Miracle-dz/UniFAR-FTUs huggingface dataset:
Lymph Node Ratio as a Prognostic Factor in Neck Dissection in Oral Cancer Patients: A Systematic Review and Meta-Analysis

Simple Summary Lymph node ratio (LNR) is a well-studied prognostic factor in colorectal and breast cancer, and it has been recently evaluated as a clinically relevant biomarker in oral squamous cell carcinoma. LNR represents the ratio of positive lymph nodes extracted in a neck dissection to the total number of nodes harvested (lymph node yield, LNY). Many single-center cohort studies and a few multicenter have assessed the significance of LNR as a prognostic factor in oral cancer. In this systematic review and meta-analysis of 32 studies and 20,994 oral cancer patients, we demonstrate that LNR is an independent prognostic indicator in patients with oral squamous cell carcinoma. Abstract Many studies have evaluated the clinical implications of lymph node ratio (LNR) as a prognostic factor in patients with oral squamous cell carcinoma (OSCC). The main purpose of this systematic review and meta-analysis was to address LNR as a prognosticator in patients with OSCC. A systematic search was conducted in the following databases: PubMed, EMBASE, Google Scholar, OpenGrey, Cochrane library, and ClinicalTrials.gov, and studies between 2009 and 2020 were sought. The pooled relative risk was calculated along with 95% confidence intervals for the following endpoints: overall survival (OS), disease-free survival (DFS), disease-specific survival (DSS), distant metastasis-free survival (DMFS), locoregional disease-free survival (LRDFS), local recurrence-free survival (LRFS), and recurrence-free survival (RFS) according to the random-effects model (Der Simonian–Laird approach). Subgroup and meta-regression analyses were performed as well. Finally, 32 cohort studies were eligible, which included 20,994 patients with OSCC. Patients were subdivided into two categories, group YES (studies that included in their analysis only patients with positive lymph nodes) and group NO (studies that did not exclude LNR = 0 patients). In the group YES, patients with high LNR had shorter OS (RR = 1.68, 95% CI: 1.47–1.91), DFS (RR = 1.68, 95% CI: 1.42–1.99), DSS (RR = 1.94, 95% CI: 1.56–2.42), DMFS (RR = 1.83, 95% CI: 1.13–2.96), LRDFS (RR = 1.55, 95% CI: 1.10–2.20), and LRFS (RR = 1.73, 95% CI: 1.41–2.13) compared to patients with low LNR. In the group NO, patients with high LNR in comparison had shorter OS (RR = 2.38, 95% CI: 1.99–2.85), DFS (RR = 2.04, 95% CI: 1.48–2.81), and DSS (RR = 2.90, 95% CI: 2.35–3.57) compared to patients with low LNR. Based on those findings, LNR might be an independent prognostic factor for OS in patients with OSCC and could be incorporated into future classification systems for better risk stratification.
Adaptive selling in business-to-business markets: Contextual boundary of a selling strategy from retailing

Abstract A recent study of a selling strategy adapted by some retail salespeople recommended that salespeople should expeditiously disengage from a customer interaction when they believe that a customer with whom they are interacting is unlikely to make a purchase (Cron et al. 2021). The question addressed in this study is whether this sales strategy is similarly effective for salespeople in a B2B context. The B2B context for this study is a low purchasing complexity environment where the majority of sales revenue stems from customers’ reordering current supplies and equipment. Similar to the outcomes observed in a retail environment, the results suggest that B2B salespeople’s use of an NC2 strategy (short for No Conversion, No Conversation) increases sales revenues if salespeople work in an environment characterized by time scarcity and if they are relationship building oriented. Critically, if B2B salespeople apply the NC2 sales strategy with a low relationship building orientation, results show detrimental effects of the strategy over time. This potentially harmful effect did not emerge in retail settings. Moreover, unlike the results in a retail setting the effectiveness of the NC2 sales strategy does not depend on salespeople’s experience. For practitioners in B2B settings these findings are meaningful and actionable.
PRR15 Is a Novel Diagnostic and Prognostic Biomarker in Papillary Thyroid Cancer and Modulates the Tumor Microenvironment

Papillary thyroid cancer (PTC), accounting for more than 80 percent of all cases of thyroid cancer, is a form of a cancerous tumor that has a very favorable prognosis. However, patients diagnosed with PTC who are already in an advanced state have a dismal outlook. This study aimed to establish the diagnostic relevance of PRR15 expression in PTC patients as well as its levels in PTC samples and its connection with immune infiltrates. The TCGA and GEO datasets were combed through to obtain information on PTC patients. The “Limma” program was used to screen for differentially expressed mRNAs (DEMs), and the results were displayed using volcano plots and heat maps. The Wilcoxon test was used to examine the level of PRR15 expression in PTC patients in comparison with that of normal tissues. To study the connection between the immune infiltration level and PRR15 expression in PTC, the single-sample sequence set enrichment analysis (ssGSEA) from the R package was utilized. The expression of PRR15 was analyzed with RT-PCR in PTC cells and normal cells. In order to evaluate the diagnostic significance of PRR15 expression, ROC assays were carried out. Experiments using CCK-8 were carried out to investigate the impact that PRR15 knockdown could have on the proliferation of PTC cells. In this study, 17 overlapped DEMs between PTC specimens and normal specimens were identified, including MPPED2, IPCEF1, SLC4A4, PKHD1L1, DIO1, CRABP1, TPO, TFF3, SPX, TCEAL2, ZCCHC12, SYTL5, PRR15, CHI3L1, SERPINA1, GABRB2, and CITED1. Our attention focused on PRR15 which was highly expressed in PTC specimens as compared with nontumor specimens. PRR15 had an AUC value of 0.926 (95% CI 0.902–0.950) for PTC based on TCGA datasets. Pan-cancer assays suggested PRR15 as an oncogenic gene in many types of tumors. Moreover, we found that PRR15 expression was positively correlated with eosinophils, NK cells, NK CD56bright cells, IDC, macrophages, DC, mast cells, and Th1 cells. Further investigations with CCK-8 demonstrated that inhibiting PRR15 resulted in a decrease in the proliferation of PTC cells. Overall, PRR15 was confirmed to be a biomarker for PTC patients and a predictor of response to immunotherapy.
ADMISSIBLE AND NONADMISSIBLE TESTS IN UNIT-ROOT-LIKE SITUATIONS

This paper investigates the asymptotic behavior of tests in situations where the likelihood is locally asymptotically quadratic. Necessary and sufficient conditions are given for a test to be admissible. Even without these restrictive parametric assumptions, it is shown that certain common procedures—such as the augmented Dickey–Fuller test in cases where no deterministic trend is present or standard tests for restrictions on cointegrating relationships—are asymptotically inadmissible. These results confirm the existence of tests that dominate these classical tests for all parameters.I express my gratitude to the editors, H. Lütkepohl and especially Peter C.B. Phillips, for their help, which enormously exceeded the usual amount of support. Also I thank the referees for their helpful comments. Their contribution greatly improved the paper. All remaining errors are mine.
Effects of Ceragenins and Antimicrobial Peptides on the A549 Cell Line and an In Vitro Co-Culture Model of A549 Cells and Pseudomonas aeruginosa

Pseudomonas aeruginosa is an important pathogen that can adhere to host tissues and epithelial surfaces, especially during chronic infections such as cystic fibrosis (CF) lung infections. The effect of ceragenins and antimicrobial peptides (AMP) on this colonization was investigated in a co-culture infection model. After determining the antimicrobial effects of the substances on P. aeruginosa planktonic cells, their cytotoxicity on the A549 cell line was also determined. After the A549 cell line was infected with P. aeruginosa, the effect of antimicrobials on intracellular bacteria as well as the effects in inhibiting the adhesion of P. aeruginosa were investigated. In addition, LDH release from cells was determined by performing an LDH experiment to understand the cytotoxicity of bacterial infection and antimicrobial treatment on cells. CSA-131 was determined as the antimicrobial agent with the highest antimicrobial activity, while the antimicrobial effects of AMPs were found to be much lower than those of ceragenins. The antimicrobial with the lowest IC50 value was determined as the combination of CSA-131 with Pluronic F127. CSA-13 has been determined to be the most effective antimicrobial with its effectiveness to both intracellular bacteria and bacterial adhesion. Nevertheless, further safety, efficacy, toxicity, and pharmacological studies of ceragenins are needed to evaluate clinical utility.
Abscisic Acid Facilitates Sucrose Import by Strawberry Fruit Explants and Cortex Disks in Vitro

Strawberry (Fragaria × ananassa Duch. cv. Tristar) fruit explants and tissue disks were used for short term in vitro studies of [14C] sucrose import. An aqueous feeding solution of 50 mm Mes, 10 mm CaCl2, 10 mm EDTA, and 600 mm [14C] sucrose at pH 5.5 resulted in maximum accumulation of sucrose by fruit expiants during 2 hr. Young fruit exhibit the highest sink activity (sucrose imported per mg fruit dry weight), a value that declined as fruit aged. Sink strength (total sucrose accumulated per fruit) was positively correlated to fruit dry weight at 13 to 15 days after pollination. Inclusion of abscisic acid (ABA) in the incubation solution of fruit expiants and cortex disks increased sucrose accumulation 45% and 37%, respectively, above control values.
Optimal, Predictive and Adaptive Control

1. Introduction. I. BASIC DETERMINISTIC THEORY OF LQ AND PREDICTIVE CONTROL. 2. Deterministic LQ Regulation - I: Riccati-Based Solution. 3. I/O Descriptions and Feedback Systems. 4. Deterministic LQ Regulation - II: Solution via Polynomial Equations. 5. Deterministic Receding Horizon Control. II. STATE ESTIMATION, SYSTEM IDENTIFICATION, LQ AND PREDICTIVE STOCHASTIC CONTROL. 6. Recursive State Filtering and System Identification. 7. LQ and Predictive Stochastic Control. III. ADAPTIVE CONTROL. 8. Single-Step-Ahead Self-Tuning Control. 9. Adaptive Predictive Control. APPENDICES. A. Some Results from Linear Systems Theory. B. Some Results of Polynomial Matrix Theory. C. Some Results on Linear Diophantine Equations. D. Probability Theory and Stochastic Processes. References. Some Often Used Abbreviations. Index.
Past, Present, and Future of Genome Modification in Escherichia coli

Escherichia coli K-12 is one of the most well-studied species of bacteria. This species, however, is much more difficult to modify by homologous recombination (HR) than other model microorganisms. Research on HR in E. coli has led to a better understanding of the molecular mechanisms of HR, resulting in technical improvements and rapid progress in genome research, and allowing whole-genome mutagenesis and large-scale genome modifications. Developments using λ Red (exo, bet, and gam) and CRISPR-Cas have made E. coli as amenable to genome modification as other model microorganisms, such as Saccharomyces cerevisiae and Bacillus subtilis. This review describes the history of recombination research in E. coli, as well as improvements in techniques for genome modification by HR. This review also describes the results of large-scale genome modification of E. coli using these technologies, including DNA synthesis and assembly. In addition, this article reviews recent advances in genome modification, considers future directions, and describes problems associated with the creation of cells by design.
Automatic Assessment of Aphasic Speech Sensed by Audio Sensors for Classification into Aphasia Severity Levels to Recommend Speech Therapies

Aphasia is a type of speech disorder that can cause speech defects in a person. Identifying the severity level of the aphasia patient is critical for the rehabilitation process. In this research, we identify ten aphasia severity levels motivated by specific speech therapies based on the presence or absence of identified characteristics in aphasic speech in order to give more specific treatment to the patient. In the aphasia severity level classification process, we experiment on different speech feature extraction techniques, lengths of input audio samples, and machine learning classifiers toward classification performance. Aphasic speech is required to be sensed by an audio sensor and then recorded and divided into audio frames and passed through an audio feature extractor before feeding into the machine learning classifier. According to the results, the mel frequency cepstral coefficient (MFCC) is the most suitable audio feature extraction method for the aphasic speech level classification process, as it outperformed the classification performance of all mel-spectrogram, chroma, and zero crossing rates by a large margin. Furthermore, the classification performance is higher when 20 s audio samples are used compared with 10 s chunks, even though the performance gap is narrow. Finally, the deep neural network approach resulted in the best classification performance, which was slightly better than both K-nearest neighbor (KNN) and random forest classifiers, and it was significantly better than decision tree algorithms. Therefore, the study shows that aphasia level classification can be completed with accuracy, precision, recall, and F1-score values of 0.99 using MFCC for 20 s audio samples using the deep neural network approach in order to recommend corresponding speech therapy for the identified level. A web application was developed for English-speaking aphasia patients to self-diagnose the severity level and engage in speech therapies.
Accurate Prediction of Required Virtual Resources via Deep Reinforcement Learning

Resource provisioning for the ever-increasing applications to host the necessary network functions necessitates the efficient and accurate prediction of required resources. However, the current efforts fail to leverage the inherent features hidden in network traffic, such as temporal stability, service correlation and periodicity, to predict the required resources in an intelligent manner, incurring coarse-grain prediction accuracies. To tackle this problem, in this paper, we propose an Accurate Prediction of Required virtual Resources (APRR) approach via Deep Reinforcement Learning (DRL). We first confirm the resource requests have more similar features and identify the high-dimensional required resources in computing, storage and bandwidth can be effectively consolidated into a single standardized value. Built upon these observations, we then model the required resources as a time-variant network matrix, which includes a number of elements, obtained from the network measurements, and some missing elements needed to be inferred. To obtain accurately predicted results, DRL-based matrix factorization with a set of available rules has been introduced into APRR and alternately executed in agent to minimize the prediction errors. Moreover, the error-prioritized designed for model training with quicker convergence. Simulation experiments on real-world datasets illustrate that APRR can accurately predict the required virtual resources compared with the related approaches.
Analysis of the Polymer Two-Layer Protective Coating Impact on Panda-Type Optical Fiber under Bending

The article discusses the effects of thermal-force on the Panda-type optical fiber. The studies used a wide temperature range. The research used two thermal cycles with exposures to temperatures of 23, 60 and −60 °C. The field of residual stresses in the fiber formed during the drawing process was determined and applied. Panda was considered taking into account a two-layer viscoelastic polymer coating under conditions of tension winding on an aluminum coil in the framework of a contact problem. The paper investigated three variants of coil radius to analyze the effect of bending on fiber behavior. The effect of the coating thickness ratio on the system deformation and optical characteristics was analyzed. Qualitative and quantitative patterns of the effect of temperature, bending, thickness of individual polymer coating layers and relaxation transitions of their materials on the Panda optical fiber deformation and optical characteristics were established. Assessment of approaches to the calculation of optical characteristics (values of the refractive indices and fiber birefringence) are given in the framework of the study. The patterns of deformation and optical behavior of the Panda-type fiber with a protective coating, taking into account the nonlinear behavior of the system materials, were original results.
Shooting for abundance: Comparing integrated multi‐sampling models for camera trap and hair trap data

Abundance estimation is a vital goal in wildlife monitoring. Camera‐traps are a tool to survey wildlife populations noninvasively and can be used for abundance estimation if individuals are identifiable. However, for species without individual identification characteristics, camera‐trap surveys have often been combined with some other survey method such as capture‐recapture (CR, using traditional tags or DNA through hair snags or scat) to inform an integrated model. We discuss and apply two integrated models involving presence‐absence data from camera traps and CR data from hair traps to compare bias and precision to estimate the population density of grizzly bears of the central Rocky Mountains of Alberta, Canada. Unlike many other studies, we found that integrating presence‐absence data with CR data does not improve the precision of the density estimates. The possible reasons for such results are discussed in detail.
Study of the microdoppler signature of a bicyclist for different directions of approach

The successful implementation of autonomous driving in an urban setting depends on the ability of the environment perception system to correctly classify vulnerable road users such as pedestrians and bicyclists in dense, complex scenarios. Self-driving vehicles include sensor systems such as cameras, lidars, and radars to enable decision making. Among these systems, radars are particularly relevant due to their operational robustness under adverse weather and night light conditions. Classification of pedestrian and car in urban settings using automotive radar has been widely investigated, suggesting that micro-Doppler signatures are useful for target discrimination. Our objective is to analyze and study the micro-Doppler signature of bicyclists approaching a vehicle from different directions in order to establish the basis of a classification criterion to distinguish bicycles from other targets including clutter. The micro-Doppler signature is obtained by grouping individual reflecting points using a clustering algorithm and observing the evolution of all the points belonging to an object in the Doppler domain over time. A comparison is then made with simulated data that uses a kinematic model of bicyclists’ movement. The suitability of the micro-Doppler bicyclist signature as a classification feature is determined by comparing it to those belonging to cars and pedestrians approaching the automotive radar system.
Understanding the Stress Distribution on Anatomic Customized Root-Analog Dental Implant at Bone-Implant Interface for Different Bone Densities

The aim of this study is to assess the stress distribution on the bone tissue and bone-implant interface of a customized anatomic root-analog dental implant (RAI) by means of finite element analysis (FEA) for different types of bone density. A mandibular right second premolar was selected from the CBCT database. A DICOM file was converted to an STL file to create a CAD model in FEA software. The bone boundary model was created, while bone density types I–IV were determined. Von Mises stress was measured at bone tissues and bone-implant interfaces. To validate the models, the RAI was 3D printed through a laser powder-bed fusion (L-PBF) approach. The results revealed that all RAI designs could not cause plastic deformation or fracture resulting in lower stress than the ultimate tensile stress of natural bone and implant. Compared to a conventional screw-type implant, RAIs possess a more favorable stress distribution pattern around the bone tissue and the bone-implant interface. The presence of a porous structure was found to reduce the stress at cancellous bone in type IV bone density.
Invasive and Noninvasive Measurement of Complex Permittivity Using ACS-Fed Stepped Impedance Sensor

Compact asymmetric coplanar strip (ACS)-fed stepped impedance sensors for complex permittivity measurement of liquids are presented in this article. The step impedance discontinuities in the feed line improve the sensitivity and compactness of the sensor. Two sensors operating at 5.6 GHz with different impedance discontinuities are designed on a low-cost substrate with an overall size of $11 \times 10 \times 1.6$ mm3. Invasive and noninvasive sensing techniques are simulated using 3-D electromagnetic simulation software and experimentally validated using the fabricated prototype. The change in reflection characteristics of the sensor with varying dielectric constant and loss tangent are analyzed for the extraction of complex permittivity. The proposed sensors’ extracted permittivity values strongly agree with the Keysight dielectric probe measurement. Invasive sensing is carried out by submerging the sensor in liquid samples, whereas noninvasive sensing is carried out by holding the sensor at a varying distance from the samples. The sensor’s single port and compact topology necessitate a minimal volume of liquid samples for measurements. According to the results of the experiments, the ACS-fed stepped impedance resonator (SIR) sensor has a maximum sensitivity of 6.6% for invasive sensing and 4.6% for noninvasive sensing. In contrast, the ACS-fed extended stub SIR sensor shows a maximum sensitivity of 10.3% and 6.2% for invasive and noninvasive sensing approaches, respectively. The sensor measures permittivity values ranging from 2 to 78.
Skeletal Class III Malocclusion Is Associated with ADAMTS2 Variants and Reduced Expression in a Familial Case

Skeletal Class III malocclusion with maxillary deficiency is a severe maxillofacial disease with unclear pathogenic mechanisms. We recruited a Han Chinese family who was clinically diagnosed with skeletal Class III malocclusion and maxillary deficiency. Using whole exome sequencing, a missense variant in ADAMTS2 (NM_014244: c.3506G>T: p.G1169V) was identified and predicted as deleterious by in silico tools. We also found ADAMTS2 variants associated with deficient maxillary development in a cohort. ADAMTS2 expression in HEK293 cells showed significant decrease due to the variant, which was also consistent in dental pulp stem cells from the proband and a healthy control. In the adamts2-knockdown zebrafish model, the length and width of the ethmoid plate, as well as the length of the palatoquadrate became significantly shorter than the control group (p < 0.001), while there was no significant difference in the length and width of the mandible. The expression of Sox3, which was required in early embryonic craniofacial development, was significantly downregulated in the adamts2-knockdown zebrafish embryos. Bioinformatic and cellular studies showed that the decreased expression of ADAMTS2 may inhibit downstream ErbB signaling pathway transduction and restrain subsequent osteogenesis in human adult mesenchymal stromal cells. Collectively, these data showed that ADAMTS2 (c.3506G>T: p.G1169V) may confer susceptibility to risk of skeletal Class III malocclusion with maxillary deficiency.