bluuebunny/crossref_metadata_2025_split huggingface dataset:
Short-term water demand forecasting using data-centric machine learning approaches
Authors: Liu Guoxuan , Savic Dragan , Fu Guangtao 
Published: March 2023

Abstract
Accurate water demand forecasting is the key to urban water management and can alleviate system pressure brought by urbanisation, water scarcity and climate change. However, existing research on water demand forecasting using machine learning is focused on model-centric approaches, where various forecasting models are tested to improve accuracy. The study undertakes a data-centric machine learning approach by analysing the impact of training data length, temporal resolution and data uncertainty on forecasting model results. The models evaluated are Autoregressive (AR) Integrated Moving Average (ARIMA), Neural Network (NN), Random Forest (RF) and Prophet. The first two are commonly used forecasting models. RF has shown similar forecast accuracy to NN but has received less attention. Prophet is a new model that has not been applied to short-term water demand forecasting, though it has had successful applications in various fields. The results obtained from four case studies show that (1) data-centric machine learning approaches offer promise for improving forecast accuracy of short-term water demands; (2) accurate forecasts are possible with short training data; (3) RF and NN models are superior at forecasting high-temporal resolution data; and (4) data quality improvement can achieve a level of accuracy increase comparable to model-centric machine learning approaches.
Decomposition of contribution to runoff changes and spatial differences of major tributaries in the middle reaches of the Yellow River based on the Budyko framework
Authors: Dai Yanyu , Lu Fan , Ruan Benqing , Song Xinyi , Du Yu , Xu Yiran 
Published: March 2023

Abstract
Quantitative differentiation of climate and human activities on runoff is important for water resources management and future water resources trend prediction. In recent years, runoff in the middle reaches of the Yellow River (MRYR) has decreased dramatically. Many studies have analyzed the causes of runoff reduction, but there is still a lack of understanding of the spatial differences in runoff contributions and their causes. Therefore, this study quantitatively distinguishes the contributions of climate and human activities to runoff changes in nine sub-basins of the MRYR based on the Budyko framework and analyses the differences in the contributions of different basins and their causes. The results show that the runoff in the nine sub-basins decreases significantly and the precipitation increases from northwest to southeast. The contribution of human activities to runoff is greater than that of climate change, especially in the Huangfuchuan (HF) River and Kuye (KY) River basins, where the contribution of human activities to runoff exceeds 90%. The greater impact of human activities in HF River and KY River is due to the significantly higher water use growth rate and normalized vegetation index trends than in other areas.
Dependence of daily precipitation and wind speed over coastal areas: evidence from China's coastline
Authors: Qi Xuyang , Qian Shuni , Chen Kebing , Li Jun , Wu Xushu , Wang Zhaoli , Deng Zifeng , Jiang Jie 
Published: March 2023

AbstractRainfall and wind speed are two important meteorological variables that have a significant impact on agriculture, human health, and socio-economic development. While individual rainfall or wind events have been widely studied, little attention has been devoted to studying the lead–lag relationship between rainfall and wind speed, particularly in coastal regions where strong dependence between rainfall and wind speed is expected. Taking China's coastline as the case study, this paper aims to explore the variation trends of wind speed and rainfall and reveal the relationships between rainfall events and wind speeds on days before and after rainfall occurrence, by using meteorological station data from 1960 to 2018. The results show that wind speed tended to decrease while rainfall showed a slight increase for most stations. The daily wind speed increased 2 days before rainfall occurrence and decreased after then, with the highest wind speed observed during rainfall onset regardless of rainfall amount. Moreover, heavier rainfall events are more likely to occur with higher wind speeds. The findings of this study potentially improve the understanding of the dependence of rainfall and wind speed, which could help rainfall or wind-related disaster mitigation.
Endocrine-disrupting substances: I. Relative risks of PFAS in drinking water
Authors: Tomei Torres Francisco Alberto , Masten Susan J. 
Published: March 2023

Abstract
Concentrations of per and polyfluorinated alkyl substances (PFAS) in drinking water are significantly lower than in vivo levels of the native target hormone. These concentrations are orders of magnitude lower than the hormone in question, particularly when corrected for transactivation. A pregnant woman can excrete about 7,000 μg/day of total estrogens. A low-dose oral contraceptive pill contains 20 μg estradiol. Soy-based baby formula contains phytoestrogens equivalent to a low-dose oral contraceptive pill. A woman on a low-dose oral hormone replacement therapy consumes about 0.5–2 mg/day of one or more estrogens. The levels of endocrine-disrupting substances (EDSs) exposure by oral, respiratory, or dermal routes have the potential to make removing PFAS from drinking water due to its estrogenic activity divert valuable resources. These levels become even less of a threat when their estrogenic potencies are compared with those of the target hormones present as contaminants in water and even more so when compared with levels commonly present in human tissues. The fact that PFAS constitute a tiny fraction compared to exposure to phytoestrogens makes the effort even more insignificant. If PFAS are to be removed from drinking water, it is not due to their estrogenic activity.
Cancer Proteomics for Cellular Dysfunction: Insights and Trends
Authors: Rani Anjna , Devi Singh Veena , Mazumder Rupa , Dua Kamal 
Published: March 2023

Background:
Cancer is an ailment with having a very low survival rate globally. Poor cancer prognosis
is primarily caused by the fact that people are found to have the disease when it is already well advanced.
The goal of this study is to compile information on new avenues of investigation into biomarkers that may facilitate
the routine detection of cancer. Proteomic analysis has recently developed into a crucial technique for cancer
biology research, working in tandem with genomic analysis. Mass spectrometry techniques are one of several
proteome analysis techniques that allow for the highly precise quantitative and qualitative recognition of hundreds
of proteins in small quantities from various biological materials. These findings might soon serve as the
foundation for better cancer diagnostic techniques.

Methods:
An exhaustive literature survey has been conducted using electronic databases such as Google Scholar,
Science Direct, and PubMed with keywords of proteomics, applications of proteomics, the technology of
proteomics, biomarkers, and patents related to biomarkers.

Result:
Studies reported till 2021 focusing on cancer proteomics and the related patents have been included in
the present review to obtain concrete findings, highlighting the applications of proteomics in cancer.

Conclusion:
The present review aims to present the overview and insights into cancer proteomics, recent breakthroughs
in proteomics techniques, and applications of proteomics with technological advancements, ranging
from searching biomarkers to the characterization of molecular pathways, though the entire process is still in its
infancy.
Obese Mouse Fat Cell-derived Extracellular Vesicles Transport
miR-99a-5p to Mitigate the Proliferation and Migration of Non-small Cell
Lung Cancer Cells
Authors: Zhai Shengping , Li Xiaoping , Lin Tiantian 
Published: March 2023

Objective:
Fat cells-derived extracellular vesicles (FC-EVs) play a role in regulating the
tumor microenvironment in cancers by transporting RNAs. MicroRNAs (miRNAs) are vital regulators
of cancer development. This study was conducted to explore the role of FC-EVs in the proliferation
and migration of non-small cell lung cancer (NSCLC) cells, providing targets for NSCLC
treatment.

Methods:
The obese mouse model was established via high‐fat diet (HFD), followed by separation
and characterization of FC-EVs (HFD-EVs). The levels of miR-99a-5p, precursor-miR-99a-5p,
and heparan sulfate-glucosamine 3-sulfotransferase 3B1 (HS3ST3B1) were measured by RT-qPCR
or Western blot assay. Cell proliferation and migration were evaluated by 3-(4, 5-dimethylthiazol-
2-yl)-2, 5-diphenyltetrazolium bromide and wound healing assays. The expression of Cy3-labeled
miR-99a-5p in A549 cells (one NSCLC cell line) was observed via confocal microscopy. The
binding of miR-99a-5p to HS3ST3B1 was analyzed by the dual luciferase assay. Rescue experiments
were performed to confirm the role of HS3ST3B1 in NSCLC cells.

Results:
miR-99a-5p was upregulated in adipose tissues, FCs, and HFD-EVs. HFD-EVs mitigated
the proliferation and migration of NSCLC cells. HFD-EVs transported miR-99a-5p into A549
cells, which upregulated miR-99a-5p expression and inhibited HS3ST3B1 expression in A549
cells. HS3ST3B1 overexpression reversed the inhibition of HFD-EVs on the proliferation and migration
of NSCLC cells.

Conclusion:
HFD-EVs transported miR-99a-5p into NSCLC cells and inhibited HS3ST3B1,
thereby inhibiting proliferation and migration of NSCLC cells.
Pan-cancer Analysis Confirms the Prognostic and Immunological Effects
of Prostate Tumor Overexpressed-1 in Human Cancers
Authors: Liu Fashun , Wan Songlin , Li Yue , Ye Zhenxiong , Li Daojiang , Li Zhen 
Published: March 2023

Background:
Prostate tumor overexpressed-1 (PTOV1) is a conserved oncogenic adaptor
protein associated with cancer progression and may be an independent prognostic marker for several
malignancies. Consequently, using pan-cancer research to explore the significance of PTOV1 is valuable,
and may reveal novel targets for cancer treatment.

Methods:
comprehensive bioinformatics analysis of PTOV1 was performed. The qRT-PCR was
utilized to confirm the aberrant PTOV1 expression in several cancer cell lines.

Results:
We observed that PTOV1 mRNA expression was high in 18 cancer tissues and was thereafter
associated with poor survival prognosis in a range of malignancies. The immune subtypes of 14 malignancies
and the molecular subtypes of six malignancies were related to PTOV1. A substantial association
between PTOV1 and immune checkpoint (ICP) genes was also observed. Tumor mutational
burden (TMB), microsatellite instability (MSI), and DNA methylation analyses indicated that PTOV1
acts as a cancer-promoting agent in a series of tumors. In addition, an enrichment study of PTOV1 and
related genes revealed that RNA splicing may be responsible for the involvement of PTOV1 in cancers.
Lastly, we also verified that PTOV1 expression was elevated in bladder cancer, breast cancer,
CESC, LIHC cell lines via qRT-PCR.

Conclusion:
Our bioinformatics research indicated that PTOV1 may be involved in tumor immunity.
Furthermore, differentially expressed PTOV1 was found to be related to poor prognosis in cancers,
and RNA splicing may be the specific mechanism for this effect. Therefore, PTOV1 mRNA and the
corresponding protein may function as potential prognostic indicators and therapeutic targets in various
cancers.
The Effectiveness, Changes in Body Weight, and Menstrual Cycle
Between 2 Rods and 1 Rod Contraceptive Implants after 12 Months of
Insertion
Authors: Santoso Budi , Susanto Lunardhi , Tunjungseto Arif , Widjayanti Yhenti , Fernandus Euvanggelia Dwilda , Devianti  Putri Berliana 
Published: March 2023

Background:
Implant as long-acting reversible contraception (LARC) is ideal for Indonesian
women. Two-rod implant (2x75mg levonorgestrel) is the most common implant used in Indonesia.
Another alternative method is a one-rod implant (1x150mg levonorgestrel).

Objective:
This study evaluated the effectiveness, changes in body weight (BW), and menstrual cycle between the one-rod and two-rod implant acceptors.

Methods:
This study was an RCT design with phase II clinical trials; 179 subjects were recruited.
Subjects were met with inclusion and exclusion criteria and divided into one-rod and two-rod groups.
The variables were evaluated within 12 months.

Results:
This study involved 70 subjects in the one-rod implant group and 66 subjects in the two-rod implant
group. At 12 months, there were no differences between the effectiveness of the two types of contraception,
and pregnancy was reported in 1.26% vs 1.31%. The concentration of levonorgestrel in the
one-rod group was 0.31 ng/dL as the median (IQR: 0.33 ng/dL) and 0.34 ng/dL as the median (IQR: 0.33
ng/dL) in the two-rod group (p = 0.438). The BW in the two-rod group increased significantly in month
six (p = 0.035) and twelve (p = 0.017) but not in the one-rod group. Menstrual cycle changes occurred in
51.4% of subjects and 57.6% of subjects in the one- and two-rod groups (p = 0.584), respectively.

Conclusion:
At 12 months, one-rod and two-rod implants had similar effectiveness and concentration of levonorgestrel. Significant weight gain happened in the two-rod group, and the weight gain increased in months six and twelve. Menstrual cycle changes have occurred in both.
Graph Convolutional Neural Network with Multi-Layer Attention Mechanism for Predicting Potential Microbe-Disease Associations
Authors: Wang Lei , Yang Xiaoyu , Kuang Linai , Zhang Zhen , Zeng Bin , Chen Zhiping 
Published: March 2023

Background:Human microbial communities play an important role in some physiological process of human beings. Nevertheless, the identification of microbe-disease associations through biological experiments is costly and time-consuming. Hence, the development of calculation models is meaningful to infer latent associations between microbes and diseases.Aims:In this manuscript, we aim to design a computational model based on the Graph Convolutional Neural Network with Multi-layer Attention mechanism, called GCNMA, to infer latent microbe-disease associations.Objective:This study aims to propose a novel computational model based on the Graph Convolutional Neural Network with Multi-layer Attention mechanism, called GCNMA, to detect potential microbedisease associations.Methods:In GCNMA, the known microbe-disease association network was first integrated with the microbe- microbe similarity network and the disease-disease similarity network into a heterogeneous network first. Subsequently, the graph convolutional neural network was implemented to extract embedding features of each layer for microbes and diseases respectively. Thereafter, these embedding features of each layer were fused together by adopting the multi-layer attention mechanism derived from the graph convolutional neural network, based on which, a bilinear decoder would be further utilized to infer possible associations between microbes and diseases.Results:Finally, to evaluate the predictive ability of GCNMA, intensive experiments were done and compared results with eight state-of-the-art methods which demonstrated that under the frameworks of both 2-fold cross-validations and 5-fold cross-validations, GCNMA can achieve satisfactory prediction performance based on different databases including HMDAD and Disbiome simultaneously. Moreover, case studies on three kinds of common diseases such as asthma, type 2 diabetes, and inflammatory bowel disease verified the effectiveness of GCNMA as well.Conclusion:GCNMA outperformed 8 state-of-the-art competitive methods based on the benchmarks of both HMDAD and Disbiome.
Exploring the Hepatotoxicity of Drugs through Machine Learning and
Network Toxicological Methods
Authors: Tang Tiantian , Gan Xiaofeng , Zhou Li , Pu Kexue , Wang Hong , Dai Weina , Zhou Bo , Mo Lingyun , Zhang Yonghong 
Published: March 2023

Background:
The prediction of the drug-induced liver injury (DILI) of chemicals is still a
key issue of the adverse drug reactions (ADRs) that needs to be solved urgently in drug development.
The development of a novel method with good predictive capability and strong mechanism interpretation
is still a focus topic in exploring the DILI.

Objective:
With the help of systems biology and network analysis techniques, a class of descriptors that
can reflect the influence of drug targets in the pathogenesis of DILI is established. Then a machine
learning model with good predictive capability and strong mechanism interpretation is developed between
these descriptors and the toxicity of DILI.

Methods:
After overlapping the DILI disease module and the drug-target network, we developed novel
descriptors according to the number of drug genes with different network overlapped distance parameters.
The hepatotoxicity of drugs is predicted based on these novel descriptors and the classical molecular
descriptors. Then the DILI mechanism interpretations of drugs are carried out with important network
topological descriptors in the prediction model.

Results:
First, we collected targets of drugs and DILI-related genes and developed 5 NT parameters (S,
Nds=0, Nds=1, Nds=2, and Nds>2) based on their relationship with a DILI disease module. Then hepatotoxicity
predicting models were established between the above NT parameters combined with molecular descriptors
and drugs through the machine learning algorithms. We found that the NT parameters had a
significant contribution in the model (ACCtraining set=0.71, AUCtraining set=0.76; ACCexternal set=0.79,
AUCexternal set=0.83) developed by these descriptors within the applicability domain, especially for Nds=2,
and Nds>2. Then, the DILI mechanism of acetaminophen (APAP) and gefitinib are explored based on
their risk genes related to ds=2. There are 26 DILI risk genes in the regulation of cell death regulated
with two steps by 5 APAP targets, and gefitinib regulated risk gene of CLDN1, EIF2B4, and AMIGO1
with two steps led to DILI which fell in the biological process of response to oxygen-containing compound,
indicating that different drugs possibly induced liver injury through regulating different biological
functions.

Conclusion:
A novel method based on network strategies and machine learning algorithms successfully
explored the DILI of drugs. The NT parameters had shown advantages in illustrating the DILI mechanism
of chemicals according to the relationships between the drug targets and the DILI risk genes in the
human interactome. It can provide a novel candidate of molecular descriptors for the predictions of other
ADRs or even of the predictions of ADME/T activity.
New Approaches for the Synthesis N-alkylated Benzo[b]thiophene Derivatives together
with their Antiproliferative and Molecular Docking Studies
Authors: El-Sharkawy Karam A. , Mohamed Abeer A. , Al Farouk Fatma O. , Mohareb Rafat M. 
Published: March 2023

Background:
2-Amino thiophene derivatives are important compounds not only for their uses in many
heterocyclic reactions but also due to their wide range of pharmaceutical and biological activities.

Objective:
The aim of this work was to explore a number of new heterocyclic derivatives, studying their inhibitions
toward cancer cell lines and studying their structure activity relation ship.

Methods:
Alkylation of 2-amino-4,5,6,7-tetrahydrobenzo[b]thiophene-3-carbonitrile was achieved through its reaction
with chloroacetone and 2-bromo-1-(4-aryl)ethanone derivatives to give compounds 3 and 11a-c. The produced compoumds
were subjected to further heterocylization reactions and cytotoxic evaluation against the three cancer cell lines
MCF-7, NCI-H460 and SF-268, together with the normal cell line WI 38. Further evaluations were obtained through
studying their inhibitions against cancer cell lines classified according to the disease. Anticancer screening against
hepatocellular carcinoma HepG2 and cervical carcinoma HeLa cell lines for all compounds together with the molecular
docking of 12c, 12d, 12e and 12f were studied.

Results:
Anti-proliferative evaluations and inhibitions for all of the synthesized compounds showed that many compounds
exhibited high inhibitions.

Conclusion:
Toward the three cancer cell lines, compounds 3, 5a, 7a, 9a, 9b, 11b, 12b, 12d, 12e, 12f, 14c, 14e, 14f,
15e, 15f, 16e, 16f, 17c, 18b, 22a and 22c were the most cytotoxic compounds. The high activities of some compounds
were attributed to the presence of the electronegative CN and or Cl groups within the molecule. Most of the tested
compounds exhibited inhibitions higher than the reference doxorubicin toward hepatocellular carcinoma HepG2 and
cervical carcinoma HeLa cell lines. The score of binding energy of compounds 12c, 12d, 12e and 12f was close to the
reference Foretinib which appeared through the molecular docking results of such compounds.
Comparison of Five Escherichia coli Strains to Achieve the
Maximum Yield of a Recombinant Immunotoxin Consisting of an
Antibody against VEGF Conjugated with MAP30 Toxin in a
Benchtop Bioreactor
Authors: Zarei Mina , Morowvat Mohammad Hossein 
Published: March 2023

Background:
Cancer is among the leading causes of death worldwide, imposing
high costs on the health systems of all societies. Extensive biological studies are required
to discover appropriate therapies. Escherichia coli has long been regarded as one of the
main biotechnological bio-factories to produce recombinant protein-based therapeutics. In
the present study, five strains of E. coli were compared to achieve the maximum production
of a previously designed recombinant immunotoxin-carrying MAP30 toxin against
VEGF-overexpressed cancer cells in a benchtop bioreactor.

Methods:
The recombinant immunotoxin coding gene sequence was extracted from the
NCBI database. The host used to produce the recombinant immunotoxin were five E. coli
strains of BL21 (DE3), DH5α, SHuffle®T7, XL1-Blue, and Rosetta-gamiTM (DE3). CaCl2
method was used for bacterial transformation. Bacterial growth measurements were performed
using optical density measurements at 600 nm. The immunotoxin production was
measured using SDS-PAGE analysis. The best-producing strain was cultivated in a 10-L
benchtop stirred tank bioreactor. Recent patents on this field were also studied.

Results:
The results demonstrated that the BL21 (DE3) strain had the highest expression of
recombinant protein in comparison to other strains. Moreover, the cell growth of E. coli
BL21 (DE3) and SHuffle®T7 strains before transformation in the LB medium, were significantly
higher in comparison to other strains. Additionally, the transformation of Rosettagami
was associated with decreased cell proliferation. The transformation of the XL1-Blue
strain did not effect cell growth. Analysis of the growth kinetics demonstrated appropriate
proliferation of the transformed BL21 (DE3) cells in the laboratory benchtop bioreactor.

Conclusions:
Based on the results of this study, the BL21 (DE3) strain could be used as a
suitable host for the production of the recombinant immunotoxin against VEGF in stirred
tank bioreactor, which can be employed for the treatment of tumors. Yet, its precise mechanism
must be explored in extensive studies.