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Fungal elicitation and precursor feeding: a novel approach to boost alkaloid yield in Catharanthus roseus cell suspension cultures
Design of a wearable compact pulsed electromagnetic field (PEMF) device for orthodontic treatments
Panduratin A inhibits topoisomerase 1 activity in acute myeloid leukaemia cells
A quasi-experimental study on responses to mechanical loads following different heart rate-based exercise intensities and water depths in postmenopausal women
Abstract The present study examined mechanical loads (ML) during a single bout of intermittent water jumps at different intensities and water depths. Nineteen postmenopausal women were non-randomly assigned to shallow-water level (SWL) or deep-water level (DWL) groups and performed four jump modes—countermovement, lunge, leg–arm open, and skipping (SKIP)—at moderate (MI) and high intensity (HI). The integral ML (IML) and average ML (AML) per jump were calculated for each condition. Baseline characteristics were compared between groups using the Mann–Whitney U test. Multi-factor analyses were performed in R, with body height adjusted as a covariate. Results showed that omnibus models revealed no significant depth × intensity interactions. For cumulative loading, a robust main effect of water depth was detected, proving that IML was generally greater in SWL than in DWL under MI. Under HI conditions, depth-modulated contrast magnitudes varied descriptively across modes, with skipping displaying a prominent exploratory post-hoc effect size. Furthermore, a significant main effect of intensity was observed for velocity metrics, wherein AML was consistently higher during MI than HI across all conditions. In conclusion, an intermittent aquatic jumping protocol in SWL at MI offers a generally superior strategy for generating higher cumulative mechanical loading. However, the localized variations in individual post-hoc patterns warrant careful interpretation strictly as exploratory trends potentially influenced by individual body height variations, rather than indicating a true statistical interaction.
Infant feeding practice and associated factors among hiv-positive mothers attending anti-retroviral therapy services in health facilities of Bahir Dar City
DT-RL: a digital twin–driven reinforcement learning framework for proactive slice admission and predictive QoS assurance in 6G networks
Risk of cardiovascular events and all-cause mortality in COPD: a real-world multicenter cohort study
eDNA metabarcoding of freshwater communities in the Royal and Heathcote National Parks
Population dynamics of a threatened pitviper in a geographic stronghold
Effect of multimedia-based education on maternal self-efficacy for preterm infant care in the NICU: a randomized controlled trial
Increased attentive use is linked to more idiosyncratic functional connections
Experience is thought to modify neural connections to adapt the network to be more optimal for the environment. Given the brain’s complexity, multiple network changes could each move the system toward optimality. Standard approaches examine each connection independently; these studies have often shown considerable inter-individual variability and modest effects (Marek et al., 2022). Here, we take a different strategy, determining how a whole-brain connection pattern differs from the typical pattern, that is, how ‘idiosyncratic’ the pattern is. We examined how the idiosyncrasy of whole-brain connection patterns varies with frequency of the use of that part of cortex for attention-demanding tasks, focusing on central versus peripheral vision in healthy individuals (who use central vision more frequently for attention-demanding tasks). We found that the whole-brain pattern of functional connections to the cortical representations of central vision is idiosyncratic, whereas patterns of connections to representations of peripheral vision were very similar person-to-person in healthy vision controls (14 females, 9 males). In a second set of analyses, we examined the brains of people with central vision loss (11 females, 10 males) who use a portion of peripheral vision (the preferred retinal locus) more frequently for attention-demanding tasks in their daily lives. The cortical representation of the preferred retinal locus exhibits more idiosyncratic connections, compared to a control brain region or compared to the same brain region in matched healthy vision controls. These results are consistent with the hypothesis that increased attentive use of a brain area results in idiosyncratic patterns of whole-brain connections. Significance Statement We found that increased attentive use of a brain region results in more idiosyncratic patterns of connections of that region to the rest of the brain. Our findings support the view that the primary visual cortex retains the capacity for plasticity well beyond the critical period and that these adaptations are idiosyncratic to the individual's experiences. This approach suggests that tailoring personalized rehabilitation plans for individuals with retinal diseases may be more effective than a ‘one size fits all’ approach. More generally, it offers a promising framework for investigating brain plasticity in both typical and clinical populations, especially in the context of sensory loss and compensatory adaptation.
Sustainable face milling of banana peel ash- and mango seed shell ash-reinforced aluminum hybrid composites using RSM–fuzzy LMAW–fuzzy MARCOS
Layer 5 and 6b extratelencephalic neurons encode distinct sound features in auditory cortex
Extratelencephalic (ET) neurons in layers (L)5 and 6b of the auditory cortex (ACtx) provide major corticofugal outputs to subcortical structures and contribute to auditory learning and experience-dependent plasticity. Although both ET subtypes innervate overlapping downstream targets, they differ in morphology, intrinsic physiology, and molecular identity, suggesting distinct functional roles. However, their in vivo response properties remain incompletely characterized. Here, we used a projection-defined viral strategy to express GCaMP8s selectively in L5 and L6b ET neurons in the ACtx of male and female mice and recorded calcium activity during presentation of diverse acoustic stimuli. L5 ET neurons predominantly exhibited sound-evoked excitation, higher response sparseness, and greater trial-to-trial reliability than L6b ET neurons; these differences were most pronounced for pure tones and sinusoidally amplitude-modulated (sAM) noise. In contrast, L6b ET neurons frequently exhibited sound-evoked suppression, particularly for more complex stimuli (including sAM noise and spectrotemporal ripples). Unsupervised clustering identified separable temporal response motifs and tuning profiles. L5 ET neurons favored single-peaked frequency tuning and monotonically increasing intensity tuning, whereas L6b ET neurons exhibited complex frequency tuning, monotonically decreasing intensity tuning, and stronger pairwise functional coupling (noise correlations). Together, these results support complementary corticofugal processing streams, with L5 ET neurons conveying more selective acoustic feature representations and L6b ET neurons conveying more integrative corticofugal signals. Significance Statement The primary auditory cortex sends extensive descending projections to subcortical structures, shaping how sounds are processed. These projections arise from distinct extratelencephalic (ET) neurons in cortical layers 5 and 6b, yet how these populations encode sound has remained unclear. Using projection-specific two-photon imaging in awake mice, we show that layer 5 ET neurons convey sparse, reliable, and selective representations of acoustic features, whereas layer 6b ET neurons more often show sound-evoked suppression, complex tuning, lower reliability, and stronger coupling to one another. These findings reveal two complementary descending channels, one relaying precise feature information and the other broader, integrative signals, that together enrich cortical control over subcortical auditory processing.
Clinical and radiographic evaluation of natural bovine bone with hyaluronic acid on osseointegration of immediate implants: a randomized clinical study
Abstract Hyaluronic acid (HA) is a natural component of the extracellular matrix (ECM) and has gained wide application in bone regeneration, particularly in maxillofacial and dental research. HA derivatives and HA-incorporated composite scaffolds demonstrate strong potential in enhancing osteogenesis and mineralization. The aim of this work was to evaluate clinical and radiographic effects of natural bovine bone with HA on immediate implants. Sixteen immediate implants were placed in medically free patients with non-restorable single-rooted maxillary anterior or premolar teeth; Patients were randomly allocated into two groups: Group 1 (Control): Implants placed in 8 fresh extraction sockets. Group 2 (Study): Implants placed in 8 fresh extraction sockets followed by grafting with a natural bovine bone material with HA. Implant stability quotient (ISQ) was measured at the time of implant surgery and 4 months postoperatively. Bone density and buccal bone width were measured immediately and 6 months postoperatively using Cone Beam CT. After 4 months, the study group showed higher ISQs values than the control group (72.25 ± 2.66 vs. 64.88 ± 1.36, p < 0.001). Bone density was significantly higher in the study group at 6 months (1163.38 ± 112.64 HU vs. 1034.13 ± 71.19 HU, p = 0.016). A significant reduction in buccal bone width was observed at 6-month follow-up in both groups. The reduction was more pronounced in the control group (15.99%; p < 0.001) than in the study group (3.88%; p = 0.014). No statistically significant differences were detected between the groups at either the immediate postoperative assessment or the 6-month follow-up. The use of natural bovine bone with hyaluronic acid after immediate implant placement enhances early implant stability, increases peri-implant bone density, and contributes to preservation of buccal bone dimensions, thereby supporting improved osseointegration. Trial registration: Current controlled trials NCT07199725 “Retrospectively registered” (registration date 02/09/2025).
Multi-approach technique implementation for segmentation of left ventricular myocardial infarction and quantification
Abstract Accurate segmentation and optimal diagnosis are required for myocardial infarction (MI) because it is a life-threatening cardiovascular condition. Therefore, its quantification is necessary to facilitate accurate diagnosis and make beneficial decisions to prevent a heart attack. Advanced cutting-edge techniques, such as deep learning, promise more precise MI segmentation and potentially provide an automated segmentation of annotated cardiac MRI (CMRI) datasets. However, segmentation accuracy is strongly affected by domain shift, low contrast of infarcted tissue, and scarcity of annotated datasets. This study introduces an innovative framework as a hybrid deep-learning framework combining Cycle-Consistent Generative Adversarial Networks (CycleGAN), Discrete Wavelet Transform (DWT), and U-Net to achieve robust MI segmentation. CycleGAN is employed to generate synthetic infarcted images from healthy MRI scans, addressing data scarcity and domain variability. DWT is integrated into the segmentation pipeline to preserve high-frequency spatial details and enhance edge localization, which are crucial for accurate infarct boundary detection. Finally, a U-Net model is trained on a hybrid dataset comprising synthetic and real images with wavelet-based feature fusion. Experiments dataset demonstrate that the proposed method significantly outperforms baseline U-Net models, achieving Dice improvements of 4–8% for infarct segmentation and 2–4% for myocardium segmentation. The results highlight the effectiveness of combining CycleGAN-based data augmentation with multi-resolution wavelet features for improved cardiac tissue delineation.
Bioefficacy of Indian Beauveria bassiana and Lecanicillium lecanii isolates against Lipaphis erysimi (Hemiptera: Aphididae) and their safety to Apis cerana (Hymenoptera: Apidae)
Joint signal processing and coding for underwater acoustic communications using ORBGRAND
Abstract Achieving high-rate and reliable underwater acoustic communications remains challenging due to severe multipath propagation, extended delay spreads, and strong Doppler-induced time variability. Consequently, channel coding plays a critical role in balancing reliability and spectral efficiency; however, reported performance results are often difficult to compare due to heterogeneous experimental platforms, channel conditions, and system configurations. In addition, the lack of standardized underwater acoustic channel models limits systematic evaluation and reproducible benchmarking. To address these challenges, we present a joint signal processing and coding (JSPC) framework that iteratively refines channel estimates using decoded codewords, enabling reliable communication with minimal parity overhead. The proposed framework employs Ordered Reliability Bits Guessing Random Additive Noise Decoding (ORBGRAND), a soft-decision variant of the Guessing Random Additive Noise Decoding (GRAND) algorithm. Additionally, to enable reproducible benchmarking across diverse underwater conditions, the proposed approach is evaluated on measured channel impulse responses from a publicly accessible repository, using channels from two distinct experiments that span contrasting regimes, from long-range mobile links to short-range shallow-water links. Results demonstrate approximately 6–8 dB of coding gain over the uncoded baseline with as few as 8 parity bits, while outperforming differentially coherent detection, particularly at low signal-to-noise ratio(SNR).