Number Of Nodes Research Articles

A protocol for assessing bias and robustness of social network metrics using GPS based radio-telemetry data

BackgroundSocial network analysis of animal societies allows scientists to test hypotheses about social evolution, behaviour, and dynamic processes. However, the accuracy of estimated metrics depends on data characteristics like sample proportion, sample size, and frequency. A protocol is needed to assess for bias and robustness of social network metrics estimated for the animal populations especially when a limited number of individuals are monitored.MethodsWe used GPS telemetry datasets of five ungulate species to combine known social network approaches with novel ones into a comprehensive five-step protocol. To quantify the bias and uncertainty in the network metrics obtained from a partial population, we presented novel statistical methods which are particularly suited for autocorrelated data, such as telemetry relocations. The protocol was validated using a sixth species, the fallow deer, with a known population size where ∼85%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\sim 85\\%$$\\end{document} of the individuals have been directly monitored.ResultsThrough the protocol, we demonstrated how pre-network data permutations allow researchers to assess non-random aspects of interactions within a population. The protocol assesses bias in global network metrics, obtains confidence intervals, and quantifies uncertainty of global and node-level network metrics based on the number of nodes in the network. We found that global network metrics like density remained robust even with a lowered sample size, while local network metrics like eigenvector centrality were unreliable for four of the species. The fallow deer network showed low uncertainty and bias even at lower sampling proportions, indicating the importance of a thoroughly sampled population while demonstrating the accuracy of our evaluation methods for smaller samples.ConclusionsThe protocol allows researchers to analyse GPS-based radio-telemetry or other data to determine the reliability of social network metrics. The estimates enable the statistical comparison of networks under different conditions, such as analysing daily and seasonal changes in the density of a network. The methods can also guide methodological decisions in animal social network research, such as sampling design and allow more accurate ecological inferences from the available data. The R package aniSNA enables researchers to implement this workflow on their dataset, generating reliable inferences and guiding methodological decisions.

Efficacy and prognostic analysis of carbon nanotracers combined with the da Vinci robot in the treatment of esophageal cancer.

Traditional methods cannot clearly visualize esophageal cancer (EC) tumor contours and metastases, which limits the clinical application of da Vinci robot-assisted surgery. To investigate the efficacy of the da Vinci robot in combination with nanocarbon lymph node tracers in radical surgery of EC. In total, 104 patients with early-stage EC who were admitted to Liuzhou worker's Hospital from January 2020 to June 2023 were enrolled. The patients were assigned to an observation group (n = 52), which underwent da Vinci robot-assisted minimally invasive esophagectomy (RAMIE) with the intraoperative use of nanocarbon tracers, and a control group (n = 52), which underwent traditional surgery treatment. The operation time, intraoperative blood loss, postoperative drainage tube indwelling time, hospital stay, number of lymph nodes dissected, incidence of complications, and long-term curative effects were comparatively analyzed. The postoperative stress response C-reactive protein (CRP), cortisol, epinephrine (E) and inflammatory response interleukin (IL)-6, IL-8, IL-10, and tumor necrosis factor-alpha (TNF-α) were evaluated. Compared with the control group, the observation group had significantly lower postoperative CRP, cortisol, and E levels (P < 0.05) with a milder inflammatory response, as indicated by lower IL-6, IL-10, and TNF-α levels (P < 0.05). Patients who underwent RAMIE had less intraoperative blood loss and shorter operation times and hospital stays than those who underwent traditional surgery. The average number of dissected lymph nodes, time of lymph node dissection, and mean smallest lymph node diameter were all significantly lower in the observation group (P < 0.05). The rate of postoperative complications was 5.77% in the observation group, significantly lower than the 15.38% observed in the control group. Furthermore, the lymphatic metastasis rate, reoperation rate, and 12- and 24-month cumulative mortality in the observation group were 1.92%, 0%, 0%, and 0%, respectively, all of which were significantly lower than those in the control group (P < 0.05). The treatment of EC using the da Vinci robot combined with nanocarbon lymph node tracers can achieve good surgical outcomes and demonstrates promising clinical applications.

Histopathologic predictors of recurrence and survival in early T stage oral tongue squamous cell carcinoma

ObjectivesRecurrence and survival in early T-stage oral tongue squamous cell carcinoma (OTSCC) may be impacted by histopathologic risk factors. This study aims to examine which of these factors predict long-term outcomes of T1 and T2 OTSCC.MethodsA retrospective review of T1 and T2 OTSCC patients treated with surgery at a single tertiary care center was conducted. Multivariate regression and Kaplan-Meier survival plots were used to identify predictors of recurrence and compare disease-free survival respectively.Results100 consecutive patients were studied. Of these, 51 were staged pT1, 49 pT2, 69 pN0, 10 pN1, and 21 pN2. Multivariate regression analysis revealed that &amp;gt;4 nodes was the strongest predictor of overall recurrence [odds ratio 1.68 (1.23–2.28), p = 0.001], while &amp;gt;4 nodes [odds ratio 1.14 (1.09–1.85), p = 0.008] and pT2 [odds ratio 1.15 (1.01–1.30), p = 0.033] were predictors of local recurrence (R2 = 0.112). Five-year disease-free survival was not significantly impacted by any risk factors except for the number of positive nodes—86% for ≤4 nodes vs. 20% for &amp;gt;4 nodes (p &amp;lt; 0.001)—and pathologic T-stage—90% for pT1 vs. 75% for pT2 (p = 0.035) regardless of adjuvant radiation and/or chemotherapy use.ConclusionsPatients who underwent adjuvant radiation and/or chemotherapy had similar survival to those who did not despite having worse overall tumor prognostic factors. Adding adjuvant therapy may equalize some high-risk histopathologic factors. In the highest risk patients—specifically those with pathologic &amp;gt;4 nodes and pT2 staging—adjuvant therapy should be considered.

Performance of Row Cover with Mulch on Early Sowing of Watermelon in Semi-Arid Region

The experiment was conducted during the months of January to March in the year 2023 at Department of Renewable Energy Engineering, Junagadh Agricultural University, Junagadh (Gujarat). A field experiment was conducted to assess the performance of mulch and row covers in early watermelon cultivation with the primary aim on strategically removing row covers days after sowing. Randomized Block Design was employed with featuring of three treatments (mulching without row cover, mulching with row cover for 30 days and mulching with row cover for 45 days). Various yield and yield attribute parameters were recorded and analysed. The research findings indicated that the removal of row covers 30 days after sowing significantly influenced main vine length (2.72 m), number of nodes per main vine (36.40), number of fruits plant-1 (2.53), yield plant-1 (4.21 kg) and yield ha-1 (62.32 t ha-1). Removal of row covers 30 days after sowing exhibited higher net returns (Rs. 4, 85, 419 ha-1) and higher benefit-cost ratio (4.52). Removal of row cover 45 days after sowing significantly reduced the watermelon yield. The results unveiled that mulching with a row cover aster 30 days of sowing is the optimal approach for increasing watermelon yield and net returns in early watermelon cultivation.

A Comparative Study of Spacing and Integrated Nutrient Management on Growth, Yield and Economics of Okra

The present investigation was conducted at Horticultural Experimental Farm, B.A.C.A., Anand Agricultural University, Anand in kharif during 2022 and 2023. Ensuring balanced plant nutrients and spacing is essential for achieving optimal okra production. Optimal plant density supports uniform and healthy growth by effectively utilizing moisture, nutrients, and light, thereby maximizing okra yield. Single source of nutrients whether chemical fertilizers, organic manures or biofertilizers that cannot improve production, productivity and soil health. Sowing okra at 60 × 30 cm spacing observed better leaf area (433.06 and 568.01 cm2) and number of nodes per plant (15.57 and 23.46) at 60 and 90 DAS. While, maximum yield per plot (9.20 kg) and yield per hectare (14.19 t) was found in 45 × 30 cm spacing. Application of 25% RDF + 50% RDN through Vermicompost + NPK consortium 1L/ha recorded maximum yield per plot (9.41 kg) and yield per hectare (15.33 t). With respect to treatment combinations, sowing of okra at 60 × 30 cm spacing with application of 25% RDF + 50% RDN through Vermicompost + NPK consortium 1L/ha recorded the highest leaf area (458.88 and 598.27 cm2) at 60 and 90 DAS. However, maximum yield per plot (10.45 kg) and yield per hectare (16.13 t) was found in 45 × 30 cm spacing with application of 25% RDF + 50% RDN through Vermicompost + NPK consortium 1L/ha. On economic point of view, maximum net realization (218555.21 ₹/ha) and BCR (2.11) was obtained in 45 × 30 cm spacing with application of 25% RDF + 50% RDN through FYM + NPK consortium 1L/ha.

Variability of morphological and phenological traits in &lt;i&gt;Vigna unguiculata&lt;/i&gt; (L.) Walp. accessions contrasting by growth type in different ecological and geographical conditions

Background. Cowpea (Vigna unguiculata (L.) Walp.) is a thermophilic crop that occupies the third place in the world in terms of cultivation area among leguminous crops. Vegetable cowpea cultivars are successfully cultivated in the open ground in Russia, in its southern regions and the south of the Far East. The creation of new cultivars adapted to cultivation in different regions and suitable for widespread industrial production, is extremely relevant. Materials and methods. The variability of 20 morphological and 3 phenological traits of six cowpea accessions with different growth habit types and architectonics was assessed in various ecological and geographical conditions at VIR experiment stations (in Astrakhan Province, Primorye Territory and Krasnodar Territory) in 2019-2022. The variability and interrelationships of the traits were estimated using basic and multivariate statistical methods. Results. The analysis revealed a significant influence of the factor complex (genotype/accession, research location) on the variability of traits. Phenological traits, the length of internodes and leaflets, depended most strongly on the research location. The range of variation in the number of nodes, branches, peduncles and beans per plant, stem thickness and bean size were determined by the genotype to a larger extent. The range of stem length variability was significantly influenced by the research location and the genotype, though the genotype had a stronger influence. The genotypes responded differently to changes in growing conditions. For the most of the studied accessions, an increase in the main stem length was noted in the monsoon climate conditions of the Primorye Territory, for some of them the type of growth habit changed from determinate to indeterminate. In our research, the plant length was positively correlated with the amount of precipitation and with the average relative air humidity. Excessive humidity and precipitation had minimal effect only on cv. ʻLyanchiheʼ, whose plants retained dwarfness and compact architectonics. Factor analysis (principal factors analysis) revealed 4 factors responsible for the main part of the variance of the analyzed traits. The characteristics of the leaf were combined in the first factor, those of architectonics and time of plant development in the second, features of the seedling and the juvenile stage in the third, and those of the bean in the fourth factor. These complexes of traits (factors) determined the variability of cowpea in our research. Conclusion. The performed analysis of morphological and phenological traits revealed a significant increase of stem length and changes in plant architectonics under conditions of high relative humidity and a large amount of precipitation. In some cases, even the type of growth habit was changed. The further research on the studied characters variability involving more accessions and the determination of molecular mechanisms that control stability of the growth habit type and length of the stem will allow us to proceed to a more efficient and rapid creation of new cultivars suitable for mechanized cultivation. This is extremely important for the development of new areas by cultivating species, the demand in which as a source of food and feed is increasing in the Russian Federation.

Galling by Trichilogaster sp. suppresses the growth of Acacia auriculiformis saplings

Pre-release efficacy assessments (PREAs) are used in weed biological control to predict the potential impact of prospective agents. These assessments enable the most damaging agents to be prioritised for host testing and release. Further, the inclusion of this information in release applications enables regulators to consider the benefits of an agent alongside any risks. We conducted a PREA to evaluate the efficacy of Trichilogaster sp., a galling wasp that is being considered for release in Florida to control invasive earleaf acacia, Acacia auriculiformis. This species is multivoltine and primarily galls vegetative buds. We performed a glasshouse trial exposing A. auriculiformis saplings to one generation of the gall wasp to determine the effect of galling on plant performance. We monitored the growth parameters and development of exposed and unexposed saplings every three weeks for twelve weeks. Above-ground and below-ground biomass were measured at the completion of the trial. One generation of galling reduced sapling height by 37.44 % (±5.57), above-ground biomass by 32.19 % (±4.00), and below-ground biomass by 38.26 % (±7.47). Galling also significantly reduced the other key growth parameters of stem weight, leaf weight, total branch length, and the number of nodes, shoot tips, and leaves. This Trichilogaster species significantly damages juvenile A. auriculiformis plants and could be an effective classical biological control agent if released in Florida, especially if it is not limited by a specialist third trophic level.

Bridge-rich and loop-less hydrogel networks through suppressed micellization of multiblock polyelectrolytes

Most triblock copolymer-based physical hydrogels form three-dimensional networks through micellar packing, and formation of polymer loops represents a topological defect that diminishes hydrogel elasticity. This effect can be mitigated by maximizing the fraction of elastically effective bridges in the hydrogel network. Herein, we report hydrogels constructed by complexing oppositely charged multiblock copolymers designed with a sequence pattern that maximizes the entropic and enthalpic penalty of micellization. These copolymers self-assemble into branched and bridge-rich network units (netmers), instead of forming sparsely interlinked micelles. We find that the storage modulus of the netmer-based hydrogel is 11.5 times higher than that of the micelle-based hydrogel. Complementary coarse grained molecular dynamics simulations reveal that in the netmer-based hydrogels, the numbers of charge-complexed nodes and mechanically reinforcing bridges increase substantially relative to micelle-based hydrogels.

Best practices towards the digitization of 3D traces from virtual outcrop models

With the expanding application of virtual outcrop models (VOMs) within the geosciences, there is a growing need to establish standards for the digital measurement of geological discontinuities exposed therein. Such standards should be tailored towards the complexities of natural outcrops, where geological discontinuities often intersect the outcrop topography and are expressed as 3D traces. Digitizing geological discontinuities expressed as trace data is typically conducted manually via polyline interpolation along the exposed trace, with discontinuity orientation estimated through planar model fitting through the polyline's component nodes. Presently, establishing quality control for such measurements lacks standardization due to the absence of robust benchmarks, with the validity of the resultant orientation data heavily reliant on the experience of the interpreter.With the aim of bridging this gap, we present the results of the manual digitization and orientation estimation of bedding planes expressed as traces across seven natural outcrops. We use two digitization strategies: one employing a previewed best-fit plane during digitization and another without. The first digitization method is carried out by an expert user who visually filters data according to visual alignment with the intended bedding prior to best fit plane estimation. In contrast, the non-visually aided method mimics acquisition by a novice user, with no a priori data filtering based upon trace geometry with respect to the outcrop. Comparison of the results obtained by these ‘expert’ and ‘novice’ acquisition modes is aimed at building benchmarks and best practices. Specifically, we analyze parameters derived from the digitized traces and their corresponding best-fit planes. We compare these parameters with the deviation of the best-fit plane from the mean orientation of the bedding surface as measured using visually-aided acquisition. Comparing these datasets reveals that visually-aided digitization yields more precise and accurate bedding measurements, characterized by traces with lower vertex collinearity. Notably, comparable results can be achieved in the non-visually assisted dataset by excluding traces with high node collinearity. Consequently, we provide robust benchmarks for trace collinearity and its relationship to best fit plane quality to aid the practical implementation of the results of this study. Furthermore, we supplement quantitative comparative analysis with recommended best practices for 3D trace digitization, such as ensuring high values of coplanarity, maintaining a quasi-constant node-to-node distance relative to the model's resolution, and ensuring a minimum number of nodes to guarantee the robustness of the fitted planar model. Critically, our study highlights the critical role tacit geological knowledge plays in the robustness of 3D trace digitization from virtual outcrop models.

Lightning graph matching

The spectral matching algorithm is a classic method for finding correspondences between two graphs, a fundamental task in pattern recognition. It has a time complexity of O(n4) and a space complexity of O(n4), where n is the number of nodes. However, such complexity limits its applicability to large-scale graph matching tasks. This paper proposes a redesign of the algorithm by transforming the graph matching problem into a one-dimensional linear assignment problem. This transformation enables efficient solving by sorting two n×1 vectors. The resulting algorithm is named the Lightning Spectral Assignment Method (LiSA), which enjoys a complexity of O(n2). Numerical experiments demonstrate the efficiency of LiSA, supported by theoretical analysis.

Complete Mesogastric Excision with D2 Lymphadenectomy for Gastric Cancer: Short-Term Results.

To compare the short-term results of complete mesogastric excision with the conventional surgical technique. An experimental study. Place and Duration of the Study: Department of Gastroenterological Surgery, Health Sciences University, Basaksehir City Hospital, Istanbul, Turkiye, from April to December 2023. Comparison of short-term results of open total gastrectomy + mesogastrectomy with standard total gastrectomy + D2 lymph node dissection at a tertiary centre in terms of peroperative results, histopathological findings, and postoperative short-term outcomes prospectively, with review of the literature. A total of 37 patients were included in the study. The groups involved 26 male and 11 female patients. The study group included 14 patients while the control group involved 23 patients. The mean blood loss (mL) was significantly lower and number of metastatic lymph nodes was significantly higher in the study group. Total mesogastric excision is a safe technique which has advantages over conventional D2 gastrectomy in terms of not only peroperative and short-term outcomes, but also disease-free survival. This is the first study from a different population of the world and initial results can contribute to the literature for universalisation. Complete mesogastrium excision, D2 lymphadenectomy, Gastric cancer, Gastrectomy, Mesogastrectomy.

Evaluation of Different Genotypes of Cucumber (Cucumis sativus) for Different Traits

The present study was conducted at the Main Experimental Station, Department of Vegetable Science, Acharya Narendra Deva University of Agriculture and Technology, Kumarganj during the Zaid season of 2023. Planting material consisted of 30 genotypes of cucumber including one check namely Pusa Uday laid out in Completely Randomized Block Design with three replications. Evaluation was done for fourteen different traits viz., days to first male flower appear, days to first female flower appear, node number at which female flower appear, days to first fruit harvest, number of primary branches per plant, inter-nodal length, vine length, fruit length, fruit diameter, average fruit weight, T.S.S, number of fruit per plant, fruit yield per plant (kg), fruit yield per hectare (q). The genotypes NDCU-23-1 (143.13q) followed by NDCU-23-11 (135.90q) and NDCU-23-12 (131.53q) performed better than the check variety i.e. Pusa Uday in term of fruit yield per plant.

Comparing the diagnostic value of 68Ga-prostate-specific membrane antigen PET/CT and multiparametric MRI in pelvic lymph node metastasis of locally advanced prostate cancer.

Multiparametric magnetic resonance imaging (mpMRI) is a commonly used method to diagnose pelvic lymph node metastasis (PLNM) in prostate cancer (PCa) patients, but there are few comparative studies on mpMRI and 68Ga-prostate-specific membrane antigen (PSMA) positron emission tomography (PET)/computed tomography (CT) in locally advanced PCa (LAPC) patients. Therefore, we designed a retrospective study to compare the diagnostic value of 68Ga-PSMA PET/CT and mpMRI for PLNM of LAPC. A retrospective study was performed on 50 patients with LAPC who underwent radical prostatectomy (RP) in Tongji Hospital from 2021 to 2023. All patients underwent PET/CT and mpMRI examination, and were diagnosed as LAPC before surgery, followed by robot-assisted laparoscopic prostatectomy or laparoscopic RP and extended pelvic lymph node dissection (ePLND). Routine postoperative pathological examination was performed. According to the results, the sensitivity, specificity, positive predictive value, and negative predictive value of 68Ga-PSMA PET/CT and mpMRI for the diagnosis of PLNM of LAPC were compared. Among the 50 patients, the mean age was 65.5±10.3 years, the preoperative total serum prostate-specific antigen (PSA) was 30.7±12.3 ng/mL, and the Gleason score was 7 [7, 8]. The difference in diagnostic efficacy between 68Ga-PSMA PET/CT and mpMRI in the preoperative diagnosis of PLNM of PCa was determined by postoperative pathological results. Based on the number of patients who developed PLNM, the sensitivity, specificity, positive predictive value, and negative predictive value of 68Ga-PSMA PET/CT were as follows: 93.75%, 100.00%, 100.00%, 97.14%, and 68.75%, 97.06%, 91.67%, 86.84% for mpMRI, respectively. Based on the number of pelvic metastatic lymph nodes, the sensitivity, specificity, positive predictive value, and negative predictive value of 68Ga-PSMA PET/CT were 95.24%, 100.00%, 100.00%, 99.48%, and 65.08%, 99.13%, 89.13%, 96.30% for mpMRI, respectively. It turned out that PET/CT was more sensitive than mpMRI in detecting PLNM of PCa, and the difference was statistically significant. 68Ga-PSMA PET/CT is more sensitive than mpMRI in the detection of PLNM in patients with LAPC. It is a promising method in the diagnosis and preoperative assessment of PLNM in LAPC.

The Influence of Particle Swarm Optimization‐Back Propagation Neural Network Hyperparameter Selection on the Prediction Accuracy of Converter Endpoint Temperature

The converter is a complex, high temperature, high pressure reactor with limited internal moitoring. At present, data‐driven models mainly focus on the prediction differences between algorithms, and there is relatively little analysis of the impact of different hyperparameters on prediction accuracy. Taking a 120 t converter in a Chinese steel plant as an example, this paper explores the application of particle swarm optimization‐back propagation neural network (PSO‐BP) in converter temperature prediction. First, the Pauta criterion or Box plot method was used to preprocess the data by prescreening. Subsequently, the influence of the activation function, learning rate, and number of hidden layer nodes of BP on the prediction accuracy of the endpoint temperature were explored. Then we investigated the influence of PSO parameters on the optimal result of BP initial value. Comparing the temperature prediction hit rate before and after optimization, the BP model has hit rates of 63.64%, 79.22%, and 87.45% within ±10, ±15, and ±20 °C, respectively, and the PSO‐BP model has hit rates of 68.40%, 84.85%, and 94.81%, respectively. In comparison, PSO‐BP extracts data features more accurately, has higher stability, and has better accuracy in predicting the endpoint temperature of the converter.

False Negative Rate of Sentinel Lymph Node Biopsy (SLNB) in Breast Cancer Patients after Neoadjuvant Chemotherapy

Background: Managing the axilla in patients with node-positive breast cancer who converted to node-negative patients after neoadjuvant chemotherapy is a challenging issue. We aim to analyze the false negative rate (FNR) of SLNB after neoadjuvant chemotherapy in this group of patients. Methods: In this cross-sectional study, we reviewed the results of SLNB and ALND in 368 breast cancer patients who underwent neoadjuvant chemotherapy from 2015 to 2019. The study included patients with pathologically proven axillary lymph nodes undergoing NAC. We collected the data from the Breast Diseases Research Center of Tehran University of Medical Sciences. Results: The average age of the patients was 46.58±10.91. Of all the patients, 205 (55.7%) had positive SLN in the histologic analysis after surgery, while 163 (44.3%) had negative results. The study also revealed that the FNR of SLNB was 9.8% (n=16). Our results showed that SLN had 86.55% sensitivity and 100% specificity in detecting involved nodes. Furthermore, after multivariable analysis, we observed that a higher number of ALND dissections were associated with a higher FNR (Odds ratio; OR=1.20, 95% CI: 1.02-1.42); while a higher number of SLN excisions was linked with a lower FNR (OR=0.41; 95% CI: 0.17-0.98). Conclusion: After NAC in breast cancer patients with positive lymph nodes, SLNB is feasible with a low FNR; the latter is correlated with the number of nodes removed during the procedure.

Machine learning-derived prognostic signature for progression-free survival in non-metastatic nasopharyngeal carcinoma.

Early detection of high-risk nasopharyngeal carcinoma (NPC) recurrence is essential. We created a machine learning-derived prognostic signature (MLDPS) by combining three machine learning (ML) models to predict progression-free survival (PFS) in patients with non-metastatic NPC. A cohort of 653 patients with non-metastatic NPC was divided into a training (n = 457) and validation (n = 196) dataset (7:3 ratio). The study included clinicopathological characteristics, hematologic markers, and MRI findings in three machine learning models-random forest (RF), extreme gradient boosting (XGBoost), and least absolute shrinkage and selection operator (LASSO)-to predict progression-free survival (PFS). A Venn diagram identified the overlapping signatures from the three ML algorithms. Cox proportional hazard analysis determined the MLDPS for PFS. The RF, XGBoost, and LASSO algorithms identified six consensus factors from the 33 signatures. Cox proportional hazards analysis showed that the MLDPS includes age, lymphocyte count, number of positive lymph nodes, and regional lymph node density. Additionally, MLDPS effectively stratified prognosis, with low-risk individuals showing better PFS than high-risk individuals (p < 0.001). MLDPS, based on clinicopathological characteristics, hematologic markers, and MRI findings, is crucial for guiding clinical management and personalizing treatments for patients with non-metastatic NPC.

The Impact of D2 Versus D1 Lymphadenectomy in Siewert II Gastroesophageal Junction (GEJ) Cancer.

Although multiple treatment options exist for gastroesophageal junction (GEJ) cancer, surgery remains the mainstay for potential cure. Extended nodal dissection with a D2 lymphadenectomy (LAD) remains controversial for Siewert II GEJ cancer. Although D2 LAD may lead to a greater lymph node harvest, its effect on survival remains elusive. The authors hypothesized that additional D2 dissection in Siewert II GEJ cancer does not lead to increased survival. This study reviewed Siewert II patients who received a D1 or D2 LAD in addition to minimally invasive esophagectomy (MIE) after receiving neoadjuvant chemoradiation or perioperative chemotherapy (2012-2022). The patients were followed for up to 5 years. The outcomes measured were survival, number of nodes sampled, and operative time. The association between D1 or D2 LAD and overall survival was analyzed with Kaplan-Meier methods and a multivariable Cox regression model. Among 155 patients, 74 % underwent D1 and 26 % underwent D2 LAD. The patients with D2 had more than 15 lymph nodes harvested more frequently than those who had D1 (83 % vs 48 %; p < 0.001), with no difference in positive nodes (2.8 ± 5.2 vs 2.1 ± 4.2; p = 0.4). The patients with D2 LAD had a longer median operative time than those who with D1 LAD (362 vs 244 min; p < 0.001). In Kaplan-Meier and multivariable Cox regression models, overall survival differed significantly between the patients undergoing D2 and those who had D1 (adjusted hazard ratio [aHR], 0.52; 95 % confidence interval [CI], 0.25-1.00; p = 0.067). Little consensus exists regarding the optimal lymph node harvest for GEJ cancers. In Siewert II cancer, D2 LAD may not be mandatory and may lead to increased operative morbidity with no significant difference in survival.

Prediction model of coal seam gas content based on kernel principal component analysis &amp; IDBO-DHKELM

Abstract Accurate coal seam gas content facilitates to effectively prevent coal and gas outburst accidents. To solve the problem, combining improved Dung Beetle optimization algorithm (IDBO) with deep hybrid kernel extreme learning machine (DHKELM), an IDBO-DHKELM coal seam gas content prediction model is proposed. Firstly, according to the influence factors of coal seam gas content and the actual situation of mine production, the index factors of coupled gas content are determined. The correlation of index factors is analyzed by SPSS 27 software through Pearson correlation coefficient matrix. Then the principle components of original data is extracted by the principal component analysis method (KPCA). Secondly, Sine chaotic mapping, fusion improved sinusoidal algorithm and fusion adaptive Gauss-Cauchy hybrid mutation perturbation are introduced to improve Dung Beetle optimization algorithm (DBO) to enhance its global search capability. Thirdly, IDBO is used to optimize the hidden layer nodes number, regularization coefficient, penalty coefficient and kernel parameter in DHKELM, which improves prediction accuracy and further avoid the phenomenon of overfitting. Finally, the principal component extracted by KPCA is taken as the input of the model and gas content as the output of the model. The results are compared and analyzed with those of PSO-BPNN, GA-BPNN, PSO-SVM and DPO-DHKELM models. The results show that the performance of the IDBO-DHKELM model is the best in each performance index. Compared with other models, mean absolute error (MAE) of test samples in IDBO-DHKELM model is reduced by 0.402, 0.4407, 0.3554 and 0.0646, respectively. The mean absolute percentage error (MAPE) is decreased by 3.67%, 4.07%, 8.27% and 6.35%, respectively. The root mean square error (RMSE) decreases by 0.7861, 0.7148, 0.3384 and 0.1186, respectively. The coefficient of determination (R2) is increased by 0.1544, 0.1404, 0.0955 and 0.0396, respectively. Finally, IDBO-DHKELM model and other models are applied to a experimental mine. the result of IDBO-DHKELM model is the closest to the actual value, which further verifies the universality and reliability of the model. Therefore, the model is more suitable for the prediction of coal seam gas content.

Genome to phenome Association for Pork Belly Parameters Elucidates Three Regulation Distinctions: Adipogenesis, muscle formation, and their transcription factors

Genome to phenome analysis is necessary in livestock areas because of its various and complex phenotypes. Pork belly is a favorable part of meat worldwide, including East Asia. A previous study has suggested that the three key transcription factors (ZNF444, NFYA and PPARG) affecting pork belly traits include total volume, the volume of total fat and muscle, and component muscles of the corresponding slice. However, other transcription factor genes affecting each slice other than pork belly component traits still needed to be identified. Thus, we aimed to analyze pork belly components at the genome to phenome level for identifying key transcription factor genes and their co-associated networks. The range of node numbers against each component trait via the association weight matrix was from 598 to 3020. Premised on the result, an in silico functional approach was performed. Each co-association network enriched three key transcription factors in adipogenesis and skeletal muscle proliferation, mesoderm development, metabolism, and gene transcription. The three key transcription factors and their related genes may be useful in comprehending their effect of pork belly construction.

Isogeometric Topology Optimization of Multi-Material Structures under Thermal-Mechanical Loadings Using Neural Networks

An isogeometric topology optimization (ITO) model for multi-material structures under thermal-mechanical loadings using neural networks is proposed. In the proposed model, a non-uniform rational B-spline (NURBS) function is employed for geometric description and analytical calculation, which realizes the unification of the geometry and computational models. Neural networks replace the optimization algorithms of traditional topology optimization to update the relative densities of multi-material structures. The weights and biases of neural networks are taken as design variables and updated by automatic differentiation without derivation of the sensitivity formula. In addition, the grid elements can be refined directly by increasing the number of refinement nodes, resulting in high-resolution optimal topology without extra computational costs. To obtain comprehensive performance from ITO for multi-material structures, a weighting coefficient is introduced to regulate the proportion between thermal compliance and compliance in the loss function. Some numerical examples are given and the validity is verified by performance analysis. The optimal topological structures obtained based on the proposed model exhibit both excellent heat dissipation and stiffness performance under thermal-mechanical loadings.