bluuebunny/crossref_metadata_2025 huggingface dataset:
Sublimation-driven morphogenesis of Zen stones on ice surfaces
Authors: Taberlet Nicolas , Plihon Nicolas
Published: September 2021

Significance

Zen stones are fascinating natural structures consisting of a stone standing on a slender ice pedestal, whose origin had long been misunderstood. We demonstrate that they are caused by a variation in the sublimation rate of the surrounding ice, which leads to the slow formation of a pedestal, adding to the very few reports of sublimation-driven pattern formation. Understanding this process sheds light on other differential ablation processes encountered on ice surfaces, such as debris-covered glaciers, whose existence is threatened by global warming, and icy bodies in space. Indeed, NASA’s Europa Lander project aims to seek biosignatures on Jupiter’s ice-covered moon, on the surface of which differential sublimation may threaten the lander stability, and this needs to be fully understood.
Processing of the Mandarin polarity itemrenhe‘any’
Authors: Wu Hongchen , Yun Jiwon
Published: September 2021

The Mandarinrenheis similar to the Englishanyin terms of polarity sensitivity (Wang 1993;Wang & Hsieh 1996;Kuo 2003;Cheng & Giannakidou 2013;Shyu 2016). However, the following phenomena regardinganyin relative clause environments have not been surveyed with respect torenhe:(a) the NPI illusion effect reported in studies like Parker & Phillips (2011;2016); (b) the subtrigging effect discussed inLeGrand (1975)and Dayal (1998;2004). We conducted two untimed, offline acceptability judgment experiments and the results suggest that: (i) NPI illusion does not appear in Mandarin in untimed offline processing, (ii) the subtrigging effect ofrenheholds, and (iii)renhecan be licensed by certain types of declarative verbs liketongyi‘agree’ andzancheng‘approve’. The results confirm the strict structural requirement of the c-commanding relation between a negation licensor andrenhe(Wang 1993) and the licensing ofrenhein non-veridical contexts (Cheng & Giannakidou 2013), and further suggest additional licensing environments forrenhe: relative clauses and declarative verbs. This requires reconsideration of positing non-veridicality as a necessary licensing condition forrenheand calls for future research on howrenheis licensed under these two licensing environments.
Leveraging Technology to Address Unhealthy Drug use in Primary Care: Effectiveness of the Substance use Screening and Intervention Tool (SUSIT)
Authors: McNeely Jennifer , Mazumdar Medha , Appleton Noa , Bunting Amanda M. , Polyn Antonia , Floyd Steven , Sharma Akarsh , Shelley Donna , Cleland Charles M.
Published: September 2021

Background: Screening for unhealthy drug use is now recommended for adult primary care patients, but primary care providers (PCPs) generally lack the time and knowledge required to screen and deliver an intervention during the medical visit. To address these barriers, we developed a tablet computer-based ‘Substance Use Screening and Intervention Tool (SUSIT)’. Using the SUSIT, patients self-administer screening questionnaires prior to the medical visit, and results are presented to the PCP at the point of care, paired with clinical decision support (CDS) that guides them in providing a brief intervention (BI) for unhealthy drug use. Methods: PCPs and their patients with moderate-risk drug use were recruited from primary care and HIV clinics. A pre-post design compared a control ‘screening only’ (SO) period to an intervention ‘SUSIT’ period. Unique patients were enrolled in each period. In both conditions, patients completed screening and identified their drug of most concern (DOMC) before the visit, and completed a questionnaire about BI delivery by the PCP after the visit. In the SUSIT condition only, PCPs received the tablet with the patient's screening results and CDS. Multilevel models with random intercepts and patients nested within PCPs examined the effect of the SUSIT intervention on PCP delivery of BI. Results: 20 PCPs and 79 patients (42 SO, 37 SUSIT) participated. Most patients had moderate-risk marijuana use (92.4%), and selected marijuana as the DOMC (68.4%). Moderate-risk use of drugs other than marijuana included cocaine (15.2%), hallucinogens (12.7%), and sedatives (12.7%). Compared to the SO condition, patients in SUSIT had higher odds of receiving any BI for drug use, with an adjusted odds ratio of 11.59 (95% confidence interval: 3.39, 39.25), and received more elements of BI for drug use. Conclusions: The SUSIT significantly increased delivery of BI for drug use by PCPs during routine primary care encounters.
Application of photoplethysmography signal in diagnosis of cerebrovascular disease and its extraction method based on Wavelet Transform
Authors: Zhu Xiangyu , Liu Jingwen , Zha Yutong
Published: September 2021

Effective monitoring of physiological information can effectively control incidence rate and mortality rate for patients with cerebrovascular disease. Among them, near infrared spectroscopy (NIRS) can measure the photoplethysmography (PPG) of cerebral cortex, which provides a new solution for the current detection of cerebrovascular physiological information. There are a lot of characteristic parameters in the pulse wave signal, which can be used to evaluate the cerebrovascular condition. In the time domain, the K value, the main wave width w, the main wave slope k were calculated to evaluate the cerebral vascular sclerosis. And in the frequency domain, the blood pressure of patients can be evaluated by the spectrum amplitude A. Besides, to improve the quality and accuracy of PPG signal parameter calculation, PPG with low noise and distortion is needed to provide support. By analyzing the interference of PPG signal in different frequency bands, the wavelet filtering method is adopted to process the signal. Through the calculation of root mean square error (RMSE) and signal-to-noise ratio (SNR), it is found that this method has excellent extraction effect on PPG signal.
Safety state monitoring analysis of seepage from Daban Reservoir
Authors: Sun Yiwen , Duan Xiangbao , Li Jingjuan
Published: September 2021

To understand the seepage security state of Daban Reservoir dam, process line analysis, correlation analysis, infiltration slope drop analysis and permeability analysis were conducted on the seepage monitoring data, and single-factor and multivariate statistical models were established to demonstrate the influencing factors of the pipe water level. The results show that the dam is generally effective in preventing seepage, the change in seepage flow before and after the seepage wall is not significant, the measured infiltration line is higher than the design value, and the correlation between the pipe water level and the reservoir water level is significant. 2020 flood season seepage occurs on the downstream slope of the dam, the height is higher than the top elevation of the drainage body, the actual operation results verify the accuracy of the analysis of the monitoring data and there is a seepage flow safety hazard. It is recommended that measures be taken to eliminate potential seepage hazards and to strengthen safety monitoring and surveillance.
A New super-resolution restoration method with Generated Adversarial Network for underground video images in coal mines
Authors: Yang Guangyao , Wang Yumo , Yi Chun , Wang Zhongqiang
Published: September 2021

The computer can be used in Super-resolution reconstruction (SR) to process low-resolution images to obtain high-resolution images. Aiming at solving problems of complex underground video image acquisition environment, uneven brightness, blurred images etc, this paper adopts the idea of deep learning to perform super-resolution restoration of underground video images in coal mines, and proposes a generational confrontation network to super-resolution underground video images in coal mines. The experiment proves that Generated Adversarial Network (GAN), while being compare with Super-resolution Deep Convolutional Neural Network (SRCNN), Efficient Sub-Pixel Convolutional Neural Network (ESPCN), Deeply Recursive Convolutional Network (DRCN) the effect of GAN method is better, because it can better realize the super-resolution restoration of underground video images in coal mines and provide preliminary support for the subsequent and further application research of underground images in coal mines.
Research on leaf image identification based on improved AlexNet neural network
Authors: Zhang Wenkun , Wen Juanjuan
Published: September 2021

The results of plant leaf classification can be used in many fields, such as plant protection. In addition, it also plays a certain role in the spread of plant diversity knowledge. At present, the recognition of plants by leaves has become a popular issue in many fields. This paper focuses more on what kind of network structure to choose to identify plant leaves. Convolution neural network is a popular network structure in recent years. Features are extracted by convolution layer, feature dimension reduction is completed by pooling layer, and finally a column vector is output by fully connected layer, and leaf recognition and classification are completed by classifier. Therefore, we construct an 11 layer convolution neural network, and select plant leaves to complete the five classification of plant leaves, and try to modify the different parameters of the network structure, choosing various optimizer, the average accuracy of the final test set is more than 99%. From the experimental results, the model has high recognition rate and high accuracy for the existing blades, which has a certain practical significance.
An Improved Multi-Target Recognition Method for Vehicle Linear Frequency Modulated Continuous Wave Radar
Authors: Lin Yiran
Published: September 2021

With the rise of unmanned driving technology and advanced driver assistance systems (ADAS), millimeter-wave radar plays a key role in the field of autonomous driving due to its unique advantages. It can provide forward target information (distance, velocity and angle) for vehicle decision-making and execution systems. Linear frequency modulated continuous wave (LFMCW) automotive radar usually divides the velocity ambiguity resolution and angle estimation into two frames for processing, which will reduce the radar data rate. This paper proposes an improved automotive LFMCW radar multi-target recognition method, which uses the TMD method to transmit sawtooth signals with different time widths and the same bandwidth, and completes range velocity calculation, angle estimation and divides the velocity ambiguity resolution within one frame. This algorithm can simplify the steps of resolving velocity ambiguity and improve the angle measurement accuracy. And it has certain reference significance for the research and application of the LFMCW automotive radar.
Research on image preprocessing for iris recognition
Authors: Mo XiuYing , Chen Tan
Published: September 2021

With the development of social economy, identity identification has penetrated into every aspect of people’s life. As the bottom technology of identity authentication, biometric technology is getting more and more attention in recent years. At present, iris based identification technology is a research hotspot with rapid development at home and abroad. Iris, as an important identification feature, has the advantages of uniqueness, stability, collectability and non-invasive. In this paper, the iris recognition algorithm is deeply studied, focusing on the image preprocessing in the iris recognition algorithm. In the aspect of positioning, the coarse to fine algorithm is used to ensure the accuracy and improve the positioning speed. The pupil location is divided into three steps. Based on the accurate pupil location, the iris is located in one step, and the effectiveness of the location is tested.
A novel αDistance Borderline-ADASYN-SMOTE algorithm for imbalanced data and its application in Alzheimer’s disease classification based on Dense Convolutional Network
Authors: Feng Yun , Li Jingjiao
Published: September 2021

The classification problem of imbalanced data has become a very important issue in the fields of machine learning and data mining. At present, relatively effective oversampling methods for processing imbalanced data include SMOTE, Borderline-SMOTE, and ADASYN. These algorithms have their own advantages; however, they do not adequately consider the distance factor, which is an important factor for balancing data precisely and reducing the misclassification probability of a minority boundary sample. Therefore, a new algorithm, αDistance Borderline-ADASYN-SMOTE algorithm, is proposed in the paper by combining the optimized Borderline-SMOTE algorithm with the optimized ADASYN algorithm. In the new algorithm, both the amount and the distance distribution of the nearest neighbor samples are considered. A few formulas are created to realize the algorithm. After being balanced by the algorithm, the data obtained from ADNI data set is trained, verified and tested by the Dense Convolutional Network. The experimental results show that the new model improves the classification performance of the Alzheimer’s disease.
Construction of Dirichlet Mixture Allocation total probability model based on multiple class text analysis
Authors: Ling Xiaoming , Zhang Feng
Published: September 2021

The LDA model is a total probability generation model for analyzing a large number of documents. It extends PLSA, another text analysis model. In this model, each document is treated as a topic hybrid model, and the topic’s proportional prior distribution is a Dirichlet distribution. The LDA model does not reflect complex dependencies between underlying topics. Based on LDA, this paper introduces a new topic generation model, DMA (Dirichlet Mixture Allocation), which models document collections more accurately than LDA when documents are obtained in multiple classes. In this paper, we build a probabilistic topic model of DMA, use the method of variational inference to approximate each parameter of the model, study the model, and finally solve the estimation of each parameter.
A Data Mining System for Potential Customers Based on One-Class Support Vector Machine
Authors: Mai Weijian , Wu Fengjie , Li Fang , Luo Wenjun , Mai Xiaoting
Published: September 2021

Commodity purchase data is usually severely skewed, which is reflected in the fact that there are far more negative data than positive data. This phenomenon makes it difficult for the binary classification model to obtain satisfactory results. Hence, we transform the binary classification problem into a one-class novelty detection problem. Specifically, this work proposes a potential customer mining system based on the One-class Support Vector Machine (OCSVM) and demonstrates its effectiveness for classification, prediction, and potential customer mining. This system allows merchants to focus on unpurchased customers with the strongest purchase intentions and to change their purchase decisions with minimal sale costs, which enables merchants to maximize their benefits.
Deep learning based news text classification software design
Authors: Xu Xiao , Wang LiJuan , Liu RuFan , Xu TianYu
Published: September 2021

New technologies such as artificial intelligence have developed at a rapid pace in recent years and are increasingly being used in the process of managing news in bulk. The development of deep learning has facilitated unprecedented progress in the field of computing and has opened our eyes to the possibility of using AI for news text classification. In this paper, based on the system requirements analysis, we describe the process of functional modules arising from the requirements analysis, design the internal details of functional modules, including algorithms and detailed principles, and finally obtain a prototype of news text classification software, which results in the pre-design expectations. The research in this paper makes the system development work more concrete, while providing software users, software developers, and analysts and testers with a unified and comprehensive understanding of the system’s functional implementation.
Improving the Relation Classification Using Convolutional Neural Network
Authors: Kamath S , Karibasappa K G , Reddy Anvitha , Kallur Arati M , Priyanka B B , Bhagya B P
Published: September 2021

Relation extraction has been the emerging research topic in the field of Natural Language Processing. The proposed work classifies the relations among the data considering the semantic relevance of words using word2vec embeddings towards training the convolutional neural network. We intended to use the semantic relevance of the words in the document to enrich the learning of the embeddings for improved classification. We designed a framework to automatically extract the relations between the entities using deep learning techniques. The framework includes pre-processing, extracting the feature vectors using word2vec embedding, and classification using convolutional neural networks. We perform extensive experimentation using benchmark datasets and show improved classification accuracy in comparison with the state-of-the-art methodologies using appropriate methods and also including the additional relations.
Application of Natural Betalain Dye from Beetroot for Improved Efficiency in Dye Sensitized Solar Cell
Authors: Soni K , Sheikh A , Jain V , Lakshmi N
Published: September 2021

C, N, S co-doped TiO2 (TU-TiO2) synthesized through solid state method using high energy ball milling was applied onto fluorine doped tin oxide (FTO) glass substrate and sintered at 450° C for half hour for use as a photoanode of dye-sensitized solar cells (DSSCs). Natural Betalain containing dye extracted from beetroot was used as sensitizer, Iodide triodide was used as electrolyte and graphite as counter electrode to obtain FTO/C, N, S-TiO2/Dye/Electrolyte/Graphite/FTO DSSC. X-ray Diffraction analysis established the formation of single phased materials retaining the basic TiO2 structure. Optical properties were enhanced with increase in molar concentration of thiourea in TiO2. Fourier Transform Infrared spectroscopy confirmed the incorporation of sulphur into TiO2. The highest of 94% relative increase is observed in solar to electric conversion efficiency for C, N, S co-doped TiO2 compared to un-doped TiO2. Along with decrease in the band gap making the cell more sensitive to visible light, the improved performance is also due to the rise in charge injection from betalain dye to the C, N, S co-doped TiO2.
Restoration of degraded underwater images with attenuation coefficients as a clue
Authors: Dikshith C S , Vernekar Sameeksha M , Desai Chaitra , Patil Ujwala , Mudenagudi Uma
Published: September 2021

Underwater image restoration methods require the knowledge of wideband attenuation coefficients per color channel. In this paper, a Generative adversarial network(GAN) framework is proposed for underwater image restoration using the attenuation coefficients as an input. Attenuation coefficients like atmospheric light and transmission map are also estimated in order to restore an underwater image. For atmospheric light estimation, a combination of Res-net and Dense-net architecture is modelled and making it a learning based Residual-Dense Network. Through literature it is evident that, the transmission map describes about the portion of the light that is not scattered and reaches the camera. Transmission map is generated using the underwater image formation model. Further, these attenuation coefficients are given as an input to the proposed GAN architecture for underwater image restoration. The GAN architecture is having a generator block and a discriminator block. The result is demonstrated on synthetic and real underwater data-set and then compared with state-of-the-art method.
Design & Simulation of S Band Planar Microwave Filter for Wireless Application
Authors: Gohil Bhumikaba , Chavda Khyati
Published: September 2021

Reducing the size of today’s wireless communication systems has become an important criterion in filter design with minimal possible value of filter parameters. To achieve this goal in wireless communication system, filter design is the most important part of the signal transmission process. Conventional filters are very important for modern wireless communication systems as they do not provide the desired miniaturization and results. Recently, using metamaterials and dielectric resonators, the filter size has been drastically reduced to a very small size for practical applications. This filter is flat and lightweight. It consumes very low power consumption. In this proposed work, a compact S-band microwave band stop filter was designed using a simple technique. These filters are small in size and light in weight. The design covers the S-band and is suitable for the desired frequency for Wi – Fi and wireless LAN application.
Modelling of partial discharge pulse propagation in transformer windings using multi conductor transmission line modelling scheme
Authors: Achankunju Annie , Joseph Jineeth , Varghese Ancy Sara , Nair K R M
Published: September 2021

Power transformers are considered as an essential component in the electric power systems. They are placed in services in different circumstances like electrical, environmental and mechanical stresses, and through the progress of operation it can affect with various threats and hazards. Transformer failure should be diagnosed earlier in order to avoid its unsafe and unstable operation. The main reason for failure of insulation in power transformer is due to partial discharges. The partial discharge activity occurring in the transformer have to be identified earlier, afore it progresses into a complete discharge, otherwise it results in the maintenance of the device and causes loss of economic cost to the utilities. In this paper, multi conductor transmission line theory is used for the modelling of transformer windings and the partial discharge pulse propagation through the winding is simulated in the MATLAB Simulink. The effect of partial discharge on the windings are also discussed.
Automatic Detection of New Vessels in Retinal Photographs using Machine Learning
Authors: Moorthi M , Gopikrishnan M. , Pooja V , Ragavi Priya M R , Vasanthakumar D K
Published: September 2021

Diabetic Retinopathy is a rare micro-vascular complication that leads to severe ocular disorder or visual impairment. This complication is unidentified due to the mild progression of its symptoms that is associated with Diabetic Mellitus. The disease is categorized by the development of abnormal new vessels developing the optical disc of the retina of the eye. In this paper, an automatic detection system for detecting new vessels in the optical disc region is detected by using image processing techniques in advance. Segmenting the vessels from the optic disc by extracting the desired region of interest. Based on this, the features are extracted and detected as the presence of new vessels in the optic disc by using a Support Vector Machine (SVM) classifier. This automatic system is trained and tested by using 65 normal images that are categorized as 33 normal images and 32 abnormal images. The discrimination performance of the system shows an accuracy of 95.38% in detecting the new vessels. It obtained sensitivity and specificity of 96.8% and 93.9% respectively.
Development of data driven adaptive edge detectors for image processing
Authors: Likhith H G , Nishanth H L , Lohit E , Rudramurthy M S , Sushma S A , Keerthan Kumar T G
Published: September 2021

In most vision processing activities, the early stage involves identifying the features in an image that provide cues to structure and properties of the object in the scene. Most common features in an image or in a scene arel edges. Edges arel significant local changes in intensity within anlimage. Most important goal of edge detection is to produce a line drawing from anlimage representing the scene. The significant features of an image such as line, curve and corners can be extracted from edges. During the stage of discovering and exploring the information from an image of that scene, edge detection is the most important and early-stage activity and as such it is prominent active area in image processing. Most popular edge detection algorithm such as Robert, Sobel, Canny, Prewitt and Laplacian of Gaussian (LoG), etc. are currently in use. This paper emphasis on an experimental study of limitations of conventional edge detectors and to devise a novel approach to resolve the conflicting issues i.e., limitations of these edge detectors in adaptive space utilizing novel methods such as Bi-dimensional Empirical Mode Decomposition (BEMD), Image Empirical Mode Decomposition (IEMD), Complete Ensemble Empirical Mode Decomposition (CEEMD) and Multivariate Decomposition techniques. Further, to study the performance of these modified edge detectors on the images of complex scenes which are of societal and agricultural importance.
Effect of Thermal Barrier Coating in Sustainable Power production of Gas Turbines
Authors: Ghosh S , Saha M , Bakshi S , Mondal S
Published: September 2021

Gas turbine play an important role in producing power for aviation industries. A sustainable operating life with high efficiency of a gas turbine requires very high temperature operation. The increase in thermal conductivity with temperature results in high thermal stress which leads to permanent deformation reducing blade life and efficiency. A thermal barrier coating is crucial to remove such detrimental effects. In the present work, an assessment of gas turbine blade materials has been carried out to study the effect of various coating techniques to improve longevity of blades. Coating methods have been found to play an important role in determining the sustainability of gas turbine performance. It was revealed that such coatings considerably reduce the heat penetration rate and thus increase the service life of gas turbine blades.
On the 𝐶_{𝑝}-equivariant dual Steenrod algebra
Authors: Sankar Krishanu , Wilson Dylan
Published: September 2021

We compute the

C
p

C_p

-equivariant dual Steenrod algebras associated to the constant Mackey functors

F

_

p

\underline {\mathbb {F}}_p

and

Z

_

(
p
)

\underline {\mathbb {Z}}_{(p)}

, as

H

Z

_

(
p
)

\mathrm {H}\underline {\mathbb {Z}}_{(p)}

-modules. The

C
p

C_p

-spectrum

H

F

_

p

∧

H

F

_

p

\mathrm {H}\underline {\mathbb {F}}_p \wedge \mathrm {H}\underline {\mathbb {F}}_p

is not a direct sum of

R
O
(

C
p

)

RO(C_p)

-graded suspensions of

H

F

_

p

\mathrm {H}\underline {\mathbb {F}}_p

when

p
p

is odd, in contrast with the classical and

C
2

C_2

-equivariant dual Steenrod algebras.
Demazure crystals for Kohnert polynomials
Authors: Assaf Sami
Published: September 2021

Kohnert polynomials are polynomials indexed by unit cell diagrams in the first quadrant defined earlier by the author and Searles that give a common generalization of Schubert polynomials and Demazure characters for the general linear group. Demazure crystals are certain truncations of normal crystals whose characters are Demazure characters. For each diagram satisfying a southwest condition, we construct a Demazure crystal whose character is the Kohnert polynomial for the given diagram, resolving an earlier conjecture of the author and Searles that these polynomials expand nonnegatively into Demazure characters. We give explicit formulas for the expansions with applications including a characterization of those diagrams for which the corresponding Kohnert polynomial is a single Demazure character.
In-home medication management by older adults: a modified ethnography study using digital photography walkabouts
Authors: Faisal Sadaf , Ivo Jessica , McMillan Colleen , Grindrod Kelly , Patel Tejal
Published: September 2021

Background

Medication mismanagement can lead to non-optimal management of chronic diseases and poor health outcomes.

Objective

The purpose of this study was to better understand meanings associated with in-home medication management and storage practices of older adults with chronic diseases.

Methods

A modified ethnographic approach using digital photography walkabouts, observation protocols and field notes were used to document in-home medication organisation and storage locations. Thematic analysis was used to generate themes and sub-themes.

Results

Data from multiple home visits of 10 participants (mean age = 76 years; 80% females) including 30 photographs, 10 observation protocols and field notes were analysed. The average number of medications used was reported to be 11.1 (range: 5–20). Themes and sub-themes include choice of storage location (sub-themes: impact on medication behaviour, visibility of medications and storage with other items), knowledge regarding appropriate medication storage conditions (sub-themes: impact on safety of patient and impact on stability of medications) and systems to manage in-home medication intake.

Discussion

In-home medication management reflects older adults perspectives regarding privacy, medication taking routine, knowledge about safe and effective storage and organisation systems. The lack of knowledge causing inappropriate medication storage not only impacts the stability of medications, but also increases risk of medication errors and safety, ultimately affecting medication intake behaviours.
Sodium hyperaccumulators in the Caryophyllales are characterized by both abnormally large shoot sodium concentrations and [Na]shoot/[Na]root quotients greater than unity
Authors: Neugebauer Konrad , Broadley Martin R , El-Serehy Hamed A , George Timothy S , Graham Neil S , Thompson Jacqueline A , Wright Gladys , White Philip J
Published: September 2021

Background and Aims

Some Caryophyllales species accumulate abnormally large shoot sodium (Na) concentrations in non-saline environments. It is not known whether this is a consequence of altered Na partitioning between roots and shoots. This paper tests the hypotheses (1) that Na concentrations in shoots ([Na]shoot) and in roots ([Na]root) are positively correlated among Caryophyllales, and (2) that shoot Na hyperaccumulation is correlated with [Na]shoot/[Na]root quotients.

Methods

Fifty two genotypes, representing 45 Caryophyllales species and 4 species from other angiosperm orders, were grown hydroponically in a non-saline, complete nutrient solution. Concentrations of Na in shoots and in roots were determined using inductively coupled plasma mass spectrometry (ICP-MS).

Key Results

Sodium concentrations in shoots and roots were not correlated among Caryophyllales species with normal [Na]shoot, but were positively correlated among Caryophyllales species with abnormally large [Na]shoot. In addition, Caryophyllales species with abnormally large [Na]shoot had greater [Na]shoot/[Na]root than Caryophyllales species with normal [Na]shoot.

Conclusions

Sodium hyperaccumulators in the Caryophyllales are characterized by abnormally large [Na]shoot, a positive correlation between [Na]shoot and [Na]root, and [Na]shoot/[Na]root quotients greater than unity.
Two Ecological Tools for Testing Slowness of Information Processing in Italian Patients with Moderate-to-Severe Traumatic Brain Injury
Authors: Valentini Federica , Fabio Valentina , Boccia Maddalena , Tanzilli Antonio , Iannetti Manuela , Cinelli Maria Cristina , De Angelis Carmela , Fasotti Luciano , Formisano Rita , Guariglia Cecilia , Ciurli Maria Paola
Published: September 2021

Slowness of Information Processing (SIP) is frequently experienced after traumatic brain injury (TBI); however, the impact of SIP on everyday functioning may be underestimated by standard neuropsychological tests. Objective: we aimed to adapt two ecological instruments assessing SIP in Italian patients with moderate-to-severe TBI, as formerly proposed by Winkens and colleagues for persons with stroke, testing also its possible relation with other neuropsychological processes and functional outcomes. Method: we performed an observational study on 37 patients with moderate-to-severe TBI and 35 demographically matched healthy controls, who underwent the Mental Slowness Observation Test (MSOT) and the Mental Slowness Questionnaire (MSQ), which had been adapted through a pilot study on independent sample of participants; extensive neuropsychological and functional evaluations were performed as well. Results: We found good clinical and psychometric properties of the Italian adaptation of the MSOT and MSQ; also, performance on MSOT significantly correlated with executive functions. Moreover, patients with TBI are significantly slower and less accurate than healthy controls on the MSOT, in particular in tasks with time limits. Even if the subjective feeling of SIP does not differ between patients and controls, we found a significant correlation between MSQ and MSOT. Finally, the performances on the MSOT correlated with measures of functional outcome and community integration. Conclusions: the results support the use of the MSOT and the MSQ to measure SIP in an ecological fashion in patients with TBI, so that specific treatments for persons with acquired brain injury can be prescribed.
Impact of Psychiatric Illnesses and Selective Serotonin Reuptake Inhibitor Medications on Baseline Neurocognitive Testing
Authors: Asfaw Zerubabbel K , Hannah Theodore C , Ali Muhammad , Li Adam Y , Spiera Zachary , Marayati Naoum Fares , Kalagara Roshini , Dreher Nickolas , Schupper Alexander J , Gometz Alex , Lovell Mark R , Choudhri Tanvir
Published: September 2021

Introduction

Neurocognitive tests are an integral component of sport-related concussion (SRC) workup. A history of psychiatric illness (HPI) is common among young athletes. Investigations of factors that influence athletes’ baseline neurocognitive function are crucial for an accurate assessment of SRC.

Objective

In this study, we aim to elucidate the effect of HPI and selective-serotonin reuptake inhibitor (SSRI) medication use on baseline neurocognitive performance in young athletes.

Methods

We conducted a retrospective cross-sectional study of Immediate Post-Concussion Assessment and Cognitive Testing assessments. A total of 268 athletes with HPI and a control group of 6,364 athletes were included. The outcomes were total symptom score based on post-concussion symptom scale, verbal memory, visual memory, visual motor, reaction time, and impulse control scores with self-reported HPI status and SSRI use.

Results

Athletes with HPI had an elevated symptom score in both univariate analysis (p < .0001) and multivariate analysis (p < .0001). HPI influence on visual memory score was not robust to multivariate analysis (p = .24). Athletes with HPI who reported SSRI medication use had the same baseline neurocognitive performance as other athletes with HPI. HPI influences athletes’ baseline neurocognitive performance by elevating symptom scores. HPI does not alter any of the objective neurocognitive composite scores in contrast to previous work.

Conclusions

Clinicians should consider the impact of HPI on baseline neurocognitive performance during the assessment of a suspected SRC. Additional research is required to bolster our findings on SSRI use and ascertain the effects of other drug classes on baseline neurocognitive performance.
Disease category-specific annotation of variants using an ensemble learning framework
Authors: Cao Zhen , Huang Yanting , Duan Ran , Jin Peng , Qin Zhaohui S , Zhang Shihua
Published: September 2021

Understanding the impact of non-coding sequence variants on complex diseases is an essential problem. We present a novel ensemble learning framework—CASAVA, to predict genomic loci in terms of disease category-specific risk. Using disease-associated variants identified by GWAS as training data, and diverse sequencing-based genomics and epigenomics profiles as features, CASAVA provides risk prediction of 24 major categories of diseases throughout the human genome. Our studies showed that CASAVA scores at a genomic locus provide a reasonable prediction of the disease-specific and disease category-specific risk prediction for non-coding variants located within the locus. Taking MHC2TA and immune system diseases as an example, we demonstrate the potential of CASAVA in revealing variant-disease associations. A website (http://zhanglabtools.org/CASAVA) has been built to facilitate easily access to CASAVA scores.
Mining plant endogenous target mimics from miRNA–lncRNA interactions based on dual-path parallel ensemble pruning method
Authors: Kang Qiang , Meng Jun , Su Chenglin , Luan Yushi
Published: September 2021

The interactions between microRNAs (miRNAs) and long non-coding RNAs (lncRNAs) play important roles in biological activities. Specially, lncRNAs as endogenous target mimics (eTMs) can bind miRNAs to regulate the expressions of target messenger RNAs (mRNAs). A growing number of studies focus on animals, but the studies on plants are scarce and many functions of plant eTMs are unknown. This study proposes a novel ensemble pruning protocol for predicting plant miRNA–lncRNA interactions at first. It adaptively prunes the base models based on dual-path parallel ensemble method to meet the challenge of cross-species prediction. Then potential eTMs are mined from predicted results. The expression levels of RNAs are identified through biological experiment to construct the lncRNA–miRNA–mRNA regulatory network, and the functions of potential eTMs are inferred through enrichment analysis. Experiment results show that the proposed protocol outperforms existing methods and state-of-the-art predictors on various plant species. A total of 17 potential eTMs are verified by biological experiment to involve in 22 regulations, and 14 potential eTMs are inferred by Gene Ontology enrichment analysis to involve in 63 functions, which is significant for further research.
Drug–drug interaction prediction with learnable size-adaptive molecular substructures
Authors: Nyamabo Arnold K , Yu Hui , Liu Zun , Shi Jian-Yu
Published: September 2021

Drug–drug interactions (DDIs) are interactions with adverse effects on the body, manifested when two or more incompatible drugs are taken together. They can be caused by the chemical compositions of the drugs involved. We introduce gated message passing neural network (GMPNN), a message passing neural network which learns chemical substructures with different sizes and shapes from the molecular graph representations of drugs for DDI prediction between a pair of drugs. In GMPNN, edges are considered as gates which control the flow of message passing, and therefore delimiting the substructures in a learnable way. The final DDI prediction between a drug pair is based on the interactions between pairs of their (learned) substructures, each pair weighted by a relevance score to the final DDI prediction output. Our proposed method GMPNN-CS (i.e. GMPNN + prediction module) is evaluated on two real-world datasets, with competitive results on one, and improved performance on the other compared with previous methods. Source code is freely available at https://github.com/kanz76/GMPNN-CS.
Are dropout imputation methods for scRNA-seq effective for scATAC-seq data?
Authors: Liu Yue , Zhang Junfeng , Wang Shulin , Zeng Xiangxiang , Zhang Wei
Published: September 2021

The tremendous progress of single-cell sequencing technology has given researchers the opportunity to study cell development and differentiation processes at single-cell resolution. Assay of Transposase-Accessible Chromatin by deep sequencing (ATAC-seq) was proposed for genome-wide analysis of chromatin accessibility. Due to technical limitations or other reasons, dropout events are almost a common occurrence for extremely sparse single-cell ATAC-seq data, leading to confusion in downstream analysis (such as clustering). Although considerable progress has been made in the estimation of scRNA-seq data, there is currently no specific method for the inference of dropout events in single-cell ATAC-seq data. In this paper, we select several state-of-the-art scRNA-seq imputation methods (including MAGIC, SAVER, scImpute, deepImpute, PRIME, bayNorm and knn-smoothing) in recent years to infer dropout peaks in scATAC-seq data, and perform a systematic evaluation of these methods through several downstream analyses. Specifically, we benchmarked these methods in terms of correlation with meta-cell, clustering, subpopulations distance analysis, imputation performance for corruption datasets, identification of TF motifs and computation time. The experimental results indicated that most of the imputed peaks increased the correlation with the reference meta-cell, while the performance of different methods on different datasets varied greatly in different downstream analyses, thus should be used with caution. In general, MAGIC performed better than the other methods most consistently across all assessments. Our source code is freely available at https://github.com/yueyueliu/scATAC-master.
The advantages of being crepuscular for bees: major pollen gain under low competition during the brief twilight period
Authors: Araújo Priscila de Cássia Souza , de Araujo Fernanda Figueiredo , Mota Theo , Schlindwein Clemens
Published: September 2021

The shift in flight activity from daylight to twilight in crepuscular bees is assumed to have evolved to escape competitors, but quantitative confirmation of this hypothesis has never been demonstrated clearly. Pseudobombax longiflorum is a chiropterophilous plant, with flowers presenting large amounts of pollen throughout anthesis, thus attracting not only nocturnal visitors, but also crepuscular and diurnal bees. In this dynamic system, the fraction of pollen that flows to different visitors and the putative role of bees in pollination remain unknown. We analysed floral biology, the frequency of visitors in periods with different light intensities and the rate of pollen removal by each visitor group. A pollinator-exclusion experiment showed that bees were not pollinators of Pseudobombax longiflorum, although they collected > 60% of the pollen from their flowers. Crepuscular bees gained the greatest amount of pollen in the few minutes when they foraged without either nocturnal or diurnal competitors, confirming the advantage of foraging under low light. During the short twilight period, these bees foraged alone and removed 26.5 and 15 times more pollen per minute than nocturnal and diurnal visitors, respectively. Therefore, pollen removal by crepuscular bees is particularly efficient when they forage in the brief period while competitors are absent.
Single-cell analysis of prostaglandin E2-induced human decidual cell in vitro differentiation: a minimal ancestral deciduogenic signal
Authors: Stadtmauer Daniel J , Wagner Günter P
Published: September 2021

The decidua is a hallmark of reproduction in many placental mammals. Differentiation of decidual stromal cells is known to be induced by progesterone and the cyclic AMP/protein kinase A (cAMP/PKA) pathway. Several candidates have been identified as the physiological stimulus for adenylyl cyclase activation, but their relative importance remains unclear. To bypass this uncertainty, the standard approach for in vitro experiments uses membrane-permeable cAMP and progestin. We phylogenetically infer that prostaglandin E2 (PGE2) likely was the signal that ancestrally induced decidualization in conjunction with progesterone. This suggests that PGE2 and progestin should be able to activate the core gene regulatory network of decidual cells. To test this prediction, we performed a genome-wide study of gene expression in human endometrial fibroblasts decidualized with PGE2 and progestin. Comparison to a cAMP-based protocol revealed shared activation of core decidual genes and decreased induction of senescence-associated genes. Single-cell transcriptomics of PGE2-mediated decidualization revealed a distinct, early-activated state transitioning to a differentiated decidual state. PGE2-mediated decidualization was found to depend upon progestin-dependent induction of PGE2 receptor 2 (PTGER2) which in turn leads to PKA activation upon PGE2 stimulation. Progesterone-dependent induction of PTGER2 is absent in opossum, an outgroup taxon of placental mammals which is incapable of decidualization. Together, these findings suggest that the origin of decidualization involved the evolution of progesterone-dependent activation of the PGE2/PTGER2/PKA axis, facilitating entry into a PKA-dominant rather than AKT-dominant cellular state. We propose the use of PGE2 for in vitro decidualization as an alternative to 8-Br-cAMP.
Domestic Violence Policing of First Nations Women in Australia: ‘Settler’ Frameworks, Consequential Harms and the Promise of Meaningful Self-Determination
Authors: Buxton-Namisnyk Emma
Published: September 2021

This article analyses domestic violence fatality reviews/coronial files for a whole-of-population study of First Nations women killed by male partners across several Australian jurisdictions between 2006 and 2016, alongside yarning/interviews with First Nations domestic violence workers, violence survivors and Elders. Findings show that most women had domestic violence-related police contact before their deaths, and these interactions were frequently harmful. Harms resulted from police inaction, including failures to respond or enforce the law. Harms also resulted from police action, with policing enhancing state surveillance of victims’ families, eroding victims’ autonomy and criminalizing victims. Findings are located in neo-colonial context, emphasizing a policy need for meaningful Indigenous self-determination and reinforcing the importance of inclusive disciplinary and epistemological practices in gender-based violence criminology.
Phylogenetics ofOchna(Ochnaceae) and a new infrageneric classification
Authors: Shah Toral , Mashimba Fandey H , Suleiman Haji O , Mbailwa Yahya S , Schneider Julio V , Zizka Georg , Savolainen Vincent , Larridon Isabel , Darbyshire Iain
Published: September 2021

Advances in high-throughput DNA sequencing are allowing faster and more affordable generation of molecular phylogenetic trees for many organisms. However, resolving relationships at species level is still challenging, particularly for taxonomically difficult groups. Until recently, the classification of Ochna had been based only on morphological data. Here, we present the first comprehensive phylogenomic study for the genus using targeted sequencing with a custom probe kit. We sampled c. 85% of species to evaluate the current infrageneric classification. Our results show that the data generated using the custom probe kit are effective in resolving relationships in the genus, revealing three sections consistent with the current classification and a new section consisting of species from Madagascar and the Mascarene Islands. Our results provide the first insights into the evolutionary relationships of several widespread and morphologically diverse species numerous poorly known and potentially new species to science. We demonstrate that for morphologically challenging groups such as Ochna, an integrated approach to classification is essential. Phylogenomic results are only informative when derived from accurately named samples. There is a symbiotic relationship between molecular phylogenomics and morphology-based taxonomy, with taxonomic expertise a requirement to accurately interpret the phylogenomic results.