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Abstract Water resources are key to economic development of the Mekong River Basin, but are threatened by climate change and affected by hydropower development. Knowledge of these drivers’ integrated impact on future hydrological alterations is limited, especially with respect to flood inundation in the lower basin. This study assesses streamflow and flood extent alterations by reservoir operations and climate change using the latest climate projections. A distributed hydrologic model is used to generate discharge and flood extent. Our findings indicate substantial changes in seasonal and annual peak discharge due to reservoir operations. Under the future hydropower scenario, the discharge at Kratie will change by + 28% ( − 10%) during the dry (wet) season. While the effects of hydropower operations vary by season, climate change tends to increase river discharge overall. Under the high-emission scenario, the wet seasonal flow at Kratie will increase by + 7% in the near-future (2026–2050), but change by -5% under integrated impact of climate change and reservoir operations. In the far-future, the wet seasonal flow at Kratie under climate change only (integrated impact) will increase by + 33% (+ 19%). Although climate change is the dominant driver of flow alterations, hydropower development is critical for reducing discharge and flood magnitude. Nonparametric statistical testing shows significant changes in the inundated area by up to + 37% during the projected periods.

Study Region: Mekong River Basin Study focus: Climate change has become a global environmental and socio-economical issue potentially affecting river hydrology and downstream flood characteristics. Climate change and upstream dam construction are the two main driving factors in altering the hydrology in the Mekong River Basin. Cambodian floodplain is located in the vulnerable flood zone to be affected by climate change and upstream dam development. This study investigated the potential impacts of future climate change and dam construction on flood hazards and agricultural damages in the Cambodian floodplain of the Mekong River. Climate outputs from MRI-AGCM3.2S under different sea surface temperature (SST) scenarios and a large ensemble climate dataset (d4PDF) under 4 K increasing scenario were used as input to a fully distributed rainfall-runoff-inundation (RRI) model for future projection of flood in the Lower Mekong Basin. New hydrological insights for the region: The results indicated that the changes in extreme flood events (25-year flood) showed an increase of agricultural flood damages under climate change impact by 18–28% under different SST scenarios. These increases would be only 5–13% under integrated impacts from future climate change and dam construction. Under the 4 K increasing scenario, the agricultural damages for extreme flood events of 10-year, 50-year, and 100-year return periods would increase by 32%, 38%, and 39% for climate change impact alone and 17%, 29%, and 31% for combined climate change and dam development scenario, respectively. The climate change impacts would have more influential on increasing the magnitude of extreme floods than the reduction caused by dam reservoir constructions in the future projection in the Cambodian floodplain of the Mekong River Basin. The impact of dam constructions tends to have greater impact on reducing smaller extreme events; however, their impacts are relatively smaller for larger extreme flood events (i.e., 100-year flood).

Abstract Identification of the sources of the spatio-temporal information of flooding is important for flood control and understanding the water dynamic. Flood disasters are generally caused by two main sources: fluvial and pluvial flooding. However, there is a gap in information and challenge of such particular information in the Mekong River Basin (MRB) known as the largest river basin in Southeast Asia. This paper aims to analyze the spatio-temporal hydrograph separations of flooding and to determine the fluvial and pluvial sources of inundation water in the Lower Mekong Basin (LMB) by using a distributed rainfall–runoff–inundation (RRI) model and time–space accounting scheme (T–SAS) method. This study focuses on the two major flood events in 2000 and 2011, whose characteristics were different. The one in 2000 was long in terms of flood duration and it was the historically largest annual total flood water with twice the peak times in July and September. The 2011 flood had the highest flooded water during the peak time; however, its annual total flood water was less than the one in 2000. The results of spatio-temporal sources of flooding indicated that during the flow peak time in 2000 and 2011, the flow at Kratie was mainly contributed by 30-day (67%) and 100-day (98%) precipitation from the upstream. The drainage area of the MRB in China, northern Lao PDR, southern Lao PDR and eastern Thailand, and Cambodia and Vietnam contributed to peak flood at Kratie by 13%, 27%, 33%, and 27% for the 2000 flood and 12%, 33%, 38%, and 16% for the 2011 flood. The source of inundated water in the LMB was derived from upstream flow (fluvial source) of 35–36% and local rainfall (pluvial source) of 64–65%. Even though flood events in 2000 and 2011 had different characteristics, the sources of flood inundation in the LMB for both events were majorly from the local rainfall rather than the upstream flow. The large annual flood volume and long during of flooding in 2000 caused severe total economic damages up to 517 million US$ in the LMB countries (Cambodia, Lao PDR, Thailand, and Vietnam), while the high peak inundated water in 2011 with shorter flood duration caused damages of 493 million US$.

Reconstruction of historical land uses helps to understand patterns, drivers, and impacts of land-use change, and is essential for finding solutions to land-use sustainability. In order to analyze the relationship between land-use change and urban flooding, this study used the Classification and Regression Tree (CART) method to extract modern (2017) land-use data based on remote sensing images. Then, the Paleo-Land-Use Reconstruction (PLUR) program was used to reconstruct the land-use maps of Xi’an during the Ming (1582) and Qing (1766) dynasties by consulting and collecting records of land-use change in historical documents. Finally, the Flo-2D model was used to simulate urban flooding under different land-use scenarios. Over the past 435 years (1582–2017), the urban construction land area showed a trend of increasing, while the unused land area and water bodies were continuously decreasing. The increase in urban green space and buildings was 20.49% and 19.85% respectively, and the unused land area changed from 0.32 km2 to 0. Urban flooding in the modern land-use scenario is the most serious. In addition to the increase in impervious areas, the increase in building density and the decrease in water areas are also important factors that aggravate urban flooding. This study can provide a reference for future land-use planning and urban flooding control policy formulation and revision in the study area.

Abstract Missions into Deep Space are planned this decade. Yet the health consequences of exposure to microgravity and galactic cosmic radiation (GCR) over years-long missions on indispensable visceral organs such as the kidney are largely unexplored. We performed biomolecular (epigenomic, transcriptomic, proteomic, epiproteomic, metabolomic, metagenomic), clinical chemistry (electrolytes, endocrinology, biochemistry) and morphometry (histology, 3D imaging, miRNA-ISH, tissue weights) analyses using samples and datasets available from 11 spaceflight-exposed mouse and 5 human, 1 simulated microgravity rat and 4 simulated GCR-exposed mouse missions. We found that spaceflight induces: 1) renal transporter dephosphorylation which may indicate astronauts’ increased risk of nephrolithiasis is in part a primary renal phenomenon rather than solely a secondary consequence of bone loss; 2) remodelling of the nephron that results in expansion of distal convoluted tubule size but loss of overall tubule density; 3) renal damage and dysfunction when exposed to a Mars roundtrip dose-equivalent of simulated GCR.

Unlike conventional CPU caches, non-datapath caches, such as host-side flash caches which are extensively used as storage caches, have distinct requirements. While every cache miss results in a cache update in a conventional cache, non-datapath caches allow for the flexibility of selective caching, i.e., the option of not having to update the cache on each miss. We propose a new, generalized, bimodal caching algorithm, Fear Of Missing Out (FOMO), for managing non-datapath caches. Being generalized has the benefit of allowing any datapath cache replacement policy, such as LRU, ARC, or LIRS, to be augmented by FOMO to make these datapath caching algorithms better suited for non-datapath caches. Operating in two states, FOMO is selective—it selectively disables cache insertion and replacement depending on the learned behavior of the workload. FOMO is lightweight and tracks inexpensive metrics in order to identify these workload behaviors effectively. FOMO is evaluated using three different cache replacement policies against the current state-of-the-art non-datapath caching algorithms, using five different storage system workload repositories (totaling 176 workloads) for six different cache size configurations, each sized as a percentage of each workload’s footprint. Our extensive experimental analysis reveals that FOMO can improve upon other non-datapath caching algorithms across a range of production storage workloads, while also reducing the write rate.

Abstract Noncanonical epitopes presented by Human Leucocyte Antigen class I (HLA-I) complexes to CD8+ T cells attracted the spotlight in the research of novel immunotherapies against cancer, infection and autoimmunity. Proteasomes, which are the main producers of HLA-I-bound antigenic peptides, can catalyze both peptide hydrolysis and peptide splicing. The prediction of proteasome-generated spliced peptides is an objective that still requires a reliable (and large) database of non-spliced and spliced peptides produced by these proteases. Here, we present an extended database of proteasome-generated spliced and non-spliced peptides, which was obtained by analyzing in vitro digestions of 80 unique synthetic polypeptide substrates, measured by different mass spectrometers. Peptides were identified through invitroSPI method, which was validated through in silico and in vitro strategies. The peptide product database contains 16,631 unique peptide products (5,493 non-spliced, 6,453 cis-spliced and 4,685 trans-spliced peptide products), and a substrate sequence variety that is a valuable source for predictors of proteasome-catalyzed peptide hydrolysis and splicing. Potential artefacts and skewed results due to different identification and analysis strategies are discussed.

Summary: Neural crest cells are multipotent cells that delaminate from the neuroepithelium, migrating throughout the embryo. Aberrant migration causes developmental defects. Animal models are improving our understanding of neural crest anomalies, but in vivo migration behaviors are poorly understood. Here, we demonstrate that murine neural crest cells display actin-based lamellipodia and filopodia in vivo. Using neural crest-specific knockouts or inhibitors, we show that the serine-threonine kinase glycogen synthase kinase-3 (GSK3) and the cytoskeletal regulator lamellipodin (Lpd) are required for lamellipodia formation while preventing focal adhesion maturation. Lpd is a substrate of GSK3, and phosphorylation of Lpd favors interactions with the Scar/WAVE complex (lamellipodia formation) at the expense of VASP and Mena interactions (adhesion maturation and filopodia formation). This improved understanding of cytoskeletal regulation in mammalian neural crest migration has general implications for neural crest anomalies and cancer.

Background: Total hip arthroplasty (THA) for developmental hip dysplasia (DDH) often requires a subtrochanteric shortening derotational osteotomy (SDO) to limit leg lengthening, mitigate risk of peripheral nerve palsy, and reduce excessive femoral anteversion. Few studies exist detailing long-term clinical outcomes and survivorship. The aim of this study is to analyze the long-term outcomes and survivorship of an SDO-THA cohort. Methods: We retrospectively reviewed all patients who underwent cementless THA with femoral osteotomy due to Crowe I-IV DDH between 1991 and 2001. Primary outcome measures included revision surgery for any reason and functional outcome measures using modified Harris Hip scores. Secondary outcome measures included mode of implant failure and radiographic assessment for osteotomy union, polyethylene wear, osteolysis, and implant loosening. Results: Our review resulted in 24 SDO-THA cases in 20 patients with a mean follow-up of 19 years (range, 8-27 years). Overall survivorship was 67%. All 8 failures were treated with acetabular revision at a mean time to revision of 11 years (range, 1-25 years). Of the failures, there were 5 cases due to polyethylene wear (62.5%), 2 cases due to acetabular loosening (25%), and 1 case due to recurrent instability (12.5%). The mean postoperative modified Harris Hip score was 76 (range, 52-91) with long-term improvement of 43 points maintained (P < .001). Conclusions: THA with SDO can produce durable long-term outcomes for the patient with DDH. It is important to consider some common reasons for revision, namely polyethylene wear and osteolysis, acetabular loosening, and recurrent acetabular dislocations.

A 74-year-old male with a prior left total knee arthroplasty presented with deformity, loosening, pain, and stiffness associated with multiple raised, erythematous, cutaneous nodules about the anterior knee. Workup was concerning for infection, but the skin nodules were atypical. The patient was sent for biopsy which revealed cutaneous diffuse large B-cell lymphoma. The revision surgery was delayed, and the patient underwent chemotherapy/radiation with complete resolution of his lymphoma. He then underwent a successful aseptic revision total knee arthroplasty. Proper identification and treatment of rare cutaneous skin lesions about a prior surgical site can limit morbidity and result in more desirable outcomes.

Abstract Escherichia coli is a Gram-negative bacterium that colonises the human intestine and virulent strains can cause severe diarrhoeal and extraintestinal diseases. The protein SslE is secreted by a range of pathogenic and commensal E. coli strains. It can degrade mucins in the intestine, promotes biofilm maturation and it is a major determinant of infection in virulent strains, although how it carries out these functions is not well understood. Here, we examine SslE from the commensal E. coli Waksman and BL21 (DE3) strains and the enterotoxigenic H10407 and enteropathogenic E2348/69 strains. We reveal that SslE has a unique and dynamic structure in solution and in response to acidification within mature biofilms it can form a unique aggregate with amyloid-like properties. Furthermore, we show that both SslE monomers and aggregates bind DNA in vitro and co-localise with extracellular DNA (eDNA) in mature biofilms, and SslE aggregates may also associate with cellulose under certain conditions. Our results suggest that interactions between SslE and eDNA are important for biofilm maturation in many E. coli strains and SslE may also be a factor that drives biofilm formation in other SslE-secreting bacteria.

Actionable knowledge graph (AKG), a specialized version of knowledge graph, was proposed recently to represent, analyze, and predict human action, thus facilitating deeper understanding of human action by robots. However, the automatic construction of AKGs from action-related corpora is still an unexplored problem. In this study, we first propose three unsupervised matrix factorization–based frameworks for AKG generation from three different perspectives: subject, context and functionality of action, respectively. Further, we propose a hybrid model based on neural network matrix factorization (NNMF) that considers multi-source signals simultaneously. It not only learns the latent action representations, but also learns the optimal learning objective rather than assuming it to be fixed. To quantitatively verify the utility of the constructed AKGs, we introduce a novel application, that is, predicting the most likely missing action records in Wikipedia biographies. Experimental results on a large-scale Wikipedia biography dataset show that the proposed model brings significant improvement over the baselines, which demonstrates the strong expressiveness of our generated AKGs.

ABSTRACT Human immunodeficiency virus type-1 (HIV-1) infection is potently inhibited by human myxovirus resistance 2 (MX2/MxB), which binds to the viral capsid and blocks the nuclear import of viral DNA. We have recently shown that phosphorylation is a key regulator of MX2 antiviral activity, with phosphorylation of serine residues at positions 14, 17, and 18 repressing MX2 function. Here, we extend the study of MX2 posttranslational modifications and identify serine and threonine phosphorylation in all domains of MX2. By substituting these residues with aspartic acid or alanine, hence mimicking the presence or absence of a phosphate group, respectively, we identified key positions that control MX2 antiviral activity. Aspartic acid substitutions of residues Ser306 or Thr334 and alanine substitutions of Thr343 yielded proteins with substantially reduced antiviral activity, whereas the presence of aspartic acid at positions Ser28, Thr151, or Thr343 resulted in enhanced activity: referred to as hypermorphic mutants. In some cases, these hypermorphic mutations, particularly when paired with other MX2 mutations (e.g., S28D/T151D or T151D/T343A) acquired the capacity to inhibit HIV-1 capsid mutants known to be insensitive to wild-type MX2, such as P90A or T210K, as well as MX2-resistant retroviruses such as equine infectious anemia virus (EIAV) and murine leukemia virus (MLV). This work highlights the complexity and importance of MX2 phosphorylation in the regulation of antiviral activity and in the selection of susceptible viral substrates. IMPORTANCE Productive infection by human immunodeficiency virus type-1 (HIV-1) requires the import of viral replication complexes into the nuclei of infected cells. Myxovirus resistance 2 (MX2/MxB) blocks this step, halting nuclear accumulation of viral DNA and virus replication. We recently demonstrated how phosphorylation of a stretch of three serines in the amino-terminal domain of MX2 inhibits the antiviral activity. Here, we identify additional positions in MX2 whose phosphorylation status reduces or enhances antiviral function (hypomorphic and hypermorphic variants, respectively). Importantly, hypermorphic mutant proteins not only increased inhibitory activity against wild-type HIV-1 but can also exhibit antiviral capabilities against HIV-1 capsid mutant viruses that are resistant to wild-type MX2. Furthermore, some of these proteins were also able to inhibit retroviruses that are insensitive to MX2. Therefore, we propose that phosphorylation comprises a major element of MX2 regulation and substrate determination.

Abstract The discovery of compounds and proteins from plants has greatly contributed to modern medicine. Vernonia amygdalina Del. (Compositae) is used by humans and primates for a variety of conditions including parasitic infection. This paper describes the serendipitous discovery that V. amygdalina extract was able to bind to, and functionally inhibit, active TGFβ1. The binding agent was isolated and identified as chlorophyll a-b binding protein AB96. Given that active TGFβ1 contributes to the pathology of many infectious diseases, inhibiting these processes may explain some of the benefits associated with the ingestion of this species. This is the first plant-derived cytokine-neutralizing protein to be described and paves the way for further such discoveries.

BackgroundMRI-guided fusion biopsy is increasingly utilized over systematic 12-core biopsy for men with MRI-visible prostate lesions.Patients and MethodsPatients with MRI visible lesions who underwent MRI-guided fusion and systematic 12-core biopsy from 2016-2020 in the Intermountain Healthcare (IHC) system were consecutively analyzed. This was in the setting of a continuous quality assurance initiative among the reading radiologists. Primary outcome was prostate cancer (PCa) detection defined by Gleason grade group (GGG) 1 or higher. Clinically significant cancer (CSC) was defined as GGG 2 or higher. Patients were stratified by biopsy date, 2016-2017 and 2018-2021, and lesions were stratified by PI-RADS v2 category.ResultsA total of 184 patients with 324 MRI-detectable lesions underwent both biopsy modalities in the IHC system from 2016 to 2021. CSC was detected in 23.5% of MRI-guided fusion biopsies. Comparing PI-RAD v2 categories 1-3 to categories 4-5, rate of CSC was 10% and 42% respectively. MRI-guided fusion and systematic 12-core biopsies were concordant for PCa in 77% of men and CSC in 83%. MRI-guided fusion biopsy detected PCa in 26/103 and CSC in 20/131 men in whom systematic 12-core biopsy was negative. Systematic 12-core biopsy detected PCa in 17/94 and CSC in 11/122 men in whom MRI-guided fusion was negative.ConclusionsOmitting MRI-guided fusion or systematic 12-core biopsy would have resulted in underdiagnosis of CSC in 11% or 6% of patients respectively. Combining biopsies increased detection rate of CSC. This was in the setting of a continuous quality assurance program at a large community-based hospital.

In many real-world networks of interest in the field of remote sensing (e.g., public transport networks), nodes are associated with multiple labels, and node classes are imbalanced; that is, some classes have significantly fewer samples than others. However, the research problem of imbalanced multi-label graph node classification remains unexplored. This non-trivial task challenges the existing graph neural networks (GNNs) because the majority class can dominate the loss functions of GNNs and result in the overfitting of the majority class features and label correlations. On non-graph data, minority over-sampling methods (such as the synthetic minority over-sampling technique and its variants) have been demonstrated to be effective for the imbalanced data classification problem. This study proposes and validates a new hypothesis with unlabeled data over-sampling, which is meaningless for imbalanced non-graph data; however, feature propagation and topological interplay mechanisms between graph nodes can facilitate the representation learning of imbalanced graphs. Furthermore, we determine empirically that ensemble data synthesis through the creation of virtual minority samples in the central region of a minority and generation of virtual unlabeled samples in the boundary region between a minority and majority is the best practice for the imbalanced multi-label graph node classification task. Our proposed novel data over-sampling framework is evaluated using multiple real-world network datasets, and it outperforms diverse, strong benchmark models by a large margin.

Deep learning algorithms have seen a massive rise in popularity for remote sensing over the past few years. Recently, studies on applying deep learning techniques to graph data in remote sensing (e.g., public transport networks) have been conducted. In graph node classification tasks, traditional graph neural network (GNN) models assume that different types of misclassifications have an equal loss and thus seek to maximize the posterior probability of the sample nodes under labeled classes. The graph data used in realistic scenarios tend to follow unbalanced long-tailed class distributions, where a few majority classes contain most of the vertices and the minority classes contain only a small number of nodes, making it difficult for the GNN to accurately predict the minority class samples owing to the classification tendency of the majority classes. In this paper, we propose a dual cost-sensitive graph convolutional network (DCSGCN) model. The DCSGCN is a two-tower model containing two subnetworks that compute the posterior probability and the misclassification cost. The model uses the cost as ”complementary information” in a prediction to correct the posterior probability under the perspective of minimal risk. Furthermore, we propose a new method for computing the node cost labels based on topological graph information and the node class distribution. The results of extensive experiments demonstrate that DCSGCN outperformed other competitive baselines on different real-world imbalanced long-tailed graphs.

Background: Infection of total knee arthroplasty is a complex problem often resulting in multiple surgeries for the patient. We examined the early to midterm results of a retained cemented “low-friction” metal-on-polyethylene articulating antibiotic spacer in total knee arthroplasty. Methods: We retrospectively reviewed patients with a total knee cemented articulating antibiotic spacer performed for joint sepsis. Patients were allowed full weight bearing and normal activities after eradication of the infection at 6 weeks postop. Two months later, patients were given the option of conversion to a revision implant vs retention of the spacer. We examined infection cure rate, mechanical failure, Knee Society Scores, range of motion, and patient factors associated with spacer retention. Results: Fifty-five knees were studied with average follow-up of 1.8 years (0.2-8.4). Among patients choosing spacer retention (40%), the average follow-up time of the spacer was 3.3 years (0.6-8.4). Five patients (9.1%) required a repeat spacer for recurrent infection. Conclusions: Usage of articulating cement antibiotic spacers with a metal-on-polyethylene bearing couple provides excellent infection eradication, while also resulting in good functional outcomes. Early evidence suggests that use of the implant can be extended beyond typical timeframes and, in certain patient populations, may be suitable for a single-stage procedure. Keywords: Infection, Total knee, Antibiotic spacer, Articulating