bluuebunny/crossref_metadata_2025_split huggingface dataset:
Effect of postgrowth processing technology and laser radiation parameters at wavelengths of 2091 and 1064 nm on the laser-induced damage threshold in ZnGeP2 single crystal
Authors: Yudin N.N. , Antipov O.L. , Gribenyukov A.I. , Eranov I.D. , Podzyvalov S.N. , Zinoviev M.M. , Voronin L.A. , Zhuravleva E.V. , Zykova M.P. 
Published: March 2021

Abstract
We report a study of the effect of postgrowth treatment of ZnGeP2 single crystals (low-temperature annealing, irradiation with fast electrons, polishing of working surfaces) and the conditions of exposure to repetitively pulsed laser radiation [wavelength (2091 or 1064 nm), pulse repetition rate, beam diameter, exposure time, sample temperature] on the laser-induced damage threshold (LIDT) of the surfaces of these crystals. It is found that thermal annealing of ZnGeP2 single crystals and their irradiation with a flux of fast electrons, which increase the LIDT at a wavelength of λ = 1064 nm, do not lead to a change in this threshold at λ = 2091 nm. It is shown that ZnGeP2 elements with lower optical losses in the spectral range 0.7 – 2.5 μm have a higher LIDT at λ = 2091 nm both immediately after fabrication and after postgrowth processing. An increase in the threshold energy density of laser radiation by a factor of 1.5 – 3 at λ = 2091 nm is revealed with a decrease in the crystal temperature from zero to –60 °C. The fact of reversible photodarkening of the propagation channel of laser radiation in ZnGeP2 in the predamage region of parameters is established by the method of digital holography.
A limited role of NKCC1 in telencephalic glutamatergic neurons for developing hippocampal network dynamics and behavior
Authors: Graf Jürgen , Zhang Chuanqiang , Marguet Stephan Lawrence , Herrmann Tanja , Flossmann Tom , Hinsch Robin , Rahmati Vahid , Guenther Madlen , Frahm Christiane , Urbach Anja , Neves Ricardo Melo , Witte Otto W. , Kiebel Stefan J. , Isbrandt Dirk , Hübner Christian A. , Holthoff Knut , Kirmse Knut 
Published: March 2021

Significance
Neuronal circuits develop in an activity-dependent manner. In vitro data suggest that the intracellular accumulation of chloride—a universal characteristic of immature nerve cells—drives network maturation through a depolarizing action of the neurotransmitter GABA. We here demonstrate that deletion of the chloride cotransporter NKCC1 from forebrain pyramidal cells severely impairs hippocampal network synchrony in vitro. In contrast, NKCC1 has weak and event type-dependent effects on spontaneous network activity in vivo, and loss of NKCC1 leaves the maturation of synaptic properties, network dynamics, and hippocampus-dependent behaviors largely unaffected. Our data reveal a subtle neural network function of NKCC1 in hippocampal glutamatergic neurons in vivo, but challenge the assertion that NKCC1 in this major cell type is central to hippocampal development.
Mapping temperature-dependent conformational change in the voltage-sensing domain of an engineered heat-activated K
+
channel
Authors: Chen Hongbo , Deng Jiahua , Cui Qiang , Chanda Baron , Henzler-Wildman Katherine 
Published: March 2021

Significance
Our understanding of fundamental biophysical mechanisms that underlie temperature-dependent gating remains limited despite the elucidation of structures of thermosensitive ion channels. According to current thinking, thermosensitive channels are expected to undergo a significant change in solvation upon activation by temperature, but direct evidence of such a change is lacking. Here, we utilize NMR spectroscopy and atomistic simulations to probe the solvation dynamics of an engineered temperature-sensitive variant of Shaker potassium channel at two different temperatures. Our studies reveal that this temperature-sensitive variant undergoes a dramatic shift in hydration at higher temperatures in contrast to the wild-type channels. Our findings illustrate how point mutations can cause dramatic differences in overall solvation dynamics and thereby alter temperature-dependent function.
A scalable empathic supervision intervention to mitigate recidivism from probation and parole
Authors: Okonofua Jason A. , Saadatian Kimia , Ocampo Joseph , Ruiz Michael , Oxholm Perfecta Delgado 
Published: March 2021

Significance
Recidivism to incarceration is a pervasive and costly societal issue and its rates are historically difficult to reduce. Psychology and criminology suggest that relationships between probation and parole officers (PPOs) and adults on probation or parole (APPs) play a pivotal role in whether APPs ultimately return to incarceration and that mindsets geared toward empathy may protect the relationship from psychological barriers to productive interactions. The present research tests the efficacy an empathic-supervision exercise with PPOs to mitigate recidivism in a longitudinal, randomized placebo-controlled field experiment with 216 officers who supervise ∼20,478 APPs. As compared to a control condition, the treatment mitigated recidivism by 13% over the course of 10 mo.
Interplays of electron and nuclear motions along CO dissociation trajectory in myoglobin revealed by ultrafast X-rays and quantum dynamics calculations
Authors: Shelby Megan L. , Wildman Andrew , Hayes Dugan , Mara Michael W. , Lestrange Patrick J. , Cammarata Marco , Balducci Lodovico , Artamonov Maxim , Lemke Henrik T. , Zhu Diling , Seideman Tamar , Hoffman Brian M. , Li Xiaosong , Chen Lin X. 
Published: March 2021

Significance
In this multidisciplinary study, we investigated local and global structural responses upon carbon monoxide (CO) dissociation from the heme iron(II) in myoglobin in solution using ultrafast X-ray absorption and scattering. CO dissociation was optically triggered and pulsed X-rays probed structural and electronic changes as a function of time, revealing an iron(II) spin state transition and nuclear motions propagating from the active site throughout the entire protein. Quantum mechanical computational results were used to assign the iron K-edge absorption features, and to map out electronic and nuclear trajectories during Fe–CO bond elongation and heme macrocycle doming. The study has important implications for heme–protein function and the interaction of small molecule substrates with the metal active centers in enzymes.
Modeling DNA trapping of anticancer therapeutic targets using missense mutations identifies dominant synthetic lethal interactions
Authors: Hamza Akil , Amitzi Leanne , Ma Lina , Driessen Maureen R. M. , O'Neil Nigel J. , Hieter Philip 
Published: March 2021

Significance
Although inhibitor-mediated trapping of protein on DNA is a viable anticancer therapeutic strategy, there are very few examples of this approach with clinical applications. One limitation to expanding this strategy is the use of gene knockout-based modeling of chemical inhibition. Instead, we propose mimicking the trapping mechanism of DNA-associated cancer targets using dominant missense mutations. These mutations inhibit enzymatic activity without impacting DNA binding and generate cytotoxic lesions in the presence of the wild-type protein. Trapped cytotoxic lesions impact DNA processes and sensitize cells to deficiencies in some DNA-associated pathways. We identified genetic backgrounds that cannot tolerate the dominant mutant proteins and demonstrated a requirement for DNA or protein binding to maintain this cytotoxicity.
Ka Mua, Ka Muri – Walking Backward Toward the Future
Authors: Ruwhiu-Karawana Kylie 
Published: March 2021

Abstract
Tourism is an economic enabler for Māori communities and offers a chance to build employment opportunities that will assist in bringing iwi, hapū and whanau members back home to support their communities and be supported by them. Marae are the cornerstone of Māori culture. From behind the fence, they look like a collection of buildings, some ornately carved, some not, but for Māori they are the tether that connects them to their people and their place in this world.

Information

© Kylie Ruwhiu-Karawana 2021
Quantitative PAT with simplified P

N
approximation
Authors: Zhao Hongkai , Zhong Yimin 
Published: March 2021

Abstract
The photoacoustic tomography (PAT) is a hybrid modality that combines the optics and acoustics to obtain high resolution and high contrast imaging of heterogeneous media. In this work, our objective is to study the inverse problem in the quantitative step of PAT which aims to reconstruct the optical coefficients of the governing radiative transport equation from the ultrasound measurements. In our analysis, we take the simplified P

N
approximation of the radiative transport equation as the physical model and then show the uniqueness and stability for this modified inverse problem. Numerical simulations based on synthetic data are presented to validate our analysis.
In pursuit of new physics with

Bd0→J/ψK0

and

Bs0→J/ψϕ

decays at the high-precision Frontier
Authors: Barel Marten Z , De Bruyn Kristof , Fleischer Robert , Malami Eleftheria 
Published: March 2021

Abstract
The decays

B

d

0

→
J
/
ψ

K

S

0

and

B

s

0

→
J
/
ψ
ϕ

play a key role for the determination of the

B

q

0

–

B

̄

q

0

(q = d, s) mixing phases ϕ

d
and ϕ

s
, respectively. The theoretical precision of the extraction of these quantities is limited by doubly Cabibbo-suppressed penguin topologies, which can be included through control channels by means of the SU(3) flavour symmetry of strong interactions. Using the currently available data and a new simultaneous analysis, we discuss the state-of-the-art picture of these effects and include them in the extracted ϕ

q
values. We have a critical look at the standard model predictions of these phases and explore the room left for new physics. Considering future scenarios for the high-precision era of flavour physics, we illustrate that we may obtain signals for physics beyond the standard model with a significance well above five standard deviations. We also determine effective colour-suppression factors of

B

d

0

→
J
/
ψ

K

0

,

B

s

0

→
J
/
ψ

K

S

0

and

B

d

0

→
J
/
ψ

π

0

decays, which serve as benchmarks for QCD calculations of the underlying decay dynamics, and present a new method using information from semileptonic

B

d

0

→

π

−

ℓ

+

ν

ℓ

and

B

s

0

→

K

−

ℓ

+

ν

ℓ

decays.
In-suit growth of Cu4SnS4 nanoplates on reduced graphene oxide (rGO) with ligand exchange exhibiting enhanced photodegradation property
Authors: Tang Yu , Liu Mengmeng , Xu Hongzhi , Xu Ming , Zhang Xiaoyan , Bao Ningzhong 
Published: March 2021

Abstract
In the present study, a novel Cu4SnS4/reduced graphene oxide (CTS/rGO) composite was successfully prepared using a simple one-pot heat-up method. Post-synthetic ligand exchange (LE) and annealing process were performed to further increase the dispersibility and the conductivity of the prepared composite. An unexpected phase transformation from CTS to Cu3SnS4 with an enhanced absorption in the near-infrared (NIR) region were observed after LE. Furthermore, the photodegradation of Rhodamine B (RhB) by the CTS/rGO composite was investigated. The CTS nanoplates with 10 wt% rGO treated through LE (CTS-10%rGO-LE) exhibited the highest (99.92%) degradation rate of RhB after 90 min of visible-light irradiation, which is approximately 10 and 1.28 times that of the pure CTS and the CTS-10%rGO treated using annealing (CTS-10%rGO-A). The enhancement of the photodegradation activity could be ascribed to the in-suit growth of CTS on rGO and the subsequent LE treatment, which effectively reduced the agglomeration of CTS and increased the electron-transfer ability of the composite materials. The CTS/rGO composite also exhibited high chemical stability of the photodegradation of RhB after four recycles. The electron paramagnetic resonance spectra reveal that ·OH and h+ are the main active species in the photocatalytic degradation of RhB with CTS-LE and CTS-10%rGO-LE photocatalysts. The in-suit growth of the CTS/rGO composite with a subsequent LE treatment has the potential to serve as an efficient photocatalysts for the degradation of organic pollutants.
Anatomy-guided PET reconstruction using l
1 bowsher prior
Authors: Kang Seung Kwan , Lee Jae Sung 
Published: March 2021

Abstract
Advances in simultaneous positron emission tomography/magnetic resonance imaging (PET/MRI) technology have led to an active investigation of the anatomy-guided regularized PET image reconstruction algorithm based on MR images. Among the various priors proposed for anatomy-guided regularized PET image reconstruction, Bowsher’s method based on second-order smoothing priors sometimes suffers from over-smoothing of detailed structures. Therefore, in this study, we propose a Bowsher prior based on the

l

1

-norm and an iteratively reweighting scheme to overcome the limitation of the original Bowsher method. In addition, we have derived a closed solution for iterative image reconstruction based on this non-smooth prior. A comparison study between the original

l

2

and proposed

l

1

Bowsher priors was conducted using computer simulation and real human data. In the simulation and real data application, small lesions with abnormal PET uptake were better detected by the proposed

l

1

Bowsher prior methods than the original Bowsher prior. The original

l

2

Bowsher leads to a decreased PET intensity in small lesions when there is no clear separation between the lesions and surrounding tissue in the anatomical prior. However, the proposed

l

1

Bowsher prior methods showed better contrast between the tumors and surrounding tissues owing to the intrinsic edge-preserving property of the prior which is attributed to the sparseness induced by

l

1

-norm, especially in the iterative reweighting scheme. Besides, the proposed methods demonstrated lower bias and less hyper-parameter dependency on PET intensity estimation in the regions with matched anatomical boundaries in PET and MRI. Therefore, these methods will be useful for improving the PET image quality based on the anatomical side information.
Feasibility of automatic detection of small hepatocellular carcinoma (≤2 cm) in cirrhotic liver based on pattern matching and deep learning
Authors: Zheng Rencheng , Wang Luna , Wang Chengyan , Yu Xuchen , Chen Weibo , Li Yan , Li Weixia , Yan Fuhua , Wang He , Li Ruokun 
Published: March 2021

Abstract

Background and objective. Early detection of hepatocellular carcinoma (HCC) is crucial for clinical management. Current studies have reported large HCC detections using automatic algorithms, but there is a lack of research on automatic detection of small HCCs (sHCCs). This study is to investigate the feasibility of automatic detection of sHCC (≤2 cm) based on pattern matching and deep learning (PM-DL) model. Materials and methods. A retrospective study included 5376 image sets from 56 cirrhosis patients (28 sHCC patients with 32 pathologically confirmed lesions and 28 non-HCC cirrhosis patients) in the training-validation cohort to build and validate the model through five-fold cross-validation. In addition, an external test cohort including 6144 image sets from 64 cirrhosis patients (32 sHCC patients with 38 lesions and 32 non-HCC cirrhosis patients) was applied to further verify the generalization ability of the model. The proposed PM-DL model consisted of three main steps: 3D co-registration and liver segmentation, screening of suspicious lesions on diffusion-weighted imaging images based on pattern matching algorithm, and identification/segmentation of sHCC lesions on dynamic contrast-enhanced images with convolutional neural network. Results. The PM-DL model achieved a sensitivity of 89.74% and a positive predictive value of 85.00% in the external test cohort for per-lesion analysis. No significant difference was observed in volumes (P = 0.13) and the largest sizes (P = 0.89) between manually delineated and segmented lesions. The DICE coefficient reached 0.77 ± 0.16. Similar performances were identified in the validation cohort. Moreover, the PM-DL model outperformed Liver Imaging Reporting and Data System (LI-RADS) in sensitivity (probable HCCs: LR-5 or LR-4, P = 0.18; definite HCCs: LR-5, P < 0.001), with a similar high specificity for per-patient analysis. Conclusion. The PM-DL model may be feasible for accurate automatic detection of sHCC in cirrhotic liver.
Toward practical driving fatigue detection using three frontal EEG channels: a proof-of-concept study
Authors: Liu Xucheng , Li Gang , Wang Sujie , Wan Feng , Sun Yi , Wang Hongtao , Bezerianos Anastasios , Li Chuantao , Sun Yu 
Published: March 2021

Abstract

Objective. Although various driving fatigue detection strategies have been introduced, the limited practicability is still an obstacle for the real application of these technologies. This study is based on the newly proposed non-hair-bearing (NHB) method to achieve practical driving fatigue detection with fewer channels from NHB areas and more efficient electroencephalogram (EEG) features. Approach. EEG data were recorded from 20 healthy subjects (15 males, age = 22.2 ± 3.2 years) in a 90 min simulated driving task using a remote wireless cap. Behaviorally, subjects demonstrated a salient fatigue effect, as reflected by a monotonic increase in reaction time. Using a sliding-window approach, we determined the vigilant and fatigued states at individual level to reduce the inter-subject differences in behavioral impairment and brain activity. Multiple EEG features, including power-spectrum density (PSD), functional connectivity (FC), and entropy, were estimated in a pairwise manner, which were set as input for fatigue classification. Main results. Intriguingly, this data-driven approach showed that the best classification performance was achieved using three EEG channel pairs located in the NHB area. The mixed features of the frontal NHB area lead to the high within-subject detection rate of driving fatigue (92.7% ± 0.92%) with satisfactory generalizability for fatigue classification across different subjects (77.13% ± 0.85%). Moreover, we found the most prominent contributing features were PSD of different frequency bands within the frontal NHB area and FC within the frontal NHB area and between frontal and parietal areas. Significance. In summary, the current work provided objective evidence to support the effectiveness of the NHB method and further improved the performance, thereby moving a step forward towards practical driving fatigue detection in real-world scenarios.
Townsend to glow discharge transition for a nanosecond pulse plasma in helium: space charge formation and resulting electric field dynamics
Authors: Simeni Marien Simeni , Zheng Yashuang , Barnat Edward V , Bruggeman Peter J 
Published: March 2021

Abstract
Stark polarization spectroscopy is used to investigate the temporal evolution of the electric field distribution in the cathode region of a nanosecond pulsed discharge in helium at 120 Torr. The measurements are performed on the He I transition at 492.19 nm, during the early stages of the discharge formation. The experimental results are compared with the predictions of a 1D fluid model. Time-resolved ICCD images show that the discharge develops as a diffuse, cathode-directed ionization wave with a Townsend-like feature before transitioning into a glow-like structure. Near anode instabilities characterized by filament formation were observed near the high voltage electrode. Within 30 ns, a reduction of the sheath thickness to about 250 μm is observed, coinciding with a gradual increase of the discharge current and proportional increase in electric field at the cathode. The cathode electric field corresponding to this sheath with a thickness of 250 μm is about 40 kV cm−1. A subsequent steep increase of the discharge current leads to a further reduction of the sheath width. The electric field evolution as obtained by the fluid model is in excellent agreement with the measurements and shows that an enhanced ionization near the cathode is causing the space charge formation responsible for the increase in electric field.
Unveiling the role of dielectric trap states on capacitively coupled radio-frequency plasma discharge: dynamic charging behaviors
Authors: Zhang Shu , Sun Guang-Yu , Volčokas Arnas , Zhang Guan-Jun , Sun An-Bang 
Published: March 2021

Abstract
The influence of charge trap states in the dielectric boundary material on capacitively coupled radio-frequency (RF) plasma discharge is investigated with theory and particle-in-cell/Monte Carlo collision simulation. It is found that the trap states of the wall material manipulated discharge properties mainly through the varying ion-induced secondary electron emission (SEE) coefficient in response to dynamic surface charges accumulated within the solid boundary. A comprehensive SEE model considering surface charging is established first, which incorporates the valence band electron distribution, electron trap density, and charge trapping through Auger neutralization and de-excitation. Theoretical analysis is carried out to reveal the effects of trap states on sheath solution, stability, plasma density and temperature, particle and power balance, etc. The theoretical work is supported by simulation results, showing the reduction of the mean RF sheath potential as charging-dependent emission coefficient increases. As the gas pressure increases, a shift of the maximum ionization rate from the bulk plasma center to the plasma-sheath interface is observed, which is also influenced by the trap states of the electrode material where the shift happens at a lower pressure with traps considered. In addition, charge traps are proven to be helpful for creating asymmetric plasma discharges with geometrically symmetric structures; such an effect is more pronounced in γ-mode discharges.
Feature optimization using Backward Elimination and Support Vector Machines (SVM) algorithm for diabetes classification
Authors: Maulidina F , Rustam Z , Hartini S , Wibowo V V P , Wirasati I , Sadewo W 
Published: March 2021

Abstract
Diabetes is a disease that occurs when the blood glucose level is higher than normal and also leads to health problems. Early and accurate diagnosis needs to be carried out on individuals affected by this disease. Furthermore, excellent treatment needs to be provided to prevent worse situations. Some studies have used several machine learning methods to diagnose diabetes. Furthermore, in this study, the Backward Elimination and Support Vector Machine (SVM) algorithm was used to classify the PIMA Indians diabetes dataset. It consisted of 268 diabetic and 500 non-diabetic patients with eight attributes. Backward Elimination is a feature selection method used to remove irrelevant features based on the linear regression model. Using this method, the right features for the model was expected. This method has some advantages which include increasing training time, decreasing complexity and improving performance and accuracy. Therefore, the performance of SVM improved. Based on the experiments, it was discovered that by combining feature selection algorithm (backward elimination) and SVM, the highest accuracy obtained was 85.71% using 90% data training. Therefore, it was concluded that Backward Elimination combined with SVM algorithm is an excellent method to classify diabetes by using the PIMA Indians diabetes dataset.