bluuebunny/crossref_metadata_2025_split huggingface dataset:
Coping strategies of the farming communities in the Indian Sundarbans region to adapt with frequent extreme weather events
Authors: Mohammad Asif , Ahmad Mokbul Morshed , Feroze Sheikh Mohammad , Dutta Tapas Kumar , Bhakat Champak , Chatterjee Anupam 
Published: November 2022

Abstract
Frequent extreme weather events result in huge economic losses for farming communities in Sundarbans region, which threatens the livelihood security of smallholder farming communities. Different agricultural enterprises in the region are facing continuous threats due to saline water intrusion, crop losses due to heavy rain, and flooding. To overcome these problems, the farming community have adopted several coping mechanisms to sustain their livelihood against the continuous threat of extreme weather events. This paper focuses on the coping mechanism followed by the farming community in the Sundarbans region of India. Primary data were collected from randomly selected 1200 farmers from six blocks of Sundarbans. Farm households who were engaged in different agricultural enterprise like cultivating field crops, livestock rearing, fish farming were selected as respondents for the study. Analytical Hierarchy Process was applied to prioritize the coping mechanism as followed by the farming communities. The coping mechanisms were assessed on the basis of ease of implementation, cost involved, effectiveness as well as durability of the strategy for long-term application. Effectiveness and durability of the coping strategy got the highest weights of 0.492 and 0.309, respectively. Coping mechanisms pertaining to managing soil health against soil salinity, raising livestock and fish species as well as cultivating field crops were identified and prioritized according to the perceived benefit of the mechanism against extreme weather events. These coping mechanisms after proper fine-tuning can help in formulating strong and sustainable development policies for agricultural and allied sectors in the Sundarbans region of India.
U.S. Infrastructure: 1929-2019
Authors: Fair Ray C 
Published: November 2022

Abstract
This paper examines the history of U.S. infrastructure since 1929 and in the process reports an interesting fact about the U.S. economy.Infrastructure as a percent of GDP began a steady decline around 1970, and thegovernment budget deficit became positive and large at roughly the same time. The infrastructure pattern in other countries does not mirror that in the United States, so the United States appears to be a special case. The overallresults suggest that the United States became less future oriented beginning around 1970. This change has persisted. This is the interesting fact. Whether it can be explained is doubtful.
Machine learning survival models trained on clinical data to identify high risk patients with hormone responsive HER2 negative breast cancer
Authors: Fanizzi Annarita , Pomarico Domenico , Rizzo Alessandro , Bove Samantha , Comes Maria Colomba , Didonna Vittorio , Giotta Francesco , Forgia Daniele La , Latorre Agnese , Pastena Maria Irene , Petruzzellis Nicole , Rinaldi Lucia , Tamborra Pasquale , Zito Alfredo , Lorusso Vito , Massafra Raffaella 
Published: November 2022

Abstract

For endocrine-positive Her2 negative breast cancer patients at an early stage, the benefit of adding chemotherapy to adjuvant endocrine therapy is controversial. Several genomic tests are available on the market but are very expensive. Therefore, there is the urgent need to explore novel reliable and less expensive prognostic tools in this setting. In this paper, we shown a machine learning survival model to estimate Invasive Disease-Free Events trained on clinical and histological data commonly collected in clinical practice. We collected clinical and cytohistological outcomes of 145 patients referred to Istituto Tumori “Giovanni Paolo II”. Three machine learning survival models are compared with the Cox proportional hazards regression according to time-dependent performance metrics evaluated in cross-validation. The c-index at 10 years obtained by random survival forest, gradient boosting, and component-wise gradient boosting is stabled with or without feature selection at approximately 0.68 in average respect to 0.57 obtained to Cox model. Moreover, machine learning survival models have accurately discriminated low- and high-risk patients, and so a large group which can be spared additional chemotherapy to hormone therapy. The preliminary results obtained by including only clinical determinants are encouraging. The integrated use of data already collected in clinical practice for routine diagnostic investigations, if properly analyzed, can reduce time and costs of the genomic tests.
Probabilistic Approach to Investigate the Impact of Distributed Generation on Voltage Deviation in Distribution System
Authors: Kumar Digamber , Padhy Bibhu Prasad 
Published: November 2022

Abstract
The stochastic nature of integrated renewable-based distributed generation (DG) and changing the conduct of power consumption adds randomness to the bus voltage. Consequently, the distribution system faces voltage limit violations on a few buses. In this scenario, the traditional voltage control methods are unable to handle the fast voltage deviation. The voltage control using the voltage controlling capability of available DGs gives a fast and dynamic solution. In this regard, the information about the impact of power perturbation at the DG-integrated buses on the voltage deviation gives insightful information for maintaining the voltage profile within its permissible limits. Evaluating this information using the traditional probabilistic load flow method is computationally inefficient for real-time applications. Therefore, a principal component analysis (PCA) based novel probabilistic voltage sensitivity index (PVSI) is proposed.It ranks the impact of power perturbation at DG-integrated buses on the voltage deviation at any bus. The PVSI is analytically obtained to reduce the computation time for ranking. Due to the advantage in computation time, the proposed method enables the distribution system operator (DSO) to use this information for fast and dynamic voltage control in a real-time scenario. Further, the proposed PVSI is validated on the 69-bus and the 141-bus distribution systems by comparing it with the traditional Monte-Carlo simulation (MCS) and Joint Differential Entropy (JDE) methods.
Microstructural stages of Intermetallic fracture during sheared deformation of AA6082 sheet
Authors: Sonkamble Vijayalaxmi , Narasimhan K. , Tewari Asim 
Published: November 2022

Abstract
Detailed high-resolution quantitative microstructural investigations were performed on AA6082(T6) sheets. The progressive deformations of a shear band formation were studied to understand the fracture mechanism in blanking process. The fracture characteristics of the alloys can be dictated by the intermetallic particles. Damage evolution of this alloy is quantitatively characterized as a function of strain. Intermetallic particles trigger the fracture in the blanking process. Larger, elongated and favorably oriented particles fracture first (with initial deformation) and with progressive deformation (blanking), smaller and rounded particles start breaking. These broken particles become the crack nucleus for matrix cracking and coalescences. Particle fracture increases with increase in imposed strain. For initial deformation stages, fragmentation of particles is taking place without significant void growth. But with subsequent deformation stages, fragmentation process has been enhanced with multi-fragmentation and re-fracturing of previously broken particles. Most probably, larger particles nucleate voids at much lower strains than smaller particles, and void growth takes place more rapidly at larger particles and they are the major contributors in the initiation of fracture throughout the deformation process. Crack has initiated in the shear band preferably from the punch side (punch side is sharper than the die side) and the cracks are found almost parallel to the loading axis.
Graph-Based Active Machine Learning Method for Diverse and Novel Antimicrobial Peptides Generation and Selection
Authors: Dossou Bonaventure F. P. , Liu Dianbo , Ji Xu , Jain Moksh , Sloot Almer M. van der , Palou Roger , Tyers Michael , Bengio Yoshua 
Published: November 2022

Abstract
As antibiotic-resistant bacterial strains are rapidly spreading worldwide, infections caused by these strains are emerging as a global crisis causing the death of millions of people every year. Antimicrobial Peptides (AMPs) are one of the candidates to tackle this problem because of their potential diversity, and ability to favorably modulate the host immune response. However, large-scale screening of new AMP candidates is expensive, time-consuming, and now affordable in developing countries, which need the treatments the most. In this work, we propose a novel active machine learning-based framework that statistically minimizes the number of wet-lab experiments needed to design new AMPs, while ensuring a high diversity and novelty of generated AMPs sequences, in multi-rounds of wet-lab AMP screening settings. Combining recurrent neural network models and a graph-based filter (GraphCC), our proposed approach delivers novel and diverse candidates and demonstrates better performances according to our defined metrics.
Human Decision-making Strategy Analysis during Large-scale Disaster
Authors: Zhang Zhiwen , Wang Hongjun , Fan Zipei , Song Xuan , Shibasaki Ryosuke 
Published: November 2022

Abstract
In recent decades, the frequency and intensity of natural disasters have increased significantly. Following these unexpected disasters, understanding and predicting human decision-making strategies during disasters will play a vital role in planning effective humanitarian relief, disaster management, and long-term societal reconstruction. However, such research is challenging due to the unavailability of reliable and large-scale human mobility data. In addition, these personal strategies during disasters become hard to predict due to the influences of multiple factors. In the decision-making process, what key (sometimes hidden) factors do people consider when making a particular mobility decision following a large-scale disaster? In this study, we construct a large human mobility database collected from mobile devices (GPS records of 66,598 users in the Greater Tokyo area), and analyze the human decision-making strategies following Japan’s 2011 Tohoku earthquake. Except for users’ location-based information, we also extract the grid-based stay areas from historical check-ins (normal days before disasters) and discover the functions of those areas combined with points of interest (POI) located in an area. In the final, we jointly model the users’ location-based factors and historical check-ins information, and develop an empirical prediction for decision-making strategies (i.e., destination, travel mode, and departure time from their workplaces) following the Tohoku earthquake. An explainable analysis is conducted to explore the fundamental laws that govern human mobility following disasters. The results are encouraging to support future interventions in analyzing decision-making strategies during disasters, and understanding the relationship between decision making and considered multiple factors/features (as well as the relationship between feature values and possible predicted values).
Immune response to pertussis vaccine in COPD patients
Authors: Feredj E. , Wiedemann A. , Krief C. , Maitre B. , Derumeaux G. , Chouaid C. , Corvoisier P. Le , Lacabaratz C. , Gallien S. , Lelièvre JD , Boyer L. 
Published: November 2022

Abstract
Exacerbation triggered by respiratory infection is an important cause of morbidity and mortality in Chronic Obstructive Pulmonary Disease (COPD) patients. Strategies aiming to preventing infection may have significant public health impact. Our previous study demonstrated decreased immunological response to seasonal flu vaccination in COPD patients, questioning the efficiency of other vaccines in this group of patients. We performed a prospective, monocenter, longitudinal study that evaluated the humoral and cellular responses upon pertussis vaccination. We included 13 patients with stable COPD and 8 healthy volunteers. No difference in circulating B and T cell subsets at baseline was noted. Both groups presented similar levels of TFH, plasmablasts and pertussis specific antibodies induction after vaccination. Moreover, monitoring T cell immunity after ex-vivo peptide stimulation revealed equivalent induction of functional and specific CD4 + T cells (IFNg, TNFa and IL-2-expressing T cells) in both groups. Our results highlight the immunological efficiency of pertussis vaccination in this particularly vulnerable population and challenge the concept that COPD patients are less responsive to all immunization strategies. Healthcare providers should stress the necessity of decennial Tdap booster vaccination in COPD patients.
Temporal evolution of mechanical stimuli from vascular remodeling in response to the severity and duration of aortic coarctation
Authors: Azarnoosh Jamasp , Ghorbannia Arash , Ibrahim El-Sayed H. , Jurkiewicz Hilda , Kalvin Lindsey , LaDisa John F. 
Published: November 2022

Abstract
Coarctation of the aorta (CoA) is one of the most common congenital cardiovascular diseases. CoA patients frequently undergo surgical repair, but hypertension (HTN) is still common. The current treatment guideline has revealed irreversible changes in structure and function, yet revised severity guidelines have not been proposed. Our objective was to quantify temporal alterations in mechanical stimuli and changes in arterial geometry in response to the range of CoA severities and durations seen clinically. Rabbits were exposed to CoA resulting in BPGpp severities of ≤ 10, 10–20, and ≥ 20 mmHg for a duration of ~ 1, 3, or 20 weeks using permanent, dissolvable, and rapidly dissolvable sutures. Elastic moduli and thickness were estimated from imaging and longitudinal fluid-structure interaction (FSI) simulations were conducted at different ages using geometries and boundary conditions measured empirically. Mechanical stimuli were characterized including blood flow velocity patterns, wall tension, and radial strain. Empirical results show vascular alternations including thickening and stiffening proximal to the coarctation with increasing severity and/or duration of CoA. FSI simulations indicate wall tension in the proximal region increases markedly with coarctation severity. Importantly, even mild CoA induced stimuli for remodeling that exceeds values seen in adulthood if not treated early and using a BPGpp lower than the current clinical threshold. The findings are aligned with observations from other species and provide some guidance for the values of mechanical stimuli that could be used to predict the likelihood of HTN in human patients with CoA.
Investigating mechanisms of laser pulse-induced reflectivity modulations in photoacoustic remote sensing with a 10 million frames-per-second camera
Authors: Haven Nathaniel Jeremy Meyer , Martell Matthew T. , Li Hoayang , Hogan James D. , Zemp Roger J. 
Published: November 2022

Abstract
Photoacoustic remote sensing has been recently developed as an all-optical imaging modality capable of imaging a variety of endogenous contrast agents label-free. Initially predicted laser pulse-induced refractive index perturbation-based interrogation beam reflectivity modulations have been found to be orders of magnitude smaller than those typically observed experimentally. In this report we utilize a 10 million frames-per-second camera to further investigate these predicted reflectivity modulations, while also exploring other potential mechanisms of laser pulse-induced reflectivity modulations. Laser-induced motion is demonstrated both laterally for gold wires suspended and submerged in air and water, respectively, and carbon fibers submerged in water, and axial motion is observed in gold wires submerged in a depth gradient of intralipid solution. This laser-induced sample motion is anticipated to cause reflectivity modulations local to the interrogation beam profile in microscopy set-ups. Non-motion-based maximum intensity modulations of 3% are also observed in gold wires submerged in water, indicating the presence of the originally predicted reflectivity modulations. Overall, these observations are important as they provide a widefield view of laser-pulse interactions unavailable in previous point scanning-based photoacoustic remote sensing microscopy configurations, where observed mechanisms occur on time-scales orders of magnitude faster than equivalent field of view point scanning capabilities.
Trans-resveratrol-3-o-b-glucoside, an alternative therapy for Alzheimer's disease, an in-silico approach
Authors: Bhattacharya Arka , Singh Rajveer 
Published: November 2022

Abstract
In neurology, Alzheimer's disease (AD) requires the greatest unmet medical need. While current medications reduce symptoms, they have only minor disease-modifying effects. However, several methods designed to stop the progression of the disease have moved to clinical trials in recent years. The most sophisticated of these focus on amyloid- β peptide formation and clearance, which is a hallmark of Alzheimer's and is suspected to play a role in disease development. Therapies that target metabolic dysfunction and aberrant tau filament aggregation, another crucial aspect of the disease, are also being tested in the clinic. Glycogen synthase kinase 3 beta (GSK3β) has been identified as a promising target for treating AD, where abnormal activation of this enzyme has been associated with hyperphosphorylation of TAU proteins. This current study aims to meet the imperative necessity of effective AD therapy with a computational multi-target drug repurposing approach. This study focused on screening the Resveratrol derivative trans-Resveratrol-3-O-b-glucoside as a GSK3β blocker. trans-Resveratrol-3-O-b-glucoside showed good binding affinity towards the biomolecular markers of AD, such as GSK3β, tau, Acetylcholinesterase, and proinflammatory mediators such as NF-κβ and COX2. Further trans-Resveratrol-3-O-b-glucoside analyzed by molecular simulation revealed that it binds tightly to the active site of GSK3β. This study suggests that trans-Resveratrol-3-O-b-glucoside can be a potential agent for treating AD.
Adsorption of BEZAKTIV BLAU V3-R and BEZAKTIV BLAU S-2R reactive dyes onto rice husk modified by the circular economy concept
Authors: Mladenovic Nina , Petkovska Jovana , Dimova Vesna , Dimitrovski Dejan , Jordanov Igor 
Published: November 2022

Abstract
Rice husk, modified with effluent from the alkaline scouring of cotton, employing an inexpensive treatment based on the circular economy concept, is used as a bio-sorbent to study the equilibrium and kinetic on cold brand BEZAKTIV BLAU V3-R (BBV3-R) and medium brand BEZAKTIV BLAU S-2R (BBS2-R) reactive dyes. 10, 20, and 40 g L-1 rice husk is used to follow adsorption of 0.1, 0.2, 0.3, 0.4, and 0.5 g L-1 BBV3-R and BBS2-R in a presence of 60 g L-1 NaCl at pH 10, at the temperature of 25oC and 40oC, respectively. Langmuir, Freundlich, and Dubinin-Radushkevic's adsorption models were employed, with the Freundlich model proving the best fit for our data. Activation energy calculated with the Dubinin-Radushkevic model (lower than 8 KJ mol-1) confirms the physical sorption of these reactive dyes onto the husk. The obtained maximum experimental absorption capacities of 122.2 mg g-1 for BBV3-R and 171.7 mg g-1 for BBS2-R using 10 g L-1 ERH are the best found in the literature. Pseudo-second-order model determines the adsorption kinetics, while the liquid film diffusion model is rate-determining. This paper demonstrates the applicability of the circular economy concept to obtain an efficient adsorbent for purifying colored effluent enriched with reactive dyes, which are one of the most challenging dyes in the textile industry from the aspect of their effluent purifying.
Machine Learning Assisted Optimization of Dielectric Resonator based mm-Wave MIMO Antenna for 5G Communication Systems
Authors: . Sumit , Kumar Vinay , Pandey Ashish , Sharma Anand , Ranjan Pinku , Tripathi Rajeev 
Published: November 2022

Abstract
In this paper, a dual-port dielectric resonator (DR) multiple-input multiple-output (MIMO) antenna along with a suspended metasurface is designed and analyzed. The metasurface is used to improve the isolation value between the port of the proposed antenna. Moreover, different machine learning (ML) algorithms (Decision Tree, Deep Neural Network, K-nearest Neighbor, Random Forest, and Extreme Gradient Boosting) are utilized for optimization purposes. The performance of different ML algorithms are compared in term of measured parameters. The prototype of the proposed antenna design is fabricated for verification purposes. The measured result confirms that the proposed antenna operates from 26.24 to 27.94 GHz, with an isolation level of more than 45 dB between the ports. The gain of the proposed antenna is around 5.0 dBi in the operating band. Due to all these features, the proposed antenna can be efficiently utilized for a 5G communication system.
Development of a novel NGS methodology for ultrasensitive circulating tumour DNA detection as a tool for early-stage breast cancer diagnosis
Authors: Jiménez-Rodríguez Begoña , Alba-Bernal Alfonso , López-López Esperanza , Quirós-Ortega María Elena , Carbajosa Guillermo , Garrido-Aranda Alicia , Álvarez Martina , Queipo-Ortuño María Isabel , Vicioso Luis , Díaz-Córdoba Gema , Roldán-Díaz María Dunia , Velasco-Suelto Jesús , Hernando Cristina , Bermejo Begoña , Julve-Parreño Ana , LLuch Ana , Pascual Javier , Comino-Méndez Iñaki , Alba Emilio 
Published: November 2022

Abstract
BACKGROUND
Breast cancer (BC) is the most prevalent cancer in women. While usually detected when localized, invasive procedures are still required for diagnosis.
METHODS
Herein, we developed a novel ultrasensitive pipeline to detect circulating tumour DNA (ctDNA) in a series of 75 plasma samples from localized BC patients prior to any medical intervention. We first performed a tumour-informed analysis to correlate the mutations found in tumour tissue and plasma. Disregarding the tumour data next, we developed an approach to detect tumour mutations in plasma.
RESULTS
We observed a mutation concordance between tumour and plasma of 29.50% with a sensitivity down to 0.03% in mutant allele frequency (AF). We detected mutations in 33.78% of the samples, identifying 8 patients with plasma-only mutations. Altogether, we determined a specificity of 86.36% and a positive predictive value of 88.46% for BC detection. We demonstrated an association between higher ctDNA median AF and higher tumour grade, multiple plasma mutations with likelihood of relapse and more frequent TP53 plasma mutations in hormone receptor-negative tumours.
CONCLUSIONS
Overall, we have developed a unique ultra-sensitive sequencing workflow with a technology not previously employed in early BC, paving the way for its application in BC screening.