bluuebunny/crossref_metadata_2025 huggingface dataset:
Brain Tumour Detection Using Machine Learning Algorithm
Authors: Keerthana A. , Kavin Kumar B. , Akshaya K.S , Kamalraj S.
Published: June 2021

Brain is the regulatory unit in human body. It controls the functions such as memory, vision, hearing, knowledge, personality, problem solving, etc. The main reason for brain tumour is the abandoned progress of brain cells. Many health organizations have recognized brain tumour as the second foremost dispute that causes a large number of human deaths all around the world. Identification of brain tumour at a premature stage offers a opportunity of effective medical treatment. Use of Magnetic Resonance Imaging images have been recognized as more detailed and more consistent images when compared to Computed Tomography images. There are various techniques to detect brain tumour or neoplasms. The most competent and effective algorithms are discussed in this paper after studying a number of appropriate research papers. Pre-processing brain images, segmenting them, feature extraction, clustering and detection of the tumour are the methodologies in most researches.
A Study on Various Trans-Humeral Prostheses Using Surface EMG
Authors: Maideen Ajmisha , Mohinarathinam A. , Kamalraj S.
Published: June 2021

Upper limb amputation arise due to cardiovascular defects, trauma, health problems, or inborn defects. A disabled person needs an assistive mechanism like the prosthetic arm to perpetrate in their day-to-day activities. A Prosthetic Arm is an artificial system for interfacing my generated signals with external physical activities, which is extensively used to communicate and control the interactions between humans and machines. Many bio-generated signals can be utilized to control prosthetic arm like Surface Electromyogram, Electro Encephalogram, etc. but here using Surface Electromyography (s EMG) signal to control Prosthetic arm. s EMG is an inquiry of electrical activity of the striated muscle which is monitored at the surface of the skin. This signal is interfaced with a prosthetic device which helps to improve the quality level of amputees. Maximum movements are the target, which includes movements of fingers, forearm, and trans-humeral areas. The body-generated signal can be extracted using various electronic equipment and can be analyzed actual brain intention on various hand movements. With the support of artificial devices, one can achieve the goal. Neural networks and advanced Embedded System applications are also included in this prostheses implementation. This paper involves the study of various trans-humeral prostheses using electromyogram.
A Survey on Prediction of Protein-Protein Interactions
Authors: Nivedha S , Bhavani S
Published: June 2021

Proteins interacts with one other to form complexes for performing the biological process in a cell. Understanding the interactions of proteins helps in recognizing the disease mechanisms. It plays a major part in the catalytic reaction, drug ability of the molecules. The wet-lab methods are tedious and expensive. However, the data that is identified in experimental methods can be processed and used. Advancement in the field led the prediction using computational approaches for signaling pathways and the recognizable proof for specific diseases. Identification of protein-protein interaction offers a way for advancements of therapeutic methods and drug design. In this paper, an analysis of prediction methodologies that are used for protein-protein interactions was described. Experimental methods and six different computational methods were discussed.
Cost-Effective Breast Cancer Screening to Reduce Mortality Rates of Women in India: A Revolution
Authors: Hamsagayathri P. , Udhayamoorthi M. , Rajakumari K. , Anandkumar N. , Mythili S.
Published: June 2021

Background: Breast cancer is one of the leading cancers among world women. Early detection and early treatment improve the survival rate of the women. In India, breast cancer is primarily identified only at later stages that increase the burden of mortality. Thus, in many developing countries, there exists the scarcity of cost-effective breast cancer screening systems.

Methods: In this paper, we have comprehensively analyzed the limitations of the existing diagnostic tools and overviewed the advancement of microwave technology in breast cancer detection. The principal focus is to augment the survival rate of the women by detecting the cancers at the early stage.

Findings: The comparative studies on sensitivity and specificity values are performed on different diagnostic tools. Microwave-based screening holds 96% sensitivity and 98% specificity values, which certainly reduces false positive and false negative rates. The detection rate is also high as 98% that enables to identify undersized tumors.

Conclusion: Microwave Technology is able to detect the early-stage cancers effectively and the performance exceeds with existing techniques.
Dehydration measurement using sweat sensor patch and pulse sensor
Authors: Manimegala P , Anitha Mary X , Biji Neha , Selshia Shiny V
Published: June 2021

Technology has increased visibly large. People find ways to easily detect their conditions. Heartbeat and dehydration level is one of the most common among people especially athletes. Athletes are drifted towards wearable technologies to track their training and recovery. Usually a dehydrated person sweats more. The sweat contains various physiological substance and health data. Based on studies sweat consists of salts and ions like [Na+], [K+], [Cl] lactate, glucose and ammonia. We are developing a wearable sweat analyser that can detect the raise in concentration of sodium and potassium. The sweat patch consist of a counter electrode created by screen printing Silver Chloride (AgCl) nanoparticles on Polydimithylsiloxane (PDMS) material and reference electrode (RE) created on the patch material by adding a platinum nanoparticle. This sweat sensor patch is connected to a microcontroller along with a temperature sensor and a Bluetooth module. The data which is collected by the sweat patch and the temperature sensor is sent via Bluetooth to mobile application were the data is displayed.
A Secure Ant Colony based Trust Computation Model in Wireless Sensor Networks
Authors: Adline Jancy Y. , Gomathy B. , Benuel Sathish Raj A.
Published: June 2021

We have always observed that the wirelessly connected sensors have difficulties inmaintaining the charge and transmitting data safely from the sender to the receiver end. Due to this the longevity of the nodes doesn’t last longer and the efficiency is found to be very less. The chances of data being lost during the transmission are also very high. This paper deals withthe methods employed to cut short the energy used by the nodes, transmit data safely from sender to receiver and also increase the efficiency. This paper associates algorithms like LEACH, ACO, ECC which in integration performs operations that selects the cluster head, reduces energy consumption. In the proposed technique transmission of data happens from thecluster member to cluster head and from the cluster head to the base station. The results showthat the LEACH-ACO algorithm significantly reduces the average energy consumption. Thethreshold value of each and every task has to be defined manually keeping in mind the risk thatthe task is able to handle. The threshold value differs with different systems. By cross checking the trust value that was obtained by computing and the threshold trust value, it is straight forward to conclude if the particular node is a safe node or an unsafe node. The proposed protocol reduces overall energy consumption by reducing the number of dead nodes, and providing an optimal path with increased packets delivery ratio.
A Safe and Comfort Bed for Cerebral Palsy Babies-Bedstead
Authors: Divakaran Sindu , Bethanney Janney J , Sudhakar T. , Antony Josephine Snowfy W , Melinda Morais M , Krishnakumar S
Published: June 2021

Cerebral palsy is a disorder that affects the muscle tone, movement and motor skills. Few percent of children affected with cerebral palsy experience problems while sleeping. These include seizure attacks, muscle spasms, pain, respiratory problems, etc. Bedstead is a unique bed designed for the cerebral palsy babies, which automatically massages the baby using vibration motors when some discomfort arises. An EMG placed on the baby senses the muscle spasms, which drives the vibration motors. Additionally, an accelerometer sensor detects seizure attacks by the principle of fall detection. During a seizure attack, a technical buzzer will alarm the caregivers, thereby reducing life-threatening conditions. If left untreated, these complications can result in dangerous situations. Another feature of this set up is that the parameters sensed is uploaded in a cloud, and also sent to caregivers as well. This bed ensures that the babies can have a deep and uninterrupted sleep with little or no intervention from the professional caregivers or family members.
IoT Based Water Surface Cleaning and Quality Checking Boat
Authors: Saran Raj B. , Murali L. , Vijayaparamesh B. , Sharan Kumar J. , Pragadeesh P.
Published: June 2021

It is foremost important to keep the water bodies around us to be clean. It is very terrible that huge amount of garbage is deposited on water bodies every year. Monitoring the quality of water that is present in the water bodies is also very important in today’s world. There is a group of people who keep on monitoring the changes that occurs on the surroundings who are named as Catchment Management Authorities. Catchment Management Authorities monitor and provide real-time day to day updates on environmental protection and tracking pollution sources. A cheap non-wired aquatic monitoring system will enable cost effective water quality measurement by collecting data as well as assisting catchment managers to maintain the health of aquatic ecosystem. The work deals with finding a way for the uncertain effects of the floating particles, hence reducing the harmfulness created on behalf of the floating particles that incorporates floating materials and curbs. Our work aims to create a garbage gathering object to monitor the lakes and ponds and also measure the condition of water by measuring several parameters. In addition, it helps to understand the water pollution using data analysis. The main feature of this project is to collect of floating objects from the surface of the water into a trash container. The Secondary work of our project is to measure the water quality by using sensors, by measuring the water quality parameters we can be able to predict the unfavourable conditions of water living creatures. pH Sensor and Turbidity Sensor are used in our project to measure the acidity and basicity of the water and turbidity level of the water respectively. The main motto is to clean the surface of the water and check the quality of the water which helps for aquatic ecosystem.
Development of PIC Based CO2 Monitoring System Device
Authors: Aravind Kumar M , Madhu Shankar K. , Malleeswaran V. , Dharani K.G.
Published: June 2021

Nowadays the air quality affects human health and energy. High levels of dioxide and alternative gasses within the air will cause serious ailments of a personality’s body. Because of the enhanced attention to the current issue, it’s necessary to develop air internal control devices. Consistent with GOST Indoor microclimate parameters’, the suitable level of greenhouse emission in a very space is 600-1000 ppm. However, the proper level of greenhouse emission concentration for health and prosperity is 380-400 ppm. The concentration level of 400-600 ppm is traditional and is usually recommended for children’s rooms, bedrooms, colleges and kindergartens. A greenhouse emission concentration on top of one thousand ppm ends up in general discomfort, weakness, distracted attention and headache. The project is meant to construct a model of the device for observation the content of dioxide and alternative gasses in indoor air. The event relies on the PIC microcontroller board, completely different gas detector modules and therefore the LCD1602 module that may be a interfaced with PIC The detector management program is developed and compiled within the IDE software package shell. The operational principle of the device is that it displays the information (CO2 level and alternative gas level indoor) from the sensors on the digital display screen with a frequency of 5 minutes.
Management of Finger Pulse using Oximeter – A Case Study in Scientific and Marketing Viability
Authors: Amuthan R.
Published: June 2021

The original purpose of this device was to create a lightweight, simple, and easy-to-use pulse oximeter for seniors and others who wanted to check their oxygen saturation at home. A portable pulse oximeter has become a must-have for all Covid-19 patients, particularly those who are quarantined at home, since the virus’s effects on the body require them to constantly track their oxygen saturation levels. The pulse oximeter’s design may be more advantageous to the average hospital patient
Recognition of dicrotic notch in arterial blood pressure pulses using signal processing techniques
Authors: Bethanney Janney J , Umashankar G , Krishnakumar S , Chandana H , Caroline Chriselda L
Published: June 2021

The Physiological condition of cardiovascular system is analyzed by arterial blood pressure pulse wave. The arterial pulse wave displays the genetic traits of the heart, average records of a heartbeat and variation in pressure as the heart spouts blood. This pulse monitoringis a standard process used to assess the cardiovascular system’s medical history. A waveform ofthe Arterial blood pressure usually involves a systolic level, diastolic occurrence, and dicrotic spike and dicrotic notch. The cardiac cavity contracting and relaxing leads to systolic and diastolic blood pressure respectively. The dicrotic notch which is a drop on the down slope shows systole termination and depicts the aorta closure of successive backward stream. The position of the dicrotic notch throughout the cardiac activity differs as per the duration of aortic closure. Dicrotic notch plays an essential part in sclerosis, occlusion, stenosis, arterial spasm and erythromelalgia diagnostic test. Hence Discrete Wavelet transform is utilized in this proposed work to examine and assess the dicrotic notch in arterial pulse wave form. Arterial pulse data are processed using a data acquisition system consisting of multiple channels sensor signal processing and a computer to collect the necessary data for future examination. The uniform peer group of 22 patients has been evaluated utilizing two distinct Haar and Daubuchies4 (db4) wavelet transformations. The peripheral wave in the patients seems to have a sharp rise and a notch on dropping slope, has been identified. The data collected are contrasted between the two techniques, and the Haar wavelet is observed to reasonably represent the best outcome.
Secure Scan Chain using Test Port for Tester Authentication
Authors: Ashna A. , Renita Pearlin T.
Published: June 2021

The principal goal of the paper is to reduce hacking from hackers. In order to reduce this access to the scan architecture of known vector is limited by test authentication and also limits the attempts for the application of test vector to observe the result of scan architecture. If the password is correct it move on to the next step otherwise, it again move to login window.
New Approach to Underwater Image Dehazing using Dark Channel Prior
Authors: Ghate Shubhangi N. , Nikose Mangesh D.
Published: June 2021

Images captured in dense, hazy, foggy atmospheric conditions or in a surrounding filled with impurities, are exposed to deterioration of the captured image which lowers the contrast, color changes, and the object features observed are difficult to recognie by normal human vision and also by some outdoor computer vision systems. This paper analyses the enhancement in the visibility of a single degraded image. The single image is processed to give two or more images of different characteristics and features. The information from these images is used to generate a solitary image with more accurate information of the scene than the original images. To maintain the important features and information of the image for good visibility regions, various parameters are used as filters using dark channel prior technique. The resultant images are then combined and weighed to reduce the unwanted attributes. The resultant image eventually is improved as compared to the resource images.
HEART DISEASE CLASSIFICATION USING MACHINE LEARNING TECHNIQUES
Authors: Radhika R. , Thomas George S.
Published: June 2021

Heart disease is one of the Leading reason for death around the world. In which machine learningis a method that predicts the emerging prospects of Heart Disease. Machine learning is used in taking care of numerous issues in information science. The basic utilization of machine learning is the forecast of a result dependent on already existing information. The machine takes the designs from the current dataset, and it is applied on an obscure dataset to foresee the result. Order method in AI is usually used for expectation. Some arrangement calculations foresee with acceptable precision, while others show a restricted exactness. Here, we play out an order dependent on various arrangement calculations like K-Nearest Neighbour, Support Vector Machine, Naïve Bayes, logistic regression, decision tree algorithm and random forest algorithm
Marine radioisotope gamma-ray spectrum analysis method based on Geant4 simulation and MLP neural network
Authors: Dai W. , Zeng Z. , Dou D. , Ma H. , Cheng J. , Li J. , Zhang H.
Published: June 2021

The monitoring of marine 137Cs using a scintillation detector relies on the spectrum analysis method to extract the 137Cs concentration. However, when in a poor statistics situation, the calculation result of the traditional net-peak-area (NPA) method has large uncertainty. In this paper, therefore, a new machine-learning-based method is presented to better analyze gamma-ray spectra with low 137Cs concentration. A multilayer perceptron (MLP) is applied to analyze the 662-keV full energy peak of 137Cs in seawater spectra; the MLP can be trained with a few measured background spectra by combining the simulated 137Cs spectra with measured background spectra. Thus, the time to prepare and measure standard samples for generating the training dataset can be saved. To validate the MLP-based method, Geant4 and background gamma-ray spectra measured by a seaborne monitoring device are used to generate an independent test dataset for testing the results obtained by the proposed MLP-based and traditional NPA methods. It is found that the MLP-based method achieves a root-mean-square error of 0.159, 2.3 times lower than that of the traditional NPA method, indicating that the MLP-based method improves the precision of 137Cs concentration calculations.
A closed-circulation system for chemical polishing of the 1.3-GHz single-cell copper cavity substrate for niobium sputtering
Authors: Yang F.Y. , Dai J. , Zhang P. , Wang N. , Li Z.Q.
Published: June 2021

A 1.3-GHz copper cavity chemical polishing system has been
developed by the Institute of High Energy Physics. It has been used
for the surface treatment of copper cavity substrates before the
niobium sputtering. The system uses the SUBU polishing agent
developed by the European Organization for Nuclear Research (CERN),
which is a four-component mixed acid with a working temperature of
72∘C. For environmental protection reasons, the system adopts
a fully closed-cycle mode, which prevents acid volatility at high
temperatures to the ambient environment. Based on the sample
experiments, polishing steps have been optimized to avoid as much as
possible the oxidation phenomenon which is easily introduced to the
copper cavity inner surface, including cooling, dilution, and
passivation process. Finally, the surface treatment process of the
closed polishing system was determined based on the optimized
polishing steps, and the oxygen isolation protection method after
treatment was put forward to accommodate long-distance
transportation of the copper cavity before coating. The experimental
results show that the surface of the copper cavity after chemical
polishing was smooth and bright with an average roughness of
0.12 μm.
Summertime linkage between Antarctic sea-ice extent and ice-shelf basal melting through Antarctic coastal water masses’ variability: a circumpolar Southern Ocean model study
Authors: Kusahara Kazuya
Published: June 2021

Recent observations indicate that two cryospheric components, namely the Antarctic sea ice and ice shelf over the Southern Ocean, have been changing over the decades. Here we analyze results from an ocean–sea ice–ice shelf model to examine variability in the Antarctic sea-ice extent and ice-shelf basal melting. The model reproduces seasonal and interannual variability in the Antarctic sea-ice extent and demonstrates that summertime ice-shelf basal melting is closely anti-correlated with the sea-ice extent anomaly. For example, the unprecedented minimum of the Antarctic sea-ice extent in the 2016 spring was accompanied by a substantial increase in the Antarctic ice-shelf melting in the model. Detailed analysis of Antarctic coastal water masses flowing into the ice-shelf cavities illustrates the physical linkage in the strong anti-correlation. This study suggests that the Antarctic summer sea-ice extent in the regions where the sea-ice edge approaches the Antarctic coastline can be a proxy for Antarctic coastal water masses and subsequent ice-shelf basal melting.
Retraction: Spatial analysis of Muslim communities’ convergence in the Nordic European region, in Scandinavian countries (IOP Conf. Ser.: Earth Environ. Sci.
302 012073)
Authors: Yu Eidemiller K , Krasnozhenova E E , Samylovskaya E A , A Kudryavtseva R-E
Published: June 2021

IOP Publishing Limited ("IOPP") is retracting this paper following an investigation which resulted from a reader complaint. The paper makes a number of unsubstantiated claims and, in the opinion of IOPP, lacks scientific rigour, clarity and objectivity. Although the paper was subject to peer review via the conference organisers, unfortunately these issues were not identified on this occasion. IOPP also has concerns regarding the discriminatory tone and sentiment of the paper, which are not appropriate in a scientific journal.

IOPP expresses its thanks to the readers and independent advisors who have shared their thoughts regarding this paper during the course of the investigation.

Retraction published: 23 June 2021
Vibration Analysis of a multi core sandwich composite beam with cutouts—A critical investigation
Authors: Senthilkumaran P , Venkatachalam R , Venkatesh Raja K
Published: June 2021

The prime objective of this research work is focused on assessing the dynamic characteristics of a multi core sandwich composite beam with cutouts.The effect of introducing additional cores in sandwich beams is investigated in detail along with various geometric configurations. Also, the impact of placing multiple cutouts with assorted geometric profiles (Circular, Square, Hexagon and Triangular) in a sandwich structure is also addressed in detail. Finite element technique is deployed for numerical analysis and parametric studies. In addition, the influence of reinforcements in the proposed structure is also presented. From the results, it is evident that square cutouts possess the highest damping capability irrespective of geometric position of its placement. Also, it may be concluded that the geometric position of placing cutoutssignificantly influences the magnitude of frequency levels ranging to maximum of 42% hike at lower modes.Moreover, introduction of cutouts leads to reduction in frequency levels for all the cases up to 20%. Furthermore, secondary investigation of multicore reinforced sandwich beams experiences a maximum reduction in frequency of about 15%.
Cloud condensation nuclei characteristics at the Southern Great Plains site: role of particle size distribution and aerosol hygroscopicity
Authors: Patel Piyushkumar N , Jiang Jonathan H
Published: June 2021

The activation ability of aerosols as cloud condensation nuclei (CCN) is crucial in climate and hydrological cycle studies, but their properties are not well known. We investigated the long-term measurements of atmospheric aerosol properties, CCN concentrations (NCCN) at supersaturation (SS = 0.1%–1.0%), and hygroscopicity at the Department of Energy’s Southern Great Plains (SGP) site to illustrate the dependence of NCCN on aerosol properties and transport pathways. Cluster analysis was applied to the back trajectories of air masses to investigate their respective source regions. The results showed that aged biomass burning aerosols from Central America were characterized by higher accumulation mode particles (Naccu; median value 805 cm−3) and relatively high aerosol hygroscopicity (κ; median value ∼0.25) values that result in the higher CCN activation and relatively high NCCN (median value 258–1578 cm−3 at a SS of 0.1%–1.0%). Aerosols from the Gulf of Mexico were characterized by higher Naccu (∼35%), and NCCN (230–1721 cm−3 at a SS of 0.1%–1.0%) with the lowest κ (∼0.17). In contrast, relatively high nucleation mode particles (Nnucl; ∼20%) and low NCCN (128–1553 cm−3 at a SS of 0.1%–1.0%) with higher κ (∼0.30) values were observed on the aerosols associated with a westerly wind. The results indicate particle size as the most critical factor influencing the ability of aerosols to activate, whereas the effect of chemical composition was secondary. Our CCN closure analysis suggests that chemical composition and mixing state information are more crucial at lower SS, whereas at higher SS, most particles become activated regardless of their chemical composition and size. This study affirms that soluble organic fraction information is required at higher SS for better NCCN prediction, but both the soluble organics fraction and mixing state are vital to reduce the NCCN prediction uncertainty at lower SS.
Metaheuristically optimized nano-MgO additive in freeze-thaw resistant concrete: a charged system search-based approach
Authors: Yazdchi Mehdi , Foroughi Asl Ali , Talatahari Siamak , Sareh Pooya
Published: June 2021

With progressive advances in the synthesis, characterization, and commercialization of nanoparticles and nanomaterials, these modern engineered materials are becoming an ingredient of innovative structural materials for various applications in civil and construction engineering. In this research, MgO nanoparticles were systematically added to normal concrete samples in order to investigate the effect of these nanomaterials on the durability of the samples under freeze and thaw conditions. The compressive and tensile strengths as well as the permeability of concrete samples containing nanoparticles were measured and compared with the corresponding values of control samples without nanoparticles. The curing time of the concrete samples, the amount of nanoparticles, and the water-cement ratios (w/c) were the variables of the experiments. Moreover, data clustering and the Charged System Search (CSS) algorithm were utilized as the numerical analysis and optimization methods. The regression analysis before clustering and after clustering proved the process of clustering is a prerequisite of regression analysis. Furthermore, the CSS optimization method showed that the optimum amount of nano MgO is 1% of the weight of cement, which can increase the compressive strength of concrete by 9.12% more than plain samples over 34 days.
Topological data analysis of task-based fMRI data from experiments on schizophrenia
Authors: Stolz Bernadette J , Emerson Tegan , Nahkuri Satu , Porter Mason A , Harrington Heather A
Published: June 2021

We use methods from computational algebraic topology to study functional brain networks in which nodes represent brain regions and weighted edges encode the similarity of functional magnetic resonance imaging (fMRI) time series from each region. With these tools, which allow one to characterize topological invariants such as loops in high-dimensional data, we are able to gain understanding of low-dimensional structures in networks in a way that complements traditional approaches that are based on pairwise interactions. In the present paper, we use persistent homology to analyze networks that we construct from task-based fMRI data from schizophrenia patients, healthy controls, and healthy siblings of schizophrenia patients. We thereby explore the persistence of topological structures such as loops at different scales in these networks. We use persistence landscapes and persistence images to represent the output of our persistent-homology calculations, and we study the persistence landscapes and persistence images using k-means clustering and community detection. Based on our analysis of persistence landscapes, we find that the members of the sibling cohort have topological features (specifically, their one-dimensional loops) that are distinct from the other two cohorts. From the persistence images, we are able to distinguish all three subject groups and to determine the brain regions in the loops (with four or more edges) that allow us to make these distinctions.
On the fragmentation phenomenon in the population optimization problem
Authors: Heo Junyoung , Kim Yeonho
Published: June 2021

In this paper, we study the population optimization problem in the logistic reaction-diffusion model. The issue of maximizing the total population in a heterogeneous environment has attracted the attention of many researchers. For the

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-dimensional box domain, it has recently been shown that resource fragmentation is better than concentration in order to maximize the total population when the diffusion rate is sufficiently small. As resource concentration is known to be beneficial for the survival of the species, this contrasting phenomenon is quite surprising. We proved that the fragmentation phenomenon occurs for any general bounded domains in

R

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\mathbb {R}^n

if the diffusion rate is sufficiently small.
A troponin T variant linked with pediatric dilated cardiomyopathy reduces the coupling of thin filament activation to myosin and calcium binding
Authors: Barrick Samantha K. , Greenberg Lina , Greenberg Michael J.
Published: June 2021

A variant in troponin T associated with dilated cardiomyopathy, R134G, decouples thin filament activation from calcium and myosin binding. These defects lead to molecular and cellular hypocontractility and sarcomeric disorganization in cardiomyocytes, demonstrating that altered molecular forces drive the disease pathogenesis.
The Golgi-associated retrograde protein (GARP) complex plays an essential role in the maintenance of the Golgi glycosylation machinery
Authors: Khakurel Amrita , Kudlyk Tetyana , Bonifacino Juan S. , Lupashin Vladimir V.
Published: June 2021

This study uncovers that the Golgi-associated retrograde protein (GARP) complex is essential for the recycling and stability of Golgi glycosylation machinery.
Circadian Control of Sodium and Blood Pressure Regulation
Authors: Soliman Reham H , Pollock David M
Published: June 2021

The attention for the control of dietary risk factors involved in the development of hypertension, includes a large effort on dietary salt restrictions. Ample studies show the beneficial role of limiting dietary sodium as a lifestyle modification in the prevention and management of essential hypertension. Not until the past decade or so have studies more specifically investigated diurnal variations in renal electrolyte excretion, which led us to the hypothesis that timing of salt intake may impact cardiovascular health and blood pressure regulation. Cell autonomous molecular clocks as the name implies, function independently to maintain optimum functional rhythmicity in the face of environmental stressors such that cellular homeostasis is maintained at all times. Our understanding of mechanisms influencing diurnal patterns of sodium excretion and blood pressure has expanded with the discovery of the circadian clock genes. In this review, we discuss what is known about circadian regulation of renal sodium handling machinery and its influence on blood pressure regulation, with timing of sodium intake as a potential modulator of the kidney clock.
Can we have it all? Repurposing target capture for repeat genomics. A commentary on: ‘Aiming off the target: recycling target capture sequencing reads for investigating repetitive DNA’
Authors: Heitkam Tony , Garcia Sònia
Published: June 2021

This article comments on:

Lucas Costa, André Marques, Chris Buddenhagen, William Wayt Thomas, Bruno Huettel, Veit Schubert, Steven Dodsworth, Andreas Houben, Gustavo Souza and Andrea Pedrosa-Harand, Aiming off the target: recycling target capture sequencing reads for investigating repetitive DNA, Annals of Botany, Volume 128, Issue 7, 2 December 2021, Pages 835–848 https://doi.org/10.1093/aob/mcab063
Large-scale preparation of sialyl-Tn antigen-containing peptides from mucin-like glycoproteins in boar seminal gel
Authors: Takeuchi Ryota , Maeda Megumi , Nakano Miran , Funahashi Hiroaki , Kimura Yoshinobu
Published: June 2021

Sialyl-Tn antigen, a tumor antigen, is a valuable ligand for the purification of proteins that specifically bind to it. Here, we developed a new method for the preparation of large amounts of sialyl-Tn antigen-containing peptides from an unused resource, boar seminal gel. The glycopeptides were prepared from the actinase E digests by a combination of gel filtration and hydrophilic partitioning.
AVPIden: a new scheme for identification and functional prediction of antiviral peptides based on machine learning approaches
Authors: Pang Yuxuan , Yao Lantian , Jhong Jhih-Hua , Wang Zhuo , Lee Tzong-Yi
Published: June 2021

Antiviral peptide (AVP) is a kind of antimicrobial peptide (AMP) that has the potential ability to fight against virus infection. Machine learning-based prediction with a computational biology approach can facilitate the development of the novel therapeutic agents. In this study, we proposed a double-stage classification scheme, named AVPIden, for predicting the AVPs and their functional activities against different viruses. The first stage is to distinguish the AVP from a broad-spectrum peptide collection, including not only the regular peptides (non-AMP) but also the AMPs without antiviral functions (non-AVP). The second stage is responsible for characterizing one or more virus families or species that the AVP targets. Imbalanced learning is utilized to improve the performance of prediction. The AVPIden uses multiple descriptors to precisely demonstrate the peptide properties and adopts explainable machine learning strategies based on Shapley value to exploit how the descriptors impact the antiviral activities. Finally, the evaluation performance of the proposed model suggests its ability to predict the antivirus activities and their potential functions against six virus families (Coronaviridae, Retroviridae, Herpesviridae, Paramyxoviridae, Orthomyxoviridae, Flaviviridae) and eight kinds of virus (FIV, HCV, HIV, HPIV3, HSV1, INFVA, RSV, SARS-CoV). The AVPIden gives an option for reinforcing the development of AVPs with the computer-aided method and has been deployed at http://awi.cuhk.edu.cn/AVPIden/.
SOMDE: a scalable method for identifying spatially variable genes with self-organizing map
Authors: Hao Minsheng , Hua Kui , Zhang Xuegong
Published: June 2021

Motivation

Recent developments of spatial transcriptomic sequencing technologies provide powerful tools for understanding cells in the physical context of tissue microenvironments. A fundamental task in spatial gene expression analysis is to identify genes with spatially variable expression patterns, or spatially variable genes (SVgenes). Several computational methods have been developed for this task. Their high computational complexity limited their scalability to the latest and future large-scale spatial expression data.

Results

We present SOMDE, an efficient method for identifying SVgenes in large-scale spatial expression data. SOMDE uses self-organizing map to cluster neighboring cells into nodes, and then uses a Gaussian process to fit the node-level spatial gene expression to identify SVgenes. Experiments show that SOMDE is about 5–50 times faster than existing methods with comparable results. The adjustable resolution of SOMDE makes it the only method that can give results in ∼5 min in large datasets of more than 20 000 sequencing sites. SOMDE is available as a python package on PyPI at https://pypi.org/project/somde free for academic use.

Availability and implementation

SOMDE is available for download from PyPI, and the source code is openly available from the Github repository https://github.com/XuegongLab/somde.

Supplementary information

Supplementary data are available at Bioinformatics online.
State of assisted reproduction technology in the coronavirus disease 2019 era and consequences on human reproductive system
Authors: Chandi Anadeep , Jain Neelam
Published: June 2021

The outbreak of the coronavirus disease 2019 (COVID-19) has created havoc on the socio-economic aspect of the world. With billions of lives being affected by this wrecking pandemic, global fertility services were also not left untouched by its impact. The possibility of sexual transmission of SARS-CoV-2 virus, its impact on male and female fertility, pregnancy, its potential teratogenic effect, and handling of gametes in the clinical laboratories were major concerns among reproductive medicine specialists, which led down all the reproductive health services, including IUI, IVF/ICSI in most of the countries. Even the people did not intend to conceive during the pandemic crisis and were hesitant to avail such services. Discrete evidence regarding the pathophysiology of COVID-19 infection and its impact on the human reproductive system is not very clear. In this review article, we intend to incorporate all the evidence related to the COVID-19 infection and its impact on human reproduction available to date. It is our responsibility to provide rightful information and to keep our patients familiar with the existing lack of clear evidence. In this COVID-19 era, it is important that the fertility management be prioritized in sub-fertile couples with diminished fertility reserve and high-risk conditions, like malignancies, that may affect their long-term fertility prospects.
Testing for unit roots based on sample autocovariances
Authors: Chang Jinyuan , Cheng Guanghui , Yao Qiwei
Published: June 2021

We propose a new unit-root test for a stationary null hypothesis $H_0$ against a unit-root alternative $H_1$. Our approach is nonparametric as $H_0$ assumes only that the process concerned is $I(0)$, without specifying any parametric forms. The new test is based on the fact that the sample autocovariance function converges to the finite population autocovariance function for an $I(0)$ process, but diverges to infinity for a process with unit roots. Therefore, the new test rejects $H_0$ for large values of the sample autocovariance function. To address the technical question of how large is large, we split the sample and establish an appropriate normal approximation for the null distribution of the test statistic. The substantial discriminative power of the new test statistic is due to the fact that it takes finite values under $H_0$ and diverges to infinity under $H_1$. This property allows one to truncate the critical values of the test so that it has asymptotic power 1; it also alleviates the loss of power due to the sample-splitting. The test is implemented in $\texttt{R}$.
Inverse moment methods for sufficient forecasting using high-dimensional predictors
Authors: Luo Wei , Xue Lingzhou , Yao Jiawei , Yu Xiufan
Published: June 2021

We consider forecasting a single time series using a large number of predictors in the presence of a possible nonlinear forecast function. Assuming that the predictors affect the response through the latent factors, we propose to first conduct factor analysis and then apply sufficient dimension reduction on the estimated factors to derive the reduced data for subsequent forecasting. Using directional regression and the inverse third-moment method in the stage of sufficient dimension reduction, the proposed methods can capture the nonmonotone effect of factors on the response. We also allow a diverging number of factors and only impose general regularity conditions on the distribution of factors, avoiding the undesired time reversibility of the factors by the latter. These make the proposed methods fundamentally more applicable than the sufficient forecasting method of Fan et al. (2017). The proposed methods are demonstrated both in simulation studies and an empirical study of forecasting monthly macroeconomic data from 1959 to 2016. Also, our theory contributes to the literature of sufficient dimension reduction, as it includes an invariance result, a path to perform sufficient dimension reduction under the high-dimensional setting without assuming sparsity, and the corresponding order-determination procedure.
Regression-Adjusted Real-Time Quality Control
Authors: Duan Xincen , Wang Beili , Zhu Jing , Zhang Chunyan , Jiang Wenhai , Zhou Jiaye , Shao Wenqi , Zhao Yin , Yu Qian , Lei Luo , Yiu Kwok Leung , Chin Kim Thiam , Pan Baishen , Guo Wei
Published: June 2021

Background

Patient-based real-time quality control (PBRTQC) has gained increasing attention in the field of clinical laboratory management in recent years. Despite the many upsides that PBRTQC brings to the laboratory management system, it has been questioned for its performance and practical applicability for some analytes. This study introduces an extended method, regression-adjusted real-time quality control (RARTQC), to improve the performance of real-time quality control protocols.

Methods

In contrast to the PBRTQC, RARTQC has an additional regression adjustment step before using a common statistical process control algorithm, such as the moving average, to decide whether an analytical error exists. We used all patient test results of 4 analytes in 2019 from Zhongshan Hospital, Fudan University, to compare the performance of the 2 frameworks. Three types of analytical error were added in the study to compare the performance of PBRTQC and RARTQC protocols: constant, random, and proportional errors. The false alarm rate and error detection charts were used to assess the protocols.

Results

The study showed that RARTQC outperformed PBRTQC. RARTQC, compared with the PBRTQC, improved the trimmed average number of patients affected before detection (tANPed) at total allowable error by about 50% for both constant and proportional errors.

Conclusions

The regression step in the RARTQC framework removes autocorrelation in the test results, allows researchers to add additional variables, and improves data transformation. RARTQC is a powerful framework for real-time quality control research.
Liquid Biopsy Hotspot Variant Assays: Analytical Validation for Application in Residual Disease Detection and Treatment Monitoring
Authors: Hallermayr Ariane , Benet-Pagès Anna , Steinke-Lange Verena , Mansmann Ulrich , Rentsch Markus , Holinski-Feder Elke , Pickl Julia M A
Published: June 2021

Background

Analysis of circulating tumor DNA (ctDNA) in plasma is a powerful approach to guide decisions in personalized cancer treatment. Given the low concentration of ctDNA in plasma, highly sensitive methods are required to reliably identify clinically relevant variants.

Methods

We evaluated the suitability of 5 droplet digital PCR (ddPCR) assays targeting KRAS, BRAF, and EGFR variants for ctDNA analysis in clinical use.

Results

We investigated assay performance characteristics for very low amounts of variants, showing that the assays had very low limits of blank (0% to 0.11% variant allele frequency, VAF) and limits of quantification (0.41% to 0.7% VAF). Nevertheless, striking differences in detection and quantification of low mutant VAFs between the 5 tested assays were observed, highlighting the need for assay-specific analytical validation. Besides in-depth evaluation, a guide for clinical interpretation of obtained VAFs in plasma was developed, depending on the limits of blank and limits of quantification values.

Conclusion

It is possible to provide comprehensive clinical reports on actionable variants, allowing minimal residual disease detection and treatment monitoring in liquid biopsy.
A Social Network Link Prediction Method Based on Stacked Generalization
Authors: Liu Xiaoyang , Li Xiang
Published: June 2021

Traditional link prediction methods of social network are vulnerable to the influence of network structure and have poor generalization, and only on a small number of networks and evaluation indicators. To improve the stability and accuracy of link prediction, this paper assembles 15 similarity indexes, introduces the idea of stacking into the link prediction of complex networks, and presents a link prediction method (Logistic-regression LightGBM Stacking Link Prediction, LLSLP). Firstly, social network link prediction is regarded as a binary classification problem. Secondly, the hyper parameters of the basic model are determined by using cross-validation and grid searching; thirdly, Logistic-regression and LightGBM are integrated by stacked generalization; Finally, take 10 different networks as practical examples. The feasibility and effectiveness of the proposed method are verified by comparing 7 evaluation indicators. The experimental results show that: the proposed method is not only more than 98.71% higher than the traditional CN (Common Neighbor) and other models are 10.52% higher on average. In addition, compared with the traditional 15 link prediction algorithms, $F1- score$ value and $MCC$ (Matthews Correlation Coefficient) value is increased by 3.2% ~ 9.7% and 5.9% ~ 14% respectively. The proposed method has good accuracy and generalization. It can also be applied to recommendation system.
Conflicts and Tentative Solutions to Protecting Personal Data in Investment Arbitration
Authors: Huang Jie (Jeanne)
Published: June 2021

Personal data protection in investment arbitration is increasingly important as more and more countries enact mandatory personal data protection laws and the COVID-19 pandemic makes online hearings the new normal. Tribunals have to consider critical, yet unresolved, normative questions, such as (i) how data protection laws would influence the trend towards transparency in investment arbitrations brought pursuant to modern investment treaties; (ii) whether a party can invoke a data protection law to reject access to fundamental documents and completely shift the power in an arbitration proceeding; (iii) whether it is good to have multiple data protection laws directly applicable in an investment arbitration; and (iv) whether the so-called Brussels Effect may take hold of investment arbitration. These questions directly address the alleged legitimacy crisis of investment arbitration (for example, procedural transparency and efficiency) in the digital era. They also have a critical impact on the fairness of proceedings and are closely related to the protection of fundamental human rights and the concern of digital surveillance. This article comprehensively maps the consensual and mandatory applications of data protection laws in investment arbitration. Adopting comparative-law and conflict-of-laws methodologies, it intends to provide tentative solutions to the four questions mentioned above.