bluuebunny/crossref_metadata_2025_split huggingface dataset:
Exogenous Production of N-acetylmuramyl-L Alanine Amidase (LysM2) from Siphoviridae Phage Affecting Anti-Gram-Negative Bacteria: Evaluation of Its Structure and Function
Authors: Miri Morteza , Yazdianpour Sepideh , Abolmaali Shamsozoha , Darvish Alipour Astaneh Shakiba 
Published: January 2022

Background: To obtain endolysin with impact(s) on gram-negative bacteria as well as gram-positive bacteria, N-acetylmuramyl L-alanine-amidase (MurNAc-LAA) from a Bacillus subtilis-hosted Siphoviridae phage (SPP1 phage, Subtilis Phage Pavia 1) was exogenously expressed in Escherichia coli (E. coli).
Methods: The sequences of MurNAc-LAA genes encoding peptidoglycan hydrolases were obtained from the Virus-Host database. The sequence of MurNAc-LAA was optimized by GenScript software to generate MurNAc-LAA-MMI (LysM2) for optimal expression in E. coli. Furthermore, the structure and function of LysM2 was evaluated in silico. The optimized gene was synthesized, subcloned in the pET28a, and expressed in E. coli BL21(DE3). The antibacterial effects of the protein on the peptidoglycan substrates were studied.
Results: LysM2, on 816 bp gene encoding a 33 kDa protein was confirmed as specific SPP1 phage enzyme. The enzyme is composed of 271 amino acids, with a half-life of 10 hr in E. coli. In silico analyses showed 34.2% alpha-helix in the secondary structure, hydrophobic N-terminal, and lysine-rich C-terminal, and no antigenic properties in LysM2 protein. This optimized endolysin revealed impacts against Proteus (sp) by turbidity, and an antibacterial activity against Klebsiella pneumoniae, Salmonella typhi-murium, and Proteus vulgaris in agar diffusion assays.
Conclusion: Taken together, our results confirmed that LysM2 is an inhibiting agent for gram-negative bacteria.
Identification and evaluation of human errors of Epoxy control room operators of a pipe Mill company using HEC technique
Authors: Shirali GH , Jafari B , Raoufian F 
Published: January 2022

Introduction: In many workplaces today, the incidence of human error can lead to catastrophic accidents in which human error is the main cause of accidents. Due to the vital role of the control room in guiding and controlling various sites of the pipe industry, especially the outer coating sector, the incidence of any error can lead to human accidents, damage to machinery, interruption in production. This study aimed to identify and evaluate human error by Human Error Calculator (HEC) method in the epoxy control room of a pipe mill company.
Materials and Methods: In the present descriptive cross-sectional study, the HEC method was used to identify and evaluate human errors. The HEC technique is provided by Risk Map Company, in which the probability of human error is based on five factors affecting the occurrence of human error, including a degree of urgency, complexity, importance, degree of individual skill, and task repetition, using a disk-shaped tool called Risk Disk is determined through direct observation, available instructions and interview with the head of the mentioned unit.
Results: According to the results of this study, out of 11 identified tasks, five job tasks with a risk number of 70% have a high probability of human error, four job tasks with a risk number of 50%, and one job task with a number There is a 40% risk of moderate human error, And a job task with a 20% risk number has an increased chance of human error.
Conclusion: The results of the present study showed that the HEC method is easy to use and is a simple and useful tool for professionals to calculate the probability of human error. In addition, HEC is a practical, effective and beneficial method for managers to reduce human error.
Prospects for architects in the Government Sector
Authors: Garg Disha , Sharma Kartik , Nayar Parul , Goyal Shubhi , Nagdeve Shruti S 
Published: January 2022

Purpose: With the conception of one’s professional life, it is essential to understand all the possibilities and opportunities that lie before them. In the case of architecture, there exists a bias towards the private sector where newly graduate students aspire to work in private practices and possibly even envision a practice of their own at a certain point in life. While there is nothing wrong with envisioning a future in the private sector, it is also essential to be aware about the public sector and understand the opportunities it provides to be able to make an informed decision. There is a preconceived notion about the monotonous nature of government jobs and a lack of awareness about students about the opportunities in this sector. Hence, it becomes crucial to understand the numerous opportunities this sector has to offer and thus, explore the potential of architects in government organizations.
Methodology: The research for this paper has been done by referring to existing literature and interviews with concerned people. With an understanding of how and why is the government sector an essential area of research for budding architects and planners. The research was done through interviews and possible case studies was done based on review of existing literature.
Main Findings: The government is one sector with tremendous possibilities in the realm of architecture but is often plagued with stereotypes and preconceptions which have emerged over the years. It is imagined to be “lazy”, “uninnovative” and “non-productive” but this sector has evolved over the recent years and is now shaping to be one of the more lucrative sectors for practice. The number of perks, benefits and a clear comparative advantage of a higher salary, added with the direct contribution towards serving the nation, the government sector clearly has an unrealised potential for architectural professionals.
Implications: With younger architetcs having preconceived notions about role of architetcs in a government sector limited to unexciting set of designs without creativity, this article may help bring a fresh thought process to choose professional sector wisely.
Client expectations in the purview of architecture
Authors: Arora Oorja , Das Shiba , Siva E S Shruthi , A S Saaral , Nagdeve Shruti 
Published: January 2022

Purpose of the study: From the conceptualisation to the construction stage, clients have a wide range of expectations from architects, and sometimes not addressing or meeting these expectations can land both of them in conflicts of interest, which might affect the architect’s career. This study attempts to unfold the dynamics of the client-architect relationships, emphasizing clients’ expectations from architects.
Methodology: This research has been conceptualised to cover various aspects of the client-architecture relationships through an in-depth literature review, followed by undertaking a survey. The literature review has touched upon different factors that shape the client-architect relationships, various architects’ theories, and how clients feel. The survey was conducted online amongst 29 architects and 12 clients to get their perspectives on the issue, giving more detailed insights into the topic.
Main Findings: The findings have revealed that clients who’ll be personally occupying and using the space have the maximum expectations from the architects who’re designing that space, with these expectations spanning the entire process starting right from their initial interactions to post-occupancy. Most of these expectations and their consequent circumstances become complex where the budgets are strict and tight, as it is difficult for the clients and the architects to compensate for the losses.
Novelty/Originality of this study: Architectural practice is an ever-evolving profession, where relationships between architects and clients are not static, but dynamic as per the changing working styles. This study represents a fresh angle of current practicing architects and their views, which may prove beneficial for architects stepping out from colleges to working industry.
Efek Obat Imunosupresan Pada Pasien Autoimun dengan COVID-19 (A Scoping Review of The Clinical Evidence)
Authors: Putra Oki Nugraha , Anggraini Mia Arum , Hardiyono Hardiyono 
Published: January 2022

The main modality in autoimmune disease is a long-term immunosuppressant treatment aiming to control disease progression and increase patient life expectancy. This scoping review aims to evaluate the effect of immunosuppressant treatment in autoimmune patients with COVID-19 on clinical outcomes and disease progression. This scoping review was conducted following the PRISMA extension for scoping review (PRISMA-ScR) guidelines. The Pubmed and Science Direct databases are used to find articles that match the study objectives. Thirteen articles met the inclusion criteria, and all of them were classified as observational studies. Most immunosuppressant treatments are the disease-modifying anti-rheumatic drugs (DMARD) and glucocorticoids. The highest number of autoimmune patients with rheumatoid arthritis (RA) was 43.4%, systemic lupus erythematosus (SLE) 13.6%, and others was 43%. Autoimmune patients with COVID-19 taking immunosuppressant medications, particularly glucocorticoids, significantly increased the risk of hospitalization and the use of ventilators. However, there was no mention of the dosage and duration of immunosuppressant therapy in most of the studies. In general, the use of immunosuppressant drugs was not associated with an increased risk of COVID-19 infection and mortality compared with the general population. Increasing age and comorbidities were associated with poor clinical outcomes. In conclusion, autoimmune patients with COVID-19 who are taking immunosuppressant therapy particularly glucocorticoid exacerbate clinical outcomes. Periodic clinical monitoring and appropriate pharmacological interventions are required in autoimmune patients with COVID-19 to improve clinical outcomes and prevent death.Keywords: Autoimmune, COVID-19, Immunosuppressant, Clinical outcome.
Synthesis of 3-amino-2-(2-chlorophenyl)quinazolin-4(3H)-one by Conventional heating and Microwave Irradiation
Authors: Muchlashi Luthfi Ahmad 
Published: January 2022

This study aims to compare conventional heating with microwave irradiation in order to obtain the optimal method for 3-amino-2-(2-chlorophenyl)quinazolin-4(3H)-one synthesis. For the compound to be completely synthesised, one needs to carry out much heating for a long period of time. 10 hours are required for the reaction using conventional heating; yet it takes only 5 minutes using microwave with 800 watts of power. It was found in this study that there was an improvement in percent yields of 3-amino-2-(2-chlorophenyl)quinazolin-4(3H)-one, 87 % using microwave irradiation compared to 79 % using reflux. Therefore, microwave-assisted method is a quicker, higher yielding method for 3-amino-2-(2-chlorophenyl)quinazolin-4(3H)-one synthesis.
Zagrożenia środowiska wynikające z nadmiernej konsumpcji
Authors: Strzyżewska Dorota 
Published: January 2022

Results o f excessive consumption that have been presented in article can be expected and some have been already observad. Interpreted development o f mankind has caused it erroneously, that person corrects nature, it interferes in to its her right and secrets, there is that in environment natural destroying. That capability o f influence causes on whole planet, that there to be guilty interactive activity o f person programed prudently. Human is first biological sort on earth, which is modify whole surface of planet to manner deep able very and global.
Występowanie i znaczenie terminu „róg” w Biblii
Authors: Bogacz Roman 
Published: January 2022

„Róg” w Piśmie Świętym w dosłownym sensie oznacza róg zwierzęcia i wyraża jego siłę i moc. Tym samym terminem w Starym Testamencie określa się rogi ołtarza całopaleń i ołtarza kadzenia symbolizujące obecność Boga, u którego azyl może znaleźć nawet zabójca, i uchwyciwszy się rogów ołtarza – uzyskać ułaskawienie. Niektóre teksty używają terminu „róg” w znaczeniu naczynia na oliwę służącą do namaszczenia ludzi wybranych do pełnienia ważnych funkcji w Izraelu. Osoba namaszczona stawała się pomazańcem Pańskim. Mogła wtedy spełniać funkcję króla, kapłana lub proroka. Funkcje te wypełniała w imieniu Boga Jahwe. „Róg” może oznaczać też wkroczenie wielkiej Bożej mocy. Tak było w przypadku zdobywania Jerycha, gdy naród głośno zakrzyknął, a kapłani zagrali na rogach. Opis zdobycia murów Jerycha ma znaczenie teologiczne. Boża moc sprawiła rozsypanie się murów warownego miasta. Obraz procesji i dęcia w rogi został sformułowany przez redaktora kapłańskiego i podkreśla znaczenie kultyczne opisanych obrzędów. Istotne znaczenie posiada użycie terminu „róg” w pieśni Anny w 1 Sm 2, 1. 10. Tworzy on inkluzję obejmującą ten kantyk. Na początku pieśni słowa „róg mój” można odczytać w znaczeniu męskiego potomka Anny – Samuela, zaś na końcu jest mowa o „rogu mesjasza”. To właśnie Samuel namaścił Dawida na króla, którego potomkiem w sensie prorockim będzie obiecany Mesjasz. Zachariasz opowiada o Nim przy obrzezaniu Jana Chrzciciela. Wciąż otwartą kwestią pozostaje tłumaczenie wyrażenia κέρας σωτηρίας. Czy przekładać je dosłownie jako „róg zbawienia”, czy raczej jako „moc zbawczą”? Wydaje się, że dosłowne tłumaczenie jest bogatsze w treść i daje głębsze zrozumienie przekazywanych prawd. W literaturze apokaliptycznej termin „róg” symbolizuje różne złe moce, które występują przeciwko Bogu i człowiekowi. Niektóre wystąpienia terminu „róg” symbolizują walkę władców ziemskich z ludem Bożym, a nawet z samym Bogiem. Apokaliptyka jednak pokazuje, że w końcowej walce zawsze zwycięża Bóg, który przewyższa swą mocą siły zła.
Do poor environmental conditions drive trachoma transmission in Burundi? A mathematical modelling study
Authors: Ndisabiye Desire , Waters Edward , Gore Russell , Sidhu Harvinder 
Published: January 2022

Trachoma is an infectious disease and it is the leading cause of preventable blindness worldwide. To achieve its elimination, the World Health Organization set a goal of reducing the prevalence in endemic areas to less than 55% by 2020, utilizing the SAFE (surgery, antibiotics, facial cleanliness, environmental improvement) strategy. However, in Burundi, trachoma prevalences of greater than 55% are still reported in 11 districts and it is hypothesized that this is due to the poor implementation of the environmental improvement factor of the SAFE strategy. In this paper, a model based on an ordinary differential equation, which includes an environmental transmission component, is developed and analysed. The model is calibrated to recent field data and is used to estimate the reductions in trachoma that would have occurred if adequate environmental improvements were implemented in Burundi. Given the assumptions in the model, it is clear that environmental improvement should be considered as a key component of the SAFE strategy and, hence, it is crucial for eliminating trachoma in Burundi.
doi:10.1017/S1446181121000389
Development of Deep Learning Models for Predicting In-Hospital Mortality Using an Administrative Claims Database: Retrospective Cohort Study
Authors: Matsui Hiroki , Yamana Hayato , Fushimi Kiyohide , Yasunaga Hideo 
Published: January 2022

Background
Administrative claims databases have been used widely in studies because they have large sample sizes and are easily available. However, studies using administrative databases lack information on disease severity, so a risk adjustment method needs to be developed.

Objective
We aimed to develop and validate deep learning–based prediction models for in-hospital mortality of acute care patients.

Methods
The main model was developed using only administrative claims data (age, sex, diagnoses, and procedures on the day of admission). We also constructed disease-specific models for acute myocardial infarction, heart failure, stroke, and pneumonia using common severity indices for these diseases. Using the Japanese Diagnosis Procedure Combination data from July 2010 to March 2017, we identified 46,665,933 inpatients and divided them into derivation and validation cohorts in a ratio of 95:5. The main model was developed using a 9-layer deep neural network with 4 hidden dense layers that had 1000 nodes and were fully connected to adjacent layers. We evaluated model discrimination ability by an area under the receiver operating characteristic curve (AUC) and calibration ability by calibration plot.

Results
Among the eligible patients, 2,005,035 (4.3%) died. Discrimination and calibration of the models were satisfactory. The AUC of the main model in the validation cohort was 0.954 (95% CI 0.954-0.955). The main model had higher discrimination ability than the disease-specific models.

Conclusions
Our deep learning–based model using diagnoses and procedures produced valid predictions of in-hospital mortality.
Domain-Specific Common Data Elements for Rare Disease Registration: Conceptual Approach of a European Joint Initiative Toward Semantic Interoperability in Rare Disease Research
Authors: Abaza Haitham , Kadioglu Dennis , Martin Simona , Papadopoulou Andri , dos Santos Vieira Bruna , Schaefer Franz , Storf Holger 
Published: January 2022

Background
With hundreds of registries across Europe, rare diseases (RDs) suffer from fragmented knowledge, expertise, and research. A joint initiative of the European Commission Joint Research Center and its European Platform on Rare Disease Registration (EU RD Platform), the European Reference Networks (ERNs), and the European Joint Programme on Rare Diseases (EJP RD) was launched in 2020. The purpose was to extend the set of common data elements (CDEs) for RD registration by defining domain-specific CDEs (DCDEs).

Objective
This study aims to introduce and assess the feasibility of the concept of a joint initiative that unites the efforts of the European Platform on Rare Disease Registration Platform, ERNs, and European Joint Programme on Rare Diseases toward extending RD CDEs, aiming to improve the semantic interoperability of RD registries and enhance the quality of RD research.

Methods
A joint conference was conducted in December 2020. All 24 ERNs were invited. Before the conference, a survey was communicated to all ERNs, proposing 18 medical domains and requesting them to identify highly relevant choices. After the conference, a 3-phase plan for defining and modeling DCDEs was drafted. Expected outcomes included harmonized lists of DCDEs.

Results
All ERNs attended the conference. The survey results indicated that genetic, congenital, pediatric, and cancer were the most overlapping domains. Accordingly, the proposed list was reorganized into 10 domain groups and recommunicated to all ERNs, aiming at a smaller number of domains.

Conclusions
The approach described for defining DCDEs appears to be feasible. However, it remains dynamic and should be repeated regularly based on arising research needs.
Panic Attack Prediction Using Wearable Devices and Machine Learning: Development and Cohort Study
Authors: Tsai Chan-Hen , Chen Pei-Chen , Liu Ding-Shan , Kuo Ying-Ying , Hsieh Tsung-Ting , Chiang Dai-Lun , Lai Feipei , Wu Chia-Tung 
Published: January 2022

Background
A panic attack (PA) is an intense form of anxiety accompanied by multiple somatic presentations, leading to frequent emergency department visits and impairing the quality of life. A prediction model for PAs could help clinicians and patients monitor, control, and carry out early intervention for recurrent PAs, enabling more personalized treatment for panic disorder (PD).

Objective
This study aims to provide a 7-day PA prediction model and determine the relationship between a future PA and various features, including physiological factors, anxiety and depressive factors, and the air quality index (AQI).

Methods
We enrolled 59 participants with PD (Diagnostic and Statistical Manual of Mental Disorders, 5th edition, and the Mini International Neuropsychiatric Interview). Participants used smartwatches (Garmin Vívosmart 4) and mobile apps to collect their sleep, heart rate (HR), activity level, anxiety, and depression scores (Beck Depression Inventory [BDI], Beck Anxiety Inventory [BAI], State-Trait Anxiety Inventory state anxiety [STAI-S], State-Trait Anxiety Inventory trait anxiety [STAI-T], and Panic Disorder Severity Scale Self-Report) in their real life for a duration of 1 year. We also included AQIs from open data. To analyze these data, our team used 6 machine learning methods: random forests, decision trees, linear discriminant analysis, adaptive boosting, extreme gradient boosting, and regularized greedy forests.

Results
For 7-day PA predictions, the random forest produced the best prediction rate. Overall, the accuracy of the test set was 67.4%-81.3% for different machine learning algorithms. The most critical variables in the model were questionnaire and physiological features, such as the BAI, BDI, STAI, MINI, average HR, resting HR, and deep sleep duration.

Conclusions
It is possible to predict PAs using a combination of data from questionnaires and physiological and environmental data.
The Application and Comparison of Machine Learning Models for the Prediction of Breast Cancer Prognosis: Retrospective Cohort Study
Authors: Xiao Jialong , Mo Miao , Wang Zezhou , Zhou Changming , Shen Jie , Yuan Jing , He Yulian , Zheng Ying 
Published: January 2022

Background
Over the recent years, machine learning methods have been increasingly explored in cancer prognosis because of the appearance of improved machine learning algorithms. These algorithms can use censored data for modeling, such as support vector machines for survival analysis and random survival forest (RSF). However, it is still debated whether traditional (Cox proportional hazard regression) or machine learning-based prognostic models have better predictive performance.

Objective
This study aimed to compare the performance of breast cancer prognostic prediction models based on machine learning and Cox regression.

Methods
This retrospective cohort study included all patients diagnosed with breast cancer and subsequently hospitalized in Fudan University Shanghai Cancer Center between January 1, 2008, and December 31, 2016. After all exclusions, a total of 22,176 cases with 21 features were eligible for model development. The data set was randomly split into a training set (15,523 cases, 70%) and a test set (6653 cases, 30%) for developing 4 models and predicting the overall survival of patients diagnosed with breast cancer. The discriminative ability of models was evaluated by the concordance index (C-index), the time-dependent area under the curve, and D-index; the calibration ability of models was evaluated by the Brier score.

Results
The RSF model revealed the best discriminative performance among the 4 models with 3-year, 5-year, and 10-year time-dependent area under the curve of 0.857, 0.838, and 0.781, a D-index of 7.643 (95% CI 6.542, 8.930) and a C-index of 0.827 (95% CI 0.809, 0.845). The statistical difference of the C-index was tested, and the RSF model significantly outperformed the Cox-EN (elastic net) model (C-index 0.816, 95% CI 0.796, 0.836; P=.01), the Cox model (C-index 0.814, 95% CI 0.794, 0.835; P=.003), and the support vector machine model (C-index 0.812, 95% CI 0.793, 0.832; P<.001). The 4 models’ 3-year, 5-year, and 10-year Brier scores were very close, ranging from 0.027 to 0.094 and less than 0.1, which meant all models had good calibration. In the context of feature importance, elastic net and RSF both indicated that TNM staging, neoadjuvant therapy, number of lymph node metastases, age, and tumor diameter were the top 5 important features for predicting the prognosis of breast cancer. A final online tool was developed to predict the overall survival of patients with breast cancer.

Conclusions
The RSF model slightly outperformed the other models on discriminative ability, revealing the potential of the RSF method as an effective approach to building prognostic prediction models in the context of survival analysis.