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Stopes suffer from unreliable wireless communication due to their harsh environment. There is a lack of confidence within industry regarding the effectiveness of existing solutions in providing reliable high-bandwidth performance in hard rock stopes. This work proposes that Wi-Fi6 is a good candidate for reliable high-bandwidth communications in underground hard rock stopes. Experiments in a tunnel and mine stope were conducted to evaluate the performance of Wi-Fi6 in terms of latency, jitter, and throughput. Different criteria, such as multi-hop systems, varying multipath, mesh routing protocols, and frequencies at different bandwidths, were used to evaluate performance. The results show that Wi-Fi6 performance is greater in stopes compared to tunnels. Signal quality evaluations were conducted using the Asus RT-AX53U running OpenWrt, and an additional experiment was conducted on the nrf7002dk running Zephyr OS to evaluate the power consumption of Wi-Fi6 against the industry standard for low-powered wireless communications, IEEE 802.15.4. Wi-Fi6 was found to be more power-efficient than IEEE 802.15.4 for Mbps communications. These experiments highlight the signal robustness of Wi-Fi6 in stope environments and also highlights its low-powered nature. This work also highlights the performance of the two most widely used open-source mesh routing protocols for Wi-Fi.

Background: Temporomandibular disorders (TMD) affect a significant portion of the population, with profound psychological, behavioral, and social repercussions. Recent investigations have explored the genetic basis underlying pain perception in individuals with TMD, aiming to elucidate the role of specific genetic factors in modulating the condition. Notably, genetic variations have been implicated in the pathogenesis of TMD, particularly genes involved in pain perception pathways. One of the primary candidates is the Catechol-O-Methyltransferase (COMT) gene, which plays a crucial role in the catecholaminergic system and has been associated with the regulation of nociceptive processes. This study seeks to investigate the correlation between COMT gene activity and pain perception among South African patients diagnosed with varying forms of TMD. Methodology: In this study, a total of 196 participants were enrolled, comprising 97 patients diagnosed with TMD and 99 control participants. The control group was meticulously matched with the TMD group for age, gender, and ethnicity. Data collection involved clinical and radiological investigations, and saliva sampling. The English version of the Diagnostic Criteria for Temporomandibular Disorders (DC/TMD) Axis I was utilized to evaluate all TMD participants, focusing on standard diagnostic measures based on clinical signs and symptoms of TMD, which primarily describe common physical manifestations of the disorder. Genomic DNA was extracted from saliva samples, enabling the analysis of single-nucleotide polymorphisms (SNPs) in the COMT gene, specifically targeting polymorphisms rs165774, rs9332377, rs6269, rs4646310, rs165656, and rs4680. Results: The current study demonstrated a pronounced gender disparity, with 80.41% of the participants being female and 19.59% male, suggesting that women in South Africa either exhibit a higher susceptibility to TMD or are more likely to seek treatment for the condition compared to men. The highest prevalence of TMD was observed in the white population (58.76%). Additionally, over 65% of TMD patients were diagnosed with at least two Axis I diagnoses, a figure that increased to 89% for those diagnosed with three Axis I diagnoses. The findings further indicated significant associations between several single-nucleotide polymorphisms (SNPs) in the Catechol-O-Methyltransferase (COMT) gene—specifically rs165656, rs9332377, rs4646310, rs6269, and rs165774—and both TMD and TMD-related pain. Myofascial pain with referral and myalgia showed a strong association with the COMT SNPs rs9332377 and rs4646310. Furthermore, COMT SNP rs4646310 was also associated with disability related to TMD. Conclusions: This study substantiates the hypothesis that pain is prevalent in a considerable proportion of patients affected by TMD. Furthermore, the findings reveal a significant association between COMT gene activity and pain perception in South African patients diagnosed with TMD.

Lesser Flamingos Phoeniconaias minor regularly collide with power lines in South Africa. Attaching light-emitting markers to overhead wires seems to be an effective mitigation measure; however, the cost of these devices is prohibitive of large-scale installation. Spatial predictions about flamingo collision risk are thus important for achieving efficient and effective proactive mitigation. In this study, collision risk was defined as a combination of factors related to threat exposure. A habitat suitability index was developed according to changes in surface water occurrence and Chlorophyll-a concentrations, which proved accurate in predicting Lesser Flamingo occurrence. Habitat suitability, and three other species and threat exposure variables, were then used in logistic regression models predicting the occurrence of historic collisions. The most parsimonious model included habitat suitability and flight height. Flamingos were only at risk of collision with power lines when flying lower than 50 m and within 3 km from the water’s edge. High-risk power line sections were thus identified from 3 km buffers around waterbodies ranked according to habitat suitability, which significantly reduced the number of power line spans predicted for proactive marking. While our models indicated that aspects related to exposure were important for predicting flamingo power line collisions, aspects related to sensitivity (e.g., nocturnal behavior) must also guide the choice of mitigation.

Recent technological advancements have fostered human–robot coexistence in work and residential environments. The assistive robot must exhibit humane behavior and consistent care to become an integral part of the human habitat. Furthermore, the robot requires an adaptive unsupervised learning model to explore unfamiliar conditions and collaborate seamlessly. This paper introduces variants of the growing hierarchical self-organizing map (GHSOM)-based computational models for assistive robots, which constructs knowledge from unsupervised exploration-based learning. Traditional self-organizing map (SOM) algorithms have shortcomings, including finite neuron structure, user-defined parameters, and non-hierarchical adaptive architecture. The proposed models overcome these limitations and dynamically grow to form problem-dependent hierarchical feature clusters, thereby allowing associative learning and symbol grounding. Infants can learn from their surroundings through exploration and experience, developing new neuronal connections as they learn. They can also apply their prior knowledge to solve unfamiliar problems. With infant-like emergent behavior, the presented models can operate on different problems without modifications, producing new patterns not present in the input vectors and allowing interactive result visualization. The proposed models are applied to the color, handwritten digits clustering, finger identification, and image classification problems to evaluate their adaptiveness and infant-like knowledge building. The results show that the proposed models are the preferred generalized models for assistive robots.

This article details the existing wearable assistive devices that could mimic a human’s active range of motion and aid individuals in recovering from stroke. The survey has identified several risk factors associated with musculoskeletal pain, including physical factors such as engaging in high-intensity exercises, experiencing trauma, aging, dizziness, accidents, and damage from the regular wear and tear of daily activities. These physical risk factors impact vital body parts such as the cervical spine, spinal cord, ankle, elbow, and others, leading to dysfunction, a decrease in the range of motion, and diminished coordination ability, and also influencing the ability to perform the activities of daily living (ADL), such as speaking, breathing and other neurological responses. An individual with these musculoskeletal disorders requires therapies to regain and restore the natural movement. These therapies require an experienced physician to treat the patient, which makes the process expensive and unreliable because the physician might not repeat the same procedure accurately due to fatigue. These reasons motivated researchers to develop and control robotics-based wearable assistive devices for various musculoskeletal disorders, with economical and accessible solutions to aid, mimic, and reinstate the natural active range of motion. Recently, advancements in wearable sensor technologies have been explored in healthcare by integrating machine-learning (ML) and artificial intelligence (AI) techniques to analyze the data and predict the required setting for the user. This review provides a comprehensive discussion on the importance of personalized wearable devices in pre- and post-clinical settings and aids in the recovery process.

Microbial fuel cells (MFCs) have garnered significant attention due to their capacity to generate electricity using renewable and carbon-neutral energy sources such as wastewater. Extensive experimental work and modeling techniques have been employed to dissect these processes and understand their respective impacts on electricity generation. The driving force is to enhance MFC performance for practical applications commercially. Among the various statistical modeling approaches, one particularly robust tool is the Design of Experiments (DoE). It serves to establish the relationships between different variables that influence MFC performance and allows for the optimization of the MFC configuration and operation for scaled-up performances in terms of bioelectricity generation. This study focused on optimizing microbial fuel cells (MFCs) for bioelectricity production using industrial wastewater treatment, employing the Box–Behnken design (BBD) methodology. Through an analysis of response surface models and ANOVA tests, it was found that a combined approach of reduced quadratic, reduced two-factor interaction, and linear models yielded sound results, particularly in voltage yield, COD removal, and current density. Second-order regression models predicted optimal conditions for various parameters, with surface area, temperature, and catholyte dosage identified as critical input variables for optimization. Under these conditions, conducted by the four-factor and three-level Box–Behnken design methodology in a double-chamber MFC unit considering eight output variables—CCV yield, % COD removal, current density, power density, % TSS removal, % CE, and % PO43−—the optimum values were 700 mV, 54.4%, 54.4 mA/m2, 73.7 mW/m2, 99%, 21.2%, and 100%, respectively. At optimum operating conditions, the results revealed a desirability of 76.6% out of a total of 92 iterations. The paper highlights the effectiveness of statistical ANOVA fit-statistics modeling and optimization in enhancing DCMFC performance, recommending its use as a sustainable bioenergy source. Furthermore, validation results supported the above optimization output response findings and confirmed the viability of biorefinery wastewater as an anolyte for scaling up DCMFC bioelectricity generation.

Assessing pain in non-verbal patients is challenging, often depending on clinical judgment which can be unreliable due to fluctuations in vital signs caused by underlying medical conditions. To date, there is a notable absence of objective diagnostic tests to aid healthcare practitioners in pain assessment, especially affecting critically-ill or advanced dementia patients. Neurophysiological information, i.e., functional near-infrared spectroscopy (fNIRS) or electroencephalogram (EEG), unveils the brain’s active regions and patterns, revealing the neural mechanisms behind the experience and processing of pain. This study focuses on assessing pain via the analysis of fNIRS signals combined with machine learning, utilising multiple fNIRS measures including oxygenated haemoglobin (ΔHBO2) and deoxygenated haemoglobin (ΔHHB). Initially, a channel selection process filters out highly contaminated channels with high-frequency and high-amplitude artifacts from the 24-channel fNIRS data. The remaining channels are then preprocessed by applying a low-pass filter and common average referencing to remove cardio-respiratory artifacts and common gain noise, respectively. Subsequently, the preprocessed channels are averaged to create a single time series vector for both ΔHBO2 and ΔHHB measures. From each measure, ten statistical features are extracted and fusion occurs at the feature level, resulting in a fused feature vector. The most relevant features, selected using the Minimum Redundancy Maximum Relevance method, are passed to a Support Vector Machines classifier. Using leave-one-subject-out cross validation, the system achieved an accuracy of 68.51%±9.02% in a multi-class task (No Pain, Low Pain, and High Pain) using a fusion of ΔHBO2 and ΔHHB. These two measures collectively demonstrated superior performance compared to when they were used independently. This study contributes to the pursuit of an objective pain assessment and proposes a potential biomarker for human pain using fNIRS.

Relation extraction from biological publications plays a pivotal role in accelerating scientific discovery and advancing medical research. While vast amounts of this knowledge is stored within the published literature, extracting it manually from this continually growing volume of documents is becoming increasingly arduous. Recently, attention has been focused towards automatically extracting such knowledge using pre-trained Large Language Models (LLM) and deep-learning algorithms for automated relation extraction. However, the complex syntactic structure of biological sentences, with nested entities and domain-specific terminology, and insufficient annotated training corpora, poses major challenges in accurately capturing entity relationships from the unstructured data. To address these issues, in this paper, we propose a Knowledge-based Intelligent Text Simplification (KITS) approach focused on the accurate extraction of biological relations. KITS is able to precisely and accurately capture the relational context among various binary relations within the sentence, alongside preventing any potential changes in meaning for those sentences being simplified by KITS. The experiments show that the proposed technique, using well-known performance metrics, resulted in a 21% increase in precision, with only 25% of sentences simplified in the Learning Language in Logic (LLL) dataset. Combining the proposed method with BioBERT, the popular pre-trained LLM was able to outperform other state-of-the-art methods.

The rising mortality and morbidity rate of head and neck cancer (HNC) in Africa has been attributed to factors such as the poor state of health infrastructures, genetics, and late presentation resulting in the delayed diagnosis of these tumors. If well harnessed, emerging molecular and omics diagnostic technologies such as liquid biopsy can potentially play a major role in optimizing the management of HNC in Africa. However, to successfully apply liquid biopsy technology in the management of HNC in Africa, factors such as genetic, socioeconomic, environmental, and cultural acceptability of the technology must be given due consideration. This review outlines the role of circulating molecules such as tumor cells, tumor DNA, tumor RNA, proteins, and exosomes, in liquid biopsy technology for the management of HNC with a focus on studies conducted in Africa. The present state and the potential opportunities for the future use of liquid biopsy technology in the effective management of HNC in resource-limited settings such as Africa is further discussed.

Antimicrobial stewardship (AMS) programs are part of the key activities that contribute to reducing antimicrobial resistance (AMR). Good knowledge, attitudes, and practices (KAP) among healthcare professionals (HCPs) are essential to improving future antimicrobial use and reducing AMR, which is a priority in Ghana. A multicenter cross-sectional survey was conducted in six public hospitals in Ghana among key HCPs to assess their level of KAP towards AMS using a validated self-administered electronic questionnaire. Data analyses included descriptive and inferential statistics using STATA version 14. Overall, 339 out of 355 HCPs responded to the questionnaire, giving a response rate of 95.5%. Most responders were nurses (n = 256, 78.2%), followed by medical doctors (n = 45, 13.3%). The study recorded both poor knowledge (8.9%) and practice levels (35.4%), as well as a good attitude (78.8%) towards AMS. Ongoing exposure to AMS structured training, exposure to continuous professional development training on AMS in the previous year, and the number of years of working experience were predictors of the HCPs’ level of knowledge (aOR = 3.02 C.I = 1.12–8.11), attitude (aOR = 0.37 C.I = 0.20–0.69) and practice (aOR = 2.09 C.I =1.09–3.99), respectively. Consequently, concentrated efforts must be made to address current low levels of knowledge and poor practices regarding AMS among HCPs in Ghana as part of ongoing strategies in the National Action Plan to reduce AMR.

In this present study, the potential application of DCMFC for the treatment of three different sourced industrial wastewater streams: biorefinery, dairy and mixed streams was investigated. Operating conditions were optimised using the Box Behnken design in response surface methodology (RSM) with three validation experimental runs. The effect of process variables, i.e., HRT (48 h), catholyte dose (0.1 gmol/L) and electrode surface area (three carbon rods argumentation-m2) on the production of electricity as voltage yield (mV), power density (mW/m2), current density (mA/m2), Columbic efficiency (%) CE and Gibbs free energy correlation with the electromotive force of the DCMFC system. Experimental results obtained were a positive response towards the predictive values according to the DoE numerical optimisation sequence. At numerical optimum MFC conditions stated above, validation experimental responses of voltage yield by biorefinery wastewater were 645.2 mV, mixed wastewater was 549 mV, and dairy wastewater was 358 mV maximum yields. The power densities and current densities were attained, for biorefinery, mixed wastewater and dairy wastewater sources respectively as; 62 mW/m2, 50 mW/m2 and 27.2 mW/m2, then current densities of 50 mA/m2, 44,008 mA/m2 and 18 mA/m2. The coulombic efficiencies of 0.34%, 0.75% and 0.22%, respectively, were achieved. The validation of predicted optimum operating conditions was successfully attained, especially through the biorefinery wastewater organic substrate. This article articulates that it is highly imperative to choose the most suitable wastewater source as the viable electron donor towards scaling up and maximising the efficiency of generating electricity in the double chamber microbial fuel cell (DCMFC). Moreover, the findings of the current study demonstrate that the DCMFC can be further upscaled through a series connection in a fed-batch mode of operation using a well-designed and simulated process control system that has been computationally designed and modelled using first order MFC model bioenergy generating models MATLAB Simulink and Simscape electrical software. These findings of the simulations were successful and illustrated that an MFC power output can be successfully stepped to be a viable bio-electrochemical technology for both industrial wastewater (IWW) treatment and simultaneous sustainable power generation.

In complex health systems, referral and outreach systems (ROS) are formal strategies for connecting and managing interdependencies between facilities in service delivery pathways. Well-functioning maternal and newborn ROS are critical to successful outcomes, and therefore, a good lens through which to examine the management of local interdependencies. We conducted a qualitative study of maternal–newborn ROS, involving interviews with 52 senior, middle, and frontline managers, in three health districts of three different provinces in South Africa. We analyse the differences in functioning of ROS as an interplay of setting (urban, rural), individual facility strengths and weaknesses, the quality of emergency medical services (EMS), and the wider provincial strategic and organisational context. ROS are strengthened by sub-district governance arrangements that recognise and enable connectedness—in particular, between primary health care and district hospital services; by informal, day-to-day communication and collaboration across levels and professions; and by hybrid clinical–managerial players as system brokers and systems thinkers. We also identify leverage points, places where small shifts could have wider system effects, most notably in the design and functioning of EMS, and in addressing small, but significant bottlenecks in supply chains in lower level facilities that negatively impact the system as a whole.

The adoption of emerging technologies in healthcare systems plays a crucial part in anti-obesity initiatives. COVID-19 has intensified the Body Mass Index (BMI) discourses in AI (Artificial Intelligence)-powered social media. However, few studies have reported on the influence of digital content on obesity prevention policies. Understanding the nature and forums of obese metaphors in social media is the first step in policy intervention. The purpose of this paper is to understand the mutual influence between obesity and COVID-19 and determine its policy implications. This paper analyzes the public responses to obesity using Twitter data collected during the COVID-19 pandemic. The emotional nature of tweets is analyzed using the NRC lexicon. The results show that COVID-19 significantly influences perceptions of obesity; this indicates that existing public health policies must be revisited. The study findings delineate prerequisites for obese disease control programs. This paper provides policy recommendations for improving social media interventions in health service delivery in order to prevent obesity.

The southeastern United States has experienced rapid growth in the Hispanic population in recent decades, giving rise to a newly forming bilingual community. The present study builds on previous work by the authors via expansion of a “variable swarm”: the analysis of multiple linguistic variables simultaneously for the same set of speakers, with the goal of understanding patterns of accommodation and change within the community. The initial study included four linguistic variables (prosodic rhythm, bilingual discourse markers, the realization of /bdg/ and vowel space), and the present study adds an additional four variables (bilingual filled pauses, subject pronoun realization, code switching, and the labiodental realization of orthographic ) for 23 speakers of Mexican and Central American origin across two sociolinguistic generations (G1 vs. G2). Results for individual speakers show a pattern of adoption of some features by speakers of both generations (such as English-influenced prosodic rhythm and phonological filled pauses), while other, possibly more salient forms directly integrated from English (English discourse markers and code switching) exhibit later, highly variable rates of adoption, suggesting that speakers may consciously manipulate these variables as part of a process of active identity construction. Likewise, G1 speakers show fewer correlations among linguistic variables than G2 speakers, and patterns reveal that some bilingual forms are incorporated in tandem due to shared phonological traits or discourse functions. The innovative swarm analysis further contributes to the advancement of techniques employed in sociolinguistic research by serving as a bridge between traditional first- and second-wave studies that focus on a single variable, and third-wave studies that focus more on variation at the individual level.

Pancreatic β cells are central to glycemic regulation through insulin production. Studies show autophagy as an essential process in β cell function and fate. Autophagy is a catabolic cellular process that regulates cell homeostasis by recycling surplus or damaged cell components. Impaired autophagy results in β cell loss of function and apoptosis and, as a result, diabetes initiation and progress. It has been shown that in response to endoplasmic reticulum stress, inflammation, and high metabolic demands, autophagy affects β cell function, insulin synthesis, and secretion. This review highlights recent evidence regarding how autophagy can affect β cells’ fate in the pathogenesis of diabetes. Furthermore, we discuss the role of important intrinsic and extrinsic autophagy modulators, which can lead to β cell failure.

Workload prediction is essential in cloud data centers (CDCs) for establishing scalability and resource elasticity. However, the workload prediction accuracy in the cloud data center could be better due to noise, redundancy, and low performance for workload prediction. This paper designs a hierarchical tree-based deep convolutional neural network (T-CNN) model with sheep flock optimization (SFO) to enhance CDCs’ power efficiency and workload prediction. The kernel method is used to preprocess historical information from the CDCs. Additionally, T-CNN model weight parameters are optimized using SFO. The suggested TCNN-SFO technology has successfully reduced excessive power consumption while correctly forecasting the incoming demand. Further, the proposed model is assessed using two benchmark datasets: Saskatchewan HTTP traces and NASA. The developed model is executed in a Java tool. Therefore, associated with existing methods, the developed technique has achieved higher accuracy of 20.75%, 19.06%, 29.09%, 23.8%, and 20.5%, as well as lower energy consumption of 20.84%, 18.03%, 28.64%, 30.72%, and 33.74% when validating the Saskatchewan HTTP traces dataset. It has also achieved higher accuracy of 32.95%, 12.05%, 32.65%, and 26.54%.

Bioenergy production from waste is one of the emerging and viable routes from renewable resources (in addition to wind and solar energy). Many developing countries can benefit from this as they are trying to solve the large amounts of unattended garbage in landfills. This waste comes in either liquid (wastewater and oil) or solid (food and agricultural residues) form. Waste has negative impacts on the environment and, consequently, any form of life that exists therein. One way of solving this waste issue is through its usage as a resource for producing valuable products, such as biofuels, thus, creating a circular economy, which is in line with the United Nations (UN) Sustainable Development Goals (SDGs) 5, 7, 8, 9, and 13. Biofuel in the form of biogas can be produced from feedstocks, such as industrial wastewater and municipal effluent, as well as organic solid waste in a process called anaerobic digestion. The feedstock can be used as an individual substrate for anaerobic digestion or co-digested with two other substrates. Research advancements have shown that the anaerobic digestion of two or more substrates produces higher biogas yields as compared to their single substrates’ counterparts. The objective of this review was to look at the anaerobic digestion process and to provide information on the potential of biogas production through the co-digestion of sugarcane processing wastewater and municipal solid waste. The study deduced that sugar wastewater and municipal solid waste can be considered good substrates for biogas production in SA due to their enormous availability and the potential to turn their negative impacts into value addition. Biogas production is a feasible alternative, among others, to boost the country from the current energy issues.

A method for estimating the stack rating of vanadium redox flow batteries (VRFBs) through constant power characterization was developed. A stack of 22 cells, each with 1500 cm2 of nominal electrode area, was constructed and tested using constant current and constant power protocols. Typical ratios of charging to discharging power that prevail in various applications (e.g., peak shaving, wind power/solar photovoltaic power integration) were employed in the test protocols. The results showed that fractional energy storage capacity utilization and round-trip energy efficiency varied linearly with the power at which the energy was charged or discharged. A zero-dimensional electrochemical model was proposed based on the area-specific resistance to account for the energy stored/extracted during constant power discharge in the state of charge (SoC) window of 20% to 80%. It was shown that this could be used to rate a given stack in terms of charging and discharging power from the point of view of its application as a power unit. The proposed method enables stack rating based on a single polarization test and can be extended to flow battery systems in general.

Background: The estimation of an individual’s age is a fundamental component of forensic odontology. Literary reports found that the efficiency of Cameriere methodology for age estimation varied among many population groups. Therefore, this study aimed to determine the applicability of the Cameriere methods to a select South African population of the KwaZulu-Natal (KZN) province. Materials and Methods: This cross-sectional retrospective study was conducted on 840 digital panoramic radiographs that met the inclusion criteria. Dental maturity was determined through the morphometric analysis of the seven left permanent mandibular and maxillary teeth in accordance with Cameriere et al. (2006). Moreover, the dental age was also calculated using the South African Black Bayesian formulae of the Cameriere method by Angelakopoulos et al. (2019). The paired sample t-test or Wilcoxon’s signed rank test assessed the significant difference between the chronological age and estimated dental age for the various formulae. A p-value < 0.05 was considered to be statistically significant. Results: The Cameriere et al. (2006) Italian formula and the South African Black Bayesian formulae of the Cameriere method by Angelakopoulos et al. (2019) underestimated and overestimated age in the South African Black and Indian population groups of the KZN province, respectively. Therefore, the authors generated a novel population-specific regression formulae (including and excluding third molars) using “step-wise regression analysis” and a “best-fit model” for the South African Black and Indian population groups of KZN. Conclusion: This study recommends that the population-specific formulae generated in this study be utilized in the KZN population to improve the accuracy of dental age estimation within this region.

Cancer immunotherapy aims to selectively target and kill tumor cells whilst limiting the damage to healthy tissues. Controlled delivery of plant, bacterial and human toxins or enzymes has been shown to promote the induction of apoptosis in cancerous cells. The 4th generation of targeted effectors are being designed to be as humanized as possible—a solution to the problem of immunogenicity encountered with existing generations. Granzymes are serine proteases which naturally function in humans as integral cytolytic effectors during the programmed cell death of cancerous and pathogen-infected cells. Secreted predominantly by cytotoxic T lymphocytes and natural killer cells, granzymes function mechanistically by caspase-dependent or caspase-independent pathways. These natural characteristics make granzymes one of the most promising human enzymes for use in the development of fusion protein-based targeted therapeutic strategies for various cancers. In this review, we explore research involving the use of granzymes as cytolytic effectors fused to antibody fragments as selective binding domains.

Targeted cancer therapy includes, amongst others, antibody-based delivery of toxic payloads to selectively eliminate tumor cells. This payload can be either a synthetic small molecule drug composing an antibody-drug conjugate (ADC) or a cytotoxic protein composing an immunotoxin (IT). Non-human cytotoxic proteins, while potent, have limited clinical efficacy due to their immunogenicity and potential off-target toxicity. Humanization of the cytotoxic payload is essential and requires harnessing of potent apoptosis-inducing human proteins with conditional activity, which rely on targeted delivery to contact their substrate. Ribonucleases are attractive candidates, due to their ability to induce apoptosis by abrogating protein biosynthesis via tRNA degradation. In fact, several RNases of the pancreatic RNase A superfamily have shown potential as anti-cancer agents. Coupling of a human RNase to a humanized antibody or antibody derivative putatively eliminates the immunogenicity of an IT (now known as a human cytolytic fusion protein, hCFP). However, RNases are tightly regulated in vivo by endogenous inhibitors, controlling the ribonucleolytic balance subject to the cell’s metabolic requirements. Endogenous inhibition limits the efficacy with which RNase-based hCFPs induce apoptosis. However, abrogating the natural interaction with the natural inhibitors by mutation has been shown to significantly enhance RNase activity, paving the way toward achieving cytolytic potency comparable to that of bacterial immunotoxins. Here, we review the immunoRNases that have undergone preclinical studies as anti-cancer therapeutic agents.

Targeted cancer immunotherapy is designed to selectively eliminate tumor cells without harming the surrounding healthy tissues. The death-associated protein kinases (DAPk) are a family of proapoptotic proteins that play a vital role in the regulation of cellular process and have been identified as positive mediators of apoptosis via extrinsic and intrinsic death-regulating signaling pathways. Tumor suppressor activities have been shown for DAPk1 and DAPk2 and they are downregulated in e.g., Hodgkin’s (HL) and B cell lymphoma (CLL), respectively. Here, we review a targeted therapeutic approach which involves reconstitution of DAPks by the generation of immunokinase fusion proteins. These recombinant proteins consist of a disease-specific ligand fused to a modified version of DAPk1 or DAPk2. HL was targeted via CD30 and B-CLL via CD22 cell surface antigens.

To date, no curative therapy is available for the treatment of most chronic inflammatory diseases such as atopic dermatitis, rheumatoid arthritis, or autoimmune disorders. Current treatments require a lifetime supply for patients to alleviate clinical symptoms and are unable to stop the course of disease. In contrast, a new series of immunotherapeutic agents targeting the Fc γ receptor I (CD64) have emerged and demonstrated significant clinical potential to actually resolving chronic inflammation driven by M1-type dysregulated macrophages. This subpopulation plays a key role in the initiation and maintenance of a series of chronic diseases. The novel recombinant M1-specific immunotherapeutics offer the prospect of highly effective treatment strategies as they have been shown to selectively eliminate the disease-causing macrophage subpopulations. In this review, we provide a detailed summary of the data generated, together with the advantages and the clinical potential of CD64-based targeted therapies for the treatment of chronic inflammatory diseases.

Some of the most promising small molecule toxins used to generate antibody drug conjugates (ADCs) include anti-mitotic agents (e.g., auristatin and its derivatives) which are designed to attack cancerous cells at their most vulnerable state during mitosis. We were interested in identifying a human cystostatic protein eventually showing comparable activities and allowing the generation of corresponding targeted fully human cytolytic fusion proteins. Recently, we identified the human microtubule associated protein tau (MAP tau), which binds specifically to tubulin and modulates the stability of microtubules, thereby blocking mitosis and presumably vesicular transport. By binding and stabilizing polymerized microtubule filaments, MAP tau-based fusion proteins skew microtubule dynamics towards cell cycle arrest and apoptosis. This biological activity makes rapidly proliferating cells (e.g., cancer and inflammatory cells) an excellent target for MAP tau-based targeted treatments. Their superior selectivity for proliferating cells confers additional selectivity towards upregulated tumor-associated antigens at their surface, thereby preventing off-target related toxicity against normal cells bearing tumor-associated antigens at physiologically normal to low levels. In this review, we highlight recent findings on MAP tau-based targeted cytolytic fusion proteins reported in preclinical immunotherapeutic studies.

Targeted human cytolytic fusion proteins (hCFPs) are humanized immunotoxins for selective treatment of different diseases including cancer. They are composed of a ligand specifically binding to target cells genetically linked to a human apoptosis-inducing enzyme. hCFPs target cancer cells via an antibody or derivative (scFv) specifically binding to e.g., tumor associated antigens (TAAs). After internalization and translocation of the enzyme from endocytosed endosomes, the human enzymes introduced into the cytosol are efficiently inducing apoptosis. Under in vivo conditions such enzymes are subject to tight regulation by native inhibitors in order to prevent inappropriate induction of cell death in healthy cells. Tumor cells are known to upregulate these inhibitors as a survival mechanism resulting in escape of malignant cells from elimination by immune effector cells. Cytosolic inhibitors of Granzyme B and Angiogenin (Serpin P9 and RNH1, respectively), reduce the efficacy of hCFPs with these enzymes as effector domains, requiring detrimentally high doses in order to saturate inhibitor binding and rescue cytolytic activity. Variants of Granzyme B and Angiogenin might feature reduced affinity for their respective inhibitors, while retaining or even enhancing their catalytic activity. A powerful tool to design hCFPs mutants with improved potency is given by in silico methods. These include molecular dynamics (MD) simulations and enhanced sampling methods (ESM). MD and ESM allow predicting the enzyme-protein inhibitor binding stability and the associated conformational changes, provided that structural information is available. Such “high-resolution” detailed description enables the elucidation of interaction domains and the identification of sites where particular point mutations may modify those interactions. This review discusses recent advances in the use of MD and ESM for hCFP development from the viewpoints of scientists involved in both fields.

The ability to detect the presence as well as classify the activities of individuals behind visually obscuring structures is of significant benefit to police, security and emergency services in many situations. This paper presents the analysis from a series of experimental results generated using a through-the-wall (TTW) Frequency Modulated Continuous Wave (FMCW) C-Band radar system named Soprano. The objective of this analysis was to classify whether an individual was carrying an item in both hands or not using micro-Doppler information from a FMCW sensor. The radar was deployed at a standoff distance, of approximately 0.5 m, outside a residential building and used to detect multiple people walking within a room. Through the application of digital filtering, it was shown that significant suppression of the primary wall reflection is possible, significantly enhancing the target signal to clutter ratio. Singular Value Decomposition (SVD) signal processing techniques were then applied to the micro-Doppler signatures from different individuals. Features from the SVD information have been used to classify whether the person was carrying an item or walking free handed. Excellent performance of the classifier was achieved in this challenging scenario with accuracies up to 94%, suggesting that future through wall radar sensors may have the ability to reliably recognize many different types of activities in TTW scenarios using these techniques.

We study trace codes induced from codes defined by an algebraic curve X. We determine conditions on X which admit a formula for the dimension of such a trace code. Central to our work are several dimension reducing methods for the underlying functions spaces associated to X.

Biogas production from wastewater as a function to curb waste and provide energy security has gained worldwide attention. Ensuring the stability of anaerobic digestion (AD) of physiochemical and biological complexity necessitates optimization. In this study, a central composite design (CCD) from a response surface methodology (RSM) was employed to evaluate and optimize the effects of bio-stimulation of banana peels coupled with magnetite on the anaerobic digestion of wastewater to produce biogas. An experimental matrix of 14 runs using the CCD, with two factors (nanoparticle and biochar load) as a function of pH, biogas production, and COD removal by the AD process was operated at a constant mesophilic temperature (37 °C) for 28 days. The analysis of variance (ANOVA) showed that the quadratic models attained were significant (p-values < 0.05) with a high coefficient of determination (R2) values closer to 1. The optimized conditions, including nanoparticle (0.46 g) and biochar (0.66 mgVS/L), resulted in biogas production (19.26 mL/day), pH (7.07), and COD removal (75.17%). This suggests 100% desirability at a 95% confidence level. This finding depicts the trade-off between biogas productivity, biodegradability, and stability of the AD process established for future consideration of using nanoparticles as bio-stimulant.

The demand for technological and industrial change has become heavily dependent on the availability and use of petroleum products as a source of energy for socio-economic development. Notwithstanding, petroleum and petrochemical products are strongly related to global economic activities, and their extensive distribution, refining processes, and final routes into the environment pose a threat to human health and the ecosystem. Additional global environmental challenges related to the toxicological impact of air, soil, and water pollutants from hydrocarbons are carcinogenic to animals and humans. Therefore, it is practical to introduce biodegradation as a biological catalyst to address the remediation of petroleum-contaminated ecosystems, adverse impacts, the complexity of hydrocarbons, and resistance to biodegradation. This review presents the bioremediation of petroleum hydrocarbon contaminants in water and soil, focusing on petroleum biodegradable microorganisms essential for the biodegradation of petroleum contaminants. Moreover, explore the mineralization and transformation of complex organic and inorganic contaminants into other simpler compounds by biological agents. In addition, physicochemical and biological factors affecting biodegradation mechanisms and enzymatic systems are expanded. Finally, recent studies on bioremediation techniques with economic prospects for petroleum spill remediation are highlighted.

This study aims to investigate the effect of metaphorical content on e-participation in healthcare. With this objective, the study assesses the awareness and capability of e-participants to navigate through healthcare metaphors during their participation. Healthcare-related e-participation data were collected from the Twitter platform. Data analysis includes (i) awareness measurements by topic modelling and sentiment analysis and (ii) participation abilities by problem-based learning models. Findings show that a lack of effort to validate metaphors harms e-participation levels and awareness, resulting in a problematic health environment. Exploring metaphors in these intricate forums has the potential to enhance service delivery. Improving web service delivery requires valuable input from stakeholders on the application of metaphors in the health domain.

Dental age estimation in the living and deceased is a fundamental aspect of forensic sciences, civil cases, medico-legal proceedings and clinical dentistry. Accordingly, this study aimed to validate the accuracy and reproducibility of the London Atlas in a select South African sample of KwaZulu-Natal. In this cross-sectional study, 760 digital panoramic radiographs (n = 760) aged between 5.00 and 23.99 years were retrospectively reviewed through consecutive sampling. Each radiograph was assessed and assigned a dental age in accordance with the London Atlas of Human Tooth Development and Eruption by AlQahtani et al. (2010). The London Atlas overestimated age with a mean difference of −0.85 to −1.26 years in the selected South African sample of KwaZulu-Natal. A statistically significant difference between the chronological and estimated dental ages was recorded. Furthermore, the South African Black and Indian males had a higher overestimation of age than their female counterparts, with a mean difference of 0.13 and 0.07 years, respectively. This overestimation was less in the South African Indian population in comparison to the SA Black population. This outcome resulted in the creation of the KZN population- and sex-specific charts and atlases for the two selected cohorts of KwaZulu-Natal. The KZN Atlases were found to be more accurate in the selected sample, with a mean absolute error of 0.57 years and no statistically significant differences between the chronological and estimated dental ages.

Antimicrobial resistance (AMR) is a serious global public-health threat. Evidence suggests that antimicrobial stewardship (AMS) is a valuable tool to facilitate rational antibiotic use within healthcare facilities. A cross-sectional situational analysis using a questionnaire was conducted to determine the current status of antimicrobial stewardship (AMS) activities in all public-sector hospitals in KwaZulu-Natal (KZN). The survey had a 79% (57, N = 72) response rate. A total of 75% of hospitals had an antimicrobial stewardship committee (AMSC), 47% (20, N = 43) had a formal written statement of support from leadership, and 7% (3, N = 43) had budgeted financial support. Only 37% (16, N = 43) had on-site or off-site support from a clinical microbiologist, and 5% (2, N = 43) had an on-site infectious disease (ID) physician. Microbiologist input on pathogen surveillance data (aOR: 5.12; 95% CI: 4.08–22.02; p-value = 0.001) and microbiological investigations prior to the commencement of antibiotics (aOR: 5.12; 95% CI: 1.08–42.01; p-value = 0.041) were significantly associated with having either on- or off-site microbiology support. Respondents that had a representative from microbiology on the AMSC were significantly associated with having and interrogating facility-specific antibiograms (P = 0.051 and P = 0.036, respectively). Those facilities that had access to a microbiologist were significantly associated with producing an antibiogram (aOR: 4.80; 95% CI: 1.25–18.42; p-value = 0.022). Facilities with an ID physician were significantly associated with having a current antibiogram distributed to prescribers within the facility (P = 0.010) and significantly associated with sending prescribers personalized communication regarding improving prescribing (P = 0.044). Common challenges reported by the facilities included suboptimal hospital management support; a lack of clinicians, pharmacists, nurses, microbiologists, and dedicated time; the lack of a multidisciplinary approach; low clinician buy-in; inadequate training; a lack of printed antibiotic guidelines; and financial restrictions for microbiological investigations. The survey identified the need for financial, IT, and management support. Microbiology and infectious disease physicians were recognized as scarce human resources.

Fuel cells are a key enabling technology for the future economy, thereby providing power to portable, stationary, and transportation applications, which can be considered an important contributor towards reducing the high dependencies on fossil fuels. Electrocatalyst plays a vital role in improving the performance of the low temperature fuel cells. Noble metals (Pt, Pd) supported on carbon have shown promising performance owing to their high catalytic activity for both electroreduction and electrooxidation and have good stability. Catalyst preparation by electrodeposition is considered to be simple in terms of operation and scalability with relatively low cost to obtain high purity metal deposits. This review emphasises the role of electrodeposition as a cost-effective method for synthesising fuel cell catalysts, summarising the progress in the electrodeposited Pt and Pd catalysts for direct liquid fuel cells (DLFCs). Moreover, this review also discusses the technological advances made utilising these catalysts in the past three decades, and the factors that impede the technological advancement of the electrodeposition process are presented. The challenges and the fundamental research strategies needed to achieve the commercial potential of electrodeposition as an economical, efficient methodology for synthesising fuel cells catalysts are outlined with the necessary raw materials considering current and future savings scenario.

Over the past decades, developments and scientific breakthroughs in the field of robotics have shown the replacement of wheeled robots with legged robots, which are often inspired by the biological characteristics of legged animals. Many industries and urban-based applications promote quadruped robots because of their dexterous ability to efficiently handle multiple tasks in the working environment. Motivated from the recent works in the field of quadruped robots, this research aims to develop and investigate gaits for a 2 DoF mammal-inspired quadruped robot that incorporates 4 hip and 4 knee servo motors as its locomotion element. Forward and inverse kinematic techniques are used to determine the joint angle required for the locomotion and stability calculation are presented to determine the center of mass/center of gravity of the robot. Three types of gaits such as walk, trot, and pace are developed while keeping the center of mass inside the support polygon using a closed-loop control system. To minimize errors and improve the performance of the robot due to its non-linearity, a meta-heuristic algorithm has been developed and addressed in this work. The fitness function is derived based on the Euclidean distance between the target and robot’s current position and kinematic equations are used to obtain the relation between joints and coordinates. Based on the literature, particle swarm optimization (PSO) was found to be a promising algorithm for this problem and is developed using Python’s ‘Pyswarms’ package. Experimental studies are carried out quantitatively to determine the convergence characteristics of the control algorithm and to investigate the distance traveled by the robot for different target positions and gaits. Comparison between experimental and theoretical results prove the efficiency of the proposed algorithm and stability of the robot during various gait movements.

Obtaining meaning-rich representations of social media inputs, such as Tweets (unstructured and noisy text), from general-purpose pre-trained language models has become challenging, as these inputs typically deviate from mainstream English usage. The proposed research establishes effective methods for improving the comprehension of noisy texts. For this, we propose a new generic methodology to derive a diverse set of sentence vectors combining and extracting various linguistic characteristics from latent representations of multi-layer, pre-trained language models. Further, we clearly establish how BERT, a state-of-the-art pre-trained language model, comprehends the linguistic attributes of Tweets to identify appropriate sentence representations. Five new probing tasks are developed for Tweets, which can serve as benchmark probing tasks to study noisy text comprehension. Experiments are carried out for classification accuracy by deriving the sentence vectors from GloVe-based pre-trained models and Sentence-BERT, and by using different hidden layers from the BERT model. We show that the initial and middle layers of BERT have better capability for capturing the key linguistic characteristics of noisy texts than its latter layers. With complex predictive models, we further show that the sentence vector length has lesser importance to capture linguistic information, and the proposed sentence vectors for noisy texts perform better than the existing state-of-the-art sentence vectors.

A single ulcerative colitis (UC) is a chronic inflammatory bowel disease (IBD) that causes inflammation of the colonic mucosa at the distal colon and rectum. The mainstay therapy involves anti-inflammatory immunosuppression based on the disease location and severity. The disadvantages of using systemic corticosteroids for UC treatment is the amplified risk of malignancies and infections. Therefore, topical treatments are safer as they have fewer systemic side effects due to less systemic exposure. In this context, pH sensitive and enzymatically triggered hydrogel of pectin (PC) and polyacrylamide (PAM) has been developed to facilitate colon-targeted delivery of budesonide (BUD) for the treatment of UC. The hydrogels were characterized by Fourier transform infrared spectroscopy (FTIR), X-ray diffraction (XRD), swelling ratio, and drug release. FT-IR spectroscopy confirmed the grafting as well loading of BUD in hydrogel. XRD showed the amorphous nature of hydrogel and increment in crystallinity after drug loading. On the other hand, SEM showed that the hydrogels exhibited a highly porous morphology, which is suitable for drug loading and also demonstrated a pH-responsive swelling behaviour, with decreased swelling in acidic media. The in-vitro release of BUD from the hydrogel exhibited a sustained release behaviour with non-ficken diffusion mechanism. The model that fitted best for BUD released was the Higuchi kinetic model. It was concluded that enzyme/pH dual-sensitive hydrogels are an effective colon-targeted delivery system for UC.

The emergence of multidrug-resistant (MDR) and extensively drug-resistant (XDR) tuberculosis (TB) has reinforced the need for the development of new anti-TB drugs. The first line drug isoniazid inhibits InhA. This is a prodrug requiring activation by the enzyme KatG. Mutations in KatG have largely contributed to clinical isoniazid resistance. We aimed to design new ‘direct’ InhA inhibitors that obviate the need for activation by KatG, circumventing pre-existing resistance. In silico molecular modelling was used as part of a rational structure-based drug-design approach involving inspection of protein crystal structures of InhA:inhibitor complexes, including the broad spectrum antibiotic triclosan (TCS). One crystal structure exhibited the unusual presence of two triclosan molecules within the Mycobacterium tuberculosis InhA binding site. This became the basis of a strategy for the synthesis of novel inhibitors. A series of new, flexible ligands were designed and synthesised, expanding on the triclosan structure. Low Minimum Inhibitory Concentrations (MICs) were obtained for benzylphenyl compounds (12, 43 and 44) and di-triclosan derivative (39), against Mycobacterium bovis BCG although these may also be inhibiting other enzymes. The ether linked di-triclosan derivative (38) displayed excellent in vitro isolated enzyme inhibition results comparable with triclosan, but at a higher MIC (125 µg mL−1). These compounds offer good opportunities as leads for further optimisation.

Coronavirus Disease 2019 (COVID-19) is a global pandemic caused by the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). While detection of SARS-CoV-2 by polymerase chain reaction with reverse transcription (RT-PCR) is currently used to diagnose acute COVID-19 infection, serological assays are needed to study the humoral immune response to SARS-CoV-2. Anti-SARS-CoV-2 immunoglobulin (Ig)G/A/M antibodies against spike (S) protein and its receptor-binding domain (RBD) were characterized in recovered subjects who were RT-PCR-positive (n = 153) and RT-PCR-negative (n = 55) using an enzyme-linked immunosorbent assay (ELISA). These antibodies were also further assessed for their ability to neutralize live SARS-CoV-2 virus. Anti-SARS-CoV-2 antibodies were detected in 90.9% of resolved subjects up to 180 days post-symptom onset. Anti-S protein and anti-RBD IgG titers correlated (r = 0.5157 and r = 0.6010, respectively) with viral neutralization. Of the RT-PCR-positive subjects, 22 (14.3%) did not have anti-SARS-CoV-2 antibodies; and of those, 17 had RT-PCR cycle threshold (Ct) values > 27. These high Ct values raise the possibility that these indeterminate results are from individuals who were not infected or had mild infection that failed to elicit an antibody response. This study highlights the importance of serological surveys to determine population-level immunity based on infection numbers as determined by RT-PCR.

With the emergence of various types of applications such as delay-sensitive applications, future communication networks are expected to be increasingly complex and dynamic. Network Function Virtualization (NFV) provides the necessary support towards efficient management of such complex networks, by virtualizing network functions and placing them on shared commodity servers. However, one of the critical issues in NFV is the resource allocation for the highly complex services; moreover, this problem is classified as an NP-Hard problem. To solve this problem, our work investigates the potential of Deep Reinforcement Learning (DRL) as a swift yet accurate approach (as compared to integer linear programming) for deploying Virtualized Network Functions (VNFs) under several Quality-of-Service (QoS) constraints such as latency, memory, CPU, and failure recovery requirements. More importantly, the failure recovery requirements are focused on the node-outage problem where outage can be either due to a disaster or unavailability of network topology information (e.g., due to proprietary and ownership issues). In DRL, we adopt a Deep Q-Learning (DQL) based algorithm where the primary network estimates the action-value function Q, as well as the predicted Q, highly causing divergence in Q-value’s updates. This divergence increases for the larger-scale action and state-space causing inconsistency in learning, resulting in an inaccurate output. Thus, to overcome this divergence, our work has adopted a well-known approach, i.e., introducing Target Neural Networks and Experience Replay algorithms in DQL. The constructed model is simulated for two real network topologies—Netrail Topology and BtEurope Topology—with various capacities of the nodes (e.g., CPU core, VNFs per Core), links (e.g., bandwidth and latency), several VNF Forwarding Graph (VNF-FG) complexities, and different degrees of the nodal outage from 0% to 50%. We can conclude from our work that, with the increase in network density or nodal capacity or VNF-FG’s complexity, the model took extremely high computation time to execute the desirable results. Moreover, with the rise in complexity of the VNF-FG, the resources decline much faster. In terms of the nodal outage, our model provided almost 70–90% Service Acceptance Rate (SAR) even with a 50% nodal outage for certain combinations of scenarios.

Differences in patient classification of myocardial injury between high-sensitivity cardiac troponin (hs-cTn) assays have largely been attributed to assay design and analytical sensitivity aspects. Our objective was to compare Ortho Clinical Diagnostics’ (OCD) hs-cTnI assay to OCD’s contemporary/conventional assay (cTnI ES) and another hs-cTnI assay (Abbott hs-cTnI) in samples obtained from different emergency departments (EDs). Two different sample types were evaluated (lithium heparin and ethylenediaminetetraacetic acid (EDTA) plasma) in a non-selected ED population (study 1, n = 469 samples) and in patients for which ED physicians ordered cardiac troponin testing (study 2, n = 1147 samples), from five different EDs. The incidence of injury in study 1 was higher with the OCD hs-cTnI assay (30.9%; 95% CI: 26.9 to 35.2) compared to that of the Abbott hs-cTnI (17.3%; 95% CI: 14.1 to 21.0) and the OCD cTnI ES (15.4%; 95% CI: 12.4 to 18.9) assays, with repeat testing identifying 4.8% (95% CI: 3.0 to 7.5) of the OCD hs-cTnI results with poor reproducibility. In study 2, 4.6% (95% CI: 3.5 to 6.0) of the results were not reported for the OCD hs-cTnI assay (i.e., poor reproducibility) with 12.7% (95%CI: 8.7 to 17.8) of the OCD hs-cTnI results positive for injury being negative for injury with the Abbott hs-cTnI assay. In summary, the OCD hs-cTnI assay yields higher rates of biochemical injury with a higher rate of poor reproducible results in different ED populations.