Adaptive spatial mode of space-time and spacefrequency OFDM system over fading channels

In this paper we present a 2 transmit 1 receive (1 Tx : 1 Rx) adaptive spatial mode (ASM) of space-time (ST) and space-frequency (SF) orthogonal frequency division multiplexing (OFDM). At low signal to noise ratio (SNR) we employ ST-OFDM and switch to SF-OFDM at a certain SNR threshold. We determine this threshold from the intersection of individual performance curves. Results show a gain of 9 dB (at a bit error rate of 10-3) is achieved by employing adaptive spatial mode compared to a fixed ST-OFDM, almost 6 dB to fixed SF-OFDM, 4 dB to Coded ST-OFDM and 2 dB to a fixed coded SF-OFDM, at a delay spread of 700 ns

The Effect of Different Dielectric Materials in Designing High-Performance Metal-Insulator-Metal (MIM) Capacitors

A Metal-Insulator-Metal (MIM) capacitor with high capacitance, high breakdown voltage, and low leakage current is aspired so that the device can be applied in many electronic applications. The most significant factors that affect the MIM capacitor’s performance is the design and the dielectric materials used. In this study, MIM capacitors are simulated using different dielectric materials and different number of dielectric layers from two layers up to seven layers.  The effect of the different dielectric constants (k) to the performance of the MIM capacitors is also studied, whereas this work investigates the effect of using low-k and high-k dielectric materials. The dielectric materials used in this study with high-k are Al2O3 and HfO2, while the low-k dielectric materials are SiO2 and Si3N4. The results demonstrate that the dielectric materials with high-k produce the highest capacitance. Results also show that metal-Al2O3 interfaces increase the performance of the MIM capacitors. By increasing the number of dielectric layers to seven stacks, the capacitance and breakdown voltage reach its highest value at 0.39 nF and 240 V, respectively

Floating-gate MOS structures and applications

Floating-gate MOS transistor (FGMOS) has proved to be suitable for low-voltage analog applications, owing to its threshold voltage programmability. This tutorial paper presents FGMOS based circuit structures and their applications in analog signal processing. The FGMOS based current mirror and its application as voltage controlled current source has been presented. The performance of these structures has been verified using PSpice simulations for 0.5 im CMOS technology at 0.75 V

Implementation of genomics in medical practice to deliver precision medicine for an Asian population

Whilst the underlying principles of precision medicine are comparable across the globe, genomic references, health practices, costs and discrimination policies differ in Asian settings compared to the reported initiatives involving European-derived populations. We have addressed these variables by developing an evolving reference base of genomic and phenotypic data and a framework to return medically significant variants to consenting research participants applicable for the Asian context. Targeting 10,000 participants, over 2000 Singaporeans, with no known pre-existing health conditions, have consented to an extensive clinical health screen, family health history collection, genome sequencing and ongoing follow-up. Genomic variants in a subset of genes associated with Mendelian disorders and drug responses are analysed using an in-house bioinformatics pipeline. A multidisciplinary team reviews the classification of variants and a research report is generated. Medically significant variants are returned to consenting participants through a bespoke return-of-result genomics clinic. Variant validation and subsequent clinical referral are advised as appropriate. The design and implementation of this flexible learning framework enables a cohort of detailed phenotyping and genotyping of healthy Singaporeans to be established and the frequency of disease-causing variants in this population to be determined. Our findings will contribute to international precision medicine initiatives, bridging gaps with ethnic-specific data and insights from this understudied population

From Big Data to Precision Medicine.

For over a decade the term "Big data" has been used to describe the rapid increase in volume, variety and velocity of information available, not just in medical research but in almost every aspect of our lives. As scientists, we now have the capacity to rapidly generate, store and analyse data that, only a few years ago, would have taken many years to compile. However, "Big data" no longer means what it once did. The term has expanded and now refers not to just large data volume, but to our increasing ability to analyse and interpret those data. Tautologies such as "data analytics" and "data science" have emerged to describe approaches to the volume of available information as it grows ever larger. New methods dedicated to improving data collection, storage, cleaning, processing and interpretation continue to be developed, although not always by, or for, medical researchers. Exploiting new tools to extract meaning from large volume information has the potential to drive real change in clinical practice, from personalized therapy and intelligent drug design to population screening and electronic health record mining. As ever, where new technology promises "Big Advances," significant challenges remain. Here we discuss both the opportunities and challenges posed to biomedical research by our increasing ability to tackle large datasets. Important challenges include the need for standardization of data content, format, and clinical definitions, a heightened need for collaborative networks with sharing of both data and expertise and, perhaps most importantly, a need to reconsider how and when analytic methodology is taught to medical researchers. We also set "Big data" analytics in context: recent advances may appear to promise a revolution, sweeping away conventional approaches to medical science. However, their real promise lies in their synergy with, not replacement of, classical hypothesis-driven methods. The generation of novel, data-driven hypotheses based on interpretable models will always require stringent validation and experimental testing. Thus, hypothesis-generating research founded on large datasets adds to, rather than replaces, traditional hypothesis driven science. Each can benefit from the other and it is through using both that we can improve clinical practice.Wellcome Trus

Unmet needs in countries participating in the undiagnosed diseases network international: an international survey considering national health care and economic indicators

BackgroundPatients, families, the healthcare system, and society as a whole are all significantly impacted by rare diseases (RDs). According to various classifications, there are currently up to 9,000 different rare diseases that have been recognized, and new diseases are discovered every month. Although very few people are affected by each uncommon disease individually, millions of people are thought to be impacted globally when all these conditions are considered. Therefore, RDs represent an important public health concern. Although crucial for clinical care, early and correct diagnosis is still difficult to achieve in many nations, especially those with low and middle incomes. Consequently, a sizeable amount of the overall burden of RD is attributable to undiagnosed RD (URD). Existing barriers and policy aspects impacting the care of patients with RD and URD remain to be investigated.MethodsTo identify unmet needs and opportunities for patients with URD, the Developing Nations Working Group of the Undiagnosed Diseases Network International (DNWG-UDNI) conducted a survey among its members, who were from 20 different nations. The survey used a mix of multiple choice and dedicated open questions covering a variety of topics. To explore reported needs and analyze them in relation to national healthcare economical aspects, publicly available data on (a) World Bank ranking; (b) Current health expenditure per capita; (c) GDP per capita; (d) Domestic general government health expenditure (% of GDP); and (e) Life expectancy at birth, total (years) were incorporated in our study.ResultsThis study provides an in-depth evaluation of the unmet needs for 20 countries: low-income (3), middle-income (10), and high-income (7). When analyzing reported unmet needs, almost all countries (N = 19) indicated that major barriers still exist when attempting to improve the care of patients with UR and/or URD; most countries report unmet needs related to the availability of specialized care and dedicated facilities. However, while the countries ranked as low income by the World Bank showed the highest prevalence of referred unmet needs across the different domains, no specific trend appeared when comparing the high, upper, and low-middle income nations. No overt trend was observed when separating countries by current health expenditure per capita, GDP per capita, domestic general government health expenditure (% of GDP) and life expectancy at birth, total (years). Conversely, both the GDP and domestic general government health expenditure for each country impacted the presence of ongoing research.ConclusionWe found that policy characteristics varied greatly with the type of health system and country. No overall pattern in terms of referral for unmet needs when separating countries by main economic or health indicators were observed. Our findings highlight the importance of identifying actionable points (e.g., implemented orphan drug acts or registries where not available) in order to improve the care and diagnosis of RDs and URDs on a global scale

Undiagnosed diseases: Needs and opportunities in 20 countries participating in the Undiagnosed Diseases Network International

Introduction: Rare diseases (RD) are a health priority worldwide, overall affecting hundreds of millions of people globally. Early and accurate diagnosis is essential to support clinical care but remains challenging in many countries, especially the low- and medium-income ones. Hence, undiagnosed RD (URD) account for a significant portion of the overall RD burden. Methods: In October 2020, the Developing Nations Working Group of the Undiagnosed Diseases Network International (DNWG-UDNI) launched a survey among its members, belonging to 20 countries across all continents, to map unmet needs and opportunities for patients with URD. The survey was based on questions with open answers and included eight different domains. Conflicting interpretations were resolved in contact with the partners involved. Results: All members responded to the survey. The results indicated that the scientific and medical centers make substantial efforts to respond to the unmet needs of patients. In most countries, there is a high awareness of RD issues. Scarcity of resources was highlighted as a major problem, leading to reduced availability of diagnostic expertise and research. Serious equity in accessibility to services were highlighted both within and between participating countries. Regulatory problems, including securing informed consent, difficulties in sending DNA to foreign laboratories, protection of intellectual property, and conflicts of interest on the part of service providers, remain issues of concern. Finally, most respondents stressed the need to strengthen international cooperation in terms of data sharing, clinical research, and diagnostic expertise for URD patients in low and medium income countries. Discussion: The survey highlighted that many countries experienced a discrepancy between the growing expertise and scientific value, the level of awareness and commitment on the part of relevant parties, and funding bodies. Country-tailored public health actions, including general syllabus of medical schools and of the education of other health professionals, are needed to reduce such gaps.VSh is supported by Health Systems Research Institute of Thailand (65-040). SJ is supported by National Medical Research Council, Singapore (Grants ID CSAINV21jun-0003 and CIRG22jul-0003).S