Phylogenetic analysis of viviparity, matrotrophy, and other reproductive patterns in chondrichthyan fishes.

The reproductive diversity of extant cartilaginous fishes (class Chondrichthyes) is extraordinarily broad, reflecting more than 400 million years of evolutionary history. Among their many notable reproductive specialisations are viviparity (live-bearing reproduction) and matrotrophy (maternal provision of nutrients during gestation). However, attempts to understand the evolution of these traits have yielded highly discrepant conclusions. Here, we compile and analyse the current knowledge on the evolution of reproductive diversity in Chondrichthyes with particular foci on the frequency, phylogenetic distribution, and directionality of evolutionary changes in their modes of reproduction. To characterise the evolutionary transformations, we amassed the largest empirical data set of reproductive parameters to date covering nearly 800 extant species and analysed it via a comprehensive molecular-based phylogeny. Our phylogenetic reconstructions indicated that the ancestral pattern for Chondrichthyes is 'short single oviparity' (as found in extant holocephalans) in which females lay successive clutches (broods) of one or two eggs. Viviparity has originated at least 12 times, with 10 origins among sharks, one in batoids, and (based on published evidence) another potential origin in a fossil holocephalan. Substantial matrotrophy has evolved at least six times, including one origin of placentotrophy, three separate origins of oophagy (egg ingestion), and two origins of histotrophy (uptake of uterine secretions). In two clades, placentation was replaced by histotrophy. Unlike past reconstructions, our analysis reveals no evidence that viviparity has ever reverted to oviparity in this group. Both viviparity and matrotrophy have arisen by a variety of evolutionary sequences. In addition, the ancestral pattern of oviparity has given rise to three distinct egg-laying patterns that increased clutch (brood) size and/or involved deposition of eggs at advanced stages of development. Geologically, the ancestral oviparous pattern arose in the Paleozoic. Most origins of viviparity and matrotrophy date to the Mesozoic, while a few that are represented at low taxonomic levels are of Cenozoic origin. Coupled with other recent work, this review points the way towards an emerging consensus on reproductive evolution in chondrichthyans while offering a basis for future functional and evolutionary analyses. This review also contributes to conservation efforts by highlighting taxa whose reproductive specialisations reflect distinctive evolutionary trajectories and that deserve special protection and further investigation.

Microbially Induced Soil Colloidal Phosphorus Mobilization Under Anoxic Conditions.

Understanding the behavior of colloidal phosphorus (Pcoll) under anoxic conditions is pivotal for addressing soil phosphorus (P) mobilization and transport and its impact on nutrient cycling. Our study investigated Pcoll dynamics in acidic floodplain soil during a 30-day flooding event. The sudden oxic-to-anoxic shift led to a significant rise in pore-water Pcoll levels, which exceeded soluble P levels by more than 2.7-fold. Colloidal fractions transitioned from dispersed forms (<220 nm) to colloid-associated microaggregates (>220 nm), as confirmed by electron microscopy. The observed increase in colloidal sizes was paralleled by their heightened ability to form aggregates. Compared to sterile control conditions, anoxia prompted the transformation of initially dispersed colloids into larger particles through microbial activity. Curiously, the 16S rRNA and ITS microbial diversity analysis indicated that fungi were more strongly associated with anoxia-induced colloidal release than bacteria. These microbially induced shifts in Pcoll lead to its higher mobility and transport, with direct implications for P release from soil into floodwaters.

Biochar pH reduction using elemental sulfur and biological activation using compost or vermicompost.

This study aimed to improve biochar's quality for arid land applications by using elemental sulfur as a pH reducer agent co-applied with compost or vermicompost as biological activators. Biochar pH was decreased by the addition of elemental sulfur, with the highest reduction from 8.1 to 7.2 occurring when co-amended with vermicompost. Elemental sulfur increased the water-soluble concentrations of calcium, magnesium, and many other elements, and stimulated substrate-induced respiration, especially when co-amended with vermicompost. The bacterial diversity community structure were significantly affected by all treatments. The Shannon index significantly increased in response to compost and sulfur treatments, while the vermicompost treatments showed higher microbial evenness and equitability diversity indices. Multivariate analyses indicated that elemental sulfur oxidation was associated with specific sulfur-oxidizing bacterial clusters. Integrating biochar with sulfur and (vermi)compost was found to be a promising sustainable technology for managing excessive biochar alkalinity, increasing its fertility and potential for application in aridlands.

Insoluble-Phytate Improves Plant Growth and Arsenic Accumulation in As-Hyperaccumulator Pteris vittata: Phytase Activity, Nutrient Uptake, and As-Metabolism.

Phytate, the principal P storage in plant seeds, is also an important organic P in soils, but it is unavailable for plant uptake. However, the As-hyperaccumulator Pteris vittata can effectively utilize soluble Na-phytate, while its ability to utilize insoluble Ca/Fe-phytate is unclear. Here, we investigated phytate uptake and the underlying mechanisms based on the phytase activity, nutrient uptake, and expression of genes involved in As metabolisms. P. vittata plants were cultivated hydroponically in 0.2-strength Hoagland nutrient solution containing 50 µM As and 0.2 mM Na/Ca/Fe-phytate, with 0.2 mM soluble-P as the control. As the sole P source, all three phytates supported P. vittata growth, with its biomass being 3.2-4.1 g plant-1 and Ca/Fe-phytate being 19-29% more effective than Na-phytate. Phytate supplied soluble P to P. vittata probably via phytase hydrolysis, which was supported by 0.4-0.7 nmol P min-1 g-1 root fresh weight day-1 phytase activity in its root exudates, with 29-545 µM phytate-P being released into the growth media. Besides, compared to Na-phytate, Ca/Fe-phytate enhanced the As contents by 102-140% to 657-781 mg kg-1 in P. vittata roots and by 43-86% to 1109-1447 mg kg-1 in the fronds, which was accompanied by 21-108% increase in Ca and Fe uptake. The increased plant As is probably attributed to 1.3-2.6 fold upregulation of P transporters PvPht1;3/4 for root As uptake, and 1.8-4.3 fold upregulation of arsenite antiporters PvACR3/3;1/3;3 for As translocation to and As sequestration into the fronds. This is the first report to show that, besides soluble Na-phytate, P. vittata can also effectively utilize insoluble Ca/Fe-phytate as the sole P source, which sheds light onto improving its application in phytoremediation of As-contaminated sites.

Dementia classification using a graph neural network on imaging of effective brain connectivity.

Alzheimer's disease (AD) and Parkinson's disease (PD) are two of the most common forms of neurodegenerative diseases. The literature suggests that effective brain connectivity (EBC) has the potential to track differences between AD, PD and healthy controls (HC). However, how to effectively use EBC estimations for the research of disease diagnosis remains an open problem. To deal with complex brain networks, graph neural network (GNN) has been increasingly popular in very recent years and the effectiveness of combining EBC and GNN techniques has been unexplored in the field of dementia diagnosis. In this study, a novel directed structure learning GNN (DSL-GNN) was developed and performed on the imaging of EBC estimations and power spectrum density (PSD) features. In comparison to the previous studies on GNN, our proposed approach enhanced the functionality for processing directional information, which builds the basis for more efficiently performing GNN on EBC. Another contribution of this study is the creation of a new framework for applying univariate and multivariate features simultaneously in a classification task. The proposed framework and DSL-GNN are validated in four discrimination tasks and our approach exhibited the best performance, against the existing methods, with the highest accuracy of 94.0% (AD vs. HC), 94.2% (PD vs. HC), 97.4% (AD vs. PD) and 93.0% (AD vs. PD vs. HC). In a word, this research provides a robust analytical framework to deal with complex brain networks containing causal directional information and implies promising potential in the diagnosis of two of the most common neurodegenerative conditions.

Insight into the availability and desorption kinetics of Se and Cd in naturally-rich soils using diffusive gradients in thin-films technique.

Cadmium (Cd) contamination of selenium (Se)-rich soils may jeopardize the nutritional benefits of Se-biofortified crops. This study employed diffusive gradients in thin-films (DGT) technique and DIFS (DGT-induced fluxes in soils) model to understand the interdependency and driving factors of Se and Cd distribution and desorption kinetics across 50 soils from south China with naturally elevated levels. DGT-labile Se was the highest (up to 2.66 µg L-1) in non-carbonate/shale-derived soils, while Cd was maximal (5.53 µg L-1) in carbonate-based soils, reflecting soil background concentrations and soil characteristics. Over one-third of the soils showed labile Se:Cd molar ratio below 0.7, suggesting Cd phytotoxicity risks. The DIFS-derived response times (Tc) and desorption rate constants (k-1) suggested that Se was resupplied to the soil solution faster than Cd in soils with higher pH and SOM level, but Se resupply was still restricted due to the rapid depletion of its labile pool. As the first study of Se and Cd release kinetics in soils, our results reveal dependence on soil parent materials, with low labile Se:Cd soils presenting greater Cd hazards. By elucidating Se and Cd lability and interactions in soils, our findings help to inform management strategies to balance reduced Cd risk with adequate Se availability.

Adaptive Gated Graph Convolutional Network for Explainable Diagnosis of Alzheimer's Disease Using EEG Data.

Graph neural network (GNN) models are increasingly being used for the classification of electroencephalography (EEG) data. However, GNN-based diagnosis of neurological disorders, such as Alzheimer's disease (AD), remains a relatively unexplored area of research. Previous studies have relied on functional connectivity methods to infer brain graph structures and used simple GNN architectures for the diagnosis of AD. In this work, we propose a novel adaptive gated graph convolutional network (AGGCN) that can provide explainable predictions. AGGCN adaptively learns graph structures by combining convolution-based node feature enhancement with a correlation-based measure of power spectral density similarity. Furthermore, the gated graph convolution can dynamically weigh the contribution of various spatial scales. The proposed model achieves high accuracy in both eyes-closed and eyes-open conditions, indicating the stability of learned representations. Finally, we demonstrate that the proposed AGGCN model generates consistent explanations of its predictions that might be relevant for further study of AD-related alterations of brain networks.

Speech patterns in responses to questions asked by an intelligent virtual agent can help to distinguish between people with early stage neurodegenerative disorders and healthy controls.

Previous research has provided strong evidence that speech patterns can help to distinguish between people with early stage neurodegenerative disorders (ND) and healthy controls. This study examined speech patterns in responses to questions asked by an intelligent virtual agent (IVA): a talking head on a computer which asks pre-recorded questions. The study investigated whether measures of response length, speech rate and pausing in responses to questions asked by an IVA help to distinguish between healthy control participants and people diagnosed with Mild Cognitive Impairment (MCI) or Alzheimer's disease (AD). The study also considered whether those measures can further help to distinguish between people with MCI, people with AD, and healthy control participants (HC). There were 38 people with ND (31 people with MCI, 7 people with AD) and 26 HC. All interactions took place in English. People with MCI spoke fewer words compared to HC, and people with AD and people with MCI spoke for less time than HC. People with AD spoke at a slower rate than people with MCI and HC. There were significant differences across all three groups for the proportion of time spent pausing and the average pause duration: silent pauses make up the greatest proportion of responses from people with AD, who also have the longest average silent pause duration, followed by people with MCI then HC. Therefore, the study demonstrates the potential of an IVA as a method for collecting data showing patterns which can help to distinguish between diagnostic groups.

IgG4 Disease-Related Ataxia.

We describe a male patient presenting with cerebellar ataxia and behavioural frontotemporal dementia in whom imaging showed cerebellar atrophy. He had significantly low N-acetyl aspartate to creatine (NAA/Cr) area ratio on MR spectroscopy of the cerebellum, primarily affecting the vermis. CT body scan showed extensive abnormal tissue within the mesentery, the retroperitoneum and perinephric areas. PET-CT showed increased tracer uptake within the wall of the aorta suggestive of an aortitis and within the perinephric tissue bilaterally. Biopsy of the perinephric tissue confirmed IgG4 disease. Treatment with steroids and mycophenolate improved his clinical state, but he developed symptoms attributed to pericardiac effusion that necessitated treatment initially with drainage and subsequently with pericardial window. After a course of rituximab, he had an episode of sepsis that did not respond to appropriate treatment and died as a result. Both the imaging findings and neurological presentation with cerebellar ataxia and behavioural frontotemporal dementia are novel in the context of IgG4 disease.

Kernel-based Nonlinear Manifold Learning for EEG-based Functional Connectivity Analysis and Channel Selection with Application to Alzheimer's Disease.

Dynamical, causal, and cross-frequency coupling analysis using the electroencephalogram (EEG) has gained significant attention for diagnosing and characterizing neurological disorders. Selecting important EEG channels is crucial for reducing computational complexity in implementing these methods and improving classification accuracy. In neuroscience, measures of (dis) similarity between EEG channels are often used as functional connectivity (FC) features, and important channels are selected via feature selection. Developing a generic measure of (dis) similarity is important for FC analysis and channel selection. In this study, learning of (dis) similarity information within the EEG is achieved using kernel-based nonlinear manifold learning. The focus is on FC changes and, thereby, EEG channel selection. Isomap and Gaussian Process Latent Variable Model (Isomap-GPLVM) are employed for this purpose. The resulting kernel (dis) similarity matrix is used as a novel measure of linear and nonlinear FC between EEG channels. The analysis of EEG from healthy controls (HC) and patients with mild to moderate Alzheimer's disease (AD) are presented as a case study. Classification results are compared with other commonly used FC measures. Our analysis shows significant differences in FC between bipolar channels of the occipital region and other regions (i.e. parietal, centro-parietal, and fronto-central) between AD and HC groups. Furthermore, our results indicate that FC changes between channels along the fronto-parietal region and the rest of the EEG are important in diagnosing AD. Our results and its relation to functional networks are consistent with those obtained from previous studies using fMRI, resting-state fMRI and EEG.

Cross-Frequency Multilayer Network Analysis with Bispectrum-based Functional Connectivity: A Study of Alzheimer's Disease.

Alzheimer's disease (AD) is a neurodegenerative disorder known to affect functional connectivity (FC) across many brain regions. Linear FC measures have been applied to study the differences in AD by splitting neurophysiological signals, such as electroencephalography (EEG) recordings, into discrete frequency bands and analysing them in isolation from each other. We address this limitation by quantifying cross-frequency FC in addition to the traditional within-band approach. Cross-bispectrum, a higher-order spectral analysis approach, is used to measure the nonlinear FC and is compared with the cross-spectrum, which only measures the linear FC within bands. This work reports the reconstruction of a cross-frequency FC network where each frequency band is treated as a layer in a multilayer network with both inter- and intra-layer edges. Cross-bispectrum detects cross-frequency differences, mainly increased FC in AD cases in δ-θ coupling. Overall, increased strength of low-frequency coupling and decreased level of high-frequency coupling is observed in AD cases in comparison to healthy controls (HC). We demonstrate that a graph-theoretic analysis of cross-frequency brain networks is crucial to obtain a more detailed insight into their structure and function. Vulnerability analysis reveals that the integration and segregation properties of networks are enabled by different frequency couplings in AD networks compared to HCs. Finally, we use the reconstructed networks for classification. The extra cross-frequency coupling information can improve the classification performance significantly, suggesting an important role of cross-frequency FC. The results highlight the importance of studying nonlinearity and including cross-frequency FC in characterising AD.

Heads or tails first? Evolution of fetal orientation in ichthyosaurs, with a scrutiny of the prevailing hypothesis.

According to a longstanding paradigm, aquatic amniotes, including the Mesozoic marine reptile group Ichthyopterygia, give birth tail-first because head-first birth leads to increased asphyxiation risk of the fetus in the aquatic environment. Here, we draw upon published and original evidence to test two hypotheses: (1) Ichthyosaurs inherited viviparity from a terrestrial ancestor. (2) Asphyxiation risk is the main reason aquatic amniotes give birth tail-first. From the fossil evidence, we conclude that head-first birth is more prevalent in Ichthyopterygia than previously recognized and that a preference for tail-first birth likely arose in derived forms. This weakens the support for the terrestrial ancestry of viviparity in Ichthyopterygia. Our survey of extant viviparous amniotes indicates that fetal orientation at birth reflects a broad diversity of factors unrelated to aquatic vs. terrestrial habitat, further undermining the asphyxiation hypothesis. We propose that birth preference is based on parturitional mechanics or carrying efficiency rather than habitat.

Tau-targeting antisense oligonucleotide MAPTRx in mild Alzheimer's disease: a phase 1b, randomized, placebo-controlled trial.

Tau plays a key role in Alzheimer's disease (AD) pathophysiology, and accumulating evidence suggests that lowering tau may reduce this pathology. We sought to inhibit MAPT expression with a tau-targeting antisense oligonucleotide (MAPTRx) and reduce tau levels in patients with mild AD. A randomized, double-blind, placebo-controlled, multiple-ascending dose phase 1b trial evaluated the safety, pharmacokinetics and target engagement of MAPTRx. Four ascending dose cohorts were enrolled sequentially and randomized 3:1 to intrathecal bolus administrations of MAPTRx or placebo every 4 or 12 weeks during the 13-week treatment period, followed by a 23 week post-treatment period. The primary endpoint was safety. The secondary endpoint was MAPTRx pharmacokinetics in cerebrospinal fluid (CSF). The prespecified key exploratory outcome was CSF total-tau protein concentration. Forty-six patients enrolled in the trial, of whom 34 were randomized to MAPTRx and 12 to placebo. Adverse events were reported in 94% of MAPTRx-treated patients and 75% of placebo-treated patients; all were mild or moderate. No serious adverse events were reported in MAPTRx-treated patients. Dose-dependent reduction in the CSF total-tau concentration was observed with greater than 50% mean reduction from baseline at 24 weeks post-last dose in the 60 mg (four doses) and 115 mg (two doses) MAPTRx groups. Clinicaltrials.gov registration number: NCT03186989 .

Effectiveness of Endophytic and Rhizospheric Bacteria from Moringa spp. in Controlling Pythium aphanidermatum Damping-Off of Cabbage.

In this study, endophytic and rhizospheric bacteria were isolated from Moringa olifera and M. perigreina from Oman, and their in vitro antagonistic activity against Pythium aphanidermatum was tested using a dual culture assay. The promising strains were tested further for their compatibility and potential for plant growth promotion, biofilm formation, antifungal volatile organic compound (VOC) production, and the biological control of P. aphanidermatum damping-off of cabbage (Brassica oleracea L.) under greenhouse conditions. A total of 12 endophytic and 27 rhizospheric bacteria were isolated from Moringa spp. Among them, Bacillus pumilus MPE1 showed the maximum antagonistic activity against P. aphanidermatum in the dual culture assay, followed by Paenibacillus glucanolyticus MPE3 and Pseudomonas indica MOR3 and MOR8. These bacterial isolates induced abundant morphological abnormalities in the hyphae of P. aphanidermatum, as observed via scanning electron microscopy. The in vitro cross-streak assay showed that these bacterial isolates were compatible among themselves, except for P. indica MOR8 × P. glucanolyticus MPE3. These antagonists released VOCs that restricted the growth of P. aphanidermatum in an in vitro assay. These antagonistic bacteria released 2,4-dimethylheptane and 4-methyloctane as the predominant volatile compounds. Of the four antagonistic bacterial strains, P. indica MOR8 was capable of forming biofilm, which is considered a trait that improves the efficacy of rhizosphere inoculants. The results of the greenhouse experiments showed that the soil treatment with B. pumilus MPE1 showed the highest reduction (59%) in the incidence of P. aphanidermatum damping-off in cabbage, evidencing its potential as a biological control agent for the management of this disease. Further research is needed to characterize the antifungal traits and activities of B. pumilus MPE1 and to assert its potential use against other soil-borne plant pathogens.

Nano and fine colloids suspended in the soil solution regulate phosphorus desorption and lability in organic fertiliser-amended soils.

Mobile colloids impact phosphorus (P) binding and transport in agroecosystems. However, their relationship to P-lability and their relative importance to P-bioavailability is unclear. In soils amended with organic fertilisers, we investigated the effects of nano (NC; 1-20 nm), fine (FC; 20-220 nm), and medium (MC; 220-450 nm) colloids suspended in soil solution on soil P-desorption and lability. The underlying hypothesis is that mobile colloids of different sizes, i.e., NC, FC, and MC, may contribute differently to P-lability in soils enriched with organic fertiliser. NC- and FC-bound Pcoll were positively correlated with P-lability parameters from diffusive gradient in thin films (DGTA-labile P concentration, r ≥ 0.88; and DGTA-effective P concentration, r ≥ 0.87). The corresponding relations with MC-bound Pcoll are weaker (r values of 0.50 and 0.51). NC- and FC-bound Pcoll were also strongly correlated with soil P-resupply (r ≥ 0.64) and desorption (r ≥ 0.79) parameters during DGTA deployment, and the mobility of these colloids was corroborated by electron microscopy of DGTA gels. MC-bound Pcoll was negatively correlated with the solid-to-solution distribution coefficient (r = -0.42), indicating this fraction is unlikely to be the source of P-release from the solid phase after P-depletion from the soil solution. We conclude that NC and FC mainly contribute to regulating soil desorbable-P supply to the soil solution in the DGTA depletion zone (in vitro proxy for plant rhizosphere), and consequently may act as critical conditioners of P-bioavailability, whereas MC tends to form complexes that lead to P-occlusion rather than lability.

EEG-Based Graph Neural Network Classification of Alzheimer's Disease: An Empirical Evaluation of Functional Connectivity Methods.

Alzheimer's disease (AD) is the leading form of dementia worldwide. AD disrupts neuronal pathways and thus is commonly viewed as a network disorder. Many studies demonstrate the power of functional connectivity (FC) graph-based biomarkers for automated diagnosis of AD using electroencephalography (EEG). However, various FC measures are commonly utilised, as each aims to quantify a unique aspect of brain coupling. Graph neural networks (GNN) provide a powerful framework for learning on graphs. While a growing number of studies use GNN to classify EEG brain graphs, it is unclear which method should be utilised to estimate the brain graph. We use eight FC measures to estimate FC brain graphs from sensor-level EEG signals. GNN models are trained in order to compare the performance of the selected FC measures. Additionally, three baseline models based on literature are trained for comparison. We show that GNN models perform significantly better than the other baseline models. Moreover, using FC measures to estimate brain graphs improves the performance of GNN compared to models trained using a fixed graph based on the spatial distance between the EEG sensors. However, no FC measure performs consistently better than the other measures. The best GNN reaches 0.984 area under sensitivity-specificity curve (AUC) and 92% accuracy, whereas the best baseline model, a convolutional neural network, has 0.924 AUC and 84.7% accuracy.

Bispectrum-based Cross-frequency Functional Connectivity: Classification of Alzheimer's disease.

Alzheimer's disease (AD) is a neurodegenerative disease known to affect brain functional connectivity (FC). Linear FC measures have been applied to study the differences in AD by splitting neurophysiological signals such as electroencephalography (EEG) recordings into discrete frequency bands and analysing them in isolation. We address this limitation by quantifying cross-frequency FC in addition to the traditional within-band approach. Cross-bispectrum, a higher-order spectral analysis, is used to measure the nonlinear FC and is compared with the cross-spectrum, which only measures the linear FC within bands. Each frequency coupling is then used to construct an FC network, which is in turn vectorised and used to train a classifier. We show that fusing features from networks improves classification accuracy. Although both within-frequency and cross-frequency networks can be used to predict AD with high accuracy, our results show that bispectrum-based FC outperforms cross-spectrum suggesting an important role of cross-frequency FC. Clinical relevance-This establishes diagnostic relevance of cross-frequency coupling in Alzheimer's disease.

Keeping patient and public partnership at the heart of medical technology development during Covid-19: examples of adaptive practice.

NIHR (National Institute for Health Research) Devices for Dignity MedTech Cooperative (D4D) and NIHR Children and Young People MedTech Cooperative (CYPMedTech) have established track records in keeping patient and public involvement (PPI) at the core of medical technology development, evaluation and implementation. The 2020 global COVID-19 pandemic presented significant challenges to maintaining this crucial focus. In this paper we describe prior successful methodologies and share examples of the adaptations made in order to continue to engage with patients and the public throughout the pandemic and beyond. We reflect on learning gained from these experiences, and new areas of scope and focus relating to broadening the reach of engagement and representation, along with associated resource requirements and impact metrics.