Semi-Coprime Array with Staggered Beam-Steering of Sub-Arrays.

A split-aperture array (SAA) is an array of sensors or antenna elements in which the array is split into two or more sub-arrays (SAs). Recently proposed SAAs, namely coprime and semi-coprime arrays, offer to attain a small half-power beamwidth (HPBW) with a small number of elements, compared to most conventional unified-aperture arrays, at the cost of reduced peak-to-side-lobe ratio (PSLR). To reduce HPBW and increase PSLR, non-uniform inter-element spacing and excitation amplitudes have proven helpful. However, all the existing arrays and beam-formers suffer increased HPBW, degraded PSLR or both when the main beam is steered away from the broadside. In this paper, we propose staggered beam-steering of SAs, a novel technique for decreasing HPBW. In this technique, we steer the main beams of the SAs of a semi-coprime array to angles slightly different from the desired steering angle. In conjunction with staggered beam-steering of SAs, we have utilized Chebyshev weights to suppress the side lobes. The results show that the beam-widening effect of Chebyshev weights can be mitigated considerably by staggered beam-steering of the SAs. Ultimately, the unified beam-pattern of the whole array offers HPBW and PSLR better than the existing SAAs, uniform and non-uniform linear arrays, especially when the desired steering angle is away from the broadside direction.

Computational Methods for Single-cell DNA Methylome Analysis.

Dissecting intercellular epigenetic differences is key to understanding tissue heterogeneity. Recent advances in single-cell DNA methylome profiling have presented opportunities to resolve this heterogeneity at the maximum resolution. While these advances enable us to explore frontiers of chromatin biology and better understand cell lineage relationships, they pose new challenges in data processing and interpretation. This review surveys the current state of computational tools developed for single-cell DNA methylome data analysis. We discuss critical components of single-cell DNA methylome data analysis, including data preprocessing, quality control, imputation, dimensionality reduction, cell clustering, supervised cell annotation, cell lineage reconstruction, gene activity scoring, and integration with transcriptome data. We also highlight unique aspects of single-cell DNA methylome data analysis and discuss how techniques common to other single-cell omics data analyses can be adapted to analyze DNA methylomes. Finally, we discuss existing challenges and opportunities for future development.

A deep dive into COVID-19-related messages on WhatsApp in Pakistan.

The spread of COVID-19 and the lockdowns that followed led to an increase in activity on online social networks. This has resulted in users sharing unfiltered and unreliable information on social networks like WhatsApp, Twitter, Facebook, etc. In this work, we give an extended overview of how Pakistan's population used public WhatsApp groups for sharing information related to the pandemic. Our work is based on a major effort to annotate thousands of text and image-based messages. We explore how information propagates across WhatsApp and the user behavior around it. Specifically, we look at political polarization and its impact on how users from different political parties shared COVID-19-related content. We also try to understand information dissemination across different social networks-Twitter and WhatsApp-in Pakistan and find that there is no significant bot involvement in spreading misinformation about the pandemic.

IL-1ß expression driven by androgen receptor absence or inactivation promotes prostate cancer bone metastasis.

We report the inverse association between the expression of androgen receptor (AR) and interleukin-1beta (IL-1ß) in a cohort of patients with metastatic castration resistant prostate cancer (mCRPC). We also discovered that AR represses the IL-1ß gene by binding an androgen response element (ARE) half-site located within the promoter, which explains the IL-1ß expression in AR-negative (ARNEG) cancer cells. Consistently, androgen-depletion or AR-pathway inhibitors (ARIs) de-repressed IL-1ß in ARPOS cancer cells, both in vitro and in vivo. The AR transcriptional repression is sustained by histone de-acetylation at the H3K27 mark in the IL-1ß promoter. Notably, patients' data suggest that DNA methylation prevents IL-1ß expression, even if the AR-signaling axis is inactive. Our previous studies show that secreted IL-1ß supports metastatic progression in mice by altering the transcriptome of tumor-associated bone stroma. Thus, in prostate cancer patients harboring ARNEG tumor cells or treated with ADT/ARIs, and with the IL-1ß gene unmethylated, IL-1ß could condition the metastatic microenvironment to sustain disease progression.

DNA methylation dynamics and dysregulation delineated by high-throughput profiling in the mouse.

We have developed a mouse DNA methylation array that contains 296,070 probes representing the diversity of mouse DNA methylation biology. We present a mouse methylation atlas as a rich reference resource of 1,239 DNA samples encompassing distinct tissues, strains, ages, sexes, and pathologies. We describe applications for comparative epigenomics, genomic imprinting, epigenetic inhibitors, patient-derived xenograft assessment, backcross tracing, and epigenetic clocks. We dissect DNA methylation processes associated with differentiation, aging, and tumorigenesis. Notably, we find that tissue-specific methylation signatures localize to binding sites for transcription factors controlling the corresponding tissue development. Age-associated hypermethylation is enriched at regions of Polycomb repression, while hypomethylation is enhanced at regions bound by cohesin complex members. Apc Min/+ polyp-associated hypermethylation affects enhancers regulating intestinal differentiation, while hypomethylation targets AP-1 binding sites. This Infinium Mouse Methylation BeadChip (version MM285) is widely accessible to the research community and will accelerate high-sample-throughput studies in this important model organism.

RRM2B Is Frequently Amplified Across Multiple Tumor Types: Implications for DNA Repair, Cellular Survival, and Cancer Therapy.

RRM2B plays a crucial role in DNA replication, repair and oxidative stress. While germline RRM2B mutations have been implicated in mitochondrial disorders, its relevance to cancer has not been established. Here, using TCGA studies, we investigated RRM2B alterations in cancer. We found that RRM2B is highly amplified in multiple tumor types, particularly in MYC-amplified tumors, and is associated with increased RRM2B mRNA expression. We also observed that the chromosomal region 8q22.3-8q24, is amplified in multiple tumors, and includes RRM2B, MYC along with several other cancer-associated genes. An analysis of genes within this 8q-amplicon showed that cancers that have both RRM2B-amplified along with MYC have a distinct pattern of amplification compared to cancers that are unaltered or those that have amplifications in RRM2B or MYC only. Investigation of curated biological interactions revealed that gene products of the amplified 8q22.3-8q24 region have important roles in DNA repair, DNA damage response, oxygen sensing, and apoptosis pathways and interact functionally. Notably, RRM2B-amplified cancers are characterized by mutation signatures of defective DNA repair and oxidative stress, and at least RRM2B-amplified breast cancers are associated with poor clinical outcome. These data suggest alterations in RR2MB and possibly the interacting 8q-proteins could have a profound effect on regulatory pathways such as DNA repair and cellular survival, highlighting therapeutic opportunities in these cancers.

Securing Machine Learning in the Cloud: A Systematic Review of Cloud Machine Learning Security.

With the advances in machine learning (ML) and deep learning (DL) techniques, and the potency of cloud computing in offering services efficiently and cost-effectively, Machine Learning as a Service (MLaaS) cloud platforms have become popular. In addition, there is increasing adoption of third-party cloud services for outsourcing training of DL models, which requires substantial costly computational resources (e.g., high-performance graphics processing units (GPUs)). Such widespread usage of cloud-hosted ML/DL services opens a wide range of attack surfaces for adversaries to exploit the ML/DL system to achieve malicious goals. In this article, we conduct a systematic evaluation of literature of cloud-hosted ML/DL models along both the important dimensions-attacks and defenses-related to their security. Our systematic review identified a total of 31 related articles out of which 19 focused on attack, six focused on defense, and six focused on both attack and defense. Our evaluation reveals that there is an increasing interest from the research community on the perspective of attacking and defending different attacks on Machine Learning as a Service platforms. In addition, we identify the limitations and pitfalls of the analyzed articles and highlight open research issues that require further investigation.

Leveraging Data Science to Combat COVID-19: A Comprehensive Review.

COVID-19, an infectious disease caused by the SARS-CoV-2 virus, was declared a pandemic by the World Health Organisation (WHO) in March 2020. By mid-August 2020, more than 21 million people have tested positive worldwide. Infections have been growing rapidly and tremendous efforts are being made to fight the disease. In this paper, we attempt to systematise the various COVID-19 research activities leveraging data science, where we define data science broadly to encompass the various methods and tools-including those from artificial intelligence (AI), machine learning (ML), statistics, modeling, simulation, and data visualization-that can be used to store, process, and extract insights from data. In addition to reviewing the rapidly growing body of recent research, we survey public datasets and repositories that can be used for further work to track COVID-19 spread and mitigation strategies. As part of this, we present a bibliometric analysis of the papers produced in this short span of time. Finally, building on these insights, we highlight common challenges and pitfalls observed across the surveyed works. We also created a live resource repository at https://github.com/Data-Science-and-COVID-19/Leveraging-Data-Science-To-Combat-COVID-19-A-Comprehensive-Review that we intend to keep updated with the latest resources including new papers and datasets.

Interaction of germline variants in a family with a history of early-onset clear cell renal cell carcinoma.

BACKGROUND: Identification of genetic factors causing predisposition to renal cell carcinoma has helped improve screening, early detection, and patient survival. METHODS: We report the characterization of a proband with renal and thyroid cancers and a family history of renal and other cancers by whole-exome sequencing (WES), coupled with WES analysis of germline DNA from additional affected and unaffected family members. RESULTS: This work identified multiple predicted protein-damaging variants relevant to the pattern of inherited cancer risk. Among these, the proband and an affected brother each had a heterozygous Ala45Thr variant in SDHA, a component of the succinate dehydrogenase (SDH) complex. SDH defects are associated with mitochondrial disorders and risk for various cancers; immunochemical analysis indicated loss of SDHB protein expression in the patient's tumor, compatible with SDH deficiency. Integrated analysis of public databases and structural predictions indicated that the two affected individuals also had additional variants in genes including TGFB2, TRAP1, PARP1, and EGF, each potentially relevant to cancer risk alone or in conjunction with the SDHA variant. In addition, allelic imbalances of PARP1 and TGFB2 were detected in the tumor of the proband. CONCLUSION: Together, these data suggest the possibility of risk associated with interaction of two or more variants.

FTIR spectroscopic study of poly(ethylene glycol)-nifedipine dispersion stability in different relative humidities.

Solid dispersion has shown to be a promising formulation strategy to enhance dissolution for hydrophobic drugs. However, solid dispersions are often thermodynamically unstable, there is a continuous interest in studying their stabilities. In this study, attenuated total reflectance Fourier transform infrared (ATR-FTIR) was used to compare the amount of crystalline nifedipine formed in different formula of poly(ethylene glycol) (PEG)-nifedipine solid dispersions when exposed at various relative humidities (RHs) for 2 h at 40°C. The ratio of the crystalline nifedipine band and an internal reference band in the out of plane δ(C-H) region has been used to indicate the relative degree of drug crystallisation in a sample. A band ratio of ∼0.05 and 0.5 was respectively indicative of a fully amorphous or crystallised drug in the formula. Results show that increasing the RH generally increases the amount of crystalline nifedipine. Formulations with low (5%, w/w) nifedipine concentration in higher molecular weight PEG were found to be better at resisting crystallisation. Deliquescence of the 10% nifedipine in PEG 4000 was observed at 77% and 100% RH with a reduction in crystalline nifedipine. All 5% (w/w) nifedipine samples were stable at RH below 77%. Crystallisation of nifedipine occurred at all RH when drug loading was increased to 10% (w/w).