The intentional damages to local Cultural Heritage sites carried out in recent months by the Islamic State have received wide coverage from the media worldwide. Earth Observation data provide important information to assess these damages in such non-accessible areas, and automated image processing techniques would be needed to speed up the analysis if a fast response is desired. This paper shows the first results of applying fast and robust change detection techniques to sensitive areas, based on the extraction of textural information and robust differences of brightness values related to pre- and post-disaster satellite images. A map highlighting potentially damaged buildings is derived, which could help experts at timely assessing the damages to the Cultural Heritage sites of interest. Encouraging results are obtained for two archaeological sites in Syria and Iraq.

Cranberries are rich in bioactive constituents reported to influence a variety of health benefits, ranging from improved immune function and decreased infections to reduced cardiovascular disease and more recently cancer inhibition. A review of cranberry research targeting cancer revealed positive effects of cranberries or cranberry derived constituents against 17 different cancers utilizing a variety of in vitro techniques; whereas, in vivo studies supported the inhibitory action of cranberries toward cancers of the esophagus, stomach, colon, bladder, prostate, glioblastoma and lymphoma. Mechanisms of cranberry-linked cancer inhibition include cellular death induction via apoptosis, necrosis and autophagy; reduction of cellular proliferation; alterations in reactive oxygen species; and modification of cytokine and signal transduction pathways. Given the emerging positive preclinical effects of cranberries, future clinical directions targeting cancer or premalignancy will be considered

In today’s Web 2.0 era, online social media has become an integral part of our lives. In the course of the information revolution, the form of information has undergone a radical change, from simple text information to today’s integrated video, image, text and audio, and there has also been a great change in the way of dissemination and access, as people nowadays do not just rely on traditional media to passively receive information, but more actively and selectively obtain information from social media. Therefore, it has become a great challenge for us to effectively utilize these massive and integrated multi-modal media information to form an effective system of retrieval, browsing, analysis and usage. Unlike movies and traditional long-form video content, micro-videos are usually short in length, between a few seconds and tens of seconds, which allows users to quickly browse different contents and make full use of the fragmented time in their lives, while users can also share their micro-videos to their friends or the public, forming a unique social way. Video contains rich multimodal information, and fusing information from multiple modalities in a video recommendation task can improve the accuracy of the video recommendation task.According to the micro-video recommendation task, a new combinatorial network model is proposed to combine the discrete features of each modality into the overall features of various modalities through the network, and then fuse the various modal features to obtain the overall video features, which will be used for recommendation. In order to verify the effectiveness of the algorithm proposed in this paper, experiments are conducted in the public dataset, and it is shown the effectiveness of our model.

This study aimed to investigate the influence of cranio-cervical position on the electromyographical activity of the core muscles during the prone plank exercise. Twenty healthy participants were enrolled in this study. Muscle activation was registered using surface electromyography (sEMG) in the rectus abdominis (RA), external oblique (EO), internal oblique (IO) and lumbar portion of erector spinae (LES). Three prone plank conditions were randomly evaluated, varying the position of the cranio-cervical segment during the prone plank performance (NEUTRAL, FLEXION and EXTENSION). sEMG signals of each individual muscle, Total Intensity (TI) and ratings of perceived exertion (RPE) were analysed with statistical significance set at P<0.05. FLEXION revealed significantly higher TI values compared to NEUTRAL and EXTENSION (p = 0.000; effect size (ES) > 0.90). The RA presented larger activation in FLEXION compared to the other variations (p < 0.05; ES >0.70). FLEXION elicited a greater response compared to NEUTRAL for both sides of EO when compared to EXTENSION, and also for both sides of IO compared to the other conditions. Both FLEXION and EXTENSION reported higher RPE values compared to NEUTRAL. Cranio-cervical segment position influenced the electromyographical activation of core musculature, showing highest values when performing prone plank in a cranio-cervical flexion.

Following the unprecedented and global health crisis of COVID-19, without a doubt, there has been a tremendous impact on international tourism for two reasons: the imposed travel restrictions discourage people from traveling, while travelers watch increased anxiety when responding to the new travel landscape. We address the problem of travelers’ changing travel risk perceptions in the COVID-19 pandemic aftermath. Our main goal is to identify and weigh critical emerging travel risks and create a risk evaluation index in which one can measure the destinations and strategic interventions’ performance for South African travelers. We found that tourist perceived risks are multidimensional, including categories like additional expenses, exchange rates, and refund-related factors. These three criteria are the most important to overall travel risk perception. We applied the developed risk evaluation index to five tourist destinations to evaluate their performance regarding the identified risks, being the UK the country with the best performance.

In recent years, the application of Indoor Positioning Systems (IPS) has experienced a significant increase in demand, with the aging of the world’s population and their changing lifestyles. While outdoor positioning systems, such as the Global Positioning System (GPS), have significantly advanced over the years, indoor positioning has been restrained by the limitations of the employed technologies. This paper presents a hybrid wireless positioning system able to locate wearables indoor accurately. It is based on the Wi-Fi and Bluetooth technologies, by using trilateration to determine the position of Bluetooth low energy (BLE) wearable devices with an accuracy of up to 1.8 meters. A graphical user interface (GUI) was used to illustrate the performance of the proposed systems, by allowing users to visualize the captured data in two dimensions and three- dimensions in real-time.

We discuss methods for calculation of critical indices and amplitudes from the perturbative expansions. Several important examples of the Stokes flow through 2D and 3D channels are brought up. Power series for the permeability derived for small values of amplitude are employed to calculation of various critical exponents in the regime of large amplitudes. Special nonlinear approximations valid for arbitrary values of the wave amplitude are derived from the expansions. The technique developed for critical phenomena is applied then for relaxation phenomena. The concept of time-translation invariance is discussed, its spontaneous violation and restoration considered. Emerging probabilistic patterns correspond to a local breakdown of time-translation invariance. Their evolution leads to the timetranslation symmetry complete (or partial) restoration. We estimate typical time extent, amplitude and direction for such restorative process. The new technique is based on explicit introduction of origin in time. After some transformations we come to the exponential and generalized, exponential-type solution with explicit finite time scale, which was only implicit in initial parametrization with polynomial approximation. The concept of crash as a relaxation phenomenon, consisting of time-translation invariance breaking and restoration, is put forward. COVID-19 related mini-crash in the time series for Shanghai Composite is discussed as an illustration.

The Indus basin is considered as the one with the highest dependence on snowmelt runoff in High Mountain Asia. The recent High Mountain Asia snow reanalysis enables to go beyond previous studies by evaluating both snowmelt and snow sublimation at the basin scale. Over 2000-2016, basin-average snowmelt is 101 11 Gt.a^-1 (121 ± 13 mm.a^-1), which represents about 25-30% of basin-average annual precipitation. Snow sublimation accounts for 11% of the mean annual snow ablation, but with a large spatial variability across the basin.

We review extrapolation methods for calculation of critical points and indices from the perturbative expansions by means of Pad\'{e}-based techniques and their various post-Pad\'{e} extensions by means of root and factor approximants. Factor approximants are applied to finding critical points. Roots are employed within the context of finding critical index. Additive self-similar approximants are discussed and $DLog$ additive recursive approximants are introduced as their generalization. They are applied to the problem of interpolation.

The CoDRIVE solution builds on R&D in the development of connected and autonomous vehicles (CAVs). The mainstay of the system is a low-cost GNSS receiver integrated with a MEMS grade IMU powered with CoDRIVE algorithms and high precision data processing software. The solution integrates RFID (radio-frequency identification) localisation information derived from tags installed in the roads around the University of Nottingham. This aids the positioning solution by correcting the long-term drift of inertial navigation technology in the absence of GNSS. The solution is informed of obscuration of GNSS through city models of skyview and elevation masks derived from 360-degree photography. The results show that predictive intelligence of the denial of GNSS and RFID aiding realises significant benefits compared to the inertial only solution. According to the validation, inertial only solutions drift over time, with an overall RMS accuracy over a 300 metres section of GNSS outage of 10 to 20 metres. After deploying the RFID tags on the road, experiments show that the RFID aided algorithm is able to constrain the maximum error to within 3.76 metres, and with 93.9% of points constrained to 2 metres accuracy overall.

There is limited information on Tamarind production, trade and value added products in Kenya. Of late, there is growing interest in the domestic and export markets due to its multiple uses. The objective of this paper is to review existing literature to identify the missing value chain links which may help catalyse the scaling up of production and commercialization of Tamarind. Selected literature and interviews with traders and extension staff in the Coastal Counties were used to collect information for this study. Tamarind fruits are mainly collected in the wild in semi-arid areas of the country and marketed through informal channels. Mombasa is the terminal market for tamarind from Kenya and Uganda, from where domestic consumers and exporters obtain their supplies. Globally, virtually every part of tamarind tree (pulp, seed, leaves, flowers, bark and roots) has either nutritional, industrial or medicinal value. Tamarind fruit contains substantial levels of carbohydrates, proteins, vitamins, minerals and essential amino acids making it potentially useful in addressing wide spread malnutrition. Its anti-oxidant, anti-inflammatory, anti-microbial, anti-fungal and anti-diabetic properties gives tamarind wider application in conventional and traditional medicine. The findings suggest that scaling up the production and commercialization of tamarind in the country requires both public and private sector investment. The structure of this partnership should consist of the following value chain links: An efficient Seed / seedling system as a source of planting materials; production hubs by farmer associations, government institutions and private farms; aggregation centres for assurance of volumes and quality; processing, value addition and product diversification; an efficient distribution systems of wholesalers and retailers, particularly supermarkets; exporters and consumers. The agenda for further research should include breeding and availing early maturing cultivars demanded by the export market, quality management andvalue addition technologies.

A proportion of osteoarthritis (OA) patients are unsatisfied with post-operative outcomes following total joint replacement surgery (TJR), with insufficient pain relief or poor functional improvement. Predicting those will have poor outcomes would be beneficial for patients and clinicians. The aim of this study was to determine the relationship between baseline anthropometric data and the concentration of pre-operative serum and peri-operative synovial fluid (SF) cytokines with 7-month post-operative outcomes in a cohort of knee and hip OA patients. 160 OA patients were recruited who were scheduled for TJR. The concentration of 24 cytokines were measured in blood and SF by multiplex assay. EQ5Dindex health status was assessed pre-operatively and at 7-months post-operatively. 13% of patients were identified as non-responders based on EQ5Dindex. Compared to responders, non-responders were of higher BMI, had greater waist and hip circumference and had higher levels of SF leptin but lower levels of SF Resistin (p<0.05). Linear regression analysis found a significant relationship between pre-operative body weight and post-operative response (ΔEQ5Dindex; r=0.222, p=0.049). The combination of body weight with SF amphiregulin and SF IL-6 provided an improved predictive model of post-operative response (r=0.470, p=0.035).

Currently, peptic ulcers are widely studied as an infectious disease caused by Helicobacter pylori primarily due to 3 supporting evidences, and this etiology may explain the other 3 observations, but cannot confront the challenges of the 15 major characteristics and the other 75 observations/phenomena of peptic ulcers. To address the challenges, a recently published Complex Causal Relationship with its accompanying methodologies was applied to analyze existing data. Peptic ulcers are identified as a psychosomatic disease triggered by psychological stress, where Helicobacter pylori plays a secondary role in only the late phase of ulceration. This new etiology elucidated all the 15 major characteristics and 81 observations/phenomena of peptic ulcers in a series of 6 articles. This sixth article focuses exclusively on the controversies surrounding the role of Helicobacter pylori in peptic ulcers. In-depth analyses find that all the 3 evidences supporting “peptic ulcers are an infectious disease caused by Helicobacter pylori” are deceiving, and 2 of the 3 explainable observations may have been misinterpreted. Meanwhile, the definitions of ‘etiological factor’ and ‘causality’ do not support ‘H. pylori infection is an etiological factor of peptic ulcers’, and existing literatures have presented a controversial view on the role of Helicobacter pylori in peptic ulcers. Interestingly, if “peptic ulcers are an infectious disease caused by Helicobacter pylori” is considered an illusion, all the characteristics of the disease, along with all the clinical, epidemiological, and laboratory observations/phenomena, can be fully understood. This illusion has misguided peptic ulcer research over the past 36 years with little progress in the field, and that may represent a widespread situation in modern medical research, resulting in a direct interference with the true understanding of disease. Evidently, keeping the concept “illusion” in mind may help remove a huge obstacle that impedes medical advance, thereby facilitating the in-depth study of disease.

The authors argue that despite significant numbers of foreign workers present in the Russian labor market Russia remains unattractive for highly skilled foreign workers. The economic crisis the country has faced since 2014 has resulted in a further outflow of foreign nationals from OECD countries. So Russia has to look for employees among newcomers from the former Soviet states, the majority of whom come from Central Asia. Russian politics with regard to immigration is characterized by the collision between “geopolitical” and domestic policy rationales. On one hand, seeking to maximize its influence in the post-Soviet space Russia provides preferential conditions of employment for citizens of partner countries in the framework of the Eurasian Economic Union. On the other hand, the Kremlin is afraid to liberalize its approach to immigration and the integration of immigrants through fear of losing popularity among the electorate. The second substantial feature of Russian immigration politics is an extreme level of securitization of migration issues. The government considers these issues primarily through a policing lens. One cannot claim that the Russian state does not take any steps to liberalize “migratory regulation”; however, the effect of these measures is being vastly reduced by new restrictions. In addition, liberally designed laws do not bring the expected results due to the corrupt practices that pervade the whole of migration regulation.

Multi- and hyper-spectral, multi-angular top-of-canopy reflectance data call for an efficient retrieval system which can improve the retrieval of standard canopy parameters (as albedo, LAI, fAPAR), and exploit the information to retrieve additional parameters (e.g. leaf pigments). Furthermore consistency between the retrieved parameters and quantification of uncertainties are required for many applications. % (2) methods We present a retrieval system for canopy and sub-canopy parameters (OptiSAIL), which is based on a model comprising SAIL, PROSPECT-D (leaf properties), TARTES (snow properties), a soil model (BRDF, moisture), and a cloud contamination model. The inversion is gradient based and uses codes % created by Automatic Differentiation. The full per pixel covariance-matrix of the retrieved parameters is computed. For this demonstration, single observation data from the Sentinel-3 SY_2_SYN (synergy) product is used. The results are compared with the MODIS 4-day LAI/fPAR product and PhenoCam site photography. OptiSAIL produces generally consistent and credible results, at least matching the quality of the technically quite different MODIS product. For most of the sites, the PhenoCam images support the OptiSAIL retrievals. The system is computationally efficient with a rate of 150 pixel per second (7 millisecond per pixel) for a single thread on a current desktop CPU using observations on 26 bands. Not all of the model parameters are well determined in all situations. Significant correlations between the parameters are found, which can change sign and magnitude over time. OptiSAIL appears to meet the design goals, puts real-time processing with this kind of system into reach, seamlessly extends to hyper-spectral and multi-sensor retrievals, and promises to be a good platform for sensitivity studies. The incorporated cloud and snow detection adds to the robustness of the system.

The established classical method of treating oil refinery effluent is flotation followed by biological treatment. Membrane bioreactors (MBRs) offer more advanced treatment, producing a clarified and potentially reusable treated effluent, but demand robust pretreatment to remove oil and grease (O&G) down to consistent, reliably low levels. An analysis of a full-scale conventional oil refinery ETP based on flotation alone, coupled with projected performance, energy consumption and costs associated with a downstream MBR, have demonstrated satisfactory performance of flotation-based pre-treatment. The flotation processes, comprising an API separator followed by dissolved air flotation (DAF), provided ~90% removal of both total suspended solids (TSS) and O&G coupled with 75% COD removal. The relative energy consumption and cost of the pretreatment, normalised against both the volume treated and COD removed, was considerably less for the API-DAF sequence compared to the MBR. The combined flotation specific energy consumption in kWh was found to be almost an order of magnitude lower than for the MBR (0.091 vs 0.86 kWh per m3 effluent treated), and the total cost (in terms of the net present value) around one sixth that of the MBR. However, the nature of the respective waste streams generated and end disposal of waste solids differs significantly between the pretreatment and MBR stages.

The multifarious internal workings of organisms are difficult to reconcile with a single feature defining a state of ‘being alive’. Indeed, definitions of life rely on emergent properties (growth, capacity to evolve, agency) only symptomatic of intrinsic functioning. Empirical studies demonstrate that biomolecules including ratcheting/rotating enzymes, ribozymes and pigment molecules undergo repetitive conformation state changes driven by energy absorption, excitation and relaxation. They exhibit disparate structures, but govern processes relying on directional physical motion (DNA transcription, translation, ATP synthesis, cytoskeleton transport, photosynthetic resonance energy transfer) and share the principle of repetitive uniplanar conformation changes driven by thermodynamic gradients, producing dependable unidirectional motion: ‘heat engines’ exploiting thermodynamic disequilibria to perform work. Recognition that disparate biological molecules share a heat engine principle governing directional motion, working in self-regulating networks, allows a mechanistic definition: life is a self-regulating process whereby matter undergoes cyclic, uniplanar conformation state changes that convert thermodynamic disequilibria into directed motion, performing work that locally reduces entropy. ‘Living things’ are structures including autonomous networks of units operating on the heat engine principle. Death is loss of integrated heat engine function. These principles are independent of any specific chemical environment, and can be applied to other biospheres.

The metal chelator PBT2 (5,7-dichloro-2-[(dimethylamino)methyl]-8-hydroxyquinoline) acts as a terdentate ligand capable of forming binary and ternary Cu2+ complexes. It was clinically trialed as an Alzheimer’s disease (AD) therapeutic but failed to progress beyond phase II. The β-amyloid (Aβ) peptide associated with AD was recently concluded to form a unique Cu(Aβ) complex that is inaccessible to PBT2. Herein, it is shown that the species ascribed to this binary Cu(Aβ) complex in fact corresponds to ternary Cu(PBT2)NIm[Aβ] complexes formed by anchoring of Cu(PBT2) on imine nitrogen (NIm) donors of His side chains. The primary site of ternary complex formation is His6, having a conditional stepwise formation constant at pH 7.4 (K [M−1] ) of log K = 6.4 ± 0.1, and a second site is supplied by His13 or His14 (log K = 4.4 ± 0.1). The stability of Cu(PBT2)NIm[H13/14] is comparable with that of the simplest ternary complexes involving free imidazole (log K = 4.22 ± 0.09) and histamine (log K = 4.00 ± 0.05). The 100-fold larger formation constant for Cu(PBT2)NIm[H6] indicates that outer-sphere ligand–peptide interactions strongly stabilize its structure. Despite the relatively high stability of Cu(PBT2)NImH6, PBT2 is a promiscuous Cu2+-binding ligand capable of forming a ternary Cu(PBT2)NIm complex with any ligand containing NIm donor. These ligands include histamine, L-His, and ubiquitous His side chains of peptides and proteins in the extracellular milieu, whose combined effect should outweigh that of a single Cu(PBT2)NIm[H6] complex regardless of its stability. We therefore conclude that PBT2 is capable of accessing Cu(Aβ) complexes with high stability but not specificity. The results have implications for future AD therapeutic strategies and understanding the role of PBT2 in the bulk transport of transition metal ions. Given the repurposing of PBT2 as a drug for breaking antibiotic resistance, ternary Cu(PBT2)NIm and analogous Zn(PBT2)NIm complexes may be relevant to its antimicrobial properties.

The aim of this work was to test polyamines as potential natural substrates of the Acinetobacter baumannii chlorhexidine efflux protein AceI using near-UV synchrotron radiation circular dichroism (SRCD) spectroscopy. The Gram-negative bacterium A. Baumannii is a leading cause of hospital-acquired infections and an important foodborne pathogen. A. Baumannii strains are becoming increasingly resistant to antimicrobial agents, including the synthetic antiseptic chlorhexidine. AceI was the founding member of the recently recognised PACE family of bacterial multidrug efflux proteins. Using the plasmid construct pTTQ18-aceI(His₆) containing the A. Baumannii aceI gene directly upstream from a His₆-tag coding sequence, expression of AceI(His₆) was amplified in E. coli BL21(DE3) cells. Near-UV (250-340 nm) SRCD measurements were performed on detergent-solubilised and purified AceI(His₆) at 20 °C. Sample and SRCD experimental conditions were identified that detected binding of the triamine spermidine to AceI(His₆). In a titration with spermidine (0-10 mM) this binding was saturable and fitting of the curve for the change in signal intensity produced an apparent binding affinity (K_D) of 3.97 +/- 0.45 mM. These SRCD results were the first experimental evidence obtained for polyamines as natural substrates of PACE proteins.

The art of representing source code is pivotal in numerous programming analysis applications. Recent strides in neural networks have marked notable successes in this realm. However, the peculiar structural characteristics inherent in programming languages have not been fully exploited in existing models. While neural models based on abstract syntax trees (ASTs) adeptly manage the tree-like nature of source codes, they fall short in discerning the diverse substructural nuances within programs. This paper introduces the Dynamic Syntax Tree Model (DSTM), an innovative approach that fuses various neural network modules into tree architectures tailored to the specific AST of the input. Distinct from preceding tree-based neural models, DSTM adeptly discerns the semantic variances across different AST substructures. We validate DSTM through rigorous testing in program classification and code clone detection, outperforming contemporary methods and demonstrating the benefits of harnessing intricate source code structures.

The three-dimensional printing of scaffolds is an interesting alternative to the traditional techniques of periodontal regeneration. This technique uses computer assisted design and manufacturing after CT scan. After 3D modelling, individualized scaffolds are printed by extrusion, selective laser sintering, stereolithography, or powder bed inkjet printing. These scaffolds can be made of one or several materials such as natural polymers, synthetic polymers, or bioceramics. They can be monophasic or multiphasic and tend to recreate the architectural structure of the periodontal tissue. In order to enhance the bioactivity and have a higher regeneration, the scaffolds can be embedded with stem cells and/or growth factors. This new technique could enhance a complete periodontal regeneration. This review summarizes the application of 3D printed scaffolds in periodontal regeneration. The process, the materials and designs, the key advantages and prospects of 3D bioprinting are highlighted, providing new ideas for tissue regeneration.

The post-2007 crisis-induced an agricultural expansion across Africa, but local level production and marketing experiences remain understudied. This study assesses the dynamics of agricultural expansion and small-scale farmers’ experiences in rural Zambia. Using a mixed research design, data were drawn from surveys, multi-level interviews, group discussions policy reviews and observations. Results show an agriculture expansion among small-scale farmers is underway due to favourable climatic conditions, land, and water availability, enabled by state subsidies, and an emerging market in commercial and supermarket outlets. However, farmers encounter production and marketing challenges related to poor tenure security, late delivery of inputs, and low financing. They face low and fluctuating prices, poor infrastructure, including low levels of mechanisation necessary to expand the production. Overall, despite an agricultural and land-use expansion taking shape, actual benefits for real transformation are largely missing – and currently over their heads. An argument is made that whilst policy actors continue to impress farmers to organise themselves to maximise benefits of an agricultural expansion, actual processes on how farmers can achieve this are missing in policy and practice. This necessitates a focus on multi-level processes aimed at addressing production, storage and marketing dynamics within a progressive coordination arrangement that centralizes small producers. Until that is addressed, the prospects for local development and poverty reduction for small-scale farmers under an agricultural expansion will be slender but continue to centrally reside in the state efforts to create enabling local and community environment that addresses real challenges. Overall, this study helps to extend the debate on diverse processes shaping rural transformation in Zambia and across sub-Saharan Africa, including the role and importance of agricultural expansion.

Inexpensive piezoelectric diaphragms can be used as sensors to facilitate both nozzle height setting and build platform leveling in FFF (Fused Filament Fabrication) 3D printers. Tests simulating nozzle contact are conducted to establish the available output and an output of greater than 8 Volts found at 20 ºC, a value which is readily detectable by simple electronic circuits. Tests are also conducted at a temperature of 80 ºC and, despite a reduction of greater than 80% in output voltage, this is still detectable. The reliability of piezoelectric diaphragms is investigated by mechanically stressing samples over 100,000 cycles at both 20 ºC and 80 ºC and little loss of output over the test duration is found. The development of a nozzle contact sensor using a single piezoelectric diaphragm is described.

his quantitative study investigates teachers' perceptions of self-efficacy during the COVID-19 pandemic and explores the correlation between these perceptions and the preservice training they received. The research addresses the cognitive connection between teachers' current self-efficacy, particularly their satisfaction with and appreciation of preservice lecturers. Utilizing a retrospective lens, the study reveals a significant correlation between teachers' evaluation of their preservice training, especially their appreciation of lecturers, and their present self-efficacy. The findings highlight that teachers, amidst the challenges of the pandemic, evaluated their self-efficacy at a remarkably high level. This underscores their resilience during a period of unprecedented uncertainty, demanding substantial personal and professional adaptability. The nuanced interplay observed suggests that teachers' sense of self-efficacy serves as a predictive variable of their mental and professional resilience when confronting uncertainty and navigating rapid and profound changes, as exemplified by the exigencies of the COVID-19 pandemic.

Background: The seasonal variation of peptic ulcers is a rhythmic phenomenon reported worldwide, exhibiting diverse patterns and controversies. Unfortunately, it has remained an unresolved mystery for more than 90 years. Numerous studies have found that this phenomenon was closely associated with multiple environmental factors, but the underlying mechanism has never been elucidated. Objectives: This study aims to elucidate the seasonal variation of peptic ulcers and identify the role of environmental factors in the disease. Methods: Based on a recently identified etiology of peptic ulcers, two inverse operations in calculus, differentiation and integration, are iterated to analyze the existing data. First, the fluctuation curve in the seasonal variation is differentiated twice into the monthly incidences caused by multiple individual environmental factors, and the fluctuation curve due to each individual environmental factor is generated separately. Second, the monthly incidences caused by the individual environmental factors are integrated twice to reproduce the fluctuation curves in the seasonal variation of peptic ulcers. Results: The differentiations of the fluctuation curves in the season variation reveal a parallel relationship between the psychological impacts of each individual environmental factor and the monthly incidences of peptic ulcers. The integrations of the monthly incidences caused by 3 environmental factors reproduce the fluctuation curves in 3 representative seasonal patterns of peptic ulcers but make the parallel relationships invisible. Discussion: The parallel relationships revealed a causal role of environmental factors in peptic ulcers, whereas the reproduction of the fluctuation curves elucidated that multiple environmental factors cause the seasonal variation of peptic ulcers by Superposition Mechanism. The regional differences in environmental factors result in the diverse patterns, as well as the controversy questioning the seasonality of peptic ulcers. Significantly, the data analyses exemplify the application of a new concept, Superposition Mechanism, which might be an indispensable methodological complement to life science and medicine.

Background: In 1962, Susser and Stein observed that the mortality rates of peptic ulcers in England and Wales increased markedly in the 1910s, maintained at a high level during the 1910s-1940s, and began to decline steadily since the early 1950s. They termed this epidemic pattern a birth-cohort phenomenon, but its mechanism has never been elucidated. Susser and Stein speculated that the occurrences of extraordinary social environmental factors roughly fit the fluctuations, but the role of environmental factors in peptic ulcers has never been identified. Objectives: This study aims to elucidate the mechanism of the birth-cohort phenomenon of peptic ulcers and identify the role of environmental factors in the disease. Methods: Starting from an etiology identified recently, this study uses the two inverse operations in calculus, differentiation, and integration, to analyze the existing data. First, a fluctuation curve in the birth-cohort phenomenon is differentiated into multiple annual mortality rates caused by individual environmental factors. Second, these annual mortality rates are integrated to reproduce the fluctuation curve in the birth-cohort phenomenon. Results: The differentiation reveals a parallel relationship between the psychological impacts of environmental factors and the mortality rates of peptic ulcers, whereas the integration reproduces a representative fluctuation curve in the birth-cohort phenomenon. The successive occurrences of multiple social environmental factors from the 1910s to 1940s maintained the increase in mortality rates, while the sustained improvements in living environments explained the steady decline of the mortality rates from the early 1950s. Conclusion: The parallel relationship suggests a causal role of environmental factors in peptic ulcers, whereas the reproduction of a representative fluctuation curve indicates that multiple environmental factors caused the birth-cohort phenomenon by Superposition Mechanism. Significantly, this study challenges the current reductionist approach used to study disease in modern medicine by demonstrating the effectiveness of an opposite methodological concept focused on integration.

PdRe/Al2O3 catalysts are highly selective for hydrogenation of furfural to furfuryl alcohol (FAL). Moreover, synergy between the metals can result in greater specific activity (higher turnover frequency, TOF) than exhibited by either metal alone. Bimetallic catalyst structure depends strongly on the metal precursors employed and their addition sequence to the support. In this work, PdRe/Al2O3 catalysts were prepared by: (i) co impregnation (CI) and sequential impregnation (SI) of  Al2O3 using HReO4 and Pd(NO3)2, (ii) SI using NH4ReO4 and [Pd(NH3)4(NO3)2], (iii) HReO4 addition to a reduced and passivated Pd/Al2O3 catalyst, and (iv) impregnation with the double complex salt (DCS), [Pd(NH3)4(ReO4)2]. Raman spectroscopy and temperature-programmed reduction (TPR) evidence larger supported PdO crystallites in catalysts prepared using Pd(NO3)2 than [Pd(NH3)4(NO3)2]. Surface [ReO4]- species detected by Raman exhibit TPR peak temperatures from ranging 85 to 260°C (versus 375°C for Re/Al2O3). After H2 reduction at 400°C, the catalysts were characterized by chemisorption, temperature-programmed hydride decomposition (TPHD), CO diffuse reflectance infrared Fourier transform spectroscopy (DRIFTS), and scanning transmission electron microscopy (STEM) with energy-dispersive x-ray (EDX) spectroscopy. The CI catalyst containing supported Pd-Re alloy crystallites had a TOF similar to Pd/Al2O3 but higher (61%) FAL selectivity. In contrast, catalysts prepared by methods (ii-iv) containing supported Pd-Re nanoparticles exhibit higher TOFs and up to 78% FAL selectivity.

The primary purpose of this article is to present a maturity model dealing with environmental manufacturing processes in a company. First, a theoretical approach is presented where evolution from the first industrial revolution to the present (Industry 4.0.) is outlined with a need to implement environmental initiatives. Chapter two contains a detailed literature review, which resulted in the creation of our own maturity model presented in the next chapter. According to some authors, Industry 4.0 is based on characteristics that have already been the focus of “lean and green” concepts. The practical part of the article is a case study that shows which areas of the manufacturing process have “environmental” potential. The goal was to move from resource consumption, pollutant emissions and more extensive manufacturing towards environmentally responsible manufacturing (ERM). Using environmental materials and methods reduces energy consumption, which generates cost savings and higher profits. Here, VSM (Value Stream Mapping) was applied to identify core processes with environmental potential. The final part of the article summarizes the work and presents future possibilities. This paper provides an understanding of the role of environmental manufacturing in the era of the 4th industrial revolution.

Futures markets are mainly used as a tool for price discovery and for risk management on the spot markets and enable diversification for international portfolio investments. With this study we aim (1) to investigate the causality relationship between futures markets and spot market and (2) to examine the causality relationship between futures markets and spot markets in different countries. We are interested in both the futures markets - spot market relations and the interactions between the markets at international level. For variables we used the the BIST30 spot index and BIST30 futures contract representing the Borsa Istanbul market and the Dow-Jones 30 index and Dow-Jones 30 futures contract, which are the most important indices representing the US markets. Daily closing price data for the period between 2nd January, 2009 and 18th June, 2018 were analyzed using correlation, unit root test, causality test and regression equations. The results of the study show that the futures markets continue their price discovery role for both the spot markets and futures markets and are influential on other futures and spot markets at international level. These findings are important for investors wanting to invest in Turkey and in similarly considered emerging market economies. It will help investors take informed decisions by providing them with a more efficient price estimations utilizing the futures markets.

This research investigated the significance of learning dispositif (LD) and emotional attachment (EA) on perceived learning success (LS) across a diaspora of Western, Russian, Asian, Middle Eastern and Chinese student cohorts. Foucault’s LD captures the disparate socio-cultural contexts, institutional milieus and more or less didactic teaching styles that moderate learning. EA is a multi-dimensional notion involving affective bonds that emerged in child psychology and spread to marketing and other fields. The sequential explanatory research reviewed the learning and EA literatures and generated an LD–EA framework to structure the quantitative phase of its mixed investigations. In 2017 and 2018, the research collected 150 responses and used a range of statistical techniques for quantitative analysis. It found that LS varied significantly across cohorts, intimating that dispositifs influence learning. Nonparametric analysis suggested that EA also influenced learning, but regressions were inconclusive. Exploratory techniques hint at a dynamic mix of emotional or cognitive motivations during the student learning journey, involving structural breaks in student/instructor relationships. Cluster analysis identified distinct student groupings, linked to years of learning. Separately, qualitative analysis of open-ended survey questions and expert interviews intimates that frequent teacher interactions can increase EA. The synthesis of quantitative with qualitative results and pedagogical reflection suggests that LD and EA both influence learning in a complex, dynamic system. The key constituents for EA are Affection, Connection, Social Presence (SP), Teaching Presence (TP) and Flow but student emotional engagement is conditioned by the socio-cultural milieu (LD) and associated factors like relationships and trust. Unlike in the Community of Learning framework, in the EA framework Cognitive Presence (CP) is an outcome of the interaction between these EA constituents, associated factors and the socio-cultural milieu. Finally, whilst awareness of culture and emotions is a useful pedagogical consideration, learning mainstays remain inclusive educational systems that identify student needs and support well-designed programmes. Within these, scaffolded modules should include a variety of engaging learning activities with non-threatening formative and trustworthy summative feedback. We acknowledge some statistical study limitations, but its tentative findings make a useful preliminary contribution

In cutting processes tool condition affects the quality of the manufactured parts. As such an essential component to prevent unplanned downtime and to assure machining quality is having information about the state of the cutting tool. The primary function of it is to alert the operator that the tool has reached or is reaching a level of ware beyond which behavior is unreliable. In this paper the tool condition is being monitored by analyzing the electric current on the main spindle via an artificial intelligence model utilizing a LSTM neural network. In the current study the tool is monitored while working on a cylindrical raw piece made of AA6013 aluminum alloy with a custom polycrystalline diamond tool, for the purposes of monitoring the ware of these tools. Spindle current characteristics were obtained using external measuring equipment to not influence the operation of the machine included in a larger production line. As a novel approach, an artificial intelligence model based on a LSTM neural network is utilized for the analysis of the spindle current obtained during a manufacturing cycle and assessing the tool ware range in real time. The neural network was designed and trained to notice significant char-acteristics of the captured current signal. The conducted research serves as a proof of concept for use of a LSTM neural network-based model as a method of monitoring the condition of cutting tools.

Widespread patterns of poor dietary behavior are a key factor causing the increasing prevalence of chronic diseases around the world. Research provides initial insights into the potential of food literacy (FL) to empower individuals to improve their dietary behavior. Yet, studies on FL interventions in working adults are scarce. This study aimed to investigate the short- and long-term effects of a comprehensive 3-week workplace health promotion program (WHPP) on FL and dietary intake (DI) and to examine the association between FL and DI in a sample of 144 German office workers (30.0% female). Using two random intercept mixed linear regression models, we found significant strong improvements for both FL (β = 0.52, p < .0001) and DI (β = 0.63, p < .0001) after the WHPP when compared to baseline. Significant long-term improvements at 18 months where strong for FL (β = 0.55, p < .0001) and small for DI (β = 0.10, p < .0001). FL showed a significant moderate effect on DI across all measurement time points (β = 0.24, p < .0001). Our study fills a gap of long-term findings in the literature on FL interventions, offers insights into underlying mechanisms, and provides recommendations for effective WHPPs.

As the global demand for water increases, so does the quantity of wastewater requiring treatment. Due to a relatively low carbon footprint, compared with conventional wastewater treatment technologies, anaerobic digestion (AD) was identified in the 1970s as a forerunner in the push for sustainability, when interest in sustainable technologies and renewable energy sources was first sparked. AD technology development ultimately resulted in the discovery of the ‘anaerobic granule’. It is a spontaneously-forming bio-aggregate of microbial cells capable of digesting pollutants and producing methane-rich biogas as a renewable source of bioenergy. The high settling velocity of such granules meant that AD systems could be operated as high-rate treatment processes, because the active, relatively-slow-growing, pollutant-removing biomass would be retained inside, and not washed out of, even bioreactors operated at extremely high volumetric loading rates. In the intervening years the emergence of the anaerobic ammonium oxidising (anammox) granule, aerobic granule, hydrogenic granule, oxygenic photogranule, and many other functionally-specialised granules, has opened new opportunities in wastewater treatment biotechnology. Whilst environmental engineering based around wastewater treatment is still a growing field of research and implementation, the granule (in all forms) is starting to catch the attention of microbial ecologists. It is a self-immobilised biofilm, with many of the properties of ‘conventional’ biofilms formed in Nature. However, as a single entity, a granule represents an entire community of microorganisms, competing or functioning cooperatively or in syntrophy. Together, inside a bioreactor, granules perform side-by-side arguably representing a meta-organism. Granules are gaining traction as the perfect samples for high-throughput studies on fundamental ecological concepts. Granular biofilms can be used to test hypotheses around drivers of diversity, community assembly, biofilm formation and maturation, community expansion and succession, community stress response, among others. This review outlines the history of three of the most influential types of granules: the anaerobic (methanogenic), aerobic and anammox granule. The main biochemical processes found in each type; their primary characteristics; and the typical makeup of the microbial community underpinning the processes are compared. Finally, the adoption of granules as the perfect ‘playground’ for experiments in microbial ecology is reviewed.

Surveillance along the Kenya-Somalia border has been a big challenge that has continuously puzzled the security personnel, due to insurgency of armed militia Al-Shabaab from Somalia , the Kenyan government proposed construction of a barrier wall. This project developed a low cost wireless sensor network surveillance system to be deployed along the Kenya-Somalia border. The research study utilized two PIR sensor for detecting human intrusion, one motion is detected the sensor transmit the data via an Xbee module. Arduino microcontroller was used to process the data collected by the sensor before transmission. The system developed has two units, the Transmitter unit and a User Graphic interface running on Tuna Term software that displays the received data. During testing, the prototype system detected human intrusion, using the Arduino serial monitor the results were displayed before being package for transmission.

Colours act as stimuli capable of inducing physiological and psychological responses, impacting on human life by influencing behaviour, cognition and emotion. Designers frequently draw upon colour association cues in designing products, packaging and marketing materials to exploit the reported human reactions to them. To date, clinicians and foot orthoses manufacturers appear to have given little thought to how the colour of foot orthoses may have impact within the broader context of a biopsychosocial model of healthcare. Here we examine research regarding colour perception and association and provide rationale for how the use of colour in foot orthoses therapy might be better exploited to improve patient outcomes and to form a focus for future research.

The terms 'open' and 'openness' are widely used across the current higher education environment particularly in the areas of repository services and scholarly communications. Open-access licensing and open-source licensing are two prevalent manifestations of open culture within higher education research environments. As theoretical ideals, open-licensing models aim at openness and academic freedom. But operating as they do within the context of global neoliberalism, to what extent are these models constructed by, sustained by, and co-opted by neoliberalism? In this paper, we interrogate the use of open-licensing within scholarly communications and within the larger societal context of neoliberalism. Through synthesis of various sources, we will examine how open access licensing models have been constrained by neoliberal or otherwise corporate agendas, how open access and open scholarship have been reframed within discourses of compliance, how open-source software models and software are co-opted by politico-economic forces, and how the language of 'openness' is widely misused in higher education and repository services circles to drive agendas that run counter to actually increasing openness. We will finish by suggesting ways to resist this trend and use open-licensing models to resist neoliberal agendas in open scholarship.

Chronic myelogenous leukemia (CML) was the first malignancy for which clinical outcome was drastically improved by kinase inhibitor therapy. Kinase inhibitors targeting other well-known oncogenes have been introduced into clinical practice, but none have shown the same magnitude of clinical benefit as ABL1 inhibition in CML. We argue that early detection is an underappreciated, but critically important factor in success of ABL1 inhibitors in treatment of CML. We show that CML provides a window into how many types of cancer may look and behave at an early stage, prior to diagnosis and the development of additional genomic alterations. The remarkable clinical benefits of ABL1 inhibition is likely due to early detection of CML at a stage in which the tumor is driven by single oncogenic alteration which can be successfully controlled by the inhibitor. Thinking of CML as a prototype for effective systemic treatment based on early cancer detection may help to develop strategies for improving treatment for other types of cancer.

In this paper the authors present a method which facilitates computationally efficient parameter estimation of dynamical systems from a continuously growing set of measurement data. It is shown that the proposed method, which utilises Sequential Monte Carlo samplers, is guaranteed to be fully parallelisable (in contrast to Markov chain Monte Carlo methods) and can be applied to a wide variety of scenarios within structural dynamics. Its ability to allow convergence of one's parameter estimates, as more data is analysed, sets it apart from other sequential methods (such as the particle filter).

This paper aims to describe the geometrical structure and explicit expressions of family of finitely parametrized probability densities over smooth manifold $M$. The geometry of family of probability densities on $M$ are inherited from probability densities on Euclidean spaces $\left\{U_\alpha \right\}$ via bundle morphisms, induced by an orientation-preserving diffeomorphisms $\rho_\alpha:U_\alpha \rightarrow M$. Current literature inherits densities on $M$ from tangent spaces via Riemannian exponential map $\exp: T_x M \rightarrow M$; densities on $M$ are defined locally on region where the exponential map is a diffeomorphism. We generalize this approach with an arbitrary orientation-preserving bundle morphism; we show that the dualistic geometry of family of densities on $U_\alpha$ can be inherited to family of densities on $M$. Furthermore, we provide explicit expressions for parametrized probability densities on $\rho_\alpha(U_\alpha) \subset M$. Finally, using the component densities on $\rho_\alpha(U_\alpha)$, we construct parametrized mixture densities on totally bounded subsets of $M$. We provide a description of inherited mixture product dualistic geometry of the family of mixture densities.

Academic interest in social and human capital is growing significantly. Similarly, their relationship with sustainability is increasing, especially compared to sustainability’s relationship with natural capital and financial and economic capital. Bibliometric and visualization research on these relationships is nonetheless insufficient. This study analyzes the evolution of the literature on natural capital, financial and economic capital, and social and human capital related to sustainability. On the other hand, the study presents a bibliometric analysis on social capital and human capital (SHC) related to sustainability. The article studies 635 references collected from the Web of Science (WoS) Core Collection database and utilizes visualization of similarities (VOS) viewer program to graphically map the material. The analysis involves co-occurrence of keywords, co-citation, and co-authorship. The results reveal not only the state of the art and the leading trends, but also the evolution regarding impact, main journals, documents, topics, authors, institutions, and countries. The study provides researchers and practitioners with a visual and schematic frame of the research on this topic.

The interaction of matter when separated by a distance is a fundamental question, whether such interactions occur through instantaneous action at a distance or retarded interaction (principle of locality)? While the mainstream consensus favors the principle of locality, instantaneous action at a distance offers certain advantages. Previously, instantaneous action at a distance has been viewed as an appearance or representation from specific gauge choices of electromagnetic potentials (retarded). In this paper, we propose a novel approach that combines Weber’s electrodynamics with the concept of vacuum polarization to explore a new possible connection between the two viewpoints: instantaneous action at a distance and the locality principle. This new approach aligns with the observation of locality in signal/energy propagation while retaining the notion of instantaneous action at a distance.

This paper builds on previous work introducing the Secure Remote Update Protocol (SRUP)—a secure communications protocol for Command & Control applications in the Internet of Things, built on top of MQTT. This paper builds on the original protocol, and introduces a number of additional message types: adding additional capabilities to the protocol. We also discuss the difficulty of proving that a physical device has an identity corresponding with a logical device on the network, and propose a mechanism to overcome this within the protocol.

Within this paper, electro-mechanical long-term tests on a thrust bearing are presented. The effects of an additional electrical load on the bearing raceways and the used lubricant are investigated. Chemical and rheological investigations are presented which show the changes in the lubricant. These investigations are compared with the electrical loads and the occurring raceway damage. In addition, a comparison is made with mechanical reference tests. This procedure makes it possible to classify the changes that occur due to the electrical load and to distinguish the effects from each other. The background to these investigations is the increased occurrence of parasitic currents in electric motors, which can lead to damage to machine elements. The phenomena that occur here are new challenges in the development of drive trains.

The urgency for a shift towards cleaner and more efficient energy alternatives for power generation is underscored by the escalating demand for energy and the detrimental environmental impacts of conventional fossil fuels. In the case of automotive power, electric vehicles are being adopted in many applications, however, challenges remain such as extended charging periods and limited ranges between charges. Therefore, this research study conducts a techno-economic analysis of renewable energy technologies (including solar modules, wind turbines and piezoelectric materials) to augment the efficiency and sustainability of electric buses. The study examines the scope for these technologies to be adopted on double-decker electric buses in London in the United Kingdom. The solar modules are placed on the rooftop and sides of the bus generating 15.9 kWh/day and the wind turbine in the front bumper of the bus generates 8.3 kWh/day. However, the piezoelectric material generated only 22.6 Wh/day, rendering it an impractical inclusion in further analysis. Therefore, both the solar modules and wind turbines combined generate 24.2 kWh/day, which can increase the driving range by 16.3 km per day and this results in savings of 19.36 minutes for charging at the stations. Investing in such projects would have a positive return as the internal rate of return (IRR) and net present value (NPV) are 2.8% and £11,175 respectively. The annual revenue would be £6,712, and the greenhouse gas (GHG) reduction would be two metric tons annually. Electricity exported to the grid, the electricity export rate, and the initial investment are identified as key factors influencing power outage in a sensitivity analysis. In conclusion, this numerical modelling study paves the way for experimental validation towards implementation of renewable energy technologies on electric bus fleets.

This research is a preliminary phase of a general effort to develop a generic-data transferring capability via human haptic thermal sensation (generic-data refers to a coded language like Morse or Braille). For the capability to be effective, it must include a large variety of short recognizable cues. Hence, we propose the concept of cues based on sequences of thermal pulses, i.e., combinations of warm and cool pulses with several levels of intensity. The objective of this study was to determine the feasibility of basing a generic-data-transfer capability on thermal cues composed of sequences of short pulses. The research included defining the basic characteristics of the stimuli parameters and developing practical methods for generating and measuring them. Several sequences were designed in light of the relevant data known to date, and tests were conducted. The thermal cues presented to the participants were sensed and recognized by touch. The results indicate high feasibility for a capability that is applicable in various scenarios. In addition, the low impact on human skin temperature due to short stimuli duration represents an inherent advantage for later implementation. This report presents promising findings and offers insights for further investigation.

The theory of heterogeneous nucleation was initially developed as a part of condensed matter physics and later it was used as an important engineering tool to design metallurgical processes. The success leads to wide applications of the theory in metallurgical practice. For example, engineering heterogeneous nucleation in ductile iron has been used to reduce shrinkage defects, suppress cementite formation, and modify size and shape of micro-structural constituencies. This demonstrates how theoretical knowledge could benefit industry practice. This overview aims to summarize the authors’ published studies in co-authorship with colleagues and students, which covered different aspects of engineering heterogeneous nucleation in multiphase cast alloys. Several approaches for engineering heterogeneous nucleation using thermodynamic simulation and practical methods for improving efficiency of nucleation using co-precipitation technique and a local transient melt supersaturation were suggested. Automated Scanning Electron Microscopy Energy-Dispersive-X-ray (SEM/EDX) analysis and a high-resolution Transmission Electron Microscopy (TEM) were used to verify simulation predictions. Practical examples of controlling micro-porosity shrinkage in cast irons with spheroidal graphite are presented to illustrate the power of engineering heterogenous nucleation. The authors appreciate the great efforts that the co-authors have made on the original, overviewed articles here.

Data from ocean color (OC) remote sensing are considered a cost-effective tool for the study of biogeochemical processes globally. Satellite-derived chlorophyll, for instance, is considered an Essential Climate Variable since it is helpful in detecting climate change impacts. Google Earth Engine (GEE) is a planetary scale tool for remote sensing data analysis. Along with OC data, such tools allow an unprecedented spatial and temporal scale analysis of water quality monitoring in a way that has never been done before. Although OC data have been routinely collected at medium (~1 km) and more recently at high (~250 m) spatial resolution, only coarse resolution (≥4 km) data are available in GEE, making them unattractive for applications in the coastal regions. Data reprojection is needed prior to making OC data readily available in the GEE. In this paper, we introduce a simple but practical procedure to reproject and ingest OC data into GEE. The procedure is applicable to OC swath (Level-2) data and is easily adaptable to higher-level products. The results showed consistent distributions between swath and reprojected data, building confidence in the introduced framework. The study aims to start a discussion on making high resolution OC data readily available in GEE.

Artisanal goldminers in Ghana are exposed to various levels and forms of health, safety and environmental threats. Without the required legislation and regulations, artisanal miners are responsible for their own health and safety at work. Consequently, understanding the probabilities of self-protection at work by artisanal goldminers is crucial. A cross-sectional survey of 500 artisanal goldminers was conducted to examine the probabilities of personal protective equipment use among artisanal goldminers in Ghana. The data was subjected to both descriptive and inferential statistics. Initial findings showed that personal protective equipment use among artisanal miners was 77.4%. Overall, higher probabilities of personal protective equipment use was observed among artisanal goldminers who work in good health and safety conditions as compared to artisanal miners who work in poor health and safety conditions. Also, personal protective equipment use was more probable among the highly educated artisanal goldminers, miners who regularly go for medical screening and the most experienced miners. Additionally, personal protective equipment use was more probable among artisanal miners who work in non-production departments and miners who work in the medium scale subsector. Inversely, personal protective equipment use was less probable among female artisanal miners and miners who earn more monthly income ($174 and above). To increase self-care and safety consciousness in artisanal mining, there is the need for a national occupational health and safety legislation in Ghana. Also, interventions and health promotion campaigns for better occupational conditions in artisanal mining should target and revise the health and safety related workplace programs and conditions

Globally, the need for sustainable urban development has never been more urgent than now. This is because estimates indicate that more than half of the global population will be living in urban areas by 2050, with South Africa estimated at 80%. This paper focuses on socio-economic nuances that impede sustainable urban development, as well as the strategies that can accelerate sustainable urban development in South Africa. Through a literature review methodology, the analysis identified the following issues as impediments to sustainable urban development in South Africa, informal settlements and businesses, lack of consultations with urban citizens, unresolved apartheid legacies, violence, crime and insecurity, and migration and immigration intricacies in South Africa. The paper also established the strategies to accelerate sustainable urban development as working with informality and against it, promoting inclusivity and access to resources, formulating, and accelerating policy reforms, investing in rural areas and towns, and finally investing in superior technology and innovation. These findings imply that there is a need to seek alternative development models and approaches, to address social challenges such as poverty, unemployment, and poor housing, aspects that impact negatively on urban development planning and sustainable development. This is because the major challenges to sustainable urban development are human-related challenges, that need social and economic solutions. There is also a need to reduce rural-urban migration by developing rural towns to ease pressure in major towns which are already spilling over.

Arsenic is naturally present in many geological formations around the world and is a major source of groundwater contamination. Bolivia is not exempt from this problem. Physical-chemical studies of water quality in deep and shallow wells have determined the presence of arsenic in some areas of the Bolivian Altiplano, valleys, and plains. In the Lake Poopó basin, water from shallow wells (up to 30 m) has arsenic concentrations that exceed the Bolivian drinking water standard and the WHO recommendation of 10 μg-As/l by up to 25 times. In peri-urban areas of Cochabamba, the arsenic content in groundwater is up to 113 μg-As/l. chronic exposure to arsenic is associated with neurological and dermatological problems and carcinogenic effects. To date, no measures have been taken to mitigate the arsenic problem for the rural population, who often rely on arsenic-contaminated water as their only source of drinking water. This contamination represents a serious threat to the health and economic well-being of the inhabitants and ecosystem in these areas. In this work, for arsenic removal, iron and iron oxide nanoparticles obtained from recycling metal chips by top-down and bottom-up technology have been used for arsenic adsorption in an experimental filter combined with other artisanal filtering materials in the form of layers. The results have shown that water contaminated with arsenic after passing through the filter reduces the concentration of total arsenic filtered from 83, 52 ppb to less than 5 ppb, less than the Bolivian standard 512 of 10 ppb for drinking water.

Identification of plants is a challenging task which aims to identify the family, genus, and species level according to morphological features. Automated deep learning-based computer vision algorithms are widely used for identifying plants and can help users to narrow down the possibilities. However, numerous morphological similarities between and within species make the classification difficult. In this paper, we tested a custom convolution neural network (CNN) and vision transformer (ViT) based models using the PyTorch framework to classify plants. We used a large dataset of 88K and 16K images for classifying plants at genus and species levels respectively. Our results show that for classifying plants at the genus level, ViT models perform better compared to CNN-based models ResNet50 and ResNet-RS-420, and other state-of-the-art CNN-based models suggested in previous studies on a similar dataset. The ViT model achieved top accuracy of 83.3% for classifying plants at the genus level. ViT models also perform better for classifying plants at the species level compared to CNN-based models ResNet50 and ResNet-RS-420, with a top accuracy of 92.5%. We show that the correct set of augmentation techniques plays an important role in classification success.

The purpose of this project was to demonstrate the potential of converting an existing internal combustion engine for hydrogen operation at relatively low cost and to optimize the combustion process for ultra-low NOx emissions. The conversion has been carried out on a 450cc naturally aspirated single cylinder engine. The first baseline measurements and setup have shown lower NOx emissions than the baseline gasoline engine (without catalytic converter) for similar power output and engine efficiency. Further optimization of the combustion process allowed to achieve Euro VI and (latest European Commission proposal) Euro VII heavy-duty NOx limits without exhaust gas aftertreatment system (EATS) in steady state conditions. The concept is very promising and the knowledge gained should allow for further optimization to achieve near zero NOx emissions at high engine efficiency and power output with supercharging. It should also allow an efficient conversion of other engine types for different applications. In particular, a project with the aim to convert a heavy duty 6 cylinder engine intended for public transport buses has started. It follows a feasibility study which has shown the competitiveness of the hydrogen engine for this application.

Background: The pathogenesis of duodenal ulcer has never been explained although the first description of this disease in medical literature appeared in 1817. Marshall et al. concluded that Helicobacter pylori was the most important etiological factor for duodenal ulcer in 1988, but the etiology based on this bacterium is controversial and how the bacterial infection leads to ulceration is presently unknown. Objectives: This study aims to identify the cause of duodenal ulcer, address the controversial issues surrounding Helicobacter pylori, elucidate the roles of gastric acid, and describe the pathological process of duodenal ulceration. Methods: First, a comprehensive systematic review on peptic ulcers (including gastric ulcer and duodenal ulcer) was conducted and the results were summarized. Second, a recently published causal relationship was employed to identify the etiology of peptic ulcers. Third, novel concepts and methods were applied to analyze the existing data on duodenal ulcer. Results: The etiology of duodenal ulcer and the roles of Helicobacter pylori and gastric acid in this disease were identified. The controversies surrounding Helicobacter pylori were addressed, and many characteristics and phenomena/observations of duodenal ulcer were elucidated. The pathological process of duodenal ulceration was described. Conclusion: Existing data accumulated over the past 300 years was sufficient, when analyzed using novel concepts, to understand the pathogenesis of duodenal ulcer. Duodenal ulcer is not an infectious disease caused by the infection of Helicobacter pylori, but a psychosomatic disease triggered by psychological stress. Helicobacter pylori plays a secondary role in only the late phase of duodenal ulceration.

Metabolic syndrome is a growing global health problem. Evidence suggests that diets rich in phytochemical-containing herbs and spices can contribute to reduced risk of chronic disease. This review assesses the scope of evidence supporting the use of herbs and spices in the diet for prevention or treatment of metabolic syndrome and associated health conditions. A search of PubMed and Scopus databases was carried out to assess the available clinical or cohort evidence for culinary doses of commonly used herbs and spices. Trials that were measuring health factors related to metabolic disorders or the health of individuals with metabolic syndrome or associated diseases were included. Out of a total of 1742 papers identified, there were 146 relevant studies on black pepper, chilli, cardamom, cinnamon, coriander, cumin, fennel, fenugreek, garlic, ginger, Nigella seed, rosemary, sage and turmeric. No relevant research was found for cloves, mint, oregano, parsley or thyme. Cinnamon, fenugreek and ginger were the herbs/spices with the most published trials on them and showed promise for glycaemic control. Cardamom appears to have potential to reduce inflammatory markers, and cinnamon, ginger and turmeric for blood lipids. Patients with type 2 diabetes were the population most likely to be included in studies, but the preventative benefits of herbs/spices in healthy populations were also investigated, particularly for chilli, ginger and cinnamon. There is evidence for the beneficial effect of culinary doses of many common herbs/spices in the prevention and treatment of metabolic syndrome and associated disorders.

Conversational spoken dialogue systems that interact with the user rather than merely reading text can be equipped with hesitations to manage the dialogue flow and the users' attention. Based on a series of empirical studies, we built an elaborated hesitation synthesis strategy for dialogue systems that inserts hesitations of scalable extent wherever needed in the ongoing utterance. So far, evaluations of hesitating systems have shown that synthesis quality is affected negatively by hesitations, but that there is improvement in interaction quality. We argue that due to its conversational nature, hesitation synthesis needs interactive evaluation rather than traditional MOS-based questionnaires. To prove this point, we dually evaluate our system’s speech synthesis component: on the one hand, linked to the dialogue system evaluation, on the other hand, in the traditional MOS way. This way we are able to analyze and discuss differences that arise due to the evaluation methodology. Our results suggest that MOS scales are not sufficient to assess speech synthesis quality, which has implications for future research that are discussed in this paper. Furthermore, our results indicate that hesitations work well to increase task performance and that an elaborated strategy is necessary to avoid likability issues.

Intrinsically disordered proteins mediate crucial biological functions through their interactions with other proteins. Mutual synergistic folding (MSF) occurs when all interacting proteins are disordered, folding into a stable structure in the course of the complex formation. In these cases, the folding and binding processes occur in parallel, lending the resulting structures uniquely heterogeneous features. Currently there are no dedicated classification approaches that would take into account the particular biological and biophysical properties of MSF complexes. Here we present a scalable clustering-based classification scheme, built on redundancy-filtered features that describe the sequence and structure properties of the complexes, and the role of the interaction, which is directly responsible for structure formation. Using this approach, we define six major types of MSF complexes, corresponding to biologically meaningful groups. Hence, the presented method also shows that differences in binding strength, subcellular localization, and regulation are encoded in the sequence and structural properties of proteins. While current structure classification methods can also handle complex structures, we show that the developed scheme is fundamentally different, and since it takes into account defining features of MSF complexes, it serves as a better representation of structures arising through this specific interaction mode.

Fire detection from satellite sensors relies on an accurate estimation of the unperturbed state of a target pixel, from which an anomaly can be isolated. Methods for estimating the radiation budget of a pixel without fire depend upon training data derived from the location's recent history of brightness temperature variation over the diurnal cycle, which can be vulnerable to cloud contamination and the effects of weather. This study proposes a new method that utilises the common solar budget found at a given latitude in conjunction with an area's local solar time to aggregate a broad-area training dataset, which can be used to model the expected diurnal temperature cycle of a location. This training data is then used in a temperature fitting process with the measured brightness temperatures in a pixel, and compared to pixel-derived training data and contextual methods of background temperature determination. Results of this study show similar accuracy between clear-sky medium wave infrared upwelling radiation and the diurnal temperature cycle estimation compared to previous methods, with demonstrable improvements in processing time and training data availability. This method can be used in conjunction with brightness temperature thresholds to provide a baseline for upwelling radiation, from which positive thermal anomalies such as fire can be isolated.

Despite the popularity of Zumba in several countries, research is scarce about its impact on older adults. Meanwhile, the integration of cognitive tasks with physical exercises, known as dual-tasking, is an evolving strategy to facilitate activities for older people. This study investigated the perceptions of older adults regarding Zumba and the potential of implementing it in a dual-task program. We conducted a qualitative-descriptive research involving 44 Filipino older adults from August to November 2020. Content analysis was employed to analyze the data. Four themes were identified: moving towards match or mismatch; balancing benefits with burdens; dual-tasking as innovative yet potentially challenging; and overcoming barriers with enablers. While Zumba is an inclusive and beneficial activity, individual and contextual limitations could hinder its suitability for older people. Dual-tasking in Zumba was also recognized as an innovative approach, although challenges should be addressed to promote its utility. Several strategies could support the design of these programs in communities. This is the first study to explore older adults’ perceptions towards Zumba and its potential utilization as a dual-tasking program. Findings could guide the implementation of appropriate Zumba and dual-tasking activities that seek to integrate cognitive and physical training for older adults.

In this report, we present a study demonstrating how machine learning methods can reveal the interaction of different risk factors of post-transplant leukemic relapse and obtain robust predictions even with a modest clinical dataset. Using a cohort of 63 pediatric patients with acute lymphocytic leukemia (ALL) and 46 patients with acute myeloid leukemia (AML) who underwent stem cell transplant at a single institution, we built predictive models of leukemic relapse with both pretransplant and posttransplant patient variables (specifically lineage-specific chimerism) using the random forest classifier. The random forest classifier revealed different important predictive factors between ALL and AML in our relapse models consistent with previous knowledge. Furthermore, it also distinguished donor CD34 chimerism as most impactful in relapse prediction compare to donor chimerism of other cellular lineages in our dataset for ALL but CD3 for AML. Our models greatly improved sensitivity and specificity at predicting relapses in cross validation compared to a reference model based on our own institutional incidence of relapse and inferential statistical principles. Local Interpretable Model-Agnostic Explanations, an interpretable machine learning tool, confirmed our Random Forest Classification result and provided an intuitive explanation of how our machine learning models made the relapse prediction for each individual patient.

The overarching aim of this paper is to investigate the lake ecosystem response to different drivers over a long term period by a paleolimnological study in Lake Narlay (46°64N, 5°91E) located in the Jura Mountains of France. It is a small, hard-water lake with a maximum water depth of 40 m and extended anoxic condition of the bottom water. Previous results on sediments analysis have documented a differential response of the lake to the environmental changes that occurred in AD 1600 when major shift in the trophic reliance on methane of the benthic food web were observed. From 1920 with intensification of modern agriculture, animal farming and the construction of a cheese making facility, the lake become eutrophic, with Oscillatoria rubescens bloom. However, the lake showed pronounced changes in an older period that remained unanswered. In this paper we aim at reconstructing in more detail the limnological conditions of this Lake over the last 1200 yrs. using combined analyses of specific algal carotenoids and subfossil diatom remains. A comparison with other proxies (chironomid, pollen, and instrumental climatic reconstruction) will be used to better identify, between the complex combination of climate and anthropogenic pressure, the driving factors that determined the ecological trajectory of Lake Narlay.

Interactome analyses have traditionally been applied to yeast, human and other model organisms due to the availability of protein-protein interactions data for these species. Recently these techniques have been applied to more diverse species using computational interaction prediction from genome sequence and other data types. This review describes the various types of computational interactome networks that can be created and how they have been used in diverse eukaryotic species, highlighting some of the key interactome studies in non-model organisms.

One control challenge in prosthetic legs is seamless transition from one gait mode to another. User intent recognition (UIR) is a high-level controller that tells a low-level controller to switch to the identified activity mode, depending on the user’s intent and environment. We propose a new framework to design an optimal UIR system with simultaneous maximum performance and parsimony for gait mode recognition. We use multi-objective optimization (MOO) to find an optimal feature subset that creates a trade-off between these two conflicting objectives. The main contribution of this paper is two-fold: (1) a new gradient-based multi-objective feature selection (GMOFS) method for optimal UIR design; and (2) the application of advanced evolutionary MOO methods for UIR. GMOFS is an embedded method that simultaneously performs feature selection and classification by incorporating an elastic net in multilayer perceptron neural network training. Experimental data are collected from six subjects, including three able-bodied subjects and three transfemoral amputees. We implement GMOFS and four variants of multi-objective biogeography-based optimization (MOBBO) for optimal feature subset selection, and we compare their performances using normalized hypervolume and relative coverage. GMOFS demonstrates competitive performance compared to the four MOBBO methods. We achieve a mean classification accuracy of 97.14% ± 1.51% and 98.45% ± 1.22% with the optimal selected subset for able-bodied and amputee subjects, respectively, while using only 23% of the available features. Results thus indicate the potential of advanced optimization methods to simultaneously achieve accurate, reliable, and compact UIR for locomotion mode detection of lower-limb amputees with prostheses.

The identification of informal settlements in urban areas is an important step in developing and implementing pro-poor urban policies. Understanding when, where and who lives inside informal settlements is critical to efforts to improve their resilience. This study aims to analyse the capability of machine-learning (ML) methods to map informal areas in Jeddah, Saudi Arabia, using very-high-resolution (VHR) imagery and terrain data. Fourteen indicators of settlement characteristics were derived and mapped using an object-based ML approach and VHR imagery. These indicators were categorised according to three different spatial levels: environ, settlement and object. The most useful indicators for prediction were found to be density and texture measures, (with random forest (RF) relative importance measures of over 25% and 23% respectively). The success of this approach was evaluated using a small, fully independent validation dataset. Informal areas were mapped with an overall accuracy of 91%. Object-based ML as a hybrid approach performed better (8%) than object-based image analysis alone due to its ability to encompass all available geospatial levels.

In semi-arid regions, climate change has affected crop growing season length and sowing time, potentially causing low yield of the rainfed staple crop pearl millet (Pennisetum glaucum L.) and food insecurity among smallholder farmers. In this study, we used 1994–2023 rainfall data from Namibia's semi-arid North-Central Region (NCR), receiving November–April summer rainfall, to analyze rainfall patterns and trends and their implications on the growing season to propose climate adaptation options for the region. The results revealed high annual and monthly rainfall variabilities, with nonsignificant negative trends for November–February rainfalls, implying a shortening growing season. Furthermore, we determined the effects of sowing date on grain yields of the early-maturing Okashana-2 and local landrace Kantana pearl millet varieties and the optimal sowing window for the region, using data from a two-year split-plot field experiment conducted at the University of Namibia-Ogongo Campus, NCR, during the rainy season. Cubic polynomial regression models were applied to grain yield data sets to predict grain production for any sowing date between January and March. Both varieties produced the highest grain yields under January sowings, with Kantana exhibiting a higher yield potential than Okashana-2. Kantana, sown by 14 January, had a yield advantage of up to 36.0% over Okashana-2, but its yield gradually reduced with delays in sowing. Okashana-2 exhibited higher yield stability across January sowings, surpassing Kantana’s yields by up to 9.4% following the 14 January sowing. We determined the pearl millet optimal sowing window for the NCR from 1–7 and 1–21 January for Kantana and Okashana-2, respectively. These results suggest that co-cultivation of early and late pearl millet varieties and growing early-maturing varieties under delayed seasons could stabilize grain production in northern Namibia and enhance farmers' climate adaptation. Semi-arid agro-region policymakers could utilize this information to adjust local seed systems and extension strategies.

In order to improve contrast and restore color for underwater image captured by camera sensors without suffering from insufficient details and color cast, a fusion algorithm for image enhancement in different color spaces based on contrast limited adaptive histogram equalization (CLAHE) is proposed in this article. The original color image is first converted from RGB color space to two different special color spaces: YIQ and HSI. The color space conversion from RGB to YIQ is a linear transformation, while the RGB to HSI conversion is nonlinear. Then, the algorithm separately operates CLAHE in YIQ and HSI color spaces to obtain two different enhancement images. The luminance component (Y) in the YIQ color space and the intensity component (I) in the HSI color space are enhanced with CLAHE algorithm. The CLAHE has two key parameters: Block Size and Clip Limit, which mainly control the quality of CLAHE enhancement image. After that, the YIQ and HSI enhancement images are respectively converted backward to RGB color. When the three components of red, green, and blue are not coherent in the YIQ-RGB or HSI-RGB images, the three components will have to be harmonized with the CLAHE algorithm in RGB space. Finally, with 4 direction Sobel edge detector in the bounded general logarithm ratio operation, a self-adaptive weight selection nonlinear image enhancement is carried out to fuse YIQ-RGB and HSI-RGB images together to achieve the final fused image. The enhancement fusion algorithm has two key factors: average of Sobel edge detector and fusion coefficient, and these two factors determine the effects of enhancement fusion algorithm. A series of evaluate metrics such as mean, contrast, entropy, colorfulness metric (CM), mean square error (MSE) and peak signal to noise ratio (PSNR) are used to assess the proposed enhancement algorithm. The experiments results showed that the proposed algorithm provides more detail enhancement and higher values of colorfulness restoration as compared to other existing image enhancement algorithms. The proposed algorithm can suppress effectively noise interference, improve the image quality for underwater image availably.

Cryoglobulinaemia is characterised by immunoglobulins which precipitate at temperatures be-low 37°C and redissolve on warming. Monoclonal IgM immunoglobulin may be associated with type I and II cryoglobulinaemia with underlying Waldenström macroglobulinemia, monoclonal gammopathy of undetermined significance or other non-Hodgkin lymphoma. We review the clinical characteristics, laboratory testing and suggest a management approach for monoclonal IgM associated cryoglobulinaemia. Laboratory testing is critical as even a minimal amount of measurable cryoglobulin may result in symptoms. Accurate detection of cryoglobulins may be challenging and care must be taken with preanalytical variables and repeat testing of monoclo-nal protein and cryoglobulins is indicated if clinical suspicion is high. Presentations range from asymptomatic disease to multisystem involvement so careful evaluation of the features and thorough interrogation of organ systems and the underlying clone is critical. Immediate man-agement is required for clinical red flag features. Due to their rarity, data to inform treatment decisions are scant and collaborative research is imperative to aid defining optimal treatment strategies.

Abstract: The SARS-CoV-2 pandemic has significantly impacted world health and economic status. In response, much work has been undertaken to provide effective, safe vaccines, antibodies and antiviral drugs with which to address this pandemic. Treatment of a pandemic population presents multiple challenges in addition to the primary issue of drug efficacy and safety, such as large scale drug manufacture and distribution, drug stability, oral dosing and pharmacoeconomic considerations. Ideally, these factors must be addressed if new candidate drugs are to be advanced for treatment of large (pandemic) populations. Subsequently, new antivirals have reached the market but choices are few. According to the NIH Covid Treatment Guidelines, only three small molecule antiviral drugs are available to treat COVID-19 disease. As such, a significant part of the research towards discovery of new antiviral drugs has focused on screening and evaluation of ‘repurposed drugs’ or previously approved or clinical stage drugs. Yet, in spite of this increased research activity, one promising clinical stage candidate drug has received little attention regarding its potential as a monotherapy or component of combination therapy for treatment of COVID-19 disease. Tucaresol, with documented human safety and pharmacokinetic data, is an orally active, stable, small molecule amenable to large scale manufacture by a proprietary two-step synthesis developed by us. Tucaresol functions as a host-targeted antiviral by selective protection/reconstitution of CD4+ T helper cells as demonstrated in HIV patients and SIV macaques. In view of similarities between HIV and SARS-CoV-2, especially with respect to host CD4+ T helper cells, and the suitability of Tucaresol for facile treatment of pandemic populations, Tucaresol is presented herein for treatment of mild-to-moderate COVID-19 patients but may also be useful for treatment of advanced disease accompanied by lymphopenia.

Spatial pattern-oriented evaluations of distributed hydrological models have contributed towards an improved realism of hydrological simulations. This advancement was supported by the broad range of readily available satellite-based datasets of key hydrological variables, such as evapotranspiration (ET). At larger scale, spatial patterns of ET are often characterized by an underlying climate gradient, and with this study, we argue that gradient dominated patterns may hamper the potential of spatial pattern-oriented evaluation frameworks. We hypothesize that the climate control of spatial patterns of ET overshadows the effect model parameters have on the simulated variability. To solve this limitation, we propose a climate normalization strategy. This is demonstrated for the Senegal River basin as modeling case study, where the dominant north-south precipitation gradient is the main driver of the observed hydrological variability. Two multi-objective calibration experiments investigate the effect of climate normalization. Both calibrations utilize observed discharge (Q) in combination with remote sensing ET data, where one is based on the original ET pattern and the other utilizes the normalized ET pattern. We identify parameter sets that balance the tradeoffs between the two independent observations and find that the calibration using the normalized ET pattern does not compromise the spatial patern performance of the original pattern. However, vice versa, this is not necessarily the case, since the calibration using the original ET pattern showed a poorer performance for the normalized pattern. Both calibrations reached comparable performance of Q. With this study, we identified a general shortcoming of spatial pattern-oriented model evaluations using ET in basins dominated by a climate gradient, but we argue that this also applies to other variables such as, soil moisture or land surface temperature.

Business processes are continuously evolving in order to adapt to changes due to various factors. One important process drift perspective yet to be investigated is the detection of branching condition changes in the process model. None of the existing process drift detection methods focus on detecting changes of branching conditions in process models. Existing branching condition detection methods do not take changes within the process into account, hence results are inadequate to represent the changes of decision criteria of the process. In this paper, we present a method which can detect branching condition changes in process models. The method takes both process models and event logs as input, and translates event logs into decision sequences for change points detection. The proposed method is evaluated by simulated event logs.

Since the discovery of the human epidermal growth factor receptor 2 (HER2) as an oncogenic driver in a subset of breast cancers and the development of HER2 directed therapies, the prognosis of HER2 amplified breast cancers has increased meaningfully. Next to monoclonal anti-HER2 antibodies and tyrosine kinase inhibitors, the antibody-drug conjugate T-DM1 is a pillar of targeted treatment of advanced HER2-positive breast cancers. Currently, several HER2 directed antibody-drug conjugates are under clinical investigation for HER2 amplified but also HER2 expressing but not amplified breast tumors. In this article, we review the current preclinical and clinical evidence of the investigational drugs A166, ALT-P7, ARX788, DHES0815A, DS-8201a, RC48, SYD985, MEDI4276 and XMT-1522.

The snow melt from the High Atlas is a critical water resource in Morocco. In spite of its importance, monitoring the spatio-temporal evolution of key snow cover properties like the snow water equivalent remains challenging due to the lack of in situ measurements at high elevation. Since 2015, the Sentinel-2 mission provides high spatial resolution images with a 5 day revisit time, which offers new opportunities to characterize snow cover distribution in mountain regions. Here we present a new data assimilation scheme to estimate the state of the snowpack without in situ data. The model was forced using MERRA-2 data and a particle filter was developed to dynamically reduce the biases in temperature and precipitation using Sentinel-2 observations of the snow cover area. The assimilation scheme was implemented using SnowModel, a distributed energy-balance snowpack model and tested in a pilot catchment in the High Atlas. The study period covers 2015-2016 snow season which corresponds to the first operational year of Sentinel-2A, therefore the full revisit capacity was not yet achieved. Yet, we show that the data assimilation led to a better agreement with independent observations of the snow height at an automatic weather station and the snow cover extent from MODIS. The performance of the data assimilation scheme should benefit from the continuous improvements in MERRA-2 reanalyses and the full revisit capacity of Sentinel-2.

The synthesis and structure of the 4,4′-dipyridyl N,N′-donor ligand with a central thiazolo[5,4-d]thiazole (tztz) unit, named Dptztz is presented. With the linker Dptztz and isophthalate (benzene-1,3-dicarboxylate, 1,3-BDC²⁻) the mixed-linker, two-dimensional coordination network [Zn(1,3-BDC)Dptztz]·DMF could be obtained (DMF = dimethylformamide), which belongs to the class of coordination polymers with interdigitated structures (CIDs). The incorporated DMF solvent molecules can be removed through solvent exchange and evacuation such that the supramolecular 3D packing of the 2D networks retains porosity for CO₂ adsorption in activated [Zn(1,3-BDC)Dptztz]. The first sorption study of a tztz-functionalized porous metal-organic framework material yields a BET surface of 417 m²/g from CO₂ adsorption. The heat of adsorption for CO₂ exhibits a relative maximum with 27.7 kJ/mol at adsorbed CO₂ of about 4 cm³/g STP which is interpreted as a gate-opening effect.

In French Polynesia, Wuhan, Delta and Omicron SARS-CoV-2 variants-of-concern (VOCs) caused epidemics with variable severities. We assessed the prevalence and titers of anti-SARS-CoV-2 antibodies related to natural infection and/or vaccination, from a representative sample (N=673) of the adult population of Tahiti recruited during November-December 2021 (after the Delta outbreak and just before the Omicron epidemic). Of the 673 participants tested, 644 (95.7%) had detectable antibodies against SARS-CoV-2-S and/or -N proteins resulting from natural infection and/or vaccination, and 388 (57.7%) were positive only for the detection of anti-N antibodies indicating natural infection. SARS-CoV-2 seroprevalence extrapolated to the adult population of Tahiti was estimated at 95.9%. Concentrations of anti-SARS-CoV-2-S antibodies significantly increased with age, number of self-reported SARS-CoV-2 infections (0 or ≥1), and number of COVID-19 vaccine doses (0, 1, 2, or 3) received by the participants. Elderly people, who are at higher risk of severe outcomes, had received more vaccine doses than younger individuals both in our sample and in the general population. The high level of antibody responses related to past infections and vaccination, especially booster doses, has likely contributed to reducing the severity of the Omicron outbreak in French Polynesia.

Recent changes in lung cancer care, including new approvals in first line and the introduction of high throughput molecular technologies in routine testing led us to question ourselves on how deeper molecular testing may be helpful for the optimal use of targeted drugs. In this article we review recent results in the scope of personalized medicine in lung cancer. We discuss biomarkers that have a therapeutic predictive value in lung cancer with a focus on recent changes and on the clinical value of large scale sequencing strategies. We review the use of second and third generation EGFR and ALK inhibitors with a focus on secondary resistance alterations. We discuss anti-BRAF and anti-MEK combo, emerging biomarkers as NRG1 and NTRKs fusions and immunotherapy. Finally we discuss the different technical issues of comprehensive molecular profiling and show how large screenings might refine the prediction value of individual markers. Based on a review of recent publications (2012–2018), we address promising approaches for the treatment of patients with lung cancers and the technical challenges associated to the identification of new predictive markers.

We have proposed a simple algorithm to retrieve the total ozone column and snow properties (spectral albedo and effective light absorption path) using the high spatial resolution single – view MSI/S-2 measurements over Antarctica.

Episcopic imaging using techniques such as High Resolution Episcopic Microscopy (HREM) and its variants, allows biological samples to be visualized in three dimensions over a large field of view. Quantitative analysis of episcopic image data is undertaken using a range of methods. In this systematic review, we look at trends in quantitative analysis of episcopic images and discuss avenues for further research. Papers published between 2011 and 2022 were analyzed for details about quantitative analysis approaches, methods of image annotation and choice of image processing software. It is shown that quantitative processing is becoming more common in episcopic microscopy and that manual annotation is the predominant method of image analysis. Our meta-analysis highlights where tools and methods require further development in this field, and we discuss what this means for the future of quantitative episcopic imaging, as well as how annotation and quantification may be automated and standardized across the field.

Postural deficits such as hyperlordosis (hollow back) or hyperkyphosis (hunchback) are relevant health issues. Diagnoses depend on the experience of the examiner and are therefore often subjective and prone to errors. Machine learning (ML) methods in combination with explainable ar-tificial intelligence (XAI) tools have proven useful for providing an objective, data-based orien-tation. However, only a few works have considered posture parameters, leaving the potential of more human-friendly XAI interpretations still untouched. Therefore, the present work proposes an objective, data-driven ML system for medical decision support that enables especially human-friendly interpretations using counterfactual explanations (CFs). Posture data for 1151 subjects were recorded by means of stereophotogrammetry. An expert-based classification of the subjects regarding the presence of hyperlordosis or hyperkyphosis was initially performed. Using a Gaussian progress classifier, the models were trained and interpreted using CFs. Label errors were flagged and re-evaluated using confident learning. Very good classification performances for both hyperlordosis and hyperkyphosis were found, whereby the re-evaluation and correction of the test labels led to a significant improvement (MPRAUC = 0.97). A statistical evaluation showed that the CFs seemed to be plausible in general. In the context of personalized medicine, the present study’s approach could be of importance for reducing diagnostic errors and thereby improving the individual adaptation of therapeutic measures. Likewise, it could be a basis for the development of apps for preventive posture assessment.

Prostate cancer (PCa) is the most commonly diagnosed cancer and the second most common cause of death due to cancer. About 30% of patients with PCa who have been castrated develop a castration-resistant form of the disease (CRPC), which is incurable. In the last decade, new treatments that control the disease have emerged, slowing progression and spread, and prolonging survival while maintaining the quality of life. These include immunotherapies; however, we do not yet know the optimal combination and sequence of these therapies with the standard ones. All therapies are not always suitable for every patient due to co-morbidities or adverse effects of therapies or both, so there is an urgent need for further work on new therapeutic options. Advances in cancer immunotherapy with an immune checkpoint inhibition mechanism (e.g., ipilimumab, an anti-CTLA-4 inhibitor) have not shown a survival benefit in patients with CRPC. Other immunological approaches have also not given clear results, which has indirectly prevented breakthrough for this type of therapeutic strategy into clinical use. Currently, the only approved form of immunotherapy for patients with CRPC is a cell-based medicine, but it is only available to patients in some parts of the world. Based on the experience gained from recently completed clinical research on immunotherapy with dendritic cell-based immunohybridomas, the aHyC dendritic cell vaccine, for patients with CRPC, we highlight the current status, and possible alternatives that should be considered in the future.

Osteoarthritis and rheumatoid arthritis are two of the most common chronic inflammatory joint diseases, for which there remains a great clinical need to develop safer and more efficacious pharmacological treatments. The pathology of both osteoarthritis and rheumatoid arthritis involves multiple tissues within the joint, including the synovial joint lining and the bone, as well as the articular cartilage in osteoarthritis. In this review, we discuss the potential for the development of oligonucleotide therapies for these disorders by examining the evidence that oligonucleotides can modulate the key cellular pathways that drive the pathology of the inflammatory diseased joint pathology as well as evidence in preclinical in vivo models that oligonucleotides can modify disease progression.

This paper examines dynamic identity, as it pertains to the IoT; and explores the practical implementation of a mitigation to some of the key weaknesses of a conventional dynamic identity model. This paper explores human-centric and machine-based observer approaches for confirming device identity, permitting automated identity confirmation for deployed systems. It also assesses the advantages of dynamic identity in the context of identity revocation permitting secure change of ownership for IoT devices. The paper explores use-cases for human and machine-based observation for authentication of device identity when devices join a C2 network, and considers the relative merits for these two approaches for different types of system.

Carbohydrates mediate a wide range of biological interactions, and understanding these processes benefits the development of new therapeutics. Isolating sufficient quantities of glycoconjugates from biological samples remains a significant challenge. With advances in chemical and enzymatic carbohydrate synthesis, the availability of complex carbohydrates is increasing and developing methods for stereoselective conjugation these polar head groups to proteins and lipids is critically important for pharmaceutical applications. The aim of this review is to provide an overview of commonly employed strategies for installing a functionalized linker at the anomeric position as well as examples of further transformations that have successfully led to glycoconjugation to vaccine constructs for biological evaluation as carbohydrate-based therapeutics.

Chemical variability in the components of T. vogelii essential oils from eastern Uganda was identified using principal component analysis (PCA) and Agglomerative hierarchical clustering (AHC). Based on the profiles of the compounds of farnesene family three chemotypes were found: farnesol (chemotype 1), springene (β- Springene and α-Springene) and the β-Farnesene were distinctive in chemotype 2 and a mixed variety of farnesol and the Springene. In the three cases, alkybenzenes; o-xylene, m-xylene and ethylbenzene were significant components in the oil. 1,4-dihydroxy-p-menth-2-ene, 5,9-undecadien-2-one, 6,10-dimethyl, and 3-cyclohexen-1-carboxaldehyde,3,4-dimethyl were other prominent constituents. The yields of the essential oils did not vary significantly however the chemical composition varied with harvesting time during the rainy and dry seasons. In choice repellency tests, chemotype 1 and chemotype 2 were more active against Sitophilus zeamais than mixed chemotype. Farnesol was found to be effective only at a higher concentration as a repellent against S. zeamais. However, further study that aims to optimize and standardize the varieties and harvesting period needed for recommendation to smallhold farmers.

Background: When we talk about zoonoses, it is undeniable that we have a human and animal population that has been isolated in studies over time. Besides human and animal health, zoonoses also impact the economy and society. Therefore, the integration of the analysis in this area is essential to optimize resources in public health decisions. We have new challenges in public health that we need to overcome in a more comprehensive method such as One Health. For better measures in public health, the dual burden of zoonoses seems a logical way to determine the integral impact of such diseases in society and thus take better measures to prevent and reduce the impact of these diseases. Methods: We follow the guidelines for “Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA). We search human and animal zoonoses on Embase, Ovid Medline, Scopus, Web of Science, and Google Scholar, from an unrestricted period until November 2021. For the search, we consider the Disability Adjusted Life Years (DALYs) for the human zoonotic burden and the animal zoonotic burden in monetary terms. A librarian collaborates to optimize the search string for the databases, and two reviewers screen eligible articles (first by title, then by abstracts, and finally, by full-text assessment.) For the analysis, we aim to convert the burden of zoonoses of all selected studies into the zoonotic Disability Adjusted Life Years (zDALYs) – including the human and animal components. Discussion: The study results will provide information on published studies that have accounted for the dual burden of zoonoses (both human and animal health aspects.) In addition, the synthesis of the available literature will address the knowledge gap in this area in order to know to what extent it is possible to convert the burden of human zoonoses to the animal burden of zoonoses and integrate them into a more comprehensive approach (dual burden of zoonoses.)

This study explores the current landscape of social value (SV) research in the construction industry, analyzing global trends and identifying future research directions. Using bibliometric analysis, research published in Elsevier Scopus database from 2013 to 2023 was extracted using the keywords "social value" AND "construction industry." Results indicate a focus on social value indicators, socioeconomic impacts, project management, innovative practices, and cross-sector collaborations. The study also reveals a lack of attention to SV in African countries' construction sectors. By highlighting research areas and identifying gaps, this study contributes to academic literature, suggesting the need for guidelines, particularly in developing countries, and advocating for further research on SV in the built environment and strategies for global social value creation.

The log interpretation and evaluation of Chang 8 tight sandstone reservoir in Longdong West area, Ordos Basin, is facing great challenges due to the existence of low resistivity contrast oil pays. To better guide the exploration of oil resources in this area, the characteristics and control mechanism of low resistivity contrast oil pays were studied in this research. Firstly, according to the relative value of apparent resistivity increase rate of the target formation, the reservoir was divided into two types: low resistivity contrast oil pay (LRCP) and high resistivity oil pay (HRP). Then, the reservoir characteristics were studied by comparing and analyzing the experimental data, formation water data and logging data collected from the two reservoir types. On this basis, the control mechanism of LRCP was studied and summarized into reservoir micro factors and regional macro factors, respectively. It is found that the reservoir rock composition between LRCP and HRP is basically the same. Compared with HRP reservoir, the average porosity and permeability of LRCP reservoir are relatively better, but the pore structure is relatively poorer because of the high content of micro pores. When the filling pressure of crude oil is sufficient enough, the high oil saturation can be formed in LRCP reservoir. The high irreducible water saturation and high formation water salinity are the main micro factors reduced the reservoir resistivity. Besides, the difference of hydrocarbon expulsion capacity of source rock and the regional difference of formation water salinity are the macro factors influenced the distribution of LRCP and HRP from vertical and horizontal of the region. The formation of LRCP is controlled by the comprehensive influence of reservoir micro factors and regional macro factors. And the comprehensive consideration of the influence of micro factors and macro factors on LRCP is suggested during the log interpretation and evaluation.

Background and Objectives: The purpose of this study was to investigate whether early surfactant administration affects the status of ductus arteriosus (DA) in preterm infants ≤32 weeks of gestational age (GA) within 24 hours of birth. Materials and Methods: It is a prospective study conducted from March 15, 2022, to November 15, 2023, in a tertiary academic center. In-born infants ≤ 32 weeks of gestation (n = 88) were enrolled. The study group was further divided into surfactant (n=44) and non-surfactant (n=44) subgroups. Results: 76% of the preterm infants who received surfactant therapy (RRR = 0.839) recorded an increase in Kindler Score at 24 hours of life (1-RR = 1-0.24 = 76%). Surfactant administration was significantly associated with decreased pre-ductal diastolic pressure (29.9 mmHg vs. 34.8 mmHg, p=0.0231), post-ductal diastolic pressure (28.7 mmHg vs. 32.2 mmHg, p=0.0178) pre-ductal MAP (41.6 mmHg vs. 46.5 mmHg, p=0.0210) post-ductal MAP (41.0 mmHg vs. 45.3 mmHg, p=0.0336). There were no significant changes in ductus arteriosus parameters at 24 hours of life. Conclusions: Early surfactant administration does not affect the status of ductus arteriosus in preterm infants ≤32 weeks of gestational age at 24 hours of life.

It is unclear how maternal glycemic status and maternal iodine status influence birth weight in mild-to-moderate iodine deficiency (ID). We studied the association between birth weight and both maternal glucose levels and iodine intake in pregnant women with mild-to-moderate ID. Glucose values were assessed using a glucose challenge test (GCT), non-fasting glucose before delivery; iodine status was assessed using an iodine food frequency questionnaire, serum thyroglobulin (Tg) and urinary iodine concentrations (UIC). Thyroid antibodies and free thyroxine (FT4) were measured. Obstetric and an-thropometric data were also collected. Large for gestational age (LGA) was predicted using a Cox proportional hazards model with multiple confounders. Tg>13g/L was in-dependently associated with LGA (adjusted hazard ratio = 3.4, 95% CI: 1.4–10.2, p=0.001). Estimated iodine intake correlated with FT4 among participants reporting io-dine-containing supplements (ICS) after adjusting for confounders (β = 0.4 95 %CI: 0.0002-0.0008, p=0.001). Newborn weight percentiles were inversely correlated with maternal FT4 values (β=-0.2 95 %CI:-0.08 - -56.49, p=0.049). We conclude that in mild-to-moderate ID regions, maternal insufficient iodine status may increase LGA risk. Iodine status and ICS intake may modify the effect maternal dysglycemia has on offspring weight.

“Sustainable development” (SD) is a term that has gained prominence in the international dia-logue around development. Many, however, continue to misunderstand the concept, its principles, its development, and its operationalization. The wide variety of definitions and interpretations of SD lean toward institutional and social prerogatives rather than unifying the essence of the con-cept, which is rooted in conventional ideas and values. By delving deeper into the paradigm, its history, and its potential, this research sought to contribute to ongoing discussions about sus-tainable development. The literature was thoroughly analyzed using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020. This historical and conceptual analysis sought to update the body of knowledge on sustainability and sustainable development by examining the metaphorical and epistemological underpinnings of SD’s various definitions, evolutions, and principles. The research concludes and argues that intergenerational justice, based on the environment, economy, and society, is the central tenet of the entire sustainable develop-ment argument. The focus and consideration of interested parties are moving from the protection of the environment to social sustainability. The future of SD is likely to continue to focus much more on the social pillar and integrate digitalization in implementing the SD concept, especially in the fourth industrial revolution. To promote ethical human behavior and activities worldwide, at national and local levels, decision-makers must be continuously cognizant of the interdepend-encies, direct interactions, and balance between the three core constructs of SD.

Hepatocellular Carcinoma (HCC) continues to pose a substantial global health challenge due to its high incidence and limited therapeutic options. In recent years, the Janus Kinase (JAK) and Signal Transducer and Activator of Transcription (STAT) pathway has emerged as a critical signaling cascade in HCC pathogenesis. The review commences with an overview of the JAK/STAT pathway, delving into the dynamic interplay between the JAK/STAT pathway and its numerous upstream activators, such as cytokines and growth factors enriched in pathogenic livers afflicted with chronic inflammation and cirrhosis. This paper also elucidates how the persistent activation of JAK/STAT signaling leads to diverse oncogenic processes during hepatocarcinogenesis, including uncontrolled cell proliferation, evasion of apoptosis, and immune escape. In the context of therapeutic implications, this review summarizes recent advancements in targeting the JAK/STAT pathway for HCC treatment. Preclinical and clinical studies investigating inhibitors and modulators of JAK/STAT signaling are discussed, highlighting their potential in suppressing the deadly disease. The insights presented herein underscore the necessity for continued research into targeting the JAK/STAT signaling pathway as a promising avenue for HCC therapy.

The Covid-19 pandemic and the initial focus on handwashing measures have again highlighted the importance of water access as an essential service in protecting human health. Yet, especially in southern cities, uneven geographies of water access – often mediated by fragmented and unequal infrastructure systems – may hamper the fight against infectious diseases. The spread of SARS-CoV-2 presented a dilemma for water providers as well as residents in water-deprived urban areas as they had to adhere to new hygiene standards and requirements, despite limited access to basic water infrastructure. Therefore, a deeper understanding of pandemic urban waterscapes – the infrastructure and governance systems as well as everyday practices and technologies – is necessary for ongoing debates on (post-) pandemic or zoonotic cities. In our paper, we focus on changes in urban (water) governance and government water projects in Nairobi since early 2020. We show that Covid-19 has contributed to changes in Nairobi’s waterscape but only in conjunction with recent changes in the city’s overall governance structure. However, if these waterscape changes lead to greater equity in water access, and if they have helped to curb the spread of SARS-CoV-2, is more than questionable.

The Coronavirus disease (COVID-19) caused by a novel coronavirus, SARS-CoV-2, has been declared as a global pandemic. Due to its infection rate and severity, it has emerged as one of the major global threats of the current generation. To support the current combat against the disease, this research aims to propose a Machine Learning based pipeline to detect the COVID-19 infection using the lung Computed Tomography scan images (CTI). This implemented pipeline consists of a number of sub-procedures ranging from segmenting the COVID-19 infection to classifying the segmented regions. The initial part of the pipeline implements the segmentation of the COVID-19 affected CTI using Social-Group-Optimization and Kapur’s Entropy thresholding, followed by k-means clustering and morphology-based segmentation. The next part of the pipeline implements feature extraction, selection and fusion to classify the infection. PCA based serial fusion technique is used in fusing the features and the fused feature vector is then employed to train, test and validate four different classifiers namely Random Forest, k-Nearest Neighbors (KNN), Support Vector Machine with Radial Basis Function, and Decision Tree. Experimental results using benchmark datasets show a high accuracy (> 91%) for the morphology-based segmentation task and for the classification task the KNN offers the highest accuracy among the compared classifiers (> 87%). However, this should be noted that this method still awaits clinical validation, and therefore should not be used to clinically diagnose the ongoing COVID-19 infection.

The flavin derivatives 10‐methyl‐isoalloxazine (MIA) and 6-fluoro-10-methyl-isoalloxazine (6F-MIA) were encapsulated in two alternative metal-organic frameworks, (MOFs) MIL53(Al) and MOF5, using a post-synthetic, diffusion-based incorporation into microcrystalline MIL-53 powders and an in-situ approach during the MOF-5 synthesis. The flavin@MOF composites were analyzed by powder X-ray diffractometry (PXRD), N2-sorption studies and time-resolved fluorescence spectroscopy and microscopy. The maximum amount of flavin dye incorporation is 3.9 wt% for MIA@MIL-53(Al) and 1.5 wt% for 6F-MIA@MIL-53(Al), 0.85 wt% for MIA@MOF-5 and 27.6 wt% for 6F-MIA@MOF-5. The emission wavelengths of all flavin@MOF composites mostly correspond to the wavelengths of the flavins in solution. Time-resolved spectroscopy showed that the fluorescence lifetime of flavin@MOF of 2-4.7 ns also corresponds to those in solution but is significantly prolonged compared to the solid flavin dyes with less than 1 ns, thereby confirming the concept of "solid solutions" for dye@MOF composites. The fluorescence quantum yield (ΦF) of the flavin@MOF composites is about half of the solution but is significantly higher compared to the solid flavin dyes. Both the fluorescence lifetime and quantum yield of flavin@MOF decrease with the flavin loading in MIL-53. Theoretical calculations using plane-wave and QM/MM methods are in good correspondence with the experimental results and explain the electronic structures as well as the photoluminescence/photophysical properties of crystalline MIA and the flavin@MOF composites. In the solids flavins π-stacking interactions of the molecules lead to a charge transfer state with low oscillator strength resulting in aggregation-caused quenching (ACQ) with low lifetimes and quantum yields. In the MOF pores the flavin molecules are separated and the computed MIA@MOF structures do not find π-stacking interactions to the pore walls but only weak van-der-Waals contacts which reasons the enhanced fluorescence lifetime and quantum yield of the flavins in the composites compared to their neat solid state. The flexible MOF MIL-53(Al) was optimized essentially to the experimental large-pore form in the guest-free state with QuantumEspresso (QE) and with MIA molecules in the pores the structure contracted to close to the experimental narrow-pore form which was also confirmed by PXRD. In summary, the incorporation of flavins in MOFs yields solid-state materials with enhanced rigidity, stabilized conformation and reduced aggregations of the flavins, leading to increased fluorescence lifetime and quantum yield as controllable photo-luminescent and photo-physical properties.

Traditional gene set enrichment analysis falters when applied to large genomic domains, where neighboring genes often share functions. This spatial dependency creates misleading enrichments, mistaking mere physical proximity for genuine biological connections. Here we present Spatial Adjusted Gene Ontology (SAGO), a novel cyclic permutation-based approach, to tackle this challenge. SAGO separates enrichments due to spatial proximity from genuine biological links by incorporating the genes' spatial arrangement into the analysis. We applied SAGO to various datasets in which the identified genomic intervals are large, including replication timing domains, H3K9me3 and H3K27me3 large domains, HiC compartments and lamina associated domains (LADs). Intriguingly, applying SAGO to prostate cancer samples with large copy number alteration (CNAs) domains eliminated most of the enriched GO terms, thus helping to accurately identify biologically relevant gene sets linked to oncogenic processes, free from spatial bias.

An imbalance in any metabolic system can be traced to its homeostasis. When homeostatic environment is not attainable then there will be a response from the body. A new shift has emerged, “the negative feedback effect of high fructose consumption;” more pain than gain. The human metabolic system daily combat fructose sugar metabolism which emanates from high consumption. This inadvently lead to a chronological series of complications arising from the feedback. These feedbacks play pivotal roles in skeletal muscle damage and other body frameworks, it also fosters toxic advanced glycation end products (AGEs), factors that impose and inflict damaging effects to the body`s energy currency and serious threat to health. These damages are missed or overlooked because of early nonspecific physiological symptoms. High level of fructose has both long- and short-term effects on human metabolic processes. These effects which are majorly through the production of reactive oxygen species (ROS) and other free radicals, are felt in the disruption of biomolecules such as causing DNA mutation, lipid peroxidation etc. these effects in turn lead to various diseases such as cancer, diabetes, atherosclerosis, and other health issues. In this review, we will focus on the damaging effects this sugar has on human health and the present solutions being applied. We will also look at the next step in combatting and controlling these negative feedbacks.

The Herpes Virus Entry Mediator (HVEM) delivers a negative signal to T cells mainly through the B and T Lymphocyte Attenuator (BTLA) molecule and thus, could represent a novel immune checkpoint during an anti-tumor immune response. A formal demonstration that HVEM can be targeted for cancer immunotherapy is however still lacking. Here, we first show that HVEM and BTLA were associated to a worse prognosis in patients with prostate adenocarcinomas, indicating a detrimental role for this pair of molecule during prostate cancer progression. We then show that a monoclonal antibody to human HVEM significantly impacted the growth of a prostate cancer cell line in immuno-compromised NOD.SCID.gc-null mice reconstituted with human T cells. Using CRISPR/Cas9, we showed that HVEM expression by the tumor was mandatory to observe the therapeutic effect. Mechanistically, tumor control was dependent on CD8+ T cells and was associated to an increase in the proliferation and number of tumor-infiltrating leukocytes. Accordingly, the expression of genes belonging to various T cell activation pathways were enriched in tumor infiltrating leukocytes, whereas genes associated with immuno-suppressive pathways were decreased, possibly resulting in modifications of leukocyte adhesion and motility. Finally, we developed a simple in vivo assay in humanized mice to directly demonstrate that HVEM was an immune checkpoint for T-cell mediated tumor control. Our results show that targeting HVEM is a promising strategy for prostate cancer immunotherapy.

Post-translational modifications (PTMs) of histone residues shape the landscape of gene expression by modulating the dynamic process of RNAPII transcription. The contribution of particular histone modifications to the definition of distinct RNAPII transcription stages remains poorly characterized in plants. Chromatin Immuno-precipitation combined with next-generation sequencing (ChIP-seq) resolves the genomic distribution of histone modifications. Here, we review histone PTM ChIP-seq data in Arabidopsis thaliana and find support for a Genomic Positioning System (GPS) that guides RNAPII transcription. We review the roles of histone PTM “readers”, “writers” and “erasers”, with a focus on the regulation of gene expression and biological functions in plants. The distinct functions of RNAPII transcription during the plant transcription cycle may in part rely on the characteristic histone PTMs profiles that distinguish transcription stages.

Financial literacy is a path to sustainability and has an important role in ensuring the financial sustainability of individuals, families, enterprises and national economies. The level of these economic indicators such as debt, payment discipline, savings and financial management all translate into prosperity or insolvency and bankruptcy and result partially from financial literacy. The higher the level of financial literacy, especially of young people, the more favorable the level of economic indicators, which translates into the economy and sustainable development. However, despite many years of research, there is still a lack of a uniform and coherent definition of financial literacy, as well as methods for measuring it. This study offers a small step forward, presenting the authors’ own view of the concept of financial literacy, the conceptualizations of financial literacy and methods used for investigating. Moreover, they present the results of a survey conducted on the financial behaviour, financial attitudes, and financial knowledge of the Polish youth and compare this to a PISA study on 15-year-old students. Results demonstrate a good, and partially very good, level of financial literacy among young Poles, showing that they are rational in their financial decision making.

Measurement-Device-Independent Quantum Key Distribution (MDI-QKD) is a two-photon protocol devised to eliminate eavesdropping attacks that interrogate or control the detector in realized quantum key distribution systems. In MDI-QKD, the measurements are carried out by an untrusted third party, and the measurement results are announced openly. Knowledge or control of the measurement results gives the third party no information about the secret key. Error-free implementation of the MDI-QKD protocol requires the crypto-communicating parties, Alice and Bob, to independently prepare and transmit single photons that are physically indistinguishable, with the possible exception of their polarization states. In this paper, we apply the formalism of quantum optics and Monte Carlo simulations to quantify the impact of small errors in wavelength, bandwidth, polarization and timing between Alice's photons and Bob's photons on the MDI-QKD quantum bit error rate (QBER). Using published single-photon source characteristics from two-photon interference experiments as a test case, our simulations predict that the finite tolerances of these sources contribute (4.04+/-20/N_sifted) to the QBER in an MDI-QKD implementation generating an N_sifted-bit sifted key.

Non-alcoholic fatty liver disease (NAFLD), a frequent complication of metabolic syndrome and visceral obesity, is characterized by marked accumulation of lipids in hepatocytes, accompanied by oxidant stress. In a substantial minority of cases, this progresses to steatohepatitis, which in turn can lead to life-threatening hepatic fibrosis and/or hepatocarcinogenesis. This essay analyzes the molecular biology underlying fat accumulation and oxidant stress in NAFLD, and identifies targets that can be addressed by nutraceutical or dietary measures. Nutraceuticals with potential for prevention or control of NAFLD – as suggested on theoretical grounds, and borne out by experience in rodent studies and/or clinical trials - include ferulic acid, melatonin, methylnicotinamide, tetrahydrocurcumin, nicotinamide riboside, carnosic acid, urolithin A, quercetin, high-dose biotin, citrulline, astaxanthin, long-chain omega-3 fatty acids, berberine, lipoic acid, silibinin, N-acetylcysteine, taurine, capsaicin, spermidine, spirulina, and carnitine. Some of these agents can also address the NLRP3 inflammasome activation and transforming growth factor-β signaling that play a role in driving the transition to steatohepatitis and fibrosis. In addition, soy isoflavones, via estrogen receptor-beta agonism, have anti-fibrotic potential, and supplemental glycine may blunt the contribution of Kupffer cells to the progression of NAFLD. Whole-food plant-based diets of modest protein content, owing to their impact on hormones such as fibroblast growth factor 21 and adiponectin, as well as on the obesity and metabolic syndrome underlying NAFLD, may also be protective in this syndrome. There is considerable potential for complex medical foods or nutraceutical supplementation regimens of rational design to aid prevention and control of NAFLD.

Background Homologous recombination deficiency (HRD) evolved into a major diagnostic marker in high grade ovarian cancer (HGOC) predicting the response to poly (Adenosine diphosphate-ribose) polymerase inhibitors (PARPi) and also platinum-based therapy. In addition to HRD, the type of peritoneal tumor spread influences treatment response and patient survival; miliary type tumor spread has poorer predicted outcome than non-miliary type tumor spread. Methods Known methods for HRD assessment were adapted for our technical requirements and the Predictive-value Integrated Genomic Instability Score (PIGIS) for HRD assessment evolved as an outcome. PIGIS was validated in HGOC samples from 122 patients. We used PIGIS to analyze whether the type of tumor spread correlated with HRD status and whether this had an impact on survival. Results We demonstrated that PIGIS can discriminate HRD positive from HRD negative samples. Tumors with miliary tumor spread are HRD negative and have a very bad prognosis with a progression free survival (PFS) of 15.6 months and an overall survival (OS) of 3.9 years. However, HRD negative non-miliary spreading tumors in our cohort had a much better prognosis (PFS 35.4 months, OS 8.9 years), similar to HRD positive tumors (PFS 34.7 months, OS 8.9 years). Conclusions Our results indicate that in a predominantly PARPi naïve cohort the type of tumor spread might be a better predictor of response to platinum sensitivity than HRD. It remains to be determined, whether this also applies for sensitivity to PARPi.