Temporal Evolution Research Articles

Numerical simulation of thermal and stress fields for multilayer and multi-pass weaving WAAM of magnesium alloy

Purpose A 3D finite element (FE) model based on the double ellipsoidal heat source was developed to investigate the evolution of temperature and stress fields during the multilayer and multi-pass wire and arc additive manufacturing (WAAM) process. This paper aims to investigate the evolution of temperature and stress fields during the multilayer and multi-pass wire and arc additive manufacturing (WAAM) process by developing a 3D finite element (FE) model based on the double ellipsoidal heat source. Design/methodology/approach Experimental thermal cycle curves and residual stresses were obtained by thermocouples and X-ray diffraction, respectively. The validity of the model was verified by the corresponding experimental results. Findings The deposition process of the upper pass led to the partial remelting of the lower deposited pass. The thermal process of the current-deposited pass alleviated the stress concentration in the previous-formed passes. A more uniform temperature distribution could be obtained by using the reciprocating deposition path. Compared to the reciprocating deposition path, the peak values of the transverse and longitudinal tensile residual stresses of the deposited sample under the unidirectional deposition path were reduced by 15 MPa and increased by 13 MPa, respectively. The heat conduction in the deposited passes could be improved by extending the inter-pass cooling time appropriately. With an increase in the inter-pass cooling time, the longitudinal residual stress in the middle region of sample along longitudinal and transverse directions showed increase and decrease–increase trends, respectively, while the transverse residual stress exhibited decrease trend. Originality/value This study enhances the understanding of temperature and stress fields evolution during the multilayer and multi-pass cold metal transfer-WAAM processes of magnesium alloy and provides the reference for parameter optimization.

A Radar-Based Methodology for Aerosol Plume Identification and Characterisation on the South African Highveld

Biomass burning on the South African Highveld annually injects substantial amounts of aerosols and trace gases into the atmosphere, impacting the global radiative balance, cloud microphysics, and regional air quality. These aerosols are transported as plumes over long distances, posing challenges to existing in situ and satellite-based monitoring techniques because of their limited spatial and temporal resolution, particularly in environments with low-level sources. This study aims to develop and validate a novel radar-based methodology to detect, track, and characterise aerosol plumes, addressing the limitations of existing in situ and satellite monitoring techniques. Using high-resolution volumetric reflectivity data from an S-band radar in Pretoria, South Africa, a traditional storm tracking algorithm is adapted to improve plume identification. Case studies of plume events in June and August 2013 demonstrate the radar’s effectiveness in distinguishing lower vertical profiles and reduced reflectivity of plumes compared with storm echoes. The adapted algorithm successfully tracked the spatial and temporal evolution of the plumes, revealing their short-lived nature. Results indicate that radar-derived geospatial characteristics have the potential to contribute significantly to understanding the impacts of plumes on local air quality. These findings underscore the critical need for high spatio-temporal resolution data to support effective air quality management and inform policy development in regions affected by biomass burning.

Spatial and Temporal Characteristics and Driving Force Analysis of Ecological Environmental Quality in Fengfeng Mining Area with Remote Sensing Ecological Index of PM2.5 Concentration

The Fengfeng mining area is an important coal-producing area in China and crucial environmental problems have been caused by large-scale exploitation of coal mines. The spatio-temporal evolution and driving factors of the ecological environment quality in this area must be explored for promoting the transformation of coal-based cities. Based on Landsat data of the Google earth engine （GEE） platform, this study constructed a new remote sensing-based ecological index （RSEInew） for the Fengfeng mining area from 2000 to 2020. The spatial and temporal evolution of RSEInew and its driving factors were evaluated by using trend analysis and geographic detector methods. The results showed that： ① From 2000 to 2020, the RSEInew of the Fengfeng mining area presented a fluctuating increasing trend （trend = 0.002 2）, and the proportion of good and excellent ecological environmental quality showed an increasing trend, rising from 24.80% in 2000 to 65.54% in 2020. ② The change in the RSEInew grade indicated that the proportion of significant improvement （3 and 4） of ecological environment quality grade in the Fengfeng Mining area from 2000 to 2020 was 10.21%, which was mainly distributed in Hexun town and Yijing town in the northwest of the Fengfeng mining area. The proportion of significant degradation （-3 and -4） was only 1.58%, mainly scattered in Linshui town and Dashe town. ③ RSEInew values increased significantly during 2000-2020 in the area accounting for 18.29%, mainly distributed in the central and northern areas and the western fringe of the Fengfeng mining area. The significantly reduced area accounted for 9.25%, mainly concentrated in the eastern area of the Fengfeng mining area. The coefficient of variation results showed that the areas with high fluctuation of RSEInew were mainly concentrated in Pengcheng town and Linshui town in the middle and eastern Fengfeng mining area. ④ From the perspective of influencing factors, the average q value of land use type （X6） during 2000-2020 was 0.290, which was much higher than other factors. The q value of social and economic factors showed an increasing trend, indicating that the spatial distribution of ecological environment quality in this region was increasingly strongly influenced by human activities. The interaction results showed that land use change was the key factor influencing ecological environment quality in the Fengfeng mining area.

Studying University-Industry Collaboration in Latin America: A Systematic Review of the period 1993-2022

This article offers a comprehensive examination of perspectives on university-industry interactions in Latin America. Distinguishing itself from international reviews, the study employs a systematic review of 274 articles across Scopus, Scielo, and Dialnet databases. The research addresses the temporal evolution, disciplinary fields, conceptual approaches, methods, and prevailing topics in the region. Findings reveal a focus on management, economics, business, social sciences, and engineering disciplines. Qualitative methods dominate, while theoretical-conceptual approaches encompass meso-institutional and micro-interactional levels. Key research themes include collaboration results, knowledge transfer, innovation, technological transfer, and collaboration barriers.

Spatiotemporal Evolution Characteristics and Influencing Factors of Industrial Carbon Emissions in the Yellow River Basin

Scientific assessment of industrial carbon emissions in the Yellow River Basin and identification of its influencing factors are of great importance for promoting green transformation, ecological protection, and high-quality development of the Yellow River Basin. Considering nine provinces in the Yellow River Basin as the research objects； using relevant data on industrial development and energy consumption in the Yellow River Basin from 2000 to 2019； and with the help of IPCC carbon emission measurement, spatial autocorrelation, and LMDI decomposition, the spatial and temporal evolution characteristics and influencing factors of carbon emissions from industries and industrial sectors in the Yellow River Basin were analyzed. Reasonable suggestions were put forward for reducing the carbon emissions from industries in the Yellow River Basin. The results showed that： ① From 2000 to 2019, industrial carbon emissions in the Yellow River Basin showed a fluctuating growth trend, with a decreasing growth rate. The spatial pattern changed from "low in the upstream and high in the middle and downstream" to "high and low value distribution," and the spatial difference gradually expanded. ② The high carbon industry was the most important source of industrial carbon emissions in the Yellow River Basin, accounting for 96.35% of the carbon emissions between the industries with a continuous growth trend, which was a significant difference. The middle and low carbon industry carbon emissions and the total proportion was low, showing different fluctuations； nine provinces and nine industrial industries had significant spatial variability. ③ Energy structure intensity, economic scale, and population scale promoted the increase in industrial carbon emissions in the Yellow River Basin and energy consumption intensity had an inhibitory effect on the increase in carbon emissions. The economic scale effect was positive and significant, which offset the negative effect of energy consumption intensity. Spatial variability was observed in the contribution value of the influence effect of the factors affecting the carbon emissions of the industry in nine provinces.

Simultaneous Probing of Electron Density and Temperature Dynamics Inside Air Plasma with Intense Terahertz Pulses

AbstractAir plasma induced by ultrafast laser pulses is an extraordinary source of electromagnetic waves, emitting microwave, terahertz (THz) radiation, and cavityless lasing in the near‐infrared and visible ranges. The temporal dynamics of the electron density have been revealed by optical pump‐probe techniques, while the evolution of the electron temperature remains elusive due to a lack of suitable methods. Here, it is demonstrated that the intense THz‐field‐enhanced fluorescence emission from the excited molecules of nitrogen is a novel tool that allows to explore the complex dynamics of the plasma density and electron temperature simultaneously. Two relaxation times of electrons in air plasma are observed and interpreted as a competition between the excitation of a triplet state by laser or THz‐field‐heated electrons and the dissociative recombination of nitrogen molecular ions. Based on the theoretical simulations, the tens of picoseconds relaxation process is attributed to the ultrafast temperature decrease, while the longer relaxation in the range of hundreds of picoseconds is ascribed to the decay of electron density. The temporal relaxation of both the electron density and temperature revealed by applying an intense THz field provides further insights into the laser‐air plasma interaction and will benefit the engineering of this exceptional source.

Spontaneous imbibition in thin anisotropic fibrous media: Experiments and numerical modeling

In this study, we investigate the temporal and spatial evolution of wetting saturation during spontaneous imbibition in anisotropic fibrous porous media using both experimental and numerical methods. We present a novel experimental approach to systematically study spontaneous imbibition in these media, allowing for a comprehensive assessment of how microstructure influences wicking performance. The experimental method, which exhibits high reproducibility, was used to validate the numerical model. The numerical model was parameterized using only the porosity and filament diameter of the porous media in conjunction with the material properties of the imbibing fluid. The essential capillary saturation curve for the numerical model was derived using the pore morphology method. The numerical results effectively replicated the experimental results. Our experimental and numerical observations revealed a diffusive wetting front within the porous media, which progressively expanded and decelerated during the imbibition process. The results highlight the significant influence of filament size on the wetting saturation dynamics and the height of the wetting front during spontaneous imbibition.

Effects of Dynamic Surface Transformation on the Activity and Stability of Mixed Co‐Mn Cubic Spinel Oxides in the Oxygen Evolution Reaction in Alkaline Media

AbstractAn atomic‐scale understanding of how electrocatalyst surfaces reconstruct and transform during electrocatalytic reactions is essential for optimizing their activity and longevity. This is particularly important for the oxygen evolution reaction (OER), where dynamic and substantial structural and compositional changes occur during the reaction. Herein, a multimodal method is developed by combining X‐ray fine structure absorption and photoemission spectroscopy, transmission electron microscopy, and atom probe tomography with electrochemical measurements to interrogate the temporal evolution of oxidation states, atom coordination, structure, and composition on Co2MnO4 and CoMn2O4 cubic spinel nanoparticle surfaces upon OER cycling in alkaline media. Co2MnO4 is activated at the onset of OER due to the formation of ≈2 nm Co‐Mn oxyhydroxides with an optimal Co/Mn ratio of ≈3. As OER proceeds, Mn dissolution and redeposition occur for the CoMn oxyhydroxides, extending the OER stability of Co2MnO4. Such dynamic dissolution and redeposition are also observed for CoMn2O4, leading to the formation of less OER‐active Mn‐rich oxides on the nanoparticle surfaces. This study provides mechanistic insights into how dynamic surface reconstruction and transformation affect the activity and stability of mixed CoMn cubic spinels toward OER.

A global–land snow scheme (GLASS) v1.0 for the GFDL Earth System Model: formulation and evaluation at instrumented sites

Abstract. Snowpacks modulate water storage over extended land regions and at the same time play a central role in the surface albedo feedback, impacting the climate system energy balance. Despite the complexity of snow processes and their importance for both land hydrology and global climate, several state-of-the-art land surface models and Earth System Models still employ relatively simple descriptions of the snowpack dynamics. In this study we present a newly-developed snow scheme tailored to the Geophysical Fluid Dynamics Laboratory (GFDL) land model version 4.1. This new snowpack model, named GLASS (Global LAnd–Snow Scheme), includes a refined and dynamical vertical-layering snow structure that allows us to track the temporal evolution of snow grain properties in each snow layer, while at the same time limiting the model computational expense, as is necessary for a model suited to global-scale climate simulations. In GLASS, the evolution of snow grain size and shape is explicitly resolved, with implications for predicted bulk snow properties, as they directly impact snow depth, snow thermal conductivity, and optical properties. Here we describe the physical processes in GLASS and their implementation, as well as the interactions with other surface processes and the land–atmosphere coupling in the GFDL Earth System Model. The performance of GLASS is tested over 10 experimental sites, where in situ observations allow for a comprehensive model evaluation. We find that when compared to the current GFDL snow model, GLASS improves predictions of seasonal snow water equivalent, primarily as a consequence of improved snow albedo. The simulated soil temperature under the snowpack also improves by about 1.5 K on average across the sites, while a negative bias of about 1 K in snow surface temperature is observed.

Prediction of Ductile Damage in Composite Material Used in Type IV Hydrogen Tanks by Artificial Neural Network and Machine Learning with Finite Element Modeling Approach

This study investigates the degradation process of composite materials used in high‐pressure hydrogen storage vessels by employing advanced computational techniques. A recurrent neural network, specifically a bidirectional long short‐term memory (Bi‐LSTM) network, is utilized to predict the temporal evolution of ductile damage. The key degradation features are extracted from finite element modeling (FEM) computations using group method of data handling algorithms and treated as time‐series data. Results demonstrate that the Bi‐LSTM network can accurately undergo both elastic and plastic behaviors of the composite under tensile strength. Additionally, traditional machine learning (ML) algorithms such as extreme gradient boosting and random forest are employed to forecast strain degradation, showing promising results. This hybrid approach combining FEM, ML, and deep learning provides a comprehensive method for predicting the degradation of composite materials, offering significant potential for optimizing the design and durability of hydrogen storage vessels.

Investigation of Temperature at Al/Glass Fiber-Reinforced Polymer Interfaces When Drilling Composites of Different Stacking Arrangements

This attempt covers an investigation of cutting temperature at interfaces of Fiber Metal Laminates (FMLs) made of glass fiber-reinforced polymer (GFRP) stacked with an Al2020 alloy. GFRP/Al/GFRP and Al/GFRP/Al composite stacks are both investigated to highlight the effect of stacking arrangement on thermal behavior within the interfaces. In a first test series, temperature history is recorded within the metal/composite stack interfaces using preinstalled thermocouples. In a second test series, a wireless telemetry system connected to K-type thermocouples implanted adjacent to the cutting edge of the solid carbide drill is used to record temperature evolution at the tool tip. Focus is put on the effects of cutting speed and stacking arrangement on the thrust force, drilling temperature, and delamination. From findings, the temperature histories show high sensitivity to the cutting speed. When cutting Al/GFRP/Al, the peak temperature is found to be much higher than that recorded in GFRP/Al/GFRP and exceeds the glass transition point of the GFRP matrix under critical cutting speeds. However, thrust force obtained at constitutive phases exhibits close magnitude when the stacking arrangement varies, regardless of cutting speed. Damage analysis is also discussed through the delamination factor at different stages of FML thickness.

Optimization method of cable structure demolition driven by digital twin evolution model

The large-span cable structure has the problems of complex component connection, cumbersome demolition process and difficult process coordination, which lead to high construction safety risk and low efficiency. Moreover, the optimization of the construction process scheme is difficult, and the convergence and visualization of the finite element analysis are insufficient, which hinders the construction efficiency. This study proposes a cable structure demolition optimization method driven by the digital twin (DT) evolution model. From the perspective of virtual-real mapping, the DT evolution process of cable structure demolition is clarified. The virtualization method of physical demolition process is formed by integrating visual modeling, simulation modeling and data modeling. Taking full account of the temporal and spatial evolution of the demolition process, a DT evolution model of the demolition process is established. Starting from the information flow, component flow, control flow and their relationship, the problems existing in the process of demolition execution are analyzed. Driven by the twin evolution process, a data mapping method for the demolition process is proposed. The safety risk is characterized by the mechanical properties (MPs) of the structure, and the expression language of the data association relationship is established. The particle swarm optimization back propagation neural network (PSO-BPNN) is improved, and an intelligent analysis method of MPs in the whole process of demolition is proposed. The best construction scheme is obtained by integrating the changes in MPs between the various demolition steps. The information of the demolition evolution process is divided into functional modules, and a DT platform is developed to efficiently guide the actual construction. Taking the demolition process of a cable truss experimental model as an example, the effectiveness of the proposed method is verified. The research results show that the DT evolution model can accurately map the actual demolition process. The deep learning method analyzes the mechanical properties of the structure and obtains the best construction scheme quickly and accurately. The development and application of the twin platform improves the degree of visualization and realizes the efficient guidance of the virtual model to the actual construction.

From birth to death: The role of upper-crustal thermal maturation and volcanism in porphyry ore formation revealed in the Yerington district

The current race towards greener energy and its intertwined increase in Cu demand imposes the need to find mineralized centers that are economic despite greater depths. To this end, a better understanding of the processes controlling the metal tonnage of porphyry Cu deposits, the main source of Cu, is needed and the focus of current research and debate. This study provides a comprehensive view of the duration of upper-crustal magmatism and its significance in the degree of metal endowment in porphyry Cu deposits using the Yerington batholith, Nevada, USA, as a case study. The Yerington district constitutes an exceptional example of an exposed porphyry Cu system where the plutonic environment that fed the porphyritic dikes is accessible. Combining trace element geochemistry and high-precision geochronology of zircon from all exposed units, we propose that the thermal maturation of the upper crust by a continuum in magmatic activity without significant hiatuses is a key factor in enabling the formation of large porphyry Cu style mineralization. The Yerington magmatic system transitioned from a volcanically active environment with coeval plutonism to a growing upper-crustal magmatic reservoir that fed the porphyritic dikes and associated mineralized centers to then resume its volcanic activity terminating the ore-forming episode. A relatively high volcanic/plutonic ratio characterized the first expressions of Jurassic magmatism in the district from ∼170 Ma with the coeval eruption of the Artesia Lake volcanics and the protracted emplacement of the McLeod Hill pluton. Geochemically, this period recorded distinctive negative Eu anomalies and a gentle increase in Yb/Dy ratios in zircon with decreasing Ti contents over a large range of zircon crystallization temperatures. This persistent magmatism over ∼2 Myr thermally matured the upper crust, enabling the growth of magmatic reservoirs that formed the Bear and Luhr Hill plutons without known volcanic activity for ∼1.3 Myr. Zircon trace element compositions indicate that this transition occurred as a continuum in decreasing negative Eu anomalies and increasing Yb/Dy with decreasing Ti contents and age across the plutonic units, reaching the more evolved signatures and lowest crystallization temperatures in the zircon from porphyry dikes. An increase in the volcanic/plutonic ratio with the eruption of the Fulstone Spring sequence likely terminated the ore-forming potential of the district. Although many other studies have provided important views on the timing and duration of porphyry Cu deposit formation, the link to their plutonic roots has been limited due to their inaccessibility. Therefore, this study offers the first complete picture into the temporal and chemical evolution of an upper-crustal magmatic system and the roles that factors like prolonged magmatism and volcanism play before and after ore formation. We argue that without the thermal maturation of the upper crust, the construction of a long-lived magmatic reservoir would not have been possible, precluding the formation of porphyry Cu deposits. Under this premise, not only the duration of ore-related magmatic activity, but the occurrence of long-lived precursor magmatism might exercise crucial control on the final tonnage of the deposit and should be considered during exploration for new porphyry deposits.

Characterization of maritime traffic noise in two areas of the Mediterranean

One of the objectives of the European JPI Oceans DeuteroNoise project is to investigate the soundscapes within different sea basins. Measurement campaigns, including the recording of raw noise data (wave files), have been carried out in the Venice Lagoon, in the North Adriatic and on Barcelona shore. In this work we will present the first results. The data acquired were processed to obtain the characteristic spectrum and noise levels; subsequently the velocity of the particles will be acquired and analysed. The analysis of the recorded data involves the design of a taxonomy of anthropogenic noise sources, e.g. large ships, small boats or any other existing noise source. The recorded data were labelled with these sources, describing their main audio characteristics (spectral distribution, temporal evolution, etc.) for their characterization and future blind identification. Throughout the measurement period, ship and route data were acquired using AIS (Automatic Identification System) and Vessel Monitoring System (VMS) in the measurement sites. The preliminary analysis and labelling, together with ship and route data, will allow the reproduction of acoustic environments of anthropogenic origin in laboratories and the creation of acoustic maps of the basins investigated using commercial software.

The genomic and transcriptomic landscape of metastastic urothelial cancer

Metastatic urothelial carcinoma (mUC) is a lethal cancer, with limited therapeutic options. Large-scale studies in early settings provided critical insights into the genomic and transcriptomic characteristics of non-metastatic UC. The genomic landscape of mUC remains however unclear. Using Whole Exome (WES) and mRNA sequencing (RNA-seq) performed on metastatic biopsies from 111 patients, we show that driver genomic alterations from mUC were comparable to primary UC (TCGA data). APOBEC, platin, and HRD mutational signatures are the most prevalent in mUC, identified in 56%, 14%, and 9% of mUC samples, respectively. Molecular subtyping using consensus transcriptomic classification in mUC shows enrichment in neuroendocrine subtype. Paired samples analysis reveals subtype heterogeneity and temporal evolution. We identify potential therapeutic targets in 73% of mUC patients, of which FGFR3 (26%), ERBB2 (7%), TSC1 (7%), and PIK3CA (13%) are the most common. NECTIN4 and TACSTD2 are highly expressed regardless of molecular subtypes, FGFR3 alterations and sites of metastases.

A Sustainable Production Segment of Global Value Chain View on Semiconductors in China: Temporal and Spatial Evolution and Investment Network

A Sustainable Production Segment of Global Value Chain View on Semiconductors in China: Temporal and Spatial Evolution and Investment Network

A hybrid mesoscale-continuum approach to understand and predict melting kinetics of Al powders during laser processing

Abstract Laser interaction with metallic powders during additive manufacturing (AM) leads to fast heating and cooling rates that can affect the quality of the final products due to the formation of defects. One of the first steps towards predicting microstructures generated during AM, therefore, requires an accurate understanding of the laser energy deposition mechanisms that determine the melting kinetics at the level of individual powders. The critical challenge, however, is the availability of computational methods that can model the laser energy absorption, heat transfer, and the related microstructure evolution in individual metal powders at the length and time scales of AM. This manuscript demonstrates the capability of a novel scale-bridging methodology that combines the mesoscale quasi-coarse-grained dynamics (QCGD) simulations with a continuum two-temperature model (TTM) to account for the atomistic mechanisms of laser energy deposition and microstructure evolution and predict the kinetics of melting of individual powders at the experimental time and length scales. The scale-bridging capability of the hybrid QCGD-TTM simulations is demonstrated here by investigating the laser-induced microstructure evolution in aluminum powders with various sizes ranging from 200 nm to 20 µm. The analysis of the evolution of temperature, pressure, phase fraction, and melt fronts suggests the melting mechanism is heterogeneous due to the interaction with a laser, and the melting time is observed to decrease exponentially as the laser intensity increases. The solid-liquid interface velocity can be quantified to identify correlations with interface temperatures, and the predicted values satisfy the theoretically reported limits of crystal stability of metals against homogeneous melting. In addition, the pre-melting is found at the grain boundaries of 20 µm polycrystalline aluminum powder, while a minute contribution to melting is observed. This manuscript demonstrates the capability of the QCGD-TTM method to capture laser-powder interaction and allow the investigation of the kinetics of laser melting.

A Study of the Evolution of Haze Microblog Concerns Based on a Co-Word Network Analysis

Haze is a phenomenon caused by excessive PM2.5 (air-borne particulate matter having a diameter of fewer than 2.5 μm) and other pollutants and results from the interaction between specific climatic conditions and human activities. It significantly impacts human health, transportation, and the natural environment and has aroused widespread concern. However, the influence of haze on human mental health, being hidden and indirect, is often overlooked. When haze pollution occurs, people express their feelings and concerns about haze events on media such as Weibo. At present, few studies focus on haze public opinion, as well as the changing trends in people’s discussion of haze since its emergence, which is of great significance for haze response and resource management. Based on the perspective of topic analysis, this study explores the psychological impact of haze on people by exploring the feelings of netizens in haze public opinion and investigates the evolution of people’s concerns based on long-term public opinion data. In this study, seven typical provinces and cities in China with severe haze pollution were selected as the research area. Based on data on the “haze” theme from Weibo from 2013 to 2019, first, the microblog posts were preprocessed, and the keyword co-word network was constructed. Second, the Louvain algorithm was used to detect the topic community. Based on this, the cosine similarity was calculated to realize the temporal evolution analysis of topics. The results show that with the development and change in haze pollution, the content and intensity of the topics netizens pay attention to have changed, including five types: merger, split, survival, transformation, and rebirth/extinction. People’s attention to haze shows obvious spatial differences, and it is related to the degree of haze pollution, which is bipolar. Areas with severe haze tend to pay more attention to haze itself and its influence, while areas with light haze pay more attention to haze control. The research results can provide valuable insights for governments and relevant departments in guiding public opinion and resource allocation.

Defining the Etiology of Renal Allograft Dysfunction Using Banff 2019 Classification: Correlation with Post-Transplant Duration and Creatinine Levels-A Comprehensive Analysis of 200 Renal Biopsies at a Tertiary Care Medical Center Hospital.

Chronic kidney disease is a growing global health issue, contributing significantly to morbidity and mortality. The incidence of end-stage renal disease (ESRD) is approximately 100 per million population. Renal transplantation remains the cornerstone treatment for ESRD, with a projected 20-year survival rate of 60%. We aim to define the etiology of renal allograft dysfunction using the Banff 2019 classification by analyzing 200 renal allograft biopsies in correlation with creatinine levels across post-transplant time frames. 200 renal allograft biopsies are analyzed using the recent Banff 2019 classification with creatinine levels and post-transplant duration correlation. The study included 150 (75%) male patients and 50 (25%) female patients, with the majority 78 (39%) representing the age group of 16-30 years. 36 (18%) biopsies were within 3-month post-transplant, while 92 (46%) were 2-year post-transplant. According to the Banff 2019 classification, 92 (46.0%) transplant rejection biopsies were identified, with most 54 (27%) exhibiting antibody-mediated rejection (Category 2), including 40 (20%) active acute antibody-mediated rejection (ABMR) and 14 (7.0%) chronic active ABMR. T-cell-mediated rejection (TCMR; Category 4) represented 12 (6%) biopsies, including 10 (5%) acute TCMR and 2 (1%) chronic active TCMR. Category 5, the miscellaneous group, represented 100 (50%) biopsies, out of which 32 (16%) exhibited calcineurin inhibitor (CNI) toxicity, 38 (19%) acute tubular necrosis, and 8 (4%) thrombotic microangiopathy. A notable variation in the dysfunction distribution across different post-transplant time frames indicated a temporal evolution in the underlying causes of allograft dysfunction. Specific Banff categories showed a robust association with renal dysfunction, potentially contributing to the elevation of creatinine levels and renal function deterioration. Our study highlights the intricate pathophysiology of renal allograft dysfunction. Most biopsies were attributed to ABMR whereas one-third of biopsies exhibited mixed lesions (ABMR and TCMR or ABMR and calcineurin inhibitor toxicity (CNIT)). Additionally, this study suggests that renal allograft rejection remains a significant contributor to graft dysfunction. A complex interplay between histological findings, Banff classification, and renal function is noted. A significant difference in the distribution of dysfunction across post-transplant time frames is noted suggesting a temporal evolution in the etiology of allograft dysfunction. Certain Banff categories demonstrate a stronger association with renal dysfunction that may influence creatinine level increase and renal function deterioration. In correspondence to the recent Banff 2019 guidelines for diagnosing ABMR, we emphasize the role of C4d staining on immunofluorescence or immunohistochemistry in allograft biopsies as imperative for timely diagnosis and immunosuppressant therapy adjustment, ultimately enhancing graft survival. Further research is needed to elucidate the underlying mechanisms driving renal dysfunction in different Banff categories, ultimately informing personalized management strategies for patients with renal allograft dysfunction. In line with the Banff 2019 guidelines for diagnosing ABMR, this study highlights the critical role of C4d staining through immunofluorescence or immunohistochemistry in allograft biopsies for early diagnosis and timely adjustment of immunosuppressive therapy, ultimately improving graft survival.

Temporal lobe evolution in Hominidae and the origin of human lobe proportions.

Objectives Evolutionary changes in hominin social complexity have been associated with increases in absolute brain size. The temporal lobes are nestled in the middle cranial fossae (MCF) of the skull, the dimensions of which allow estimation of temporal lobe volume (TLV) in extant and fossil taxa. Materials and Methods The main aim of this study is to determine where along the hominid phylogeny, major temporal lobe size transitions occurred. We used computed tomography (CT) scans of crania, 3D photogrammetry data, and laser surface scans of endocranial casts to measure seven MCF metrics in 11 extant anthropoid taxa using multiple regressions to estimate TLV in 5 extant hominids and 10 fossil hominins. Phylogenetic comparative methods mapped temporal lobe size, brain size, and temporal lobe proportions onto phylogenetic trees broadly for Hominidae and specifically for Hominini. Results Extant Homo sapiens were not an outlier in relative brain size, temporal lobe size, or proportions of the temporal lobes, but some proportions within the lobe were uniquely altered. The most notable changes in relative temporal lobe size and proportions saw a decrease in relative temporal lobe size and proportions in the genus Pan compared to other extant great apes and fossil hominins while there was a relative increase in the temporal lobe width and length in Australopithecus-Paranthropus clade compared to the genus Homo and other extant great apes including modern humans. Discussion We do not find support for the social brain, environmental or functional craniology hypotheses alone but think it prudent to consider the implications of cerebral reorganization between the temporal lobes and other regions of the brain within the context of these hypotheses and with future investigation is warranted.