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Abstract Background There is a demand for non-destructive systems in plant phenotyping which could precisely measure plant traits for growth monitoring. In this study, the growth of chilli plants (Capsicum annum L.) was monitored in outdoor conditions. A non-destructive solution is proposed for growth monitoring in 3D using a single mobile phone camera based on a structure from motion algorithm. A method to measure leaf length and leaf width when the leaf is curled is also proposed. Various plant traits such as number of leaves, stem height, leaf length, and leaf width were measured from the reconstructed and segmented 3D models at different plant growth stages. Results The accuracy of the proposed system is measured by comparing the values derived from the 3D plant model with manual measurements. The results demonstrate that the proposed system has potential to non-destructively monitor plant growth in outdoor conditions with high precision, when compared to the state-of-the-art systems. Conclusions In conclusion, this study demonstrated that the methods proposed to calculate plant traits can monitor plant growth in outdoor conditions.

Introduction: Laparoscopic distal pancreatectomy (LDP) has potential advantages over its open equivalent open distal pancreatectomy (ODP) for pancreatic disease in the neck, body and tail. Within the United Kingdom (UK), there has been no previous experience describing the role of robotic distal pancreatectomy (RDP). This study evaluated differences between ODP, LDP and RDP. Methods: Patients undergoing distal pancreatectomy performed in the Department of Hepatobiliary and Pancreatic Surgery at the Freeman Hospital between September 2007 and December 2018 were included from a prospectively maintained database. The primary outcome measure was length of hospital stay, and the secondary outcome measures were complication rates graded according to the Clavien–Dindo classification. Results: Of the 125 patients, the median age was 61 years and 46% were male. Patients undergoing RDP (n = 40) had higher American Society of Anesthesiologists grading III compared to ODP (n = 38) and LDP (n = 47) (57% vs. 37% vs. 38%, P = 0.02). RDP had a slightly lower but not significant conversion rate (10% vs. 13%, P = 0.084), less blood loss (median: 0 vs. 250 ml, P < 0.001) and a higher rate of splenic preservation (30% vs. 2%, P < 0.001) and shorter operative time, once docking time excluded (284 vs. 300 min, P < 0.001) compared to LDP. RDP had a higher R0 resection rate than ODP and LDP (79% vs. 47% vs. 71%, P = 0.078) for neoplasms. RDP was associated with significantly shorter hospital stay than LDP and ODP (8 vs. 9 vs. 10 days, P = 0.001). While there was no significant different in overall complications across the groups, RDP was associated with lower rates of Grade C pancreatic fistula than ODP and LDP (2% vs. 5% vs. 6%, P = 0.194). Conclusion: Minimally invasive pancreatic resection offers potential advantages over ODP, with a trend showing RDP to be marginally superior when compared to conventional LDP, but it is accepted that that this is likely to be at greater expense compared to the other current techniques.

Background Northern England has been experiencing a persistent rise in the number of primary liver cancers, largely driven by an increasing incidence of hepatocellular carcinoma (HCC) secondary to alcohol-related liver disease and non-alcoholic fatty liver disease. Here we review the effect of the COVID-19 pandemic on primary liver cancer services and patients in our region.Objective To assess the impact of the COVID-19 pandemic on patients with newly diagnosed liver cancer in our region.Design We prospectively audited our service for the first year of the pandemic (March 2020–February 2021), comparing mode of presentation, disease stage, treatments and outcomes to a retrospective observational consecutive cohort immediately prepandemic (March 2019–February 2020).Results We observed a marked decrease in HCC referrals compared with previous years, falling from 190 confirmed new cases to 120 (37%). Symptomatic became the the most common mode of presentation, with fewer tumours detected by surveillance or incidentally (% surveillance/incidental/symptomatic; 34/42/24 prepandemic vs 27/33/40 in the pandemic, p=0.013). HCC tumour size was larger in the pandemic year (60±4.6 mm vs 48±2.6 mm, p=0.017), with a higher incidence of spontaneous tumour haemorrhage. The number of new cases of intrahepatic cholangiocarcinoma (ICC) fell only slightly, with symptomatic presentation typical. Patients received treatment appropriate for their cancer stage, with waiting times shorter for patients with HCC and unchanged for patients with ICC. Survival was associated with stage both before and during the pandemic. 9% acquired COVID-19 infection.Conclusion The pandemic-associated reduction in referred patients in our region was attributed to the disruption of routine healthcare. For those referred, treatments and survival were appropriate for their stage at presentation. Non-referred or missing patients are expected to present with more advanced disease, with poorer outcomes. While protective measures are necessary during the pandemic, we recommend routine healthcare services continue, with patients encouraged to engage.

Abstract MicroRNAs are small (~ 22nt long) noncoding RNAs (ncRNAs) that regulate gene expression at the post-transcriptional level. Over 2000 microRNAs have been described in humans and many are implicated in human pathologies including tissue fibrosis. Hepatic stellate cells (HSC) are the major cellular contributors to excess extracellular matrix deposition in the diseased liver and as such are important in the progression of liver fibrosis. We employed next generation sequencing to map alterations in the expression of microRNAs occurring across a detailed time course of culture-induced transdifferentiation of primary human HSC, this a key event in fibrogenesis. Furthermore, we compared profiling of human HSC microRNAs with that of rat HSC so as to identify those molecules that are conserved with respect to modulation of expression. Our analysis reveals that a total of 229 human microRNAs display altered expression as a consequence of HSC transdifferentiation and of these 104 were modulated early during the initiation phase. Typically modulated microRNAs were targeting kinases, transcription factors, chromatin factors, cell cycle regulators and growth factors. 162 microRNAs changed in expression during transdifferentiation of rat HSC, however only 17 underwent changes that were conserved in human HSC. Our study therefore identifies widespread changes in the expression of HSC microRNAs in fibrogenesis, but suggests a need for caution when translating data obtained from rodent HSC to events occurring in human cells.

Objective Severe acute pancreatitis (SAP) is associated with high mortality (15%–30%). Current guidelines recommend these patients are best managed in a multidisciplinary team setting. This study reports experience in the management of SAP within the UK’s first reported hub-and-spoke pancreatitis network.Design All patients with SAP referred to the remote care pancreatitis network between 2015 and 2017 were prospectively entered onto a database by a dedicated pancreatitis specialist nurse. Baseline characteristics, aetiology, intensive care unit (ICU) stay, interventions, complications, mortality and follow-up were analysed.Results 285 patients admitted with SAP to secondary care hospitals during the study period were discussed with the dedicated pancreatitis specialist nurse and referred to the regional service. 83/285 patients (29%; 37 male) were transferred to the specialist centre mainly for drainage of infected pancreatic fluid collections (PFC) in 95% (n=79) of patients. Among the patients transferred; 29 (35%) patients developed multiorgan failure with an inpatient mortality of 14% (n=12/83). The median follow-up was 18.2 months (IQR=11.25–35.51). Multivariate analysis showed that transferred patients had statistically significant longer overall hospital stay (p

Introduction: In kidney transplantation, total laparoscopic live donor nephrectomy (TLLDN) in the presence of multiple renal arteries (MRA) is technically challenging and has traditionally been associated with higher complication rates. We report our experience of using MRA grafts procured by TLLDN. Materials and Methods: Patients undergoing TLLDN at our center (2004–2014) was identified from a prospectively maintained database and divided into single renal arteries (SRA) or MRA groups. Recipient perioperative parameters, postoperative complications, and long-term graft survival were analyzed. Results: Of 465 patients, 106 had MRA and 359 had an SRA. There were six vascular complications in the SRA group and two in the MRA group (1.7% vs. 1.8%). There were eight ureteric complications requiring intervention in the SRA group compared to three in the MRA group (4% vs. 3%; P = 0.45). Acute rejection was observed in 12% of the SRA group compared to 9% in the MRA group (P = 0.23). One-, 5- and 10-year graft survivals were 98.2%, 91.3%, and 89.8% in the MRA group versus 98.0%, 90.4%, and 77.5% in the SRA group (log-rank P = 0.13). Conclusion: The use of MRA grafts procured by TLLDN has comparable complication rates to SRA grafts and should not preclude selection for renal transplantation.

The exponential growth in remote sensing, coupled with advancements in integrated circuits (IC) design and fabrication technology for communication, has prompted the progress of Wireless Sensor Networks (WSN). WSN comprises of sensor nodes and hubs fit for detecting, processing, and communicating remotely. Sensor nodes have limited resources such as memory, energy and computation capabilities restricting their ability to process large volume of data that is generated. Compressing the data before transmission will help alleviate the problem. Many data compression methods have been proposed but mainly for image processing and a vast majority of them are not pertinent on sensor nodes because of memory impediment, energy utilization and handling speed. To overcome this issue, authors in this research have chosen Run Length Encoding (RLE) and Adaptive Huffman Encoding (AHE) data compression techniques as they can be executed on sensor nodes. Both RLE and AHE are capable of balancing compression ratio and energy utilization. In this paper, a hybrid method comprising RLE and AHE, named as H-RLEAHE, is proposed and further investigated for sensor nodes. In order to verify the efficacy of the data compression algorithms, simulations were run, and the results compared with the compression techniques employing RLE, AHE, H-RLEAHE, and without the use of any compression approach for five distinct scenarios. The results demonstrate the RLE’s efficiency, as it surpasses alternative data compression methods in terms of energy efficiency, network speed, packet delivery rate, and residual energy throughout all iterations.

The application of the Internet of Things (IoT) in wireless sensor networks (WSNs) poses serious challenges in preserving network longevity since the IoT necessitates a considerable amount of energy usage for sensing, processing, and data communication. As a result, there are several conventional algorithms that aim to enhance the performance of WSN networks by incorporating various optimization strategies. These algorithms primarily focus on the network layer by developing routing protocols to perform reliable communication in an energy-efficient manner, thus leading to an enhanced network life. For increasing the network lifetime in WSNs, clustering has been widely accepted as an important method that groups sensor nodes (SNs) into clusters. Additionally, numerous researchers have been focusing on devising various methods to increase the network lifetime. The prime factor that helps to maximize the network lifetime is the minimization of energy consumption. The authors of this paper propose a multi-objective optimization approach. It selects the optimal route for transmitting packets from source to sink or the base station (BS). The proposed model employs a two-step approach. The first step employs a trust model to select the cluster heads (CHs) that manage the data communication between the BS and nodes in the cluster. Further, a novel hybrid algorithm, combining a particle swarm optimization (PSO) algorithm and a genetic algorithm (GA), is proposed to determine the routes for data transmission. To validate the efficacy of the proposed hybrid algorithm, named PSOGA, simulations were conducted and the results were compared with the existing LEACH method and PSO, with a random route selection for five different cases. The obtained results establish the efficiency of the proposed approach, as it outperforms existing methods with increased energy efficiency, increased network throughput, high packet delivery rate, and high residual energy throughout the entire iterations.

Use of 3D sensors in plant phenotyping has increased in the last few years. Various image acquisition, 3D representations, 3D model processing and analysis techniques exist to help the researchers. However, a review of approaches, algorithms, and techniques used for 3D plant physiognomic analysis is lacking. In this paper, we investigate the techniques and algorithms used at various stages of processing and analysing 3D models of plants, and identify their current limiting factors. This review will serve potential users as well as new researchers in this field. The focus is on exploring studies monitoring the plant growth of single plants or small scale canopies as opposed to large scale monitoring in the field.

In this paper, we compare the performance of a novel adaptive protocol with the fixed power transmission protocol using experimental data when the distance between the transmitter and the receiver is fixed. In fixed power transmission protocol, corresponding to the distance between the sensor and the hub, there is a fixed power level that provides the optimal or minimum value in terms of energy consumption while maintaining a threshold Quality of Service (QoS) parameter. This value is bounded by the available output power levels of a given radio transceiver. The proposed novel adaptive power control protocol tracks and supersedes that energy expenditure by using an intelligent algorithm to ramp up or down the output power level as and when required. This protocol does not use channel side information in terms of received signal strength indication (RSSI) or link quality indication (LQI) for channel estimation to decide the transmission power. It also controls the number of allowed retransmissions for error correction. Experimental data have been collected at different distances between the transmitting sensor and the hub. It can be observed that the energy consumption of the fixed power level is at least 25% more than the proposed adaptive protocol for comparable packet success rate.

In this paper, we have presented a novel transmission protocol which is suited for battery-powered sensors that are worn by a patient when under medical treatment, and allow constant monitoring of health indices. These body-wearable sensors log data from the patient and transmit the data to a base-station or gateway, via a wireless link at specific intervals. The signal link quality varies because the distance between the patient and the gateway is not fixed. This may lead to packet drops that increase the energy consumption due to repeated retransmission. The proposed novel transmission power control protocol combines a state based adaptive power control (SAPC) algorithm and an intelligent adaptive drop-off algorithm, to track the changes in the link quality, in order to maintain an acceptable Packet success rate (PSR)(~99%). This removes the limitation of the SAPC by making the drop-off rate adaptive. Simulations were conducted to emulate a subject’s movement in different physical scenarios—an indoor office environment and an outdoor running track. The simulation results were validated through experiments in which the transmitter, together with the sensor mounted on the subject, and the subject themselves were made to move freely within the communicable range. Results showed that the proposed protocol performs at par with the best performing SAPC corresponding to a fixed drop-off rate value.

This study proposes an automatic sign language translator, which is developed as assistive technology to help the hearing/speech impaired communities to communicate with the rest of the world. The system architecture, which includes feature extraction and recognition stages is described in detail. The signs are classified into two types: static and dynamic. Various types of sign features are presented and analysed. Recognition stage considers the hidden Markov model and segmentation signature. Real-time implementation of the system with the use of Windows7 and LINUX Fedora 16 operating systems with VMware workstation is presented in detail. The system has been successfully tested on Malaysian sign language.