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Scholarship long assumed that only men inhabited the architectural site of Qumran and thus failed to query the sex of the residents. Similarly specialists of the Dead Sea scrolls focused on these texts without linking them to the material evidence of the archeological site. This article explores the reasons why scholars assumed the absence of women at Qumran. It uses the material evidence from archeological digs to argue the contrary.

The story of this star-cross'd couple begins in fair Verona, where Juliet, in a heart-stopping array of dresses, falls head-over-heels for who else but Romeo. Oh, Romeo.

After a spring season exploding with pyrotechnics, designers are learning to forgo frills in favor of strong, clean lines and a newfound maturity.

Background Perineuronal nets (PNNs) are specialized aggregations of extracellular matrix (ECM) molecules surrounding specific neurons in the central nervous system (CNS). PNNs are supposed to control synaptic transmission and are frequently associated with neurons firing at high rates, including principal neurons of auditory brainstem nuclei. The origin of high-frequency activity of auditory brainstem neurons is the indefatigable sound-driven transmitter release of inner hair cells (IHCs) in the cochlea. Results Here, we show that synaptic poles of IHCs are ensheathed by basket-like ECM complexes formed by the same molecules that constitute PNNs of neurons in the CNS, including brevican, aggreccan, neurocan, hyaluronan, and proteoglycan link proteins 1 and 4 and tenascin-R. Genetic deletion of brevican, one of the main components, resulted in a massive degradation of ECM baskets at IHCs, a significant impairment in spatial coupling of pre- and postsynaptic elements and mild impairment of hearing. Conclusions These ECM baskets potentially contribute to control of synaptic transmission at IHCs and might be functionally related to PNNs of neurons in the CNS.

Accurate identification of Mpox is essential for timely diagnosis and treatment. However, traditional image-based diagnostic methods often struggle with challenges such as body hair obscuring skin lesions and complicating accurate assessment. To address this, the study introduces a novel deep learning-based approach to enhance Mpox detection by integrating a hair removal process with an upgraded U-Net model. The research developed the “Mpox Skin Lesion Dataset (MSLD)” by combining images of skin lesions from Mpox, chickenpox, and measles. The proposed methodology includes a pre-processing step to effectively remove hair from dermoscopic images, improving the visibility of skin lesions. This is followed by applying an enhanced U-Net architecture, optimized for efficient feature extraction and segmentation, to detect and classify Mpox lesions accurately. Experimental evaluations indicate that the proposed approach significantly improves the accuracy of Mpox detection, surpassing the performance of existing models. The achieved accuracy, recall, and F1 scores for Mpox detection were 90%, 89%, and 86%, respectively. The proposed method offers a valuable tool for assisting physicians and healthcare practitioners in the early diagnosis of Mpox, contributing to improved clinical outcomes and better management of the disease.

In a developing nervous system, axonal arbors often undergo complex rearrangements before neural circuits attain their final innervation topology. In the lateral line sensory system of the zebrafish, developing sensory axons reorganize their terminal arborization patterns to establish precise neural microcircuits around the mechanosensory hair cells. However, a quantitative understanding of the changes in the sensory arbor morphology and the regulators behind the microcircuit assembly remain enigmatic. Here, we report that Semaphorin7A (Sema7A) acts as an important mediator of these processes. Utilizing a semi-automated three-dimensional neurite tracing methodology and computational techniques, we have identified and quantitatively analyzed distinct topological features that shape the network in wild-type and Sema7A loss-of-function mutants. In contrast to those of wild-type animals, the sensory axons in Sema7A mutants display aberrant arborizations with disorganized network topology and diminished contacts to hair cells. Moreover, ectopic expression of a secreted form of Sema7A by non-hair cells induces chemotropic guidance of sensory axons. Our findings propose that Sema7A likely functions both as a juxtracrine and as a secreted cue to pattern neural circuitry during sensory organ development.

The timely identification of melanoma and other cutaneous malignancies is fundamentally reliant on the acquisition of high-caliber dermoscopic images. However, factors such as hair interference, reflections, and various illumination artifacts frequently obscure essential characteristics of lesions, thereby impeding both clinical evaluations and automated diagnostic processes. This research introduces an innovative deep learning framework based on U-Net architecture, meticulously designed for the effective removal of hair and artifacts from dermoscopic images while ensuring the preservation of intricate lesion attributes, including pigmentary networks and irregular edges. The model underwent training predominantly on the HAM10000 dataset, which comprises 10,015 images sourced from multiple origins, and was rigorously validated on separate portions of both the HAM10000 dataset and the ISIC 2018 dataset. The training employed a hybrid loss function of Dice and Binary Cross-Entropy, alongside extensive data augmentation techniques and meticulous artifact mask creation. Quantitative assessments reveal significant improvements in image quality: Peak Signal-to-Noise Ratio (PSNR) increased from 21.5 dB to 34.1 dB, and Structural Similarity Index Measure (SSIM) enhanced from 0.79 to 0.89 for HAM10000 and from 0.77 to 0.92 for ISIC 2018, with Intersection over Union (IoU) ranging between 0.85 and 0.87 across the datasets. Subsequent melanoma classification utilizing a pre-trained model demonstrated notable improvements, with accuracy advancing from 84.2% to between 90.3% and 91.5%, F1-score rising from 81.6% to between 90.2% and 91.5%, along with an increase in prediction confidence. This methodology shows robust generalization capabilities across diverse artifact densities, types of lesions, and imaging environments, thereby establishing itself as a potent and adaptable preprocessing technique for standardized dermoscopic examinations and dependable AI-supported skin cancer diagnostics.

Self-injurious behaviors (SIB) are common in autistic people. SIB is mainly studied as a broad category, rather than by specific SIB types. We aimed to determine associations of distinct SIB types with common psychiatric, emotional, medical, and socio-demographic factors. Participants included 323 autistic youth (~50% non-/minimally-speaking) with high-confidence autism diagnoses ages 4-21 years. Data were collected by the Autism Inpatient Collection during admission to a specialized psychiatric inpatient unit (www.sfari.org/resource/autism-inpatient-collection/). Caregivers completed questionnaires about their child, including SIB type and severity. The youth completed assessments with clinicians. Elastic net regressions identified associations between SIB types and factors. No single factor relates to all SIB types. SIB types have unique sets of associations. Consistent with previous work, more repetitive motor movements and lower adaptive skills are associated with most types of SIB; female sex is associated with hair/skin pulling and self-rubbing/scratching. More attention-deficit/hyperactivity disorder symptoms are associated with self-rubbing/scratching, skin picking, hair/skin pulling, and inserts finger/object. Inserts finger/object has the most medical condition associations. Self-hitting against surface/object has the most emotion dysregulation associations. Specific SIB types have unique sets of associations. Future work can develop clinical likelihood scores for specific SIB types in inpatient settings, which can be tested with large community samples. Current approaches for SIB focus on the behavior functions, but there is an opportunity to further develop interventions by considering the specific SIB type in assessment and treatment. Identifying factors associated with specific SIB types may aid with screening, prevention, and treatment of these often-impairing behaviors.

The endoplasmic reticulum (ER) is connected to the plasma membrane (PM) through the plant-specific NETWORKED protein, NET3C, and phylogenetically conserved vesicleassociated membrane protein-associated proteins (VAPs). Ten VAP homologues (VAP27-1 to 27-10) can be identified in the Arabidopsis genome and can be divided into three clades. Representative members from each clade were tagged with fluorescent protein and expressed in Nicotiana benthamiana. Proteins from clades I and III localized to the ER as well as to ER/PM contact sites (EPCSs), whereas proteins from clade II were found only at the PM. Some of the VAP27-labelled EPCSs localized to plasmodesmata, and we show that the mobility of VAP27 at EPCSs is influenced by the cell wall. EPCSs closely associate with the cytoskeleton, but their structure is unaffected when the cytoskeleton is removed. VAP27-labelled EPCSs are found in most cell types in Arabidopsis, with the exception of cells in early trichome development. Arabidopsis plants expressing VAP27-GFP fusions exhibit pleiotropic phenotypes, including defects in root hair morphogenesis. A similar effect is also observed in plants expressing VAP27 RNAi. Taken together, these data indicate that VAP27 proteins used at EPCSs are essential for normal ER–cytoskeleton interaction and for plant development.

Melanoma is one of the most dangerous skin cancers. The current melanoma segmentation is mainly based on FCNs (fully connected networks) and U-Net. Nevertheless, these two kinds of neural networks are prone to parameter redundancy, and the gradient of neural networks disappears that occurs when the neural network backpropagates as the neural network gets deeper, which will reduce the Jaccard index of the skin lesion image segmentation model. To solve the above problems and improve the survival rate of melanoma patients, an improved skin lesion segmentation model based on deformable 3D convolution and ResU-NeXt++ (D3DC- ResU-NeXt++) is proposed in this paper. The new modules in D3DC-ResU-NeXt++ can replace ordinary modules in the existing 2D convolutional neural networks (CNNs) that can be trained efficiently through standard backpropagation with high segmentation accuracy. In particular, we introduce a new data preprocessing method with dilation, crop operation, resizing, and hair removal (DCRH), which improves the Jaccard index of skin lesion image segmentation. Because rectified Adam (RAdam) does not easily fall into a local optimal solution and can converge quickly in segmentation model training, we also introduce RAdam as the training optimizer. The experiments show that our model has excellent performance on the segmentation of the ISIC2018 Task I dataset, and the Jaccard index achieves 86.84%. The proposed method improves the Jaccard index of segmentation of skin lesion images and can also assist dermatological doctors in determining and diagnosing the types of skin lesions and the boundary between lesions and normal skin, so as to improve the survival rate of skin cancer patients.