Have prisons become a dangerous place? Disproportionality, safety and mental health in British Prisons

In 2016, the Chief Inspector of Prisons reported that the prison service was in crisis and that ‘our prisons have become unacceptably violent and dangerous places’. In this year’s HMIP report he argues that ‘the situation has not improved; in fact, it has become worse’. It is clear that prisons in England and Wales are in chaos. A record number of people are dying, violence is at an all-time high and the living conditions for those in prison are far below acceptable standards. Moreover, there is significant evidence that many of the negative outcomes and deteriorating conditions of prison life are even worse for Black, Asian and ethnic minority (BME) prisoners

Mass infection is not an option: we must do more to protect our young.

Funder: Medical Research CouncilFunder: Wellcome Trus

Vulnerability

A panel session with the UK's Independent SAGE group

Fabrication and Characterization of Acute Myocardial Infarction Myoglobin Biomarker Based on Chromium-Doped Zinc Oxide Nanoparticles

In this article, we describe the fabrication and characterization of a sensor for acute myocardial infarction that detects myoglobin biomarkers using chromium (Cr)-doped zinc oxide (ZnO) nanoparticles (NPs). Pure and Cr-doped ZnO NPs (13 × 1017, 20 × 1017, and 32 × 1017 atoms/cm3 in the solid phase) were synthesized by a facile low-temperature sol-gel method. Synthesized NPs were examined for structure and morphological analysis using various techniques to confirm the successful formation of ZnO NPs. Zeta potential was measured in LB media at a negative value and increased with doping. XPS spectra confirmed the presence of oxygen deficiency in the synthesized material. To fabricate the sensor, synthesized NPs were screen-printed over a pre-fabricated gold-coated working electrode for electrochemical detection of myoglobin (Mb). Cr-doped ZnO NPs doped with 13 × 1017 Cr atomic/cm3 revealed the highest sensitivity of ~37.97 μA.cm−2nM−1 and limit of detection (LOD) of 0.15 nM for Mb with a response time of ≤10 ms. The interference study was carried out with cytochrome c (Cyt-c) due to its resemblance with Mb and human serum albumin (HSA) abundance in the blood and displayed distinct oxidation potential and current values for Mb. Cr-doped ZnO NP-based Mb biosensors showed 3 times higher sensitivity as compared to pure ZnO NP-based sensors

Machine learning-based technique for gain and resonance prediction of mid band 5G Yagi antenna

In this study, we present our findings from investigating the use of a machine learning (ML) technique to improve the performance of Quasi-Yagi–Uda antennas operating in the n78 band for 5G applications. This research study investigates several techniques, such as simulation, measurement, and an RLC equivalent circuit model, to evaluate the performance of an antenna. In this investigation, the CST modelling tools are used to develop a high-gain, low-return-loss Yagi–Uda antenna for the 5G communication system. When considering the antenna’s operating frequency, its dimensions are 0.642λ0×0.583λ0 . The antenna has an operating frequency of 3.5 GHz, a return loss of −43.45 dB, a bandwidth of 520 MHz, a maximum gain of 6.57 dB, and an efficiency of almost 97%. The impedance analysis tools in CST Studio’s simulation and circuit design tools in Agilent ADS software are used to derive the antenna’s equivalent circuit (RLC). We use supervised regression ML method to create an accurate prediction of the frequency and gain of the antenna. Machine learning models can be evaluated using a variety of measures, including variance score, R square, mean square error, mean absolute error, root mean square error, and mean squared logarithmic error. Among the nine ML models, the prediction result of Linear Regression is superior to other ML models for resonant frequency prediction, and Gaussian Process Regression shows an extraordinary performance for gain prediction. R-square and var score represents the accuracy of the prediction, which is close to 99% for both frequency and gain prediction. Considering these factors, the antenna can be deemed an excellent choice for the n78 band of a 5G communication system

Label-Free Myoglobin Biosensor Based on Pure and Copper-Doped Titanium Dioxide Nanomaterials

In this study, using pure and copper-doped titanium dioxide (Cu-TiO2) nanostructures as the base matrix, enzyme-less label free myoglobin detection to identify acute myocardial infarction was performed and presented. The Cu-TiO2 nanomaterials were prepared using facile sol–gel method. In order to comprehend the morphologies, compositions, structural, optical, and electrochemical characteristics, the pure and Cu-TiO2 nanomaterials were investigated by several techniques which clearly revealed good crystallinity and high purity. To fabricate the enzyme-less label free biosensor, thick films of synthesized nanomaterials were applied to the surface of a pre-fabricated gold screen-printed electrode (Au-SPE), which serves as a working electrode to construct the myoglobin (Mb) biosensors. The interference study of the fabricated biosensor was also carried out with human serum albumin (HSA) and cytochrome c (cyt-c). Interestingly, the Cu-doped TiO2 nanomaterial-based Mb biosensor displayed a higher sensitivity of 61.51 µAcm−2/nM and a lower detection limit of 14 pM with a response time of less than 10 ms

Label-Free Electrochemical Sensor Based on Manganese Doped Titanium Dioxide Nanoparticles for Myoglobin Detection: Biomarker for Acute Myocardial Infarction

A label free electrochemical sensor based on pure titanium oxide and manganese (Mn)-doped titanium oxide (TiO2) nanoparticles are fabricated and characterized for the sensitive detection of myoglobin (Mb) levels to analyze the cardiovascular infarction. Pristine and Mn-doped TiO2 nanoparticles were synthesized via the sol-gel method and characterized in order to understand their structure, morphologies, composition and optical properties. The structural properties revealed that the pure- and doped-TiO2 nanoparticles possess different TiO2 planes. FTIR studies confirm the formation of metal oxide nanoparticles by exhibiting a well-defined peak in the range of 600–650 cm−1. The values of the optical band gap, estimated from UV-Vis spectroscopy, are decreased for the Mn-doped TiO2 nanoparticles. UV-Vis spectra in the presence of myoglobin (Mb) indicated interaction between the TiO2 nanoparticles and myoglobin. The SPE electrodes were then fabricated by printing powder film over the working electrode and tested for label-free electrochemical detection of myoglobin (Mb) in the concentration range of 0–15 nM Mb. The fabricated electrochemical sensor exhibited a high sensitivity of 100.40 μA-cm−2/nM with a lowest detection limit of 0.013 nM (0.22 ng/mL) and a response time of ≤10 ms for sample S3. An interference study with cyt-c and Human Serum Albumin (HSA) of the sensors show the selective response towards Mb in 1:1 mixture

Quasi-Yagi antenna design for LTE applications and prediction of gain and directivity using machine learning approaches

In recent years, improvements in wireless communication have led to the development of microstrip or patch antennas. The article discusses using simulation, measurement, an RLC equivalent circuit model, and machine learning to assess antenna performance. The antenna's dimensions are 1.01 λ0×0.612λ0 with respect to the lowest operating frequency, the maximum achieved gain is 6.76 dB, the maximum directivity is 8.21 dBi, and the maximum efficiency is 83.05%. The prototype's measured return loss is compared to CST and ADS simulations. The prediction of gain and directivity of the antenna is determined using a different supervised regression machine learning (ML) method. The performance of ML models is measured by the variance score, R square, mean square error (MSE), mean absolute error (MAE), root mean square error (RMSE), and mean squared logarithmic error (MSLE), etc. With errors of less than unity and an accuracy of roughly 98%, Ridge regression gain prediction outperforms the other seven ML models. Gaussian process regression is the best method for predicting directivity. Finally, modeling results from CST and ADS, as well as measured and anticipated results from machine learning, reveal that the suggested antenna is a good candidate for LTE

Open science communication:The first year of the UK's Independent Scientific Advisory Group for Emergencies

The COVID-19 pandemic has shone a light on the complex relationship between science and policy. Policymakers have had to make decisions at speed in conditions of uncertainty, implementing policies that have had profound consequences for people's lives. Yet this process has sometimes been characterised by fragmentation, opacity and a disconnect between evidence and policy. In the United Kingdom, concerns about the secrecy that initially surrounded this process led to the creation of Independent SAGE, an unofficial group of scientists from different disciplines that came together to ask policy-relevant questions, review the evolving evidence, and make evidence-based recommendations. The group took a public health approach with a population perspective, worked in a holistic transdisciplinary way, and were committed to public engagement. In this paper, we review the lessons learned during its first year. These include the importance of learning from local expertise, the value of learning from other countries, the role of civil society as a critical friend to government, finding appropriate relationships between science and policy, and recognising the necessity of viewing issues through an equity lens.</p