Miracle-dz/UniFAR-FTUs huggingface dataset:
Yeast eIF2A has a minimal role in translation initiation and uORF-mediated translational control in vivo

Initiating translation of most eukaryotic mRNAs depends on recruitment of methionyl initiator tRNA (Met-tRNAi) in a ternary complex (TC) with GTP-bound eukaryotic initiation factor 2 (eIF2) to the small (40S) ribosomal subunit, forming a 43S preinitiation complex (PIC) that attaches to the mRNA and scans the 5’-untranslated region (5’ UTR) for an AUG start codon. Previous studies have implicated mammalian eIF2A in GTP-independent binding of Met-tRNAi to the 40S subunit and its recruitment to specialized mRNAs that do not require scanning, and in initiation at non-AUG start codons, when eIF2 function is attenuated by phosphorylation of its α-subunit during stress. The role of eIF2A in translation in vivo is poorly understood however, and it was unknown whether the conserved ortholog in budding yeast can functionally substitute for eIF2. We performed ribosome profiling of a yeast deletion mutant lacking eIF2A and isogenic wild-type (WT) cells in the presence or absence of eIF2α phosphorylation induced by starvation for amino acids isoleucine and valine. Whereas starvation of WT confers changes in translational efficiencies (TEs) of hundreds of mRNAs, the eIF2AΔ mutation conferred no significant TE reductions for any mRNAs in non-starved cells, and it reduced the TEs of only a small number of transcripts in starved cells containing phosphorylated eIF2α. We found no evidence that eliminating eIF2A altered the translation of mRNAs containing putative IRES elements, or harboring uORFs initiated by AUG or near-cognate start codons, in non-starved or starved cells. Thus, very few mRNAs (possibly only one) appear to employ eIF2A for Met-tRNAi recruitment in yeast cells, even when eIF2 function is attenuated by stress.
A green alternative to fragrant agarwood sesquiterpenoid production

Certain endangered Thymelaeaceous trees are major sources of the fragrant and highly valued resinous agarwood, comprised of hundreds of oxygenated sesquiterpenoids (STPs). Despite growing pressure on natural agarwood sources, the chemical complexity of STPs severely limits synthetic production. Here, we catalogued the chemical diversity in 58 agarwood samples by two-dimensional gas chromatography–mass spectrometry and partially recreated complex STP mixtures through synthetic biology. We improved STP yields in the unicellular alga Chlamydomonas reinhardtii by combinatorial engineering to biosynthesise nine macrocyclic STP backbones found in agarwood. A bioprocess following green-chemistry principles was developed that exploits ‘milking’ of STPs without cell lysis, solvent–solvent STP extraction, solvent–STP nanofiltration, and bulk STP oxy-functionalisation to obtain terpene mixtures like those of agarwood. This process occurs with total solvent recycling and enables continuous production. Our synthetic-biology approach offers a sustainable alternative to harvesting agarwood trees to obtain mixtures of complex, fragrant, oxygenated STPs.
Detection of disease-specific signatures in B cell repertoires of lymphomas using machine learning

The classification of B cell lymphomas - mainly based on light microscopy evaluation by a pathologist - requires many years of training. Since the B cell receptor (BCR) of the lymphoma clonotype and the microenvironmental immune architecture are important features discriminating different lymphoma subsets, we asked whether BCR repertoire next-generation sequencing (NGS) of lymphoma-infiltrated tissues in conjunction with machine learning algorithms could have diagnostic utility in the subclassification of these cancers. We trained a random forest and a linear classifier via logistic regression based on patterns of clonal distribution, VDJ gene usage and physico-chemical properties of the top-n most frequently represented clonotypes in the BCR repertoires of 620 paradigmatic lymphomas - nodular lymphocyte predominant B cell lymphoma (NLPBL), diffuse large B cell lymphoma (DLBCL) and chronic lymphocytic leukemia (CLL) - as well as 291 control tissues. With regard to DLBCL and CLL, the models demonstrated optimal performance when utilizing only the most prevalent clonotype for classification, while in NLPBL - that has a dominant background of non-malignant bystander cells - a broader array of clonotypes enhanced model accuracy. Surprisingly, the straightforward logistic regression model performed best in this seemingly complex classification problem, suggesting linear separability in our chosen dimensions. It achieved a weighted F1-score of 0.84 on a test cohort including 125 cases from all three lymphoma entities and 58 healthy individuals. Together, we provide proof-of-concept that at least the 3 studied lymphoma entities can be differentiated from each other using BCR repertoire NGS on lymphoma-infiltrated tissues by a trained machine learning model. Author Summary Lymphoma, a complex group of malignant blood cancers, poses a significant diagnostic challenge due to its diverse subtypes. Yet, precise classification is crucial for tailored treatment. In our research, we developed a machine learning algorithm and conducted comprehensive validation to discern distinct B cell lymphoma subtypes. We therefore leveraged B cell repertoires of lymphoma-infiltrated tissue, as ascertained through next-generation sequencing. Our data offers three key insights: We detail the creation and training of our machine learning algorithm, explaining how we selected features and designed the model. We demonstrate the algorithm’s diagnostic precision using sequencing data from a test-set of patients. Moreover, through a deep dive into the most distinguishing aspects of our algorithm, we unveil distinctive disease-related patterns present within the malignant B cell and its surrounding environment. This analysis showed that both the malignant lymphoma cell, but also healthy bystander immune cells contribute to the distinctive architecture that characterizes a specific lymphoma subtype. We hope our work will contribute towards creating tools to diagnose lymphoma more easily and accurately ultimately leading to better outcomes for patients with this type of cancer.
Cross-pathway integration of cAMP signals through cGMP and calcium-regulated phosphodiesterases in mouse striatal cholinergic interneurons

Acetylcholine plays a key role in striatal function, yet the intricate dynamics of cyclic nucleotide signaling which govern the firing properties of cholinergic interneurons (ChINs) have remained elusive. Since phosphodiesterases determine the dynamics of cyclic nucleotides, in this study, we used FRET biosensors and pharmacological compounds to examine phosphodiesterase activity in ChINs in mouse brain slices. Intriguingly, these neurons displayed strikingly low levels and slow cAMP responsiveness compared to medium-sized spiny neurons (MSNs). Our experiments revealed that PDE1, PDE3 and PDE4 are important regulators of cAMP level in ChINs. Notably, the induction of cGMP production by nitric oxide (NO) donors increases cAMP by inhibiting PDE3 - a mechanism hitherto unexplored in neuronal context. Furthermore, the activation of NMDA or metabotropic glutamate receptors increases intracellular calcium, consequently activating PDE1 and thereby decreasing both cAMP and cGMP. This interplay of phosphodiesterases enables the control of cAMP by the neuromodulatory influences of glutamate and NO. Remarkably, the NO/cGMP signal results in different effects: NO enhances cAMP in ChINs by inhibiting PDE3, whereas it reduces cAMP levels in MSNs by activating PDE2A. These findings underscore the specificity of intracellular signaling in ChINs compared to MSNs and show how the NO-cGMP pathway affects these various neuronal types differently. These observations have significant implications for understanding the regulation of the striatal network and the integration of dopaminergic signals and suggest innovative therapeutic strategies for addressing basal ganglia disorders with unmet medical need.
Conservation of the ancestral function of GTSF1 in transposon silencing in the unicellular eukaryote Paramecium tetraurelia

The PIWI-interacting RNA (piRNA) pathway is crucial for transposon repression and the maintenance of genomic integrity. Gametocyte specific factor 1 (GTSF1), an indispensable auxiliary factor of PIWI, was recently shown to potentiate the catalytic activity of PIWI in many metazoans. Whether the requirement of GTSF1 extends to PIWI proteins beyond metazoans is unknown. In this study, we identified a homolog of GTSF1 in the unicellular eukaryote Paramecium tetraurelia (PtGTSF1) and found that its role as a PIWI-cofactor is conserved. PtGTSF1 interacts with PIWI (Ptiwi09) and Polycomb Repressive Complex 2 (PRC2) and is essential for PIWI-dependent DNA elimination of transposons during sexual development. PtGTSF1 is crucial for the degradation of PIWI-bound small RNAs recognizing the organism’s own genomic sequences. Without PtGTSF1, self-matching small RNAs are not degraded and results in an accumulation of H3K9me3 and H3K27me3, which disturbs transposon recognition and slows down their elimination. Our results demonstrate that the PIWI-GTSF1 interaction also exists in unicellular eukaryotes with the ancestral function of transposon silencing.
Migration in Kenya: beyond Harris-Todaro

ABSTRACT This paper examines the impact of agrarian structures on the migration behavior and destination of rural household heads and individuals in Kenya. To explore the complexity of migration we extend the standard Harris-Todaro framework to account for land inequality and size as well as type of destination. Using probit regressions, we show that Kenyan household heads born in districts with higher land inequality, smaller per capita land and lower per capita rural income are more likely to migrate. We show that for individuals whose incomes are squeezed by larger land inequality, migration from villages to smaller cities, and villages in different districts could be a preferable strategy to migrating to Greater Nairobi. The impact of land inequality is larger for male than female migration and insignificant for females’ rural-to-rural migration. Moreover, the level of education, age, marital status, gender, religion and distance to Nairobi play a role in migration behavior.
Automating Seccomp Filter Generation for Linux Applications

Software vulnerabilities undermine the security of applications. By blocking unused functionality, the impact of potential exploits can be reduced. While seccomp provides a solution for filtering syscalls, it requires manual implementation of filter rules for each individual application. Recent work has investigated approaches to automate this task. However, as we show, these approaches make assumptions that are not necessary or require overly time-consuming analysis. In this paper, we propose Chestnut, an automated approach for generating strict syscall filters with lower requirements and limitations. Chestnut comprises two phases, with the first phase consisting of two static components, i.e. , a compiler and a binary analyzer, that statically extract the used syscalls. The compiler-based approach of Chestnut is up to factor 73 faster than previous approaches with the same accuracy. On the binary level, our approach extends over previous ones by also applying to non-PIC binaries. An optional second phase of Chestnut is dynamic refinement to restrict the set of allowed syscalls further. We demonstrate that Chestnut on average blocks 302 syscalls (86 . 5%) via the compiler and 288 (82 . 5%) using the binary analysis on a set of 18 applications. Chestnut blocks the dangerous exec syscall in 50% and 77 . 7% of the tested applications using the compiler-and binary-based approach, respectively. For the tested applications, Chestnut blocks exploitation of more than 61% of the 175 CVEs that target the kernel via syscalls.
A multivariate Wald‐Wolfowitz rank test against serial dependence

Rank-based cross-covariance matrices, extending to the case of multivariate observed series the (univariate) rank autocorrelation coefficients introduced by Wald and Wolfowitz (1943), are considered. A permutational central limit theorem is established for the joint distribution of such matrices, under the null hypothesis of (multivariate) randomness as well as under contiguous alternatives of (multivariate) ARMA dependence. A rank-based, permutationaily distribution-free test of the portmanteau type is derived, and its asymptotic local power is investigated. Finally, a modified rank-based version of Tiao and Box's model specification procedure is proposed, which is likely to be more reliable under non-Gaussian conditions, and more robust against gross errors. RESUME Des matrices de covariances croisees fondees sur les rangs, generalisant au cas des series multivariees les coefficients d'autocorrelation de rangs introduits par Wald et Wolfowitz (1943) sont considerees. Un theoreme central-limite permutationnel est etabli pour ces matrices, sous Phypothese que la serle sous-jacente constitue la realisation d'un bruit blanc multivarie, ainsi que sous des contre-hypotheses contigues de dependance ARMA. Un test de rangs du type portemanteau est egalement construit, et sa puissance asymptotique locale est explicitement calculee. Enfin, une version fondee sur les rangs de la procedure d'identification de Tiao et Box est proposee. Celle-ci est plus fiable que la procedure usuelle sous des conditions non gaussiennes, et plus robuste par rapport a la presence de valeurs aberrantes.
Vital effects in coccolith calcite: Cenozoic climate‐pCO2 drove the diversity of carbon acquisition strategies in coccolithophores?

[1] Coccoliths, calcite plates produced by the marine phytoplankton coccolithophores, have previously shown a large array of carbon and oxygen stable isotope fractionations (termed “vital effects”), correlated to cell size and hypothesized to reflect the varying importance of active carbon acquisition strategies. Culture studies show a reduced range of vital effects between large and small coccolithophores under high CO2, consistent with previous observations of a smaller range of interspecific vital effects in Paleocene coccoliths. We present new fossil data examining coccolithophore vital effects over three key Cenozoic intervals reflecting changing climate and atmospheric partial pressure of CO2 (pCO2). Oxygen and carbon stable isotopes of size-separated coccolith fractions dominated by different species from well preserved Paleocene-Eocene thermal maximum (PETM, ∼56 Ma) samples show reduced interspecific differences within the greenhouse boundary conditions of the PETM. Conversely, isotope data from the Plio-Pleistocene transition (PPT; 3.5–2 Ma) and the last glacial maximum (LGM; ∼22 ka) show persistent vital effects of ∼2‰. PPT and LGM data show a clear positive trend between coccolith (cell) size and isotopic enrichment in coccolith carbonate, as seen in laboratory cultures. On geological timescales, the degree of expression of vital effects in coccoliths appears to be insensitive topCO2 changes over the range ∼350 ppm (Pliocene) to ∼180 ppm (LGM). The modern array of coccolith vital effects arose after the PETM but before the late Pliocene and may reflect the operation of more diverse carbon acquisition strategies in coccolithophores in response to decreasing Cenozoic pCO2.
Unsupervised Detection of Interictal Epileptiform Discharges in Routine Scalp EEG

Epilepsy affects more than 50 million people and is one of the most prevalent neurological disorders and has a high impact on the quality of life of those suffering from it. However, 70% of epilepsy patients can live seizure free with proper diagnosis and treatment. Patients are evaluated using scalp EEG recordings which is cheap and non-invasive. Diagnostic yield is however low and qualified personnel need to process large amounts of data in order to accurately assess patients. MindReader is an unsupervised classifier which detects spectral anomalies and generates a hypothesis of the underlying patient state over time. The aim is to highlight abnormal, potentially epileptiform states, which could expedite analysis of patients and let qualified personnel attest the results. It was used to evaluate 95 scalp EEG recordings from healthy adults and adult patients with epilepsy. Interictal Epileptiform discharges (IED) occurring in the samples had been retroactively annotated, along with the patient state and maneuvers performed by personnel, to enable character ization of the classifier’s detection performance. The performance was slightly worse than previous benchmarks on pediatric scalp EEG recordings, with a 7% and 33% drop in specificity and sensitivity, respectively. Electrode positioning and partial spatial extent of events saw notable impact on performance. However, no correlation between annotated disturbances and reduction in performance could be found. Additional explorative analysis was performed on serialized intermediate data to evaluate the analysis design. Hyperparameters and electrode montage options were exposed to optimize for the average Mathew’s correlation coefficient (MCC) per electrode per patient, on a subset of the patients with epilepsy. An increased window length and lowered amount of training along with an common average montage proved most successful. The
An evaluation and comparison of AutoML solutions: Azure AutoML and EvalML

Automated machine learning (AutoML) supports the development of machine learning systems by automating tasks that traditionally require manual, time-consuming labor and extensive expertise in the field. This paper presents two AutoML systems, one open-source ( EvalML ) and one commercial solution ( Azure AutoML ), and evaluates their range of functionality as well as practical usability. The results show that commercial tools provide better usability for beginners, while open-source alternatives tend to stand out with greater configurability.
What Consistency Does Your Key-Value Store Actually Provide?

Many key-value stores have recently been proposed as platforms for always-on, globally-distributed, Internet-scale applications. To meet their needs, these stores often sacrifice consistency for availability. Yet, few tools exist that can verify the consistency actually provided by a key-value store, and quantify the violations if any. How can a user check if a storage system meets its promise of consistency? If a system only promises eventual consistency, how bad is it really? In this paper, we present efficient algorithms that help answer these questions. By analyzing the trace of interactions between the client machines and a key-value store, the algorithms can report whether the trace is safe, regular, or atomic, and if not, how many violations there are in the trace. We run these algorithms on traces of our eventually consistent key-value store called Pahoehoe and find few or no violations, thus showing that it often behaves like a strongly consistent system during our tests.
Vegetation cover change detection in Chamela-Cuixamala, Mexico

In Mexico, and everywhere else, the ecosystems are constantly changing either by natural factors or anthropogenic activity. Remote sensing has been a key tool to monitoring these changes throughout history and also to understanding the ecological dynamics. Hence, sustainable development plans have been created in order to improve the decisionmaking process; thus, this paper analyses deforestation impact in a very important natural resourcing area in Mexico, considering land cover changes. The study area is located in the coast of Jalisco, Mexico, where deforestation and fragmentation as well as high speed touristic development have been the causes of enormous biodiversity losses; the Chamela-Cuixamala Biosphere Reserve is located within this area. It has great species richness and vast endemism. The exploitation of this biome is widespread all over the country and it has already had an impact in the reserve. The change detection multi-temporal study uses Landsat satellite imagery during the 1970-2003 time period. Thus, the objective of change detection analysis is to detect and localize environmental changes through time. The change detection method consists in producing an image of change likelihood (by post-classification, multivariate alteration detection) and thresholding it in order to produce the change map. Experimental results confirmed that the patterns of land use and land cover changes have increased significantly over the last decade. This study also analyzes the deforestation impact on biodiversity. The analysis validation was carried out using field and statistic data. Spatial-temporal changing range enables the analysis of the structural and dynamic effects on the ecosystem and it enhances better decision-making and public environmental policies to decrease or eliminate deforestation, the creation of natural protected areas as a biodiversity conservation method, and counteracting the global warming phenomena.
School of Informatics Informatics Research Review Sarcasm Detection in English Tweets: Dataset and Feature Creation Approaches

Due to its polarity ﬂipping nature, sarcasm detection in textual data is a challenging, yet important task in Natural Language Processing. This review discusses sarcasm detection in English tweets using the binary classiﬁcation approach. Speciﬁcally, we studied dataset creation approaches and feature creation approaches across literature. We found that two common dataset creation approaches are hashtag-based annotation and manual annotation. Moreover, these approaches represent diﬀerent sarcasm phenomena. Related to sarcasm context, we found two diﬀerent feature types: local features and contextual features. Finally, studies show that sarcasm detection systems perform best when both local features and contextual features are used
Screening and diagnostic tools for depressive disorders used in primary care: an integrative review

Introduction: The prevalence of depressive disorders in the general population is high, and primary health care (PHC) is essential in dealing with these issues. The introduction of screening and diagnostic tools for these disorders in PHC is one of the suggested strategies to enhance the quality of care. Nevertheless, there is limited understanding regarding their effectiveness. Objective: To identify the screening instruments and diagnostic interviews that have been widely used in studies involving users with depressive symptoms seeking help at PHC. Methods: This was an integrative literature review. For the period from October to December 2020, searches were conducted in the LILACS and MEDLINE databases. Research articles carried out in PHC that utilized one or more screening and/or diagnostic instruments for depressive disorders were included. At least two researchers analyzed titles, abstracts, and full texts, extracting data using a standardized spreadsheet. The results were presented in a descriptive and narrative manner. Results: After using eligibility criteria, 413 articles were selected. There were 22 screening instruments used for screening and diagnosis of depression in studies carried out in PHC. The most cited screening instrument was the Patient Health Questionnaire, and the most used diagnostic interview was the Mini International Neuropsychiatric Interview. Studies published after 2011, carried out in Europe, with a non-elderly adult population and with the purpose of identifying the prevalence of depression in different social groups predominated. Most studies used long instruments, with complex analytical criteria, without validation/adaptation for PHC, and with restrictions on the analyzed health problems, making them limited for general use in PHC. Conclusions: The use of screening and diagnostic instruments for depression in PHC can be a relevant strategy to improve the care provided to this population. Nevertheless, additional research is required to analyze the benefits and harms of this approach, as well as a greater effort to select the best instruments to be used.