Number Of Nodes Research Articles

Convolutional neural network classification of beam profiles from silicon photonics gratings

Convolutional neural network (CNN) models consist of CNN block(s), and dense neural network (DNN) block(s) are used to perform image classification on beam profiles in light beams coupled out from silicon photonics (SiPh) mixed-pitch gratings. The beam profiles are first simulated and segregated into three categories based on their corresponding height above the SiPh gratings. With one CNN block, one DNN block, and 128 nodes in the DNN block, classification accuracy of 98.68% is achieved when classifying 454 beam profile images to their corresponding categories. Expanding the number of CNN blocks, DNN blocks, and nodes, 64 CNN models are constructed, trained, and evaluated. Out of the 64 CNN models, 52 of them achieved classification accuracy of >95%.

A DRL-based Partial Charging Algorithm for Wireless Rechargeable Sensor Networks

Breakthroughs in Wireless Energy Transfer technologies have revitalized Wireless Rechargeable Sensor Networks. However, how to schedule mobile chargers rationally has been quite a tricky problem. Most of the current work does not consider the variability of scenarios and how many mobile chargers should be scheduled as the most appropriate for each dispatch. At the same time, the focus of most work on the mobile charger scheduling problem has always been on reducing the number of dead nodes, and the most critical metric of network performance, packet arrival rate, is relatively neglected. In this article, we develop a DRL-based Partial Charging algorithm. Based on the number and urgency of charging requests, we classify charging requests into four scenarios. And for each scenario, we design a corresponding request allocation algorithm. Then, a Deep Reinforcement Learning algorithm is employed to train a decision model using environmental information to select which request allocation algorithm is optimal for the current scenario. After the allocation of charging requests is confirmed, to improve the Quality of Service, i.e., the packet arrival rate of the entire network, a partial charging scheduling algorithm is designed to maximize the total charging duration of nodes in the ideal state while ensuring that all charging requests are completed. In addition, we analyze the traffic information of the nodes and use the Analytic Hierarchy Process to determine the importance of the nodes to compensate for the inaccurate estimation of the node’s remaining lifetime in realistic scenarios. Simulation results show that our proposed algorithm outperforms the existing algorithms regarding the number of alive nodes and packet arrival rate.

Interaction Effects of Vegetation and Soil Factors on Microbial Communities in Alpine Steppe Under Degradation

To clarify the regulating effect of vegetation and soil factors on microbial communities in the alpine steppe under degradation on the Qinghai-Xizang Plateau, the alpine steppe in the Sanjiangyuan area of the Qinghai-Tibet Plateau was chosen. We analyzed the differences in vegetation and soil factors in different stages of degradation （non-degradation, moderate degradation, and severe degradation） and detected the variations in microbial community characteristics in the alpine steppe under different degradation stages using high-throughput sequencing technology. Eventually, redundancy analysis （RDA） and multiple regression matrixes （MRM） based on the similarity or dissimilarity matrix were used to identify key environmental factors regulating microbial （bacterial and fungal） community changes under degradation. The results showed that the degradation of the alpine steppe significantly changed the community coverage, height, biomass, and important value of graminae； significantly reduced the contents of soil organic matter, total nitrogen, total phosphorus, and silt； and increased the soil bulk density and sand content. Degradation did not change the composition of bacteria and fungi, but their composition proportions changed and also resulted in the loss of microbial richness （Chao1 index and Richness index） but did not significantly change the microbial diversity （Shannon index）. With the occurrence of degradation, the vegetation characteristics, soil physicochemical properties, and microbial diversity showed a consistent change trend. Combined with the characteristics of the network topology changes （the number of nodes and clustering coefficient significantly decreased）, it was found that degradation of the alpine steppe led to the decline of interspecies interactions, decentralization of network, and homogenization of microorganisms, but the cooperation relations among the species were maintained （positive correlation connections accounted for more than 90% in all degradation stages）. Under the alpine steppe degradation, the vegetation-soil interaction had the greatest effect on soil bacterial community, whereas soil physicochemical properties had the greatest influence on soil fungal community. Specifically, vegetation community height, biomass, and soil bulk density were the mutual factors regulating soil microorganisms, whereas the vegetation Simpson index, important value of graminae, soil total phosphorus, total potassium, and silt content were the unique factors affecting the soil bacterial community, and soil pH and total nitrogen content were the particular factors affecting the soil fungal community.

Lightweight adaptive Byzantine fault tolerant consensus algorithm for distributed energy trading

With the rapid development of smart grid, constructing distributed energy trading market (DETM) based on blockchain to coordinate distributed energy resources (DER) has become a future direction. However, existing consensus algorithms of blockchain face many challenges in large-scale energy trading scenarios, such as high resource overhead, slow transaction procedure. To solve the above crucial problems for wide deployment of distributed energy trading, this study proposes a novel consensus algorithm named Lightweight adaptive Byzantine fault tolerant consensus (LA-BFT), and a reputation calculation method based on behavioral characteristics for selection of consensus nodes. The LA-BFT consists of two parts: (i) weak consensus for normal cases. By introducing threshold signature mechanism, weak consensus simplifies the consensus process to achieve linear communication complexity O(n). (ii) byzantine node detection scheme is enable for malicious cases. With consensus committee, the detection scheme can detect the potential byzantine nodes by cross-validation, which ensures transaction safety. The reputation calculation method is presented to cooperate with LA-BFT to elect leaders and candidates for consensus procedure. Once a round of consensus is completed, the reputation of each node needs to be updated, only nodes with high reputation are eligible to become leaders or committee nodes in the next round. With the reputation calculation method, honest nodes and byzantine nodes can be effectively identified, ensuring the security of the consensus process. Numerical results indicate that LA-BFT exhibits superior performance on communication overhead and bandwidth occupancy in large-scale concurrent energy trading scenarios. When the number of nodes is 50, under normal scenarios, LA-BFT’s communication overhead is notably lower, constituting a mere 6.02% of PBFT and 24.26% of SHBFT, while bandwidth occupancy amounts to merely 5.55% of PBFT and 9.76% of SHBFT.

Construction of the prognostic nomogram and treatment recommendation in patients with mixed endometrial carcinoma treated with hysterectomy.

The study aimed to identify the independent prognostic factors of mixed endometrial carcinoma (MEC) patients treated with hysterectomy and to explore the optimal treatment modalities for overall survival (OS) and cancer-specific survival (CSS). Using the Surveillance, Epidemiology, and End Results (SEER) database, a total of 12,848 MEC patients treated with hysterectomy were screened out. Independent prognostic factors were identified by Cox regression analysis and used to construct the nomogram. The concordance index (C-index) of OS and CSS were 0.807 and 0.834 in the training set. Validation of the nomogram revealed that the receiver operating curve (ROC) maintained good discrimination, the decision curve analysis (DCA) had a high net benefit rate, and the calibration curves showed high consistency. Patients were grouped by the nomogram formula and the number of positive regional lymph nodes (NPR-Lymph node) to evaluate the therapeutic outcomes of chemotherapy, radiotherapy, neoadjuvant treatment and lymph node operation. Survival analysis revealed that chemotherapy could improve the prognosis for OS and CSS in the high-risk group and in the group with NPR-Lymph node counts above 1 (P < 0.05). Radiotherapy was associated with better OS and CSS in the intermediate-risk and high-risk groups, and in the group with NPR-Lymph node counts above 0 (P < 0.05). Lymphadenectomy was found to prolong OS and CSS in the high-risk group (P < 0.05), while neoadjuvant treatment did not prolong OS and CSS in any group. Thus, in this study, the nomogram for MEC patients treated with hysterectomy was successfully built and validated which could effectively predict the prognosis and identify at-risk population to guide clinical decision-making. The NPR-Lymph node was identified as a potentially strong prognostic indicator with good clinical value.

Interval many‐objective dynamic charging planning in wireless rechargeable sensor networks

SummaryCharging path planning in wireless sensor networks (WSNs) refers to designing an efficient charging path for sensor nodes in the network. However, most charging schemes mainly consider the planning of charging paths and pay little attention to the impact of uncertainties, such as road conditions and environment on the planning of charging paths, as well as ignoring the charging problem of new nodes in need of charging. Road conditions and the environment directly affect the energy consumption of wireless charging vehicles (WCVs) during traveling. To address the aforementioned challenges, this article proposes an interval many‐objective charging path scheme model, the WCV consumption is an uncertain value, it will change according to the environment, and road conditions, so we represent it as an interval parameter with upper and lower bounds. An interval high‐dimensional multi‐objective model with target energy consumption, path distance, number of dead nodes, and communication delay is constructed. Second, to implement this model, an interval SPEA2 algorithm (I‐SPEA2) that introduces an environmental response mechanism is proposed. I‐SPEA2 treats individual target interval values as ranges of values on a two‐dimensional coordinate axis, forming a quadrilateral, calculates individual size probabilities based on the area to determine the dominant relationship, and combines fixed distance and interval overlap to eliminate redundant individuals. The simulation results show that the interval dynamic model is effective in prolonging the lifecycle of WSN as well as the proposed algorithm reduces the mortality rate of the nodes by 15%, 28%, 13%, 16%, and 21% compared with other algorithms.

Robotic versus laparoscopic right colectomy for nonmetastatic pT4 colon cancer: A European multicentre propensity score-matched analysis.

Minimally invasive surgery has been increasingly adopted for locally advanced colon cancer. However, evidence comparing robotic (RRC) versus laparoscopic right colectomy (LRC) for nonmetastatic pT4 cancers is lacking. This was a multicentre propensity score-matched (PSM) study of a cohort of consecutive patients with pT4 right colon cancer treated with RRC or LRC. The two surgical approaches were compared in terms of R0, number of lymph nodes harvested, intra- and postoperative complication rates, overall (OS), and disease-free survival (DFS). Among a total of 200 patients, 39 RRC were compared with 78 PS-matched LRC patients. The R0 rate was similar between RRC and LRC (92.3% vs. 96.2%, respectively; p = 0.399), as was the odds of retrieving 12 or more lymph nodes (97.4% vs. 96.2%; p = 1). No significant difference was noted for the mean operating time (192.9 min vs. 198.3 min; p = 0.750). However, RRC was associated with fewer conversions to laparotomy (5.1% vs. 20.5%; p = 0.032), less blood loss (36.9 vs. 95.2 mL; p < 0.0001), fewer postoperative complications (17.9% vs. 41%; p = 0.013), a shorter time to flatus (2 vs. 2.8 days; p = 0.009), and a shorter hospital stay (6.4 vs. 9.5 days; p < 0.0001) compared with LRC. These results were confirmed even when converted procedures were excluded from the analysis. The 1-, 3- and 5-year OS (p = 0.757) and DFS (p = 0.321) did not significantly differ between RRC and LRC. Adequate oncological outcomes are observed for RRC and LRC performed for pT4 right colon cancer. However, RRC is associated with lower conversion rates and improved short-term postoperative outcomes.

Communication-Efficient Multi-Party Computation for RMS Programs

Despite much progress, general-purpose secure multi-party computation (MPC) with active security may still be prohibitively expensive in settings with large input datasets. This particularly applies to the secure evaluation of graph algorithms, where each party holds a subset of a large graph. Recently, Araki et al. (ACM CCS '21) showed that dedicated solutions may provide significantly better efficiency if the input graph is sparse. In particular, they provide an efficient protocol for the secure evaluation of “message passing” algorithms, such as the PageRank algorithm. Their protocol's computation and communication complexity are both O ~ ( M · B ) instead of the O ( M 2 ) complexity achieved by general-purpose MPC protocols, where M denotes the number of nodes and B the (average) number of incoming edges per node. On the downside, their approach achieves only a relatively weak security notion; 1 -out-of- 3 malicious security with selective abort. In this work, we show that PageRank can instead be captured efficiently as a restricted multiplication straight-line (RMS) program, and present a new actively secure MPC protocol tailored to handle RMS programs. In particular, we show that the local knowledge of the participants can be leveraged towards the first maliciously-secure protocol with communication complexity linear in M , independently of the sparsity of the graph. We present two variants of our protocol. In our communication-optimized protocol, going from semi-honest to malicious security only introduces a small communication overhead, but results in quadratic computation complexity O ( M 2 ) . In our balanced protocol, we still achieve a linear communication complexity O ( M ) , although with worse constants, but a significantly better computational complexity scaling with O ( M · B ) . Additionally, our protocols achieve security with identifiable abort and can tolerate up to n − 1 corruptions.

Clustered Routing Using Chaotic Genetic Algorithm with Grey Wolf Optimization to Enhance Energy Efficiency in Sensor Networks.

As an alternative to flat architectures, clustering architectures are designed to minimize the total energy consumption of sensor networks. Nonetheless, sensor nodes experience increased energy consumption during data transmission, leading to a rapid depletion of energy levels as data are routed towards the base station. Although numerous strategies have been developed to address these challenges and enhance the energy efficiency of networks, the formulation of a clustering-based routing algorithm that achieves both high energy efficiency and increased packet transmission rate for large-scale sensor networks remains an NP-hard problem. Accordingly, the proposed work formulated an energy-efficient clustering mechanism using a chaotic genetic algorithm, and subsequently developed an energy-saving routing system using a bio-inspired grey wolf optimizer algorithm. The proposed chaotic genetic algorithm-grey wolf optimization (CGA-GWO) method is designed to minimize overall energy consumption by selecting energy-aware cluster heads and creating an optimal routing path to reach the base station. The simulation results demonstrate the enhanced functionality of the proposed system when associated with three more relevant systems, considering metrics such as the number of live nodes, average remaining energy level, packet delivery ratio, and overhead associated with cluster formation and routing.

Identification of SNP Markers and Candidate Genes Associated with Major Agronomic Traits in Coffea arabica.

Genome-wide association studies (GWASs) allow for inferences about the relationships between genomic variants and phenotypic traits in natural or breeding populations. However, few have used this methodology in Coffea arabica. We aimed to identify chromosomal regions with significant associations between SNP markers and agronomic traits in C. arabica. We used a coffee panel consisting of 195 plants derived from 13 families in F2 generations and backcrosses of crosses between leaf rust-susceptible and -resistant genotypes. The plants were phenotyped for 18 agronomic markers and genotyped for 21,211 SNP markers. A GWAS enabled the identification of 110 SNPs with significant associations (p < 0.05) for several agronomic traits in C. arabica: plant height, plagiotropic branch length, number of vegetative nodes, canopy diameter, fruit size, cercosporiosis incidence, and rust incidence. The effects of each SNP marker associated with the traits were analyzed, such that they can be used for molecular marker-assisted selection. For the first time, a GWAS was used for these important agronomic traits in C. arabica, enabling applications in accelerated coffee breeding through marker-assisted selection and ensuring greater efficiency and time reduction. Furthermore, our findings provide preliminary knowledge to further confirm the genomic loci and potential candidate genes contributing to various structural and disease-related traits of C. arabica.

Variability, Heritability and Genetic Advance for Yield and Shoot Fly Resistance Associated Traits in Recombinant Inbred Lines (RILs) of Sorghum [Sorghum bicolor (L.) Moench

The present investigation was carried out with 210 Recombinant Inbred Lines (RILs) at the Instructional Dairy Farm of the Govind Ballabh Pant University of Agriculture and Technology, Pantnagar, Uttarakhand, India and the experiment was conducted during kharif season, 2018-19 in augmented design with three replications with the objective to access variability, heritability and genetic advance for 18 traits studied to select suitable shoot fly resistace RILs. The analysis of variance revealed highly significant differences for the trait viz., seedling height, days to 50 per cent flowering, plant height, stem diameter, number of nodes per plant, leaf length, leaf width, 1000 grain weight, dry fodder yield and green fodder yield, while significant differences were found in case of oviposition and dead heart per cent at 21 days after emergence. Eleven traits showed higher value of Genotypic coefficient of variation (GCV) than environment coefficient of variation (ECV). Total soluble solid (TSS) percent showed highest GCV followed by green fodder yield while dead heart per cent at 14 days after emergence showed highest ECV followed by days to emergence and Hydrocyanic acid content. Among the traits with high heritability, only plant height, dry fodder yield, and green fodder yield exhibited high genetic advance suggesting the additive gene action, making these traits suitable targets for effective selection in the RIL population. Further, molecular markers can be identified for key traits used in marker-assisted selection, and selected RILs can be evaluated in multi-environment trials for stability and adaptability.

Patient characteristics associated with delayed time to adjuvant chemotherapy among women treated for stage I-IIIA breast cancer.

For patients with breast cancer, delays in chemotherapy initiation have been adversely associated with recurrence and survival. We evaluated patient-level factors associated with delayed chemotherapy initiation, from both diagnosis and surgery, in a community-based cohort of women with early-stage breast cancer. For the Optimal Breast Cancer Chemotherapy Dosing study, we identified a cohort of 34,109 women diagnosed with stage I-IIIA breast cancer at two U.S. integrated healthcare delivery systems between 2004 and 2019. We used logistic regression to calculate odds ratios (OR) and 95% confidence intervals (CI) to identify patient factors associated with delays in chemotherapy initiation after diagnosis (≥90 days) and surgery (≥60 days). Among 10,968 women receiving adjuvant chemotherapy, 21.1% experienced delays in chemotherapy initiation after diagnosis and 21.3% after surgery. Older age, non-Hispanic Black and Hispanic race and ethnicity, and ER+ and/or PR+ disease were associated with increased likelihood of delays to chemotherapy initiation after diagnosis and surgery. People diagnosed in 2012-2019 (vs. 2005-2011), with a higher grade and larger tumor size were less likely to experience delays. Other factors were associated with a higher likelihood of delays specifically from diagnosis (earlier stage, mastectomy vs. breast-conserving surgery), or surgery (higher comorbidity, increased nodal number). Women diagnosed with breast cancer who were at highest risk of progression and recurrence were less likely to experience delays in chemotherapy initiation after diagnosis and surgery. Understanding reasons for chemotherapy delays beyond patient factors may be potentially important to reduce risk of breast cancer recurrence and progression.

The effect of ileal resection length on postoperative complications and prognosis in right colon cancer

BackgroundThere is a lack of literature on the length of the terminal ileum to be resected in right hemicolectomy for colon cancer. Therefore, we aimed to determine the mean ileal loop length and the effect of this variation on postoperative complications and long-term oncological outcomes in patients who underwent right hemicolectomy.MethodsRight hemicolectomy surgeries performed for colon cancer in a tertiary care hospital between January 2011 and December 2018 were retrospectively analyzed from a prospective database. Two patient groups were established based on the mean length of the resected ileum above and below 7 cm. The two groups were compared for clinicopathological data, postoperative complications, mortality, long-term overall survival (OS) and disease-free survival (DFS). The factors contributing to OS and DFS were analyzed.ResultsThe study included 217 patients. Body mass index (BMI) values were significantly higher in the ileum resection length > 7 cm group (p = 0.009). Pathological N stage, tumor diameter, and number of metastatic lymph nodes were significantly higher in the ileum resection length > 7 cm group (p = 0.001, p = 0.001, and p = 0.026, respectively). There was no significant difference for postoperative complication and mortality rates between the two groups. The mean follow-up period was 61.2 months (2-120) in all patients. The total number of deaths was 29 (11.7%) while the 60-month OS was 83.5% and 50-month DFS was 81.8%. There was no significant difference between the groups in terms of OS and DFS rates (p > 0.05).ConclusionsExcessive resection of the distal ileum in right hemicolectomy does not provide any benefit in terms of prognosis and complications.The ileum resection length and values close to it in our study appear to be sufficient.

Clonal growth characteristics and diversity patterns of different Clintonia udensis (Liliaceae) diploid and tetraploid cytotypes in the Hualongshan Mountains

Polyploidization plays an important role in plant evolution and biodiversity. However, intraspecific polyploidy compared to interspecific polyploidy received less attention. Clintonia udensis (Liliaceae) possess diploid (2n = 2x = 14) and autotetraploid (2n = 4x = 28) cytotypes. In the Hualongshan Mountains, the autotetraploids grew on the northern slope, while the diploids grew on the southern slopes. The clonal growth characteristics and clonal architecture were measured and analyzed by field observations and morphological methods. The diversity level and differentiation patterns for two different cytotypes were investigated using SSR markers. The results showed that the clonal growth parameters, such as the bud numbers of each rhizome node and the ratio of rhizome branches in the autotetraploids were higher than those in the diploids. Both the diploids and autotetraploids appeared phalanx clonal architectures with short internodes between ramets. However, the ramets or genets of the diploids had a relatively scattered distribution, while those of the autotetraploids were relatively clumping. The diploids and autotetraploids all allocated more biomass to their vegetative growth. The diploids had a higher allocation to reproductive organs than that of autotetraploids, which indicated that the tetraploids invested more resources in clonal reproduction than diploids. The clone diversity and genetic diversity of the autotetraploids were higher than that of the diploids. Significant genetic differentiation between two different cytotypes was observed (P < 0.01). During establishment and evolution, C. udensis autotetraploids employed more clumping phalanx clonal architecture and exhibited more genetic variation than the diploids.

Construction of the structure of rural electric networks taking into account the availability of renewable sources of electricity

It is noted that although the share of renewable electricity sources continues to grow and develop rapidly, the industry still faces many challenges, including how to continue to reduce electricity losses and annualized costs, improve operation and maintenance efficiency, maintain grid stability, ensure safety and reliability of the power supply system containing renewable sources of electrical energy. It is shown that an effective solution to these problems is possible only on the basis of the analysis of the prospects for the development of local electric power systems that contain renewable sources of electric energy, the development of technical and organizational support mechanisms that will contribute to the construction of modern system (schematic) solutions. It is proposed to apply the potential surface method for building the optimal structure of the rural electrical network during its design and modernization in the presence of renewable sources of electrical energy, which allows optimizing the structure of the network from the point of view of electrical energy losses and reducing annualized costs. The algorithm for forming the structure of power supply systems is described, which implements the simultaneous solution of the tasks of determining the number of load nodes, distributing electrical receivers between them, determining the design of power sources, taking into account the discreteness of the design of system elements. The results of numerical modeling based on the proposed algorithm are presented on the example of solving the problem of reconstruction of a section of the rural electricity network.

Quantifying local and global mass balance errors in physics-informed neural networks

Physics-informed neural networks (PINN) have recently become attractive for solving partial differential equations (PDEs) that describe physics laws. By including PDE-based loss functions, physics laws such as mass balance are enforced softly in PINN. This paper investigates how mass balance constraints are satisfied when PINN is used to solve the resulting PDEs. We investigate PINN’s ability to solve the 1D saturated groundwater flow equations (diffusion equations) for homogeneous and heterogeneous media and evaluate the local and global mass balance errors. We compare the obtained PINN’s solution and associated mass balance errors against a two-point finite volume numerical method and the corresponding analytical solution. We also evaluate the accuracy of PINN in solving the 1D saturated groundwater flow equation with and without incorporating hydraulic heads as training data. We demonstrate that PINN’s local and global mass balance errors are significant compared to the finite volume approach. Tuning the PINN’s hyperparameters, such as the number of collocation points, training data, hidden layers, nodes, epochs, and learning rate, did not improve the solution accuracy or the mass balance errors compared to the finite volume solution. Mass balance errors could considerably challenge the utility of PINN in applications where ensuring compliance with physical and mathematical properties is crucial.

Intraparenchymal Indocyanine Green Use Improves Nodal Yield During Minimally Invasive Tumor Nephrectomy in Children.

Introduction: Correctly identifying positive lymph nodes associated with pediatric renal tumors is key to guiding management. Recommended targets for lymph node sampling are commonly missed during tumor nephrectomy, particularly if minimally invasive surgery (MIS) is performed. Indocyanine green (ICG) is used for lymph node mapping in adult oncology with excellent efficacy and safety profile. Materials and Methods: A prospective study was undertaken at a single-quaternary-level pediatric surgery center. All patients undergoing MIS radical or partial nephrectomy for renal tumors 2016-2023 were included. Patients managed from 2020 onwards received intra-parenchymal ICG prior to lymph node sampling. Main Results: Twenty-five patients underwent MIS nephrectomy at mean age 2 years 10 months. Eighteen patients were pre-ICG and 7 received ICG. ICG administration successfully demonstrated fluorescent nodes in all patients. Median number of nodes sampled was three pre-ICG and seven with ICG (P = 0.009). Forty-six nodes were sampled across 7 ICG patients-33 fluorescent, 10 non-fluorescent, and 3 identified histologically. Three nodes overall contained active disease, two pre-ICG and one fluorescent node with ICG. Neither operative time (180 pre-ICG versus 161 minutes ICG, P = 0.7) nor length of stay (72 versus 84 hours, P = 0.3) were significantly affected by ICG administration. There were no adverse events associated with ICG use. Conclusions: ICG is safe and effective at identifying nodes in MIS resection of pediatric renal tumors with the potential to increase the number of nodes sampled. Further research is needed, specifically a randomized control trial with extended follow-up.

241 Predictors of Post-Radioiodine Ablation Outcomes in Thyroid Cancer: Analysis of Four Years of Data from a Regional Centre

Abstract Aim To assess correlations between patient demographics, tumour and lymph node pathology and outcomes for patients with non-metastatic thyroid cancer. Outcomes examined were evidence of post-ablation residual disease on blood testing or ultrasound or thyroid cancer recurrence. Method Electronic records for thyroid cancer patients at a regional centre undergoing thyroidectomy followed by Radioiodine-131 ablation in the years 2018-2021 were reviewed with 191 records contributing to the analysis. Patient demographics, tumour characteristics, numbers of positive lymph nodes and radioiodine dosages were compared against patient outcomes. Results Increasing age, T stage of the tumour and lymph node involvement demonstrated the strongest correlations with negative outcomes. Age appeared to be a strong risk factor for thyroid cancer recurrence with 70% of the patient’s experiencing recurrence aged over 60 despite patients over 60 making up only 33% of the sample. Removal of over 4 metastatic lymph nodes has previously been found to predict poorer patient outcomes. The findings support this however over 50% of positive nodes removed had a stronger correlation with negative outcomes compared with raw numbers retrieved. 52.2% of patients with over 50% positive lymph nodes experienced poorer outcomes compared with 40.1% who had over 4 positive nodes removed. The correlation was stronger still for those with over 4 positive nodes of which over 50% were positive of whom 57.9% demonstrated poor outcomes. Conclusions Rather than raw numbers of lymph nodes removed, proportion of metastatic nodes of those harvested or a combination of total numbers and proportion appeared to give a better indicator of prognosis.

Is the burden of metastatic lymph node stations a prognostic factor in patients with resected lung cancer?

ObjectivesThe burden of metastatic lymph node (LN) stations might reflect a distinct N subcategory with a more aggressive biology and behaviour than the traditional N classification.MethodsBetween 2008 and 2018, we analyzed 1236 patients with pN1/2 lung cancer. Survival was analyzed based on LN station metastasis, determining the optimal threshold for the number of metastatic LN stations that provided additional prognostic information. N prognostic subgrouping was performed using thresholds for the number of metastatic LN stations with the maximum chi-square log-rank value, and validated at each pT-stage.ResultsSurvival showed stepwise statistical deterioration with an increase in the number of metastatic LN stations., Threshold values for the number of metastatic LN stations were determined and N prognostic subgroupswas created as sN-alpha; one LN station metastases (n = 632), sN-beta; two-three LN stations metastases (n = 505), and sN-gamma; ≥4 LN stations metastasis (n = 99). The 5-year survival rate was 57.7% for sN-alpha, 39.2% for sN-beta, and 12.7% for sN-gamma (chi-square log rank = 97.906, p < 0.001). A clear tendency of survival deterioration was observed from sN-alpha to sN-gamma in the same pT stage, except for pT4 stage. Multivariate analysis showed that age (p < 0.001), sex (p = 0.002), tumour histology (p < 0.001), IASLC-proposed N subclassification (p < 0.001), and sN prognostic subgroups (p < 0.001) were independent risk factors for survival.ConclusionThe burden of metastatic LN stations is an independent prognostic factor for survival in patients with lung cancer. It could provide additional prognostic information to the N classification.

Efficient Error-Rate Estimation for Optical Transmission Systems Using Artificial Neural Networks

ABSTRACT With the advancement in modulation formats, optical transmission systems are becoming more adaptable and dynamic. Predicting bit errors of higher-order modulation formats is a challenging problem for the optimum design of communication systems. The existing simulation methods have persisted by employing iterative procedures that frequently incur high computing expenses and time consumption. On the other hand, deep learning (DL) algorithms have demonstrated remarkable efficacy as practical computing tools, presenting a viable approach to accelerate modulation simulations. In this paper, an artificial neural network (ANN) based bit error rate (BER) estimation scheme is proposed for the popular modulation forms including 56 Gbps 16-quadrature amplitude modulation (16QAM), 100 Gbps 32QAM, and 120 Gbps 64QAM optical system. Based on constellation diagrams (CDs) acquired with different launch power, laser linewidth, transmission distance, and OSNR, amplitude histograms (AHs) are generated via a manual preprocessing method. The properly trained ANN architecture exhibits a computational speed that surpasses traditional simulation methods by a factor exceeding 347. Moreover, the design considerations including the number of layers, nodes, activation functions, learning rate, optimizers, and evolution epochs are also investigated in detail. This research paves the way for optical transmission systems to use fast DL-based optimization strategies.