Browse Articles

Capacitance enhancement by ion-laminated borophene-like layered materials

A novel label-free method to determine equilibrium dissociation constants of antibodies binding to cell surface proteins

Author Correction: Multi-omic and single-cell profiling of chromothriptic medulloblastoma reveals genomic and transcriptomic consequences of genome instability

Determinants maintaining healthcare personnel’s motivation during COVID-19 pandemic in Uganda

The transcriptional response of cortical neurons to concussion reveals divergent fates after injury

Abstract Traumatic brain injury (TBI) is a risk factor for neurodegeneration, however little is known about how this kind of injury alters neuron subtypes. In this study, we follow neuronal populations over time after a single mild TBI (mTBI) to assess long ranging consequences of injury at the level of single, transcriptionally defined neuronal classes. We find that the stress-responsive Activating Transcription Factor 3 (ATF3) defines a population of cortical neurons after mTBI. Using an inducible reporter linked to ATF3, we genetically mark these damaged cells to track them over time. We find that a population in layer V undergoes cell death acutely after injury, while another in layer II/III survives long term and remains electrically active. To investigate the mechanism controlling layer V neuron death, we genetically silenced candidate stress response pathways. We found that the axon injury responsive dual leucine zipper kinase (DLK) is required for the layer V neuron death. This work provides a rationale for targeting the DLK signaling pathway as a therapeutic intervention for traumatic brain injury. Beyond this, our approach to track neurons after a mild, subclinical injury can inform our understanding of neuronal susceptibility to repeated impacts.

Pulsed laser deposition assisted epitaxial growth of cesium telluride photocathodes for high brightness electron sources

Investigating the Antiscale Magnetic Treatment Controversy: Insights from the Model Calcium Carbonate Scalant

Abstract The antiscale magnetic treatment (ASMT) claims to utilize magnetic field to combat scaling. However, its underlying mechanism, effectiveness, and reliability remain controversial. To address these contentious aspects, we analyze the influence of a magnetic field on the different stages of typical scale formation, using $${\text{CaCO}}_{3}$$ as a model scale. For simplification, we consider the working fluid, such as in domestic and industrial settings, as a homogeneous mixture of a supersaturated, multi-ionic solution and a suspension of neutral multiphase contaminants, a fraction of which is magnetic. We argue that the combined effects of pH variation and catalytic role of magnetic contaminants are crucial factors affecting the properties of the resultant scale. Based on these considerations, we clarify the controversy by showing that each side holds a valid piece of the overall picture of the ASMT process. Indeed, the two viewpoints on magnetic field’s influence on scaling can be explained along the following scenarios: (i) Within a non-contaminated, supersaturated solution, there is no significant field influence because, under typical laboratory conditions, the Lorentz force does not practically affect the scaling process. (ii) Within a high-pH, magnetically-contaminated, supersaturated solution, the field does have an influence: Here, gradient-force-driven agglomerated particulates can act as templates for heterogeneous nucleation and growth.

Gut–brain axis and neuropsychiatric health: recent advances

Alu-Sc-mediated exonization generated a mitochondrial LKB1 gene variant found only in higher order primates

Sensitivity of coronary hemodynamics to vascular structure variations in health and disease

Nanosilicon application changes the morphological attributes and essential oil compositions of hemp (Cannabis sativa L.) under water deficit stress

A bioinformatic approach to characterize the vitellogenin receptor and the low density lipoprotein receptor superfamily in the newt Cynops orientalis

Abstract The Low Density Lipoprotein receptors (LDLRs) gene family includes 15 receptors: very low-density lipoprotein receptor (VLDLR), LDLR, Sorting-related receptor with A-type repeats (SORLA), and 12 LDL receptor-related proteins (LRPs): LRP1, LRP1B, LRP2, LRP3, LRP4, LRP5, LRP6, LRP8, LRP10, LRP11, LRP12, LRP13. Most of these are involved in the transduction of key signals during embryonic development and in the regulation of cholesterol homeostasis. In oviparous animals, the VLDL receptor is also known as VTGR since it facilitates the uptake of vitellogenin in ovary. In tetrapods, information concerning genes encoding these proteins is limited to a few taxa. Here, we report the characterization of VTGR in the amphibian Cynops orientalis. The secondary structure analyses and the expression profiles obtained from hepatic and gonadal tissues of C. orientalis supported the role of VTGR as vitellogenin oocyte membrane receptor in this species. Moreover, to get a holistic view of the evolutionary history of this gene superfamily, we extended our investigation to all 15 genes belonging to the LDLR superfamily analyzing through a phylogenetic analysis a total of 161 sequences belonging to 11 genera of vertebrates. The position of LRP8 in the tree and its expression findings in C. orientalis ovary allowed us to suggest that other proteins of the LDLR superfamily could act as receptors during vitellogenesis.

Optimizing photovoltaic power plant forecasting with dynamic neural network structure refinement

Sustainability metrics targeted optimization and electric discharge process modelling by neural networks

Effect of external infusion connection devices replacement frequency on catheter related bloodstream infection

Metabolomic analysis of murine tissues infected with Brucella melitensis

Brucella is a gram negative, facultative intracellular bacterial pathogen that constitutes a substantial threat to human and animal health. Brucella can replicate in a variety of tissues and can induce immune responses that alter host metabolite availability. Here, mice were infected with B. melitensis and murine spleens, livers, and female reproductive tracts were analyzed by GC-MS to determine tissue-specific metabolic changes at one-, two- and four- weeks post infection. The most remarkable changes were observed at two-weeks post-infection when relative to uninfected tissues, 42 of 329 detected metabolites in reproductive tracts were significantly altered by Brucella infection, while in spleens and livers, 68/205 and 139/330 metabolites were significantly changed, respectively. Several of the altered metabolites in host tissues were linked to the GABA shunt and glutaminolysis. Treatment of macrophages with GABA did not alter control of B. melitensis infection, and deletion of the putative GABA transporter BMEI0265 did not alter B. melitensis virulence. While glutaminolysis inhibition did not affect control of B. melitensis in macrophages, glutaminolysis was required for macrophage IL-1β production in response to B. melitensis. In summary, these results indicate that Brucella infection alters host tissue metabolism and that these changes could have effects on inflammation and the outcome of infection.

Optimizing key parameters for grinding energy efficiency and modeling of particle size distribution in a stirred ball mill

Abstract Fine grinding using a stirred ball mill can enhance ore liberation but incurs high energy consumption, which can be minimized by optimizing operating conditions. This study explores the impact of key operational parameters—grinding time, stirrer tip speed, solid concentration, and feed size—on grinding efficiency, evaluated using specific energy inputs, in stirred milling of Egyptian copper ore. The particle size distribution (PSD) of ground products was simulated using the Gates–Gaudin-Schuhmann model (GGS) and the Rosin-Rammler-Benne (RRB) function. Taking minimum energy consumption into account, the finest particles (100% ~1 μm) were achieved at the maximum stirrer speed of 500 rpm and a moderate solid concentration of 33.3% after 17 h of grinding, consuming approximately 1225 kWh/t. Experimental data demonstrated a linear correlation between the natural logarithm of the cumulative retained fraction and particle size (µm). The proposed model accurately describes PSDs across different solid concentrations and grinding durations.

Site-selective photo-crosslinking for the characterisation of transient ubiquitin-like protein-protein interactions

Non-covalent protein-protein interactions are one of the most fundamental building blocks in cellular signalling pathways. Despite this, they have been historically hard to identify using conventional methods due to their often weak and transient nature. Using genetic code expansion and incorporation of commercially available unnatural amino acids, we have developed a highly accessible method whereby interactions between biotinylated ubiquitin-like protein (UBL) probes and their binding partners can be stabilised using ultraviolet (UV) light-induced crosslinks. The stabilised protein complexes can be purified using affinity purification and identified by mass spectrometry. The resultant covalent bonds can withstand even the harshest washing conditions, allowing for the removal of indirect binders whilst retaining and capturing weak and transient interactors that are commonly lost during wash steps. This technique is widely applicable and highly effective for identifying site-selective non-covalent interactors. Members of our team have previously demonstrated the benefit of this method using the small ubiquitin-like modifier (SUMO). Here, we provide further proof-of-principle validation of the method and highlight its generality by applying an optimised workflow to a lesser studied UBL, interferon stimulated gene 15 (ISG15). We show that this method is able to capture known ISG15 interactors from a complex protein mixture in a site-selective manner, only capturing proteins that specifically interact with the region of ISG15 where the unnatural amino acid was incorporated. This exquisite degree of sensitivity and specificity greatly improves upon previous screens aimed at identifying downstream non-covalent binders, or readers, of ISG15. Taken together, the approach opens the possibility of characterising previously undetected protein-protein interactions, with the potential of elucidating molecular mechanisms behind the most complex and poorly understood processes in the cell.

Comparison of aesthetic perception and acceptability of silver diamine fluoride staining between Spanish and Italian parents

Distributed Denial of Services (DDoS) attack detection in SDN using Optimizer-equipped CNN-MLP

Software-Defined Networks (SDN) provides more control and network operation over a network infrastructure as an emerging and revolutionary paradigm in networking. Operating the many network applications and preserving the network services and functions, the SDN controller is regarded as the operating system of the SDN-based network architecture. The SDN has several security problems because of its intricate design, even with all its amazing features. Denial-of-service (DoS) attacks continuously impact users and Internet service providers (ISPs). Because of its centralized design, distributed denial of service (DDoS) attacks on SDN are frequent and may have a widespread effect on the network, particularly at the control layer. We propose to implement both MLP (Multilayer Perceptron) and CNN (Convolutional Neural Networks) based on conventional methods to detect the Denial of Services (DDoS) attack. These models have got a complex optimizer installed on them to decrease the false positive or DDoS case detection efficiency. We use the SHAP feature selection technique to improve the detection procedure. By assisting in the identification of which features are most essential to spot the incidents, the approach aids in the process of enhancing precision and flammability. Fine-tuning the hyperparameters with the help of Bayesian optimization to obtain the best model performance is another important thing that we do in our model. Two datasets, InSDN and CICDDoS-2019, are utilized to assess the effectiveness of the proposed method, 99.95% for the true positive (TP) of the CICDDoS-2019 dataset and 99.98% for the InSDN dataset, the results show that the model is highly accurate.