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Global issues such as environmental problems and food security are currently of concern to all of us. Circular bioeconomy is a promising approach towards resolving these global issues. The production of bioenergy and biomaterials can sustain the energy–environment nexus as well as substitute the devoid of petroleum as the production feedstock, thereby contributing to a cleaner and low carbon environment. In addition, assimilation of waste into bioprocesses for the production of useful products and metabolites lead towards a sustainable circular bioeconomy. This review aims to highlight the waste biorefinery as a sustainable bio-based circular economy, and, therefore, promoting a greener environment. Several case studies on the bioprocesses utilising waste for biopolymers and bio-lipids production as well as bioprocesses incorporated with wastewater treatment are well discussed. The strategy of waste biorefinery integrated with circular bioeconomy in the perspectives of unravelling the global issues can help to tackle carbon management and greenhouse gas emissions. A waste biorefinery–circular bioeconomy strategy represents a low carbon economy by reducing greenhouse gases footprint, and holds great prospects for a sustainable and greener world.

As the Internet of Things (IoT) expands, robust tools for assessing privacy risk are increasingly critical. This research introduces a quantitative framework for evaluating IoT privacy risks, centered on two algorithmically derived scores: the Personalized Privacy Assistant (PPA) score and the PrivacyCheck score, both developed by the Center for Identity at The University of Texas. We analyze the correlation between these scores across multiple types of sensitive data—including email, social security numbers, and location—to understand their effectiveness in detecting privacy vulnerabilities. Our approach leverages Bayesian networks with cycle decomposition to capture complex dependencies among risk factors and applies entropy-based metrics to quantify informational uncertainty in privacy assessments. Experimental results highlight the strengths and limitations of each tool and demonstrate the value of combining data-driven risk scoring, information-theoretic analysis, and network modeling for privacy evaluation in IoT environments.

Tgf-β1 contributes to fibrosis during chronic injury by abrogating Tnf-directed apoptosis of fibro/adipogenic progenitor cells during muscle regeneration Depending on the inflammatory milieu, injury can result either in a tissue's complete regeneration or in its degeneration and fibrosis, the latter of which could potentially lead to permanent organ failure. Yet how inflammatory cells regulate matrix-producing cells involved in the reparative process is unknown. Here we show that in acutely damaged skeletal muscle, sequential interactions between multipotent mesenchymal progenitors and infiltrating inflammatory cells determine the outcome of the reparative process. We found that infiltrating inflammatory macrophages, through their expression of tumor necrosis factor (TNF), directly induce apoptosis of fibro/adipogenic progenitors (FAPs). In states of chronic damage, however, such as those in mdx mice, macrophages express high levels of transforming growth factor β1 (TGF-β1), which prevents the apoptosis of FAPs and induces their differentiation into matrix-producing cells. Treatment with nilotinib, a kinase inhibitor with proposed anti-fibrotic activity, can block the effect of TGF-β1 and reduce muscle fibrosis in mdx mice. Our findings reveal an unexpected anti-fibrotic role of TNF and suggest that disruption of the precisely timed progression from a TNF-rich to a TGF-β−rich environment favors fibrotic degeneration of the muscle during chronic injury.

Anaerobic digestion of food waste appears promising to generate biogas in the context of the growing energy demand and the circular economy. In particular, anaerobic digestion causes less air and solid waste pollution compared to incineration, gasification and pyrolysis. Actual research on biogas production using food waste focuses on the performance of substrates such as manure and bacteria, yet few investigations evaluate the impact of anaerobic digestion on the environment. Here, we review the steps of anaerobic digestion, factors that influence the process, and food waste as main and co-substrate to increase biogas yield. High metabolic activity of anaerobes is optimized by controlling temperature, pH, retention time, carbon-to-nitrogen ratio, volatile fatty acid and organic loading rate. We discuss the effect of pre-treatments such as biological, thermal, chemical and mechanical treatments, on anaerobic digestion performance. The impacts of food waste treatments on the environment are compared by life cycle analysis.

Over time, the many different ways in which we collect and use data have become more complex as we communicate and interact with an ever-increasing variety of modern technologies. Although people often say they care about their privacy, they do not have a deep understanding of what devices around them are collecting their identity information, what identity information is being collected, and how that collected data will affect them. This research is dedicated to developing a personalized privacy assistant to help users regain control, understand their own identity management, and process and simplify the large amount of information from the Internet of Things (IoT). This research constructs an empirical study to obtain the comprehensive list of identity attributes that are being collected by IoT devices. We build a statistical model to simulate the identity theft and to help calculate the privacy risk score based on the identity attributes collected by IoT devices. We discuss how well each feature of our Personal Privacy Assistant (PPA) works and compare the PPA and related work to a list of fundamental features for privacy protection.

PurposeMounting fiscal constraints and increasing complexity of public services have led governments to search for alternative service delivery mechanisms. The public–private partnership (PPP) is one type of service arrangement in which the public and private sectors enter into a long-term cooperative relationship for the purpose of delivering a public good or service. Despite increasing private sector participation in developing nations, there is a need for more systematic assessment of PPPs in such countries. The purpose of this paper is to explore the factors that affect the adoption and implementation of projects in the context of developing countries.Design/methodology/approachA multiple case holistic design is employed to analyze 19 select projects across several developing countries to identify and pool clusters of variables that facilitate or impede PPPs.FindingsThe results indicate five broad categories of political, economic, legislative, financial and management requisites.Research limitations/implicationsA limitation of this research is that the cases were not selected at random. However, the projects are spread across several areas such as public health, public utilities, public works, transportation and water/wastewater infrastructure in different countries. This allows the authors to examine how the common factors apply across different contextual settings.Originality/valueThis paper seeks to contribute to the literature by examining several developing countries to identify and pool clusters of variables that facilitate or impede the effective implementation of PPP projects in the context of such regions.

A bstract We propose a manifestly supersymmetric generalization of the solvable T T ¯ deformation of two-dimensional field theories. For theories with (1 , 1) and (0 , 1) supersymmetry, the deformation is defined by adding a term to the superspace Lagrangian built from a superfield containing the supercurrent. We prove that the energy levels of the resulting deformed theory are determined exactly in terms of those of the undeformed theory. This supersymmetric deformation extends to higher dimensions, where we conjecture that it might provide a higher-dimensional analogue of T T ¯ , producing supersymmetric Dirac or Dirac-Born-Infeld actions in special cases.

Adult tissue-resident macrophages (RMs) are either maintained by blood monocytes or through self-renewal. While the presence of a nurturing niche is likely crucial to support the survival and function of self-renewing RMs, evidence regarding its nature is limited. Here, we identify fibro-adipogenic progenitors (FAPs) as the main source of colony-stimulating factor 1 (CSF1) in resting skeletal muscle. Using parabiosis in combination with FAP-deficient transgenic mice ( Pdgfrα CreERT2 × DTA) or mice lacking FAP-derived CSF1 ( Pdgfrα CreERT2 × Csf1 flox/null ), we show that local CSF1 from FAPs is required for the survival of both TIM4 - monocyte-derived and TIM4 + self-renewing RMs in adult skeletal muscle. The spatial distribution and number of TIM4 + RMs coincide with those of dipeptidyl peptidase IV (DPPIV) + FAPs, suggesting their role as CSF1-producing niche cells for self-renewing RMs. This finding identifies opportunities to precisely manipulate the function of self-renewing RMs in situ to further unravel their role in health and disease. Although macrophages infiltrating the skeletal muscles are known to be important in muscle growth and repair, much less is known about muscle-resident macrophages. Here, the authors identify a fibro-adipogenic progenitor niche involved in the maintenance of skeletal muscle-resident macrophages.

The increase in the prevalence of multidrug-resistant Acinetobacter baumannii (MDRAB) strains is a serious public health concern. Antimicrobial peptides (AMPs) are a possible solution to this problem. In this study, we examined whether AMPs could be derived from phage endolysins. We synthesized four AMPs based on an amphipathic helical region in the C-terminus of endolysin LysAB2 encoded by the A . baumannii phage ΦAB2. These peptides showed potent antibacterial activity against A . baumannii (minimum inhibitory concentration, 4–64 μM), including some MDR and colistin-resistant A . baumannii . Of the four peptides, LysAB2 P3, with modifications that increased its net positive charge and decreased its hydrophobicity, showed high antibacterial activity against A . baumannii but little haemolytic and no cytotoxic activity against normal eukaryotic cells. The results of electron microscopy experiments and a fluorescein isothiocyanate staining assay indicated that this peptide killed A . baumannii through membrane permeabilization. Moreover, in a mouse intraperitoneal infection model, at 4 h after the bacterial injection, LysAB2 P3 decreased the bacterial load by 13-fold in ascites and 27-fold in blood. Additionally, LysAB2 P3 rescued sixty percent of mice heavily infected with A . baumannii from lethal bacteremia. Our results confirmed that bacteriophage endolysins are a promising resource for developing effective AMPs.

The rising prevalence and swift spread of multidrug-resistant gram-negative bacteria (MDR-GNB), especially Klebsiella pneumoniae (KP), present a critical global health threat highlighted by the World Health Organization, with mortality rates soaring approximately 50% with inappropriate antimicrobial treatment. This study aims to advance a novel strategy to develop an artificial intelligence-clinical decision support system (AI-CDSS) that combines machine learning (ML) with matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS), aiming to significantly improve the accuracy and speed of diagnosing antibiotic resistance, directly addressing the grave health risks posed by the widespread dissemination of pan drug-resistant gram-negative bacteria across numerous countries. A comprehensive dataset comprising 165,299 bacterial specimens and 11,996 KP isolates was meticulously analyzed using MALDI-TOF MS technology. Advanced ML algorithms were harnessed to sculpt predictive models that ascertain resistance to quintessential antibiotics, particularly levofloxacin and ciprofloxacin, by using the amassed spectral data. Our ML models revealed remarkable proficiency in forecasting antibiotic resistance, with the random forest classifier emerging as particularly effective in predicting resistance to both levofloxacin and ciprofloxacin, achieving the highest area under the curve of 0.95. Performance metrics across different models, including accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and F -score, were detailed, underlining the potential of these algorithms in aiding the development of precision treatment strategies. This investigation highlights the synergy between MALDI-TOF MS and ML as a beacon of hope against the escalating threat of antibiotic resistance. The advent of AI-CDSS heralds a new era in clinical diagnostics, promising a future in which rapid and accurate resistance prediction becomes a cornerstone in combating infectious diseases. Through this innovative approach, we answered the challenge posed by KP and other multidrug-resistant pathogens, marking a significant milestone in our journey toward global health security.