Miracle-dz/UniFAR-FTUs huggingface dataset:
Molecular switch architecture determines response properties of signaling pathways

Significance Molecular switches are key components of intracellular signal-transduction pathways. Experimental work has established that the activity of molecular switches is regulated by three distinct mechanisms: 1) activation, 2) derepression, and 3) concerted (activation and derepression). However, it remains unclear how the choice of signaling mechanism influences the performance properties of the switch. Here, we characterize each switch design in terms of dose–response relationship, response time, and abilities to process upstream fluctuations. Our results highlight unique features of each mechanism and provide insight into the operating principles that underlie many different signaling pathways. Many intracellular signaling pathways are composed of molecular switches, proteins that transition between two states—on and off. Typically, signaling is initiated when an external stimulus activates its cognate receptor that, in turn, causes downstream switches to transition from off to on using one of the following mechanisms: activation, in which the transition rate from the off state to the on state increases; derepression, in which the transition rate from the on state to the off state decreases; and concerted, in which activation and derepression operate simultaneously. We use mathematical modeling to compare these signaling mechanisms in terms of their dose–response curves, response times, and abilities to process upstream fluctuations. Our analysis elucidates several operating principles for molecular switches. First, activation increases the sensitivity of the pathway, whereas derepression decreases sensitivity. Second, activation generates response times that decrease with signal strength, whereas derepression causes response times to increase with signal strength. These opposing features allow the concerted mechanism to not only show dose–response alignment, but also to decouple the response time from stimulus strength. However, these potentially beneficial properties come at the expense of increased susceptibility to upstream fluctuations. We demonstrate that these operating principles also hold when the models are extended to include additional features, such as receptor removal, kinetic proofreading, and cascades of switches. In total, we show how the architecture of molecular switches govern their response properties. We also discuss the biological implications of our findings.
Comprehensive Characterization and Validation of Chromosome-Specific Highly Polymorphic SSR Markers From Pomegranate (Punica granatum L.) cv. Tunisia Genome

The simple sequence repeat (SSR) survey of ‘Tunisia’ genome (296.85 Mb) identified a total of 365,279 perfect SSRs spanning eight chromosomes, with a mean marker density of 1,230.6 SSRs/Mb. We found a positive trend in chromosome length and the SSR abundance as marker density enhanced with a shorter chromosome length. The highest number of SSRs (60,708) was mined from chromosome 1 (55.56 Mb), whereas the highest marker density (1,294.62 SSRs/Mb) was recorded for the shortest chromosome 8 (27.99 Mb). Furthermore, we categorized all SSR motifs into three major classes based on their tract lengths. Across the eight chromosomes, the class III had maximum number of SSR motifs (301,684, 82.59%), followed by the class II (31,056, 8.50%) and the class I (5,003, 1.37%). Examination of the distribution of SSR motif types within a chromosome suggested the abundance of hexanucleotide repeats in each chromosome followed by dinucleotides, and these results are consistent with ‘Tunisia’ genome features as a whole. Concerning major repeat types, AT/AG was the most frequent (14.16%), followed by AAAAAT/AAAAAG (7.89%), A/C (7.54%), AAT/AAG (5.23%), AAAT/AAAG (4.37%), and AAAAT/AAAAG (1.2%) types. We designed and validated a total of 3,839 class I SSRs in the ‘Tunisia’ genome through electronic polymerase chain reaction (ePCR) and found 1,165 (30.34%) SSRs producing a single amplicon. Then, we selected 906 highly variable SSRs (> 40 nt) from the ePCR-verified class I SSRs and in silico validated across multiple draft genomes of pomegranate, which provided us a subset of 265 highly polymorphic SSRs. Of these, 235 primers were validated on six pomegranate genotypes through wet-lab experiment. We found 221 (94%) polymorphic SSRs on six genotypes, and 187 of these SSRs had ≥ 0.5 PIC values. The utility of the developed SSRs was demonstrated by analyzing genetic diversity of 30 pomegranate genotypes using 16 HvSSRs spanning eight pomegranate chromosomes. In summary, we developed a comprehensive set of highly polymorphic genome-wide SSRs. These chromosome-specific SSRs will serve as a powerful genomic tool to leverage future genetic studies, germplasm management, and genomics-assisted breeding in pomegranate.
Real-Time In-Network Image Compression via Distributed Dictionary Learning

Multi-camera networks are increasingly becoming pervasive in many monitoring and surveillance applications, and have attracted much attention in distributed systems with collaborative, real-time decision-making capabilities. While in-network data compression brings significant energy savings in camera nodes, signal representation using sparse approximations and overcomplete dictionaries have been shown to outperform traditional compression methods. In this work, an end-to-end and real-time solution is designed and implemented to enable energy-efficient and robust dictionary learning in distributed camera networks by leveraging the spatial correlation of the collected multimedia data. Traditional distributed dictionary learning relies on consensus-building algorithms, which involve communicating with neighboring nodes until convergence is achieved. Existing methods, however, do not exploit spatial correlations in camera networks for improved energy efficiency. In contrast, low-computational-complexity metrics are employed in this work to quantify and exploit the spatial correlation across camera nodes in a wireless network for efficient distributed dictionary learning and in-network image compression. The performance of the proposed approach is validated through extensive simulations on public datasets as well as via real-world experiments on a testbed composed of Raspberry Pi nodes.
Ethnic Politics and Job Performance in the Kenyan Police 1957-1970∗

Using a panel of 6,725 Kenyan police officers 1957-1970, we show how ethnic politics encroached and changed the daily behavior of the members of the police force as soon as after Kenya’s first multiparty elections in 1961. We find a significant detoriation in discipline for Kikuyu officers after 1961, when the Kikuyu-dominated KANU party emerged as the most powerful political force. We investigate the channels of this detoriation in discipline. We find little evidence for selection: The quality of new recruits did not detoriate, nor did well-performing police officers disproportionately leave the force at independence. We also find little evidence that the reorganisation of police divisions caused the divergent trends. The effects appear to be driven by individual policemen changing their behaviour. (JEL: ) ∗We are grateful to Jean-Marie Baland, Denis Cogneau, Richard Disney, Remi Jedwab, Nathan Nunn, Hosny Zoabi, and seminar audiences at the 2015 World Economic History Congress, 2015 AEHN Meeting, NES CSDSI Conference 2015, PSE, and Sussex. We thank Kenya Police HQ for granting us access to the Kenya Police Service Registers. We are also grateful to Kenya National Archives for support in retrieving them. Data collection was funded by an ESRC First Grant Scheme to the third author (RES-061-25-0456). We thank LABEX OSE ”ouvrir la Science Economique” at PSE for supporting our additional data processing work. †Paris School of Economics; Email: oliver.vanden. eynde@ens.fr. Corresponding author. ‡School of Government & International Affairs, Durham University; E-mail: p.m. kuhn@durham.ac. uk. §Department of Economics, University of Sussex, Jubilee Building, Falmer, BN1 9SN, UK. Also affiliated with the Center for the Study of African Economies, University of Oxford, Department of Economics, Manor Road, Oxford, OX1 3UQ, UK, and Research Associate, Department of Economics, Stellenbosch University, South Africa. E-mail: A.Moradi@sussex.ac. uk.
U-Net Model for Logging Detection Based on the Sentinel-1 and Sentinel-2 Data

Illegal logging in Ukraine is a big problem that negatively affects both environmental and socio-economic indicators of the country. The main reason for this problem is the lack of independent control over the forest industry. Lack of control, in turn, makes it possible to provide inaccurate information about the permitted logging and to hide the fact of logging. The solution to this problem is the use of modern approaches of Remote Sensing and deep learning to implement mechanisms for forestry monitoring and logging detection based on the satellite data. Most researches on satellite-based logging detection technology are based on the optical satellite missions. However, for countries with temperate and cold climates, the use of such approaches is problematic in winter and autumn due to the lack of vegetative biomass and the high percentage of clouds and snow in satellite images. In this study, we assessed a methodology for detecting logging based on optical and radar images of Copernicus satellite missions, namely Sentinel-l and 2. The obtained results show that when using this approach, it is possible to monitor and detect logging with high accuracy both in summer and in winter with the frequency of data updates once a week. The basis of this methodology is a convolutional neural network with U -Net architecture, which input is a stack of optical and radar images in summer and spring, and works on radar images only in winter and autumn.
Clinical Concept Extraction with Lexical Semantics to Support Automatic Annotation

Extracting clinical concepts, such as problems, diagnosis, and treatment, from unstructured clinical narrative documents enables data-driven approaches such as machine and deep learning to support advanced applications such as clinical decision-support systems, the assessment of disease progression, and the intelligent analysis of treatment efficacy. Various tools such as cTAKES, Sophia, MetaMap, and other rules-based approaches and algorithms have been used for automatic concept extraction. Recently, machine- and deep-learning approaches have been used to extract, classify, and accurately annotate terms and phrases. However, the requirement of an annotated dataset, which is labor-intensive, impedes the success of data-driven approaches. A rule-based mechanism could support the process of annotation, but existing rule-based approaches fail to adequately capture contextual, syntactic, and semantic patterns. This study intends to introduce a comprehensive rule-based system that automatically extracts clinical concepts from unstructured narratives with higher accuracy and transparency. The proposed system is a pipelined approach, capable of recognizing clinical concepts of three types, problem, treatment, and test, in the dataset collected from a published repository as a part of the I2b2 challenge 2010. The system’s performance is compared with that of three existing systems: Quick UMLS, BIO-CRF, and the Rules (i2b2) model. Compared to the baseline systems, the average F1-score of 72.94% was found to be 13% better than Quick UMLS, 3% better than BIO CRF, and 30.1% better than the Rules (i2b2) model. Individually, the system performance was noticeably higher for problem-related concepts, with an F1-score of 80.45%, followed by treatment-related concepts and test-related concepts, with F1-scores of 76.06% and 55.3%, respectively. The proposed methodology significantly improves the performance of concept extraction from unstructured clinical narratives by exploiting the linguistic and lexical semantic features. The approach can ease the automatic annotation process of clinical data, which ultimately improves the performance of supervised data-driven applications trained with these data.
Integration of thermo mechanical strains into optimal tolerance design of mechanical assembly using NSGA II and FE simulation

In the design of mechanical assembly, the dimension-chain tools take into account the manufacturing dispersion of the parts and assembly defects. This ensures the interchangeability of the different components and guarantees that an assembly can carry out different service functions, as it is modeled in infinitely rigid solids. However, this approach does not take thermo-mechanical effects and deformation due to inertia effects like gravity, angular velocity etc., into account. Most materials change length as they change temperature. As a result of this change, the dimensions and tolerances of a product become at variance with the design values. Hence, thermal effects must be taken into account when designing a product that will undergo temperature cycling and yet, the different operating regimes of an assembly make it indispensable that the effects caused by the thermodynamic cycle should be integrated. In this regard, a finite element model of a machine assembly is created in order to determine the deformation due to change in temperature and inertia effects. The aim of this article is to include the deformation determined by Finite element analysis in the dimension chain thereby controlling clearances in the mechanical assembly. The approach first generates a Cost-tolerance model using neural network where the inputs are parameters and tolerance levels. Then, Finite element analysis of the machine assembly is carried out. The deformation obtained by FEA is then included in the dimension chain. Finally, optimization is done using Non-dominated sorting genetic algorithm II (NSGA II). The results provide designers with optimal component parameters and tolerance values, and the critical components and the manufacturing cost. The approach can also guarantee that the parameter and tolerance values found remain within tolerance for the temperature variation. Then, the product can function as intended under a wide range of temperature conditions for the duration of its life.
Chronic rapamycin treatment decreases hepatic IL‐6 protein, but increases autophagy markers as a protective effect against the overtraining‐induced tissue damage

Regular endurance exercise is a non‐pharmacological strategy to protect the liver against diseases. Conversely, exercise may be harmful when excessive, the so‐called overtraining. As expected, mice who underwent an overtraining protocol presented higher levels of proinflammatory cytokines in the serum and liver. Based on the relationship among overtraining, inflammation and mammalian target of rapamycin complex 1 (mTORC1) upregulation, the present study verified if animals submitted to an overtraining protocol, but with inhibition of the mTOR pathway via rapamycin injections could mitigate the liver and serum inflammation. Once autophagy can be linked to the improvement of hepatic dysfunction, we also investigated if the inhibition of mTORC1 by rapamycin can improve hepatic autophagy. The animals were randomized into four groups: control (CT; sedentary mice), overtraining by downhill running (OT; mice submitted to the downhill running‐based overtraining protocol), overtraining by downhill running with chronic administration of rapamycin (OT/Rapa; mice submitted to the downhill running‐based overtraining protocol with intraperitoneal injections of rapamycin) and aerobic (AER; submitted to aerobic training protocol). The serum and liver of the animals were used for biochemical analysis, reverse transcription‐quantitative polymerase chain reaction (RT‐qPCR) and immunoblotting. The main results are (a) OT and OT/Rapa protocols decreased the performance; (b) the protein levels of interleukin 6 (IL‐6) were higher for the OT group; the OT/Rapa group reduced the autophagic genes, increased the microtubule‐associated protein light chain 3 II/I (LC3II/LC3I) protein ratio and decreased the sequestosome 1 (SQSTM1) protein. In conclusion, rapamycin appears efficiently to increase the autophagy proteins and decrease IL‐6 protein in the liver of overtraining mice.
Effects of Insect Growth Regulators on Ambrosia Beetles (Coleoptera: Curculionidae)

Abstract Ambrosia beetles, especially the granulate ambrosia beetle, Xylosandrus crassiusculus (Motschulsky) (Coleoptera: Curculionidae), are serious pests in ornamental nurseries in Georgia during the spring months. Growers spray pyrethroid insecticides to prevent ambrosia beetle attacks on the tree trunk around bud break. Repeated pyrethroid insecticide applications can harm beneficial arthropods and cause a resurgence of minor, secondary pests. Insect growth regulators (IGRs), such as novaluron and azadirachtin, have demonstrated transovarial activity on many insect pests, for which the viability of the eggs was reduced after adult exposure. IGRs, particularly azadirachtin, are also repellent to many insect pests. Thus, the objective of this study was to determine the transovarial and repellent activity of IGRs on ambrosia beetles. Two experiments were conducted in ornamental nurseries in 2019 and 2021. In the first experiment, novaluron, azadirachtin, and permethrin were sprayed as stand-alone and combination treatments with permethrin on maple (Acer) tree bolts. The number of ambrosia beetle attacks was significantly lower in treatments with permethrin in both years. Novaluron and azadirachtin treatments neither reduced ambrosia beetle attacks on the ethanol-infused bolts nor suppressed the recovery of X. crassiusculus from the bolts, suggesting the lack of repellent and transovarial activity, respectively. For the second experiment, novaluron alone and in combination with 1×, 4×, and 8× bark penetrant at the label rate were sprayed on ethanol-infused bolts. None of the novaluron treatments with or without bark penetrant elicited transovarial activity in X. crassiusculus, as the beetle recovery was similar among treatments.
Złożoność wokół nas : część pierwsza : gra w chaos = Complexity is around us : part one : the chaos game / Dawid Lubiszewski.

Zlozone zjawiska – takie jak struktury czy procesy – interesują naukowcow na calym świecie. Z wielu powodow, dla ktorych zlozonośc jest tak bardzo popularnym przedmiotem badan, pozwole sobie wymienic zaledwie trzy. Pierwszym jest fakt, z pozoru banalny, ze zyjemy w zlozonym świecie. Lecz zlozonośc nie ogranicza sie jedynie do naszego otoczenia: my sami mozemy nazwac siebie prawdopodobnie najbardziej zlozonymi jednostkami istniejącymi w świecie; zawdzieczamy to oczywiście naszemu mozgowi, ktory sklada sie z miliardow neuronow tworzących jeszcze wiecej polączen miedzy sobą. Zatem zrozumienie zlozonych zjawisk moze ulatwic nam lepsze poznanie nas samych. Z drugiej strony zlozonośc jest ciekawym przedmiotem badan, poniewaz zaskakuje naukowcow i to przynajmniej na dwa sposoby.
Sharp weighted $L^p$ estimates for spectral multipliers

Let $L$ be a non-negative self-adjoint operator on $L^2(X)$. Assume that operator $L$ generates the analytic semigroup $\{e^{-tL}\}_{t>0}$ whose kernels satisfy the standard Gaussian upper bounds. This paper investigates the sharp weighted inequalities for spectral multipliers $F(L)$ and their commutators with BMO functions. The obtained results in this paper can be considered to be improvements of those in \cite{DSY}[Weighted norm inequalities, Gaussian bounds and sharp spectral multipliers, {\it J. Funct. Anal. }, {\bf 260} (2011), 1106-1131].
Hierarchical Forecasting of Load Demand With Smart Meter Data for Distribution Networks

Load forecasting is an essential task for the use of technologies such as energy storage systems and distributed energy resources in modern distribution networks. However, these technologies can increase the complexity of the operation of the distribution system due to the variability of its operation. Therefore, accurate load forecasting is necessary, and this will require the use of all available data held by the utility at all voltage levels. In this sense, a hierarchical structure is created in distribution systems, where smart meters allow obtaining granular data. In this paper, we present the hierarchical time series approach using different forecasting models to predict the load demand of a primary substation one hour ahead. To evaluate the performance of forecasting models, the Mean Absolute Percentage Error (MAPE) indicator is used. In this case, the bottom-up approach is used to forecast at the top level. The forecast results reveal that the hierarchical structure provides better performance with the forecast models employed.
BEIR-PL: Zero Shot Information Retrieval Benchmark for the Polish Language

The BEIR dataset is a large, heterogeneous benchmark for Information Retrieval (IR) in zero-shot settings, garnering considerable attention within the research community. However, BEIR and analogous datasets are predominantly restricted to the English language. Our objective is to establish extensive large-scale resources for IR in the Polish language, thereby advancing the research in this NLP area. In this work, inspired by mMARCO and Mr.~TyDi datasets, we translated all accessible open IR datasets into Polish, and we introduced the BEIR-PL benchmark -- a new benchmark which comprises 13 datasets, facilitating further development, training and evaluation of modern Polish language models for IR tasks. We executed an evaluation and comparison of numerous IR models on the newly introduced BEIR-PL benchmark. Furthermore, we publish pre-trained open IR models for Polish language,d marking a pioneering development in this field. Additionally, the evaluation revealed that BM25 achieved significantly lower scores for Polish than for English, which can be attributed to high inflection and intricate morphological structure of the Polish language. Finally, we trained various re-ranking models to enhance the BM25 retrieval, and we compared their performance to identify their unique characteristic features. To ensure accurate model comparisons, it is necessary to scrutinise individual results rather than to average across the entire benchmark. Thus, we thoroughly analysed the outcomes of IR models in relation to each individual data subset encompassed by the BEIR benchmark. The benchmark data is available at URL {\bf https://huggingface.co/clarin-knext}.
Type-B Goldstone modes and a logarithmic spiral in the staggered $\rm SU(4)$ ferromagnetic spin-orbital model

It is found that the staggered $\rm SU(4)$ ferromagnetic spin-orbital model accommodates highly degenerate ground states arising from spontaneous symmetry breaking with type-B Goldstone modes. The spontaneous symmetry breaking pattern is ${\rm SU(4)} \rightarrow {\rm U(1)} \times {\rm U(1)} \times {\rm U(1)}$, with three type-B Goldstone modes. An abstract fractal underlies the ground state subspace with the fractal dimension identified with the number of type-B Goldstone modes. This connection is established by evaluating the entanglement entropy, which exhibits a universal finite system-size scaling behaviour. The latter in turn implies that the entanglement entropy scales logarithmically with the block size in the thermodynamic limit. In addition, the ground state degeneracies, depending on the boundary conditions adopted, constitute the two Fibonacci-Lucas sequences. In the limit of large system size their asymptotic forms become a self-similar logarithmic spiral. As a result, the model has a non-zero residual entropy $S_{\! r} = -2 \ln R $, where $R=(\! \sqrt{6}-\! \sqrt{2})/2$.