Columnar Model Research Articles

Evaluation of the value of combined thyroid function-related indexes in the prognosis prediction of patients with differentiated thyroid cancer.

This study aims to evaluate the prognostic significance of thyroid function-related indices in patients with differentiated thyroid cancer (DTC). This retrospective analysis included 90 patients diagnosed with DTC and treated at our hospital from January 2010 to January 2019. Patients were classified into 2 groups based on whole-body imaging results: 67 with a favorable prognosis and 23 with a poor prognosis. The study compared clinical data and thyroid function indices between these groups to assess their efficacy in prognostic prediction. Patients in the poor prognosis group had a higher occurrence of T3-4 stage cancer (P = .006) and ≥2 lymph node metastases (P = .019). Notably, levels of total thyroxine (TT4), thyroid-stimulating hormone (TSH), and thyroglobulin antibody (Tg-Ab) were significantly elevated in this group (P < .001 for each). Receiver operating characteristic analysis revealed substantial predictive accuracy for TT4, TSH, and Tg-Ab (area under curve of 0.747, 0.820, and 0.720, respectively). The columnar graphical model used for prediction demonstrated a high concordance index (C-index = 0.919), superior to single-indicator evaluations. Thyroid function indices, specifically TT4, TSH, and Tg-Ab, play a crucial role in the prognostic assessment of patients with DTC. The column-line diagram model effectively enhances prophetic prediction, aiding in clinical decision-making.

Can We Explain Thousands of Molecularly Identified Mouse Neuronal Types? From Knowing to Understanding.

At the end of 2023, the Whole Mouse Brain Atlas was announced, revealing that there are about 5300 molecularly defined neuronal types in the mouse brain. We ask whether brain models exist that contemplate how this is possible. The conventional columnar model, implicitly used by the authors of the Atlas, is incapable of doing so with only 20 brain columns (5 brain vesicles with 4 columns each). We argue that the definition of some 1250 distinct progenitor microzones, each producing at least 4-5 neuronal types over time, may be sufficient. Presently, this is nearly achieved by the prosomeric model amplified by the secondary dorsoventral and anteroposterior microzonation of progenitor areas, plus the clonal variation in cell types produced, on average, by each of them.

The first microwave and submillimetre closure study using particle models of oriented ice hydrometeors to simulate polarimetric measurements of ice clouds

Abstract. The first closure study involving passive microwave and submillimetre measurements of ice clouds with the consideration of oriented particles is presented, using a unique combination of polarised observations from the ISMAR spectral-like radiometer, two radars with frequencies of 35 and 95 GHz, and a variety of in situ instruments. Of particular interest to this study are the large V–H polarised brightness temperature differences measured from ISMAR above a thick frontal ice cloud. Previous studies combining radar and passive submillimetre measurements have not considered polarisation differences. Moreover, they have assumed particle habits a priori. We aim to test whether the large V–H measurements can be simulated successfully by using an atmospheric model consistent with in situ microphysics. An atmospheric model is constructed using information from the in situ measurements, such as the ice water content, the particle size distribution, and the mass and shape of particles, as well as background information obtained from dropsonde profiles. Columnar and dendritic aggregate particle models are generated specifically for this case, and their scattering properties are calculated using the independent monomer approximation under the assumption of horizontal orientation. The scattering properties are used to perform polarised radiative transfer simulations using ARTS to test whether we can successfully simulate the measured large V–H differences. Radar measurements are used to extrapolate the 1-D microphysical profile to derive a time series of particle size distributions which are used to simulate ISMAR brightness temperatures. These simulations are compared to the observations. It is found that particle models that are consistent with in situ microphysics observations are capable of reproducing the brightness temperature depression and polarisation signature measured from ISMAR at the dual-polarised channel of 243 GHz. However, it was required that a proportion of the particles were changed in order to increase the V–H polarised brightness temperature differences. Thus, we incorporated millimetre-sized dendritic crystals, as these particles were observed in the probe imagery. At the second dual-polarised channel of 664 GHz, the brightness temperature depressions were generally simulated at the correct locations; however, the simulated V–H was too large. This work shows that multi-frequency polarisation information could be used to infer realistic particle shapes, orientations, and representations of the split between single crystals and aggregates within the cloud.

Human Adapted Prosomeric Model: A Future for Brainstem Tumor Classification

This study reevaluates the conventional understanding of midbrain anatomy and neuroanatomical nomenclature in the context of recent genetic and anatomical discoveries. The authors assert that the midbrain should be viewed as an integral part of the forebrain due to shared genetic determinants and evolutionary lineage. The isthmo-mesencephalic boundary is recognized as a significant organizer for both the caudal midbrain and the isthmo-cerebellar area. The article adopts the prosomeric model, redefining the whole brain as neuromeres, offering a more precise depiction of brain development, including processes like proliferation, neurogenesis, cell migration, and differentiation. This shift in understanding challenges traditional definitions of the midbrain based on external brain morphology. The study also delves into the historical context of neuroanatomical models, including the columnar model proposed by Herrick in 1910, which has influenced our understanding of brain structure. Furthermore, the study has clinical implications, affecting neuroanatomy, neurodevelopmental studies, and the diagnosis and treatment of brain disorders. It emphasizes the need to integrate molecular research into human neuroanatomical studies and advocates for updating neuroanatomical terminology to reflect modern genetic and molecular insights. The authors propose two key revisions. First, we suggest reclassifying the isthmo-cerebellar prepontine region as part of the hindbrain, due to its role in cerebellar development and distinct location caudal to the genetically-defined midbrain. Second, we recommend redefining the anterior boundary of the genetically-defined midbrain to align with genetic markers. In conclusion, the authors highlight the importance of harmonizing neuroanatomical nomenclature with current scientific knowledge, promoting a more precise and informed understanding of brain structure, which is crucial for both research and clinical applications related to the human brain.

Functional Implications of the Prosomeric Brain Model.

Brain models present a viewpoint on the fundamental structural components of the brain and their mutual organization, generally relative to a particular concept of the brain axis. A model may be based on adult brain structure or on developmental morphogenetic aspects. Brain models usually have functional implications, depending on which functional properties derive from the postulated organization. This essay examines the present scenario about brain models, emphasizing the contrast between columnar or other longitudinal models and transverse subdivisional neuromeric models. In each case, the main functional implications and apparent problems are explored and commented. Particular attention is given to the modern molecularly based 'prosomeric model', which postulates a set of 20 transverse prosomeres as the developmental units that serve to construct all the cerebral parts and the particular typology of many different neuronal populations within the forebrain and the hindbrain, plus a number of additional spinal cord units. These metameric developmental units (serially repeated, but with unique molecular profiles) confer to this model remarkable functional properties based mainly on its multiplicity and modularity. Many important brain functions can be decomposed into subfunctions attended to by combined sets of neuronal elements derived from different neuromeres. Each neuromere may participate in multiple functions. Most aspects related to creation of precise order in neural connections (axonal navigation and synaptogenesis) and function is due to the influence of neuromeric anteroposterior and dorsoventral positional information. Research on neuromeric functionality aspects is increasing significantly in recent times.

External validation of the prediction model of intradialytic hypotension: a multicenter prospective cohort study

Objective Intradialytic hypotension (IDH) is a common and serious complication in patients with Maintenance Hemodialysis (MHD). The purpose of this study is to externally verify three IDH risk prediction models recently developed by Ma et al. and recalibrate, update and present the optimal model to improve the accuracy and applicability of the model in clinical environment. Methods A multicenter prospective cohort study of patients from 11 hemodialysis centers in Sichuan Province, China, was conducted using convenience sampling from March 2022 to July 2022, with a follow-up period of 1 month. Model performance was assessed by: (1) Discrimination: Evaluated through the computation of the Area Under Curve (AUC) and its corresponding 95% confidence intervals. (2) Calibration: scrutinized through visual inspection of the calibration plot and utilization of the Brier score. (3) The incremental value of risk prediction and the utility of updating the model were gauged using NRI (Net Reclassification Improvement) and IDI (Integrated Discrimination Improvement). Decision Curve Analysis (DCA) was employed to evaluate the clinical benefit of updating the model. Results The final cohort comprised 2235 individuals undergoing maintenance hemodialysis, exhibiting a 14.6% occurrence rate of IDH. The externally validated Area Under the Curve (AUC) values for the three original prediction models were 0.746 (95% CI: 0.718 to 0.775), 0.709 (95% CI: 0.679 to 0.739), and 0.735 (95% CI: 0.706 to 0.764) respectively. Conversely, the AUC value for the recalibrated and updated columnar plot model reached 0.817 (95% CI: 0.791 to 0.842), accompanied by a Brier score of 0.081. Furthermore, Decision Curve Analysis (DCA) exhibited a net benefit within the threshold probability range of 15.2% to 87.1%. Conclusion Externally validated, recalibrated, updated, and presented IDH prediction models may serve as a valuable instrument for evaluating IDH risk in clinical practice. Furthermore, they hold the potential to guide clinical providers in discerning individuals at risk and facilitating judicious clinical intervention decisions.

Aggregation Behavior of Ethoxylated Phytosterol Surfactants in Different Protic Solvents: An Insight from Dielectric Relaxation Studies.

The micellar aggregation behavior of biocompatible surfactants, phytosterol ethoxylates (BPS-n, n is the oxyethylene (EO) chain length), containing polyoxyethylene chains in four organic solvents (glycerol (G), ethylene glycol (EG), formamide (FA), and N,N-dimethylformamide (DMF)) was studied by dielectric spectroscopy in the frequency range of 40 Hz to 110 MHz. Only the BPS-n/EG and BPS-n/G systems show distinct dielectric loss peaks near 104 Hz, indicating that BPS-n forms micellar aggregates in EG and G solvents, and the interfacial polarization (IP) between the aggregates and solvents leads to this relaxation. The dielectric spectra were analyzed based on the IP theory and a columnar dispersion model. The dielectric parameters of micelles, the permittivity and conductivity of micelles and solvents, and the volume fraction of the aggregates in solvents were calculated. On this basis, the binding numbers of each EO group on the hydrophobic chain to the solvent molecules EG and G were calculated to be 0.42 and 0.22, respectively. It was suggested that the aggregation appears to be related to the magnitude of the hydrogen bonding interactions. The lower number of EO group-binding solvents is because of the strong steric effect of alcohol solvents and the magnitude of the hydrogen bonding force.

An analytical model and experimental validation of annular heat source during scanning electron beam treatment

In this investigation, the maximum depth of penetration and power density coefficient are calculated by simulating the motion trajectories of electrons in surface layer of 5CrNiMo steel using Monte Carlo method, and the number of electrons is 500,000 in simulation. Then tomographic calculation is used to determine the distribution of power density coefficient on the section perpendicular to electron incidence direction, and a Gaussian columnar heat source model that matches the properties of electron beam is established. Finally, a mobile annular heat source model of scanning electron beam is then derived, combining it with the real operating mode. The temperature field during surface modification is simulated by using the mobile annular heat source model, and the simulation findings are compared to experimental data. The findings of Monte Carlo simulation demonstrate that the power density coefficient is Gaussian distributed on the section perpendicular to the depth of electron incidence and the peak power density at the center of each section changes with depth. The thickness and microstructure of re-melting layer predicted from the temperature field simulation is very close to the one from experimental findings. In our investigation, on top of deriving a model for the heat source of a single beam spot, we have also solved the problem of mathematical representation of the beam spot when it is doing high-speed rotational and linear composite motions. The aforementioned findings demonstrate that our study can provide methodological references for the construction of heat source models for different materials during electron beam treatment.

Risk Factors for Recurrence After Radiofrequency Surgery of the V2 Branch of the Trigeminal Nerve

BACKGROUND: The factors influencing relapse after radiofrequency operation of the V2 branch of the trigeminal neuralgia are yet to be identified. OBJECTIVES: The risk factors affecting recurrence after radiofrequency operation of the V2 branch of the trigeminal neuralgia were analyzed, and a curative effect prediction model was constructed. STUDY DESIGN: A retrospective study. SETTING: This study was conducted at the Affiliated Hospital of Jiaxing University, People’s Republic of China. METHODS: The records of patients with maxillary nerve pain in the V2 branch of the trigeminal nerve who underwent computed tomography-guided foramen rotundum radiofrequency treatment at the Pain Department of the Affiliated Hospital of Jiaxing College from April 2014 through December 2020 were collected and randomly divided into training (n = 137) and test (n = 59) groups at a 7:3 ratio. The outcome variable was whether or not recurrence was observed 2 years postsurgery. Independent predictors were screened by LASSO (least absolute shrinkage and selection operator) regression analysis. Based on these findings, a nomogram prediction model was explored further and developed using multifactor logistic regression analysis. Also, the feasibility of the nomogram prediction model for recurrence after radiofrequency was assessed using a validation group. Finally, the discriminatory power, accuracy, and clinical utility of the prediction model were evaluated using the receiver operating characteristic (ROC), area under the curve (AUC), calibration curve, and decision curve analysis (DCA), respectively. RESULTS: LASSO regression, combined with multifactorial logistic regression analysis, identified factors such as age, duration, branches, and numbness that influence V2 trigeminal nerve pain recurrence in patients 2 years post-radiofrequency surgery (P &lt; 0.05). The above variables were used to construct the nomogram prediction models. The AUC of the nomogram prediction model predicted that the recurrence post V2 radiofrequency was 0.726 in the training group and 0.611 in the test group. The DCA showed that the columnar plot prediction model predicted the risk of recurrence post-radiofrequency of the V2 branch of the trigeminal nerve had a threshold probability of 0 – 0.9. LIMITATIONS: This was a single-center study. CONCLUSION: A highly accurate nomogram prediction model (predictor variables include age, duration, branches, and numbness) was developed to improve the early identification and screening of patients at high risk of recurrence after V2 trigeminal nerve radiofrequency surgery. KEY WORDS: Trigeminal neuralgia, maxillary neuralgia, radiofrequency, risk factors, prediction model

Investigations on Flexural Strength of a Columnar Saline Model Ice under Circular Plate Central Loading

The properties of ice strength have a significant impact on the design and safety of structures in ice-infested waters. To analyze the flexural strength of columnar saline model ice, we conducted circular plate center loading tests at the Small Ice Model Basin of the China Ship Scientific Research Center (CSSRC SIMB) in China. The tests involved varying the loading rate and ice temperature, and a numerical model was developed using FEM and LS-DYNA for validation and comparison. The results of the tests revealed the crack propagation process, stress distribution, load response, and failure mode of the model ice. The model ice displayed typical brittle failure, and the flexural strength was linearly related to ice temperature but not significantly correlated with loading rate. The porosity of the model ice affected the load response and time of failure but not the failure mode. The model ice with 7% porosity had a 7.8% reduction in load response compared to the nonporous model ice. This study provides a reliable method for measuring and analyzing the flexural strength of model ice. It also serves as a foundation for further research on the interaction between structures and ice sheets.

Identification of cuproptosis-related subtypes, characterization of immune microenvironment infiltration, and development of a prognosis model for osteoarthritis.

Osteoarthritis (OA) is a prevalent chronic joint disease with an obscure underlying molecular signature. Cuproptosis plays a crucial role in various biological processes. However, the association between cuproptosis-mediated immune infifiltration and OA progression remains unexplored. Therefore, this study elucidates the pathological process and potential mechanisms underlying cuproptosis in OA by constructing a columnar line graph model and performing consensus clustering analysis. Gene expression profifile datasets GSE12021, GSE32317, GSE55235, and GSE55457 of OA were obtained from the comprehensive gene expression database. Cuproptosis signature genes were screened by random forest (RF) and support vector machine (SVM). A nomogram was developed based on cuproptosis signature genes. A consensus clustering was used to distinguish OA patients into different cuproptosis patterns. To quantify the cuproptosis pattern, a principal component analysis was developed to generate the cuproptosis score for each sample. Single-sample gene set enrichment analysis (ssGSEA) was used to provide the abundance of immune cells in each sample and the relationship between these significant cuproptosis signature genes and immune cells.To quantify the cuproptosis pattern, a principal component analysis technique was developed to generate the cuproptosis score for each sample. Cuproptosis-related genes were extracted and subjected to differential expression analysis to construct a disease prediction model and confifirmed by RT-qPCR. Seven cuproptosis signature genes were screened (DBT, LIPT1, GLS, PDHB, FDX1, DLAT, and PDHA1) to predict the risk of OA disease. A column line graph model was developed based on these seven cuproptosis signature genes, which may assist patients based on decision curve analysis. A consensus clustering method was used to distinguish patients with disorder into two cuproptosis patterns (clusters A and B). To quantify the cuproptosis pattern, a principal component analysis technique was developed to generate the cuproptosis score for each sample. Furthermore, the OA characteristics of patients in cluster A were associated with the inflflammatory factors IL-1b, IL-17, IL-21, and IL-22, suggesting that the cuproptosis signature genes play a vital role in the development of OA. In this study, a risk prediction model based on cuproptosis signature genes was established for the fifirst time, and accurately predicted OA risk. In addition, patients with OA were classifified into two cuproptosis molecule subtypes (clusters A and B); cluster A was highly associated with Th17 immune responses, with higher IL-1b, IL-17, and IL-21 IL-22 expression levels, while cluster B had a higher correlation with cuproptosis. Our analysis will help facilitate future research related cuproptosis-associated OA immunotherapy. However, the specifific mechanisms remain to be elucidated.

The Influence of the Preoperative Albumin to Alkaline Phosphatase Ratio on Overall Survival in Post-Radical Surgery for Colorectal Cancer and the Construction of a Nomogram Prediction Model.

The albumin to alkaline phosphatase ratio (AAPR) is a newly developed blood biomarker that has been reported to have prognostic value in several types of cancers. The aim of this study was to investigate the predictive value of AAPR in overall survival after radical colon cancer surgery in patients with stage I-III colorectal cancer (CRC). The clinical data of 221 eligible patients with stage I ∼ III CRC were retrospectively analyzed. A series of survival analyses were performed to assess the prognostic value of AAPR. Univariate and multifactorial Cox analyses were performed to identify independent risk factors. Columnar graph prediction models were further constructed based on independent risk factors such as AAPR, and their predictive properties were validated. The optimal cutoff value of preoperative AAPR for postoperative overall survival (OS) in patients undergoing laparoscopic radical CRC was .495 as shown by univariate and multifactorial Cox regression analysis. The factors of age ≤65years, Tumor-Node-Metastasis (TNM) stage I-II, tumor grading (high/medium differentiation), CEA ≤5, and AAPR ≥.495 were associated with better OS (P < .05). Preoperative AAPR level was a good predictor of postoperative survival in patients undergoing laparoscopic radical CRC surgery, and AAPR <.495 was an independent risk factor for decreased postoperative OS.

Analysis of risk factors associated with different degrees of postpartum hemorrhage in patients with pregnancy-induced hypertension and construction of a prediction model using line graph

Objective This study aims to analyze the risk factors associated with different degrees of postpartum hemorrhage in patients with pregnancy-induced hypertension and construct a prediction model using line graph. Methods The patients who were treated in our hospital for pregnancy-induced hypertension from January 2021 to December 2022 were enrolled as the study subjects. Their clinical data were collected, and the risk factors associated with postpartum hemorrhage in patients with pregnancy-induced hypertension were analyzed by single-factor and multi-factor logistic regression. The nomogram prediction model was constructed and validated internally, and the discrimination and consistency of the model were verified by the ROC curve and calibration graph. Results In this experiment, 125 out of the 482 patients with hypertensive disorder complicating pregnancy experienced different degrees of postpartum hemorrhage, with an incidence of 25.93%. Multivariate Logistic regression analysis showed that patients with severe disease (OR = 2.454), the degree of proteinuria +++ or ++++ (OR = 6.754, 7.206), fetal body mass ≥4000 g (OR = 5.972), uterine atony (OR = 11.789), abnormal HDL-C (OR = 3.174), abnormal LDL-C (OR = 8.812), and abnormal VEGF (OR = 7.702) had a higher risk of postpartum hemorrhage (p < .05). The risk of postpartum hemorrhage was lower in patients with onset gestational week ≥28 weeks (OR = 0.158, 0.025) and delivery gestational week ≥28 weeks (OR = 0.085, 0.152) (p < .05). Columnar line graph models for postpartum hemorrhage in patients with gestational hypertension were constructed based on nine independent risk factors, and the model differentiation (AUC 0.912 and 0.919, respectively) and precision (goodness of fit HL χ 2 = 8.441, p = .392, χ 2 = 7.741, p = .459) were better in the modeling and validation groups. Conclusion The severity of disease, the gestational week upon onset, the gestational week upon delivery, the degree of proteinuria, systolic blood pressure, diastolic blood pressure, uterine atony, HDL-C, LDL-C, VEGF are factors affecting the incidence of postpartum hemorrhage in patients with hypertensive disorder complicating pregnancy. The prediction model based on the above factors can accurately evaluate the risk of different degrees of postpartum hemorrhage in patients with hypertensive disorder complicating pregnancy.

Exploring the diagnostic value of eosinophil-to-lymphocyte ratio to differentiate Kawasaki disease from other febrile diseases based on clinical prediction model

Kawasaki disease (KD) is a febrile disease that affects children under 5 years of age and leads to serious cardiovascular complications such as coronary artery disease. The development of markers that can predict early is important to reduce the under- and misdiagnosis of KD. The aim of this research was to develop a diagnostic predictive model to differentiate Kawasaki disease (KD) from other febrile diseases using eosinophil-to-lymphocyte ratio (ELR) and other biomarkers. We recruited a total of 190 children with KD and 1604 children with other febrile diseases. We retrospectively collected clinical information from the children, which included laboratory data on the day of admission, such as white blood cells (WBC), hemoglobin (HGB), calcitoninogen (PCT), hypersensitive c-reactive protein (CRP), snake prognostic nutritional index (PNI), peripheral blood neutrophil–lymphocyte ratio (NLR), platelet-lymphocyte ratio (PLR), and ELR. We performed analyses using univariate analysis, multivariate logistic regression, and column line plots, and evaluated the diagnostic parameters of the predictive models. ELR was significantly increased in patients with KD. After multivariate logistic regression, WBC, HGB, CRP, NLR, ELR and PNI were finally included as indicators for constructing the prediction model. The ROC curve analysis suggested that the C-index of the diagnostic prediction model was 0.921. The calibration curve showed good diagnostic performance of the columnar graph model. The cut-off value of ELR alone for KD was 0.04, the area under the ROC curve was 0.809. Kids with KD show highly expressive level of ELR compared to children with febrile disease, which can be used to diagnose KD, and column line graphs constructed together with other indicators can help pediatricians to identify KD more effectively from febrile children.

Network-Structured BST/MBO Composites Made from Core-Shell-Structured Granulates.

A finite element method (FEM)-based simulation approach to predict the tunability in composite materials was developed and tested with analytical data. These tests showed good prediction capabilities of the simulation for the test data. The simulation model was then used to predict the tunability of a network-structured composite, where the dielectric phase formed clusters in a paraelectric network. This was achieved by simulating a reciprocal core-shell unit cell of said network. The simulation showed a high tunability for this network model, exceeding the tunability of the analytically evaluated layered, columnar, and particulate model. The simulation results were experimentally verified with a Ba0.6Sr0.4TiO3/Mg3B2O6 (BST/MBO) composite, where core-shell granulates were made with a two-step granulation process. These structured samples showed higher tunability and dielectric loss than the unstructured samples made for comparison. Overall, the structured samples showed higher tunability to loss ratios, indicating their potential for use in tunable radio frequency applications, since they may combine high performance with little energy loss.

Columnar graphical model of poor prognosis of severe acute pancreatitis complicated by carbapenem resistant Enterobacteriaceae infection

Columnar graphical model of poor prognosis of severe acute pancreatitis complicated by carbapenem resistant Enterobacteriaceae infection

Chemical order transitions within extended interfacial segregation zones in NbMoTaW

Interfacial segregation and chemical short-range ordering influence the behavior of grain boundaries in complex concentrated alloys. In this study, we use atomistic modeling of a NbMoTaW refractory complex concentrated alloy to provide insight into the interplay between these two phenomena. Hybrid Monte Carlo and molecular dynamics simulations are performed on columnar grain models to identify equilibrium grain boundary structures. Our results reveal extended near-boundary segregation zones that are much larger than traditional segregation regions, which also exhibit chemical patterning that bridges the interfacial and grain interior regions. Furthermore, structural transitions pertaining to an A2-to-B2 transformation are observed within these extended segregation zones. Both grain size and temperature are found to significantly alter the widths of these regions. An analysis of chemical short-range order indicates that not all pairwise elemental interactions are affected by the presence of a grain boundary equally, as only a subset of elemental clustering types are more likely to reside near certain boundaries. The results emphasize the increased chemical complexity that is associated with near-boundary segregation zones and demonstrate the unique nature of interfacial segregation in complex concentrated alloys.

Numerical simulation of the effect of rib spacing on the heat transfer performance of micro pin-fin heat sink

This paper aims to deeply understand the influence of different columnar rib spacing on the comprehensive thermal hydraulic performance of miniature columnar rib radiator, and further explore the influence of flow and heat flow density Taking 10 miniature columnar rib radiator models as research objects, the single-phase heat transfer process of square miniature columnar rib radiator was studied. The experimental results show that for 10 kinds of miniature columnar rib radiators, the relationship between the maximum temperature difference at the bottom of the ribbed substrate will change with the change of temperature, with linear arrangement and cross arrangement.The maximum and minimum values are, respectively, when the longitudinal spacing is 0.75mm and 0.25 mm, and the maximum and minimum values are, respectively, when the transverse spacing is 0.5 mm and 1.25 mm.

Prosomeric Hypothalamic Distribution of Tyrosine Hydroxylase Positive Cells in Adolescent Rats.

Most of the studies on neurochemical mapping, connectivity, and physiology in the hypothalamic region were carried out in rats and under the columnar morphologic paradigm. According to the columnar model, the entire hypothalamic region lies ventrally within the diencephalon, which includes preoptic, anterior, tuberal, and mamillary anteroposterior regions, and sometimes identifying dorsal, intermediate, and ventral hypothalamic partitions. This model is weak in providing little or no experimentally corroborated causal explanation of such subdivisions. In contrast, the modern prosomeric model uses different axial assumptions based on the parallel courses of the brain floor, alar-basal boundary, and brain roof (all causally explained). This model also postulates that the hypothalamus and telencephalon jointly form the secondary prosencephalon, separately from and rostral to the diencephalon proper. The hypothalamus is divided into two neuromeric (transverse) parts called peduncular and terminal hypothalamus (PHy and THy). The classic anteroposterior (AP) divisions of the columnar hypothalamus are rather seen as dorsoventral subdivisions of the hypothalamic alar and basal plates. In this study, we offered a prosomeric immunohistochemical mapping in the rat of hypothalamic cells expressing tyrosine hydroxylase (TH), which is the enzyme that catalyzes the conversion of L-tyrosine to levodopa (L-DOPA) and a precursor of dopamine. This mapping was also combined with markers for diverse hypothalamic nuclei [agouti-related peptide (Agrp), arginine vasopressin (Avp), cocaine and amphetamine-regulated transcript (Cart), corticotropin releasing Hormone (Crh), melanin concentrating hormone (Mch), neuropeptide Y (Npy), oxytocin/neurophysin I (Oxt), proopiomelanocortin (Pomc), somatostatin (Sst), tyrosine hidroxilase (Th), and thyrotropin releasing hormone (Trh)]. TH-positive cells are particularly abundant within the periventricular stratum of the paraventricular and subparaventricular alar domains. In the tuberal region, most labeled cells are found in the acroterminal arcuate nucleus and in the terminal periventricular stratum. The dorsal retrotuberal region (PHy) contains the A13 cell group of TH-positive cells. In addition, some TH cells appear in the perimamillary and retromamillary regions. The prosomeric model proved useful for determining the precise location of TH-positive cells relative to possible origins of morphogenetic signals, thus aiding potential causal explanation of position-related specification of this hypothalamic cell type.

Prosomeric classification of retinorecipient centers: a new causal scenario.

The retina is known to target many superficial areas in the brain. These have always been studied under the tenets of the classic columnar brain model, which was not designed to produce causal explanations, being functionally oriented. This has led over the years to a remarkable absence of understanding or even hypothetical thinking about why the optic tract takes its precise course, why there are so many retinal targets (some of them at surprising sites), what mechanism places each one of them exactly at its standard position, which processes specify spatial aspects of retinotopy and differential physiological properties within the visual system, and so on, including questions about conserved and changing evolutionary aspects of the visual structures. The author posits that the origin of the current causally uninformative state of the field is the columnar model, which worked as a subliminal or cryptic dogma that disregards the molecular developmental advances accruing during the last 40years, and in general distracts the attention of neuroscientists from causal approaches. There is now an alternative brain model, known as the prosomeric model, that does not have these problems. The author aims to show that once the data on retinal projections are mapped and analyzed within the prosomeric model the scenario changes drastically and multiple opportunities for formulating hypotheses for causal explanation of any aspects about the visual projections become apparent (emphasis is made on mouse and rabbit data, but any set of data on retinal projections in vertebrates can be used, as shown in some examples).