bluuebunny/crossref_metadata_2025_split huggingface dataset:
Quantitative profiling of comprehensive composition in compound herbal injections: An NMR approach applied on Shenmai injection
Authors: Li Wenzhu , Zhao Fang , Xie Xinyuan , Yang Jiayu , Pan Jianyang , Qu Haibin 
Published: June 2022

AbstractIntroductionCompound herbal injections (CHIs) can be regarded as a significant innovation in the modernisation of herbal medicine. Therefore, improving the quality control level of CHIs has always been an active research topic in traditional herbal medicine.ObjectivesIn this study, Shenmai injection was used as a representative sample for investigating the ability of proton nuclear magnetic resonance (1H NMR) in the quality evaluation of CHIs.MethodsA quantitative 1H NMR method was developed to simultaneously determine the contents of total ginsenosides, polysorbate 80, and 20 primary metabolites in Shenmai injection. Multivariate statistical analysis was combined to compare differences between samples from different manufacturers.ResultsIt was found that the combined measurement uncertainty of each component is less than 1.61%, which demonstrates the reliability of the method. Furthermore, the components determined by this method account for up to 92.64% of the total solids, which is an unprecedented success in the analysis of Shenmai injection. In the end, the method was applied to the quality comparison of Shenmai injection from six manufacturers. The results showed that the differences among the samples from the six manufacturers were reflected in multiple types of components.ConclusionThis study fully demonstrates the superiority of the quantitative 1H NMR method in comprehensive composition profiling of CHIs, which is conducive to improving the quality control level of Shenmai injection. Further, the present study can be used as a reference study for the research on the quality and safety of CHIs.
Time‐related biases in perinatal pharmacoepidemiology: A systematic review of observational studies
Authors: Ukah Ugochinyere Vivian , Aibibula Wusiman , Platt Robert W. , Dayan Natalie , Reynier Pauline , Filion Kristian B. 
Published: June 2022

AbstractBackgroundTime‐related biases, such as immortal time and time‐window bias, frequently occur in pharmacoepidemiologic research. However, the prevalence of these biases in perinatal pharmacoepidemiology is not well understood.ObjectiveTo describe the frequency of time‐related biases in observational studies of medications commonly used during pregnancy (antibiotic, antifungal, and antiemetic drugs) via systematic review.MethodWe searched Medline and EMBASE for observational studies published between January 2013 and September 2020 and examining the association between antibiotic, antifungal, or antiemetic drugs and adverse pregnancy outcomes, including spontaneous abortion, stillbirth, preterm delivery, small‐for‐gestational age, pre‐eclampsia, and gestational diabetes. The proportion of studies with time‐related biases was estimated overall and by type (immortal time bias, time‐window bias).ResultsOur systematic review included 20 studies (16 cohort studies, 3 nested case–control studies, and 1 case–control study), of which 12 examined antibiotic, 6 antiemetic, and 2 anti‐fungal drugs. Eleven studies (55%) had immortal time bias due to the misclassification of unexposed, event‐free person‐time between cohort entry and exposure initiation as exposed. No included study had time‐window bias. The direction of effect varied for both studies with and without time‐related bias, with many studies reporting very wide confidence intervals around the effect estimates, thus making the direction of effect less interpretable. However, studies with time‐related bias were more likely to show protective or null associations compared with studies without time‐related bias.ConclusionTime‐related biases occur frequently in observational studies of drug effects during pregnancy. The use of appropriate study design and analytical approaches is needed to prevent time‐related biases and ensure study validity.
Anxiety and psychological needs of high school students: COVID‐19 period
Authors: Erden Zehra Şeyda , Aliyev Ramin 
Published: June 2022

AbstractThe study aims to examine the relationship between anxiety and psychological needs of high school students during the COVID‐19 pandemic period. The fact that the COVID‐19 virus poses a threat to mental health as well as physical health, the effects of the pandemic period on the psychological needs and anxiety level of adolescents, especially in a critical developmental stage, and the scarcity of studies concerning adolescent mental health in this period, constituted the purpose of the current study. The quantitative (N = 502) and qualitative data (N = 29) of the study, in which triangulation design was used, were collected from high school students studying in the Southeast of Turkey. In the quantitative part of the research Multiple Linear Regression was implemented and content analysis was used in the qualitative part. The results indicate that love, belonging and power, which are subdimensions of basic psychological needs, are significant predictors of anxiety level. Additionally, it is seen that the need for freedom subdimension is the least predictive variable for the anxiety level. It is concluded that there are moderate negative and significant relationships between the general and subdimensions of basic psychological needs and the level of state anxiety. In this sense, it has been found that the level of anxiety tends to decrease as the level of the satisfaction of basic psychological needs increases. The results in the qualitative analysis part largely overlap with the results in the quantitative part. Suggestions based on the findings of the study are made to researchers.
rrQNet: Protein contact map quality estimation by deep evolutionary reconciliation
Authors: Roche Rahmatullah , Bhattacharya Sutanu , Shuvo Md. Hossain , Bhattacharya Debswapna 
Published: June 2022

AbstractProtein contact maps have proven to be a valuable tool in the deep learning revolution of protein structure prediction, ushering in the recent breakthrough by AlphaFold2. However, self‐assessment of the quality of predicted structures are typically performed at the granularity of three‐dimensional coordinates as opposed to directly exploiting the rotation‐ and translation‐invariant two‐dimensional (2D) contact maps. Here, we present rrQNet, a deep learning method for self‐assessment in 2D by contact map quality estimation. Our approach is based on the intuition that for a contact map to be of high quality, the residue pairs predicted to be in contact should be mutually consistent with the evolutionary context of the protein. The deep neural network architecture of rrQNet implements this intuition by cascading two deep modules—one encoding the evolutionary context and the other performing evolutionary reconciliation. The penultimate stage of rrQNet estimates the quality scores at the interacting residue‐pair level, which are then aggregated for estimating the quality of a contact map. This design choice offers versatility at varied resolutions from individual residue pairs to full‐fledged contact maps. Trained on multiple complementary sources of contact predictors, rrQNet facilitates generalizability across various contact maps. By rigorously testing using publicly available datasets and comparing against several in‐house baseline approaches, we show that rrQNet accurately reproduces the true quality score of a predicted contact map and successfully distinguishes between accurate and inaccurate contact maps predicted by a wide variety of contact predictors. The open‐source rrQNet software package is freely available at https://github.com/Bhattacharya-Lab/rrQNet.
Water quality assessment using synchrotron‐based TXRF
Authors: Garg Vijay Kumar , Tanta Ankush , Lal Srivastav Arun , Tiwari M. K. , Sharma Ajay , Kanwar Varinder Singh 
Published: June 2022

AbstractThe pollutants released from pharmaceutical, steel, paper, and battery industries into water cause stress on the natural ecosystems, may mix with soil and water, enter into human food chain, and hence cause irreparable damage to the biotic system. Hence, the appropriate monitoring of water along with determination of heavy metals is very important for human beings. In present paper, total reflection X‐ray fluorescence (TXRF) spectrometry technique is employed to determine the level of different contaminants in the water samples gathered from the various sites of an identified industrial area. Experimentation is carried out at Raja Ramanna Centre for Advanced Technology (RRCAT), Indore‐India by using TXRF, which is one the advance techniques of element determination up to ppb levels. The elemental concentration of Cl, K, Ca, Fe, Cu, Zn, Ga, Br, Sr, As, Pb, and Ni is quantified and compared with the limits established by the WHO (World Health Organization) and BIS (Bureau of Indian Standard) guidelines regarding drinking water use. The levels of the Pb, Fe, As, Ni, Cr, Co, and Mn exceeded the values suggested by WHO and BIS at some locations/sites. To better understand the situation, water quality parameters such as Water Quality Index (WQI), Heavy metal Pollution Index (HPI), Contamination Index (CI), Metal Enrichment Index (MEI), and Heavy Metal Evaluation Index (HEI) have also been assessed for all the sites close to industrial hub. Among all sites except at A3 and A6, WQI is found to be much greater than WHO and BIS established limit. Level of arsenic in the water at A1 location was found 73 ppb. However, lead metal in water was found to be very high at all the six studied locations, and at A1 location, it is found extremely high 2613 ppb. Therefore, water at A1 and A2 sites is found to be unfit for drinking.Practitioner Points

Total reflection X‐ray fluorescence (TXRF) spectrometry technique is employed to determine the level of different contaminants in the water samples
The elemental concentration of Cl, K, Ca, Fe, Cu, Zn, Ga, Br, Sr, As, Pb, and Ni is quantified and compared with the limits prescribed by the WHO
Water Quality Index (WQI), Heavy metal Pollution Index (HPI), Contamination Index (CI), Metal Enrichment Index (MEI), and Heavy Metal Enrichment Index (MEI) have also been assessed for all the sites
Water at some sites is found unfit for drinking purpose. Based on the observations, some remedial measures are suggested to reduce the level of water contaminants up to desired levels.
Resistance and tolerance of Mycobacterium tuberculosis to antimicrobial agents—How M. tuberculosis can escape antibiotics
Authors: Li Haoran , Yuan Jinfeng , Duan Shujuan , Pang Yu 
Published: June 2022

AbstractTuberculosis (TB) poses a serious threat to public health worldwide since it was discovered. Until now, TB has been one of the top 10 causes of death from a single infectious disease globally. The treatment of active TB cases majorly relies on various anti‐tuberculosis drugs. However, under the selection pressure by drugs, the continuous evolution of Mycobacterium tuberculosis (Mtb) facilitates the emergence of drug‐resistant strains, further resulting in the accumulation of tubercle bacilli with multiple drug resistance, especially deadly multidrug‐resistant TB and extensively drug‐resistant TB. Researches on the mechanism of drug action and drug resistance of Mtb provide a new scheme for clinical management of TB patients, and prevention of drug resistance. In this review, we summarized the molecular mechanisms of drug resistance of existing anti‐TB drugs to better understand the evolution of drug resistance of Mtb, which will provide more effective strategies against drug‐resistant TB, and accelerate the achievement of the EndTB Strategy by 2035.This article is categorized under:
Infectious Diseases > Molecular and Cellular Physiology
Process Mining: A 360 Degree Overview
Authors: van der Aalst Wil M. P. 
Published: June 2022

AbstractProcess mining enables organizations to uncover their actual processes, provide insights, diagnose problems, and automatically trigger corrective actions. Process mining is an emerging scientific discipline positioned at the intersection between process science and data science. The combination of process modeling and analysis with the event data present in today’s information systems provides new means to tackle compliance and performance problems. This chapter provides an overview of the field of process mining introducing the different types of process mining (e.g., process discovery and conformance checking) and the basic ingredients, i.e., process models and event data. To prepare for later chapters, event logs are introduced in detail (including pointers to standards for event data such as XES and OCEL). Moreover, a brief overview of process mining applications and software is given.
Predictive Process Monitoring
Authors: Di Francescomarino Chiara , Ghidini Chiara 
Published: June 2022

AbstractPredictive Process Monitoring [29] is a branch of process mining that aims at predicting the future of an ongoing (uncompleted) process execution. Typical examples of predictions of the future of an execution trace relate to the outcome of a process execution, to its completion time, or to the sequence of its future activities
Streaming Process Mining
Authors: Burattin Andrea 
Published: June 2022

AbstractStreaming process mining refers to the set of techniques and tools which have the goal of processing a stream of data (as opposed to a finite event log). The goal of these techniques, similarly to their corresponding counterparts described in the previous chapters, is to extract relevant information concerning the running processes. This chapter presents an overview of the problems related to the processing of streams, as well as a categorization of the existing solutions. Details about control-flow discovery and conformance checking techniques are also presented together with a brief overview of the state of the art.
Responsible Process Mining
Authors: Mannhardt Felix 
Published: June 2022

AbstractThe prospect of data misuse negatively affecting our life has lead to the concept of responsible data science. It advocates for responsibility to be built, by design, into data management, data analysis, and algorithmic decision making techniques such that it is made difficult or even impossible to intentionally or unintentionally cause harm. Process mining techniques are no exception to this and may be misused and lead to harm. Decisions based on process mining may lead to unfair decisions causing harm to people by amplifying the biases encoded in the data by disregarding infrequently observed or minority cases. Insights obtained may lead to inaccurate conclusions due to failing to considering the quality of the input event data. Confidential or personal information on process stakeholders may be leaked as the precise work behavior of an employee can be revealed. Process mining models are usually white-box but may still be difficult to interpret correctly without expert knowledge hampering the transparency of the analysis. This chapter structures the topic of responsible process mining based on the FACT criteria: Fairness, Accuracy, Confidentiality, and Transparency. For each criteria challenges specific to process mining are provided and the current state of the art is briefly summarized.
Using Process Mining in Healthcare
Authors: Martin Niels , Wittig Nils , Munoz-Gama Jorge 
Published: June 2022

AbstractThis chapter introduces a specific application domain of process mining: healthcare. Healthcare is a very promising domain for process mining given the significant societal value that can be generated by supporting process improvement in a data-driven way. Within a healthcare organisation, a wide variety of processes is being executed, many of them being highly complex due to their loosely-structured and knowledge-intensive nature. Consequently, performing process mining in healthcare is challenging, but can generate significant societal impact. To provide more insights in process mining in healthcare, this chapter first provides an overview of healthcare processes and healthcare process data, as well as their particularities compared to other domains. Afterwards, an overview of common use cases in process mining in healthcare research is presented, as well as insights from a real-life case study. Subsequently, an overview of open challenges to ensure a widespread adoption of process mining in healthcare is provided. By tackling these challenges, process mining will become able to fully play its role to support evidence-based process improvement in healthcare and, hence, contribute to shaping the best possible care for patients in a way that is sustainable in the long run.
Foundations of Process Discovery
Authors: van der Aalst Wil M. P. 
Published: June 2022

AbstractProcess discovery is probably the most interesting, but also most challenging, process mining task. The goal is to take an event log containing example behaviors and create a process model that adequately describes the underlying process. This chapter introduces the baseline approach used in most commercial process mining tools. A simplified event log is used to create a so-called Directly-Follows Graph (DFG). This baseline is used to explain the challenges one faces when trying to discover a process model. After introducing DFG discovery, we focus on techniques that are able to discover models allowing for concurrency (e.g., Petri nets, process trees, and BPMN models). The chapter distinguishes two types of approaches able to discover such models: (1) bottom-up process discovery and (2) top-down process discovery. The Alpha algorithm is presented as an example of a bottom-up technique. The approach has many limitations, but nicely introduces the idea of discovering local constraints. The basic inductive mining algorithm is presented as an example of a top-down technique. This approach, combined with frequency-based filtering, works well on most event logs. These example algorithms are used to illustrate the foundations of process discovery.
Foundations of Process Enhancement
Authors: de Leoni Massimiliano 
Published: June 2022

AbstractProcess models are among the milestones for Business Process Management and Mining, and used to describe a business process or to prescribe how its instances should be carried out. It follows that they need to fulfill certain properties to be useful. If they aim to represent how the process is currently being executed, they need to be precise and recall the behavior observed in reality. If the goal is to ensure that the process is executed according to laws and regulations, its model should only allow the behavior that is valid from a domain viewpoint and provides some guarantee to ensure good performance level. Process enhancement is the type of Process Mining that aims at models that fulfill these properties, and the literature further splits it into two subfields: process extension and process improvement. Process extension aims to incorporate the process perspectives on data, decision, resources and time into the model: their inclusion in process models enable designers to fine-tune the model specifications, thus obtaining models with higher levels of precision. Process improvement passes through an “improved” process model. If the model contains portions of behavior that lead to unsatisfactory outcomes (high costs, low customer satisfactions, etc.) or that violate norms and regulations, one would like those portions to be disallowed by the model. In case some executions are observed in reality and are not allowed by the model, they should be incorporated into the model if they are observed to generally yield good performances. This chapter discusses these two types of process enhancement, and illustrates some basic and some advanced techniques to tackle it, highlighting the pros and cons, and the underlaying assumptions.
Systematic review exploring the relationship between sexual abuse and lower urinary tract symptoms
Authors: Selai Caroline , Elmalem Michael S. , Chartier-Kastler Emmanuel , Sassoon Natalia , Hewitt Sam , Rocha Maria Francisca , Klitsinari Larisa , Panicker Jalesh N. 
Published: June 2022

Abstract
Introduction and hypothesis
Patients presenting with lower urinary tract symptoms (LUTS) may report a history of sexual abuse (SA), and survivors of SA may report LUTS; however, the nature of the relationship is poorly understood. The aim of this review is to systematically evaluate studies that explore LUT dysfunction in survivors of SA.

Methods
A systematic literature search of six databases, Cochrane Database of Systematic Reviews, MEDLINE, EMBASE, CINAHL, AMED, and PsycINFO, was performed. The last search date was June 2021 (PROSPERO CRD42019122080). Studies reporting the prevalence and symptoms of LUTS in patients who have experienced SA were included. The literature was appraised according to the PRISMA statement. The quality of the studies was assessed.

Results
Out of 272 papers retrieved, 18 publications met the inclusion criteria: studies exploring LUTS in SA survivors (n=2), SA in patients attending clinics for their LUTs (n=8), and cross-sectional studies (n=8). SA prevalence ranged between 1.3% and 49.6%. A history of SA was associated with psychosocial stressors, depression, and anxiety. LUTS included urinary storage symptoms, voiding difficulties, voluntary holding of urine and urinary tract infections. Most studies were of moderate quality. Assessment of SA and LUTS lacked standardisation.

Conclusions
The review highlights the need for a holistic assessment of patients presenting with LUTS. Although most of the studies were rated as being of ‘moderate’ quality, the evidence suggests the need to provide a “safe space” in clinic for patients to share sensitive information about trauma. Any such disclosure should be followed up with further assessment.
Defining Surgical Workforce Density Targets to Meet Child and Neonatal Mortality Rate Targets in the Age of the Sustainable Development Goals: A Global Cross‐Sectional Study
Authors: Truche Paul , Smith Emily R. , Ademuyiwa Adesoji , Buda Alexandra , Nabukenya Mary T. , Kaseje Neema , Ameh Emmanuel A. , Greenberg Sarah , Evans Faye , Bickler Stephen , Meara John G. , Rice Henry E. 
Published: June 2022

AbstractObjectivesTo reduce preventable deaths of newborns and children, the United Nations set a target rate per 1000 live births of 12 for neonatal mortality (NMR) and 25 for under‐5 mortality (U5MR). The purpose of this paper is to define the minimum surgical workforce needed to meet these targets and evaluate the relative impact of increasing surgeon, anesthesia, and obstetrician (SAO) density on reducing child mortality.MethodsWe conducted a cross‐sectional study of 192 countries to define the association between surgical workforce density and U5MR as well as NMR using unadjusted and adjusted B‐spline regression, adjusting for common non‐surgical causes of childhood mortality. We used these models to estimate the minimum surgical workforce to meet the sustainable development goals (SDGs) for U5MR and NMR and marginal effects plots to determine over which range of SAO densities the largest impact is seen as countries scale‐up SAO workforce.ResultsWe found that increased SAO density is associated with decreased U5MR and NMR (P < 0.05), adjusting for common non‐surgical causes of child mortality. A minimum SAO density of 10 providers per 100,000 population (95% CI: 7–13) is associated with an U5MR of < 25 per 1000 live births. A minimum SAO density of 12 (95% CI: 9–20) is associated with an NMR of < 12 per 1000 live births. The maximum decrease in U5MR, on the basis of our adjusted B‐spline model, occurs from 0 to 20 SAO per 100,000 population. The maximum decrease in NMR based on our adjusted B‐spline model occurs up from 0 to 18 SAO, with additional decrease seen up to 80 SAO.ConclusionsScale‐up of the surgical workforce to 12 SAO per 100,000 population may help health systems meet the SDG goals for childhood mortality rates. Increases in up to 80 SAO/100,000 continue to offer mortality benefit for neonates and would help to achieve the SDGs for neonatal mortality reduction.