Kerr-optomechanical spectroscopy of multimode diamond resonators

Diamond microdisk cavities play a key role in optomechanical and spin-optomechanical technologies. Previous optomechanical studies of these devices have focused exclusively on their fundamental radial breathing mode. Accessing other mechanical modes of these structures is desirable for identifying routes towards improving their optomechanical properties, implementing multimode optomechanical systems, and broadening the accessible range of resonant spin--phonon coupling processes. Here we perform broadband optomechanical spectroscopy on diamond microdisks, and observe high quality factor mechanical modes with frequencies up to 10 GHz. Through Fano interference of their optomechanical response with diamond's Kerr nonlinear optical response, we estimate that optomechanical coupling of these high frequency modes can exceed 10 kHz, making them attractive for high-frequency multimode optomechanics. In combination with their per-phonon stress of a few kPa, these properties makes them excellent candidates for spin-optomechanical coupling

High throughput detection of M6P/IGF2R intronic hypermethylation and LOH in ovarian cancer

Cell surface mannose 6-phosphate/insulin-like growth factor II receptors (M6P/IGF2R) bind and target exogenous insulin-like growth factor II (IGF2) to the prelysosomes where it is degraded. Loss of heterozygosity (LOH) for M6P/IGF2R is found in cancers, with mutational inactivation of the remaining allele. We exploited the normal allele-specific differential methylation of the M6P/IGF2R intron 2 CpG island to rapidly evaluate potential LOH in ovarian cancers, since every normal individual is informative. To this end, we developed a method for bisulfite modification of genomic DNA in 96-well format that allows for rapid methylation profiling. We identified ovarian cancers with M6P/IGF2R LOH, but unexpectedly also found frequent abnormal acquisition of methylation on the paternally inherited allele at intron 2. These results demonstrate the utility of our high-throughput method of bisulfite modification for analysis of large sample numbers. They further show that the methylation status of the intron 2 CpG island may be a useful indicator of LOH and biomarker of disease

BASP1 interacts with estrogen receptor α and modifies the tamoxifen response

AbstractTamoxifen binds to oestrogen receptor α (ERα) to elicit distinct responses that vary by cell/tissue type and status, but the factors that determine these differential effects are unknown. Here we report that the transcriptional corepressor BASP1 interacts with ERα and in breast cancer cells, this interaction is enhanced by tamoxifen. We find that BASP1 acts as a major selectivity factor in the transcriptional response of breast cancer cells to tamoxifen. In all, 40% of the genes that are regulated by tamoxifen in breast cancer cells are BASP1 dependent, including several genes that are associated with tamoxifen resistance. BASP1 elicits tumour-suppressor activity in breast cancer cells and enhances the antitumourigenic effects of tamoxifen treatment. Moreover, BASP1 is expressed in breast cancer tissue and is associated with increased patient survival. Our data have identified BASP1 as an ERα cofactor that has a central role in the transcriptional and antitumourigenic effects of tamoxifen.</jats:p

Diamond Integrated Quantum Photonics: A Review

Integrated quantum photonics devices in diamond have tremendous potential for many quantum applications, including long-distance quantum communication, quantum information processing, and quantum sensing. These devices benefit from diamond's combination of exceptional thermal, optical, and mechanical properties. Its wide electronic bandgap makes diamond an ideal host for a variety of optical active spin qubits that are key building blocks for quantum technologies. In landmark experiments, diamond spin qubits have enabled demonstrations of remote entanglement, memory-enhanced quantum communication, and multi-qubit spin registers with fault-tolerant quantum error correction, leading to the realization of multinode quantum networks. These advancements put diamond at the forefront of solid-state material platforms for quantum information processing. Recent developments in diamond nanofabrication techniques provide a promising route to further scaling of these landmark experiments towards real-life quantum technologies. In this paper, we focus on the recent progress in creating integrated diamond quantum photonic devices, with particular emphasis on spin-photon interfaces, cavity optomechanical devices, and spin-phonon transduction. Finally, we discuss prospects and remaining challenges for the use of diamond in scalable quantum technologies.Comment: 31 pages, 8 figure

Cyber Attack Evaluation Dataset for Deep Packet Inspection and Analysis

To determine the effectiveness of any defense mechanism, there is a need for comprehensive real-time network data that solely references various attack scenarios based on older software versions or unprotected ports, and so on. This presented dataset has entire network data at the time of several cyber attacks to enable experimentation on challenges based on implementing defense mechanisms on a larger scale. For collecting the data, we captured the network traffic of configured virtual machines using Wireshark and tcpdump. To analyze the impact of several cyber attack scenarios, this dataset presents a set of ten computers connected to Router1 on VLAN1 in a Docker Bridge network, that try and exploit each other. It includes browsing the web and downloading foreign packages including malicious ones. Also, services like File Transfer Protocol (FTP) and Secure Shell (SSH) were exploited using several attack mechanisms. The presented dataset shows the importance of updating and patching systems to protect themselves to a greater extent, by following attack tactics on older versions of packages as compared to the newer and updated ones. This dataset also includes an Apache Server hosted on the different subset on VLAN2 which is connected to the VLAN1 to demonstrate isolation and cross- VLAN communication. The services on this web server were also exploited by the previously stated ten computers. The attack types include: Distributed Denial of Service, SQL Injection, Account Takeover, Service Exploitation (SSH, FTP), DNS and ARP Spoofing, Scanning and Firewall Searching and Indexing (using Nmap), Hammering the services to brute-force passwords and usernames, Malware attacks, Spoofing, and Man-in-the-Middle Attack. The attack scenarios also show various scanning mechanisms and the impact of Insider Threats on the entire network

YOLO-based Segmented Dataset for Drone vs. Bird Detection for Deep and Machine Learning Algorithms

The use of unmanned aerial vehicles (UAVs) has been rapidly increasing in both professional and recreational settings, leading to concerns about the safety and security of people and facilities. One area of research that has emerged in response to this concern is the development of detection systems for UAVs. However, many existing systems have limitations, such as detection failures or false detection of other aerial objects, including birds. To address this issue, the development of a standard dataset that provides images of both drones and birds is essential for training accurate and effective detection models. In this context, we present a dataset consisting of images of drones and birds operating in various environments. This dataset will serve as a valu- able resource for researchers and developers working on UAV detection and classification systems. The dataset was created using Roboflow software, which enabled us to efficiently edit and manipulate the images using AI-assisted bounding boxes, polygons, and instance segmentation. The software supports a wide range of input and output formats, making it easy to import and export the dataset in different machine learning frameworks. To ensure the highest possible accuracy, we manually segmented each im- age from edge to edge, providing the YOLO model with detailed and accurate information for training. The dataset includes both training and testing sets, allowing for the evaluation of model performance and accuracy. Our dataset offers several advant- ages over existing datasets, including the inclusion of both drones and birds, which are commonly misclassified by detection systems. Additionally, the images in our dataset were collected in diverse environments, providing a wide range of scenarios for model training and testing. The presented dataset provides a valuable resource for researchers and developers working on UAV detection and classification systems. The inclusion of both drones and birds, as well as the diverse range of environments and scenarios, makes this dataset a unique and essential tool for training accurate and effective models. We hope that this dataset will contribute to the advancement of UAV detection and classification systems, improving safety and security in both professional and recreational settings

Seasonal effect and long-term nutritional status following exit from a Community-Based Management of Severe Acute Malnutrition program in Bihar, India.

BACKGROUND/OBJECTIVES: Children aged 6 months to 5 years completing treatment for severe acute malnutrition (SAM) in a Médecins Sans Frontières Community Management of Acute Malnutrition (CMAM) program in Bihar, India, showed high cure rates; however, the program suffered default rates of 38%. This report describes the nutritional status of 1956 children followed up between 3 and 18 months after exiting the program. SUBJECTS/METHODS: All children aged 6-59 months discharged as cured with mid-upper arm circumference (MUAC) ⩾120 mm or who defaulted from the program with MUAC <115 mm were traced at 3, 6, 9, 12 and 18 months (±10 days) before three exit reference dates: first at the end of the food insecure period, second after the 2-month food security and third after the 4-month food security. RESULTS: Overall, 68.7% (n=692) of defaulters and 76.2% (n=1264) of children discharged as cured were traced. Combined rates of non-recovery in children who defaulted with MUAC <115 mm were 41%, 30.1%, 9.9%, 6.1% and 3.6% at 3, 6, 9, 12 and 18 months following exit, respectively. Combined rates of relapse among cured cases (MUAC ⩾120 mm) were 9.1%, 2.9%, 2.1%, 2.8% and 0% at 3, 6, 9, 12 and 18 months following discharge, respectively. Prevalence of undernutrition increased substantially for both groups traced during low food security periods. Odds of death were much higher for children defaulting with MUAC <110 mm when compared with children discharged as cured, who shared the same mortality risk as those defaulting with MUAC 110-<115 mm. CONCLUSIONS: Seasonal food security predicted short-term nutritional status after exit, with relapse rates and non-recovery from SAM much higher during food insecurity. Mortality outcomes suggest that a MUAC of 110 mm may be considered an appropriate admission point for SAM treatment programs in this context