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Identifying genuine polymorphic variants is a significant challenge in sequence data analysis, although detecting low-frequency variants in sequence data is essential for estimating demographic parameters and investigating genetic processes, such as selection, within populations. Arbuscular mycorrhizal (AM) fungi are multinucleate organisms, in which individual nuclei collectively operate as a population, and the extent of genetic variation across nuclei has long been an area of scientific interest. In this study, we investigated the patterns of polymorphism discovery and the alternate allele frequency distribution by comparing polymorphism discovery in 2 distinct genomic sequence datasets of the AM fungus model species, Rhizophagus irregularis strain DAOM197198. The 2 datasets used in this study are publicly available and were generated either from pooled spores and hyphae or amplified single nuclei from a single spore. We also estimated the intraorganismal variation within the DAOM197198 strain. Our results showed that the 2 datasets exhibited different frequency patterns for discovered variants. The whole-organism dataset showed a distribution spanning low-, intermediate-, and high-frequency variants, whereas the single-nucleus dataset predominantly featured low-frequency variants with smaller proportions in intermediate and high frequencies. Furthermore, single nucleotide polymorphism density estimates within both the whole organism and individual nuclei confirmed the low intraorganismal variation of the DAOM197198 strain and that most variants are rare. Our study highlights the methodological challenges associated with detecting low-frequency variants in AM fungal whole-genome sequence data and demonstrates that alternate alleles can be reliably identified in single nuclei of AM fungi.

Arbuscular mycorrhizal (AM) fungi form an obligate symbiosis with the roots of the majority of land plants and are found in all terrestrial ecosystems. The source and structure of genetic variation in AM fungi has remained an enigma due to difficulties in the axenic cultivation and generation of high-quality genome assemblies from most species. Furthermore, how AM fungi survive long-term without a known single nucleus per cell stage in their life cycle is puzzling. Purifying selection acting at the nuclear level has been hypothesized as a mechanism to purge deleterious mutations. In this study, we aimed to characterize both intra- and inter-organismal genetic variation in AM fungi by analyzing genomic information from individual nuclei of three strains of two species in the genus Claroideoglomus. We observed overall low levels of genetic variation within the strains, most of which represent rare variants and with average dN/dS ratios within nuclei indicating that these are kept at low frequency by purifying selection. We also observed sites that are maintained as polymorphic across both strains and species, and discuss different models to explain this pattern. The results in this study affirm our conceptual understanding that nuclei in AM fungal strains function as populations of asexually reproducing units with strong signals of purifying selection on nuclei within the strains.

Arbuscular mycorrhizal (AM) fungi are obligate root symbionts that play crucial roles in terrestrial ecosystems by aiding land plants in nutrient and water uptake in exchange for photosynthetically fixed carbon. Their nuclear genomes exhibit considerable variation in size across families, ranging from approximately 40 megabases in Paraglomeraceae to several hundred megabases in Gigasporaceae. Notable variations in nuclear size have also been observed in species within Diversisporales, suggesting that genome size may also vary within species. Three species in this order also stood out in our earlier work with broad taxonomic sampling of AM fungi, by exhibiting a distinct separation of forward-scattered light during nuclei sorting with Fluorescence-Activated Cell Sorting (FACS), indicating that nuclei of two distinct sizes existed within these species. AM fungi are multinucleate with coenocytic hyphae and mating is thought to be controlled by allele variation in the mating-type (mat) locus. Species where all nuclei within the cytoplasm contain only one mat allele are referred to as homokaryotic while strains with nuclei carrying different mat alleles are called heterokaryotic.

Here, we compare genome characteristics of the two nuclear sizes by investigating ploidy level, mat alleles across nuclei, and the SNP density of three species in Diversisporales. Irrespective of nuclei size estimated from FACS sorting, all nuclei were found to be haploid. All three species exhibited low intra-organismal genetic variation, and identical mat allele were found when the mat locus was identified across single nuclei within the three species. We thus conclude that the three strains are homokaryotic for the mating type. Interestingly, we observed substantial structural divergence of the putative mat locus in two of the three species, corroborating a previous report where the HD1-like and HD-2 mat genes were found on different genomic contigs in strains of Gigasporaceae. Together, our analysis suggests that the observed different nuclear sizes in species within Diversisporales are due to asynchronous nuclear division within spores.

Arbuscular mycorrhizal (AM) fungi are essential to terrestrial ecosystems, yet their genomic study is fraught with challenges. Traditional sequencing methods are often limited by difficulties in obtaining pure, high-quality DNA due to the obligate symbiosis of AM fungi with land plants. Here, we explore the previously developed wokflow to sequence AM fungal genomes using single nuclei instead of whole DNA extracts. We present a refined workflow that involves the isolation of spores, sorting of individual nuclei through fluorescence-activated cell sorting (FACS), whole genome amplification (WGA), sequencing, and assembly. We assess variability in nuclear sorting accuracy across different

AM fungal taxa, as well as diversity in genome assembly sizes and completeness across different AM fungal families. This method assessment paves the way for the continuous development towards comprehensive genomic studies of AM fungi without the need for extensive culturing.

Gamma-ray bursts (GRBs) have long been considered a possible source of high-energy neutrinos. While no correlations have yet been detected between high-energy neutrinos and GRBs, the recent observation of GRB 221009A-the brightest GRB observed by Fermi-GBM to date and the first one to be observed above an energy of 10 TeV-provides a unique opportunity to test for hadronic emission. In this paper, we leverage the wide energy range of the IceCube Neutrino Observatory to search for neutrinos from GRB 221009A. We find no significant deviation from background expectation across event samples ranging from MeV to PeV energies, placing stringent upper limits on the neutrino emission from this source.

Large neutrino detectors like IceCube monitor for core-collapse supernovae using low-energy (MeV) neutrinos with a detection reach from a supernova neutrino burst to the Magellanic Cloud. However, some models predict the emission of high-energy neutrinos of GeV-TeV from core-collapse supernovae through the interaction of ejecta with circumstellar material with energies of TeV-PeV produced through choked jets. In this paper, we explore the detection horizon of IceCube for core-collapse supernovae using high-energy neutrinos from these models. We examine the potential of two high-energy neutrino data samples from IceCube, one that performs best in the northern sky and one that has better sensitivity in the southern sky. We demonstrate that, by using high-energy neutrinos from core-collapse supernovae, the detection reach can be extended to the megaparsec range, far beyond what is accessible through low-energy neutrinos. Looking ahead to IceCube-Gen2, this reach will be extended considerably.

The IceCube Neutrino Observatory is uniquely sensitive to MeV neutrinos emitted during a core-collapse supernova. The Supernova Data Acquisition System (SNDAQ) monitors in real-time the detector rate deviation searching for bursts of MeV neutrinos. We present a new analysis stream that uses SNDAQ to respond to external alerts from gravitational waves detected in LIGO-Virgo-KAGRA

 GRB 221009A is the brightest Gamma Ray Burst (GRB) ever observed. The observed extremelyhigh flux of high and very-high-energy photons provide a unique opportunity to probe the predictedneutrino counterpart to the electromagnetic emission. We have used a variety of methods to searchfor neutrinos in coincidence with the GRB over several time windows during the precursor, promptand afterglow phases of the GRB. MeV scale neutrinos are studied using photo-multiplier ratescalers which are normally used to search for galactic core-collapse supernovae neutrinos. GeVneutrinos are searched starting with DeepCore triggers. These events don’t have directionallocalization, but instead can indicate an excess in the rate of events. 10 GeV - 1 TeV and >TeVneutrinos are searched using traditional neutrino point source methods which take into accountthe direction and time of events with DeepCore and the entire IceCube detector respectively. The>TeV results include both a fast-response analysis conducted by IceCube in real-time with timewindows of T0 − 1 to T0 + 2 hours and T0 ± 1 day around the time of GRB 221009A, as well asan offline analysis with 3 new time windows up to a time window of T0 − 1 to T0 + 14 days, thelongest time period we consider. The combination of observations by IceCube covers 9 ordersof magnitude in neutrino energy, from MeV to PeV, placing upper limits across the range forpredicted neutrino emission.

Numerous models predict MeV neutrino production correlated with gravitational waves from neutron star merger systems. The IceCube Neutrino Observatory is sensitive to the MeV neutrinos from bright transients such as core-collapse supernovae via the Supernova Data Acquisition system (SNDAQ). We perform a search for MeV neutrinos correlated with gravitational waves, using the data recorded by SNDAQ and the times of the gravitational wave detections. We searched in four different search windows: 0.5 s, 1.5 s, 4 s, and 10 s. We then perform a binomial test on the two populations in the catalog: those events that contain no neutron star (Binary black hole mergers) and events that contain at least one neutron star (binary neutron star mergers and black hole - neutron star mergers).

Cyanobacteria can be utilized as a platform for direct phototrophic conversion of CO₂ to produce several types of carbon-neutral biofuels. One promising compound to be produced photobiologically in cyanobacteria is isobutene. As a volatile compound, isobutene will quickly escape the cells without building up to toxic levels in growth medium or get caught in the membranes. Unlike liquid biofuels, gaseous isobutene may be collected from the headspace and thus avoid the costly extraction of a chemical from culture medium or from cells. Here we investigate a putative synthetic pathway for isobutene production suitable for a photoautotrophic host. First, we expressed α-ketoisocaproate dioxygenase from Rattus norvegicus (RnKICD) in Escherichia coli. We discovered isobutene formation with the purified RnKICD with the rate of 104.6 ​± ​9 ​ng (mg protein)^-1 min^-1 using α-ketoisocaproate as a substrate. We further demonstrate isobutene production in the cyanobacterium Synechocystis sp. PCC 6803 by introducing the RnKICD enzyme. Synechocystis strain heterologously expressing the RnKICD produced 91 ​ng ​l^-1 OD₇₅₀ ^-1 ​h^-1. Thus, we demonstrate a novel sustainable platform for cyanobacterial production of an important building block chemical, isobutene. These results indicate that RnKICD can be used to further optimize the synthetic isobutene pathway by protein and metabolic engineering efforts.

Metabolic fluxes and their control mechanisms are fundamental in cellular metabolism, offering insights for the study of biological systems and biotechnological applications. However, quantitative and predictive understanding of controlling biochemical reactions in microbial cell factories, especially at the system level, is limited. In this work, we present ARCTICA, a computational framework that integrates constraint-based modelling with machine learning tools to address this challenge. Using the model cyanobacterium Synechocystis sp. PCC 6803 as chassis, we demonstrate that ARCTICA effectively simulates global-scale metabolic flux control. Key findings are that (i) the photosynthetic bioproduction is mainly governed by enzymes within the Calvin-Benson-Bassham (CBB) cycle, rather than by those involve in the biosynthesis of the end-product, (ii) the catalytic capacity of the CBB cycle limits the photosynthetic activity and downstream pathways and (iii) ribulose-1,5-bisphosphate carboxylase/oxygenase (RuBisCO) is a major, but not the most, limiting step within the CBB cycle. Predicted metabolic reactions qualitatively align with prior experimental observations, validating our modelling approach. ARCTICA serves as a valuable pipeline for understanding cellular physiology and predicting rate-limiting steps in genome-scale metabolic networks, and thus provides guidance for bioengineering of cyanobacteria.

Understanding energy and redox homeostasis and carbon partitioning is crucial for systems metabolic engineering of cell factories. Carbon metabolism alone cannot achieve maximal accumulation of metabolites in production hosts, since an efficient production of target molecules requires energy and redox balance, in addition to carbon flow. The interplay between cofactor regeneration and heterologous production in photosynthetic microorganisms is not fully explored. To investigate the optimality of energy and redox metabolism, while overproducing alkenes-isobutene, isoprene, ethylene and 1-undecene, in the cyanobacterium Synechocystis sp. PCC 6803, we applied stoichiometric metabolic modelling. Our network-wide analysis indicates that the rate of NAD(P)H regeneration, rather than of ATP, controls ATP/NADPH ratio, and thereby bioproduction. The simulation also implies that energy and redox balance is interconnected with carbon and nitrogen metabolism. Furthermore, we show that an auxiliary pathway, composed of serine, one-carbon and glycine metabolism, supports cellular redox homeostasis and ATP cycling. The study revealed non-intuitive metabolic pathways required to enhance alkene production, which are mainly driven by a few key reactions carrying a high flux. We envision that the presented comparative in-silico metabolic analysis will guide the rational design of Synechocystis as a photobiological production platform of target chemicals.

The use of photosynthetic microbes as synthetic biology hosts for the sustainable production of commodity chemicals and even fuels has received increasing attention over the last decade. The number of studies published, tools implemented, and resources made available for microalgae have increased beyond expectations during the last few years. However, the tools available for genetic engineering in these organisms still lag those available for the more commonly used heterotrophic host organisms. In this mini-review, we provide an overview of the photosynthetic microbes most commonly used in synthetic biology studies, namely cyanobacteria, chlorophytes, eustigmatophytes and diatoms. We provide basic information on the techniques and tools available for each model group of organisms, we outline the state-of-the-art, and we list the synthetic biology tools that have been successfully used. We specifically focus on the latest CRISPR developments, as we believe that precision editing and advanced genetic engineering tools will be pivotal to the advancement of the field. Finally, we discuss the relative strengths and weaknesses of each group of organisms and examine the challenges that need to be overcome to achieve their synthetic biology potential.

Background

Bariatric surgery, Roux-en-Y gastric bypass (RYGBP) in particular, is associated with weight loss as well as low bone mineral density. Bone mineral density relies upon multiple factors, some of which are lifestyle factors. The aim of this study was to compare lifestyle factors in order to eliminate them as culprits of the suspected difference in BMD in RYGBP operated and controls.

Materials and methods

Study participants included 71 RYGBP-operated women (42.3 years, BMI 33.1 kg/m²) and 94 controls (32.4 years, BMI 23.9 kg/m²). Each completed a DEXA scan, as well as survey of lifestyle factors (e.g. physical activity in daily life, corticosteroid use, and calcium intake). All study participants were premenopausal Caucasian women living in the same area. Blood samples were taken in RYGBP-patients.

Results

BMD was significantly lower in RYGBP, femoral neck 0.98 vs. 1.04 g/cm² compared to controls, despite higher BMI (present and at 20 years of age) and similar physical activity and calcium intake. In a multivariate analysis, increased time since surgery and age were negatively associated with BMD of the femoral neck and total hip in RYGBP patients.

Conclusion

Despite similar lifestyle, RYGBP was followed by a lower BMD compared to controls. Thus, the reduced BMD in RYGBP cannot be explained, seemingly nor prevented, by lifestyle factors. As the reduction in BMD was associated with time since surgery, strict follow-up is a lifelong necessity after bariatric surgery, and especially important in younger bariatric patients.

Introduction: Apart from massive weight loss, metabolic and bariatric surgery, especially gastric bypass (RYGBP), can cause nutritional deficiencies. Proton pump inhibitors (PPI), relatively often used after RYGBP, are associated with reduced calcium absorption. We have studied the long-term impact of PPI upon calcium homeostasis among RYGBP patients. 

Methods: In the Scandinavian Obesity Surgery Registry (SOReg), 550 primary RYGBP patients, with eGFR > 60 ml/min/1.73m2, had PTH and 25-OH D levels registered at 10 years. To avoid the impact of hypovitaminosis D, those with 25-OH D >75 nmol/L were selected.

Results: At 10 years, 10.3% of patients reported continuous PPI treatment, i.e., daily use during the last month. In an age adjusted logistic regression model, continuous PPI treatment was associated with a tripled risk (OR: 3.97 [1.35-11.70]) of having a pathological PTH level (>7 pmol/L).

Conclusion: This unique study has shown a correlation between continuous PPI use and pathological PTH levels, thereby inferring that the medication may have detrimental effects upon calcium homeostasis among gastric bypass patients. The risk of having pathological PTH levels was more than tripled among those with PPI treatment, highlighting the importance of specialized follow-up while also suggesting that a limited duration of PPI treatment is preferable.

Background: By treating obesity, one of the major epidemics of this past century, through bariatric surgery, we may cause complications due to malnourishment in a growing population. At present, vitamin D deficiency is of interest, especially in patients with inferior absorption of fat-soluble nutrients after biliopancreatic diversion with duodenal switch (BPD/DS).

Methods: Twenty BPD/DS patients, approximately 4 years postoperatively, were randomized to either intramuscular supplementation of vitamin D with a single dose of 600,000 IU cholecalciferol, or a control group. Patients were instructed to limit their supplementation to 1400 IU of vitamin D and to avoid the influence of UV-B radiation; the study was conducted when sunlight is limited (December to May).

Results: Despite oral supplementation, a pronounced deficiency in vitamin D was seen (injection 19.3; control 23.2 nmol/l) in both groups. The cholecalciferol injection resulted in elevated 25[OH]D levels at 1 month (65.4 nmol/l), which was maintained at 6 months (67.4 nmol/l). This resulted in normalization of intact parathyroid hormone (PTH) levels. No changes in vitamin D or PTH occurred in the control group.

Conclusions: In BPD/DS patients, having hypovitaminosis D despite full oral supplementation, a single injection of 600,000 IU of cholecalciferol was effective in elevating vitamin D levels and normalizing levels of intact PTH. The treatment is simple and highly effective and thus recommended, especially in cases of reduced UV-B radiation.

Prostate-specific membrane antigen (PSMA) and gastrin-releasing peptide receptor (GRPR) are promising targets for molecular imaging of prostate cancer (PCa) lesions. Due to the heterogenic overexpression of PSMA and GRPR in PCa, a heterodimeric radiotracer with the ability to bind to both targets could be beneficial. Recently, our group reported the novel heterodimer BQ7800 consisting of a urea-based PSMA inhibitor, the peptide-based GRPR antagonist RM26 and NOTA chelator. The study reported herein, aimed to improve the affinity of BQ7800 towards PSMA by changing the composition of the two linkers connecting the PSMA- and GRPR-targeting motifs. Three novel heterodimeric analogues were synthesized by incorporation of phenylalanine in the functional linker of the PSMA-binding motif and/or shortening the PEG-linker coupled to RM26. The heterodimers were labeled with indium-111 and evaluated in vitro. In the competitive binding assay, BQ7812, featuring phenylalanine and shorter PEG-linker, demonstrated a nine-fold improved affinity towards PSMA. In the in vivo biodistribution study of [In-111]In-BQ7812 in PC3-pip tumor-bearing mice (PSMA and GRPR positive), the activity uptake was two-fold higher in the tumor and three-fold higher in kidneys than for [In-111]In-BQ7800. Herein, we showed that the affinity of a bispecific PSMA/GRPR heterodimer towards PSMA could be improved by linker modification.

Simple Summary Prostate cancer continues to be the most frequently diagnosed form of cancer and the leading cause of cancer-related deaths among men. For a successful treatment plan and outcome, an early diagnosis, correct staging, and monitoring of treatment response are crucial. To improve this, a radiotracer could be used to target the two most abundant proteins overexpressed in prostate cancer: prostate-specific membrane antigen (PSMA) and gastrin-releasing peptide receptor (GRPR). To date, no such heterodimeric radiotracer is used in the clinic. In this study, we have preclinically characterized and evaluated a galium-68 labelled PSMA/GRPR-targeting radiotracer for PET imaging of prostate cancer. We hope that the findings from this study will be able to contribute to designing better heterodimeric ligands, promote clinical translation of a heterodimer, and serve as a step towards a first-in-human study. The development of radioligands targeting prostate-specific membrane antigen (PSMA) and gastrin-releasing peptide receptor (GRPR) has shown promising results for the imaging and therapy of prostate cancer. However, studies have shown that tumors and metastases can express such targets heterogeneously. To overcome this issue and to improve protein binding, radioligands with the ability to bind both PSMA and GRPR have been developed. Herein, we present the preclinical characterization of [Ga-68]Ga-BQ7812; a PSMA/GRPR-targeting radioligand for the diagnostic PET imaging of prostate cancer. This study aimed to evaluate [Ga-68]Ga-BQ7812 to promote the translation of such imaging probes into the clinic. [Ga-68]Ga-BQ7812 demonstrated rapid and specific binding to both targets in a PSMA/GRPR-expressing PC3-pip cell line. Results from the biodistribution study in PC3-pip xenografted mice showed specific binding to both targets, with the highest activity uptake at 1 h pi in tumor (PSMA+/GRPR+, 10.4 +/- 1.0% IA/g), kidneys (PSMA+, 45 +/- 16% IA/g), and pancreas (GRPR+, 5.6 +/- 0.7% IA/g). At 3h pi, increased tumour-to-organ ratios could be seen due to higher retention in the tumor compared with other PSMA or GRPR-expressing organs. These results, together with low toxicity and an acceptable estimated dosimetry profile (total effective dose = 0.0083 mSv/MBq), support the clinical translation of [Ga-68]Ga-BQ7812 and represent a step towards its first clinical trial.

Prostate-specific membrane antigen (PSMA) is overexpressed in the majority of prostate cancer cells and is considered to be an important target for the molecular imaging and therapy of prostate cancer. Herein, we present the design, synthesis, and evaluation of 11 PSMA-binding radioligands with modified linker structures, focusing on the relationship between molecular structure and targeting properties. The linker design was based on 2-naphthyl-L-alanine-tranexamic acid, the linker structure of PSMA-617. X-ray crystal-structure analysis of PSMA and structure-based design were used to generate the linker modifications, suggesting that substitution of tranexamic acid could lead to interactions with Phe546, Trp541, and Arg43 within the binding cavity. After synthesis through SPPS, analogues were labelled with indium-111 and evaluated in vitro for their specific binding, affinity, and cellular retention. Selected compounds were further evaluated in vivo in PSMA-expressing tumour-bearing mice. Based on the results, 2-naphthyl-L-alanine appears to be crucial for good targeting properties, whereas tranexamic acid could be replaced by other substituents. [¹¹¹In]In-BQ7859, consisting of a 2-naphthyl-L-alanine-L-tyrosine linker, demonstrated favourable targeting properties. The substitution of tranexamic acid for L-tyrosine in the linker led to an improved tumour-to-blood ratio, highlighting [¹¹¹In]In-BQ7859 as a promising PSMA-targeting radioligand.

Assessment of model fitness is a key part of machine learning. The standard paradigm of model evaluation is analysis of the average loss over future data. This is often explicit in model fitting, where we select models that minimize the average loss over training data asa surrogate, but comes with limited theoretical guarantees. In this paper, we consider the problem of characterizing a batch of out-of-sample losses of a model using a calibration dataset. We provide finite-sample limits on the out-of-sample losses that are statistically valid under quite general conditions and propose a diagonistic tool that is simple to compute andinterpret. Several numerical experiments are presented to show how the proposed  method quantifies the impact of distribution shifts, aids the analysis of regression, and enables model selection as well as hyperparameter tuning.

Inventory control is subject to service-level requirements, in which sufficient stock levels must be maintained despite an unknown demand. We propose a data-driven order policy that certifies any prescribed service level under minimal assumptions on the unknown demand process. The policy achieves this using any online learning method along with integral action. We further propose an inference method that is valid in finite samples. The properties and theoretical guarantees of the method are illustrated using both synthetic and real-world data. 

Background/Objective

Increased intracranial pressure (ICP) is a feared secondary brain insult in neurointensive care (NIC) of traumatic brain injury (TBI). A system that predicts ICP insults before they emerge may facilitate early optimization of the physiology, which in turn may lead to that the predicted ICP insult will never occur. The aim of this study was to evaluate the performance of different AI models in predicting risk of ICP insults. 

Methods

The models were trained to predict risk of ICP insults starting within 30 minutes, using the Uppsala High Frequency TBI (UHF-TBI) dataset. A restricted dataset consisting of monitoring data only was used, and an unrestricted dataset using monitoring data as well as clinical data, demographic data and radiological evaluations. Four different model classes were compared: Gaussian Process Regression (GP), Logistic Regression (LR), Random Forest classifier (RF) and Extreme Gradient Boosted Decision Trees (XGBoost).

Results

Six hundred and two TBI patients were included (total monitoring 138 411 hours). On the task of predicting upcoming ICP insults, the GP model performed similar on the UHF-TBI dataset (sensitivity 93.2% and specificity 93.9%), as in earlier smaller studies. Using a more flexible model (XGBoost) resulted in slightly better performance (sensitivity 93.8% and specificity 94.6%). Adding more clinical variables and features further improved the performance of the models slightly (XGBoost: sensitivity 94.1% and specificity of 94.6%). Using AUROC as performance measure, the XGBoost models also performed slightly better than the other models. 

Conclusions

AI models have potential to become valuable tools for prediction of ICP insults in advance during NIC. The fact that common off-the-shelf models, such as XGBoost, performed well in predicting ICP insults opens for new possibilities, which can lead to faster advances in the field and faster clinical implementations.

A sudden increase in intracranial pressure carries severe risk for patients in neurocritical care.  Machine learning models have been developed to predict such events, but do not account for the impact of treatment decisions, reducing their utility. Causal inference can be employed to estimate the impact of treatments, supporting medical staff in attending to those patients for whom treatment is beneficial. This work investigates the feasibility of conditional average treatment effect (CATE) estimation using observational data from the Neurocritical Care Unit at Uppsala Akademiska University Hospital. We find that the selected CATE estimators have difficulties in estimating the treatment effect on a semi-synthetic data set. When deployed on the real data set, the CATE estimators show poor mutual agreement. We discuss the challenges and suggest future work to address them.

A series of benzothiadiazole-Au(I) complexes with phosphine or carbene ligands has been synthesized, structurally characterized and investigated for their photophysical properties. These are the first organometallic Au(I) complexes that contains a C-Au bond on the highly electron-deficent benzothiadiazole unit. The complexes exhibit room temperature phosphorescence in solution (Fphos = 0.03-0.06), which is notable for mononuclear Au(I) complexes without intervening alkynyl bridges. [NK1] Their emissive nature was attributed to the triplet manifold of the benzothiadiazole motif that is efficently populated due to the direct linkage of gold.  Hence, we show that direct C-auration of a weakly emissive aromatic heterocycle can gives access to efficent room temperature phosphorescence - a feature  that is  higly desirable for numerous applications within materials and life sciences.

Despite growing interest in 2,1,3-benzothiadiazole (BTD) as an integral component of many functional molecules, methods for the functionalization of its benzenoid ring have remained limited, and many even simply decorated BTDs have required de novo synthesis. We show that regioselective Ir-catalyzed C–H borylation allows access to versatile 5-boryl or 4,6-diboryl BTD building blocks, which undergo functionalization at the C4, C5, C6, and C7 positions. The optimization and regioselectivity of C–H borylation are discussed. A broad reaction scope is presented, encompassing ipso substitution at the C–B bond, the first examples of ortho-directed C–H functionalization of BTD, ring closing reactions to generate fused ring systems, as well as the generation and capture reactions of novel BTD-based heteroarynes. The regioselectivity of the latter is discussed with reference to the Aryne Distortion Model.

The 2,1,3-benzothiadiazole (BTD) unit is a prominent building block commonly used in various research areas such as optoelectronics and bioimaging. Despite its great versatility, the development of strategies to elaborate BTD has been largely neglected, including exploring its reactivity and understanding how regioselective functionalization can be used to tune the fluorescence emission. Previous focus has primarily been on C4- or C4,C7-substitutions. Here, a series of unsymmetrical mono – and disubstituted BTDs was synthesized and characterized for their photophysical properties. The reaction scope includes all six possible substituent patterns on the BTD benzoid ring (C4-, C5-, C4,C5-, C4,C6-, C4,C7- and C5,C6-substitution), which comprise arrangements that previously been synthetically challenging to access. By introducing a methoxy and/or a phenyl group we demonstrate that the emissive behavior of BTD derivatives strongly depends on the position of the substituent (s). We show that regioselective substitution on BTD can engender long-lived fluorescence and circumvent strong fluorescence quenching in polar protic solvents, which is a limitation of many previously described BTD derivatives.

Contact‐dependent growth inhibition (CDI) allows bacteria to recognize kin cells in mixed bacterial populations. In Escherichia coli, CDI mediated effector delivery has been shown to be species‐specific, with a preference for the own strain over others. This specificity is achieved through an interaction between a receptor‐binding domain in the CdiA protein and its cognate receptor protein on the target cell. But how conserved this specificity is has not previously been investigated in detail. Here, we show that class II CdiA receptor‐binding domains and their Enterobacter cloacae analog are highly promiscuous, and can allow for efficient effector delivery into several different Enterobacteriaceae species, including Escherichia, Enterobacter, Klebsiella and Salmonella spp. In addition, although we observe a preference for the own receptors over others for two of the receptor‐binding domains, this did not limit cross‐species effector delivery in all experimental conditions. These results suggest that class II CdiA proteins could allow for broad‐range and cross‐species growth inhibition in mixed bacterial populations.

The C-terminal (CT) toxin domains of contact-dependent growth inhibition (CDI) CdiA proteins target Gram-negative bacteria and must breach both the outer and inner membranes of target cells to exert growth inhibitory activity. Here, we examine two CdiA-CT toxins that exploit the bacterial general protein secretion machinery after delivery into the periplasm. A Ser281Phe amino acid substitution in transmembrane segment 7 of SecY, the universally conserved channel-forming subunit of the Sec translocon, decreases the cytotoxicity of the membrane depolarizing orphan10 toxin from enterohemorrhagic Escherichia coli EC869. Target cells expressing secY(S281F) and lacking either PpiD or YfgM, two SecY auxiliary factors, are fully protected from CDI-mediated inhibition either by CdiA-CTo10EC869 or by CdiA-CTGN05224, the latter being an EndoU RNase CdiA toxin from Klebsiella aerogenes GN05224 that has a related cytoplasm entry domain. RNase activity of CdiA-CTGN05224 was reduced in secY(S281F) target cells and absent in secY(S281F) Delta ppiD or secY(S281F) Delta yfgM target cells during competition co-cultures. Importantly, an allele-specific mutation in secY (secY(G313W)) renders DppiD or Delta yfgM target cells specifically resistant to CdiA-CTGN05224 but not to CdiA-CTo10EC869, further suggesting a direct interaction between SecY and the CDI toxins. Our results provide genetic evidence of a unique confluence between the primary cellular export route for unfolded polypeptides and the import pathways of two CDI toxins. IMPORTANCE Many bacterial species interact via direct cell-to-cell contact using CDI systems, which provide a mechanism to inject toxins that inhibit bacterial growth into one another. Here, we find that two CDI toxins, one that depolarizes membranes and another that degrades RNA, exploit the universally conserved SecY translocon machinery used to export proteins for target cell entry. Mutations in genes coding for members of the Sec translocon render cells resistant to these CDI toxins by blocking their movement into and through target cell membranes. This work lays the foundation for understanding how CDI toxins interact with the protein export machinery and has direct relevance to development of new antibiotics that can penetrate bacterial cell envelopes.

Single particle imaging of proteins in the gas phase with x-ray free-electron lasers holds great potential to study fast protein dynamics, but is currently limited by weak and noisy data. A further challenge is to discover the proteins' orientation as each protein is randomly oriented when exposed to x-rays. Algorithms such as the expand, maximize, and compress (EMC) exist that can solve the orientation problem and reconstruct the three-dimensional diffraction intensity space, given sufficient measurements. If information about orientation were known, for example, by using an electric field to orient the particles, the reconstruction would benefit and potentially reach better results. We used simulated diffraction experiments to test how the reconstructions from EMC improve with particles' orientation to a preferred axis. Our reconstructions converged to correct maps of the three-dimensional diffraction space with fewer measurements if biased orientation information was considered. Even for a moderate bias, there was still significant improvement. Biased orientations also substantially improved the results in the case of missing central information, in particular in the case of small datasets. The effects were even more significant when adding a background with 50% the strength of the averaged diffraction signal photons to the diffraction patterns, sometimes reducing the data requirement for convergence by a factor of 10. This demonstrates the usefulness of having biased orientation information in single particle imaging experiments, even for a weaker bias than what was previously known. This could be a key component in overcoming the problems with background noise that currently plague these experiments.

Coherent diffractive imaging with X-ray free-electron lasers could enable structural studies of macromolecules at room temperature. This type of experiment could provide a means to study structural dynamics on the femtosecond time scale. However, the diffraction from a single protein is weak compared to the incoherent scattering from background sources, which negatively affects the reconstruction analysis. In this work, we evaluated the effects of the presence of background on the analysis pipeline. Background measurements from the European X-ray free-electron laser were combined with simulated diffraction patterns, and treated by a standard reconstruction procedure including orientation recovery with the expand, maximize and compress algorithm and 3D phase retrieval. Background scattering did have an adverse effect on the estimated resolution of the reconstructed density maps. Still, the reconstructions generally worked when the signal-to-background ratio was 0.6 or better, at the momentum transfer shell of the highest reconstructed resolution. The results also suggest that the signal-to-background requirement increases at higher resolution. This study gives an indication of what is possible at current setups at X-ray free-electron lasers, with regards to expected background strength and establishes a target for experimental optimization of the background.

Demand-side flexibility (DSF) generally refers to adjusting electricity demand to better match supply and capacity in the grid. Widely seen as a central pillar of the energy transition, it is anticipated to play a key role in decarbonization. Despite its recent popularity, its efficacy as a policy instrument remains limited. This paper argues that DSF is being hindered by an overreliance on what is seen as its primary lever – the price signal. The price signal “paradigm” refers to how the general problem of fostering a more flexible demand side has been constrained to the narrow task of having users respond to price signals. The consequence of this development has been for policymakers and system operators to limit themselves to the role of information providers and neutral market facilitators, absolving themselves of more direct obligations and shifting the bulk of the responsibility onto end users who are required to actively participate in the electricity market. This paper explores the emergence of this paradigm, shedding light on the historical factors that have shaped the evolution of DSF, and the limitations inherent to its contemporary form. It outlines policy recommendations and an alternative approach that centers on shifting more responsibility towards system operators.

Demand response refers to changes in power consumption by electricity users in response to certain conditions on the electricity market. Anticipated to play a major role in the energy transition, demand response is conventionally exercised through network tariffs, which serve as a medium for price signals intended to incentivize and guide electricity users on how to best behave. Considerable attention has been devoted to the unresolved question of whether users are willing to respond to these signals, a question premised on the implicit assumption of cognizant users making informed decisions. Less attention has been dedicated to evaluating the validity of this assumption, and the extent to which users actually internalize these signals prior to making any decision on how to respond. This study posits that prior to making an informed decision on how to act, an electricity user must first “qualify” through meeting a set of preconditions. These preconditions are captured by a proposed three-stage framework that involves i) receiving the price signal (being aware of the tariff), ii) processing the price signal (comprehending its features) and iii) assimilating the price signal (understanding how behavior influences costs). Evaluating this framework using a survey, the study finds that only 3.8–8.5 % of respondents clear all three stages. This minority is substantially more likely to contain older villa-residents, who are comparatively more concerned with their costs and read their bills more frequently. The findings of this study demonstrate that the “audience” of tariff-based price signals are a small fraction of what is commonly supposed, and that research and policy should shift from a dominant focus on the magnitude of these price signals, towards alternative or improved strategies for communication and engagement.

Conventionally, demand response functions by communicating to electricity users through price signals embedded in their tariffs. These signals are intended to encourage a change in behavior, which hinges on the ability of users to understand their tariff and link it to the appropriate curtailment actions. This study focuses on demand-based tariffs, evaluating user's understanding of these tariffs and the conceptual grasp of power (rate of energy consumption) that they implicitly require. It also explores whether users exposed to these tariffs for extended periods develop a better understanding of them. Using a survey, the following points are sequentially evaluated: (1) Respondents' abilities to intuitively distinguish between energy/power (2) Their understanding of the different effects of curtailment actions under four distinct tariffs (3) Whether those subject to demand-based pricing outperform those subject to energy-based pricing. Despite a weaker conceptual understanding of power compared to energy, there were no significant differences between respondents' understanding of energy and demand-based tariffs. Comparing those subject to energy and demand-based pricing reveals that a majority were unaware of their own tariff (and hence which group they fall into), but for the minority of users who correctly identified their own tariffs, those subject to demand-based pricing outperform their energy-based counterparts. When presented with clear and instructive tariffs, respondents are largely able to deduce the consequences of curtailment actions, despite a weak conceptual understanding of power. A deeper problem is that the price signal may be entirely disregarded by an apathetic majority, reaching only an inquisitive minority.

Reducing peak demand in distribution grids is associated with benefits such as delayed infrastructural investments, decreased losses and a reduced risk of power deficits. One instrument aimed at reducing peak demand is the demand charge, a capacity-based component in a network tariff that intends to encourage users to reduce their peak usage. Using ten years of data from a Swedish distribution network, this study demonstrates that demand charges may be unsuitable for managing the problems they are intended to address. Two critical misalignments in the design of these demand charges are identified: 1) Demand charges are most commonly based on maximum billing demand – a given user's maximum monthly peak – whereas the problem of peak demand overwhelmingly concerns maximum system peaks in the distribution grid as a whole. The lack of coincidence between these peaks suggest that demand charges are, by design, ineffective for reducing peak demand. 2) The peaks which determine a distribution system's maximum capacity requirements are rare, seasonal and largely temperature-driven events, whereas demand charges mainly target users' habitual daily patterns, encouraging daily shifts from peak to off-peak hours. As long as the main driver of network costs, maximum system peaks, are absent in their design, demand charges will neither reflect the costs that users impose on the grid nor provide them with the correct price signals on how to best act.

Demand-side flexibility is often encouraged via demand response programs, where users are incentivized to adjust their electricity consumption based on price signals in tariffs. The design and implementation of these signals are vital, as they link conditions of the energy system with user behaviour. As a result of energy system separation into stages of generation, many users are required to simultaneously respond to two price signals. While existing research has mainly examined responses to a single signal, this study highlights the complexities when users must react to two signals simultaneously. It presents a case study using two distinct price signals: real-time pricing (RTP) and a demand charge, analysing the implications such dual price signals could have on users. The study reveals the empirical incompatibility of these signals, leading to conflicting user goals. Such interference complicates decision-making for demand flexibility, potentially eroding user trust in retailers and system operators. The study ends with proposing an alternative tariff, resolving the dual price situation, which facilitates user comprehension and decision-making, and thus enhances the potential for demand flexibility.

This study explores the metabolic differences between human and murine plasma in addition to differences between murine subcutaneous and visceral white adipose tissue. A quantitative and semi-quantitative targeted method was developed and validated for this purpose. The quantitative method includes tryptophan and its metabolites in addition to tyrosine, phenylalanine, taurine, B vitamins, neopterin, cystathionine and hypoxanthine. While the semi-quantitative method includes; 3-indoleacetic acid, 5-hydroxyindoleacetic acid, acetylcholine, asymmetric dimethylarginine, citrulline and methionine. Sample preparation was based on protein precipitation, while quantification was conducted using ultrahigh-performance liquid chromatography coupled to a quadrupole Orbitrap tandem mass spectrometer with electrospray ionization in the parallel reaction monitoring (PRM) mode. The low limit of quantification for all metabolites ranged from 1 to 200 ng mL^-1. Matrix effects and recoveries for stable isotope labelled internal standards were evaluated, with most having a coefficient of variation (CV) of less than 15%. Results showed that a majority of the analytes passed both the intra- and interday precision and accuracy criteria. The comparative analysis of human and murine plasma metabolites reveals species-specific variations within the tryptophan metabolic pathway. Notably, murine plasma generally exhibits elevated concentrations of most compounds in this pathway, with the exceptions of kynurenine and quinolinic acid. Moreover, the investigation uncovers noteworthy metabolic disparities between murine visceral and subcutaneous white adipose tissues, with the subcutaneous tissue demonstrating significantly higher concentrations of tryptophan, phenylalanine, tyrosine, and serotonin. The findings also show that even a semi-quantitative method can provide comparable results to quantitative methods from other studies and be effective for assessing metabolites in a complex sample. Overall, this study provides a robust platform to compare human and murine metabolism, providing a valuable insight to future investigations. A validated HRMS method for measuring tryptophan metabolites and related compounds has been developed, with simple sample preparation, successfully applied in human and murine plasma, as well as murine white adipose tissue.

Caspofungin has a liver-dependent metabolism. Reduction of the dose is recommended based on Child-Pugh (C-P) score. In critically ill patients, drug pharmacokinetics (PK) may be altered. The aim of this study was to investigate the prevalence of abnormal liver function tests, increased C-P scores, their effects on caspofungin PK, and whether pharmacokinetic-pharmacodynamic (PK/PD) targets were attained in patients with suspected candidiasis. Intensive care unit patients receiving caspofungin were prospectively included. PK parameters were determined on days 2, 5, and 10, and their correlations to the individual liver function tests and the C-P score were analyzed. Forty-six patients were included with C-P class A (n = 5), B (n = 40), and C (n = 1). On day 5 (steady state), the median and interquartile range for area under the curve from 0 to 24 h (AUC(0-24)), clearance (CL), and central volume of distribution (V-1) were 57.8 (51.6 to 69.8) mg.h/liter, 0.88 (0.78 to 1.04) liters/h, and 11.9 (9.6 to 13.1) liters, respectively. The C-P score did not correlate with AUC(0-24) (r = 0.03; P = 0.84), CL (r = -0.07; P = 0.68), or V-1 (r = 0.19; P = 0.26), but there was a bilirubin-driven negative correlation with the elimination rate constant (r = -0.46; P = 0.004). Hypoalbuminemia correlated with low AUC(0-24) (r = 0.45; P = 0.005) and was associated with higher clearance (r = -0.31; P = 0.062) and somewhat higher V-1 (r = -0.15; P = 0.37), resulting in a negative correlation with the elimination rate constant (r = -0.34; P = 0.042). For Candida strains with minimal inhibitory concentrations of >= 0.064 mu g/ml, PK/PD targets were not attained in all patients. The caspofungin dose should not be reduced in critically ill patients in the absence of cirrhosis, and we advise against the use of the C-P score in patients with trauma- or sepsis-induced liver injury.

Background

Recent studies have shown low caspofungin concentrations in critically ill patients. In some patients, the therapeutic target, area under the total plasma concentration curve in relation to the minimal inhibition concentration (AUC_tot/MIC), seems not to be achieved and therapeutic drug monitoring (TDM) has been proposed. Caspofungin is highly protein-bound and the effect of reduced plasma protein levels on pharmacodynamics has not been investigated.

Objectives

Fungal killing activity of caspofungin in vitro was investigated under varying levels of human plasma protein.

Methods

Time-kill studies were performed with clinically relevant caspofungin concentrations of 1-9 mg/L on four blood isolates of C. glabrata, three susceptible and one strain with reduced susceptibility, in human plasma and plasma diluted to 50% and 25% using Ringer's acetate.

Results

Enhanced fungal killing of the three susceptible strains was observed in plasma with lower protein content (p < .001). AUC_tot/MIC required for a 1 log₁₀ CFU/ml kill at 24 h in 50% and 25% plasma was reduced with 36 + 12 and 80 + 9%, respectively. The maximum effect was seen at total caspofungin concentrations of 4–9 × MIC. For the strain with reduced susceptibility, growth was significantly decreased at lower protein levels.

Conclusions

Reduced human plasma protein levels increase the antifungal activity of caspofungin in vitro, most likely by increasing the free concentration. Low plasma protein levels in critically ill patients with candidemia might explain a better response to caspofungin than expected from generally accepted target attainment and should be taken into consideration when assessing TDM based on total plasma concentrations.

Recreational anglers collectively spend a great amount of time on fishing activities. While being out fishing, these anglers perceive various components of aquatic ecosystems, allowing them to accumulate knowledge of the ecological status of the system and the fish they are targeting in it. Such fishers' ecological knowledge (FEK) is increasingly being recognized as a valuable source of information in fisheries research and management, but it has also been criticized for a lack of accuracy. People's perceptions of their environment are shaped by a variety of psychological and environmental factors, and the same is expected for anglers' perceptions of fish stocks and the resulting FEK. This study assessed anglers' perceived change in salmon and sea trout stocks at the Mo center dot rrum river in Sweden, comparing the year that they first fished there to the present (2019). In addition, effects on these perceptions of the anglers' motivations to fish, satisfaction with catches, and their engagement with this fishery were examined. The majority of the respondents perceived a negative trend for both species, even when the reported total catches during the year they first fished at Mo center dot rrum were lower than the present catches. Binomial regression models indicated significant influences of engagement at Mo center dot rrum on the anglers' stock trend perceptions. The results of this study emphasize the importance of understanding different individual angler characteristics when collecting and interpreting FEK. Moreover, the fact that anglers with a longer history at Mo center dot rrum had a more negative perception overall, regardless of actual stock status in their first year in this fishery, suggests the occurrence of the shifting baseline syndrome in recreational fisheries. Therefore, the prevailing narrative on stock trends and its effect on angler perceptions should be considered when using FEK.

The incorporation of theories and methods from tourism research in recreational fisheries research is not common practice, despite many parallels between the two research fields. Adopting a tourism perspective could contribute to our understanding of angling tourists' attitudes and preferences and help identify the factors affecting the recruitment and retention of anglers. The concept of destination loyalty reflects a tourist's intention to revisit or recommend a destination, and provides a suitable avenue for studying (repeated) visits to a fishing destination. This study assessed the destination loyalty of angling tourists targeting Atlantic salmon (Salmo salar L.) and sea trout (Salmo trutta L.) at the Mo spacing diaeresis rrum river in southern Sweden. A structural equation model validated positive relationships between the psychological dimensions of destination image, satisfaction, place attachment and destination loyalty. In addition, centrality of the angling activity to the anglers' life was identified as a precursor of place attachment and subsequent loyalty. Moreover, anglers with a high experience use history for fishing at Mo spacing diaeresis rrum scored higher than other anglers on several of the indicators for image, satisfaction, place attachment and loyalty. These results show how applying a tourism perspective to traditional recreational fisheries research may help to explain angler loyalty to a recreational fishery, which ultimately contributes to the management of visitor numbers and experiences. The measurement model used in this study can inform future studies involving angling destination loyalty and contribute to creating a universal measurement scale for this construct and its antecedents. Finally, this study holds several implications for fishing destination marketing and management in terms of supporting d