bluuebunny/crossref_metadata_2025_split huggingface dataset:
ATG7 and ATG14 restrict cytosolic and phagosomal Mycobacterium tuberculosis replication in human macrophages
Authors: Aylan Beren , Bernard Elliott M. , Pellegrino Enrica , Botella Laure , Fearns Antony , Athanasiadi Natalia , Bussi Claudio , Santucci Pierre , Gutierrez Maximiliano G. 
Published: March 2023

AbstractAutophagy is a cellular innate-immune defence mechanism against intracellular microorganisms, including Mycobacterium tuberculosis (Mtb). How canonical and non-canonical autophagy function to control Mtb infection in phagosomes and the cytosol remains unresolved. Macrophages are the main host cell in humans for Mtb. Here we studied the contributions of canonical and non-canonical autophagy in the genetically tractable human induced pluripotent stem cell-derived macrophages (iPSDM), using a set of Mtb mutants generated in the same genetic background of the common lab strain H37Rv. We monitored replication of Mtb mutants that are either unable to trigger canonical autophagy (Mtb ΔesxBA) or reportedly unable to block non-canonical autophagy (Mtb ΔcpsA) in iPSDM lacking either ATG7 or ATG14 using single-cell high-content imaging. We report that deletion of ATG7 by CRISPR–Cas9 in iPSDM resulted in increased replication of wild-type Mtb but not of Mtb ΔesxBA or Mtb ΔcpsA. We show that deletion of ATG14 resulted in increased replication of both Mtb wild type and the mutant Mtb ΔesxBA. Using Mtb reporters and quantitative imaging, we identified a role for ATG14 in regulating fusion of phagosomes containing Mtb with lysosomes, thereby enabling intracellular bacteria restriction. We conclude that ATG7 and ATG14 are both required for restricting Mtb replication in human macrophages.
Global detection of human variants and isoforms by deep proteome sequencing
Authors: Sinitcyn Pavel , Richards Alicia L. , Weatheritt Robert J. , Brademan Dain R. , Marx Harald , Shishkova Evgenia , Meyer Jesse G. , Hebert Alexander S. , Westphall Michael S. , Blencowe Benjamin J. , Cox Jürgen , Coon Joshua J. 
Published: March 2023

AbstractAn average shotgun proteomics experiment detects approximately 10,000 human proteins from a single sample. However, individual proteins are typically identified by peptide sequences representing a small fraction of their total amino acids. Hence, an average shotgun experiment fails to distinguish different protein variants and isoforms. Deeper proteome sequencing is therefore required for the global discovery of protein isoforms. Using six different human cell lines, six proteases, deep fractionation and three tandem mass spectrometry fragmentation methods, we identify a million unique peptides from 17,717 protein groups, with a median sequence coverage of approximately 80%. Direct comparison with RNA expression data provides evidence for the translation of most nonsynonymous variants. We have also hypothesized that undetected variants likely arise from mutation-induced protein instability. We further observe comparable detection rates for exon–exon junction peptides representing constitutive and alternative splicing events. Our dataset represents a resource for proteoform discovery and provides direct evidence that most frame-preserving alternatively spliced isoforms are translated.
Panoramic visual statistics shape retina-wide organization of receptive fields
Authors: Gupta Divyansh , Młynarski Wiktor , Sumser Anton , Symonova Olga , Svatoň Jan , Joesch Maximilian 
Published: March 2023

AbstractStatistics of natural scenes are not uniform—their structure varies dramatically from ground to sky. It remains unknown whether these nonuniformities are reflected in the large-scale organization of the early visual system and what benefits such adaptations would confer. Here, by relying on the efficient coding hypothesis, we predict that changes in the structure of receptive fields across visual space increase the efficiency of sensory coding. Using the mouse (Mus musculus) as a model species, we show that receptive fields of retinal ganglion cells change their shape along the dorsoventral retinal axis, with a marked surround asymmetry at the visual horizon, in agreement with our predictions. Our work demonstrates that, according to principles of efficient coding, the panoramic structure of natural scenes is exploited by the retina across space and cell types.
A multi-sensor dataset with annotated activities of daily living recorded in a residential setting
Authors: Tonkin Emma L. , Holmes Michael , Song Hao , Twomey Niall , Diethe Tom , Kull Meelis , Perello Nieto Miquel , Camplani Massimo , Hannuna Sion , Fafoutis Xenofon , Zhu Ni , Woznowski Przemysław R. , Tourte Gregory J. L. , Santos-Rodríguez Raúl , Flach Peter A. , Craddock Ian 
Published: March 2023

AbstractSPHERE is a large multidisciplinary project to research and develop a sensor network to facilitate home healthcare by activity monitoring, specifically towards activities of daily living. It aims to use the latest technologies in low powered sensors, internet of things, machine learning and automated decision making to provide benefits to patients and clinicians. This dataset comprises data collected from a SPHERE sensor network deployment during a set of experiments conducted in the ‘SPHERE House’ in Bristol, UK, during 2016, including video tracking, accelerometer and environmental sensor data obtained by volunteers undertaking both scripted and non-scripted activities of daily living in a domestic residence. Trained annotators provided ground-truth labels annotating posture, ambulation, activity and location. This dataset is a valuable resource both within and outside the machine learning community, particularly in developing and evaluating algorithms for identifying activities of daily living from multi-modal sensor data in real-world environments. A subset of this dataset was released as a machine learning competition in association with the European Conference on Machine Learning (ECML-PKDD 2016).
HIDSAG: Hyperspectral Image Database for Supervised Analysis in Geometallurgy
Authors: Ehrenfeld Alejandro , Egaña Álvaro F. , Santibañez-Leal Felipe , Garrido Felipe , Ojeda Marcia , Townley Brian , Navarro Felipe 
Published: March 2023

AbstractSupervised analysis using spectral data requires a well-informed characterisation of the response variables and abundant spectral data points. The presented hyperspectral dataset comes from five sets of geometallurgical samples, each characterised by different methods. To provide the spectral data, all mineral samples were scanned with SPECIM VNIR and SWIR hyperspectral cameras. For each subset the following data are provided 1) hyperspectral reflectance images in the VNIR spectral range (400–1000 nm wavelength); 2) hyperspectral reflectance images in the SWIR spectral range (900–2500 nm wavelength); 3) hyperspectral reflectance images in the VNIR-SWIR range (merged to SWIR spatial resolution); 4) RGB images constructed from hyperspectral data using a Bilateral Filter based sensor fusion method; 5) response variables representing mineral sample characterisation results, provided as training and validation data. This dataset is intended for use in general regression and classification research and experiments. All subsets were validated using machine learning models with satisfactory results.
The risk assessment of rockburst intensity in the highway tunnel based on the variable fuzzy sets theory
Authors: Wang Ai-Feng , Yang Xiu-Tao , Gu Xin-Bao 
Published: March 2023

AbstractRockbursts have important influences on construction safety, so the risk assessment of rockburst intensity has great significance. Firstly, the depth of the rockburst, the uniaxial compressive strength, the stress concentration coefficients, the brittleness coefficients, and the elastic energy index are selected as the evaluation index. Secondly, an assessment model is developed based on the fuzzy variable theory. And the model is proposed to assess the rockburst intensity in the highway tunnel. Finally, the results demonstrate that the results derived from the proposed model are consistent with the current specifications; the accurate rate comes to 100%. The method can determine the risk level of rockburst intensity and provide an alternative scheme. Hence, the study can accurately present a new approach to assess the rockburst intensity in the future.
Climate warming and increasing Vibrio vulnificus infections in North America
Authors: Archer Elizabeth J. , Baker-Austin Craig , Osborn Timothy J. , Jones Natalia R. , Martínez-Urtaza Jaime , Trinanes Joaquín , Oliver James D. , González Felipe J. Colón , Lake Iain R. 
Published: March 2023

AbstractVibrio vulnificus is an opportunistic bacterial pathogen, occurring in warm low-salinity waters. V. vulnificus wound infections due to seawater exposure are infrequent but mortality rates are high (~ 18%). Seawater bacterial concentrations are increasing but changing disease pattern assessments or climate change projections are rare. Here, using a 30-year database of V. vulnificus cases for the Eastern USA, changing disease distribution was assessed. An ecological niche model was developed, trained and validated to identify links to oceanographic and climate data. This model was used to predict future disease distribution using data simulated by seven Global Climate Models (GCMs) which belong to the newest Coupled Model Intercomparison Project (CMIP6). Risk was estimated by calculating the total population within 200 km of the disease distribution. Predictions were generated for different “pathways” of global socioeconomic development which incorporate projections of greenhouse gas emissions and demographic change. In Eastern USA between 1988 and 2018, V. vulnificus wound infections increased eightfold (10–80 cases p.a.) and the northern case limit shifted northwards 48 km p.a. By 2041–2060, V. vulnificus infections may expand their current range to encompass major population centres around New York (40.7°N). Combined with a growing and increasingly elderly population, annual case numbers may double. By 2081–2100 V. vulnificus infections may be present in every Eastern USA State under medium-to-high future emissions and warming. The projected expansion of V. vulnificus wound infections stresses the need for increased individual and public health awareness in these areas.
A hierarchical dynamic Bayesian learning network for EMG-based early prediction of voluntary movement intention
Authors: Chen Yongming , Zhang Haihong , Wang Chuanchu , Ang Kai Keng , Ng Soon Huat , Jin Huiwen , Lin Zhiping 
Published: March 2023

AbstractDecoding human action intention prior to motion onset with surface electromyograms (sEMG) is an emerging neuroengineering topic with interesting clinical applications such as intelligent control of powered prosthesis/exoskeleton devices. Despite extensive prior works in the related fields, it remains a technical challenge due to considerable variability of complex multi-muscle activation patterns in terms of volatile spatio-temporal characteristics. To address this issue, we first hypothesize that the inherent variability of theidlestate immediately preceding the motion initiation needs to be addressed explicitly. We therefore design a hierarchical dynamic Bayesian learning network model that integrates an array of Gaussian mixture model – hidden Markov models (GMM-HMMs), where each GMM-HMM learns the multi-sEMG processes either during the idle state, or during the motion initiation phase of a particular motion task. To test the hypothesis and evaluate the new learning network, we design and build a upper-limb sEMG-joystick motion study system, and collect data from 11 healthy volunteers. The data collection protocol adapted from the psychomotor vigilance task includes repeated and randomized binary hand motion tasks (push or pull) starting from either of two designatedidlestates: relaxed (with minimal muscle tones), or prepared (with muscle tones). We run a series of cross-validation tests to examine the performance of the method in comparison with the conventional techniques. The results suggest that the idle state recognition favors the dynamic Bayesian model over a static classification model. The results also show a statistically significant improvement in motion prediction accuracy by the proposed method (93.83±6.41%) in comparison with the conventional GMM-HMM method (89.71±8.98%) that does not explicitly account for the idle state. Moreover, we examine the progress of prediction accuracy over the course of motion initiation and identify the important hidden states that warrant future research.
Impact of reactivity controlled compression ignition (RCCI) mode engine operation in diesel engine powered with B20 blend of waste cooking oil biodiesel
Authors: Anish M. , Jayaprabakar J. , Bency P. , Joy Nivin , Jayaprakash V. , Arunkumar K. , Aravind Kumar J. , Praveenkumar T. R. , Ghfar Ayman A. , Rajasimman M. , Ravindran Balasubramani 
Published: March 2023

AbstractThe purpose of this study is to conduct an experimental assessment of the impact of RCCI (reactivity regulated compression ignition) on the performance, emissions, and combustion of a CRDI engine. A fuel mix (20% biodiesel, 80% diesel, and a NaOH catalyst) is generated. The produced combination is evaluated for attributes using standards established by the American Society for Testing and Materials (ASTM). The engine research included three distinct kinds of injections: 10% Pen RCCI, 20% Pen RCCI, and 30% Pen RCCI. Increasing the injection pressure increases the brake thermal efficiency, often known as BTE. NOx emissions increased as a consequence of higher injection pressures and improved combustion. However, when the injection rate is increased, the Specific Fuel Consumption (SFC) falls. The CO2 and hydrocarbon emissions, as well as the smoke opacity values, increased as the charge increased. The resultant mixture may be utilized in a CI engine with pre-mixed ignition to improve overall engine performance as well as combustion characteristics.
Longitudinal changes in disgust sensitivity during pregnancy and the early postpartum period, and the role of recent health problems
Authors: Dlouhá Daniela , Roberts S. Craig , Hlaváčová Jana , Nouzová Kamila , Kaňková Šárka 
Published: March 2023

AbstractDisgust is an essential part of the behavioral immune system, protecting the individual from infection. According to the Compensatory Prophylaxis Hypothesis (CPH), disgust sensitivity increases in times of immunosuppression, potentially including pregnancy. We aimed to replicate a previous study observing longitudinal changes in disgust sensitivity in pregnant women. Additionally, for the first time, we explored how recent health problems influence these changes. To do this, we obtained disgust sensitivity measures from 94 women in each trimester and in early postpartum. In contrast to the original study, where disgust sensitivity was highest in the first trimester, we found that overall and animal reminder disgust increased across pregnancy and after birth. In line with the CPH, women who were recently sick in the first trimester had elevated disgust sensitivity at that time. Although disgust sensitivity was significantly higher in the second trimester and postpartum period compared to the first trimester in mothers pregnant with a male fetus, the overall results regarding the effect of fetus sex on disgust sensitivity were mixed. It seems that changing levels of disgust sensitivity during pregnancy and postpartum result from a suite of physiological and psychological changes that occur during this sensitive period of a woman’s life.
Universal scaling laws and density slopes for dark matter haloes
Authors: Xu Zhijie 
Published: March 2023

AbstractSmalls scale challenges suggest some missing pieces in our current understanding of dark matter. A cascade theory for dark matter is proposed to provide extra insights, similar to the cascade phenomenon in hydrodynamic turbulence. The kinetic energy is cascaded in dark matter from small to large scales involves a constant rate $$\varepsilon _u$$

ε
u

($$\approx -\,4.6\times 10^{-7}{\text { m}}^2/{\text { s}}^3$$

≈
-

4.6
×

10

-
7

m

2

/

s

3

). Confirmed by N-body simulations, the energy cascade leads to a two-thirds law for kinetic energy $$v_r^2$$

v
r
2

on scale r such that $$v_r^2 \propto (\varepsilon _u r)^{2/3}$$

v
r
2

∝

(

ε
u

r
)

2
/
3

. Equivalently, a four-thirds law can be established for mean halo density $$\rho _s$$

ρ
s

enclosed in the scale radius $$r_s$$

r
s

such that $$\rho _s \propto \varepsilon _u^{2/3}G^{-1}r_s^{-4/3}$$

ρ
s

∝

ε
u

2
/
3

G

-
1

r
s

-
4
/
3

, which was confirmed by galaxy rotation curves. Critical properties of dark matter might be obtained by identifying key constants on relevant scales. First, the largest halo scale $$r_l$$

r
l

can be determined by $$-u_0^3/\varepsilon _u$$

-

u
0
3

/

ε
u

, where $$u_0$$

u
0

is the velocity dispersion. Second, the smallest scale $$r_{\eta }$$

r
η

is dependent on the nature of dark matter. For collisionless dark matter, $$r_{\eta } \propto (-{G\hbar /\varepsilon _{u}}) ^{1/3}\approx 10^{-13}\,{\text {m}}$$

r
η

∝

(
-

G
ħ
/

ε
u

)

1
/
3

≈

10

-
13

m

, where $$\hbar$$
ħ
is the Planck constant. An uncertainty principle for momentum and acceleration fluctuations is also postulated. For self-interacting dark matter, $$r_{\eta } \propto \varepsilon _{u}^2 G^{-3}(\sigma /{{m}})^3$$

r
η

∝

ε

u

2

G

-
3

(
σ
/
m
)

3

, where $$\sigma /m$$

σ
/
m

is the cross-section of interaction. On halo scale, the energy cascade leads to an asymptotic density slope $$\gamma =-\,4/3$$

γ
=
-

4
/
3

for fully virialized haloes with a vanishing radial flow, which might explain the nearly universal halo density. Based on the continuity equation, halo density is analytically shown to be closely dependent on the radial flow and mass accretion, such that simulated haloes can have different limiting slopes. A modified Einasto density profile is proposed accordingly.
Advancing neutron diffraction for accurate structural measurement of light elements at megabar pressures
Authors: Haberl Bianca , Guthrie Malcolm , Boehler Reinhard 
Published: March 2023

AbstractOver the last 60 years, the diamond anvil cell (DAC) has emerged as the tool of choice in high pressure science because materials can be studied at megabar pressures using X-ray and spectroscopic probes. In contrast, the pressure range for neutron diffraction has been limited due to low neutron flux even at the strongest sources and the resulting large sample sizes. Here, we introduce a neutron DAC that enables break-out of the previously limited pressure range. Key elements are ball-bearing guides for improved mechanical stability, gem-quality synthetic diamonds with novel anvil support and improved in-seat collimation. We demonstrate a pressure record of 1.15 Mbar and crystallographic analysis at 1 Mbar on the example of nickel. Additionally, insights into the phase behavior of graphite to 0.5 Mbar are described. These technical and analytical developments will further allow structural studies on low-Z materials that are difficult to characterize by X-rays.
A polygenic and family risk score are both independently associated with risk of type 2 diabetes in a population-based study
Authors: Duschek Elena , Forer Lukas , Schönherr Sebastian , Gieger Christian , Peters Annette , Kronenberg Florian , Grallert Harald , Lamina Claudia 
Published: March 2023

AbstractThe availability of polygenic scores for type 2 diabetes (T2D) raises the question, whether assessing family history might become redundant. However, family history not only involves shared genetics, but also shared environment. It was the aim of this study to assess the independent and combined effects of one family risk score (FamRS) and a polygenic score (PGS) on prevalent and incident T2D risk in a population-based study from Germany (n = 3071). The study was conducted in 2004/2005 with up to 12 years of follow-up. The FamRS takes into account not only the number of diseased first grade relatives, but also age at onset of the relatives and age of participants. 256 prevalent and additional 163 incident T2D cases were recorded. Prevalence of T2D increased sharply for those within the top quantile of the PGS distribution resulting in an OR of 19.16 (p < 2 × 10–16) for the top 20% compared to the remainder of the population, independent of age, sex, BMI, physical activity and FamRS. On the other hand, having a very strong family risk compared to average was still associated with an OR of 2.78 (p = 0.001), independent of the aforementioned factors and the PGS. The PGS and FamRS were only slightly correlated (r2Spearman = 0.018). The combined contribution of both factors varied with varying age-groups, though, with decreasing influence of the PGS with increasing age. To conclude, both, genetic information and family history are relevant for the prediction of T2D risk and might be used for identification of high risk groups to personalize prevention measures.
Characterization of radiation-resistance mechanism in Spirosoma montaniterrae DY10T in terms of transcriptional regulatory system
Authors: Cho Changyun , Lee Dohoon , Jeong Dabin , Kim Sun , Kim Myung Kyum , Srinivasan Sathiyaraj 
Published: March 2023

AbstractTo respond to the external environmental changes for survival, bacteria regulates expression of a number of genes including transcription factors (TFs). To characterize complex biological phenomena, a biological system-level approach is necessary. Here we utilized six computational biology methods to infer regulatory network and to characterize underlying biologically mechanisms relevant to radiation-resistance. In particular, we inferred gene regulatory network (GRN) and operons of radiation-resistance bacterium Spirosoma montaniterrae DY10$$^T$$

T

and identified the major regulators for radiation-resistance. Our results showed that DNA repair and reactive oxygen species (ROS) scavenging mechanisms are key processes and Crp/Fnr family transcriptional regulator works as a master regulatory TF in early response to radiation.
Predicting choice behaviour in economic games using gaze data encoded as scanpath images
Authors: Byrne Sean Anthony , Reynolds Adam Peter Frederick , Biliotti Carolina , Bargagli-Stoffi Falco J. , Polonio Luca , Riccaboni Massimo 
Published: March 2023

AbstractEye movement data has been extensively utilized by researchers interested in studying decision-making within the strategic setting of economic games. In this paper, we demonstrate that both deep learning and support vector machine classification methods are able to accurately identify participants’ decision strategies before they commit to action while playing games. Our approach focuses on creating scanpath images that best capture the dynamics of a participant’s gaze behaviour in a way that is meaningful for predictions to the machine learning models. Our results demonstrate a higher classification accuracy by 18% points compared to a baseline logistic regression model, which is traditionally used to analyse gaze data recorded during economic games. In a broader context, we aim to illustrate the potential for eye-tracking data to create information asymmetries in strategic environments in favour of those who collect and process the data. These information asymmetries could become especially relevant as eye-tracking is expected to become more widespread in user applications, with the seemingly imminent mass adoption of virtual reality systems and the development of devices with the ability to record eye movement outside of a laboratory setting.