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Abstract Background This study evaluates the efficacy of intravitreal Faricimab in reducing pigment epithelium detachment (PED) and fluid volumes in both treatment-naïve eyes and eyes unresponsive to anti-VEGF mono-therapies, all diagnosed with type 1 macular neovascularization (T1 MNV) over a period of 12-month. Methods A retrospective, single-center cohort study was conducted at the Jules Gonin Eye Hospital, Lausanne, Switzerland. Clinical records of treatment-naïve and non-responder switch patients presenting T1 MNV secondary to neovascular age-related macular degeneration (nAMD) from September 2022 to March 2023 were reviewed. Patients received a loading dose of three monthly Faricimab injections followed by a treat-and-extend (T&E) regimen. Multimodal imaging, including structural OCT and AI-assisted analysis, was used to quantify PED volumes and related fluid biomarkers at baseline, 3-month, 6-month, and 12-month follow-up. Statistical analyses included linear mixed models to evaluate differences and trends in intraretinal (IRF), subretinal fluid (SRF) and PED volumes. Results 65 eyes of 65 patients were enrolled (female: 70.7%; mean age = 80.7yrs, SD = 6.9yrs). 80% had received anti-VEGF treatment (Switch group) and 20% were treatment-Naïve at baseline. At 12 months, intravitreal treatments were more frequent in the Switch group (mean number = 8.3 vs. 6.0; p = 0.009). BCVA improved at the 12-month follow-up in Naïve eyes (+ 6.9 ETDRS letters from baseline, p = 0.053) and was maintained in Switch eyes. No cases of intraocular inflammation were observed. Significant reduction in SRF and IRF volumes were noted in both groups. A significant reduction in PED volume was observed over the follow-up period in both groups (mean slope = -206 nL, 95%CL = -273/-138; p-value

Societal Impact Statement Seagrass meadows are a globally important habitat subject to significant loss. As efforts to restore these sensitive habitats are hampered by their high cost and low levels of reliability, rigorous guidance is required to improve effectiveness and ensure they are cost‐effective. Here, we define 10 golden rules for how we can undertake seagrass restoration. We do this by considering that for seagrass restoration to be successful, it needs to take place with people and not against people. The framework we present aims to direct efforts for seagrass restoration that are holistic and achieve broad goals for people, biodiversity and the planet. Summary The world has lost a significant proportion of its seagrass, and although glimmers of hope for its recovery exist, losses and degradations continue. First and foremost, evidence highlights the need to put the world on a global pathway to seagrass net gain. Achieving this outcome requires that conservation of what remains is a priority, but reaching net gain requires seagrass coverage to increase at rates unlikely to be achieved naturally; large‐scale active restoration is required to fill this gap. Novel finance mechanisms aligned to the climate emergency and biodiversity crises are increasingly leading to larger scale restoration projects. However, no clear framework exists for developing or prioritising approaches. With seagrass restoration expensive and unreliable, rigorous guidance is required to improve effectiveness and ensure it is cost‐effective. Building on evidence from terrestrial and marine sources, here, we apply the ‘10 golden rules’ concept, first outlined for reforestation and later applied to coral reefs, to seagrass restoration. In doing so, we follow international standards for ecological restoration and view seagrass restoration in a broad context, whereby regeneration can be achieved either by planting or by enhancing and facilitating natural recovery. These rules somewhat differ from those on reforestation and coral reef restoration, principally due to the relative immaturity of seagrass restoration science. These 10 golden rules for seagrass restoration are placed within a coupled social‐ecological systems context, and we present a framework for conservation more broadly, to achieve multiple goals pertaining to people, biodiversity and the planet.

Abstract Central serous chorioretinopathy (CSCR) is a retinal disease characterised by the accumulation of subretinal fluid, which often resolves spontaneously in acute cases. However, approximately one-third of patients experience recurrences that may cause severe and irreversible vision. This study aimed to identify parameters derived from optical coherence tomography (OCT) that are associated with CSCR recurrence. Our dataset included 5211 OCT scans from 344 eyes of 255 patients diagnosed with CSCR. 178 eyes were identified as recurrent, 109 as non-recurrent, and 57 were excluded. We extracted parameters using artificial intelligence algorithms based on U-Nets, convolutional kernels, and morphological operators. We applied inferential statistics to evaluate differences between the recurrent and non-recurrent groups, and we used a logistic regression predictive model, reporting the coefficients as a measure of biomarker importance. We identified nine predictive biomarkers for CSCR recurrence: age, intraretinal fluid, subretinal fluid, pigment epithelial detachments, choroidal vascularity index, integrity of photoreceptors and retinal pigment epithelium layer, choriocapillaris and choroidal stroma thickness, and thinning of the outer nuclear layer, and of the inner nuclear layer combined with the outer plexiform layer. These results could enable future developments in the automatic detection of CSCR recurrence, paving the way for translational medical applications.

Abstract Studies for the preservation of seagrass beds biotopes have met difficulties in establishing appropriate methods assessing their health. We tested the efficiency of space occupation by seagrasses scattered worldwide (dgrass index), which proved to be dependent on clonal growth form and morphometric plasticity. dgrass correlated with the above-ground to below-ground biomass ratio. However, the latter was misleading when high ratios resulted from low below-ground biomass. Nutrient Posphate-limitations were revealed in situations of theoretical Nitrogen-limitation. Enhanced nutrient supply benefitted seagrasses only up to a threshold after which it became detrimental. Better nurtured, healthier meadows with denser canopies increased the organic matter in the sediment and had associated greater abundances of benthic macrofauna. Hence, seagrass biotopes could benefit from moderate anthropogenic nutrient additions. However, organic matter above ≈6% and/or reduced riverine discharges (dams upstream and climate-change-related droughts) were detrimental to healthy meadows, jeopardizing ecosystem services such as macrofauna abundances and carbon sinks.

The NANOGrav 15 yr data provide compelling evidence for a stochastic gravitational-wave (GW) background at nanohertz frequencies. The simplest model-independent approach to characterizing the frequency spectrum of this signal consists of a simple power-law fit involving two parameters: an amplitude A and a spectral index γ . In this Letter, we consider the next logical step beyond this minimal spectral model, allowing for a running (i.e., logarithmic frequency dependence) of the spectral index, ${\gamma }_{{\rm{run}}}(f)=\gamma +\beta \mathrm{ln}\left(f/{f}_{{\rm{ref}}}\right)$ . We fit this running-power-law (RPL) model to the NANOGrav 15 yr data and perform a Bayesian model comparison with the minimal constant-power-law (CPL) model, which results in a 95% credible interval for the parameter β consistent with no running, $\beta \in \left[-0.80,2.96\right]$ , and an inconclusive Bayes factor, ${ \mathcal B }\left({\rm{RPL}}\,{\rm{versus}}\,{\rm{CPL}}\right)=0.69\pm 0.01$ . We thus conclude that, at present, the minimal CPL model still suffices to adequately describe the NANOGrav signal; however, future data sets may well lead to a measurement of nonzero β . Finally, we interpret the RPL model as a description of primordial GWs generated during cosmic inflation, which allows us to combine our results with upper limits from Big Bang nucleosynthesis, the cosmic microwave background, and LIGO–Virgo–KAGRA.

Abstract In the spectrum of colorectal tumors, microsatellite-stable (MSS) tumors with DNA polymerase ε (POLE) mutations exhibit a hypermutated profile, holding the potential to respond to immunotherapy similarly to their microsatellite-instable (MSI) counterparts. Yet, due to their rarity and the associated testing costs, systematic screening for these mutations is not commonly pursued. Notably, the histopathological phenotype resulting from POLE mutations is theorized to resemble that of MSI. This resemblance not only could facilitate their detection by a transformer-based Deep Learning (DL) system trained on MSI pathology slides, but also indicates the possibility for MSS patients with POLE mutations to access enhanced treatment options, which might otherwise be overlooked. To harness this potential, we trained a Deep Learning classifier on a large dataset with the ground truth for microsatellite status and subsequently validated its capabilities for MSI and POLE detection across three external cohorts. Our model accurately identified MSI status in both the internal and external resection cohorts using pathology images alone. Notably, with a classification threshold of 0.5, over 75% of POLE driver mutant patients in the external resection cohorts were flagged as “positive” by a DL system trained on MSI status. In a clinical setting, deploying this DL model as a preliminary screening tool could facilitate the efficient identification of clinically relevant MSI and POLE mutations in colorectal tumors, in one go.

Abstract Deep Learning (DL) can predict biomarkers from cancer histopathology. Several clinically approved applications use this technology. Most approaches, however, predict categorical labels, whereas biomarkers are often continuous measurements. We hypothesize that regression-based DL outperforms classification-based DL. Therefore, we develop and evaluate a self-supervised attention-based weakly supervised regression method that predicts continuous biomarkers directly from 11,671 images of patients across nine cancer types. We test our method for multiple clinically and biologically relevant biomarkers: homologous recombination deficiency score, a clinically used pan-cancer biomarker, as well as markers of key biological processes in the tumor microenvironment. Using regression significantly enhances the accuracy of biomarker prediction, while also improving the predictions’ correspondence to regions of known clinical relevance over classification. In a large cohort of colorectal cancer patients, regression-based prediction scores provide a higher prognostic value than classification-based scores. Our open-source regression approach offers a promising alternative for continuous biomarker analysis in computational pathology.

Evidence has emerged for a stochastic signal correlated among 67 pulsars within the 15 yr pulsar-timing data set compiled by the NANOGrav collaboration. Similar signals have been found in data from the European, Indian, Parkes, and Chinese pulsar timing arrays. This signal has been interpreted as indicative of the presence of a nanohertz stochastic gravitational-wave background (GWB). To explore the internal consistency of this result, we investigate how the recovered signal strength changes as we remove the pulsars one by one from the data set. We calculate the signal strength using the (noise-marginalized) optimal statistic, a frequentist metric designed to measure the correlated excess power in the residuals of the arrival times of the radio pulses. We identify several features emerging from this analysis that were initially unexpected. The significance of these features, however, can only be assessed by comparing the real data to synthetic data sets. After conducting identical analyses on simulated data sets, we do not find anything inconsistent with the presence of a stochastic GWB in the NANOGrav 15 yr data. The methodologies developed here can offer additional tools for application to future, more sensitive data sets. While this analysis provides an internal consistency check of the NANOGrav results, it does not eliminate the necessity for additional investigations that could identify potential systematics or uncover unmodeled physical phenomena in the data.

In 2017-2019 southeast Australia experienced one of its most severe droughts since 1900. Rainfall over the region encompassing the Murray-Darling Basin was consistently below average for three consecutive cool seasons, an unprecedented event on record. A strong positive Indian Ocean Dipole event has been previously suggested to have intensified the conditions of the drought in 2019, however the state of the climate drivers cannot fully explain the onset and development of this drought. In this study, we adopt a different approach to investigate processes other than remote climate drivers that may have triggered the drought. Using a Lagrangian model to identify moisture sources to the Murray Darling Basin, we show that local processes were crucial in explaining the onset and development of the drought. We identify the oceanic and terrestrial sources of atmospheric moisture over the Murray Darling Basin and show for the first time a significant decline in rainfall moisture supply from the Tasman Sea in 2017 and 2018. We further show that anomalous atmospheric circulation transported the expected moisture northward toward the Maritime Continent. Our results provide an explanation for the moisture and rainfall deficit that caused the 2017-19 southeast Australian drought. Understanding the processes that led to the 2017-2019 drought is an important step towards improved predictions and planning for future multi-year droughts in Australia.

Abstract Prolonged droughts and heatwaves are common causes of agricultural failure in Australia, yet the origins of these climate anomalies remain understudied. Here, we use a Lagrangian trajectory model driven by atmospheric reanalysis and constrained by satellite data to unravel the sources of precipitation and heat over the Southeastern Australia wheat belt. Furthermore, we assess the impact of local and upwind drought conditions on the moisture and heat imports to the region. Results indicate that the most extreme crop failure events over the wheat belt (i.e., 1994, 2002, and 2006) were associated with persistent high‐pressure systems. The ocean provided on average 72% of the moisture for precipitation and 39% of the heat arriving over the wheat belt, with the moisture sources substantially decreasing during crop failure events. Upwind drought further intensified rainfall deficits and heat stress during these events due to lower moisture and higher heat imports to the region. This was particularly clear during the initial phase of the Millennium Drought in 2002. Then, yield deficits exceeded 50%, and ∼4% of the precipitation originated from drought‐affected regions upwind, compared to the 9% that was expected climatologically from those regions. Simultaneously, the heat import from these regions upwind increased by ∼10 W m−2, from the climatological 23%–25%, during this event. While these results indicate a limited potential for upwind land management to mitigate downwind agricultural loss in the Southeastern Australia wheat belt, other agricultural regions with a higher climatic dependency on remote land may benefit from such strategies.

Abstract Background Poor performance is a multifactorial syndrome of racehorses, commonly associated with subclinical disorders, which can be diagnosed by exercise testing. Objectives Describe the prevalence of medical causes of poor performance in Standardbreds unassociated with lameness, and evaluate their relationships with fitness variables measured by exercise treadmill test. Animals Hospital population of 259 nonlame Standardbred trotters referred for poor performance. Methods The horses' medical records were retrospectively reviewed. Horses underwent a diagnostic protocol including resting examination, plasma lactate concentration, treadmill test with continuous ECG and assessment of fitness variables, creatine kinase activity, treadmill endoscopy, postexercise tracheobronchoscopy, bronchoalveolar lavage (BAL), and gastroscopy. The prevalence of different disorders was evaluated, including cardiac arrhythmias, exertional myopathies, dynamic upper airway obstructions (DUAOs), exercise‐induced pulmonary hemorrhage (EIPH), moderate equine asthma (MEA), and gastric ulcers (EGUS). The associations of these disorders with fitness variables were investigated individually and using multivariable models. Results Moderate equine asthma and EGUS were the most common disorders, followed by EIPH, DUAOs, cardiac arrhythmias, and exertional myopathies. Hemosiderin score was positively correlated with BAL neutrophils, eosinophils, and mast cells; increased creatine kinase activity was associated with BAL neutrophilia, DUAOs, premature complexes, and squamous gastric disease. Treadmill velocity at a plasma lactate concentration of 4 mmol/L and at heart rate of 200 beats per minute was negatively affected by BAL neutrophilia, multiple DUAOs, exertional myopathies, and squamous gastric disease. Conclusions The multifactorial nature of poor performance was confirmed, with MEA, DUAOs, myopathies and EGUS representing the main diseases involved in fitness impairment.

Recently we found compelling evidence for a gravitational-wave background with Hellings and Downs (HD) correlations in our 15 yr data set. These correlations describe gravitational waves as predicted by general relativity, which has two transverse polarization modes. However, more general metric theories of gravity can have additional polarization modes, which produce different interpulsar correlations. In this work, we search the NANOGrav 15 yr data set for evidence of a gravitational-wave background with quadrupolar HD and scalar-transverse (ST) correlations. We find that HD correlations are the best fit to the data and no significant evidence in favor of ST correlations. While Bayes factors show strong evidence for a correlated signal, the data does not strongly prefer either correlation signature, with Bayes factors ∼2 when comparing HD to ST correlations, and ∼1 for HD plus ST correlations to HD correlations alone. However, when modeled alongside HD correlations, the amplitude and spectral index posteriors for ST correlations are uninformative, with the HD process accounting for the vast majority of the total signal. Using the optimal statistic, a frequentist technique that focuses on the pulsar-pair cross-correlations, we find median signal-to-noise ratios of 5.0 for HD and 4.6 for ST correlations when fit for separately, and median signal-to-noise ratios of 3.5 for HD and 3.0 for ST correlations when fit for simultaneously. While the signal-to-noise ratios for each of the correlations are comparable, the estimated amplitude and spectral index for HD are a significantly better fit to the total signal, in agreement with our Bayesian analysis.

Abstract The histopathological phenotype of tumors reflects the underlying genetic makeup. Deep learning can predict genetic alterations from pathology slides, but it is unclear how well these predictions generalize to external datasets. We performed a systematic study on Deep-Learning-based prediction of genetic alterations from histology, using two large datasets of multiple tumor types. We show that an analysis pipeline that integrates self-supervised feature extraction and attention-based multiple instance learning achieves a robust predictability and generalizability.

Abstract Anti-vascular endothelial growth factor (anti-VEGF) therapies have become the standard of care in the treatment of neovascular age-related macular degeneration (nAMD) and diabetic macular edema (DME), resulting in a remarkable decrease in disease-related vision loss. However, the need for regular injections places a significant burden on patients, caregivers, and the healthcare system and improvements in vision may not be maintained long term. As a result of its drying potency and duration of action, brolucizumab, an intravitreal anti-VEGF therapy approved for the treatment of nAMD and DME, could decrease injection frequency for patients and provide an efficacious treatment; however, balancing its benefits and risks can be challenging. There have been reports of intraocular inflammation (IOI) in patients treated with brolucizumab, which, if left untreated, may result in severe vision loss. Recent evidence, however, indicates that early recognition of IOI and prompt and aggressive systemic corticosteroid treatment in response to posterior segment involvement can lead to favorable outcomes in these relatively rare but severe cases. A series of consensus meetings were conducted in 2022 between Swiss medical retina experts and diabetologists, discussing the current data for brolucizumab and exploring various challenges to its use, including the associated risk of IOI. The outcome is a collation of practical insights and guidance for ophthalmologists on the use of brolucizumab in patients with nAMD and DME, including patient selection and assessment, treatment regimen and monitoring, and the recognition and management of adverse events.

Abstract Regeneration of the neuromuscular junction (NMJ) leverages on extensive exchange of factors released from motor axon terminals (MATs), muscle fibers and perisynaptic Schwann cells (PSCs), among which hydrogen peroxide (H2O2) is a major pro-regenerative signal. To identify critical determinants of NMJ remodeling in response to injury, we performed temporal transcriptional profiling of NMJs from 2 month-old mice during MAT degeneration/regeneration, and cross-referenced the differentially expressed genes with those elicited by H2O2 in SCs. We identified an enrichment in extracellular matrix (ECM) transcripts, including Connective Tissue Growth Factor (Ctgf), which is usually expressed during development. We discovered that Ctgf levels are increased in a Yes-associated protein (YAP)-dependent fashion in response to rapid, local H2O2 signaling generated by stressed mitochondria in the injured sciatic nerve, a finding highlighting the importance of signals triggered by mechanical force to motor nerve repair. Through sequestration of Ctgf or inactivation of H2O2, we delayed the recovery of neuromuscular function by impairing SC migration and, in turn, axon-oriented re-growth. These data indicate that H2O2 and its downstream effector Ctgf are pro-regenerative factors that enable axonal growth, and reveal a striking ECM remodeling process during nerve regeneration upon local H2O2 signaling. Our study identifies key transcriptomic changes at the regenerating NMJ, providing a rich source of pro-regenerative factors with potential for alleviating the consequences of peripheral nerve injuries.

Abstract Introduction Considering the reported efficacy of monoclonal antibodies (mAbs) directed against the Spike (S) protein of severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) in reducing disease severity, the aim of this study was to investigate the innate immune response before and after mAbs treatment in 72 vaccinated and 31 unvaccinated SARS‐CoV‐2 patients. Methods The mRNA levels of IFN‐I, IFN‐related genes and cytokines were evaluated using RT/real‐time quantitative PCR. Results Vaccinated patients showed increased rate of negative SARS‐CoV‐2 PCR tests on nasopharyngeal swab compared with unvaccinated ones after mAbs treatment (p = .002). Unvaccinated patients had lower IFN‐α/ω and higher IFN‐related genes (IFNAR1, IFNAR2, IRF9, ISG15, ISG56 and IFI27) and cytokines (IL‐6, IL‐10 and TGF‐β) mRNA levels compared to vaccinated individuals before mAbs (p

Backgroundwe evaluated the concordance between immunohistochemical p53 staining and TP53 mutations in a series of HGSOC. Moreover, we searched for prognostic differences between p53 overexpression and null expression groups.Methodspatients affected by HGSOC were included. For each case p53 immunohistochemical staining and molecular assay (Sanger sequencing) were performed. Kaplan-Meier survival analyses were undertaken to determine whether the type of TP53 mutation, or p53 staining pattern influenced overall survival (OS) and progression free survival (PFS).Results34 HGSOC were considered. All cases with a null immunohistochemical p53 expression (n=16) showed TP53 mutations (n=9 nonsense, n=4 in-frame deletion, n=2 splice, n=1 in-frame insertion). 16 out of 18 cases with p53 overexpression showed TP53 missense mutation. Follow up data were available for 33 out of 34 cases (median follow up time 15 month). We observed a significant reduction of OS in p53 null group [HR = 3.64, 95% CI 1.01-13.16].Conclusionimmunohistochemical assay is a reliable surrogate for TP53 mutations in most cases. Despite the small cohort and the limited median follow up, we can infer that HGSOC harboring p53 null mutations are a more aggressive subgroup.

Abstract To identify drivers of sensitivity and resistance to Protein Arginine Methyltransferase 5 (PRMT5) inhibition, we perform a genome-wide CRISPR/Cas9 screen. We identify TP53 and RNA-binding protein MUSASHI2 (MSI2) as the top-ranked sensitizer and driver of resistance to specific PRMT5i, GSK-591, respectively. TP53 deletion and TP53 R248W mutation are biomarkers of resistance to GSK-591. PRMT5 expression correlates with MSI2 expression in lymphoma patients. MSI2 depletion and pharmacological inhibition using Ro 08-2750 (Ro) both synergize with GSK-591 to reduce cell growth. Ro reduces MSI2 binding to its global targets and dual treatment of Ro and PRMT5 inhibitors result in synergistic gene expression changes including cell cycle, P53 and MYC signatures. Dual MSI2 and PRMT5 inhibition further blocks c-MYC and BCL-2 translation. BCL-2 depletion or inhibition with venetoclax synergizes with a PRMT5 inhibitor by inducing reduced cell growth and apoptosis. Thus, we propose a therapeutic strategy in lymphoma that combines PRMT5 with MSI2 or BCL-2 inhibition.

BackgroundAnti-PCSK9 monoclonal antibodies are effective in reducing LDL-C and cardiovascular events by neutralizing circulating PCSK9. PCSK9, however, is also expressed in tissues, including the pancreas, and studies on PCSK9 KO mice have shown impaired insulin secretion. Statin treatment is already known to affect insulin secretion. Our aim was to conduct a pilot study to evaluate the effect of anti-PCSK9 mAb on glucose metabolism and β-cell function in humans.MethodsFifteen non-diabetic subjects, candidates for anti-PCSK9 mAb therapy, were enrolled. All underwent OGTT at baseline and after 6 months of therapy. During OGTT, insulin secretion parameters were derived from C-peptide by deconvolution (β cell glucose sensitivity). Surrogate insulin sensitivity indices were also obtained from OGTT (Matsuda).ResultsGlucose levels during OGTT were unchanged after 6 months of anti-PCSK9 mAb treatment, as well as insulin and C-peptide levels. The Matsuda index remained unchanged, while β-cell glucose sensitivity improved post-therapy (before: 85.3 ± 65.4; after: 118.6 ± 70.9 pmol min-1m-2mM-1; p

The recent integration of citizen science with modern technology has greatly increased its applications and has allowed more people than ever to contribute to research across all areas of science. In particular, citizen science has been instrumental in the detection and monitoring of novel introduced species across the globe. This study provides the first records of Miomantis caffra Saussure, 1871, the South African Mantis, from the Australian mainland and uses records from four different citizen science and social media platforms in conjunction with museum records to track the spread of the species through the country. A total of 153 wild mantises and oothecae were observed across four states and territories (New South Wales, Norfolk Island, Victoria, and Western Australia) between 2009 and 2021. The large number of observations of the species in Victoria and the more recent isolated observations in other states and territories suggest that the species initially arrived in Geelong via oothecae attached to plants or equipment, likely from the invasive population in New Zealand. From there it established and spread outwards to Melbourne and eventually to other states and territories, both naturally and with the aid of human transport. We also provide a comparison of M. caffra to similar native mantises, specifically Pseudomantis albofimbriata (Stål, 1860), and comment on the potential impact and further spread of the species within Australia. Finally, we reiterate the many benefits of engaging directly with citizen scientists in biodiversity research and comment on the decision to include them in all levels of this research investigation.

The cosmic merger history of supermassive black hole binaries (SMBHBs) is expected to produce a low-frequency gravitational wave background (GWB). Here we investigate how signs of the discrete nature of this GWB can manifest in pulsar timing arrays (PTAs) through excursions from, and breaks in, the expected ${f}_{\mathrm{GW}}^{-2/3}$ power law of the GWB strain spectrum. To do this, we create a semianalytic SMBHB population model, fit to North American Nanohertz Observatory for Gravitational Waves (NANOGrav’s) 15 yr GWB amplitude, and with 1000 realizations, we study the populations’ characteristic strain and residual spectra. Comparing our models to the NANOGrav 15 yr spectrum, we find two interesting excursions from the power law. The first, at 2 nHz, is below our GWB realizations with a p -value significance p = 0.05–0.06 (≈1.8 σ –1.9 σ ). The second, at 16 nHz, is above our GWB realizations with p = 0.04–0.15 (≈1.4 σ –2.1 σ ). We explore the properties of a loud SMBHB that could cause such an excursion. Our simulations also show that the expected number of SMBHBs decreases by 3 orders of magnitude, from ∼10 ^6 to ∼10 ^3 , between 2 and 20 nHz. This causes a break in the strain spectrum as the stochasticity of the background breaks down at ${26}_{-19}^{+28}\,\mathrm{nHz}$ , consistent with predictions pre-dating GWB measurements. The diminished GWB signal from SMBHBs at frequencies above the 26 nHz break opens a window for PTAs to detect continuous GWs from individual SMBHBs or GWs from the early Universe.

We present the results of a Bayesian search for gravitational wave (GW) memory in the NANOGrav 12.5 yr data set. We find no convincing evidence for any gravitational wave memory signals in this data set. We find a Bayes factor of 2.8 in favor of a model that includes a memory signal and common spatially uncorrelated red noise (CURN) compared to a model including only a CURN. However, further investigation shows that a disproportionate amount of support for the memory signal comes from three dubious pulsars. Using a more flexible red-noise model in these pulsars reduces the Bayes factor to 1.3. Having found no compelling evidence, we go on to place upper limits on the strain amplitude of GW memory events as a function of sky location and event epoch. These upper limits are computed using a signal model that assumes the existence of a common, spatially uncorrelated red noise in addition to a GW memory signal. The median strain upper limit as a function of sky position is approximately 3.3 × 10 ^−14 . We also find that there are some differences in the upper limits as a function of sky position centered around PSR J0613−0200. This suggests that this pulsar has some excess noise that can be confounded with GW memory. Finally, the upper limits as a function of burst epoch continue to improve at later epochs. This improvement is attributable to the continued growth of the pulsar timing array.

The radio galaxy 3C 66B has been hypothesized to host a supermassive black hole binary (SMBHB) at its center based on electromagnetic observations. Its apparent 1.05 yr period and low redshift (∼0.02) make it an interesting testbed to search for low-frequency gravitational waves (GWs) using pulsar timing array (PTA) experiments. This source has been subjected to multiple searches for continuous GWs from a circular SMBHB, resulting in progressively more stringent constraints on its GW amplitude and chirp mass. In this paper, we develop a pipeline for performing Bayesian targeted searches for eccentric SMBHBs in PTA data sets, and test its efficacy by applying it to simulated data sets with varying injected signal strengths. We also search for a realistic eccentric SMBHB source in 3C 66B using the NANOGrav 12.5 yr data set employing PTA signal models containing Earth term-only as well as Earth+pulsar term contributions using this pipeline. Due to limitations in our PTA signal model, we get meaningful results only when the initial eccentricity e _0 < 0.5 and the symmetric mass ratio η > 0.1. We find no evidence for an eccentric SMBHB signal in our data, and therefore place 95% upper limits on the PTA signal amplitude of 88.1 ± 3.7 ns for the Earth term-only and 81.74 ± 0.86 ns for the Earth+pulsar term searches for e _0 < 0.5 and η > 0.1. Similar 95% upper limits on the chirp mass are (1.98 ± 0.05) × 10 ^9 and (1.81 ± 0.01) × 10 ^9 M _☉ . These upper limits, while less stringent than those calculated from a circular binary search in the NANOGrav 12.5 yr data set, are consistent with the SMBHB model of 3C 66B developed from electromagnetic observations.

The millisecond pulsar J1713+0747 underwent a sudden and significant pulse shape change between 2021 April 16 and 17 (MJDs 59320 and 59321). Subsequently, the pulse shape gradually recovered over the course of several months. We report the results of continued multifrequency radio observations of the pulsar made using the Canadian Hydrogen Intensity Mapping Experiment and the 100 m Green Bank Telescope in a 3 yr period encompassing the shape change event, between 2020 February and 2023 February. As of 2023 February, the pulse shape had returned to a state similar to that seen before the event, but with measurable changes remaining. The amplitude of the shape change and the accompanying time-of-arrival residuals display a strong nonmonotonic dependence on radio frequency, demonstrating that the event is neither a glitch (the effects of which should be independent of radio frequency, ν ) nor a change in dispersion measure alone (which would produce a delay proportional to ν ^−2 ). However, it does bear some resemblance to the two previous “chromatic timing events” observed in J1713+0747, as well as to a similar event observed in PSR J1643−1224 in 2015.

Pulsar timing arrays (PTAs) are designed to detect low-frequency gravitational waves (GWs). GWs induce achromatic signals in PTA data, meaning that the timing delays do not depend on radio frequency. However, pulse arrival times are also affected by radio-frequency-dependent “chromatic” noise from sources such as dispersion measure (DM) and scattering delay variations. Furthermore, the characterization of GW signals may be influenced by the choice of chromatic noise model for each pulsar. To better understand this effect, we assess if and how different chromatic noise models affect the achromatic noise properties in each pulsar. The models we compare include existing DM models used by the North American Nanohertz Observatory for Gravitational waves (NANOGrav) and noise models used for the European PTA Data Release 2 (EPTA DR2). We perform this comparison using a subsample of six pulsars from the NANOGrav 15 yr data set, selecting the same six pulsars as from the EPTA DR2 six-pulsar data set. We find that the choice of chromatic noise model noticeably affects the achromatic noise properties of several pulsars. This is most dramatic for PSR J1713+0747, where the amplitude of its achromatic red noise lowers from ${\mathrm{log}}_{10}{A}_{\mathrm{RN}}=-{14.1}_{-0.1}^{+0.1}$ to $-{14.7}_{-0.5}^{+0.3}$ , and the spectral index broadens from ${\gamma }_{\mathrm{RN}}={2.6}_{-0.4}^{+0.5}$ to ${\gamma }_{\mathrm{RN}}={3.5}_{-0.9}^{+1.2}$ . We also compare each pulsar's noise properties with those inferred from the EPTA DR2, using the same models. From the discrepancies, we identify potential areas where the noise models could be improved. These results highlight the potential for custom chromatic noise models to improve PTA sensitivity to GWs.

Abstract Background Neoadjuvant therapy with dual HER2 blockade improved pathological complete response (pCR) rate in HER2‐positive breast cancer patients. Nevertheless, it would be desirable to identify patients exquisitely responsive to single agent trastuzumab to minimize or avoid overtreatment. Herein, we evaluated the predictive and prognostic value of basal primary tumor miRNA expression profile within the trastuzumab arm of NeoALTTO study (ClinicalTrials.gov Identifier: NCT00553358). Methods RNA samples from baseline biopsies were randomized into training (n = 45) and testing (n = 47) sets. After normalization, miRNAs associated with Event‐free survival (EFS) and pCR were identified by univariate analysis. Multivariate models were implemented to generate specific signatures which were first confirmed, and then analyzed together with other clinical and pathological variables. Results We identified a prognostic signature including hsa‐miR‐153‐3p (HR 1.831, 95% CI: 1.34–2.50) and hsa‐miR‐219a‐5p (HR 0.629, 95% CI: 0.50–0.78). For two additional miRNAs (miR‐215‐5p and miR‐30c‐2‐3p), we found a statistically significant interaction term with pCR (p.interaction: 0.017 and 0.038, respectively). Besides, a two‐miRNA signature was predictive of pCR (hsa‐miR‐31‐3p, OR 0.70, 95% CI: 0.53–0.92, and hsa‐miR‐382‐3p, OR: 1.39, 95% CI: 1.01–1.91). Notably, the performance of this predictive miRNA signature resembled that of the genomic classifiers PAM50 and TRAR, and did not improve when the extended models were fitted. Conclusion Analyses of primary tumor tissue miRNAs hold the potential of a parsimonious tool to identify patients with differential clinical outcomes after trastuzumab based neoadjuvant therapy.

Habitat suitability modelling (HSM) is a tool that is increasingly being used to help guide decision making for conservation management. It can also be used to focus efforts of restoration in our oceans. To improve on model performance, the best available environmental data along with species distribution data are needed. Marine habitats tend to have ecological niches defined by physical environmental conditions and of particular importance for shallow water species is wave energy. In this study we examined the relative improvements to HSM outputs that could be achieved by producing high-resolution Delft-3D modelled wave height data to see if model predictions at a fine-scale can be improved. Seagrasses were used as an exemplar and comparisons at fine-scale showed considerable differences in the area predicted suitable for seagrass growth and greatly increased the importance of waves as a predictor variable when compared with open-source low resolution wave energy data.

BackgroundThe absence of breast cancer cells in surgical specimens, i.e., pathological complete response (pCR), is widely recognized as a favorable prognostic factor after neoadjuvant therapy. In contrast, the presence of disease at surgery characterizes a prognostically heterogeneous group of patients. Here, we challenged circulating microRNAs (miRNAs) at the end of neoadjuvant therapy as potential prognostic biomarkers in the NeoALTTO study.MethodsPatients treated within the trastuzumab arm (i.e., pre-operative weekly trastuzumab for 6 weeks followed by the addition of weekly paclitaxel for 12 weeks; post-operative FEC for 3 cycles followed by trastuzumab up to complete 1 year of treatment) were randomized into a training (n= 54) and testing (n= 72) set. RT-PCR-based high-throughput miRNA profile was performed on plasma samples collected at the end of neoadjuvant treatment of both sets. After normalization, circulating miRNAs associated with event free survival (EFS) were identified by univariate and multivariate Cox regression model.ResultsStarting from 23 circulating miRNAs associated with EFS in the training set, we generated a 3-circulating miRNA prognostic signature consisting of miR-185-5p, miR-146a-5p, miR-22-3p, which was confirmed in the testing set. The 3-circulating miRNA signature showed a C-statistic of 0.62 (95% confidence interval [95%CI] 0.53-0.71) in the entire study cohort. By resorting to a multivariate Cox regression model we found a statistical significant interaction between the expression values of miR-194-5p and pCR status (p.interaction =0.005) with an estimate Hazard Ratio (HR) of 1.83 (95%CI 1.14- 2.95) in patients with pCR, and 0.87 (95%CI 0.69-1.10) in those without pCR. Notably, the model including this interaction along with the abovementioned 3-circulating miRNA signature provided the highest discriminatory capability with a C-statistic of 0.67 (95%CI 0.58-0.76).ConclusionsCirculating miRNAs are informative to identify patients with different prognosis among those with heterogeneous response after trastuzumab-based neoadjuvant treatment, and may be an exploitable tool to select candidates for salvage adjuvant therapy.

We present observations and timing analyses of 68 millisecond pulsars (MSPs) comprising the 15 yr data set of the North American Nanohertz Observatory for Gravitational Waves (NANOGrav). NANOGrav is a pulsar timing array (PTA) experiment that is sensitive to low-frequency gravitational waves (GWs). This is NANOGrav’s fifth public data release, including both “narrowband” and “wideband” time-of-arrival (TOA) measurements and corresponding pulsar timing models. We have added 21 MSPs and extended our timing baselines by 3 yr, now spanning nearly 16 yr for some of our sources. The data were collected using the Arecibo Observatory, the Green Bank Telescope, and the Very Large Array between frequencies of 327 MHz and 3 GHz, with most sources observed approximately monthly. A number of notable methodological and procedural changes were made compared to our previous data sets. These improve the overall quality of the TOA data set and are part of the transition to new pulsar timing and PTA analysis software packages. For the first time, our data products are accompanied by a full suite of software to reproduce data reduction, analysis, and results. Our timing models include a variety of newly detected astrometric and binary pulsar parameters, including several significant improvements to pulsar mass constraints. We find that the time series of 23 pulsars contain detectable levels of red noise, 10 of which are new measurements. In this data set, we find evidence for a stochastic GW background.

We report multiple lines of evidence for a stochastic signal that is correlated among 67 pulsars from the 15 yr pulsar timing data set collected by the North American Nanohertz Observatory for Gravitational Waves. The correlations follow the Hellings–Downs pattern expected for a stochastic gravitational-wave background. The presence of such a gravitational-wave background with a power-law spectrum is favored over a model with only independent pulsar noises with a Bayes factor in excess of 10 ^14 , and this same model is favored over an uncorrelated common power-law spectrum model with Bayes factors of 200–1000, depending on spectral modeling choices. We have built a statistical background distribution for the latter Bayes factors using a method that removes interpulsar correlations from our data set, finding p = 10 ^−3 (≈3 σ ) for the observed Bayes factors in the null no-correlation scenario. A frequentist test statistic built directly as a weighted sum of interpulsar correlations yields p = 5 × 10 ^−5 to 1.9 × 10 ^−4 (≈3.5 σ –4 σ ). Assuming a fiducial f ^−2/3 characteristic strain spectrum, as appropriate for an ensemble of binary supermassive black hole inspirals, the strain amplitude is ${2.4}_{-0.6}^{+0.7}\times {10}^{-15}$ (median + 90% credible interval) at a reference frequency of 1 yr ^−1 . The inferred gravitational-wave background amplitude and spectrum are consistent with astrophysical expectations for a signal from a population of supermassive black hole binaries, although more exotic cosmological and astrophysical sources cannot be excluded. The observation of Hellings–Downs correlations points to the gravitational-wave origin of this signal.

Pulsar timing array collaborations, such as the North American Nanohertz Observatory for Gravitational Waves (NANOGrav), are seeking to detect nanohertz gravitational waves emitted by supermassive black hole binaries formed in the aftermath of galaxy mergers. We have searched for continuous waves from individual circular supermassive black hole binaries using NANOGrav’s recent 12.5 yr data set. We created new methods to accurately model the uncertainties on pulsar distances in our analysis, and we implemented new techniques to account for a common red-noise process in pulsar timing array data sets while searching for deterministic gravitational wave signals, including continuous waves. As we found no evidence for continuous waves in our data, we placed 95% upper limits on the strain amplitude of continuous waves emitted by these sources. At our most sensitive frequency of 7.65 nHz, we placed a sky-averaged limit of h _0 < (6.82 ± 0.35) × 10 ^−15 , and h _0 < (2.66 ± 0.15) × 10 ^−15 in our most sensitive sky location. Finally, we placed a multimessenger limit of ${ \mathcal M }\lt (1.41\pm 0.02)\times {10}^{9}\,{M}_{\odot }$ on the chirp mass of the supermassive black hole binary candidate 3C 66B.

We present multiwavelength high-spatial resolution (∼0.″1, 70 pc) observations of UGC 4211 at z = 0.03474, a late-stage major galaxy merger at the closest nuclear separation yet found in near-IR imaging (0.″32, ∼230 pc projected separation). Using Hubble Space Telescope/Space Telescope Imaging Spectrograph, Very Large Telescope/MUSE+AO, Keck/OSIRIS+AO spectroscopy, and the Atacama Large Millimeter/submillimeter Array (ALMA) observations, we show that the spatial distribution, optical and near-infrared emission lines, and millimeter continuum emission are all consistent with both nuclei being powered by accreting supermassive black holes (SMBHs). Our data, combined with common black hole mass prescriptions, suggest that both SMBHs have similar masses, $\mathrm{log}\left({M}_{\mathrm{BH}}/{M}_{\odot }\right)$ ∼ 8.1 (south) and $\mathrm{log}\left({M}_{\mathrm{BH}}/{M}_{\odot }\right)$ ∼ 8.3 (north), respectively. The projected separation of 230 pc (∼6× the black hole sphere of influence) represents the closest-separation dual active galactic nuclei (AGN) studied to date with multiwavelength resolved spectroscopy and shows the potential of nuclear (

Evidence for a low-frequency stochastic gravitational-wave background has recently been reported based on analyses of pulsar timing array data. The most likely source of such a background is a population of supermassive black hole binaries, the loudest of which may be individually detected in these data sets. Here we present the search for individual supermassive black hole binaries in the NANOGrav 15 yr data set. We introduce several new techniques, which enhance the efficiency and modeling accuracy of the analysis. The search uncovered weak evidence for two candidate signals, one with a gravitational-wave frequency of ∼4 nHz, and another at ∼170 nHz. The significance of the low-frequency candidate was greatly diminished when Hellings–Downs correlations were included in the background model. The high-frequency candidate was discounted due to the lack of a plausible host galaxy, the unlikely astrophysical prior odds of finding such a source, and since most of its support comes from a single pulsar with a commensurate binary period. Finding no compelling evidence for signals from individual binary systems, we place upper limits on the strain amplitude of gravitational waves emitted by such systems. At our most sensitive frequency of 6 nHz, we place a sky-averaged 95% upper limit of 8 × 10 ^−15 on the strain amplitude. We also calculate an exclusion volume and a corresponding effective radius, within which we can rule out the presence of black hole binaries emitting at a given frequency.

The NANOGrav 15 yr data set shows evidence for the presence of a low-frequency gravitational-wave background (GWB). While many physical processes can source such low-frequency gravitational waves, here we analyze the signal as coming from a population of supermassive black hole (SMBH) binaries distributed throughout the Universe. We show that astrophysically motivated models of SMBH binary populations are able to reproduce both the amplitude and shape of the observed low-frequency gravitational-wave spectrum. While multiple model variations are able to reproduce the GWB spectrum at our current measurement precision, our results highlight the importance of accurately modeling binary evolution for producing realistic GWB spectra. Additionally, while reasonable parameters are able to reproduce the 15 yr observations, the implied GWB amplitude necessitates either a large number of parameters to be at the edges of expected values or a small number of parameters to be notably different from standard expectations. While we are not yet able to definitively establish the origin of the inferred GWB signal, the consistency of the signal with astrophysical expectations offers a tantalizing prospect for confirming that SMBH binaries are able to form, reach subparsec separations, and eventually coalesce. As the significance grows over time, higher-order features of the GWB spectrum will definitively determine the nature of the GWB and allow for novel constraints on SMBH populations.

The 15 yr pulsar timing data set collected by the North American Nanohertz Observatory for Gravitational Waves (NANOGrav) shows positive evidence for the presence of a low-frequency gravitational-wave (GW) background. In this paper, we investigate potential cosmological interpretations of this signal, specifically cosmic inflation, scalar-induced GWs, first-order phase transitions, cosmic strings, and domain walls. We find that, with the exception of stable cosmic strings of field theory origin, all these models can reproduce the observed signal. When compared to the standard interpretation in terms of inspiraling supermassive black hole binaries (SMBHBs), many cosmological models seem to provide a better fit resulting in Bayes factors in the range from 10 to 100. However, these results strongly depend on modeling assumptions about the cosmic SMBHB population and, at this stage, should not be regarded as evidence for new physics. Furthermore, we identify excluded parameter regions where the predicted GW signal from cosmological sources significantly exceeds the NANOGrav signal. These parameter constraints are independent of the origin of the NANOGrav signal and illustrate how pulsar timing data provide a new way to constrain the parameter space of these models. Finally, we search for deterministic signals produced by models of ultralight dark matter (ULDM) and dark matter substructures in the Milky Way. We find no evidence for either of these signals and thus report updated constraints on these models. In the case of ULDM, these constraints outperform torsion balance and atomic clock constraints for ULDM coupled to electrons, muons, or gluons.

Pulsar timing arrays (PTAs) are galactic-scale gravitational wave (GW) detectors. Each individual arm, composed of a millisecond pulsar, a radio telescope, and a kiloparsecs-long path, differs in its properties but, in aggregate, can be used to extract low-frequency GW signals. We present a noise and sensitivity analysis to accompany the NANOGrav 15 yr data release and associated papers, along with an in-depth introduction to PTA noise models. As a first step in our analysis, we characterize each individual pulsar data set with three types of white-noise parameters and two red-noise parameters. These parameters, along with the timing model and, particularly, a piecewise-constant model for the time-variable dispersion measure, determine the sensitivity curve over the low-frequency GW band we are searching. We tabulate information for all of the pulsars in this data release and present some representative sensitivity curves. We then combine the individual pulsar sensitivities using a signal-to-noise ratio statistic to calculate the global sensitivity of the PTA to a stochastic background of GWs, obtaining a minimum noise characteristic strain of 7 × 10 ^−15 at 5 nHz. A power-law-integrated analysis shows rough agreement with the amplitudes recovered in NANOGrav’s 15 yr GW background analysis. While our phenomenological noise model does not model all known physical effects explicitly, it provides an accurate characterization of the noise in the data while preserving sensitivity to multiple classes of GW signals.

The North American Nanohertz Observatory for Gravitational Waves (NANOGrav) has reported evidence for the presence of an isotropic nanohertz gravitational-wave background (GWB) in its 15 yr data set. However, if the GWB is produced by a population of inspiraling supermassive black hole binary (SMBHB) systems, then the background is predicted to be anisotropic, depending on the distribution of these systems in the local Universe and the statistical properties of the SMBHB population. In this work, we search for anisotropy in the GWB using multiple methods and bases to describe the distribution of the GWB power on the sky. We do not find significant evidence of anisotropy. By modeling the angular power distribution as a sum over spherical harmonics (where the coefficients are not bound to always generate positive power everywhere), we find that the Bayesian 95% upper limit on the level of dipole anisotropy is ( C _l _=1 / C _l _=0 ) < 27%. This is similar to the upper limit derived under the constraint of positive power everywhere, indicating that the dipole may be close to the data-informed regime. By contrast, the constraints on anisotropy at higher spherical-harmonic multipoles are strongly prior dominated. We also derive conservative estimates on the anisotropy expected from a random distribution of SMBHB systems using astrophysical simulations conditioned on the isotropic GWB inferred in the 15 yr data set and show that this data set has sufficient sensitivity to probe a large fraction of the predicted level of anisotropy. We end by highlighting the opportunities and challenges in searching for anisotropy in pulsar timing array data.

Seagrasses are declining globally, in large part due to increased anthropogenic coastal nutrient loads that enhance smothering by macroalgae, attenuate light, and are toxic when in excessive concentrations of inorganic nitrogen and phosphorus. However, as sanitation is improved many seagrass meadows have been observed to recover, with a few studies suggesting that they may even benefit from mild anthropogenic nutrient additions. Monitoring seagrass demography and health has faced difficulties in establishing the adequate variables and metrics. Such uncertainty in the methods has caused uncertainty of the significance of results presented and compromised extrapolations to other seasons, areas, or species. One solution has come from within the plant self-thinning theories. During the 1980s, an interspecific boundary line (IBL) was determined as the upper limit of the combination of plant density and above-ground biomass for any stand on Earth, setting their maximum possible efficiency in space occupation. Recently, two meta-analyses to determine specific IBLs for algae and for seagrasses have been performed. The recently updated seagrass dataset comprises 5,052 observations from 78 studies on 18 species. These IBLs opened new perspectives for monitoring: the observed distance of a stand to the respective IBL (i.e., each stand’s relative efficiency of space occupation) was demonstrated to be a valuable indicator of a population’s health. Thus, this metric can be used to determine the impact of nutrients and pollutants on algae and seagrass populations. Furthermore, because the IBLs are common to all species, they may be used to compare all species from any location worldwide. This novel approach showed that Halodule wrightii, Halodule beaudettei, Halophila baillonii, Zostera marina, and Zostera noltei meadows benefit from anthropogenic additions of nitrogen and phosphorus, as long as these additions are moderate. In fact, the healthier Z. noltei meadows in Portugal (and among the healthiest meadows worldwide) were the ones exposed to effluents from wastewater treatment plants (WWTP) and a food factory. We conclude that those effluents are providing water with enough quality and that their optimal management should coordinate the technological solutions of the WWTP with the natural potential of seagrass meadows as water purifiers and biomass producers.

Coastal ecosystems, including coral reefs, mangroves, and seagrass, are in global decline. Mitigation approaches include restoration and other managed recovery interventions. To maximise success, these should be guided by an understanding of the environmental niche and geographic limits of foundational species. However, the choices of data, variables, and modelling approaches can be bewildering when embarking on such an exercise, and the biases associated with such choices are often unknown. We reviewed the current available knowledge on methodological approaches and environmental variables used to model and map habitat suitability for coastal ecosystems. While our focus is on seagrass, we draw on information from all marine macrophyte studies for greater coverage of approaches at different scales around the world. We collated 75 publications, of which 35 included seagrasses. Out of all the publications, we found the most commonly used predictor variables were temperature (64%), bathymetry (61%), light availability (49%), and salinity (49%), respectively. The same predictor variables were also commonly used in the 35 seagrass Habitat Suitability Models (HSM) but in the following order: bathymetry (74%), salinity (57%), light availability (51%), and temperature (51%). The most popular method used in marine macrophyte HSMs was an ensemble of models (29%) followed by MaxEnt (17%). Cross-validation was the most commonly used selection procedure (24%), and threshold probability was the favoured model validation (33%). Most studies (87%) did not calculate or report uncertainty measures. The approach used to create an HSM was found to vary by location and scale of the study. Based upon previous studies, it can be suggested that the best approach for seagrass HSM would be to use an ensemble of models, including MaxEnt along with a selection procedure (Cross-validation) and threshold probability to validate the model with the use of uncertainty measures in the model process.

In the area of drug discovery, repurposing strategies represent an approach to discover new uses of approved drugs besides their original indications. We used this approach to investigate the effects of dimethyl fumarate (DMF), a drug approved for relapsing–remitting multiple sclerosis and psoriasis treatment, on early injury associated with diabetic retinopathy (DR). We used an in vivo streptozotocin (STZ)-induced diabetic rat model. Diabetes was induced by a single injection of STZ in rats, and after 1 week, a group of animals was treated with a daily intraperitoneal injection of DMF or a vehicle. Three weeks after diabetes induction, the retinal expression levels of key enzymes involved in DR were evaluated. In particular, the biomarkers COX-2, iNOS, and HO-1 were assessed via Western blot and immunohistochemistry analysis. Diabetic rats showed a significant retinal upregulation of COX-2 and iNOS compared to the retina of normal rats (non-diabetic), and an increase in HO-1 was also observed in the STZ group. This latter result was due to a mechanism of protection elicited by the pathological condition. DMF treatment significantly induced the retinal expression of HO-1 in STZ-induced diabetic animals with a reduction in iNOS and COX-2 retinal levels. Taken together, these results suggested that DMF might be useful to counteract the inflammatory process and the oxidative response in DR. In conclusion, we believe that DMF represents a potential candidate to treat diabetic retinopathy and warrants further in vivo and clinical evaluation.

Summary: The exposome concept encourages holistic consideration of the non-genetic factors (environmental exposures including lifestyle) that influence an individual’s health over their life course. However, disconnect between the concept and practical application has promoted divergent interpretations of the exposome across disciplines and reinforced separation of the environmental (emphasizing exposures) and biological (emphasizing responses) research communities. In particular, while knowledge of biological responses can help to distinguish actual (i.e. experienced) from potential exposures, the inclusion of endogenous processes has generated confusion about the position of the exposome in a multi-omics systems biology context. We propose a reattribution of “exposome” to exclusively represent the totality of contact with external factors that a biological entity experiences, and introduce the term “functional exposomics” to denote the systematic study of exposure-phenotype interaction. This reoriented definition of the exposome allows a more readily integrable dataset for multi-omics and systems biology research.