bluuebunny/crossref_metadata_2025_split huggingface dataset:
Peningkatan Manajemen Usaha Pada Pembenihan Ikan ‘Kolam As Syifa’ Cindai Alus
Authors: Mahreda Emmy Sri , Mustika Rina , Febrianty Irma , Lindawati Lindawati , Pardian Pajar 
Published: November 2021

AbstractThe purpose of the community partnership program activities: improving business management through bookkeeping and report training (logbooks, cash books, inventory, and balance sheets) and increase knowledge to assess business viability through profit analysis training, Cost-Revenue Ratio, Payment Period, and Break-Even Point. The methods applied are 1. the stage of the situation and condition of the partners, 2. the stage of preparation of all materials and materials for training activities, 3. the stage of counseling and management training (training on preparing and presenting reports), analytical training according to final needs, and 4. the stage of evaluation. Community partnership program activities to improve business management for hatcheries at the As Syifa Pond Business provide increased understanding and skills of fish breeders about professional business management through increasing ability in compiling simple financial reports/bookkeeping and in analyzing business financially. This increase in understanding and skills is not only felt for current operations but also business development in the future.Keywords: management, business, bookkeeping, feasibility, As Syifa Abstrak Tujuan kegiatan PKM adalah: perbaikan manajemen usaha melalui pelatihan penyusunan pembukuan dan laporan keuangan (log book, buku kas, buku persediaan dan neraca) dan meningkatkan pengetahuan untuk menilai kelayakan usaha melalui pelatihan menganalisis keuntungan, Revenue Cost Ratio, Payback Period dan Break Even Point. Metode kegiatan yang diterapkan adalah: 1. tahap analisis situasi dan kondisi mitra, 2. tahap persiapan semua bahan dan materi untuk kegiatan pelatihan, 3. tahap penyuluhan dan pelatihan manajemen usaha (pelatihan pembuatan dan penyajian laporan keuangan), pelatihan menganalisis kelayakan usaha secara finasial, dan 4. tahap evaluasi PKM. Kegiatan PKM perbaikan manajemen usaha terhadap pembenih ikan pada Usaha Kolam As Syifa memberikan peningkatan pemahaman dan keterampilan pembenih ikan tentang manajemen usaha yang profesional melalui peningkatan kemampuan dalam menyusun laporan keuangan/pembukuan sederhana dan dalam menganalisis kelayakan usaha secara finansial. Peningkatan pemahaman dan keterampilan ini bukan hanya dirasakan untuk operasional saat ini, tetapi juga untuk pengembangan usaha di masa depan.Kata kunci: manajemen, usaha, pembukuan, kelayakan, As Syifa
­­BVES Is a Novel Interactor of ANO5 and Regulates Myoblast Differentiation
Authors: Li Haiwen , Xu Li , Gao Yandi , Zuo Yuanbojiao , Yang Zuocheng , Zhao Lingling , Chen Zhiheng , Guo Jacky Shuliang , Han Renzhi 
Published: November 2021

Abstract
Background: Anoctamin 5 (ANO5) is a membrane protein belonging to the TMEM16/Anoctamin family and its deficiency leads to the development of limb girdle muscular dystrophy R12 (LGMDR12). However, little has been known about the interactome of ANO5 and its cellular functions. Results: In this study, we exploited a proximal labeling approach to identify the interacting proteins of ANO5 in C2C12 myoblasts stably expressing ANO5 tagged with BioID2. Mass spectrometry identified 41 unique proteins specifically from ANO5-BioID2 samples but not BioID2 fused with ANO6 or MG53, including BVES and POPDC3. The interaction between ANO5 and BVES was further confirmed by co-immunoprecipitation (Co-IP), and the N-terminus of ANO5 mediated the interaction with BVES through its C-terminus. ANO5 and BVES were co-localized in muscle cells and enriched at the endoplasmic reticulum (ER) membrane. Genome editing-mediated ANO5 or BVES disruption significantly suppressed C2C12 myoblast differentiation with little impact on proliferation. Conclusions:Taken together, these data suggest that BVES is a novel interacting protein of ANO5, involved in regulation of muscle differentiation.
Clinical Experience With Venetoclax in Patients With Newly Diagnosed, Relapsed, or Refractory Acute Myeloid Leukemia.
Authors: Fleischmann Maximilian , Scholl Sebastian , Frietsch Jochen J , Hilgendorf Inken , Schrenk Karin , Hammersen Jakob , Prims Florian , Thiede Christian , Hochhaus Andreas , Schnetzke Ulf 
Published: November 2021

Abstract
Background: Diagnosis of acute myeloid leukemia (AML) is associated with poor outcome in elderly and unfit patients. Recently, approval of the BCL-2 inhibitor Venetoclax (VEN) in combination with hypomethylating agents (HMA) led to a significant improvement of response rates and survival. Further, application in the relapsed or refractory (r/r) AML setting or in context of allogeneic stem cell transplantation (alloHSCT) seems feasible.Methods and Patients: Fifty-six consecutive adult AML patients on VEN from January 2019 to June 2021 were analyzed retrospectively. Patients received VEN either as first-line treatment, as subsequent therapy (r/r AML excluding prior alloHSCT), or at relapse after alloHSCT. VEN was administered orally in 28-day cycles either combined with HMA or low-dose cytarabine (LDAC).Results: After a median follow-up of 4.7 (range, 0.8 – 24.3) months median overall survival (OS) from start of VEN treatment was 13.3 (2.2 - 20.5) months, 5.0 (0.8 - 24.3) months and 4.0 (1.5 - 22.1) months for first-line, subsequent line treatment and at relapse post-alloHSCT, respectively. Median OS was 11.5 (10 - 22.3) months from start of VEN when subsequent alloHSCT was carried out. Overall response rate (composite complete remission + partial remission) was 51.8% for the total cohort (61.1% for VEN first-line treatment, 52.2% for subsequent line and 42.8% at relapse post-alloHSCT). Subgroup analysis revealed a significantly reduced median OS in FLT3-ITD mutated AML with 3.4 (1.9 - 8.0) months versus 10.4 (0.8 - 24.3) months for non-mutated cases, (HR 3.59, 95% CI 0.94 - 13.72, p = 0.001). Patients harboring NPM1 or IDH1/2 mutations lacking parallel FLT3-ITD mutations showed a survival advantage over patients without those mutations (11.2 (5 - 24.3) months versus 5.0 (0.8 - 22.1) months, respectively, (HR 0.53, 95% CI 0.23 – 1.21, p = 0.131). The most common adverse events were hematological, with grade 3 and 4 neutropenia and thrombocytopenia reported in 44.6% and 14.5% of patients, respectively. Conclusion: Detailed analyses on efficacy for common clinical scenarios such as first-line treatment, subsequent therapy (r/r AML), and application prior to and post-alloHSCT are presented. The findings suggest VEN treatment combinations efficacious not only in first-line setting but also in r/r AML. Furthermore, VEN might play a role in a subgroup of patients with failure to conventional chemotherapy as a salvage regimen aiming for potential curative alloHSCT.
A Deep Learning Post-Discharge Mortality Prediction Method Considering Diagnosis Information for ICU Patients
Authors: Liu Mucan , Guo Chonghui , Cui Shaoze 
Published: November 2021

Abstract
Background and Objective: Mortality prediction is widely used to stratify patients into different risk categories and to provide prognosis evaluation. Nowadays, scoring systems, which predict mortality with some scores reflecting the severity of diseases and physiological states of patients in ICU, have been widely applied for in-hospital mortality prediction. Many research works which focus on designing better machine learning models and algorithms for mortality prediction also have achieved great performance. However, it is not enough to make post-discharge prognosis of mortality only with the aid of better models and algorithms while richer patient related information can make big differences. In this study, we propose a deep learning method considering patient diagnosis information for post-discharge prognosis of mortality. This method can help to significantly improve the performance of prediction. Further more, we propose a method of calculating disease Shapley values to evaluate the mortality risk brought by disease factors. Methods: Deep learning models including long short-term memory (LSTM) and temporal convolutional network (TCN) are trained with patient physiological time series data and diagnosis information of different prevalence to predict post-discharge mortality risk of different time windows. Disease Shapley values to evaluate the mortality risk brought by disease factors are the weighted average of marginal contributions of diseases to patient mortality. Experiments of several post-discharge mortality prediction tasks of different time windows are conducted on the large freely accessible MIMIC-III dataset. To provide more sufficient comparison, the diagnosis information is also introduced for traditional machine learning models. Results: In our experiment, LSTM achieves highest AUROC and the improvements of which are 8.67%, 9.68%, 13.33%, 12.32% and 12.25% with the help of diagnosis information for five post-discharge mortality prediction tasks of different time window. Several patient examples are shown to present the mortality risk brought by disease factors, of which the analysis results are in line with clinical experiences. Conclusions: In general, our proposed method can improve performance of ICU patient post-discharge mortality prediction and help to evaluate how much do different kinds of diseases which a patient suffers from increase his mortality, thus providing support for clinical decisions.
The Most Significant Surgical Risk Factors of Giant Intracranial Meningiomas: Localization Matters Much More than Grading and Volume of Peritumoral Brain Edema. A Retrospective Clinical Neuroradiological and Immunohistochemical Study
Authors: Armocida Daniele , Bevacqua Giuseppina , Catapano Antonia , Palmieri Mauro , Arcidiacono Umberto Aldo , Pesce Alessandro , Cofano Fabio , Picotti Veronica , Salvati Maurizio , Garbossa Diego , D'Andrea Giancarlo , Santoro Antonio , Frati Alessandro 
Published: November 2021

Abstract
Background: Giant intracranial meningiomas (GIMs) are extremely rare and are usually considered arduous to resect totally with poorer prognosis. The real mechanisms by which a meningioma can grow to be defined as "giant" are unknown, as well as the real biological , radiological profile and the different outcomes.Methods: We performed a retrospective review of a consecutive series of surgically-treated patients suffering from intracranial Meningioma. All the patients were assigned on the ground of the preoperative imaging to the Giant and Medium/Large Meningiomas. We investigated whether the presence large diameter on radiological diagnosis is indicative for different mortality rate, grading, characteristic and clinical/neurological outcome.Results: The study shows that surgically treated giant meningiomas have a higher risk of developing complications in the postoperative phase (Chi square= 11.121, dF=1, p=0.001). The direct proportional relationship between peritumoral brain edema (PBE) volume and tumor volume was present only in the medium/large group and was not present in the giant meningioma group. When comparing the degree of performance there is a statistically significant difference between localization and KPS immediately postoperatively (p=0.04) particularly for sphenopetroclival meningiomas (p=0.071), and partially with GIM of the olfactory groove with arterial encasement. The most frequently encountered complications include the occurrence of ischemia (p=0.049), infection (p=0.03), and the occurrence of postoperative seizures.Conclusions: We identified that the major surgical risk factor for GIMs is location, where the petro-clival region and, to a lesser extent the anterior basicranium offer a greater risk of neurovascular involvement and arterial encasement. On other hand, the risk correlated with PBE is poorer in GIM although there is a well-noted correlation between the Edema volume and outcome in meningiomas.
Associations Between Short-Term Ambient Particulate Matter Exposure and The Risk of Severe COVID-19
Authors: Li Zhong-Qi , Tao Bi-Lin , Zhan Meng-Yao , Hu Zhi-Liang , Yi Yong-Xiang , Wang Jian-Ming 
Published: November 2021

Abstract
Background

Previous studies have suggested the relation between outdoor air pollution and the onset risk of Corona Virus Disease 2019 (COVID-19). However, there is a lack of data related to the severity of the disease, especially in China.
Methods

We recruited a group of COVID-19 patients diagnosed in a recent large-scale outbreak caused by Delta variants in eastern China. We collected ambient air pollution data of the same period where the case occurred and applied a generalized linear model (GLM) to analyze the effects of short-term outdoor ambient particulate matter (PM) exposure on the risk of severe COVID-19.
Results

A total of 476 confirmed adult patients were recruited, of which 42 (8.82%) had a severe illness. With a unit increase in PM10, the risk of severe COVID-19 increased by 47.64% (95% confidence interval [CI]: 27.67, 70.73) at lag 0-14 days, 65.00% (95% CI: 35.18, 101.40) at lag 0-21 days, and 69.44% (95% CI: 37.03, 109.50) at lag 0-28 days, respectively. The association remained significant at lag 0-14 days or 0-28 days in the multi-pollutant models. With a unit increase in PM2.5, the risk of severe COVID-19 increased by 49.47% (95% CI: 19.61, 86.78) at lag 0-14 days, 97.61% (95% CI: 45.92, 167.60) at lag 0-21 days, and 120.27% (95% CI: 56.53, 209.98) at lag 0-28 days, respectively. The association remained significant at lag 0-21 days or 0-28 days in the multi-pollutant models.
Conclusions

Our results indicated that short-term exposure to outdoor PM was positively related to the risk of severe COVID-19, and reducing air pollution may contribute to the control of COVID-19.
An Efficient SMOTE Based Machine Learning classification for Prediction &amp; Detection of PCOS
Authors: Dutta Pijush , Paul Shobhandeb , Majumder Madhurima 
Published: November 2021

Abstract
Polycystic ovary syndrome (PCOS) is a well known catabolism based and reproductive endocrinopathies disease which affects up to 18% of women in reproductive age. PCOS results irregularity in menstrual periods which might directed to numerous entanglements such as embodiment of cysts (follicles) in either or both ovaries as well as impotence for giving birth, turmoil in androgen levels, increased BMI and some other deviancy in hormone level of LH, FSH, DHEAS, Fasting insulin and Fasting blood sugar. Detection & prevention of this disease as early as possible it has attracted research attention. Overall research in this therapeutic unhealthiness categorisation using machine learning technique is considered less to some extent because of data imbalance. In this research we can make a lay on the line for a novel forecast model using Synthetic Minority Oversampling Technique (SMOTE) with five machine learning algorithms like Logistic Regression, Random Forest, Decision Tree Support vector machine and K-Nearest Neighbor (KNN) model which automates the PCOS detection in an early stage. The framework of the recommended classification of PCOS is comprised of three different layers. The first flapping which is responsible for handling the missing values & oversampling the splinter class in datasets. In second layer, the most eloquent attributes are tabbed using Principal component analysis (PCA). In third layer, the projected model is skilled & its efficiency is reckoned in a fourth layer in terms of classification accuracy, Training time (TT), F1 score, Recall (Sensitivity), Precision & Area under the ROC (AUROC). The propounded algorithm clarify the outperformed of its corresponded & achieves the preeminent accuracy, precision, F1 score, Sensitivity, training time & AUROC. The best model achieve accuracy, Training time (TT), F1 score, Recall (Sensitivity), Precision & Area under the ROC (AUROC) is 97.11%, 0.010 sec, 98%, 98%, 98% & 95.6% respectively.
The Intellectual Assets in Europe
Authors: Alberto Costantiello , Lucio Laureti , Angelo Leogrande 
Published: November 2021

Abstract
In this article we investigate the determinants of the Intellectual Assets in Europe. We use data from the European Innovation Scoreboard of the European Commission in the period 2000-2019 for 36 countries. Data are analyzed using Panel with Fixed Effects, Random Effects, WLS, Pooled OLS, Dynamic Panel at 1 Stage. Results show that the presence of Intellectual Assets in Europe is positively associated with “Enterprise Births”, “Top R&D Spending Enterprises per 10 mln Population”, “Employment Share Manufacturing”, “Share High and Medium high-tech Manufacturing”, “Attractive Research Systems”, “Finance and Support”, “Innovators”, “Sales Impact” and is negatively associated to “Government Procurement of Advanced Technology Products” and “Share Knowledge-Intensive Services”
Advanced Tracing Methods for Container Messaging Systems Analysis
Authors: DENYS Pierre-Frédérick , Dagenais Michel R. , Pepin Martin 
Published: November 2021

Abstract
Containers are increasingly used for software deployment, because of the modularity they offer for packagingand isolating applications. However, this implies a reliable communication system between computing elementsin different containers. Thence, conventional messaging systems have evolved and adapted to increasing loadsand Edge Computing. Inter-container communications, with Message Oriented Middleware, provide insight intothe execution of distributed applications, as well as a deeper input for analysis. However detecting messagelosses and slowdowns on this type of infrastructure is a challenge. Actual tracing solutions for this task werecompared to identify shortcomings and possible improvements. New tracing methods are proposed to addressthese shortcomings and open the door to more versatile tracing tools. This paper focuses on the approachestaken to extract information from messages, and achieve advanced analysis. Two new methods are presented,each providing a detailed picture of the distributed system, while being better suited for different use cases,depending on the environmental constraints.
The frequency of bacterial pathogens of mastitis and their antibiotic susceptibility in Saanen and Alpine goats
Authors: Esmaeili Hossein , Shakeri Amir Pasha , Tajik Parviz , Hamedi Mona 
Published: November 2021

Abstract
Mastitis is mammary gland inflammation and as the purpose of Saanen and Alpine farms is milk production, control of mastitis is important. Antimicrobial resistance among mastitis pathogens has gradually been increasing. The aim of the present study was to isolate pathogenic bacteria from mastitis cases in Saanen and Alpine goats and to determine their antibiotic resistance in milk. The milk sample of 26 Saanen and 29 Alpine breeds with clinical mastitis was collected and cultured on general microbiological media. Then, the colonies were stained by gram staining and were assessed by differential media and biochemical tests. PCR was performed for the detection of Mycoplasma spp. The isolated bacteria were tested against 12 antibacterial disks. The significant difference in drug resistance levels between the Saanen and Alpine breeds was statistically assessed. Mycoplasma spp. was detected in 12.73% of samples. The frequency of the isolated bacteria was Escherichia coli (29.1%), Trueperella pyogenes (25.5%), Staphylococcus aureus (16.4%), Streptococcus agalactiae (9.1%), coagulase negative staphylococci (5.5%), and Corynebacterium pseudotuberculosis (1.8%) respectively. The significant difference between Saanen and Alpine was observed in antibiotic resistance to amoxicillin which showed more resistance in the Alpine breed. All the isolated bacteria showed multidrug resistance. Based on the obtained data using of antibiotics more accurate and using antibiogram test by clinician is necessary in the treatment of mastitis.
Multiomics Combined Analysis of Low-Phosphorus and Drought Stresses in Lotus Japonicus
Authors: Peijie+ Ma , Yajiao+ Li , Jianhong Shu , Ziyuan Wang , Xi Chen , Xiaoli Wang 
Published: November 2021

Abstract
BackgroundLotus japonicus is a perennial herb in Leguminosae. It is a good feed source and improves soil. It is also an excellent honey source and medicinal plant. Low-phosphorus and drought stresses are among the main abiotic stress factors limiting the production of pulse roots. MethodsIn this experiment, the effects of low-phosphorus and drought stresses on Baimai roots were analyzed under three treatments: control (zl1), low-phosphorus stress (zl2) and drought stress (zl3). Results A total of 2176, 3026 and 2980 differentially expressed genes were screened in zl1 vs. zl2, zl1 vs. zl3 and zl2 vs. zl3, respectively. The differentially expressed genes were enriched in functions related to cells, membranes, ion binding, enzyme activity and resistance to low-phosphorus and drought stresses. The enriched KEGG pathways included the MAPK signaling pathway-plant, flavor biosynthesis, starch and sucrose metabolism and plant hormone signal transmission. In particular, a large number of differentially expressed genes were enriched in the response to plant hormone signal transmission pathways among different treatments, and gene expression changes were analyzed. In addition, the differentially expressed genes identified under drought stress and the phase response genes identified under osmotic stress were upregulated. Differential metabolites were mainly enriched in the important metabolic pathways of flavonoid biosynthesis, arginine and proline metabolism and starch and sucrose metabolism. Differentially expressed proteins were mainly enriched in GO terms related to cell, membrane, ion binding and enzyme activity functions, and the main enriched KEGG pathways included the ribosome, starch and sucrose metabolism and plant hormone signal transmission pathways. ConclusionIn conclusion, these results of transcriptome, metabolome and proteome sequencing are helpful for understanding the response mechanisms, gene changes, metabolite changes and protein changes in Baimai roots under low-phosphorus and drought stress conditions to lay a foundation for future research on Lotus japonicus.
Towards Spectral Efficiency Enhancement for IoT Aided Smart Transportation: A Compressive OFDM Transmission and Regularized Recovery Approach
Authors: Zhang Yusi , Li Yong , Fang Xiaojie , Sha Xuejun , Feng Yuqing , Wang Weizhi 
Published: November 2021

Abstract
The increasing number of vehicles brings ubiquitous connectivity and huge information interaction, implementing with limited spectrum resource. Focusing on the higher spectral efficiency requirement, a compressive OFDM system is proposed in this paper. The idea of compressing the transmission of OFDM signal for spectral efficiency enhancement origins from GP extrapolation algorithm for bandlimited signal. In the proposed scheme, a truncation filter with deliberately designed compressed ratio and truncation mode is performed on the OFDM signal to generate the compressive OFDM signal. At the receiver, up-sampling and iterative extrapolation are conducted to recover from the partial signal. Simulation results show that the compressive OFDM signal could be compressed up to 0.5, presenting better compressive capability than the typical nonorthogonal SEFDM system. Further considering the ill-posed problem caused by the noise, a regularization approach is adopted to retain the convergence of recovery. Moreover, the proposed compressive OFDM system possesses the spectrally efficient advantage than SEFDM system. At the compressed ratio 0.5, the compressive OFDM system possesses better BER than SEFDM. At 10dB E b /N 0 , the throughput rate of the compressive OFDM is 2 times and 1.6 times higher than OFDM and SEFDM, respectively.