Miracle-dz/UniFAR-FTUs huggingface dataset:
Safety of Cryopreserved Stem Cell Infusion through a Peripherally Inserted Central Venous Catheter

Simple Summary Autologous and allogeneic hematopoietic stem cell transplantations (HSCTs) are considered the standard of care for many hematological and non-hematological malignancies. It is generally believed that cryopreserved hematopoietic stem cells should be administered rapidly to minimize their exposure to cytotoxic dimethyl sulfoxide (DMSO). A fast peripheral blood stem cell (PBSC) infusion rate is usually achieved through conventionally inserted central venous catheters (CICCs). Therefore, CICCs are widely used in PBSC transplantation settings. Nevertheless, their insertion may be associated with serious complications. Peripherally inserted central venous catheters (PICCs) are not associated with such complications but are rarely used in cryopreserved PBSC transplantation due to the presumption that prolonged PBSC infusion through the CICC would impair their viability. We demonstrated that prolonged PBSC infusion through the PICC has no effect on transplantation outcomes. Therefore, we suggest implementing PICCs in a cryopreserved transplantation setting, as PICCs are more comfortable for patients, easier to insert, cost-effective, and have fewer serious line-related complications. Abstract The management of patients undergoing stem cell transplantation requires a multipurpose central venous catheter (CVC) to facilitate drug administration, parenteral nutrition, transfusion of blood products, and collection of blood samples. Peripherally inserted central venous catheters (PICCs) appear to meet these requirements but are rarely used for stem cell infusion. We aimed to retrospectively assess the safety and feasibility of stem cell infusion through PICC and to evaluate its impact on transplantation kinetics. We retrospectively analyzed the outcomes of peripheral blood stem cell (PBSC) transplantation in patients receiving cryopreserved autologous or allogeneic PBSC by PICCs and compared the results with patients receiving transplants through a conventionally inserted central venous catheter (CICC). Despite statistically significant differences in CD34+ dose, infusion rate, and total length of administration, the clinical outcomes of transplantation, exemplified by platelet and neutrophil engraftment, along with the length of hospitalization, were not affected by the prolonged infusion time and lower infusion velocity in the PICC group. Our study showed that the clinical outcomes of PBSC transplantation did not differ between the PICC and CICC groups, suggesting that both types of catheters can be implemented in a PBSC transplantation setting.
Semantics of Higher-Order Recursion Schemes

Higher-order recursion schemes are equations defining recursively new operations from given ones called "terminals". Every such recursion scheme is proved to have a least interpreted semantics in every Scott's model of λ-calculus in which the terminals are interpreted as continuous operations. For the uninterpreted semantics based on infinite λ-terms we follow the idea of Fiore, Plotkin and Turi and work in the category of sets in context, which are presheaves on the category of finite sets. Whereas Fiore et al proved that the presheaf Fλ of λ-terms is an initial Hλ-monoid, we work with the presheaf Rλ of rational infinite λ-terms and prove that this is an initial iterative Hλ-monoid. We conclude that every guarded higher-order recursion scheme has a unique uninterpreted solution in Rλ.
Privacy Preserving Machine Learning : Related Work

A practical scenario of PPML is where only one central party has the entire data on which the ML algorithm has to be learned. Agrawal and Ramakrishnan [1] proposed the first method to learn a Decision Tree classifier on a database without revealing any information about individual records. They consider public model private data setting where the algorithm and its parameters are public whereas the data, over which the algorithm is trained, is kept private. The privacy of individual records in data is achieved through data perturbation. This notion of privacy was later identified as differential privacy by Dwork [2]. Though the data is private in the scenario considered by Agrawal and Ramakrishnan, where algorithm is trained on the data and only the learned algorithm is revealed, some orthogonal scenarios consider publishing the perturbed data thereby leading to public data setting with respect to the perturbed data. Chaudhuri and Monteleoni [3] proposed learning of Logistic Regression with differential privacy in which they consider the same setting of public model private data as above. However, a major distinction is that they perturb the learning algorithm rather than the data itself (unlike the method of Agrawal and Ramakrishnan) thereby not revealing information about individual data points upon which the algorithm is learned. Chaudhuri et al. [4] further generalized this solution for learning Support Vector Machine (SVM) under the same settings. Zhang et al. [5] gave a differential privacy solution to learning Logistic and Linear Regression models by perturbing the objective function similar to Chaudhuri and Monteleoni under the public model private data setting. Rubinstein et al. [6] came up with differentially private learning of SVM under public model private data setting, where differential privacy is guaranteed by introducing weight regularization in objective loss function. Jain et al. [7] proposed a mechanism of training Deep Belief Networks with differential privacy using drop-out (unlike the above methods of objective perturbation which rely on regularization for differential privacy). Like all the above differential privacy mechanisms, Jain et al. also consider public model private data setting.
Porphyrin Co(III)-Nitrene Radical Mediated Pathway for Synthesis of o-Aminoazobenzenes

Azobenzenes are versatile compounds with a range of applications, including dyes and pigments, food additives, indicators, radical reaction initiators, molecular switches, etc. In this context, we report a general method for synthesizing o-aminoazobenzenes using the commercially available cobalt(II) tetraphenyl porphyrin [CoII(TPP)]. The net reaction is a formal dimerization of two phenyl azides with concomitant loss of two molecules of dinitrogen. The most commonly used methodology to synthesize azobenzenes is based on the initial diazotization of an aromatic primary amine at low temperatures, which then reacts with an electron rich aromatic nucleophile. As such, this limits the synthesis of azobenzenes with an amine functionality. In contrast, the method we report here relies heavily on the o-amine moiety and retains it in the product. The reaction is metal catalyzed and proceeds through a porphyrin Co(III)-nitrene radical intermediate, which is known to form on activation of organic azides at the cobalt center. The synthesized o-aminoazobenzenes are bathochromatically shifted, as compared to azobenzenes without amine substituents. Based on the crystal structure of one of the products, strong H-bonding between the N-atom of the azo functionality and the H of the NH2 substituent is shown to stabilize the trans isomeric form of the product. The NH2 substituents offers possibilities for further functionalization of the synthesized azo compounds.
Face identification using soft computing tool

In this paper we have proposed a novel approach for face identification as application of Genetic Algorithm, based on PCA and applicable to colour images. Our proposed method is a search procedure to find out the face of interest from a 2D image. For optimization of search procedure we have used here Genetic algorithm with a fitness function. The proposed method is based on Eigenfaces method using PCA and a term distance from face space (DFFS) which is a Euclidian distance that is used here in fitness function for GA. And for quicker convergence we have first extracted the skin area from the scene image from YCbCr colour model. This method has specific application like locating a criminal from a scene image. This method is very fast due to reduction in search space.
A facile and scalable process for size-controllable separation of nanodiamond particles as small as 4 nm.

Interest in nanometer-sized particles has increased in recent years, especially from the viewpoint of nanobiology and nanomedicine. Among them are gold, silver, and silica nanoparticles, quantum dots, magnetic beads, carbon nanotubes, and nanodiamond (ND) particles. Since the physical properties of these nanoparticles are largely dependent on their size and shape, control of their structure is vital for advancing both fundamental studies and technological applications. In this context, sizeselective preparation has been established for gold, silver, and silica nanoparticles, quantum dots, and magnetic beads (iron oxide). We have also investigated structure-based separation of single-walled carbon nanotubes by using molecular recognition or ultrasonic atomization. Quite recently, shape, as well as size, was successfully controlled in the preparation of silver and gold nanoparticles. The availability of a wide spectrum of nanoparticle size and shape enables precise investigation of their effects in biological systems. In contrast, the size and shape control of ND is much less developed than that of other nanoparticles. This restricts the potential applications of ND particles, in spite of their many prominent physical properties. Although the biological and medicinal applications of ND have been extensively investigated quite recently due to its biocompatibility, it is somewhat difficult to fully investigate the effect of its size in,
Excel 2013 Charts and Graphs

Use Excel 2013s radically revamped charting and graphing tools to communicate more clearly, powerfully, and quickly so you drive your message home, and get the decisions and actions youre looking for! This book reveals data visualization techniques you wont find anywhere else and shows you how to use Excel 2013 to create designer-quality charts and graphs that stand out from the crowd. It will help you make the most of new features ranging from Power View to Recommended Charts, and instantly share your insights with anyone, anywhereeven on the Web and social networks. Learning advanced Excel techniques has never been easier. Youll find simple, step-by-step instructions, real-world examples and case studies, and more than a dozen YouTube videos, straight from MrExcel! Create stunning data visualizations instantly with Excel 2013s new Recommended Charts Use charts to instantly reveal trends, differences, and relationships Map your data with Excel 2013, MapPoint, and the new GeoFlow add-in Quickly generate combo charts that once required complex, frustrating procedures Use sparklines to imbue worksheets with more context and insight Highlight and clarify the meaning of data with DataBars, color scales, icon sets, and other conditional formatting tools Post charts to Facebook, Twitter, or LinkedIn, directly from Excel Build stock charts that help you make smarter investments Solve non-standard problems such as noncontiguous data or custom data sequences Generate new charts automatically with Excel VBA Uncover visual tricks that people use to lie with Excel About MrExcel Library: Every book in the MrExcel Library pinpoints a specific set of crucial Excel tasks and presents focused skills and examples for performing them rapidly and effectively. Selected by Bill Jelen, Microsoft Excel MVP and mastermind behind the leading Excel solutions website MrExcel.com, these books will: Dramatically increase your productivitysaving you 50 hours a year or more Present proven, creative strategies for solving real-world problems Show you how to get great results, no matter how much data you have Help you avoid critical mistakes that even experienced users make
GENPass: A Multi-Source Deep Learning Model for Password Guessing

The password has become today's dominant method of authentication. While brute-force attack methods such as HashCat and John the Ripper have proven unpractical, the research then switches to password guessing. State-of-the-art approaches such as the Markov Model and probabilistic context-free grammar (PCFG) are all based on statistical probability. These approaches require a large amount of calculation, which is time-consuming. Neural networks have proven more accurate and practical in password guessing than traditional methods. However, a raw neural network model is not qualified for cross-site attacks because each dataset has its own features. Our work aims to generalize those leaked passwords and improves the performance in cross-site attacks. In this paper, we propose GENPass, a multi-source deep learning model for generating “general” password. GENPass learns from several datasets and ensures the output wordlist can maintain high accuracy for different datasets using adversarial generation. The password generator of GENPass is PCFG+LSTM (PL). We are the first to combine a neural network with PCFG. Compared with Long short-term memory (LSTM), PL increases the matching rate by 16%–30% in cross-site tests when learning from a single dataset. GENPass uses several PL models to learn datasets and generate passwords. The results demonstrate that the matching rate of GENPass is 20% higher than by simply mixing datasets in the cross-site test. Furthermore, we propose GENPass with probability (GENPass-pro), the updated version of GENPass, which can further increase the matching rate of GENPass.
Interferometrischer Liniensensor zur Formmessung von rotationssymmetrischen optisch glatten Oberflächen

Zusammenfassung Es wird ein neuartiges Formmesssystem basierend auf einem interferometrischen Liniensensor vorgestellt. Der Liniensensor wird mit einem Bewegungssystem so nachgeführt, dass die Beleuchtungsstrahlen möglichst senkrecht zur Prüflingsoberfläche stehen. Das linienbasierte Formmesssystem bietet die Möglichkeit einer schnellen und flexiblen Messung. In den Überlappbereichen sind redundante Informationen enthalten, die zu einer genaueren Auswertung führen als bei einem Punktsensor. Die Formmessung mit Punktsensoren ist langsam, und die Genauigkeit ist durch das Bewegungssystem limitiert, da die Bewegungsachsen systematische und zufällige Führungsfehler haben. Diese Führungsfehler können nicht ohne Weiteres eliminiert werden und führen deshalb zu einer erhöhten Messunsicherheit bei der Formmessung. Bei vollflächig messenden Systemen werden i. Allg. sehr große Optiken benötigt, die mit hohen Kosten verbunden sind. In diesem Beitrag wird der Aufbau des linienbasierten Systems und exemplarisch einige Messergebnisse vorgestellt.
Use and Acceptance of Drinking Fountains: A Pilot Study in Two Secondary Schools in Dortmund, Germany

(1) Background: Water drinking is essential to reduce obesity in children, but effective means for implementation remain controversial. Our study assesses students’ and teachers’ use of and attitudes towards drinking fountains in two urban secondary schools. (2) Methods: In a cross-sectional study, answers from students and teachers to a 28- and 19-item questionnaire, respectively, containing closed- and open-ended questions and short interviews with the schools’ two principals were described and analysed using the question-specific number of responses as the denominator. (3) Results: Questionnaires of one hundred sixty-two students and ten teachers were analysed; 36.1% of students responded. Students viewed the schools’ two fountains as a good idea (73.3%, n = 118), recommended them to other schools (73.1%, n = 117), and felt able to distinguish healthy from unhealthy drinks (70.5%, n = 110). In contrast, 55.7% (n = 88) reported using the fountains regularly; over a week, 39.8% (n = 47) used them less than once; 26.3% (n = 31) used them one to two times. Only about a third (26.5%, n = 43) reported consuming more water since the fountains’ installation. Teachers’ responses were similar to students’; principals stressed planning and costs. (4) Conclusions: A discrepancy between a good attitude towards and actual use of drinking fountains may exist; school communities may need to look for measures to overcome it.
Low Dose Leucovorin and Bolus and C ontinuous Infusion of 5-fluorouracil ( M odified FO LFO X 4 ) as a

Purpose: To determine the activity and the toxicity associated with a low dose regimen of leucovorin (LV) plus 5-fluorouracil (5-FU) combined with oxaliplatin every two weeks (modified FOLFOX 4) as a salvage therapy for advanced gastric cancer patients. Materials and Methods: Between December 2003 and December 2004, 33 patients were enrolled in this study. The patients were treated with oxaliplatin 85 mg/m as a 2-hour infusion on the first day plus LV 20 mg/m over 10 minutes. Subsequently, the patients were given a 5-FU bolus 400 mg/m followed by a 22-hour continuous infusion of 600 mg/m on days 1~2. The treatment was repeated at 2 week intervals. Results: The median age of the patients was 50 years (range: 31~74), 82% (27/33) had the Eastern Cooperative Oncology Group performance status was 0 and 1. Of the 30 patients who could be evaluated for their tumor response, 8 achieved a partial response, with an overall response rate of 26.7% (95% confidence interval (CI): 20.5 ~32.7%). Fifteen patients (50% ) showed stable disease and 7 patients (23.3% ) progressed during the course of treatment. The median time from the start of chemotherapy to progression was 3.5 months (95% CI: 2.6~4.4 months) and the median overall survival time was 7.9 months (95% CI: 5.9~9.9 months). The major grade 3/4 hematological toxicity encountered included neutropenia (45.4% ) and thrombocytopenia (3.0% ). Neutropenic fever occurred during only 2 of the 178 cycles. The most common non-hematological toxicity encountered was grade 1/2 nausea/vomiting, which occurred in 18.2% of patients, diarrhea in 12.1% and neuropathy in 15.2% . There were no treatment related deaths. C onclusion: The modified FO LFO X 4 regimen appears to be a safe and e ffective salvage therapy for advanced gastric cancer patients. (C ancer R es Treat. 2005;37:279-283) 󰠏󰠏󰠏󰠏󰠏󰠏󰠏󰠏󰠏󰠏󰠏󰠏󰠏󰠏󰠏󰠏󰠏󰠏󰠏󰠏󰠏󰠏󰠏󰠏󰠏󰠏󰠏󰠏󰠏󰠏󰠏󰠏󰠏󰠏󰠏󰠏󰠏󰠏󰠏󰠏󰠏󰠏󰠏󰠏󰠏󰠏󰠏󰠏󰠏󰠏󰠏󰠏󰠏󰠏󰠏 Key W ords: Stomach neoplasms, Salvage chemotherapy, Oxaliplatin, 5-fluorouracil
Optimal Dynamic Scheduling of Electric Vehicles in a Parking Lot Using Particle Swarm Optimization and Shuffled Frog Leaping Algorithm

In this paper, the optimal dynamic scheduling of electric vehicles (EVs) in a parking lot (PL) is proposed to minimize the charging cost. In static scheduling, the PL operator can make the optimal scheduling if the demand, arrival, and departure time of EVs are known well in advance. If not, a static charging scheme is not feasible. Therefore, dynamic charging is preferred. A dynamic scheduling scheme means the EVs may come and go at any time, i.e., EVs’ arrival is dynamic in nature. The EVs may come to the PL with prior appointments or not. Therefore, a PL operator requires a mechanism to charge the EVs that arrive with or without reservation, and the demand for EVs is unknown to the PL operator. In general, the PL uses the first-in-first serve (FIFS) method for charging the EVs. The well-known optimization techniques such as particle swarm optimization and shuffled frog leaping algorithms are used for the EVs’ dynamic scheduling scheme to minimize the grid’s charging cost. Moreover, a microgrid is also considered to reduce the charging cost further. The results obtained show the effectiveness of the proposed solution methods.
Racial/Ethnic Differences in the Effects of Adverse Childhood Experiences on Substance Misuse among Justice-Involved Children

Abstract Background: Adverse childhood experiences (ACEs) are linked to substance use (SU) and substance use disorders (SUD). However, this relationship has yet to be tested among justice-involved children (JIC), and it is unclear if racial/ethnic differences exist. This study aimed to determine: (1) whether ACEs are associated with increased risk of SU and SUD among JIC; and (2) if the effects of ACEs on SU and SUD are moderated by race/ethnicity. Methods: Bivariate and multivariate logistic regression analyses were employed to examine a statewide dataset of 79,960 JIC from the Florida Department of Juvenile Justice. Marginal odds were estimated to examine how race moderates the relationship between ACEs and SU and SUD. Results: Results showed higher ACEs scores were linked to SU and SUD. Black JIC were 2.46 times more likely, and Latinx JIC were 1.40 times more likely to report SU than white JIC. Specifically, Black and Latinx JIC with a higher average ACEs score were more likely to report SU but less likely to have ever been diagnosed with a SUD when compared to white JIC with equivalent ACEs. Conclusions: Study results highlight the need to develop trauma-informed and culturally appropriate interventions for SU and SUD among JIC.
LISTERIA MONOCYTOGENESIN RAW MILK AND DAIRY PRODUCTS;OCCURRENCE AND TRIALS FOR THEREDUCTION OF THEIR RESISTANCE TO ANTIBIOTICS.

Fatma I. El-hofy 1 , Ashraf A. Abd El-tawab 2 , Elham A. Mobarez 3 , Mohamed F. Abo El-Maati, Nancy Y. Tawkol 4 and Ahmed A. Hefny 4 . 1. Bacteriology, Immunology and Mycology Department, Faculty of Veterinary Medicine, Benha University, 13736, Egypt. 2. Pharmacology Department, Animal Health Research Institute, Dokki, Giza, 12618, Egypt. 3. Biochemistry Department, Faculty of Agriculture, Zagazig University, 44511, Egypt. 4. Veterinary Hospital, Faculty of Veterinary Medicine, Zagazig University, 44511, Egypt. Manuscript Info Abstract . Manuscript History Received: 01 October 2018 Final Accepted: 03 November 2018 Published: December 2018
Optical constants of crystalline and amorphous semiconductors : numerical data and graphical information

Preface. Acknowledgments. Abbreviations and Acronyms. Introductory Remarks. A. Group-VI Semiconductors. B. III-V Binary Semiconductors. C. III-V Alloy Semiconductors. D. II-VI Semiconductors. E. IV-VI Semiconductors. F. Amorphous Semiconductors. Index.
Using Different Models of Machine Learning to Predict Attendance at Medical Appointments

Outpatient absenteeism is a recurring problem worldwide and in Brazil, it is a chronic problem. The number of appointments and exams scheduled and not performed, due to the non-attendance of patients, reaches significantly high rates and can be seen in all regions in the country and in different types of care and medical specialties. This practice generates waste of resources, disorganizes the offer of services, and limits the guarantee of care at different levels of assistance. In addition, it causes a series of dissatisfactions from users of the health system who really need and have not yet been able to access consultations and exams. This imbalance causes the misuse of the offer, an increase in the queue and waiting time, as well as a financial loss since it is paid for by a professional who is idle due to the absence of patients. It is necessary to understand the profile of these missing patients and to try to discover the reasons that lead this person to be absent, in order to predict a future absence for consultation. Thus, this work presents the study of different machine learning models in order to help predict whether or not the patient will attend the scheduled appointment. As a result, an end-to-end machine learning process was developed, considering exploratory data analysis, pre-processing, creation of machine learning models, analysis of results, and deployment of the most appropriate model in a web application. As a result, it was found that the Decision Tree algorithm represents an interesting choice as a final model for use in future observations.