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It is widely recognized that collisional mountain belt topography is generated by crustal thickening and lowered by river bedrock erosion, linking climate and tectonics 1 – 4 . However, whether surface processes or lithospheric strength control mountain belt height, shape and longevity remains uncertain. Additionally, how to reconcile high erosion rates in some active orogens with long-term survival of mountain belts for hundreds of millions of years remains enigmatic. Here we investigate mountain belt growth and decay using a new coupled surface process 5 , 6 and mantle-scale tectonic model 7 . End-member models and the new non-dimensional Beaumont number, Bm, quantify how surface processes and tectonics control the topographic evolution of mountain belts, and enable the definition of three end-member types of growing orogens: type 1, non-steady state, strength controlled (Bm > 0.5); type 2, flux steady state 8 , strength controlled (Bm ≈ 0.4−0.5); and type 3, flux steady state, erosion controlled (Bm < 0.4). Our results indicate that tectonics dominate in Himalaya–Tibet and the Central Andes (both type 1), efficient surface processes balance high convergence rates in Taiwan (probably type 2) and surface processes dominate in the Southern Alps of New Zealand (type 3). Orogenic decay is determined by erosional efficiency and can be subdivided into two phases with variable isostatic rebound characteristics and associated timescales. The results presented here provide a unified framework explaining how surface processes and lithospheric strength control the height, shape, and longevity of mountain belts. Using the new Beaumont number presented, it is concluded that the topographic evolution of collisional mountain belts is determined by the combination of plate velocity, crustal rheology and surface process efficiency.

Climate extremes threaten the land carbon sink and it is important to understand their impact in a changing climate. A recent study provides new insights on reduced forest carbon uptake during the severe 2022 drought and heatwave across Europe.

Crustal thickening associated with orogenic growth elevates topography, causing orographic enhancement of precipitation, which in turn facilitates local erosion and possibly intensifies localization of deformation. How these three processes—deformation, precipitation, and erosion—coordinate during orogenic growth remains unknown. Here, we present a numerical model where tectonics, surface processes, and orographic precipitation are tightly coupled, and explore the impact on low, intermediate, and high erodibility orogens. We show that, for intermediate erodibility models, rock uplift rates and precipitation rates correlate well with erosion rates during the formation of orogenic plateaus with high correlation coefficients of ~0.9 between rock uplift and erosion rates, and ~0.8 between precipitation and erosion rates. We demonstrate a cyclicity of correlation evolution among uplift, precipitation, and erosion rates through the development of new faults propagating outward. These results shed insights into the relative tectonic or climatic control on erosion in active orogens (e.g., Himalayas, Central Andes, and Southern Alps of New Zealand), and provide a plausible explanation for several conflicting data and interpretations in the Himalayas, which depend on the stage of maturity of the newest fault and the relative locations to old faults. This study presents a model showing how tectonics, climate, and erosion interact during orogen growth. It reveals a cyclic correlation between uplift, precipitation, and erosion, offering insights into exogenic controlled orogens like the Himalayas.

This study aimed to investigate how electromyography (EMG) cluster analysis of the rectus femoris (RF) could help to better interpret gait analysis in patients with cerebral palsy (CP). The retrospective gait data of CP patients were categorized into two groups: initial examination (E1, 881 patients) and subsequent examination (E2, 377 patients). Envelope-formatted EMG data of RF were collected. Using PCA and a combined PSO-K-means algorithm, main clusters were identified. Patients were further classified into crouch, jump, recurvatum, stiff and mild gait for detailed analysis. The clusters (labels) were characterized by a significant peak EMG activity during mid-swing (L1), prolonged EMG activity during stance (L2), and a peak EMG activity during loading response (L3). Notably, L2 contained 76% and 92% of all crouch patients at E1 and E2, respectively. Comparing patients with a crouch gait pattern in L2-E1 and L2-E2, two subgroups emerged: patients with persistent crouch (G1) and patients showing improvement at E2 (G2). The minimum activity of RF during 20–45% of the gait was significantly higher (p = 0.025) in G1 than in G2. A greater chance of improvement from crouch gait might be associated with lower RF activity during the stance phase. Using our findings, we could potentially establish an approach to improve clinical decision-making regarding treatment of patients with CP.

Duchenne gait, characterized by an ipsilateral trunk lean towards the affected stance limb, compensates for weak hip abductor muscles, notably the gluteus medius (GM). This study aims to investigate how electromyographic (EMG) cluster analysis of GM contributes to a better understanding of Duchenne gait in patients with cerebral palsy (CP). We analyzed retrospective gait data from 845 patients with CP and 65 typically developed individuals. EMG activity of GM in envelope format were collected and examined with gait kinematics and kinetics parameters in frontal plane and hip abductor strength, and hip abduction passive range of motion. Six key EMG envelope features during ten gait phases were extracted and normalized. A hybrid K-means-PSO clustering algorithm was employed, followed by hierarchical clustering. The identified clusters were characterized by having a low (cluster_1), medium (cluster_2), and high (cluster_3) activity of GM during loading response. The patients in cluster_1 also exhibited pathological gait characteristics, including increased trunk lateral lean and weak hip abductor, which are associated with Duchenne gait. The patients in this cluster were subclustered according to their response to the intervention: SUB_1 with a significant improvement in trunk obliquity, pelvic obliquity, and hip abduction after intervention, and SUB_2 without such improvement. Comparing pre-treatment EMG and clinical exam of the sub_clusters, SUB_1 had significantly higher activity of GM during 50–87% of the gait cycle with a greater passive range of hip abduction compared to SUB_2. This study established a relationship between EMG of GM and frontal plane gait abnormalities in patients with CP, highlighting potential improvement in Duchenne gait with prolonged GM activity during swing after the intervention.

Despite an increasingly detailed understanding of endogenous and environmental growth-controlling signals and their signaling networks, little is known on how these networks are integrated with the cell expansion machinery. Members of the CrRLK1L family control cell wall properties and cell expansion in a variety of developmental and environmental contexts. Two recent reports provide exciting new insights into the mode of action of these RLKs. One study shows that one family member, FERONIA (FER), is required for the production of hydroxyl radicals in the female gametophyte, which causes pollen tube rupture and sperm cell release during fertilization. Another study shows that FER is a receptor for a signaling peptide (Rapid Alkalinization Factor 1 [RALF1]) that triggers cell wall alkalinization and growth arrest, possibly through the inhibition of plasma membrane H⁺-ATPase activity. RALF1 belongs to a large gene family, with a wide range of expression patterns. Other CrRLK1L family members therefore may also be receptors for RALF-like peptides. These findings have important implications for our understanding of the control of cell wall integrity during growth and raise new intriguing questions.

Background Mass spectrometry has become the analytical method of choice in metabolomics research. The identification of unknown compounds is the main bottleneck. In addition to the precursor mass, tandem MS spectra carry informative fragment peaks, but the coverage of spectral libraries of measured reference compounds are far from covering the complete chemical space. Compound libraries such as PubChem or KEGG describe a larger number of compounds, which can be used to compare their in silico fragmentation with spectra of unknown metabolites. Results We created the MetFrag suite to obtain a candidate list from compound libraries based on the precursor mass, subsequently ranked by the agreement between measured and in silico fragments. In the evaluation MetFrag was able to rank most of the correct compounds within the top 3 candidates returned by an exact mass query in KEGG. Compared to a previously published study, MetFrag obtained better results than the commercial MassFrontier software. Especially for large compound libraries, the candidates with a good score show a high structural similarity or just different stereochemistry, a subsequent clustering based on chemical distances reduces this redundancy. The in silico fragmentation requires less than a second to process a molecule, and MetFrag performs a search in KEGG or PubChem on average within 30 to 300 seconds, respectively, on an average desktop PC. Conclusions We presented a method that is able to identify small molecules from tandem MS measurements, even without spectral reference data or a large set of fragmentation rules. With today's massive general purpose compound libraries we obtain dozens of very similar candidates, which still allows a confident estimate of the correct compound class. Our tool MetFrag improves the identification of unknown substances from tandem MS spectra and delivers better results than comparable commercial software. MetFrag is available through a web application, web services and as java library. The web frontend allows the end-user to analyse single spectra and browse the results, whereas the web service and console application are aimed to perform batch searches and evaluation.

Several studies have highlighted the advantages of employing artificial intelligence (AI) models in gait analysis. However, the credibility and practicality of integrating these models into clinical gait routines remain uncertain. This study critically evaluates an AI model’s ability to predict lower extremity joint moments during gait in patients with cerebral palsy (CP). We employed a three-step approach to assess the feasibility of a previously developed AI model that predicted joint moments during walking for 622 patients with CP, using joint kinematics as input. First, we established clinically relevant thresholds for lower extremity joint moments, categorizing into three labels: acceptable (Green), acceptable with caution (Yellow), and unacceptable (Red). This categorization was based on the normalized root mean square error (nRMSE) between lab-measured and predicted joint moments. We explored the relationship between gait kinematics and joint moments by correlating the kinematic inputs with their respective output labels. Finally, we developed a linear discrimination analysis (LDA) model to predict labels for newly predicted joint. Assessing the validity of thresholds, an ANOVA one-way analysis and Bonferroni post-hoc statistical tests were performed to find significant differences between the nRMSE values for each label. The hip joint exhibited the largest population of Green labels (84%), while the ankle joint had the smallest (50%). Regressive differences in joint kinematics and gait profile scores were observed across all labels. The LDA model achieved an accuracy of 85.2% and an F-score of 92% for predicting Green label in hip joint moment. Additionally, more severe patient conditions were associated with an increase in Red-labeled predictions. Our findings highlight significant differences in nRMSE among labels, demonstrating the effectiveness of the proposed thresholds for labeling joint moments. Overall, the AI model’s performance was rated as moderate, and the three-step approach proved valuable for assessing the feasibility of AI models in clinical settings.

Modification of pectin, a component of the plant cell wall, is required to facilitate signaling by a RALF peptide, which is essential for many physiological and developmental processes.Modification of pectin, a component of the plant cell wall, is required to facilitate signaling by a RALF peptide, which is essential for many physiological and developmental processes.

In order to improve the understanding foot function in the presence of planovalgus foot deformity, functional joint center determination is applied to the ankle and midfoot for application in 3D-gait analysis. Gait data of 36 patients with planovalgus (PV) foot deformity as well as of 33 typically developing (TD) subjects were collected using foot markers according to the Heidelberg Foot Measurement method. During single-limb stance subjects performed a circular movement of the foot and ankle (CIR) by drawing a circle with the hallux in the air. Midfoot joint center location as well as kinematics was calculated based (a) on functional calibration, (b) via a simple midpoint approach, and (c) via linear regression. All typically developing participants were able to perform the CIR movement with sufficient ROM for calibration whereas 10 % of the participants with idiopathic PV foot deformity and 72 % of the participants with a neurogenic PV foot were not able to perform this movement adequately. Nevertheless, the regression approach led to almost the same location of the midfoot joint center compared to the functional method with similar kinematics. PV feet show substantially larger Forefoot/Hindfoot flexion and Forefoot/Hindfoot adduction in gait compared to TD feet. On top, feet with neurologic background show reduced ROM of these angles in gait. The CIR movement task may prove useful in future studies monitoring active ranges of ankle and midfoot motion since the kinematics of this task may also be directly assessed via the proposed regression approach.