh Janani , Cullerton Katherine , Jan Stephen 
Published: March 2022

Summary
Alcohol labelling laws and policy are contentious and highly politicized. Very few countries have been able to implement health warnings on alcohol labels due to complex legal and governance systems and coordinated industry lobbying. In 2020, Australia and New Zealand implemented a mandatory and evidence-based legal standard for pregnancy warning labels on alcohol products. This article discusses some of the challenges faced in achieving policy change and how these barriers were overcome by public health advocacy groups to build the evidence, counter industry conflicts of interest, consumer test health messages, mobilize community support and gather political support. Reflecting on the decades of ineffective regulation and politicization of this health issue, lessons for other countries include the importance of creating and maintaining relationships with decision makers and regularly updating them with evidence and recommendations, highlighting industry failures and tactics, building broad-based coalitions and sharing lived-experiences.