Miracle-dz/UniFAR-FTUs huggingface dataset:
Slowing Amazon deforestation through public policy and interventions in beef and soy supply chains

The recent 70% decline in deforestation in the Brazilian Amazon suggests that it is possible to manage the advance of a vast agricultural frontier. Enforcement of laws, interventions in soy and beef supply chains, restrictions on access to credit, and expansion of protected areas appear to have contributed to this decline, as did a decline in the demand for new deforestation. The supply chain interventions that fed into this deceleration are precariously dependent on corporate risk management, and public policies have relied excessively on punitive measures. Systems for delivering positive incentives for farmers to forgo deforestation have been designed but not fully implemented. Territorial approaches to deforestation have been effective and could consolidate progress in slowing deforestation while providing a framework for addressing other important dimensions of sustainable development.
The science of sustainable supply chains

Recent advances in the science and technology of global supply chain management offer near–real-time demand-response systems for decision-makers across production networks. Technology is helping propel “fast fashion” and “lean manufacturing,” so that companies are better able to deliver products consumers want most. Yet companies know much less about the environmental and social impacts of their production networks. The failure to measure and manage these impacts can be explained in part by limitations in the science of sustainability measurement, as well as by weaknesses in systems to translate data into information that can be used by decision-makers inside corporations and government agencies. There also remain continued disincentives for firms to measure and pay the full costs of their supply chain impacts. I discuss the current state of monitoring, measuring, and analyzing information related to supply chain sustainability, as well as progress that has been made in translating this information into systems to advance more sustainable practices by corporations and consumers. Better data, decision-support tools, and incentives will be needed to move from simply managing supply chains for costs, compliance, and risk reduction to predicting and preventing unsustainable practices.
Complex HuR function in pancreatic cancer cells

Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal cancers with dismal patient outcomes. The underlying core genetic drivers of disease have been identified in human tumor specimens and described in genetically engineered mouse models. These genetic drivers of PDAC include KRAS signaling, TP53 mutations, and genetic loss of the SMAD4 tumor suppressor protein. Beyond the known mutational landscape of PDAC genomes, alternative disrupted targets that extend beyond conventional genetic mutations have been elusive and understudied in the context of PDAC cell therapeutic resistance and survival. This last point is important because PDAC tumors have a unique and complex tumor microenvironment that includes hypoxic and nutrient‐deprived niches that could select for cell populations that garner therapeutic resistance, explaining tumor heterogeneity in regards to response to different therapies. We and others have embarked in a line of investigation focused on the key molecular mechanism of posttranscriptional gene regulation that is altered in PDAC cells and supports this pro‐survival phenotype intrinsic to PDAC cells. Specifically, the key regulator of this mechanism is a RNA‐binding protein, HuR (ELAVL1), first described in cancer nearly two decades ago. Herein, we will provide a brief overview of the work demonstrating the importance of this RNA‐binding protein in PDAC biology and then provide insight into ongoing work developing therapeutic strategies aimed at targeting this molecule in PDAC cells.
Modeling muscle

A biohybrid machine offers insight into muscle growth, adaptation, and repair Communication underlies the complexity of biological systems. Adaptive behaviors ranging from self-assembly to self-healing showcase the ability of such systems to sense and adapt to dynamic environments based on signaling between living cells. This signaling takes on many forms—biochemical, mechanical, and electrical—and uncovering it has become as much the purview of regenerative medicine as of fundamental biology. We cannot reverse-engineer native tissues if we do not understand the fundamental design rules and principles that govern their assembly from the bottom up (1).
Animal cultures matter for conservation

Understanding the rich social lives of animals benefits international conservation efforts Animal culture, defined as “information or behavior—shared within a community—which is acquired from conspecifics through some form of social learning” (1), can have important consequences for the survival and reproduction of individuals, social groups, and potentially, entire populations (1, 2). Yet, until recently, conservation strategies and policies have focused primarily on broad demographic responses and the preservation of genetically defined, evolutionarily significant units. A burgeoning body of evidence on cultural transmission and other aspects of sociality (3) is now affording critical insights into what should be conserved (going beyond the protection of genetic diversity, to consider adaptive aspects of phenotypic variation), and why specific conservation programs succeed (e.g., through facilitating the resilience of cultural diversity) while others fail (e.g., by neglecting key repositories of socially transmitted knowledge). Here, we highlight how international legal instruments, such as the Convention on the Conservation of Migratory Species of Wild Animals (CMS), can facilitate smart, targeted conservation of a wide range of taxa, by explicitly considering aspects of their sociality and cultures.
A review of population initialization techniques for evolutionary algorithms

Although various population initialization techniques have been employed in evolutionary algorithms (EAs), there lacks a comprehensive survey on this research topic. To fill this gap and attract more attentions from EA researchers to this crucial yet less explored area, we conduct a systematic review of the existing population initialization techniques. Specifically, we categorize initialization techniques from three exclusive perspectives, i.e., randomness, compositionality and generality. Characteristics of the techniques belonging to each category are carefully analysed to further lead to several sub-categories. We also discuss several open issues related to this research topic, which demands further in-depth investigations.
A WSLA-based monitoring system for grid service - GSMon

The target of grid monitoring and management is to monitor services in the grid for fault detection, performance analysis, performance tuning, load balancing and scheduling. This paper introduces the design and implementation of a WSLA-based grid monitoring and management system - GSMon. In GSMon, we use a service-oriented architecture, give monitoring information of the grid service a unique format by using WSLA and develop extended modules in the OGSA container. GSMon conforms to grid service and Web service standards and uses dynamic deployment technology to solve the problem of monitoring new grid services. It is a novel infrastructure for grid monitoring and management with high flexibility and scalability.
Conversion of Channelrhodopsin into a Light-Gated Chloride Channel

Optogenetic Insights Mapping functional neural circuits for many behaviors has been almost impossible, so Vogelstein et al. (p. 386, published online 27 March; see the Perspective by O'Leary and Marder) developed a broadly applicable optogenetic method for neuron-behavior mapping and used it to phenotype larval Drosophila and thus developed a reference atlas. As optogenetic experiments become routine in certain fields of neuroscience research, creating even more specialized tools is imperative (see the Perspective by Hayashi). By engineering channelrhodopsin, Wietek et al. (p. 409, published online 27 March) and Berndt et al. (p. 420) created two different light-gated anion channels to block action potential generation during synaptic stimulation or depolarizing current injections. These new tools not only improve understanding of channelrhodopsins but also provide a way to silence cells. A class of directly light-gated anion channels can be used to block neuronal output in a fully reversible fashion. [Also see Perspective by Hayashi and Report by Berndt et al.] The field of optogenetics uses channelrhodopsins (ChRs) for light-induced neuronal activation. However, optimized tools for cellular inhibition at moderate light levels are lacking. We found that replacement of E90 in the central gate of ChR with positively charged residues produces chloride-conducting ChRs (ChloCs) with only negligible cation conductance. Molecular dynamics modeling unveiled that a high-affinity Cl–-binding site had been generated near the gate. Stabilizing the open state dramatically increased the operational light sensitivity of expressing cells (slow ChloC). In CA1 pyramidal cells, ChloCs completely inhibited action potentials triggered by depolarizing current injections or synaptic stimulation. Thus, by inverting the charge of the selectivity filter, we have created a class of directly light-gated anion channels that can be used to block neuronal output in a fully reversible fashion.
Regional seismic waveform inversion using swarm intelligence algorithms

Inversion is a critical and challenging task in geophysical research. Geophysical inversion can be formulated as an optimization problem to find the best parameters whose forward synthesis data most fit the observed data. The inverse problems are usually highly non-linear, multi-modal as well as ill-posed, so conventional optimization algorithms cannot handle it very efficiently. In the past decades, genetic algorithm (GA) and its many variants are widely applied to inverse problems and achieve great success. Swarm intelligence algorithms are a family of global optimizers inspired by swarm phenomena in nature, and have shown better performance than GA for diverse optimization problems. However, swarm intelligence algorithms are not utilized for geophysical inversion problems until recently and only limited number of works are reported. In this paper, we try to apply two swarm intelligence algorithms, Particle Swarm Optimization (PSO) and Fireworks Algorithm (FWA), to the regional seismic waveform inversion. To explore the advantages and disadvantages of swarm intelligence algorithms over GA, synthetic experiments are conducted by using these two swarm intelligence algorithm and several GA variants as well as Differential Evolution (DE). The experimental results show that, both swarm intelligence algorithms outperform the widely used GA, DE, and the models estimated by swarm intelligence algorithms are closer to the true solution. The promising results imply that swarm intelligence algorithms are a potentially more powerful tool for inversion problems.
Where Do Batteries End and Supercapacitors Begin?

Electrochemical measurements can distinguish between different types of energy storage materials and their underlying mechanisms. Batteries keep our devices working throughout the day–that is, they have a high energy density–but they can take hours to recharge when they run down. For rapid power delivery and recharging (i.e., high power density), electrochemical capacitors known as supercapacitors (1) are used. One such application is regenerative braking, used to recover power in cars and electric mass transit vehicles that would otherwise lose braking energy as heat. However, supercapacitors have low energy density.
Genome Sequence of the Tsetse Fly (Glossina morsitans): Vector of African Trypanosomiasis

Africa's Bane Tsetse are blood-feeding, fast-flying flies that transmit a range of Trypanosoma spp. protozoan pathogens, which cause sleeping sickness in humans and their nagana in their livestock. The International GlossinaGenome Initiative (p. 380) sequenced the genome of Glossina morsitans and identified the genes for many attributes of the tsetse's remarkable biology, including viviparity and the expression of analogs of mammalian milk proteins. Tsetse are host to several specific symbionts that appear to synthesize essential nutrients for the fly and also to hitherto undiscovered parasitoid-derived viruses. Deeper exploration of this genome will reveal what makes these fly species so host- and trypanosome specific. Blood-sucking tsetses transmit protozoan parasites, harbor multiple symbionts, reproduce viviparously, and lactate. Tsetse flies are the sole vectors of human African trypanosomiasis throughout sub-Saharan Africa. Both sexes of adult tsetse feed exclusively on blood and contribute to disease transmission. Notable differences between tsetse and other disease vectors include obligate microbial symbioses, viviparous reproduction, and lactation. Here, we describe the sequence and annotation of the 366-megabase Glossina morsitans morsitans genome. Analysis of the genome and the 12,308 predicted protein–encoding genes led to multiple discoveries, including chromosomal integrations of bacterial (Wolbachia) genome sequences, a family of lactation-specific proteins, reduced complement of host pathogen recognition proteins, and reduced olfaction/chemosensory associated genes. These genome data provide a foundation for research into trypanosomiasis prevention and yield important insights with broad implications for multiple aspects of tsetse biology.
Water Loss from the Great Lakes

Knowledge of the drivers behind recent record low water levels in the North American Great Lakes can help water resource management planning. As marine coastal populations experience and plan for rising ocean levels (1), residents along the coasts of Earth's largest lake system are encountering the opposite problem: persistent low water levels and a receding shoreline. In January 2013, federal agencies from the United States and Canada documented the lowest water levels ever recorded on lakes Michigan and Huron (2). Only 6 years earlier, historically low water levels were recorded on Lake Superior (3), which feeds into the Lake Michigan-Huron system. These low water levels are symptoms of an imbalance in the water budget of the Great Lakes. Adapting to, and potentially mitigating, low water level conditions requires improved quantification of the factors that drive the imbalance.
Performances in GA-based menu production for hospital meals

One of the approaches for improving quality of life of patients, undergoing hospitalization, is to improve hospital meals that are provided to them. It is a very cumbersome task to plan daily menus for hospital meals, which have many conditions for nutrition and allergies, and this task becomes difficult if patients' preferences of foods are also considered for enhancing patients' quality of life. This task can be formulated as an optimization problem with menu items as variables and specific nutritional and allergic as its constraints. We have proposed a menu creation scheme to satisfy these conditions with being incorporated patients' food preferences, and shows a preliminary result for creating menus for a patient. A genetic algorithm was utilized to better and more efficiently locate combinations of menu dishes for individual patients. This paper shows that a variety of menus can be produced for patients with several ranges of ages, three types of physical activities, and allergic conditions.
Integration of systems glycobiology with bioinformatics toolboxes, glycoinformatics resources, and glycoproteomics data

The glycome constitutes the entire complement of free carbohydrates and glycoconjugates expressed on whole cells or tissues. ‘Systems Glycobiology’ is an emerging discipline that aims to quantitatively describe and analyse the glycome. Here, instead of developing a detailed understanding of single biochemical processes, a combination of computational and experimental tools are used to seek an integrated or ‘systems‐level’ view. This can explain how multiple biochemical reactions and transport processes interact with each other to control glycome biosynthesis and function. Computational methods in this field commonly build in silico reaction network models to describe experimental data derived from structural studies that measure cell‐surface glycan distribution. While considerable progress has been made, several challenges remain due to the complex and heterogeneous nature of this post‐translational modification. First, for the in silico models to be standardized and shared among laboratories, it is necessary to integrate glycan structure information and glycosylation‐related enzyme definitions into the mathematical models. Second, as glycoinformatics resources grow, it would be attractive to utilize ‘Big Data’ stored in these repositories for model construction and validation. Third, while the technology for profiling the glycome at the whole‐cell level has been standardized, there is a need to integrate mass spectrometry derived site‐specific glycosylation data into the models. The current review discusses progress that is being made to resolve the above bottlenecks. The focus is on how computational models can bridge the gap between ‘data’ generated in wet‐laboratory studies with ‘knowledge’ that can enhance our understanding of the glycome. WIREs Syst Biol Med 2015, 7:163–181. doi: 10.1002/wsbm.1296
Could Autism Be Treated Prenatally?

Treatment of rodent models of autism spectrum disorder with a drug that alters the function of a neurotransmitter ameliorates autistic-like behavior in offspring. [Also see Report by Tyzio et al.] Autism spectrum disorder (ASD) has presented a conundrum: How can the behavioral signs and symptoms that define the condition arise from different etiologies and lead to so many varied phenotypes? Genetics and the environment, including prenatal and perinatal factors, long have been suspected to interact in the causation of ASD. Evidence for neuronal dysfunction and the frequent development of epilepsy strongly support increased excitatory and decreased inhibitory neuronal activity in ASD. In particular, altered functions of γ-aminobutyric acid (GABA), the main inhibitory neurotransmitter (in the mature brain), have been of interest because GABA's effects are excitatory during prenatal development but become inhibitory at birth (1, 2). What has been unclear is the cellular physiology that underlies this “GABA switch. ” On page 675 of this issue, Tyzio et al. (3) show that a defect in this switch is associated with abnormal chloride concentration in neurons in two different animal models of ASD. Normal electrophysiology and behavior can be restored in their offspring by the prenatal administration of the compound bumetanide, which blocks a key chloride transporter. The findings raise the possibility of preventing the autistic phenotype in offspring by predelivery pharmacological treatment.
Helicobacter pylori and increased epithelial cell proliferation: a risk factor for cancer

Objective: Epidemiological studies have identified a link between Helicobacter pylori and gastric carcinoma. Epithelial cell proliferation is an indicator of risk of adenocarcinoma. We aimed to assess the effect of H. pylori on gastric epithelial cell proliferation. Subjects: A total of 124 patients were recruited and assigned to three study groups. Group 1 (control group) comprised 28 H. pylori-negative patients with normal gastric mucosa; group 2 comprised 28 H. pylori-negative patients with type C (chemically induced) gastritis and group 3 comprised 68 H. pylori-positive patients with type B (bacteria induced) gastritis. Four antral mucosal biopsy specimens were taken for cell proliferation analysis using the in vitro bromodeoxyuridine immunohistochemical technique. Ten entire gastric pits of more than 100 cells were counted per patient and the labelling index percentage (L1%) was calculated (the total number of proliferating cells expressed as a percentage of the total number of cells per pit). Results: Patients with H. pylori-positive gastritis had a significantly higher Ll% (4.5 ±; 0.2%; P<0.05) than patients with H. pylori-negative gastritis (3.3 ±0.2%) or controls (2.9±0.1%). This increased cell proliferation was not associated with the severity of inflammation or the presence or absence of an ulcer on gastroscopy. Conclusion: Increased cell proliferation was associated with H. pylori infection, which may indicate that H. pylori is a risk factor in the pathogenesis of gastric cancer.
Immune Activation with HIV Vaccines

Future HIV vaccine research should consider the balance between responses that favor protection and those that lead to susceptibility to infection. The development of a safe and effective HIV vaccine is perhaps the most important and challenging goal remaining in HIV-AIDS research. Recent progress using a poxvirus vector prime and envelope protein boost strategy demonstrated a modest but statistically significant level of efficacy and established the concept that a vaccine could prevent HIV infection (1), and approaches to boost durability and efficacy are currently in the planning stages (2). But the results of two vaccine concepts based on recombinant adenovirus serotype-5 (rAd5) (3–5) pointed to a potential major problem—that such vaccines might increase susceptibility to HIV infection. This also raised the question of whether the problem extends to some or all of the other recombinant adenovirus vectors currently in development or to other vector-based vaccines.
Computational modeling of single‐cell mechanics and cytoskeletal mechanobiology

Cellular cytoskeletal mechanics plays a major role in many aspects of human health from organ development to wound healing, tissue homeostasis and cancer metastasis. We summarize the state‐of‐the‐art techniques for mathematically modeling cellular stiffness and mechanics and the cytoskeletal components and factors that regulate them. We highlight key experiments that have assisted model parameterization and compare the advantages of different models that have been used to recapitulate these experiments. An overview of feed‐forward mechanisms from signaling to cytoskeleton remodeling is provided, followed by a discussion of the rapidly growing niche of encapsulating feedback mechanisms from cytoskeletal and cell mechanics to signaling. We discuss broad areas of advancement that could accelerate research and understanding of cellular mechanobiology. A precise understanding of the molecular mechanisms that affect cell and tissue mechanics and function will underpin innovations in medical device technologies of the future. WIREs Syst Biol Med 2018, 10:e1407. doi: 10.1002/wsbm.1407