Evaluating Waterbirth Risks: A Deep Dive into the POOL Cohort Study’s Research GapsWei-Zhen Tang a, Tai-Hang Liu a,**, Xia Lan a,*

In this paper, we consider the following 2D incompressible chemotaxis-Navier-Stokes equations with the fractional diffusion { ∂ t n + u · ∇ n − ∆ n = − ∇ · ( n ∇ c )+ n ( 1 − n )( n − a ) , ∂ t c + u · ∇ c − ∆ c = − cn , ∂ t u + u · ∇ u + ∧ 2 α u + ∇ P = − n ∇ ϕ , where ∧ : = ( − ∆ ) 1 2 and α ∈ [ 1 2 , 1 ] . By establishing the new priori estimates, we get the global well-posedness for the above system with large initial data.

Amoxicillin-induced DRESS syndrome without eosinophilia

Chinese hamster ovary (CHO) bioprocesses, the dominant platform for therapeutic protein production, are increasingly used to produce complex multispecific proteins. Product quantity and quality are affected by intracellular conditions, but these are challenging to measure and often overlooked during process optimization studies. pH is known to impact quality attributes like protein aggregation across upstream and downstream processes, yet the effects of intracellular pH on cell culture performance are largely unknown. Recently, advances in protein biosensors have enabled investigations of intracellular environments with high spatiotemporal resolution. In this study, we integrated a fluorescent pH-sensitive biosensor into a bispecifc (bisAb)-producing cell line to investigate changes in endoplasmic reticulum pH (pH ER). We then investigated how changes in lactate metabolism impacted pH ER, cellular redox, and product quality in fed-batch and perfusion bioreactors. Our data show pH ER rapidly increased during exponential growth to a maximum of pH 7.7, followed by a sharp drop in stationary phase in all perfusion and fed-batch conditions. pH ER decline in stationary phase was driven by an apparent loss of cellular pH regulation that occurred despite differences in redox profiles. Protein aggregate levels correlated positively with pH ER, indicating that aggregation is not solely driven by oxidative stress and cellular protein load. Improved characterization of intracellular changes in bioprocesses can help guide media optimizations, improve bioprocess control strategies or provide new targets for cell engineering.

Geographical distribution and diversity patterns of bird species are influenced by climate change. The Rouget’s rail (Rougetius rougetii) is a ground-dwelling endemic bird species distributed in Ethiopia and Eritrea. It is a near-threatened species menaced by habitat loss, one of the main causes of population declines for bird species. The increasing effects of climate change may further threaten the species’ survival. So far, the spatial distribution of this species is not fully documented. With this study, we develop current potential suitable habitat and predict the future habitat shift of R. rougetii based on environmental data such as bioclimatic variables, population density, vegetation cover, and elevation using ten algorithms. We evaluated the importance of environmental factors in shaping the bird’s distribution and how it shifts under climate change scenarios. We used 182 records of R. rougetii from Ethiopia and nine bioclimatic, population density, vegetation cover, and elevation variables to run the 10 model algorithms. Among 10 algorithms, eight were selected for ensembling models according to their predictive abilities. The current suitable habitats for R. rougetii were predicted to cover an area of about 82,000 km² despite being highly fragmented. The model suggested that temperature seasonality (bio4), elevation, and mean daily air temperatures of the driest quarter (bio9) contributed the most to delimiting suitable areas for this species. R. rougetii is sensitive to climate change associated with elevation, leading to a large, shrinking distribution of suitable areas. The projected spatial and temporal pattern of habitat loss of R. rougetii suggests the importance of climate change mitigation and implementing long-term conservation and management strategies for this threatened endemic bird species.

Quinone outside inhibitor (QoI) fungicide resistance in A. alternata populations was reported in Brazil for the first time in 2019, in São Paulo orchards, and the mutation G143A in cytochrome b ( cytb) was found in resistant isolates. Our study investigated the infectious process, production of Reactive Oxygen Species (ROS) and fungal cell death in resistant (QoI-R) and sensitive (QoI-S) Alternaria alternata pathotype tangerine (Aapt) isolates. Morphological characterization of Aapt isolates was performed using Confocal Laser Scanning Microscopy (CLSM). Alternaria brown spot (ABS) symptoms were produced by Aapt isolates on tangelo cv. BRS Piemonte. Germination of QoI-R conidia and production of germ tubes on tangelo leaflets treated with 100µg mL -1 of pyraclostrobin 18 hours after inoculation (hai) was observed using Scanning Electron Microscopy (SEM). At the same time, QoI-S conidial germination was inhibited on tangelo leaflets treated with pyraclostrobin. ROS production and cell death in Aapt isolates at high fungicide concentration were observed using CLSM. QoI-S conidia exhibited high ROS production, indicating high oxidative stress. When dyed with Propidium Iodate (PI), QoI-S conidia emitted red fluorescence, showing cell death and confirming their sensitive phenotype. In contrast, QoI-R conidia neither produced ROS nor exhibited red fluorescence, indicating no cell death and confirming their resistant phenotype. Therefore, our findings evidence that microscopic techniques may help characterize events during fungi-plant interactions, ROS production, cell death, and Aapt phenotypes resistant and sensitive to QoIs using fluorometric protocols. Research Highlights Our findings demonstrate the infection process, reactive oxygen species production, and cell death of Aapt isolates resistant and sensitive to quinone outside inhibitors (QoIs). Confocal laser scanning microscopy may help to characterize phenotypes resistant and sensitive to QoIs using fluorometric protocols.

Demographic processes can substantially affect a species’ response to changing ecological conditions, necessitating the combined consideration of genetic responses to environmental variables and neutral genetic variation. Using a seascape genomics approach combined with population demographic modelling, we explored the interplay of demographic and environmental factors that shaped the current population structure in Indo-Pacific bottlenose dolphins (Tursiops aduncus) along most of the western Australian coastline. We combined large-scale environmental data gathered via remote sensing with RADseq genomic data from 138 individuals at 19 sampling sites. Using population genetic and outlier detection anaylses, we identified three distinct genetic clusters, coinciding with tropical, subtropical and temperate provincial bioregions. In contrast to previous studies, our demographic models indicated that populations occupying the paleo-shoreline split into two demographically independent lineages before the last glacial maximum (LGM). A subsequent split after the LGM at 8.5 kya gave rise to the Shark Bay population, thereby creating the three currently observed clusters. Although multi-locus heterozygosity declined from north to south, dolphins from the southernmost cluster inhabiting temperate waters had higher heterozygosity in potentially adaptive loci, compared to dolphins from subtropical and tropical waters. These findings suggest ongoing adaptation to cold temperate waters in the southernmost cluster, possibly linked to distinct selective pressures between the different bioregions. Our study demonstrated that in the marine realm, without apparent physical boundaries, only a combined approach can fully elucidate the intricate environmental and genetic interactions shaping the evolutionary trajectory of marine mammals.

The deployment of large language models (LLMs) on edge devices and non-server environments presents significant challenges, primarily due to constraints in memory usage, computational power, and inference time. This paper investigates the feasibility of running LLMs across such devices by focusing on optimizing memory usage, employing quantization techniques, and reducing inference time. Specifically, we utilize LLaMA 2 for biomedical text summarization and implement Low-Rank Adaptation (LoRA) quantization to compress the model size to compress the model size and fine-tune it using limited resources. Our study systematically evaluates memory consumption during both training and inference phases, demonstrating substantial reductions through efficient LoRA quantization. Our results indicate that with careful optimization, it is feasible to deploy sophisticated LLMs like LLaMA 2 on low powered devices, thereby broadening the scope of their application in resource-constrained environments.

Fluctuations in genomic repetitive fractions (repeatome) are known to impact several facets of evolution, such as ecological adaptation and speciation processes. Therefore, investigating the divergence of repetitive elements can provide insights into an important evolutionary force. However, it is not clear how the different repetitive element lineages are impacted by the ecological changes. To discuss this, we used the Neotropical legume genus Erythrostemon (Caesalpinioideae) as a model, given its ancient origin (~33 Mya), lineage-specific niche conservatism, macroecological heterogeneity, and disjunct distribution in Meso- and South American (MA and SA, respectively) lineages. We performed a comparative repeatomic analysis of 18 Erythrostemon species to test the impact of environmental variables over repeats diversification. Overall, repeatome composition was diverse, with high abundances of satDNAs and Ty3/gypsy-Tekay transposable elements, predominantly in the MA and SA lineages, respectively. Remarkably the first divergent lineages (E. pannosus and E. placidus) of the MA clade preserve plesiomorphic Tekay and satDNA patterns. This pattern was altered in the MA-sensu stricto subclade with a striking genomic differentiation (expansion of satDNA and retraction of Tekay) associated with the colonization of a new environment in Central America around 20 Mya. Our data reveal that the current species-specific Tekay pool was the result of two bursts of amplification probably in the Miocene, with distinct patterns for the MA and SA repeatomes. This suggests a strong role of the Tekay elements as modulators of the genome-environment interaction in Erythrostemon, providing macroevolutionary insights about mechanisms of repeatome differentiation and plant diversification across space and time.

Various test structures can be used to determine the specific contact resistivity of ohmic contacts. The transmission line model test structure and circular transmission line model test structure are the most commonly used. The analytical expressions of the former are straightforward and effectively describe the electrical behavior of a contact, while the concentric geometry of the latter eliminates complications during fabrication. In this paper, we present a hybrid test structure that combines the advantages of the transmission line and the circular transmission line models. The analytical expressions of the new structure are presented, and its finite-element modeling is undertaken. The effect of contact geometry on this test structure is also discussed. Using the presented test structure, determining contact parameters does not require any error corrections.

With organizations seeking faster, cheaper, and smarter ways of delivering higher quality software, many are looking towards generative artificial intelligence (AI) to drive efficiencies and innovation throughout the software development lifecycle. However, generative AI can suffer from several fundamental issues, including a lack of traceability in concept generation and decision making, the potential for making incorrect inferences (hallucinations), shortcomings in response quality, and bias. Quality engineering (QE) has long been utilized to enable more efficient and effective delivery of higher quality software. A core aspect of QE is adopting quality models to support various lifecycle practices, including requirements definition, quality risk assessments and testing. In this position paper, we introduce the application of QE to AI systems, consider shortcomings in existing AI quality models from the International Organization for Standardization (ISO), and propose extensions to ISO models based on the results of a survey. We also reflect on skills that IT graduates may need in the future, to support delivery of better-quality AI.

A document by Chien Chieh Wang. Click on the document to view its contents.

Background: In the context of precision diagnosis for various subtypes of melanoma, identifying biomarkers with clinical translational potential from a molecular standpoint is crucial for a more comprehensive characterization of the disease. MUC18 is highly expressed in both tumor cells and tumor vasculature in major melanoma subtypes and is restricted to normal tissues. Methods: A noninvasive imaging approach for MUC18 in melanoma utilizing an immune Positron Emission Tomography (PET) radionuclide-conjugated drug (RDC) with an 89Zr-labeled humanized anti-MUC18 monoclonal antibody (mAb) was developed. A375, Sk-Mel-28, HMVII, and A549 cells and tumor model mice were conducted. Immuno-PET was employed to assess the specificity and targeting of three distinct melanoma cell line-derived xenografts (CDXs) and patient-derived tumor xenografts (PDXs) in immunodeficient mice. Results: The developed RDC, named 89Zr-IP150, demonstrated robust in vitro stability and high binding affinity, ensuring reliable and specific PET imaging of small, medium, and large subcutaneous tumors in human melanoma mouse xenotransplantation models. Notably, for the first time, the clinical translational potential of 89Zr-IP150 was successfully validated using a PDX model. Conclusions: These findings present a noninvasive, real-time method for the early screening of MUC18 (+) melanoma patients and are important for studying the early-stage biological distribution of MUC18-targeted antibody‒drug conjugates (ADCs).

Background: Trauma plays an important role in the development and maintenance of psychosis. However, it is still under-examined in daily clinical practice. The current study investigated the rates of recording of trauma-exposure in the clinical histories of a first-episode psychosis (FEP) cohort attending an early intervention psychosis service. Methods: This study used a retrospective chart review methodology in a six-year epidemiologically complete (FEP) first-episode psychosis cohort attending an early intervention psychosis service. The Trauma and Life Events Checklist was used to define the rate and types of trauma exposure reported in clinical histories. The relationships were examined between recorded trauma-exposure and positive and negative symptoms, depression, and duration of untreated psychosis at first assessment. Results: A high rate of recorded trauma exposure was found, indicating that clinicians are recording trauma-exposure in daily practice through clinical histories. Childhood trauma exposure was recorded in 47.4% of the sample. No significant relationships between the recorded trauma-exposure and symptom measures were found. A significant relationship was found between interpersonal stressors and positive symptoms, and work-related stress and negative symptoms, highlighting the important of proximal stressful life events. Discussion: Even though clinicians were not assessing trauma systematically using standardised instruments, this study found that clinicians were recording trauma-exposure in daily practice. The high rates of trauma-exposure highlighted the need for trauma to be systematically diagnosed, assessed and treated in planning services for people with psychosis. This study found no significant relationship between recorded trauma-exposure and symptomology.

The design and development of efficient bifunctional electrocatalysts for fuel cells and rechargeable metal-air batteries have become increasingly urgent. This study systematically investigated the OER/ORR catalytic activities of NiN4, NiN3, NiN3H2, NiN4X, NiN3X, and NiN3H2X (X denotes axial ligand) through density functional theory (DFT) calulations. This study unveils two distinct reaction pathways for ORR and OER, involving proton-electron pairs adsorbed from both the solution and the catalyst surface. When proton-electron pairs are adsorbed from the solution. The introduction of N defects, two hydrogen atoms, and axial ligands, can significantly reduce the ORR overpotential. Specifically, NiN3, NiN3H2, NiN3X, and NiN3H2X (X = CN, NO2, and NH2) exhibited superior ORR activity compared to Pt. Meanwhile, the introduction of N-defects (NiN3) and two H atoms (NiN3H2) significantly improves their OER overpotential. To sum up, NiN3 and NiN3H2 show promise as pH-universal bifunctional electrocatalysts for both ORR and OER. On the other hand, when proton-electron pairs are adsorbed from the catalyst surface, the reaction energy barrier becomes the crucial metric for assessing catalytic activity. Our investigation reveals that NiN3H2 consistently exhibits optimal ORR activity across a wide pH range, regardless of the source of proton-electron pair (solvent or catalyst surface).

Drought is one of the most devastating causes of yield losses in crops like maize and the anticipated increases in severity and duration of drought spells due to climate change pose an imminent threat to agricultural productivity. To understand the drought response, phenotypic and molecular studies are typically performed at a given time point after drought onset, representing a steady-state adaptation response. Because growth is a dynamic process, we monitored the drought response with high temporal resolution and examined cellular and transcriptomic changes after rehydration at 4 and 6 days after leaf four appearance. These data showed that the division zone activity is a determinant for full organ growth recovery upon rehydration. Moreover, a prolonged maintenance of cell division by the ectopic expression of PLASTOCHRON1 extends the ability to resume growth after rehydration. The transcriptome analysis also indicated that GROWTH-REGULATING FACTORS (GRFs) affected leaf growth by impacting cell division duration, which was confirmed by a prolonged recovery potential of the GRF1-overexpression line after rehydration. Finally, we used a multiplex genome editing approach to evaluate the most promising differentially expressed genes from the transcriptome study and as such narrowed down the gene space from forty to seven genes for future functional characterization.

Network security is experiencing huge challenges as network attacks on traffic data become more frequent and sophisticated. In this paper, we employ hybrid deep learning models and low-rank approximation to present a novel method for multi-label categorization of network assaults on traffic data. Our suggested solution, LR-CNN-MLP, consists of three models While the CNN and MLP models extract features and categorise data, respectively, the low-rank approximation model reduces the input's dimensionality. Overall, by combining hybrid models and low-rank approximation, our proposed LR-CNN-MLP approach provides a promising solution for multi-label categorization of network attacks on traffic data.

Acquired resistance to first-line treatments in various cancers both promotes cancer recurrence as well as limits effective treatment. This is true for epidermal growth factor receptor (EGFR) mutations, for which secondary EGFR mutations are one of the principal mechanisms conferring resistance to the covalent inhibitor osimertinib. Thus, it is very important to develop a deeper understanding of the secondary mutational resistance mechanisms associated with EGFR mutations arising in tumors treated with osimertinib to expedite the development of innovative therapeutic drugs to overcome acquired resistance. This work uses all-atom molecular dynamics (MD) simulations to investigate the conformational variation of two reported EGFR mutants (L858R/L718Q and L858R/L792H) that resist osimertinib. The wild-type EGFR kinase domain and the L858R mutant are used as the reference. Our MD simulation results revealed that both the L718Q and L792 secondary mutations induce additional hydrogen bonds between the residues in the active pocket and the residues with the water molecules. These additional hydrogen bonds reduce the exposure area of C797, the covalent binding target of osimertinib. The additional hydrogen bonds also influence the binding affinity of the EGFR kinase domain by altering the secondary structure and flexibility of the amino acid residues in the domain. Our work highlights how the two reported mutations may alter both residue-residue and residue-solvent hydrogen bonds, affecting protein binding properties, which could be helpful for future drug discovery.