bluuebunny/crossref_metadata_2025 huggingface dataset:
The Shape of Data: a Theory of the Representation of Information in the Cerebellar Cortex
Authors: Gilbert Mike
Published: December 2021

This paper presents a model of rate coding in the cerebellar cortex. The pathway of input to output of the cerebellum forms an anatomically repeating, functionally modular network, whose basic wiring is preserved across vertebrate taxa. Each network is bisected centrally by a functionally defined cell group, a microzone, which forms part of the cerebellar circuit. Input to a network may be from tens of thousands of concurrently active mossy fibres. The model claims to quantify the conversion of input rates into the code received by a microzone. Recoding on entry converts input rates into an internal code which is homogenised in the functional equivalent of an imaginary plane, occupied by the centrally positioned microzone. Homogenised means the code exists in any random sample of parallel fibre signals over a minimum number. The nature of the code and the regimented architecture of the cerebellar cortex mean that the threshold can be represented by space so that the threshold can be met by the physical dimensions of the Purkinje cell dendritic arbour and planar interneuron networks. As a result, the whole population of a microzone receives the same code. This is part of a mechanism which orchestrates functionally indivisible behaviour of the cerebellar circuit and is necessary for coordinated control of the output cells of the circuit. In this model, fine control of Purkinje cells is by input rates to the system and not by learning so that it is in conflict with the for-years-dominant supervised learning model.
Pointwise monotonicity of heat kernels
Authors: Alonso-Orán Diego , Chamizo Fernando , Martínez Ángel D. , Mas Albert
Published: December 2021

In this paper we present an elementary proof of a pointwise radial monotonicity property of heat kernels that is shared by the Euclidean spaces, spheres and hyperbolic spaces. The main result was discovered by Cheeger and Yau in 1981 and rediscovered in special cases during the last few years. It deals with the monotonicity of the heat kernel from special points on revolution hypersurfaces. Our proof hinges on a non straightforward but elementary application of the parabolic maximum principle. As a consequence of the monotonicity property, we derive new inequalities involving classical special functions.
Experimental study on rock-breaking mechanism of micro-coring PDC bit
Authors: Huang Shengsong , Gao Qingchun , Ye Xian , Yang Yingxin , Huang Kuilin
Published: December 2021

In this paper, in order to study the rock-breaking mechanism of the micro-coring PDC bit, a series of unit breaking experiments containing 2 breaking forms (static-pressure breaking and fracture breaking) are conducted on core columns of sandstone, limestone, and granite. Besides, a full-sized micro-coring PDC bit with a diameter of 152.4 mm is designed and manufactured and is used to conduct an indoor experiment on multiple sized sandstone core columns. The unit breaking experiment results show that ROP (rate of penetration) of the fracture breaking is higher than the static-pressure breaking. The indoor experiment results of the full-sized bit show that ROP of the micro-coring PDC bit is 49–112% higher than the conventional bit and that the diameter of core column shows greater influence on ROP, while the height of the column shows smaller. Moreover, the micro-coring PDC bit realizes volumetric fracture on all of the three types of rock samples. Since volumetric fracture produces large rock debris, the rock-breaking efficiency and ROP of the micro-coring PDC bit will be improved significantly.
Self-Assembled Nanomicelles of Affibody-Drug Conjugate with Excellent Therapeutic Property to Cure Ovary and Breast Cancers
Authors: Xia Xuelin , Yang Xiaoyuan , Huang Wei , Xia Xiaoxia , Yan Deyue
Published: December 2021

Affibody molecules are small non-immunoglobulin affinity proteins, which can precisely target to some cancer cells with specific overexpressed molecular signatures. However, the relatively short in vivo half-life of them seriously limited their application in drug targeted delivery for cancer therapy. Here an amphiphilic affibody-drug conjugate is self-assembled into nanomicelles to prolong circulation time for targeted cancer therapy. As an example of the concept, the nanoagent was prepared through molecular self-assembly of the amphiphilic conjugate of ZHER2:342-Cys with auristatin E derivate, where the affibody used is capable of binding to the human epidermal growth factor receptor 2 (HER2). Such a nanodrug not only increased the blood circulation time, but also enhanced the tumor targeting capacity (abundant affibody arms on the nanoagent surface) and the drug accumulation in tumor. As a result, this affibody-based nanoagent showed excellent antitumor activity in vivo to HER2-positive ovary and breast tumor models, which nearly eradicated both small solid tumors (about 100 mm3) and large established tumors (exceed 500  mm3). The relative tumor proliferation inhibition ratio reaches 99.8% for both models.
A Graph Mining Approach for Ranking and Discovering the Interesting Frequent Subgraph Patterns
Authors: Ur Rehman Saif , Liu Kexing , Ali Tariq , Nawaz Asif , Fong Simon James
Published: December 2021

Graph mining is a well-established research field, and lately it has drawn in considerable research communities. It allows to process, analyze, and discover significant knowledge from graph data. In graph mining, one of the most challenging tasks is frequent subgraph mining (FSM). FSM consists of applying the data mining algorithms to extract interesting, unexpected, and useful graph patterns from the graphs. FSM has been applied to many domains, such as graphical data management and knowledge discovery, social network analysis, bioinformatics, and security. In this context, a large number of techniques have been suggested to deal with the graph data. These techniques can be classed into two primary categories: (i) a priori-based FSM approaches and (ii) pattern growth-based FSM approaches. In both of these categories, an extensive research work is available. However, FSM approaches are facing some challenges, including enormous numbers of frequent subgraph patterns (FSPs); no suitable mechanism for applying ranking at the appropriate level during the discovery process of the FSPs; extraction of repetitive and duplicate FSPs; user involvement in supplying the support threshold value; large number of subgraph candidate generation. Thus, the aim of this research is to make do with the challenges of enormous FSPs, avoid duplicate discovery of FSPs, and use the ranking for such patterns. Therefore, to address these challenges a new FSM frameworkA RAnkedFrequent pattern-growthFramework (A-RAFF) is suggested. Consequently, A-RAFF provides an efficacious answer to these challenges through the initiation of a new ranking measure called FSP-Rank. The proposed ranking measure FSP-Rank effectively reduced the duplicate and enormous frequent patterns. The effectiveness of the techniques proposed in this study is validated by extensive experimental analysis using different benchmark and synthetic graph datasets. Our experiments have consistently demonstrated the promising empirical results, thus confirming the superiority and practical feasibility of the proposed FSM framework.
An Adoptable Multi-Criteria Decision-Making Analysis to Select a Best Hair Mask Product-Extended Weighted Aggregated Sum Product Assessment Method
Authors: Narayanamoorthy Samayan , Brainy J. V. , Manirathinam Thangaraj , Kalaiselvan Samayan , Kureethara Joseph Varghese , Kang Daekook
Published: December 2021

Hair masks (HMs) act as one of the solutions for most of the hair problems like dandruff, frizziness, breakage, premature- greying and so on. Due to its various benefits, HM products are acquiring more popularity among the individuals. As there are different varieties of HM products available in the market, the confusion arises in choosing a HM which suits the individual’s hair profile and causes less side effects. Here, we have employed multi-criteria decision-making (MCDM) combined with fuzzy set theory to obtain better results. We used the extended Weighted Aggregated Sum Product Assessment (WASPAS) method based on trapezoidal interval type-2 fuzzy set (TIT2FS) in this research paper to handle vagueness and complexity in real-world problems. For determining the objective weights of the criteria, we used the entropy method of weight finding. An example of selecting a hair mask product (HMP) among four alternatives based on five criteria is provided to illustrate the applicability of the proposed method. In comparison to other MCDM methods, the approach yielded more practical results. By doing a sensitive study, the method’s stability is also assessed.
Multi-objective Aggregate Production Planning for Multiple Products: A Local Search-Based Genetic Algorithm Optimization Approach
Authors: Liu Lan-Fen , Yang Xin-Feng
Published: December 2021

The diversity of products and fierce competition make the stability and production cost of manufacturing industry more important. So, the purpose of this paper is to deal with the multi-product aggregate production planning (APP) problem considering stability in the workforce and total production costs, and propose an efficient algorithm. Taking into account the relationship of raw materials, inventory cost and product demand, a multi-objective programming model for multi-product APP problem is established to minimize total production costs and instability in the work force. To improve the efficiency of the algorithm, the feasible region of the planned production and the number of workers in each period are determined and a local search algorithm is used to improve the search ability. Based on the analysis of the feasible range, a genetic algorithm is designed to solve the model combined with the local search algorithm. For analyzing the effect of this algorithm, the information entropy strategy, NSGA-II strategy and multi-population strategy are compared and analyzed with examples, and the simulation results show that the model is feasible, and the NSGA-II algorithm based on the local search has a better performance in the multi-objective APP problem.
A Deep-Fusion Network for Crowd Counting in High-Density Crowded Scenes
Authors: Khan Sultan Daud , Salih Yasir , Zafar Basim , Noorwali Abdulfattah
Published: December 2021

People counting has been investigated extensively as a tool to increase the individual’s safety and to avoid crowd hazards at public places. It is a challenging task especially in high-density environment such as Hajj and Umrah, where millions of people gathered in a constrained environment to perform rituals. This is due to large variations of scales of people across different scenes. To solve scale problem, a simple and effective solution is to use an image pyramid. However, heavy computational cost is required to process multiple levels of the pyramid. To overcome this issue, we propose deep-fusion model that efficiently and effectively leverages the hierarchical features exits in various convolutional layers deep neural network. Specifically, we propose a network that combine multiscale features from shallow to deep layers of the network and map the input image to a density map. The summation of peaks in the density map provides the final crowd count. To assess the effectiveness of the proposed deep network, we perform experiments on three different benchmark datasets, namely, UCF_CC_50, ShanghaiTech, and UCF-QNRF. From experiments results, we show that the proposed framework outperforms other state-of-the-art methods by achieving low Mean Absolute Error (MAE) and Mean Square Error (MSE) values.
Chinese Named Entity Recognition Method in History and Culture Field Based on BERT
Authors: Liu Shuang , Yang Hui , Li Jiayi , Kolmanič Simon
Published: December 2021

With rapid development of the Internet, people have undergone tremendous changes in the way they obtain information. In recent years, knowledge graph is becoming a popular tool for the public to acquire knowledge. For knowledge graph of Chinese history and culture, most researchers adopted traditional named entity recognition methods to extract entity information from unstructured historical text data. However, the traditional named entity recognition method has certain defects, and it is easy to ignore the association between entities. To extract entities from a large amount of historical and cultural information more accurately and efficiently, this paper proposes one named entity recognition model combining Bidirectional Encoder Representations from Transformers and Bidirectional Long Short-Term Memory-Conditional Random Field (BERT-BiLSTM-CRF). First, a BERT pre-trained language model is used to encode a single character to obtain a vector representation corresponding to each character. Then one Bidirectional Long Short-Term Memory (BiLSTM) layer is applied to semantically encode the input text. Finally, the label with the highest probability is output through the Conditional Random Field (CRF) layer to obtain each character’s category. This model uses the Bidirectional Encoder Representations from Transformers (BERT) pre-trained language model to replace the static word vectors trained in the traditional way. In comparison, the BERT pre-trained language model can dynamically generate semantic vectors according to the context of words, which improves the representation ability of word vectors. The experimental results prove that the model proposed in this paper has achieved excellent results in the task of named entity recognition in the field of historical culture. Compared with the existing named entity identification methods, the precision rate, recall rate, and $$F_1$$

F
1

value have been significantly improved.
Z-Hesitant Fuzzy Network Model with Reliability and Transparency of Information for Decision Systems
Authors: Yaakob Abdul Malek , Shafie Shahira , Gegov Alexander , Rahman Siti Fatimah Abdul
Published: December 2021

Decision-making environment often encounters complexity along its processes, especially in the context of multidisciplinary scientific research. This can commonly be seen in engineering, computing, finance, astrology and other different areas. It is of great restriction in dealing with the practical problems which have diverse demands and properties. There is a growing body of literature that recognizes the importance of dealing with the complexity in decision making environment. The reliability and the transparency are the dominant feature of the integration of fuzzy network and Z-numbers. However, much of the research up to now has been descriptive in nature of the features. Hence, this proposed method is unique and novel because it offers some interesting insight of dealing with reliability and transparency of information in Z-hesitant fuzzy network decision-making environment. The fuzzy networks have the functionality under rule bases of fuzzy systems where it is recognized by its transparency and precision. The proposed method makes use of fuzzy network with the incorporation of hesitant fuzzy sets to assimilate decision information towards alternatives. For the validation and applicability purposes of the proposed method, the case study of stock evaluation assessed by a number of decision makers has been utilized as a real-world problem. The performance of the proposed method is evaluated respectively by applying the Spearman’s rho correlation. The result shows that the proposed method performs as the established method with the consideration of additional dominant features.
UAV Intelligent Coverage Navigation Based on DRL in Complex Geometrical Environments
Authors: Liu Shuai , Bai Yuebin
Published: December 2021

Unmanned aerial vehicle (UAV) is one of the preferred tools for coverage detection missions, because of its maneuverability and flexibility. It is challenging for the UAV to decide a track by itself in a complex geometrical environment. This paper presents a UAV intelligent navigation method based on deep reinforcement learning (DRL). We propose using geographic information systems (GIS) as the DRL training environment to overcome the inconsistency between the training environment and the test environment. We creatively save the flight path in the form of an image. The combination of the knowledge-based Monte Carlo tree search method and local search method can not only effectively avoid falling into local search, but also ensure learning the optimal search direction under the limitation of computing power. Experiments show that the trained UAV can find an excellent flight path by intelligent navigation, and able to make effective flight decisions in a complex geometrical environment.
Enhancing ELECTRE I Method with Complex Spherical Fuzzy Information
Authors: Akram Muhammad , Al-Kenani Ahmad N. , Shabir Maria
Published: December 2021

This article is concerned to delineate the strategic approach of ELiminating Et Choice Translating REality (ELECTRE) method for multi-attribute group decision-making (MAGDM) in terms of complex spherical fuzzy sets. The feasible, well-suited, and marvelous structure of complex spherical fuzzy set compliments the decision-making efficiency and ranking calibre of ELECTRE I approach to present a beneficial and supreme aptitude strategy for MAGDM. Beside the proposed methodology, a few non-fundamental properties of complex spherical fuzzy weighted averaging (CSFWA) operator inclusive of shift invariance, homogenous, linearity, and additive property are also explored. The proposed procedure validates the individual opinions into an acceptable form by the dint of CSFWA operator and the aggregated opinions are further analyzed by the proposed complex spherical fuzzy- ELECTRE I (CSF-ELECTRE I) method. Within the consideration of proposed methodology, normalized Euclidean distances of complex spherical fuzzy numbers are also contemplated. In CSF-ELECTRE I method, the score, accuracy, and refusal degrees determine the concordance and discordance sets for each pair of alternatives to calculate the concordance and discordance indices, respectively. Based on aggregated outranking matrix, a decision graph is constructed to attain the ELECTREcally outranked solutions and the best alternative. This article provides supplementary approach at the final step to profess a linear ranking order of the alternatives. The versatility and feasibility of the presented method are embellished with two case studies from the business and IT field. Moreover, to ratify the intensity and aptitude of the presented methodology, we provide a comparative study with complex spherical fuzzy-TOPSIS method.
Few-Shot Abnormal Network Traffic Detection Based on Multi-scale Deep-CapsNet and Adversarial Reconstruction
Authors: Ma Wengang , Zhang Yadong , Guo Jin , Yu Qian
Published: December 2021

Detecting various attacks and abnormal traffic in the network is extremely important to network security. Existing detection models used massive amounts of data to complete abnormal traffic detection. However, few-shot attack samples can only be intercepted in certain special scenarios. In addition, the discrimination of traffic attributes will also be affected by the change of feature attitude. But the traditional neural network model cannot detect this kind of attitude change. Therefore, the accuracy and efficiency of few-shot sample abnormal traffic detection are very low. In this paper, we proposed a few-shot abnormal network traffic detection method. It was composed of the multi-scale Deep-CapsNet and adversarial reconstruction. First, we designed an improved EM vector clustering of the Deep-CapsNet. The attitude transformation matrix was used to complete the prediction from low-level to high-level features. Second, a multi-scale convolutional capsule was designed to optimize the Deep-CapsNet. Third, an adversarial reconstruction classification network (ARCN) was proposed. The supervised source data classification and the unsupervised target data reconstruction were achieved. Moreover, we proposed an adversarial training strategy, which alleviated the noise interference during reconstruction. Fourth, the few-shot sample classification were obtained by combining multi-scale Deep-CapsNet and adversarial reconstruction. The ICSX2012 and CICIDS2017 datasets were used to verify the performance. The experimental results show that our method has better training performance. Moreover, it has the highest accuracy in two-classification and multi-classification. Especially it has good anti-noise performance and short running time, which can be used for real-time few-shot abnormal network traffic detection.
Application of Improved Differential Evolution Algorithm in Solving Equations
Authors: Ning Guiying , Zhou Yongquan
Published: December 2021

The problem of finding roots of equations has always been an important research problem in the fields of scientific and engineering calculations. For the standard differential evolution algorithm cannot balance the convergence speed and the accuracy of the solution, an improved differential evolution algorithm is proposed. First, the one-half rule is introduced in the mutation process, that is, half of the individuals perform differential evolutionary mutation, and the other half perform evolutionary strategy reorganization, which increases the diversity of the population and avoids premature convergence of the algorithm; Second, set up an adaptive mutation operator and a crossover operator to prevent the algorithm from falling into the local optimum and improve the accuracy of the solution. Finally, classical high-order algebraic equations and nonlinear equations are selected for testing, and compared with other algorithms. The results show that the improved algorithm has higher solution accuracy and robustness, and has a faster convergence speed. It has outstanding effects in finding roots of equations, and provides an effective method for engineering and scientific calculations.
Epidemiology of Dermatophytes Isolated from Clinical Samples in a Hospital in Eastern Saudi Arabia: A 20-Year Survey
Authors: Alshehri Bashayer Ali , Alamri Aisha M. , Rabaan Ali A. , Al-Tawfiq Jaffar A.
Published: December 2021

Background

Dermatophytes are group of fungi that cause superficial infections via enzymes that degrade keratin in human skin. Several factors, including climate, gender, age, lifestyle, human migration, cultural habits, and socioeconomic status influence the prevalence of dermatophyte infections. We analyzed the prevalence of dermatophyte isolates in a hospital in Eastern Saudi Arabia from 2000 to 2019.

Methods

The data on fungal cultures were obtained from the Laboratory Information System of the Mycology Laboratories at Johns Hopkins Aramco Healthcare, and were used for the analysis. Fungal isolates were examined microscopically for the presence of specialized hyphal structures and conidia. The Vitek® MS microbial identification system (biomerieux) was used if the culture type was not identified microscopically.

Results

Among the 10,021 samples analyzed, 3040 (30.33%) were positive for fungi and only 398 (3.97%) were dermatophytes. Microsporum species was the most common dermatophyte accounting for 50.5% (n = 201) followed by trichophyton with 36.9% (n = 147). The most common positive samples were scrapping (251, 63%) and hair (68, 17%). Culture positivity relative to the age groups revealed a cluster of positive dermatophyte species in children < 10 years of age with 215 (54%) of all cases and among 10–19 years of age with 60 (15) of the cases (p < 0.001). Microsporum species were the prevalent dermatophytes in patients  < 10 years of age, while Epidermophyton species were the most frequent dermatophyte species in age groups 10–19, 20–29, and 30–39 years. However, Trichophyton species were the most frequent dermatophyte species in individuals 70–79 years. The percentage of Microsporum and Trichophyton species decreased significantly over time (p < 0.001). In addition, there was a significant seasonal variation in relation to Trichophyton species. A comparison between the most frequent species showed that there was no difference in relation to gender, but there was a difference in relation to the specimen type and age group.

Conclusion

Dermatophytosis was common among children and adolescent with the most common samples were scrapping and hair. There was a significant reduction in Microsporum and Trichophyton species over time.
An Integrated Approach to Assess Knowledge/Perceptions and Attitudes/Practices (KAP) Regarding Major Neglected Tropical Diseases Endemic in the Mbengwi Health District, North West Region, Cameroon
Authors: Sumo Laurentine , Ntonifor Ngum H. , Lenou-Nanga Cédric G. , Chenkumo-Kengmoni Nicanor , Amana-Bokagne Vanessa T. , Awah Chembo G. , Niamsi-Emalio Yannick , Nana-Djeunga Hugues C.
Published: December 2021

Background and objectives

Preventive chemotherapy (PCT) is the main strategy currently used to control and/or eliminate onchocerciasis, lymphatic filariasis (LF) and soil transmitted helminthiasis (STH), and community participation (through implementation or adherence to PCT) is critical. This study aimed at investigating knowledge/perceptions of populations of the Mbengwi health district (North West Region, Cameroon), in relation to their attitudes/practices regarding the most prevalent neglected tropical diseases (NTDs).

Methods

A household-based cross-sectional survey was carried out in the Mbengwi health district (North West Region, Cameroon) using the cluster sampling technique. Clusters were selected using the probability proportionate to estimate size strategy. In each cluster, the random walk technique was used for the selection of households, and a structure questionnaire was administered to 2–3 of its members.

Results

A total of 254 households from 26 clusters were visited, and 514 individuals were interviewed. The sex ratio of interviewees (1.08) was unbiased, and their ages ranged between 10 and 99 years old. Though most of the respondents declared having already heard of these NTDs (41.6%, 73.9% and 90.5% for onchocerciasis, LF and STH, respectively), only a minority of them were aware of how they are acquired/transmitted (8.9%, 9.2% and 32.7% for onchocerciasis, LF and STH, respectively), or prevented (23.1%, 18.9% and 47.2% for onchocerciasis, LF and STH, respectively).

Conclusions

This study revealed poor knowledge/perceptions and wrong attitudes/practices of interviewees as regards to these NTDs, and these misconceptions can seriously affect the adherence and contribution of populations to the success of PCTs. It appears compulsory to reinforce information, education, and communication, with a focus on the rationale and importance behind PCTs, to optimize/improve community participation.
Comparison of the Characteristics of Asymptomatic and Presymptomatic Patients with Coronavirus Disease 2019 in the Republic of Korea
Authors: Hyun Miri , Lee Ji Yeon , Park Jae Seok , Kim Jin Young , Kim Hyun Ah
Published: December 2021

Purpose

This retrospective study aimed to evaluate the baseline characteristics of asymptomatic patients with coronavirus disease 2019 at admission and to follow-up their clinical manifestations and radiological findings during hospitalization.

Methods

Patients with coronavirus disease 2019 who were asymptomatic at admission were divided into two groups—those with no symptoms until discharge (group A) and those who developed symptoms after admission (group B). Patients who could not express their own symptoms were excluded.

Results

Overall, 127 patients were enrolled in the study, of whom 19 and 108 were assigned to groups A and B, respectively. The mean age and median C-reactive protein level were higher in group B than in group A. All patients in group A and one-third of patients in group B had normal initial chest radiographs; 15.8% and 48.1% of patients in groups A and B, respectively, had pneumonia during hospitalization. One patient in group B, whose condition was not severe at the time of admission, deteriorated due to aggravated pneumonia and was transferred to a tertiary hospital.

Conclusion

We summarize the clinical characteristics during hospitalization of patients with coronavirus disease 2019 who were purely asymptomatic at the time of admission. The majority of asymptomatic patients with coronavirus disease 2019 were discharged without significant events during hospitalization. However, it may be difficult to predict subsequent events from initial chest radiographs or oxygen saturation at admission.
Factors that Influence 2-Year Progression-Free Survival Among Head and Neck Cancer Patients
Authors: Irawan Cosphiadi , Benbella Larangga Gempa , Rachman Andhika , Mansjoer Arif
Published: December 2021

Objectives

The majority of patients with head and neck cancer (HNC) come to the hospital at advanced stages. This research was conducted to determine the mortality, 2-year progression-free survival (PFS) and factors that influenced PFS of HNC patients.

Methods

A retrospective cohort study was conducted among locally advanced HNC patients who underwent chemoradiation for the first time at RSCM from January 2015 to December 2017. Data were retrieved through medical records. Laboratory data were taken 2–4 weeks prior and 2–4 weeks after chemoradiation. PFS observation started from the first day of chemoradiation until disease progression or death. PFS data were recorded in two groups: ≤ 2 years and > 2 years. The Chi-square test was used for bivariate analysis with the Fischer-exact test as an alternative. Variables will be further tested using multivariate logistic regression tests.

Results

Among 216 subjects, there were 103 (47.69%) patients who did not reach overall survival (OS) > 2 years. There were 108 (50%) patients who had PFS > 2 years. Based on the results of multivariate analysis, it was found that smoking, hemoglobin level ≤ 12 g/dl, ECOG (Eastern Cooperative Oncology Group) 1–2, and negative therapeutic response were associated with poor PFS. Hazard ratio (HR) for 2-year PFS for Brinkman index > 250 was 1.36 (95% CI 0.93–2.00; p = 0.02); HR for Hb ≤ 12 g/dl was 1.65 (95% CI 1.13–2.42; p = 0.01); HR for ECOG 1–2 was 4.05 (95% CI 1.49–11.00; p < 0.01); and HR for negative therapeutic response was 2.37 (95% CI 1.43–3.94; p < 0.01).

Conclusion

Mortality of HNC patients within 2 years is 47.69%, with a 2-year PFS reaching 50%. Cigarette smoking, low hemoglobin levels, poor performance status, and negative therapeutic response (non-responders) negatively affect the 2-year PFS.
Combined Blood Indexes of Systemic Inflammation as a Mirror to Admission to Intensive Care Unit in COVID-19 Patients: A Multicentric Study
Authors: Hamad Dina Ali , Aly Mai Mostafa , Abdelhameid Marwa Ahmed , Ahmed Shimaa Abdalla , Shaltout Asmaa Salah , Abdel-Moniem Alaa Eldin , Ragheb Ahmed Mohamed Reda , Attia Mohammed Nahed , Meshref Taghreed Sayed
Published: December 2021

Background

The Coronavirus 2019 is a pandemic that has spread worldwide, threatening human health. The main cause of death in patients with COVID-19 is a systemic pro-inflammatory mechanism that quickly progresses to acute respiratory distress syndrome. Hematological ratios as affordable indicators of inflammatory response were studied in COVID-19 patients. The study aimed to study the importance of the blood cell indexes of the systemic inflammatory response, as the Aggregate Index of Systemic Inflammation (AISI), neutrophils lymphocyte to platelet ratio (NLPR), systemic immune-inflammation index (SII) and, systemic inflammation response index (SIRI) in predicting intensive care unit (ICU) admission of COVID-19 patients.

Methods

495 COVID-19 patients managed in four tertiary centers; divided into non-ICU and ICU groups.

Results

Total leucocyte count (TLC), AISI, NLPR, SII, and SIRI were more elevated in the ICU group (P < 0.001 for all except AMC P = 0.006), while this group had less absolute lymphocyte count (ALC) (P = 0.047).

We estimated the optimal cut-off values of the hematological ratio; AISI (729), NLPR (0.0195), SII (1346), and SIRI (2.5). SII had the highest specificity (95.6%), while NLPR had the highest sensitivity (61.3%). Age, AISI, CRP, D-dimer, and oxygen aid were the independent predictors for ICU admission in COVID-19 in multivariate logistic regression.

Conclusion

AISI is a predictor for severity and ICU admission in COVID-19 patients, SII is a predictor of survival, while NLPR and SIRI have an additive role that needs further evaluation.
Almost Cosymplectic $$(k,\mu )$$-metrics as $$\eta$$-Ricci Solitons
Authors: Wang Wenjie
Published: December 2021

In this paper, we study $$\eta$$
η
-Ricci solitons on almost cosymplectic $$(k,\mu )$$

(
k
,
μ
)

-manifolds. As an application, it is proved that if an almost cosymplectic $$(k,\mu )$$

(
k
,
μ
)

-metric with $$k<0$$

k
<
0

represents a Ricci soliton, then the potential vector field of the Ricci soliton is a strict infinitesimal contact transformation, and the corresponding almost cosymplectic manifold is locally isometric to a Lie group whose local structure is determined completely by $$k<0$$

k
<
0

. In addition, a concrete example is constructed to illustrate the above result.
Changes in Frequency and Patterns of Marital Sexual Activity During COVID-19: Evidence from Longitudinal Data Prior to, During and After Lockdown in Singapore
Authors: Tan Poh Lin
Published: December 2021

Background

The literature suggests that the pandemic has affected sexual activity and sexual desire around the world, potentially due to increased levels of stress, movement restrictions under lockdown conditions, and changes in relationship quality.

Aim

To investigate changes in frequency and patterns of marital sexual activity and the role of potential factors underlying these changes, during and after COVID-19 lockdown.

Methods

This longitudinal study followed 409 heterosexual married female participants who completed a baseline survey in April–July 2018 and biweekly online surveys over the next 14 weeks; an online survey in May 2020 during the lockdown in Singapore; and an online survey in June 2020 after the lockdown was lifted. Participants were recruited in 2018 using street intercept and screened for inclusion and exclusion criteria. Logistic fixed-effects models were used to test for changes in frequency and patterns of marital sexual activity and assess underlying factors. Pseudo-design-based sample weights were applied.

Outcomes

The 2018 in-person baseline survey collected information on demographic characteristics and ideal frequency of marital sex, while follow-up online questionnaires in May–June 2020 included items on exact dates of marital sexual activity of previous weeks; stress and fatigue levels; both spouses’ stay-at-home statuses during lockdown; and marital satisfaction.

Results

409 heterosexual married women were included in this study. Compared to pre-pandemic levels, the proportion of participants not having marital sex within a week remained stable while weekly sexual frequency increased, with more evenly distributed sexual activity on weekdays and weekends. Stress, fatigue, and marital satisfaction levels predicted probability of non-activity and sexual frequency.

Clinical Translation

The increase in weekly sexual frequency has implications for sexual and reproductive health, including sexual satisfaction and prevalence of infertility and low birth weight associated with waiting time to pregnancy.

Strengths & Limitations

The longitudinal nature of the dataset provides unique insights into differences in frequency of marital sexual activity during compared to before the pandemic. Unlike previous studies, detailed data on exact dates of sexual activity allow for detection of differences in sexual activity by day of the week. However, dates of sexual activity were recorded retrospectively and may contain recall errors. Data were collected only from wives and hence dates of marital sexual activities were not cross-validated with husbands.

Conclusion

Results point to more active and flexible marital sex lives during the pandemic, with effects that persisted after the lockdown ended.
MEAN–VARIANCE INSURANCE DESIGN WITH COUNTERPARTY RISK AND INCENTIVE COMPATIBILITY
Authors: Boonen Tim J. , Jiang Wenjun
Published: December 2021

This paper studies the optimal insurance design from the perspective of an insured when there is possibility for the insurer to default on its promised indemnity. Default of the insurer leads to limited liability, and the promised indemnity is only partially recovered in case of a default. To alleviate the potential ex post moral hazard, an incentive compatibility condition is added to restrict the permissible indemnity function. Assuming that the premium is determined as a function of the expected coverage and under the mean–variance preference of the insured, we derive the explicit structure of the optimal indemnity function through the marginal indemnity function formulation of the problem. It is shown that the optimal indemnity function depends on the first and second order expectations of the random recovery rate conditioned on the realized insurable loss. The methodology and results in this article complement the literature regarding the optimal insurance subject to the default risk and provide new insights on problems of similar types.
Cost-effectiveness analysis of whole-genome sequencing during an outbreak of carbapenem-resistantAcinetobacter baumannii
Authors: Elliott Thomas M. , Harris Patrick N. , Roberts Leah W. , Doidge Michelle , Hurst Trish , Hajkowicz Krispin , Forde Brian , Paterson David L. , Gordon Louisa G.
Published: December 2021

Background:

Whole-genome sequencing (WGS) shotgun metagenomics (metagenomics) attempts to sequence the entire genetic content straight from the sample. Diagnostic advantages lie in the ability to detect unsuspected, uncultivatable, or very slow-growing organisms.

Objective:

To evaluate the clinical and economic effects of using WGS and metagenomics for outbreak management in a large metropolitan hospital.

Design:

Cost-effectiveness study.

Setting:

Intensive care unit and burn unit of large metropolitan hospital.

Patients:

Simulated intensive care unit and burn unit patients.

Methods:

We built a complex simulation model to estimate pathogen transmission, associated hospital costs, and quality-adjusted life years (QALYs) during a 32-month outbreak of carbapenem-resistantAcinetobacter baumannii(CRAB). Model parameters were determined using microbiology surveillance data, genome sequencing results, hospital admission databases, and local clinical knowledge. The model was calibrated to the actual pathogen spread within the intensive care unit and burn unit (scenario 1) and compared with early use of WGS (scenario 2) and early use of WGS and metagenomics (scenario 3) to determine their respective cost-effectiveness. Sensitivity analyses were performed to address model uncertainty.

Results:

On average compared with scenario 1, scenario 2 resulted in 14 fewer patients with CRAB, 59 additional QALYs, and $75,099 cost savings. Scenario 3, compared with scenario 1, resulted in 18 fewer patients with CRAB, 74 additional QALYs, and $93,822 in hospital cost savings. The likelihoods that scenario 2 and scenario 3 were cost-effective were 57% and 60%, respectively.

Conclusions:

The use of WGS and metagenomics in infection control processes were predicted to produce favorable economic and clinical outcomes.
Hospital mattress failures—A hidden patient danger
Authors: Hooker Edmond A.
Published: December 2021

The objective of the research was to assess the condition of beds and mattresses in 4 US hospitals. In total, 727 beds and mattresses were evaluated, and 523 (72%) had damage: 340 (47%) required mattress cover replacement and 183 (25%) required replacement of the entire mattress.
Correlates of vocational outcomes of youth with co-occurring mental illness and substance use disorders: Evidence from a vocational rehabilitation program
Authors: Akinola Olayemi A. , Horsman Euchay Ngozi , Dunkley Lisa
Published: December 2021

Youth with co-occurring mental illness and substance use disorders are at higher risk for vocational rehabilitation exclusion. This study aimed to (a) explore the personal factors associated with vocational outcomes of youth with co-occurring mental illness and substance use disorders and (b) highlight services that have shown the greatest promise for this population in the state-federal rehabilitation program in the United States. Our analytic sample was extracted from the Rehabilitation Services Administration’s Case Service Report data set for 2013, 2014, and 2015 fiscal years. Multiple regression analyses results identified personal factors such as gender, race/ethnicity, level of education, and severity of disability as predictors of the achievement of competitive employment, hours worked, and income. The receipt of vocational rehabilitation services such as job search support, job placement assistance, vocational training, and on-the-job support are significantly associated with the achievement of competitive employment, higher work hours, and income. These finding have implications for vocational rehabilitation practice as they highlight who is at higher risk for poor outcomes, effective services, and additional factors to consider when working with youth with co-occurring mental illness and substance use disorders.
A novel statistical method predicts mutability of the genomic segments of the SARS-CoV-2 virus
Authors: Darooneh Amir Hossein , Przedborski Michelle , Kohandel Mohammad
Published: December 2021

The SARS-CoV-2 virus has made the largest pandemic of the 21st century, with hundreds of millions of cases and tens of millions of fatalities. Scientists all around the world are racing to develop vaccines and new pharmaceuticals to overcome the pandemic and offer effective treatments for COVID-19 disease. Consequently, there is an essential need to better understand how the pathogenesis of SARS-CoV-2 is affected by viral mutations and to determine the conserved segments in the viral genome that can serve as stable targets for novel therapeutics. Here, we introduce a text-mining method to estimate the mutability of genomic segments directly from a reference (ancestral) whole genome sequence. The method relies on calculating the importance of genomic segments based on their spatial distribution and frequency over the whole genome. To validate our approach, we perform a large-scale analysis of the viral mutations in nearly 80,000 publicly available SARS-CoV-2 predecessor whole genome sequences and show that these results are highly correlated with the segments predicted by the statistical method used for keyword detection. Importantly, these correlations are found to hold at the codon and gene levels, as well as for gene coding regions. Using the text-mining method, we further identify codon sequences that are potential candidates for siRNA-based antiviral drugs. Significantly, one of the candidates identified in this work corresponds to the first seven codons of an epitope of the spike glycoprotein, which is the only SARS-CoV-2 immunogenic peptide without a match to a human protein.
Changing In-Group Boundaries: The Effect of Immigration on Race Relations in the United States
Authors: FOUKA VASILIKI , TABELLINI MARCO
Published: December 2021

How do social group boundaries evolve? Does the appearance of a new out-group change the in-group’s perceptions of other out-groups? We introduce a conceptual framework of context-dependent categorization in which exposure to one minority leads to recategorization of other minorities as in- or out-groups depending on perceived distances across groups. We test this framework by studying how Mexican immigration to the United States affected white Americans’ attitudes and behaviors toward Black Americans. We combine survey and crime data with a difference-in-differences design and an instrumental variables strategy. Consistent with the theory, Mexican immigration improves whites’ racial attitudes, increases support for pro-Black government policies, and lowers anti-Black hate crimes while simultaneously increasing prejudice against Hispanics. Results generalize beyond Hispanics and Blacks, and a survey experiment provides direct evidence for recategorization. Our findings imply that changes in the size of one group can affect the entire web of intergroup relations in diverse societies.
Novel SNPs and haplotypes identified in theCD4gene and their influence on deregressed MACE EBV indexes of milk-related traits in Simmental breed
Authors: Napolitano Francesco , Grandoni Francesco , De Matteis Giovanna , Degano Lorenzo , Vicario Daniele , Buttazzoni Luca
Published: December 2021

Cluster of differentiation 4 (CD4) is the accessory protein non-covalently bound to the T cell receptor that recognizes an invariant region of MHC class II on antigen presenting cells. Its cytoplasmic tail, physically associated with a protein tyrosine kinase, is important in the activation of helper/inducer T lymphocytes. InBos taurus,CD4gene is located on chromosome 5 from which two isoforms are transcribed, with a different number of amino acids due to splicing of exon 7 and variation in the reading frame. The aim of this study was to investigate the sequence of the entireCD4gene in Simmental sires to evaluate the effects of genomic variants on the indexes of the bulls for milk, fat and protein yields, as well as somatic cell score. The associations among genomic variants and indexes were analysed using the Allele and GLM procedures of SAS 9.4. The analysis indicated that only four of the thirty-one identified SNPs influenced the considered traits. Identified variants insist on coding zones and intronic sequences, where we revealed the presence of sites for transcription factors. To evaluate the existence of haplotypic effects, combinations among the four genomic variants (SNP 3, SNP 8, SNP 11 and SNP 19) were investigated. Six different haplotypic alleles were identified, but only four of them were frequent enough to allow for an evaluation of any haplotypic effect (at least six copies in the examined sample): Hap1, Hap2, Hap3 and Hap6. The analysis of associations between the selected haplotypes in theCD4gene with milk related indexes showed that bulls with Hap2 (T-A-C-C) had better indexes for milk and protein yields (P< 0.05), whereas the presence of the Hap1 haplotype (A-G-A-T) caused a significant decrease of the index for protein yield (P< 0.05). Frequencies of the two alleles Hap1 and Hap2 (9 and 36% respectively) make them of interest for their possible inclusion in breeding schemes and support the hypothesis of considering this gene as a candidate for the improvement of milk-related traits in the Simmental breed.
Amen to daat: on the foundations of Jewish epistemology
Authors: Lebens Samuel
Published: December 2021

I argue that the Hebrew Bible adopts a non-doxastic account of propositional faith. In coming to this conclusion, we shall discover that Biblical Hebrew has no word for belief. What ramifications might this have had for biblical and Jewish epistemology? I begin to trace the sort of epistemic norms that might emerge from an epistemology that approaches knowledge by thinking about faith, rather than belief.
A SIMPLE NONPARAMETRIC APPROACH FOR ESTIMATION AND INFERENCE OF CONDITIONAL QUANTILE FUNCTIONS
Authors: Fang Zheng , Li Qi , Yan Karen X.
Published: December 2021

In this paper, we present a new nonparametric method for estimating a conditional quantile function and develop its weak convergence theory. The proposed estimator is computationally easy to implement and automatically ensures quantile monotonicity by construction. For inference, we propose to use a residual bootstrap method. Our Monte Carlo simulations show that this new estimator compares well with the check-function-based estimator in terms of estimation mean squared error. The bootstrap confidence bands yield adequate coverage probabilities. An empirical example uses a dataset of Canadian high school graduate earnings, illustrating the usefulness of the proposed method in applications.