Inflammation-targeted nanomedicine against brain cancer: From design strategies to future developments.

Brain cancer is an aggressive type of cancer with poor prognosis. While the immune system protects against cancer in the early stages, the tumor exploits the healing arm of inflammatory reactions to accelerate its growth and spread. Various immune cells penetrate the developing tumor region, establishing a pro-inflammatory tumor milieu. Additionally, tumor cells may release chemokines and cytokines to attract immune cells and promote cancer growth. Inflammation and its associated mechanisms in the progression of cancer have been extensively studied in the majority of solid tumors, especially brain tumors. However, treatment of the malignant brain cancer is hindered by several obstacles, such as the blood-brain barrier, transportation inside the brain interstitium, inflammatory mediators that promote tumor growth and invasiveness, complications in administering therapies to tumor cells specifically, the highly invasive nature of gliomas, and the resistance to drugs. To resolve these obstacles, nanomedicine could be a potential strategy that has facilitated advancements in diagnosing and treating brain cancer. Due to the numerous benefits provided by their small size and other features, nanoparticles have been a prominent focus of research in the drug-delivery field. The purpose of this article is to discuss the role of inflammatory mediators and signaling pathways in brain cancer as well as the recent advances in understanding the nano-carrier approaches for enhancing drug delivery to the brain in the treatment of brain cancer.

Epigenetics of glioblastoma multiforme: From molecular mechanisms to therapeutic approaches.

Glioblastoma multiforme (GBM) is the most common form of brain cancer and one of the most aggressive cancers found in humans. Most of the signs and symptoms of GBM can be mild and slowly aggravated, although other symptoms might demonstrate it as an acute ailment. However, the precise mechanisms of the development of GBM remain unknown. Due to the improvement of molecular pathology, current researches have reported that glioma progression is strongly connected with different types of epigenetic phenomena, such as histone modifications, DNA methylation, chromatin remodeling, and aberrant microRNA. Furthermore, the genes and the proteins that control these alterations have become novel targets for treating glioma because of the reversibility of epigenetic modifications. In some cases, gene mutations including P16, TP53, and EGFR, have been observed in GBM. In contrast, monosomies, including removals of chromosome 10, particularly q23 and q25-26, are considered the standard markers for determining the development and aggressiveness of GBM. Recently, amid the epigenetic therapies, histone deacetylase inhibitors (HDACIs) and DNA methyltransferase inhibitors have been used for treating tumors, either single or combined. Specifically, HDACIs are served as a good choice and deliver a novel pathway to treat GBM. In this review, we focus on the epigenetics of GBM and the consequence of its mutations. We also highlight various treatment approaches, namely gene editing, epigenetic drugs, and microRNAs to combat GBM.

TBK1-medicated DRP1 phosphorylation orchestrates mitochondrial dynamics and autophagy activation in osteoarthritis.

Mitochondrial dynamics, including mitochondrial fission and fusion, are critical for maintaining mitochondrial functions. Evidence shows that TANK-binding kinase 1 (TBK1) regulates mitochondrial fusion and fission and then mitophagy. Since a previous study demonstrates a strong correlation between mitophagy and osteoarthritis (OA), we herein investigated the potential role of TBK1 in OA process and mitochondrial functions. We demonstrated a strong correlation between TBK1 and OA, evidenced by significantly downregulated expression of TBK1 in cartilage tissue samples of OA patients and in the chondrocytes of aged mice, as well as TNF-α-stimulated phosphorylation of TBK1 in primary mouse chondrocytes. TBK1 overexpression significantly attenuated TNF-α-induced apoptosis and abnormal mitochondrial function in primary mouse chondrocytes. Furthermore, TBK1 overexpression induced remodeling of mitochondrial morphology by directly phosphorylating dynamin-related protein 1 (DRP1) at Ser637, abolishing the fission of DRP1 and preventing its fragmentation function. Moreover, TBK1 recruitment and DRP1 phosphorylation at Ser637 was necessary for engulfing damaged mitochondria by autophagosomal membranes during mitophagy. Moreover, we demonstrated that APMK/ULK1 signaling contributed to TBK1 activation. In OA mouse models established by surgical destabilization of the medial meniscus, intraarticular injection of lentivirus-TBK1 significantly ameliorated cartilage degradation via regulation of autophagy and alleviation of cell apoptosis. In conclusion, our results suggest that the TBK1/DRP1 pathway is involved in OA and pharmacological targeting of the TBK1-DRP1 cascade provides prospective therapeutic benefits for the treatment of OA.

Time trends, projections, and spatial distribution of low birthweight in Australia, 2009-2030: Evidence from the National Perinatal Data Collection.

INTRODUCTION: Infants with low birthweight (LBW, birthweight <2500 g) have increased in many high-resource countries over the past two decades. This study aimed to investigate the time trends, projections, and spatial distribution of LBW in Australia, 2009-2030. METHODS: We used standard aggregate data on 3 346 808 births from 2009 to 2019 from Australia's National Perinatal Data Collection. Bayesian linear regression model was used to estimate the trends in the prevalence of LBW in Australia. RESULTS: Wefound that the prevalence of LBW was 6.18% in 2009, which has increased to 6.64% in 2019 (average annual rate of change, AARC = +0.76%). If the national trend remains the same, the projected prevalence of LBW in Australia will increase to 7.34% (95% uncertainty interval, UI = 6.99, 7.68) in 2030. Observing AARC across different subpopulations, the trend of LBW was stable among Indigenous mothers, whereas it increased among non-Indigenous mothers (AARC = +0.81%). There is also an increase among the most disadvantaged mothers (AARC = +1.08%), birthing people in either of two extreme age groups (AARC = +1.99% and +1.53% for <20 years and ≥40 years, respectively), and mothers who smoked during pregnancy (AARC = +1.52%). Spatiotemporal maps showed that some of the Statistical Area level 3 (SA3) in Northern Territory and Queensland had consistently higher prevalence for LBW than the national average from 2014 to 2019. CONCLUSION: Overall, the prevalence of LBW has increased in Australia during 2009-2019; however, the trends vary across different subpopulations. If trends persist, Australia will not achieve the Sustainable Development Goals (SDGs) target of a 30% reduction in LBW by 2030. Centering and supporting the most vulnerable subpopulations is vital to progress the SDGs and improves perinatal and infant health in Australia.

Adolescent girls in aquaculture ecozones at risk of nutrient deficiency in Bangladesh development and validation of an integrated metric.

BACKGROUND: This study developed and validated an integrated metric that enhances understanding of linkages between agro-ecological and socio-economic factors that are important for explaining nutritional wellbeing in relation to fish consumption; especially among adolescent girls who are at risk of nutritional deficiency in Bangladesh. Currently, there is no metric that takes account of environmental, cultural and economic contexts when considering fish consumption and dietary health from a policy perspective. METHODS: The study was designed as a bi-seasonal survey, repeated in the same population of adolescent girls recruited during the dry and wet seasons. Sampling was stratified by five settings (four aqua-agroecological zones and one processing plant community), with 60 girls recruited in each. Associations between candidate predictors (salinity, diet diversity, religion, socio-economic status and women's autonomy score) and dependent variables representing nutritional outcomes (anthropometry, omega-3 index and micronutrient levels) were explored in multivariable regressions. The fitted model with its predictors was validated, and a risk score derived from responses to a few short questions on religion, salinity zone, female autonomy, diet diversity and tilapia consumption. RESULTS: The omega-3 index showed the clearest distinction between seasons, by salinity and religion. Higher female autonomy, religion (being Hindu rather than Muslim), geographical location (living in a high or mid-saline area), and a higher dietary diversity were the strongest predictors of whole blood omega-3 index. The c-index for the prognostic model was 0.83 and 0.76 in the wet and dry season respectively, indicating good predictive accuracy. There appeared to be a clear trend in risk scores differentiating between those 'chronically at risk' and those 'never at risk'. CONCLUSIONS: Observational data on different aquaculture-ecozones defined by salinity enabled us to establish linkages between seasonal fish intake, religion, diet diversity, female autonomy and nutritional wellbeing. The purpose of the metric is to reveal these specific linkages in practice. This tool should improve targeting of timely, preventative and cost-effective nutritional interventions to adolescent girls most at-risk from low omega-3 levels in communities where seafood is produced.

Computer-Aided Bleeding Detection Algorithms for Capsule Endoscopy: A Systematic Review.

Capsule endoscopy (CE) is a widely used medical imaging tool for the diagnosis of gastrointestinal tract abnormalities like bleeding. However, CE captures a huge number of image frames, constituting a time-consuming and tedious task for medical experts to manually inspect. To address this issue, researchers have focused on computer-aided bleeding detection systems to automatically identify bleeding in real time. This paper presents a systematic review of the available state-of-the-art computer-aided bleeding detection algorithms for capsule endoscopy. The review was carried out by searching five different repositories (Scopus, PubMed, IEEE Xplore, ACM Digital Library, and ScienceDirect) for all original publications on computer-aided bleeding detection published between 2001 and 2023. The Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) methodology was used to perform the review, and 147 full texts of scientific papers were reviewed. The contributions of this paper are: (I) a taxonomy for computer-aided bleeding detection algorithms for capsule endoscopy is identified; (II) the available state-of-the-art computer-aided bleeding detection algorithms, including various color spaces (RGB, HSV, etc.), feature extraction techniques, and classifiers, are discussed; and (III) the most effective algorithms for practical use are identified. Finally, the paper is concluded by providing future direction for computer-aided bleeding detection research.

Deep Learning-Based Segmentation of 3D Volumetric Image and Microstructural Analysis.

As a fundamental but difficult topic in computer vision, 3D object segmentation has various applications in medical image analysis, autonomous vehicles, robotics, virtual reality, lithium battery image analysis, etc. In the past, 3D segmentation was performed using hand-made features and design techniques, but these techniques could not generalize to vast amounts of data or reach acceptable accuracy. Deep learning techniques have lately emerged as the preferred method for 3D segmentation jobs as a result of their extraordinary performance in 2D computer vision. Our proposed method used a CNN-based architecture called 3D UNET, which is inspired by the famous 2D UNET that has been used to segment volumetric image data. To see the internal changes of composite materials, for instance, in a lithium battery image, it is necessary to see the flow of different materials and follow the directions analyzing the inside properties. In this paper, a combination of 3D UNET and VGG19 has been used to conduct a multiclass segmentation of publicly available sandstone datasets to analyze their microstructures using image data based on four different objects in the samples of volumetric data. In our image sample, there are a total of 448 2D images, which are then aggregated as one 3D volume to examine the 3D volumetric data. The solution involves the segmentation of each object in the volume data and further analysis of each object to find its average size, area percentage, total area, etc. The open-source image processing package IMAGEJ is used for further analysis of individual particles. In this study, it was demonstrated that convolutional neural networks can be trained to recognize sandstone microstructure traits with an accuracy of 96.78% and an IOU of 91.12%. According to our knowledge, many prior works have applied 3D UNET for segmentation, but very few papers extend it further to show the details of particles in the sample. The proposed solution offers a computational insight for real-time implementation and is discovered to be superior to the current state-of-the-art methods. The result has importance for the creation of an approximately similar model for the microstructural analysis of volumetric data.

Natural Compounds Targeting the Autophagy Pathway in the Treatment of Colorectal Cancer.

Autophagy is a highly conserved intracellular degradation pathway by which misfolded proteins or damaged organelles are delivered in a double-membrane vacuolar vesicle and finally degraded by lysosomes. The risk of colorectal cancer (CRC) is high, and there is growing evidence that autophagy plays a critical role in regulating the initiation and metastasis of CRC; however, whether autophagy promotes or suppresses tumor progression is still controversial. Many natural compounds have been reported to exert anticancer effects or enhance current clinical therapies by modulating autophagy. Here, we discuss recent advancements in the molecular mechanisms of autophagy in regulating CRC. We also highlight the research on natural compounds that are particularly promising autophagy modulators for CRC treatment with clinical evidence. Overall, this review illustrates the importance of autophagy in CRC and provides perspectives for these natural autophagy regulators as new therapeutic candidates for CRC drug development.

The promise and pitfalls of community-based monitoring with a focus on Canadian examples.

Community-based monitoring (CBM) is a widely used form of scientific data collection in which local community members directly participate in ecosystem research and also help the process by sharing traditional ecological knowledge and local understanding of land and resources. This paper presents a review of the challenges and opportunities associated with CBM projects in Canada and internationally. While Canadian cases are the primary focus, international examples are drawn on to provide additional context. Based on our review of 121 documents and publications, we found that CBM helps fill science research gaps by providing access to continuous data sets on the ecosystems being studied. CBM also increases the credibility of the data among users, as the community itself takes part in the environmental monitoring process. CBM supports cross-cultural learning and the co-production of knowledge by using traditional ecological knowledge with science, thereby enabling researchers, scientists and community members to learn from one another. Our review suggests that although there are multiple successes, CBM faces several challenges that constrain its progress including funding shortages, lack of support for local stewardship, and inadequate training for local users in the operation of equipment and data collection methods. Data sharing and rights on the use of data are also constraining to the long-term success of CBM programs.

A Comprehensive Review on Lane Marking Detection Using Deep Neural Networks.

Lane marking recognition is one of the most crucial features for automotive vehicles as it is one of the most fundamental requirements of all the autonomy features of Advanced Driver Assistance Systems (ADAS). Researchers have recently made promising improvements in the application of Lane Marking Detection (LMD). This research article has taken the initiative to review lane marking detection, mainly using deep learning techniques. This paper initially discusses the introduction of lane marking detection approaches using deep neural networks and conventional techniques. Lane marking detection frameworks can be categorized into single-stage and two-stage architectures. This paper elaborates on the network's architecture and the loss function for improving the performance based on the categories. The network's architecture is divided into object detection, classification, and segmentation, and each is discussed, including their contributions and limitations. There is also a brief indication of the simplification and optimization of the network for simplifying the architecture. Additionally, comparative performance results with a visualization of the final output of five existing techniques is elaborated. Finally, this review is concluded by pointing to particular challenges in lane marking detection, such as generalization problems and computational complexity. There is also a brief future direction for solving the issues, for instance, efficient neural network, Meta, and unsupervised learning.

Antidiabetic Potential of Commonly Available Fruit Plants in Bangladesh: Updates on Prospective Phytochemicals and Their Reported MoAs.

Diabetes mellitus is a life-threatening disorder affecting people of all ages and adversely disrupts their daily functions. Despite the availability of numerous synthetic-antidiabetic medications and insulin, the demand for the development of novel antidiabetic medications is increasing due to the adverse effects and growth of resistance to commercial drugs in the long-term usage. Hence, antidiabetic phytochemicals isolated from fruit plants can be a very nifty option to develop life-saving novel antidiabetic therapeutics, employing several pathways and MoAs (mechanism of actions). This review focuses on the antidiabetic potential of commonly available Bangladeshi fruits and other plant parts, such as seeds, fruit peals, leaves, and roots, along with isolated phytochemicals from these phytosources based on lab findings and mechanism of actions. Several fruits, such as orange, lemon, amla, tamarind, and others, can produce remarkable antidiabetic actions and can be dietary alternatives to antidiabetic therapies. Besides, isolated phytochemicals from these plants, such as swertisin, quercetin, rutin, naringenin, and other prospective phytochemicals, also demonstrated their candidacy for further exploration to be established as antidiabetic leads. Thus, it can be considered that fruits are one of the most valuable gifts of plants packed with a wide spectrum of bioactive phytochemicals and are widely consumed as dietary items and medicinal therapies in different civilizations and cultures. This review will provide a better understanding of diabetes management by consuming fruits and other plant parts as well as deliver innovative hints for the researchers to develop novel drugs from these plant parts and/or their phytochemicals.

DL-3-n-butylphthalide ameliorates diabetes-associated cognitive decline by enhancing PI3K/Akt signaling and suppressing oxidative stress.

DL-3-n-Butylphthalide (DL-NBP), a small molecular compound extracted from the seeds of Apium graveolens Linn (Chinese celery), has been shown to exert neuroprotective effects due to its anti-inflammatory, anti-oxidative and anti-apoptotic activities. DL-NBP not only protects against ischemic cerebral injury, but also ameliorates vascular cognitive impairment in dementia patients including AD and PD. In the current study, we investigated whether and how DL-NBP exerted a neuroprotective effect against diabetes-associated cognitive decline (DACD) in db/db mice, a model of type-2 diabetes. db/db mice were orally administered DL-NBP (20, 60, 120 mg· kg-1· d-1) for 8 weeks. Then the mice were subjected to behavioral test, their brain tissue was collected for morphological and biochemical analyses. We showed that oral administration of DL-NBP significantly ameliorated the cognitive decline with improved learning and memory function in Morris water maze testing. Furthermore, DL-NBP administration attenuated diabetes-induced morphological alterations and increased neuronal survival and restored the levels of synaptic protein PSD95, synaptophysin and synapsin-1 as well as dendritic density in the hippocampus, especially at a dose of 60 mg/kg. Moreover, we revealed that DL-NBP administration suppressed oxidative stress by upregulating Nrf2/HO-1 signaling, and increased brain-derived neurotrophic factor (BDNF) expression by activating PI3K/Akt/CREB signaling in the hippocampus. These beneficial effects of DL-NBP were observed in high glucose-treated PC12 cells. Our results suggest that DL-NBP may be a potential pharmacologic agent for the treatment of DACD.

Detecting Parkinson Disease Using a Web-Based Speech Task: Observational Study.

BACKGROUND: Access to neurological care for Parkinson disease (PD) is a rare privilege for millions of people worldwide, especially in resource-limited countries. In 2013, there were just 1200 neurologists in India for a population of 1.3 billion people; in Africa, the average population per neurologist exceeds 3.3 million people. In contrast, 60,000 people receive a diagnosis of PD every year in the United States alone, and similar patterns of rising PD cases-fueled mostly by environmental pollution and an aging population-can be seen worldwide. The current projection of more than 12 million patients with PD worldwide by 2040 is only part of the picture given that more than 20% of patients with PD remain undiagnosed. Timely diagnosis and frequent assessment are key to ensure timely and appropriate medical intervention, thus improving the quality of life of patients with PD. OBJECTIVE: In this paper, we propose a web-based framework that can help anyone anywhere around the world record a short speech task and analyze the recorded data to screen for PD. METHODS: We collected data from 726 unique participants (PD: 262/726, 36.1% were women; non-PD: 464/726, 63.9% were women; average age 61 years) from all over the United States and beyond. A small portion of the data (approximately 54/726, 7.4%) was collected in a laboratory setting to compare the performance of the models trained with noisy home environment data against high-quality laboratory-environment data. The participants were instructed to utter a popular pangram containing all the letters in the English alphabet, "the quick brown fox jumps over the lazy dog." We extracted both standard acoustic features (mel-frequency cepstral coefficients and jitter and shimmer variants) and deep learning-based embedding features from the speech data. Using these features, we trained several machine learning algorithms. We also applied model interpretation techniques such as Shapley additive explanations to ascertain the importance of each feature in determining the model's output. RESULTS: We achieved an area under the curve of 0.753 for determining the presence of self-reported PD by modeling the standard acoustic features through the XGBoost-a gradient-boosted decision tree model. Further analysis revealed that the widely used mel-frequency cepstral coefficient features and a subset of previously validated dysphonia features designed for detecting PD from a verbal phonation task (pronouncing "ahh") influence the model's decision the most. CONCLUSIONS: Our model performed equally well on data collected in a controlled laboratory environment and in the wild across different gender and age groups. Using this tool, we can collect data from almost anyone anywhere with an audio-enabled device and help the participants screen for PD remotely, contributing to equity and access in neurological care.

Amaryllidaceae Alkaloids of Norbelladine-Type as Inspiration for Development of Highly Selective Butyrylcholinesterase Inhibitors: Synthesis, Biological Activity Evaluation, and Docking Studies.

Alzheimer's disease (AD) is a multifactorial neurodegenerative condition of the central nervous system (CNS) that is currently treated by cholinesterase inhibitors and the N-methyl-d-aspartate receptor antagonist, memantine. Emerging evidence strongly supports the relevance of targeting butyrylcholinesterase (BuChE) in the more advanced stages of AD. Within this study, we have generated a pilot series of compounds (1-20) structurally inspired from belladine-type Amaryllidaceae alkaloids, namely carltonine A and B, and evaluated their acetylcholinesterase (AChE) and BuChE inhibition properties. Some of the compounds exhibited intriguing inhibition activity for human BuChE (hBuChE), with a preference for BuChE over AChE. Seven compounds were found to possess a hBuChE inhibition profile, with IC50 values below 1 µM. The most potent one, compound 6, showed nanomolar range activity with an IC50 value of 72 nM and an excellent selectivity pattern over AChE, reaching a selectivity index of almost 1400. Compound 6 was further studied by enzyme kinetics, along with in-silico techniques, to reveal the mode of inhibition. The prediction of CNS availability estimates that all the compounds in this survey can pass through the blood-brain barrier (BBB), as disclosed by the BBB score.

Recent Progress on Biological Activity of Amaryllidaceae and Further Isoquinoline Alkaloids in Connection with Alzheimer's Disease.

Alzheimer's disease (AD) is a progressive age-related neurodegenerative disease recognized as the most common form of dementia among elderly people. Due to the fact that the exact pathogenesis of AD still remains to be fully elucidated, the treatment is only symptomatic and available drugs are not able to modify AD progression. Considering the increase in life expectancy worldwide, AD rates are predicted to increase enormously, and thus the search for new AD drugs is urgently needed. Due to their complex nitrogen-containing structures, alkaloids are considered to be promising candidates for use in the treatment of AD. Since the introduction of galanthamine as an antidementia drug in 2001, Amaryllidaceae alkaloids (AAs) and further isoquinoline alkaloids (IAs) have been one of the most studied groups of alkaloids. In the last few years, several compounds of new structure types have been isolated and evaluated for their biological activity connected with AD. The present review aims to comprehensively summarize recent progress on AAs and IAs since 2010 up to June 2021 as potential drugs for the treatment of AD.

Semisynthetic Derivatives of Selected Amaryllidaceae Alkaloids as a New Class of Antimycobacterial Agents.

The search for novel antimycobacterial drugs is a matter of urgency, since tuberculosis is still one of the top ten causes of death from a single infectious agent, killing more than 1.4 million people worldwide each year. Nine Amaryllidaceae alkaloids (AAs) of various structural types have been screened for their antimycobacterial activity. Unfortunately, all were considered inactive, and thus a pilot series of aromatic esters of galanthamine, 3-O-methylpancracine, vittatine and maritidine were synthesized to increase biological activity. The semisynthetic derivatives of AAs were screened for their in vitro antimycobacterial activity against Mycobacterium tuberculosis H37Ra and two other mycobacterial strains (M. aurum, M. smegmatis) using a modified Microplate Alamar Blue Assay. The most active compounds were also studied for their in vitro hepatotoxicity on the hepatocellular carcinoma cell line HepG2. In general, the derivatization of the original AAs was associated with a significant increase in antimycobacterial activity. Several pilot derivatives were identified as compounds with micromolar MICs against M. tuberculosis H37Ra. Two derivatives of galanthamine, 1i and 1r, were selected for further structure optimalization to increase the selectivity index.

Structure Elucidation and Cholinesterase Inhibition Activity of Two New Minor Amaryllidaceae Alkaloids.

Two new minor Amaryllidaceae alkaloids were isolated from Hippeastrum × hybridum cv. Ferrari and Narcissus pseudonarcissus cv. Carlton. The chemical structures were identified by various spectroscopic (one- and two-dimensional (1D and 2D) NMR, circular dichroism (CD), high-resolution mass spectrometry (HRMS) and by comparison with literature data of similar compounds. Both isolated alkaloids were screened for their human acetylcholinesterase (hAChE) and butyrylcholinesterase (hBuChE) inhibition activity. One of the new compounds, a heterodimer alkaloid of narcikachnine-type, named narciabduliine (2), showed balanced inhibition potency for both studied enzymes, with IC50 values of 3.29 ± 0.73 µM for hAChE and 3.44 ± 0.02 µM for hBuChE. The accommodation of 2 into the active sites of respective enzymes was predicted using molecular modeling simulation.

COVID-19 impacts and adaptations in Asia and Africa's aquatic food value chains.

The COVID-19 pandemic is a shock affecting all areas of the global food system. We tracked the impacts of COVID-19 and associated policy responses on the availability and price of aquatic foods and production inputs during 2020, using a high frequency longitudinal survey of 768 respondents in Bangladesh, Egypt, India, Myanmar, Nigeria. We found the following: (1) Aquatic food value chains were severely disrupted but most effects on the availability and accessibility of aquatic foods and production inputs were short-lived. (2) Impacts on demand for aquatic foods, production inputs, and labor have been longer lasting than impacts on their supply. (3) Retail prices of aquatic foods spiked briefly during March-May 2020 but trended down thereafter, whereas prices of production inputs rose. These trends suggest a deepening 'squeeze' on the financial viability of producers and other value chain actors. (4) Survey respondents adapted to the challenges of COVID-19 by reducing production costs, sourcing alternative inputs, diversifying business activities, leveraging social capital, borrowing, seeking alternative employment, and reducing food consumption. Many of these coping strategies are likely to undermine well-being and longer-term resilience, but we also find some evidence of proactive strategies with potential to strengthen business performance. Global production of aquatic food likely contracted significantly in 2020. The importance of aquatic food value chains in supporting livelihoods and food and nutrition security in Asia and Africa makes their revitalization essential in the context of COVID-19 recovery efforts. We outline immediate and longer-term policies and interventions to support this goal.

Minimotif Miner 4: a million peptide minimotifs and counting.

Minimotif Miner (MnM) is a database and web system for analyzing short functional peptide motifs, termed minimotifs. We present an update to MnM growing the database from ∼300 000 to >1 000 000 minimotif consensus sequences and instances. This growth comes largely from updating data from existing databases and annotation of articles with high-throughput approaches analyzing different types of post-translational modifications. Another update is mapping human proteins and their minimotifs to know human variants from the dbSNP, build 150. Now MnM 4 can be used to generate mechanistic hypotheses about how human genetic variation affect minimotifs and outcomes. One example of the utility of the combined minimotif/SNP tool identifies a loss of function missense SNP in a ubiquitylation minimotif encoded in the excision repair cross-complementing 2 (ERCC2) nucleotide excision repair gene. This SNP reaches genome wide significance for many types of cancer and the variant identified with MnM 4 reveals a more detailed mechanistic hypothesis concerning the role of ERCC2 in cancer. Other updates to the web system include a new architecture with migration of the web system and database to Docker containers for better performance and management. Weblinks:minimotifminer.org and mnm.engr.uconn.edu.

High temperature acclimation alters upper thermal limits and growth performance of Indian major carp, rohu, Labeo rohita (Hamilton, 1822).

Increase in water temperature due to anthropogenic and climatic changes is expected to affect physiological functions of fish. In this study, we determined high temperature tolerance (CTmax) of a common aquacultured Indian major carp, rohu, Labeo rohita fingerlings (15.96 ± 0.72 g BW, 11.56 ± 0.42 cm TL) followed by acclimatization at three temperatures (30, 33, 36 °C). To determine the CTmax, we analyzed the major hemato-biochemical indices - hemoglobin (Hb), red blood cell (RBC), white blood cell (WBC), blood glucose levels, and erythrocytic nuclear abnormalities (ENAs) and erythrocytic cellular abnormalities (ECAs) of peripheral erythrocytes in the fish sampled at the start and end point at each acclimated temperature. Significantly decreased CTmax of the fish was found at 36 °C compared to 30 °C and 33 °C. The fish in the highest (36 °C) temperature were found with significantly lower Hb and RBC content and significantly higher WBC and blood glucose levels than that of the fishes in the lowest (30 °C) temperature both at the start and end points. The highest frequencies of ENAs and ECAs were found in the highest (36 °C) temperature group compared to the lowest (30 °C) temperature group at both the points. We also evaluated growth performance of the rohu fingerlings reared in the three temperatures for 60 days. The growth parameters - final weight gain, percent weight gain and specific growth rate were the highest at 33 °C and the lowest at 36 °C. The present study revealed that the highest temperature (36 °C) tested here may be hazardous to rohu and the temperature should be kept below 36 °C in the aquaculture setting to avoid physiological damage and growth and production loss to the fish.