bluuebunny/crossref_metadata_2025_split huggingface dataset:
Cluster Weighted Model Based on TSNE algorithm for High-Dimensional Data
Authors: Olobatuyi Kehinde 
Published: October 2022

Abstract
Similar to many Machine Learning models, both accuracy and speed of the Cluster weighted models (CWMs) can be hampered by high-dimensional data, leading to previous works on a parsimonious technique to reduce the effect of ”Curse of dimensionality” on mixture models. In this work, we review the background study of the cluster weighted models (CWMs). We further show that parsimonious technique is not sufficient for mixture models to thrive in the presence of huge high-dimensional data. We discuss a heuristic for detecting the hidden components by choosing the initial values of location parameters using the default values in the ”FlexCWM” R package. We introduce a dimensionality reduction technique called T-distributed stochastic neighbor embedding (TSNE) to enhance the parsimonious CWMs in high-dimensional space. Originally, CWMs are suited for regression but for classification purposes, all multi-class variables are transformed logarithmically with some noise. The parameters of the model are obtained via expectation maximization algorithm. The effectiveness of the discussed technique is demonstrated using real data sets from different fields.
Comprehensive sensitivity analysis on static and dynamic reservoir parameters impacting near wellbore injectivity during CO2 sequestration
Authors: Ahmadi Parvin , Rezaei Gomari Sina , Ahmad Faizan , Rahman Mohammad Aziz 
Published: October 2022

Carbon capture and storage (CCS) is proved to be effective measure for reducing CO2 emissions. whilst the world still highly depends on the use of fossil fuel energy, this method is necessary for reaching the world’s 1.5 °C goal.In CCS, CO2 is hindered from entering the atmosphere by capturing it from sources of emission and storing it in geological formation. Saline aquifers among all possible underground formations are most common targeted ones for CO2 storage due to their frequent presence, and large storage capacity. However, this storage option suffers from sufficient well injectivity to inject large volumes of CO2 at acceptable rates through a minimum number of wells.The injectivity impairment / reinforcement happens through mineral dissolution, fine particle movement, salt precipitation and hydrate formation (known so far). Each of these mechanisms will be more dominant in injectivity alteration at different distance from the injection point depending on reservoir pressure and temperature, formation water salinity, rock mineralogy, and flow rate of CO2 injection as well as its dryness.Incorporating all the finding into radial flow near wellbore will help gaining insight into the resultant of injectivity changes over time and distant from injection point. In this study we have chosen Eclipse 300 together with an open-source code to investigate the impact of formation characteristics, CO2 -Brine-Rock interaction, pressure, temperature as well as injection rate on injectivity alteration. The goal for this work is to provide a workflow which can help predicting injectivity alteration using the existing tools.Simulation results show that the high homogenous horizontal permeability in combination with vertical flow baffles in the formation (among all other parameters) has positive impact on storage capacity by increasing residual trapping. However, permeability is affected severely by salt precipitation during CO2 injection. Combined static and dynamic parameter study demonstrate that the injection rate plays a crucial role in size and expansion of CO2 plume as well as growth rate of dry out zone length, amount of salt precipitation and length of equilibrium region. The higher the injection rate, the quicker activation of the capillary and gravity force which leads to drag more brine to near well-bore resulting in higher volume fraction of salt precipitation. However, low injection rate could result in smaller CO2 plume, shorter dry out zone and longer equilibrium region in term of distance from injection point.
Socio-political development of CC(U)S in the Baltic Sea region
Authors: Karimi Farid 
Published: October 2022

According to EU goals and the Paris Agreement, an urgent need exists to reduce CO2 emissions while still securing energy supply. Thus, the timely deployment of carbon capture and storage (CCS) is seemingly unavoidable, especially for the cement and steel industries. However, diverse perceptions of CCS among stakeholders such as experts, politicians, and laypeople exist that could hinder the deployment of the technology, not least in the Baltic Sea Region (BSR). Hence, this research discusses these diverse perceptions and their roots. Furthermore, when it comes to political developments of CCS, after the unprovoked Russian invasion of Ukraine, the whole process of the energy transition in the region is under shadow for the seemingly mid-term while the approach to the energy security and security of supply needs to be revisited. In other words, the countries of the BSR need to manage the energy crisis in the region while following their plans for decarbonisation. In this light, CCS is, therefore, an option to secure energy supply from undesired alternatives like fossil fuels for the short-term and also biomass while curbing CO2 emissions. In sum, this research also discusses the role of CCS in energy security and security of supply concerning the Russian invasion of Ukraine.
Bio-CCS as a policy measure to achieve climate goals – the pioneering support scheme in Sweden
Authors: Söderholm Svante , Carnbrand Håkansson Nicki 
Published: October 2022

By 2045, Sweden is to have zero net emissions of greenhouse gases into the atmosphere. After 2045, Sweden should achieve negative emissions. To accomplish this, the use of bioenergy with carbon capture and storage (bio-CCS) will be important. Sweden should aim to capture and store two million tonnes of biogenic carbon dioxide per year by 2030. However, the feasible potential for bio-CCS in Sweden amounts to at least 10 million tonnes of biogenic carbon dioxide per year in a 2045 perspective. To support the development and deployment of CCS the Swedish energy Agency has been given two governmental assignments.1. The first task/assignment, given in December 2020, was to establish a national centre for CCS. This task entails planning, coordination and promotion of CCS throughout the country. The Swedish Energy Agency will carry out its work in dialogue with both national and international stakeholders: industries, academia, governmental authorities and the Government Offices of Sweden. The present tasks for the centre are to implement a support system for bio-CCS and ensure that it is line with international conventions, such as the UN Convention on Biological Diversity and its moratorium on geo-engineering, and the London Convention and the London Protocol. The centre is also working with questions related to the accounting and reporting of negative carbon dioxide emissions in relation to national and international climate goals as well as following the emergence of a carbon market – voluntary and/or regulated – for negative emissions.2. The second assignment was to roll-out the support system earlier proposed by the agency. The Swedish Energy Agency has concluded that a reverse action as the most cost-effective support system as well as to be compatible with EU state aid rules. The support system for bio-CCS has a budget framework of 3.6 billion €. A reverse auction means that, for example, a pulp and paper industry or a combined heat and power plant can submit a bid on how much carbon dioxide they can capture and store, and at what cost. The one who can deliver bio-CCS according to the stipulated requirements at the lowest cost, wins the auction. The Swedish Energy Agency hope to launch the first round of auction in 2023 and have the first storage of Swedish captured carbon dioxide taking place in 2026.Other countries can use Sweden’s knowledge and experiences when implementing bio-CCS. Exchanging knowledge, experiences and ideas with other countries are important to achieve large-scale deployment of bio-CCS in the Nordic-Baltic region and net-zero emissions in 2045.
Strategic planning for exchanging treated urban wastewater for agricultural water with the approach of supplying sustainable urban water: a case study of Mashhad, Iran
Authors: Esfandiari Sasan , Dourandish Arash , Firoozzare Ali , Taghvaeian Saleh 
Published: October 2022

Abstract
Considering the increasing demand for urban water and the low water-use efficiency in the agricultural sector, an exchange of treated urban wastewater for agricultural water could be a fundamental solution for administering sustainable management of water resources. However, implementing the intersectoral water exchange project is influenced by the economic, social, and environmental conditions of each region, especially in developing countries. Therefore, this study sought to identify the best possible strategies for exchanging treated urban wastewater for agricultural water in Mashhad city, located in Iran, a country that suffers from decreased urban water resources. Based on the interviews conducted with the experts and the strengths, weaknesses, opportunities and threats analysis, 28 influential factors were identified for the water exchange project, and eleven strategies were developed for its implementation. The results of the analytic hierarchy process (AHP) and F-TOPSIS indicated that organizing educational and awareness campaigns regarding the exchange project's goals, devising an appropriate organizational structure, enhancing treated wastewater's quality according to the WHO standards, taking the necessity of conducting social, cultural, and economic studies into account, developing consumers' awareness programs, and supporting, training, and counseling the farmers and setting the required incentives for them were among the main factors involved in the implementation of the water exchange project in Mashhad.
Assessing the Therapeutic and Toxicological Profile of Novel Acetylcholinesterase
Reactivators: Value of In Silico And In Vitro Data
Authors: Ramalho Teodorico C. , de Castro Alexandre A. , Leal Daniel H.S. , Teixeira Jessika P. , da Cunha Elaine F.F. , Kuca Kamil 
Published: October 2022

:
Organophosphorus compounds (OP) make up an important class of inhibitors,
mostly employed as pesticides, even as chemical weapons. These toxic substances act
through the inhibition of the acetylcholinesterase (AChE) enzyme, which results in elevated
synaptic acetylcholine (ACh) levels, leading to serious adverse effects under the cholinergic
syndrome. Many reactivators have been developed to combat the toxic effects of
these AChE inhibitors. In this line, the oximes highlight because of their good reactivating
power of cholinesterase enzymes. To date, no universal antidotes can reactivate
AChE inhibited by any OP agent.

:
This review summarizes the intoxication process by neurotoxic OP agents, along with
the development of reactivators capable of reversing their effects, approaching aspects
like the therapeutic and toxicological profile of these antidotes.

:
Computational methods and conscious in vitro studies, capable of significantly predicting
the toxicological profile of these drug candidates, might support the process of development
of these reactivators before entering in vivo studies in animals, and then clinical
trials. These approaches can assist in the design of safer and more effective molecules, reducing
related cost and time for the process.
Electrochemical Nanomaterial-based Sensors/Biosensors for Drug Monitoring
Authors: Negahdary Masoud , Angnes Lúcio , Barros Azeredo Nathália Florência , Santos Berlane Gomes , de Oliveira Thawan Gomes , de Oliveira Lins Renato Soares , dos Santos Lima Irlan 
Published: October 2022

Abstract:
Determining the amount of medication used is essential for correctly managing treatment systems. The unauthorized use of drugs and the importance of determining the absorbed and re-quired dose of drugs in target organs are essential factors that justify the design of new drug moni-toring systems. Electrochemical sensors and biosensors based on nanomaterials have been devel-oped for drug monitoring in the past few years. The use of nanomaterials to optimize the analyte de-tection process and facilitate electron transfer in electrochemical processes has enhanced intermo-lecular interactions and increased diagnostic sensitivity. Considering this review, in the first part, the evaluation of cancer drugs is examined, which can be used to determine the exact dose of the drug required in different stages of cancer. Accurate monitoring of cancer drugs can increase patient life expectancy, reduce side effects, and increase economic savings. In the next section, sensors and biosensors designed for antibiotics are examined. Accurate measurement of antibiotics for deter-mining the effectiveness of the dose in controlling infections and preventing antibiotic resistance is possible with the help of these drug diagnostic platforms. In the next part, the diagnosis of different hormones is considered. Abnormal amounts (low/high) of hormones cause multiple physiological complications and various disabilities. Therefore, accurate determination of hormone levels can ef-fectively treat hormonal changes. In the last section, other drugs, including drugs and analgesics for which the use of electrochemical diagnostic platforms can significantly help drug distribution and social health systems, are also discussed.
Resveratrol-Mediated Regulation of Mitochondria Biogenesis-associated
Pathways in Neurodegenerative Diseases: Molecular Insights and Potential
Therapeutic Applications
Authors: Shaito Abdullah , Al-Mansoob Maryam , Ahmad Salma M.S. , Haider Mohammad Z. , Eid Ali H. , Posadino Anna Maria , Pintus Gianfranco , Giordo Roberta 
Published: October 2022

Abstract:
Neurodegenerative disorders include different neurological conditions that affect nerve
cells, causing the progressive loss of their functions and ultimately leading to loss of mobility, coordination,
and mental functioning. The molecular mechanisms underpinning neurodegenerative disease
pathogenesis are still unclear. Nonetheless, there is experimental evidence to demonstrate that the perturbation
of mitochondrial function and dynamics play an essential role. In this context, mitochondrial
biogenesis, the growth, and division of preexisting mitochondria, by controlling mitochondria number,
plays a vital role in maintaining proper mitochondrial mass and function, thus ensuring efficient synaptic
activity and brain function. Mitochondrial biogenesis is tightly associated with the control of cell
division and variations in energy demand in response to extracellular stimuli; therefore, it may represent
a promising therapeutic target for developing new curative approaches to prevent or counteract
neurodegenerative disorders. Accordingly, several inducers of mitochondrial biogenesis have been
proposed as pharmacological targets for treating diverse central nervous system conditions. The naturally
occurring polyphenol resveratrol has been shown to promote mitochondrial biogenesis in various
tissues, including the nervous tissue, and an ever-growing number of studies highlight its neurotherapeutic
potential. Besides preventing cognitive impairment and neurodegeneration through its antioxidant
and anti-inflammatory properties, resveratrol has been shown to be able to enhance mitochondria
biogenesis by acting on its main effectors, including PGC-1α, SIRT1, AMPK, ERRs, TERT,
TFAM, NRF-1 and NRF-2. This review aims to present and discuss the current findings concerning
the impact of resveratrol on the machinery and main effectors modulating mitochondrial biogenesis in
the context of neurodegenerative diseases.
MIRNA146a And Diabetes-Related Complications: A Review
Authors: Maratni Ni Putu Tesi , Saraswati Made Ratna , Ayu Dewi Ni Nyoman , Suastika Ketut 
Published: October 2022

Abstract:
Diabetes mellitus, defined as long-standing hyperglycemic conditions caused by a defect
in insulin production and activity, has become a major healthcare burden as the number of catastrophic
and life-threatening complications rises. Microvascular complications (neuropathy, retinopathy,
and nephropathy), and also diabetes-related macrovascular complications are common
problems that arise as the life expectancy of diabetic patients has increased despite improved
treatment options. While it is impossible to pinpoint the specific crucial timing when the complications
become fully entrenched, looking for novel sensitive biomarkers to identify physiological
changes in the initial stages would be needed. An increasing amount of data shows that miRNAs,
particularly miRNA146a, are stable in a range of body fluids and can be used to identify pathogenic
changes at the cellular or tissue level. In this brief review, we highlight the important functioning
of miRNA146a and its putative target of action in diabetic microvascular and cardiovascular complications.
A decrease in miRNA146a levels may play a critical role in the onset and development
of diabetes complications, whereas its anti-inflammatory properties were revealed to be associated
with the pathogenesis of numerous diabetic complications, including diabetic nephropathy,
retinopathy, neuropathy, and diabetes-related cardiovascular disorders, even tending to be a
potential biomarker of the disease's inflammatory status.
Dual-path Network for Liver and Tumor Segmentation in CT Images
Using Swin Transformer Encoding Approach
Authors: Yang Zhen , Li Shuzhou 
Published: October 2022

Background:
Liver and tumor segmentation from CT images is a complex and crucial step
in achieving full-course adaptive radiotherapy and also plays an essential role in computer-aided clinical
diagnosis systems. Deep learning-based methods play an important role in achieving automatic
segmentation.

Objective:
This research aims to improve liver tumor detection performance by proposing a dual path
feature extracting strategy and employing Swin-Transformer.

Methods:
The hierarchical Swin-Transformer is embedded into the encoder and decoder and combined
with CNN to form a dual coding path structure incorporating long-range dependencies and multi-scale
contextual connections to capture coarse-tuned features at different semantic scales. The features of the
two encoding paths and the upsampling path are fused, tested and validated with LITS and in-house
datasets.

Results:
The proposed method has a DG of 97.95% and a DC of 96.2% for liver segmentation; a DG
of 80.6% and a DC of 68.1% for tumor segmentation; and a classification study of the tumor dataset
shows a DG of 91.1% and a DC of 87.2% for large and continuous tumors and a DG of 71.7% and a
DC of 66.4% for small and scattered tumors.

Conclusions:
Swin-Transformer can be used as a robust encoder for medical image segmentation networks
and, combined with CNN networks, can better recover local spatial information and enhance
feature representation. Accurate localization before segmentation can achieve better results for small
and scattered tumors.
In-Silico Prediction of Novel Fused Quinazoline Based Topoisomerase
Inhibitors as Anticancer Agents
Authors: Kumawat Mukesh Kumar , Kaur Ramandeep , Kumar Kapil 
Published: October 2022

Background:
The prospective uses of tryptanthrin and its analogues in cancer chemotherapy are well known, and they are also predicated on their capacity to reverse drug resistance in cancer therapy.

Objective:
The current project entails developing a novel hybrid analogue that includes modifying the tryptanthrin molecule at the C-6 carbonyl position and is expected to exhibit substantial anticancer action.

Method:
In the ATPase domain of human topoisomerase II, a series of 162 substituted Schiff base analogues of tryptanthrin were developed, and molecular docking experiments were done using Gold 5.1 software interfaced with Hermes 1.6.2. (PDB ID: 1ZXM).

Results:
Most of the compounds were found to have Goldscore above 100 and were forming interactions with the residues like ASN91, ALA92, ASN95, ARG98, ASN120, ILE125, ILE141, PHE142, SER149, THR215, and ILE217. Compound RK-149 was having highest Goldscore of 132.59 forming an interaction with ASN91 but had lesser Goldscore as compared to standard drug etoposide and has better score than tryptanthrin.

Conclusion:
The nitrogen in the imine bond of the proposed compounds is responsible for significant interactions, demonstrating their anticancer potential.
Automatic Estimation of the Most Likely Drug Combination in Electronic Health Records Using the Smooth Algorithm: Development and Validation Study
Authors: Ouchi Dan , Giner-Soriano Maria , Gómez-Lumbreras Ainhoa , Vedia Urgell Cristina , Torres Ferran , Morros Rosa 
Published: October 2022

Background
Since the use of electronic health records (EHRs) in an automated way, pharmacovigilance or pharmacoepidemiology studies have been used to characterize the therapy using different algorithms. Although progress has been made in this area for monotherapy, with combinations of 2 or more drugs the challenge to characterize the treatment increases significantly, and more research is needed.

Objective
The goal of the research was to develop and describe a novel algorithm that automatically returns the most likely therapy of one drug or combinations of 2 or more drugs over time.

Methods
We used the Information System for Research in Primary Care as our reference EHR platform for the smooth algorithm development. The algorithm was inspired by statistical methods based on moving averages and depends on a parameter Wt, a flexible window that determines the level of smoothing. The effect of Wt was evaluated in a simulation study on the same data set with different window lengths. To understand the algorithm performance in a clinical or pharmacological perspective, we conducted a validation study. We designed 4 pharmacological scenarios and asked 4 independent professionals to compare a traditional method against the smooth algorithm. Data from the simulation and validation studies were then analyzed.

Results
The Wt parameter had an impact over the raw data. As we increased the window length, more patient were modified and the number of smoothed patients augmented, although we rarely observed changes of more than 5% of the total data. In the validation study, significant differences were obtained in the performance of the smooth algorithm over the traditional method. These differences were consistent across pharmacological scenarios.

Conclusions
The smooth algorithm is an automated approach that standardizes, simplifies, and improves data processing in drug exposition studies using EHRs. This algorithm can be generalized to almost any pharmacological medication and model the drug exposure to facilitate the detection of treatment switches, discontinuations, and terminations throughout the study period.