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Cells self-organize into functional, ordered structures during tissue morphogenesis, a process that is evocative of colloidal self-assembly into engineered soft materials. Understanding how intercellular mechanical interactions may drive the formation of ordered and functional multicellular structures is important in developmental biology and tissue engineering. Here, by combining an agent-based model for contractile cells on elastic substrates with endothelial cell culture experiments, we show that substrate deformation-mediated mechanical interactions between cells can cluster and align them into branched networks. Motivated by the structure and function of vasculogenic networks, we predict how measures of network connectivity like percolation probability and fractal dimension as well as local morphological features including junctions, branches, and rings depend on cell contractility and density and on substrate elastic properties including stiffness and compressibility. We predict and confirm with experiments that cell network formation is substrate stiffness dependent, being optimal at intermediate stiffness. We also show the agreement between experimental data and predicted cell cluster types by mapping a combined phase diagram in cell density substrate stiffness. Overall, we show that long-range, mechanical interactions provide an optimal and general strategy for multicellular self-organization, leading to more robust and efficient realizations of space-spanning networks than through just local intercellular interactions.

Breast cancer remains a leading cause of cancer-related deaths among women, emphasizing the critical need for early detection and monitoring techniques. Conventional imaging modalities such as mammography, MRI, and ultrasound have face sensitivity, specificity, cost, and patient comfort limitations. This study introduces a handheld Near-Infrared Diffuse Optical Tomography (NIR DOT) probe for breast cancer imaging. The NIRscan probe utilizes multi-wavelength light-emitting diodes (LEDs) and a linear charge-coupled device (CCD) sensor to acquire real-time optical data, reconstructing cross-sectional images of breast tissue based on scattering and absorption coefficients. With wavelengths optimized for the differential optical properties of tissue components, the probe enables functional imaging, distinguishing between healthy and malignant tissues. Clinical evaluations have demonstrated its potential for precise tumor localization and monitoring therapeutic responses, achieving a sensitivity of 94.7% and specificity of 84.2%. By incorporating machine learning algorithms and a modified diffusion equation (MDE), the system enhances the accuracy and speed of image reconstruction, supporting rapid, non-invasive diagnostics. This development represents a significant step forward in portable, cost-effective solutions for breast cancer detection, with potential applications in low-resource settings and diverse clinical environments.

Breast cancer remains a leading cause of cancer-related deaths among women, emphasizing the critical need for early detection and monitoring techniques. Conventional imaging modalities such as mammography, MRI, and ultrasound have face sensitivity, specificity, cost, and patient comfort limitations. This study introduces a handheld Near-Infrared Diffuse Optical Tomography (NIR DOT) probe for breast cancer imaging. The NIRscan probe utilizes multi-wavelength light-emitting diodes (LEDs) and a linear charge-coupled device (CCD) sensor to acquire real-time optical data, reconstructing cross-sectional images of breast tissue based on scattering and absorption coefficients. With wavelengths optimized for the differential optical properties of tissue components, the probe enables functional imaging, distinguishing between healthy and malignant tissues. Clinical evaluations have demonstrated its potential for precise tumor localization and monitoring therapeutic responses, achieving a sensitivity of 94.7% and specificity of 84.2%. By incorporating machine learning algorithms and a modified diffusion equation (MDE), the system enhances the accuracy and speed of image reconstruction, supporting rapid, non-invasive diagnostics. This development represents a significant step forward in portable, cost-effective solutions for breast cancer detection, with potential applications in low-resource settings and diverse clinical environments. [ABSTRACT FROM AUTHOR]

Prior to the start of the LHC Run 3, the US ATLAS Software and Computing operations program established three shared Tier 3 Analysis Facilities (AFs). The newest AF was established at the University of Chicago in the past year, joining the existing AFs at Brookhaven National Lab and SLAC National Accelerator Lab. In this paper, we will describe both the common and unique aspects of these three AFs, and the resulting distributed facility from the user’s perspective, including how we monitor and measure the AFs. The common elements include enabling easy access via Federated ID, file sharing via EOS, provisioning of similar Jupyter environments using common Jupyter kernels and containerization, and efforts to centralize documentation and user support channels. The unique components we will cover are driven in turn by the requirements, expertise and resources at each individual site. Finally, we will highlight how the US AFs are collaborating with other ATLAS and LHC wide (IRIS-HEP and HSF) user analysis support activities, evaluating tools like ServiceX and Coffea-Casa.

Breast cancer patients undergoing neoadjuvant chemotherapy (NAC) require precise and accurate evaluation of treatment response. Residual cancer burden (RCB) is a prognostic tool widely used to estimate survival outcomes in breast cancer. In this study, we introduced a machine-learning-based optical biosensor called the Opti-scan probe to assess residual cancer burden in breast cancer patients undergoing NAC. The Opti-scan probe data were acquired from 15 patients (mean age: 61.8 years) before and after each cycle of NAC. Using regression analysis with k-fold cross-validation, we calculated the optical properties of healthy and unhealthy breast tissues. The ML predictive model was trained on the optical parameter values and breast cancer imaging features obtained from the Opti-scan probe data to calculate RCB values. The results show that the ML model achieved a high accuracy of 0.98 in predicting RCB number/class based on the changes in optical properties measured by the Opti-scan probe. These findings suggest that our ML-based Opti-scan probe has considerable potential as a valuable tool for the assessment of breast cancer response after NAC and to guide treatment decisions. Therefore, it could be a promising, non-invasive, and accurate method for monitoring breast cancer patient’s response to NAC.

The 21st century is seeing dramatic shifts in the business environment. In order for organizations to adapt to these shifts, they must be more flexible and learning oriented. To thrive in this environment, organizational leaders must facilitate and build the capacity for learning throughout the organization. Organizational leadership is looking for employees to bring more than technical competencies or subject-matter expertise to their work; they are requiring specific learning-oriented competencies such as critical thinking, problem-solving, agility, adaptability, initiative, communication, and collaboration among others. To that end, business education is under heavy criticism for failure to produce the workforce needed to meet the demands of the 21st century. We set out to develop a conceptual teaching and learning model anchored in transformative and constructivist perspectives of learning that engages the whole learner in learning, reflective practice, and interactions with learning facilitators, other learning agents, and actors in a learning context.

The development of reverse transcription–polymerase chain reaction using degenerate primers against conserved regions of most potyviral genomes enabled sampling of the potyvirome. However, these assays usually involve sampling potential host plants, but identifying infected plants when they are asymptomatic is challenging, and many plants, especially wild ones, contain inhibitors to DNA amplification. We used an alternative approach which utilized aphid vectors and indicator plants to identify potyviruses capable of infecting common bean (Phaseolus vulgaris). Aphids were collected from a range of asymptomatic leguminous weeds and trees in Iran, and transferred to bean seedlings under controlled conditions. Bean plants were tested serologically for potyvirus infections four-weeks post-inoculation. The serological assay and symptomatology together indicated the presence of one potyvirus, and symptomology alone implied the presence of an unidentified virus. The partial genome of the potyvirus, encompassing the complete coat protein gene, was amplified using generic potyvirus primers. Sequence analysis of the amplicon confirmed the presence of an isolate of Wisteria vein mosaic virus (WVMV), a virus species not previously identified from Western Asia. Phylogenetic analyses of available WVMV sequences categorized them into five groups: East Asian-1 to 3, North American and World. The Iranian isolate clustered with those in the World group. Multiple sequence alignment indicated the presence of some genogroup-specific amino acid substitutions among the isolates studied. Chinese isolates were sister groups of other isolates and showed higher nucleotide distances as compared with the others, suggesting a possible Eastern-Asian origin of WVMV, the main region where Wisteria might have originated.

Nanoparticle green synthesis is an emerging scientific field that focuses on the production of nanoparticles by living cells. In this work, the potential of extracts of alfalfa leaves (Medicago sativa) as affordable and fabricated zirconium nanoparticles (ZrNPs) was evaluated for the first time. alfalfa alcoholic extract and zirconium precursor were used to produce ZrNPs, the production and characteristics of nanoparticles were measured using the Tyndall effect, UV‐vis, FT‐IR, EDX, DLS, and SEM tests, antioxidant and antibacterial properties, and toxicity Measurement of nanoparticles on breast cancer (MDA‐MB‐468) and human healthy cells (HFF) was investigated. The optimization of nanoparticle production was evaluated with ANN and GN algorithms. The analysis showed that nanoparticles have spherical‐like ZrNPs shapes with an average of 50 nm size. in addition, ZrNPs have significant cytotoxic effects against human breast cancer (MDA‐MB‐468) cells, and Human foreskin fibroblast (HFF) cells exhibited the highest resistance to ZrNPs. There was a significant agreement between the results of the designed ANN‐GA model and the experimental data. In this research, ZrNP was successfully created using alfalfa extract as a renewable source of stabilizing and reducing agents with an environmentally friendly approach, and they showed significant antioxidant, antibacterial, and anticancer properties. [ABSTRACT FROM AUTHOR]

This paper presents a single-axis angular rate sensor that is robust to variations in its operating voltage and frequencies. The sensor is developed to overcome the shortcomings of conventional mode-matched Micromachined Vibratory Gyroscopes in open loop operations, namely narrow frequency bandwidths and unstable scale factors. The developed sensor utilizes inherent forcing and inertial nonlinearities from electrostatic forces and fabrication imperfections to auto-parametrically excite the sense mode via 2:1 auto-parametric resonance, which yields a broader bandwidth frequency response for the sensor’s sense mode. The experimental results demonstrated −3 dB frequency bandwidth of 500 Hz, a scale factor of 50 μV/°/s, and a dynamic range of ±330°/s.

Abstract One of the main reasons for pipeline breakage and leakage is aging. This results in a decline in system efficiency, and associated levels of service, which in turn may endanger public health and safety. Since the most expensive and significant part of a water supply system is the distribution network, repair strategies are necessary to protect the value of the assets when a pipeline reaches the end of its useful life. Usually, repair works are taken as a reaction to the detection of a leak, pressure improvement and other factors that eventually results in an inefficient management of allocated funds. Therefore, a careful computational analysis should be performed to efficiently utilize the allocated budge. This paper presents a near‐optimized budget allocation model that is able to find the near‐optimal scheduling plan for renewal and/or replacement. The aim of this paper is to suggest a new model in order to minimize the number of breaks over a given planning horizon and the overall cost of repair of water distribution networks (WDNs), including indirect damage cost, direct damage cost, and failure repair cost while fulfilling functional requirements. The model will identify the pipe segment that needs replacement, time of replacement, and the necessary interventions that should be carried out for the network. It also identifies those pipelines that need repair. The solution is usually constrained by the yearly limited budget. The optimization procedure results in a solution alternative that decision‐makers could use for their operational requirements. The outcome of the developed model is validated based on the available leakage and breakage data from the city of Montreal database and an existing validated model. The model forecast that, in the next 20 years, 19.7% of the network will need open trench repair, 25.3% could be repaired using trenchless techniques, while 50.9% of the pipelines will remain intact.

An electrical Impedance based tool is designed and developed to aid physicians performing clinical exams focusing on cancer detection. Current research envisions improvement in sensor-based measurement technology to differentiate malignant and benign lesions in human subjects. The tool differentiates malignant anomalies from nonmalignant anomalies using Electrical Impedance Spectroscopy (EIS). This method exploits cancerous tissue behavior by using EIS technique to aid early detection of cancerous tissue.

Abstract Plant viruses have important global impacts on crops, and identifying their centre and date of emergence is important for planning control measures. Turnip mosaic virus (TuMV) is a member of the genus Potyvirus in the family Potyviridae and is a major worldwide pathogen of brassica crops. For two decades, we have collected TuMV isolates, mostly from brassicas, in Turkey and neighbouring countries. This region is thought to be the centre of emergence of this virus. We determined the genomic sequences of 179 of these isolates and used these to estimate the timescale of the spread of this virus. Our Bayesian coalescent analyses used synonymous sites from a total of 417 novel and published whole-genome sequences. We conclude that TuMV probably originated from a virus of wild orchids in Germany and, while adapting to wild and domestic brassicas, spread via Southern Europe to Asia Minor no more than 700 years ago. The population of basal-B group TuMVs in Asia Minor is older than all other populations of this virus, including a newly discovered population in Iran. The timescale of the spread of TuMV correlates well with the establishment of agriculture in these countries.

ABSTRACT Construction labor productivity is affected by many factors such as scope changes, weather conditions, managerial policies, and operational variables. Labor productivity is critical in project development. Its modeling, however, can be a very complex task for it requires consideration of the factors stated above. In this article, a novel methodology is proposed for quantifying the impact of multiple factors on productivity. The data used in the present study was prepared using data processing techniques and was subsequently used in the development of a predictive model for labor productivity utilizing radial basis function neural network. The model focuses on labor productivity in a formwork installation using data gathered from two high‐rise buildings in the downtown area of Montreal, Canada. The predictive capability of the developed model is then compared with other techniques including adaptive neuro‐fuzzy inference system, artificial neural network, radial basis function (RBF), and generalized regression neural network. The results show that LU‐RBF predicts productivity more accurately and thus can be utilized members of project teams to validate the estimated productivity based on available data. The advantages and limitations of the proposed model are discussed in this article.