dbAPIS: a database of anti-prokaryotic immune system genes

Anti-prokary otic immune sy stem (APIS) proteins, typically encoded b y phages, prophages, and plasmids, inhibit prokaryotic immune systems (e.g. restriction modification, to xin-antito xin, CRISPR-Cas). A gro wing number of APIS genes ha v e been characterized and dispersed in the literature. Here w e de v eloped dbAPIS ( https:// bcb.unl.edu/ dbAPIS ), as the first literature curated data repository for experimentally verified APIS genes and their associated protein f amilies. T he k e y features of dbAPIS include: (i) e xperimentally v erified APIS genes with their protein sequences, functional annotation, PDB or AlphaFold predicted str uct ures, genomic context, sequence and str uct ural homologs from different microbiome / virome databases; (ii) classification of APIS proteins into sequence-based families and construction of hidden Mark o v models (HMMs); (iii) user-friendly web interface for data browsing by the inhibited immune system types or by the hosts, and functions for searching and batch downloading of pre-computed data; (iv) Inclusion of all types of APIS proteins (e x cept f or anti-CRISPRs) that inhibit a v ariety of prokary otic defense systems (e.g. RM, TA, CB A SS , Thoeris, Gabija). The current release of dbAPIS contains 41 verified APIS proteins and ∼4400 sequence homologs of 92 families and 38 clans. dbAPIS will facilitate the discovery of novel anti-defense genes and genomic islands in phages, by providing a user-friendly data repository and a web resource for an easy homology search against known APIS proteins

Propozycja standardu ekologicznej kompensacji dla obszarowych zanieczyszczeń z rolnictwa

Non-point source water pollution mainly comes from farmland chemical fertilizers which has become an obstacle of agricultural sustainability and ecological health. As a public policy tool for assessing global ecological crisis and environmental pollution, ecological compensation is important for regional agricultural sustainability. Ecological compensation that farmers receive from governments is based on their reduction of fertilizer application at optimal ecological and economic levels. In this study we estimated the ecological compensation standards for nitrogen non-point pollution in Yixng city with contingent valuation method and cost-benefit method.  Results showed that the range of theoretical values of ecological compensation of nitrogen in Yixing City depended upon its optimal ecological and economic nitrogen application levels. The willingness of farmers to accept the compensation was positively correlated with their farming experience and education. There were about half of farmers willing to accept the compensation. Based on the present study, we found Yixing’s ecological compensation standard for controlling nitrogen non-point pollution was 620.0 yuan/hm2 at the current economic development level.Obszarowe zanieczyszczeń wód z rolnictwa pochodzą ze stosowania nawozów sztucznych, stanowiących przeszkodę na drodze do osiągnięcia rolniczej zrównoważoności i równowagi ekologicznej. W tym kontekście ekologiczna kompensacja, stanowiąca narzędzie polityczne do oceny kryzysu ekologicznego i ogólnego poziomu zanieczyszczenia środowiska, okazuje się także ważna w wymiarze lokalnej zrównoważoności rolniczej. Wysokość świadczeń, które rolniczy dostają od władz, jest uwarunkowana poziomem redukcji stosowania nawozów, którego celem jest osiągnięcie poziomu optymalnego zarówno zer strony ekologicznej, jak i ekonomicznej. W tym artykule, przy pomocy  Metoda wyceny warunkowej i metody kosztów i korzyści, ustaliliśmy standardy ekologicznej kompensacji dla miasta Yixng. Otrzymane rezultaty pozwalają na stwierdzenie, że zakres teoretycznych wartości ekologicznej kompensacji dla azotu w Yixing zależy od ustalenia optymalnych ekologicznych i ekonomicznych pozimów stosowania azotu. Zainteresowanie rolników otrzymaniem odszkodowania okazało się być pozytywnie skorelowane z ich doświadczeniem rolniczym i poziomem wykształcenia. Chęć jego otrzymania zgłosiła połowa z nich. Ustaliliśmy ponadto, że standard ekologicznej kompensacji dla Yixing odnoszący się kontrolowania obszarowych zanieczyszczeń związanych z nawozami azotowymi wynosi 620.0 yuan/hm2 , przy założeniu obecnego poziomu rozwoju ekonomicznego

A Matrix Ensemble Kalman Filter-based Multi-arm Neural Network to Adequately Approximate Deep Neural Networks

Deep Learners (DLs) are the state-of-art predictive mechanism with applications in many fields requiring complex high dimensional data processing. Although conventional DLs get trained via gradient descent with back-propagation, Kalman Filter (KF)-based techniques that do not need gradient computation have been developed to approximate DLs. We propose a multi-arm extension of a KF-based DL approximator that can mimic DL when the sample size is too small to train a multi-arm DL. The proposed Matrix Ensemble Kalman Filter-based multi-arm ANN (MEnKF-ANN) also performs explicit model stacking that becomes relevant when the training sample has an unequal-size feature set. Our proposed technique can approximate Long Short-term Memory (LSTM) Networks and attach uncertainty to the predictions obtained from these LSTMs with desirable coverage. We demonstrate how MEnKF-ANN can "adequately" approximate an LSTM network trained to classify what carbohydrate substrates are digested and utilized by a microbiome sample whose genomic sequences consist of polysaccharide utilization loci (PULs) and their encoded genes.Comment: 18 pages, 6 Figures, and 6 Table

Public participation in urban design with augmented reality technology based on indicator evaluation

Decision-making processes in traditional urban design approaches are mainly top-down. Such processes have defects including not only taking a long time to examine design results but also leading to irreversible impacts after design implementation. Policymakers and researchers stress the importance of collaborating with different stakeholders in the process of urban design policy and guideline making in order to minimize these negative impacts. However, introducing public participation into urban design from the bottom up is challenging, especially when the process involves abstract urban design concepts such as indicators. This paper explores a new workflow aimed at enhancing public participation to cooperate in urban design work with the help of a newly designed platform tool powered by mobile augmented-reality technologies. The platform is intuitive to use and displays scenes of potential urban design results by superimposing the virtual models onto real-world environments on mobile devices. The public stakeholders are provided with this platform on-site to evaluate the initial values of urban design indicators by interacting with the prototype design along with an immersive experience. They can also grow familiar with the concepts of the given indicators during this process, which helps them better understand the implications of guidelines in future published urban design drafts and estimate the potential results. Their feedback is collected, which can help urban designers further optimize the indicators in urban design guideline making in order to improve their rationality. This process of urban design involving public participation is repeatable, which makes it possible to continuously adjust the design results. A user study was conducted to examine the platform’s usability and its ability to enhance public familiarity with the concepts of given indicators and their willingness to participate in urban design evaluation. The study also attests to the possibility of a workflow that integrates public feedback with the urban design process

Lumos: Learning Agents with Unified Data, Modular Design, and Open-Source LLMs

We introduce Lumos, a novel framework for training language agents that employs a unified data format and a modular architecture based on open-source large language models (LLMs). Lumos consists of three distinct modules: planning, grounding, and execution. The planning module breaks down a task into a series of high-level, tool-agnostic subgoals, which are then made specific by the grounding module through a set of low-level actions. These actions are subsequently executed by the execution module, utilizing a range of off-the-shelf tools and APIs. In order to train these modules effectively, high-quality annotations of subgoals and actions were collected and are made available for fine-tuning open-source LLMs for various tasks such as complex question answering, web tasks, and math problems. Leveraging this unified data and modular design, Lumos not only achieves comparable or superior performance to current, state-of-the-art agents, but also exhibits several key advantages: (1) Lumos surpasses GPT-4/3.5-based agents in complex question answering and web tasks, while equalling the performance of significantly larger LLM agents on math tasks; (2) Lumos outperforms open-source agents created through conventional training methods and those using chain-of-thoughts training; and (3) Lumos is capable of effectively generalizing to unseen interactive tasks, outperforming larger LLM-based agents and even exceeding performance of specialized agents.Comment: Project website: https://allenai.github.io/lumos

Saikosaponin A Alleviates Symptoms of Attention Deficit Hyperactivity Disorder through Downregulation of DAT and Enhancing BDNF Expression in Spontaneous Hypertensive Rats

The disturbed dopamine availability and brain-derived neurotrophic factor (BDNF) expression are due in part to be associated with attention deficit hyperactivity disorder (ADHD). In this study, we investigated the therapeutical effect of saikosaponin a (SSa) isolated from Bupleurum Chinese DC, against spontaneously hypertensive rat (SHR) model of ADHD. Methylphenidate and SSa were orally administered for 3 weeks. Activity was assessed by open-field test and Morris water maze test. Dopamine (DA) and BDNF were determined in specific brain regions. The mRNA or protein expression of tyrosine hydroxylase (TH), dopamine transporter (DAT), and vesicles monoamine transporter (VMAT) was also studied. Both MPH and SSa reduced hyperactivity and improved the spatial learning memory deficit in SHRs. An increased DA concentration in the prefrontal cortex (PFC) and striatum was also observed after treating with the SSa. The increased DA concentration may partially be attributed to the decreased mRNA and protein expression of DAT in PFC while SSa exhibited no significant effects on the mRNA expression of TH and VMAT in PFC of SHRs. In addition, BDNF expression in SHRs was also increased after treating with SSa or MPH. The obtained result suggested that SSa may be a potential drug for treating ADHD

Automated Raman based cell sorting with 3D microfluidics

Raman activated cell sorting has emerged as a label-free technology that can link phenotypic function with genotypic properties of cells. However, its broad implementation is limited by challenges associated with throughput and the complexity of biological systems. Here, we describe a three-dimensional hydrodynamic focusing microfluidic system for a fully automated, continuous Raman activated cell sorting (3D-RACS). The system consists of a 3D printed detection chamber (1 mm3) that is integrated with a PDMS based sorting unit, optical sensors and an in-line collection module. It has the ability to precisely position cells in the detection chamber for Raman measurements, effectively eliminating spectroscopic interference from the device materials. This enables the sorting of a range of cell sizes (from 1 μm bacteria to 10's μm mammalian cells) with stable operation over >8 hours and high throughput. As a proof-of-concept demonstration, Raman-activated sorting of mixtures of Chlorella vulgaris and E. coli has demonstrated a purity level of 92.0% at a throughput of 310 cells per min. The platform employed in this demonstration features a simple “Raman window” detection system, enabling it to be built on a standard, inverted microscope. Together with its facile and robust operation, it provides a versatile tool for function-based flow cytometry and sorting applications in the fields of microbiology, biotechnology, life science and diagnostics

Laser powder bed fusion of WC-reinforced Hastelloy-X composite: Microstructure and mechanical properties

Nickel-based superalloys such as Hastelloy X (HX) are widely used in gas turbine engines for their exceptional oxidation resistance and high-temperature strength. The addition of ceramic reinforcement further enhances these superalloys’ mechanical performance and high-temperature properties. For this reason, this paper investigates the microstructure and mechanical property of laser powder bed fusion (LPBF) additively manufactured HX–1 wt% WC (tungsten carbide) composite specimens. The results demonstrate that the LPBF-fabricated composite was observed to have several pores and microcracks, whilst only pores were detected in the as-fabricated pure HX. Compared to the fabricated pure HX, the tensile yield strength of such HX composite parts was increased by 13% without undue sacrifices to ductility, suggesting that the very limited number of microcracks were not sufficient to degrade the mechanical performance. The significantly increased dislocations were considered to be the primary contributor for the mechanical performance enhancement in the LPBF-fabricated composite material. The findings offer a promising pathway to employ LPBF process to fabricate advanced microcrack-free composites with high-strength through a careful selection of ceramic reinforcement materials

Rankitect: Ranking Architecture Search Battling World-class Engineers at Meta Scale

Neural Architecture Search (NAS) has demonstrated its efficacy in computer vision and potential for ranking systems. However, prior work focused on academic problems, which are evaluated at small scale under well-controlled fixed baselines. In industry system, such as ranking system in Meta, it is unclear whether NAS algorithms from the literature can outperform production baselines because of: (1) scale - Meta ranking systems serve billions of users, (2) strong baselines - the baselines are production models optimized by hundreds to thousands of world-class engineers for years since the rise of deep learning, (3) dynamic baselines - engineers may have established new and stronger baselines during NAS search, and (4) efficiency - the search pipeline must yield results quickly in alignment with the productionization life cycle. In this paper, we present Rankitect, a NAS software framework for ranking systems at Meta. Rankitect seeks to build brand new architectures by composing low level building blocks from scratch. Rankitect implements and improves state-of-the-art (SOTA) NAS methods for comprehensive and fair comparison under the same search space, including sampling-based NAS, one-shot NAS, and Differentiable NAS (DNAS). We evaluate Rankitect by comparing to multiple production ranking models at Meta. We find that Rankitect can discover new models from scratch achieving competitive tradeoff between Normalized Entropy loss and FLOPs. When utilizing search space designed by engineers, Rankitect can generate better models than engineers, achieving positive offline evaluation and online A/B test at Meta scale.Comment: Wei Wen and Kuang-Hung Liu contribute equall