Miracle-dz/UniFAR-FTUs huggingface dataset:
Functional characterization of cytochrome P450s associated with pyrethroid resistance in the olive fruit fly Bactrocera oleae

Resistance to pyrethroid insecticides has evolved in Bactrocera oleae populations in Greece, threatening the efficacy of control interventions based on this insecticide class. Here we report the collection of populations from Crete with resistance levels reaching up to 331-folds, compared to susceptible laboratory strains and show that pyrethroid resistance is substantially suppressed by the PBO synergist, suggesting the involvement of detoxification enzymes. To identify specific candidate genes implicated in resistance, we performed comparative transcriptomic analysis, between the pyrethroid resistant populations from Crete and the susceptible laboratory strains, using both whole bodies and Malpighian tubules. Several genes were found differentially transcribed between resistant and susceptible flies in each comparison, with P450s being among the most highly over-expressed detoxification genes in pyrethroid resistant populations. Four of the over-expressed P450s (Cyp6A61, Cyp6G6, Cyp4P6 and Cyp6G28) were recombinantly expressed in Escherichia coli and in vitro metabolism assays revealed that CYP6A61 is capable of metabolizing alpha-cypermethrin, while CYP6G6, CYP4P6 and CYP6G28 are capable of metabolizing deltamethrin. No metabolism of neonicotinoid insecticides was recorded. We further silenced CYP6G6 in vivo, via RNAi, which led to a small, but significant increase in deltamethrin toxicity. The study provides valuable information towards the development of molecular diagnostics and evidence-based insecticide resistance management strategies.
Matrix Gla Protein acts as a driver of stemness and tumor initiation in ovarian cancer

Ovarian cancer (OC) displays the highest mortality among gynecological tumors, mainly due to early peritoneal dissemination, the high frequency of tumor relapse following primary debulking and the development of chemoresistance. All these events are thought to be initiated and sustained by a subpopulation of neoplastic cells, termed ovarian cancer stem cells (OCSC), that are endowed with self-renewing and tumor-initiating properties. This implies that interfering with OCSC function should offer novel therapeutic perspectives to defeat OC progression. To this aim, a better understanding of the molecular and functional makeup of OCSC in clinically relevant model systems is essential. We have profiled the transcriptome of OCSC vs. their bulk cell counterpart from a panel of patient-derived OC cell cultures. This revealed that Matrix Gla Protein (MGP), classically known as a calcification-preventing factor in cartilage and blood vessels, is markedly enriched in OCSC. Functional assays showed that MGP confers several stemness-associated traits to OC cells, including a transcriptional reprogramming. Patient-derived organotypic cultures pointed to the peritoneal microenvironment as a major inducer of MGP expression in OC cells. Furthermore, MGP was found to be necessary and sufficient for tumor initiation in OC mouse models, by shortening tumor latency and increasing dramatically the frequency of tumor-initiating cells. Mechanistically, MGP-driven OC stemness was mediated by the stimulation of Hedgehog signaling, in particular through the induction of the Hedgehog effector GLI1, thus highlighting a novel MGP/Hedgehog pathway axis in OCSC. Finally, MGP expression was found to correlate with poor prognosis in OC patients, and was increased in tumor tissue after chemotherapy, supporting the clinical relevance of our findings. Thus, MGP is a novel driver in OCSC pathophysiology, with a major role in stemness and in tumor initiation.
Predicting customer churn: A systematic literature review

Abstract Churn prediction is an active topic for research and machine learning approaches have made significant contributions in this domain. Models built to address customer churn, aim to identify customers who are at a high risk of terminating services offered by a company. Hence, an effective machine learning model indirectly contributes to the revenue growth of an organization, by identifying “at risk” customers, well in advance. This improves the success rate of retention campaigns and reduces costs associated with churn. The aim of this study is to explore the state-of-the-art machine learning techniques used in churn prediction. A systematic literature review, that is driven by 5 research questions and rigorous quality assessment criteria, is presented. There are 38 primary studies that are selected out of 420 studies published between 2018 and 2021. The review identifies popular machine learning techniques used in churn prediction and provides directions for future research. Firstly, the study finds that churn models lack generalization capability across industry domains. Hence, it identifies a need for researchers to explore techniques that extend beyond model experimentation, to improve efficiency of classifiers across domains. Secondly, it is observed that the traditional approaches to churn prediction depend significantly on demographic, product-usage, and revenue features alone. However, recent papers have integrated social network analysis-related features in churn models and achieved satisfactory results. Furthermore, there is a lack of scientific work that utilizes information-rich content of customer-company-interaction instances via email, chat conversations and other means. This area is the least explored. Thirdly, there is scope to investigate the effect of hybrid sampling strategies on model performance. This has not been extensively evaluated in literature. Lastly, there is no formal guideline on correct evaluation parameters to be used for models applied on imbalanced churn datasets. This is a grey area that requires greater attention.
Visuomotor interactions in the mouse forebrain mediated by extrastriate cortico-cortical pathways

The mammalian visual system can be broadly divided into two functional processing pathways: a dorsal stream supporting visually and spatially guided actions, and a ventral stream enabling object recognition. In rodents, the majority of visual signaling in the dorsal stream is transmitted to frontal motor cortices via extrastriate visual areas surrounding V1, but exactly where and to what extent V1 feeds into motor-projecting visual regions is not well known. To address this we employed a dual labeling strategy in male and female mice in which efferent projections from V1 were labeled anterogradely, and motor-projecting neurons in higher visual areas were labeled with retrogradely traveling adeno-associated virus (rAAV-retro) injected in M2. In flattened sections of dorsal cortex, the most pronounced colocalization V1 output and M2 input occurred in extrastriate areas AM, PM, RL and AL. Coronal sections further showed that neurons in both superficial and deep layers in these regions project to M2, but high resolution volumetric reconstructions revealed that the vast majority of putative synaptic contacts from V1 onto M2-projecting neurons occurred in layer 2/3. These findings support the existence of a dorsal processing stream in the mouse visual system, where visual signals reach motor cortex largely via feedforward projections in anteriorly and medially located extrastriate areas. Significance Statement Visually guided motor behavior depends on the long-distance relay of signals from visual cortex to frontal motor cortices, but the neuroanatomical connections linking visual and motor systems in rodents are not fully charted. Here, we characterized such pathways by injecting anterograde tracers in primary visual cortex (V1) and retrogradely traveling virus in motor cortex (M2), and visualizing where the projections overlapped. We found preferential colocalization of V1 output and M2-projecting neurons in anteriorly- and medially-located higher visual areas. 3D volumetric reconstructions further showed high rates of putative synaptic connections mainly in superficial layers. Thus, visual signals in the mouse dorsal visual stream reach motor areas at least in part via superficial, feedforward connections in a subset of extrastriate areas.
Autorepression-based conditional gene expression system in yeast for variation-suppressed control of protein dosage

Conditional control of gene expression allows an experimenter to investigate many aspects of a gene’s function. In the model organism Saccharomyces cerevisiae, a number of methods to control gene expression are widely practiced, including induction by metabolites, small molecules, and even light. However, all current methods suffer from at least one of a set of drawbacks, including need for specialized growth conditions, leaky expression, or the requirement of specialized equipment. Here we describe protocols using two transformations to construct strains that carry a new controller, in which all these drawbacks are overcome. In these strains, the expression of a controlled gene (gene of interest, or GOI) is repressed by the bacterial repressor TetR, and induced by anhydrotetracycline. TetR also regulates its own expression, creating an autorepression loop. This autorepression allows tight control of gene expression/ protein dosage with low cell to cell variation in expression. A second repressor, TetR-Tup1, prevents any leaky expression. We also present a protocol showing a particular workhorse application of such strains, to generate synchronized cell populations. We turn off the expression of the cell cycle regulator CDC20 completely, arresting the cell population, and then back on so that the synchronized cells resume cell cycle progression. This control system can be applied to any endogenous or exogenous gene for precise expression. Basic Protocol 1 Generating a parent WTC846 strain. Basic Protocol 2 Generating a WTC846 strain with controlled expression of the targeted gene Alternate Protocol 1 CRISPR-mediated promoter replacement Basic Protocol 3 Cell cycle synchronization/Arrest and Release using the WTC846-K3::CDC20 strain
Fast and accurate protein function prediction from sequence through pretrained language model and homology-based label diffusion

Protein function prediction is an essential task in bioinformatics which benefits disease mechanism elucidation and drug target discovery. Due to the explosive growth of proteins in sequence databases and the diversity of their functions, it remains challenging to fast and accurately predict protein functions from sequences alone. Although many methods have integrated protein structures, biological networks or literature information to improve performance, these extra features are often unavailable for most proteins. Here, we propose SPROF-GO, a Sequence-based alignment-free PROtein Function predictor which leverages a pretrained language model to efficiently extract informative sequence embeddings and employs self-attention pooling to focus on important residues. The prediction is further advanced by exploiting the homology information and accounting for the overlapping communities of proteins with related functions through the label diffusion algorithm. SPROF-GO was shown to surpass state-of-the-art sequence-based and even network-based approaches by more than 14.5%, 27.3% and 10.1% in AUPR on the three sub-ontology test sets, respectively. Our method was also demonstrated to generalize well on non-homologous proteins and unseen species. Finally, visualization based on the attention mechanism indicated that SPROF-GO is able to capture sequence domains useful for function prediction. Key points SPROF-GO is a sequence-based protein function predictor which leverages a pretrained language model to efficiently extract informative sequence embeddings, thus bypassing expensive database searches. SPROF-GO employs self-attention pooling to capture sequence domains useful for function prediction and provide interpretability. SPROF-GO applies hierarchical learning strategy to produce consistent predictions and label diffusion to exploit the homology information. SPROF-GO is accurate and robust, with better performance than state-of-the-art sequence-based and even network-based approaches, and great generalization ability on non-homologous proteins and unseen species
Adult born hippocampal granule cells promote pattern separation by bidirectionally modulating the remapping of place and cue cells

The hippocampal dentate gyrus (DG) exhibits a unique form of neural plasticity that results from continuous integration of adult born neurons, referred to as ‘adult neurogenesis’. Recent studies have proposed that adult neurogenesis promotes the ability to encode new memories without interference from previously stored memories that share similar features, through a neural computation known as pattern separation. However, due to lack of in vivo physiological evidence, the manner in which adult neurogenesis contributes to pattern separation remains unknown. Here, we investigate the contribution of functionally integrated yet immature adult born granule cells (iGCs) to DG computations by examining how chronic ablation or acute chemogenetic silencing of iGCs affects the activity of mature granule cells (mGCs) using in vivo 2-photon Ca2+ imaging. In both cases we observed altered remapping of mGCs but in opposite directions depending on their tuning selectivity. Rather than broadly modulating the activity of all mGCs, iGCs promote the remapping of place cells but limit the remapping of mGCs representing sensory cues (cue cells). We propose that these properties of iGCs explain their role in pattern separation because they promote the formation of non-overlapping representations for identical sensory cues encountered in different locations. Conversely, the absence of iGCs shifts the DG network to a state dominated by sensory cue information, a situation that is consistent with the overgeneralization often observed in anxiety disorders such as PTSD.
A sustainable competing dynamic - Real-time Bangla license plate detection and recognition system using YOLOv5 and SSD: A deep learning application

Abstract In this day and age, the programmed procurement of a tag and acknowledgment assumes a significant part in observing and coordinating vehicles in significant urban communities. It is hard to recognize a driver or proprietor of a vehicle that disregards traffic controls or plays out any incidental movement out and about. It will require a great deal of investment for a cop to review the plate of every vehicle. Subsequently, a mechanized tag acknowledgment framework can tackle these sorts of issues. This is the proposed technique; two Deep Learning calculations are utilized to distinguish the permit number and characters on the tag from the constant picture. The primary YOLOv5 model tracks down the main in the live video of a vehicle out and about. Then, at that point, cut out the area of the permit numbers in the video. The cut casing is then embedded into a second SSD (Single Shot Detection) to identify slugs on that tag. The prepared model acquires a high precision of 96.2% over a sum of 400 picture databases.
A hybrid convolutional long short-term memory (CNN-LSTM) based natural language processing (NLP) model for sentiment analysis of customer product reviews in Bangla

Abstract Sentiment Analysis (SA) examines how people feel about products, services, people, organizations, events etc. Most of the Natural language processing on SA research has focused on English. In the case of Bangla, it lacks sufficient study as well as a proper dataset. All previous analyses employed KNN, NB, and other methods. We develop a natural language processing (NLP) model for separating opinion and sentiment from Bangla customer surveys. This technology isolates extreme client opinions to help with business and marketing decisions. Bangladesh is embracing e-commerce and f-commerce. Client comments and evaluations are becoming more significant in judging product or service quality, and this industry is evolving toward internet distribution. Organizations utilize client audits to check product quality. Our objective is to systematically collect client feedback and understand their product reaction. We used a hybrid CNN-LSTM based NLP model to classify Bangla texts in 3 viewpoint categories (positive, negative and neutral). We tested our model using a Bangla dataset that we generated. For our dataset, we collected polls and comments from websites and social media. Finally, among the three evaluation matrices, the f-1 score is providing the highest average, and the three-opinion technique is 87.22 percent accurate in determining the performance of our task.
Carbon stock and dynamic in the middle Xingu forests at eastern Amazonia

ABSTRACT Studies on the quantification and spatial distribution of stored carbon, as well as the potential for carbon uptake by forests, are of great importance. We assessed the spatial distribution and dynamic of carbon in forest plots in the Middle Xingu. These plots are part of the vegetation monitoring area of the Belo Monte Dam. We estimated the biomass of trees with diameter at breast height (DBH) ≥10 cm using an allometric equation with forest inventory data in the eastern Amazonian region in the Xingu River area. We evaluated the vegetation structure and carbon stock dynamic in 2012, 2014, and 2016, and temporal effects on carbon uptake. The periodic annual increase in carbon was significantly higher between 2012 and 2014 (2.5 Mg ha−1) than between 2014 and 2016 (1.84 Mg ha−1). Plant biomass was highly influenced by the abundance of species with low diameters (between 10 cm and 50 cm DBH). Despite the mortality rate being higher than the recruitment in the second period (2014–2016), we found that the forest did function as a carbon sink but showing increases in carbon emissions.
BOUNDARY ELEMENT-PERTURBATION METHOD USING NEUMANN SERIES EXPANSION FOR SEISMIC RESPONSE ANALYSIS OF THE GROUND WITH IRREGULAR SURFACE

山地や谷地形などの地形の起伏や, 埋没谷などの基盤の不整形性を有する地盤では複雑な地震応答を示すことが指摘されている. このため様々な地震応答解析が行われているが, 複雑な応答が生じるメカニズムについては依然として不明な点が多い. 本研究では, 地表面の起伏に着目し, 起伏が地震応答に与える影響を検討するための手法として, 境界要素法と摂動解法を組み合わせた新たな手法を展開した. この手法には地震応答を入射波と次数ごとの散乱波の寄与に分離できる利点がある. また, 本解析手法の適用例を示し, 地盤の応答関数が散乱波の寄与から解釈できること, 散乱波の寄与は散乱波の発生地点の地表面の法線方向とこの地点から地震応答を考える地点への方向との関係, 及び両地点の距離の二つの要因の影響を受けることなどを指摘した.
Gene editing and high-throughput screening in Leishmania without requiring

8 CRISPR/Cas9 gene editing has revolutionised loss-of-function experiments in Leishmania, the 9 causative agent of leishmaniasis. As Leishmania lack a functional non-homologous DNA end joining 10 pathway however, obtaining null mutants typically requires additional donor DNA, selection of drug 11 resistance-associated edits or time-consuming isolation of clones. Genome-wide loss-of-function 12 screens across different conditions and across multiple Leishmania species are therefore unfeasible 13 at present. Here, we report a CRISPR/Cas9 cytosine base editor (CBE) toolbox that overcomes these 14 limitations. We employed CBEs in Leishmania to introduce STOP codons by converting cytosine into 15 thymine and created www.leishbaseedit. net for CBE primer design in kinetoplastids. Through reporter 16 assays and by targeting singleand multi-copy genes in L. mexicana, L. major, L. donovani and L. 17 infantum, we demonstrate how this tool can efficiently generate functional null mutants by expressing 18 just one single-guide RNA, reaching up to 100% editing rate in non-clonal populations. We then 19 generated a Leishmania-optimised CBE and successfully targeted an essential gene in a plasmid 20 library delivered loss-of-function screen in L. mexicana. Since our method does not require DNA 21 double-strand breaks, homologous recombination, donor DNA or isolation of clones, we believe that 22 this enables for the first time functional genetic screens in Leishmania via delivery of plasmid libraries. 23
Absorption Cross Sections of O2(a1Δg) and O2(X3Σg−) in the Region from 1087 to 1700 Å

An attempt has been made to measure the absorption cross sections of O2 in the metastable state a1Δg from 1087 to 1700 A. The absorption is measurable (σa ≥ 3 × 10−19 cm2) from around 1500 A toward.
Modelling laser interferometers for the measurement of the Avogadro constant

In dimensional measurements by laser interferometry, when the accuracy approaches 10-9 λ, wavefront aberra tions cause systematic variations of the fringe period. This paper illustrates how these effects are modeled and experimentally studied in the measurements of the lattice parameter and the topographical survey of 1 kg Si spheres, which measurements are necessary to "count" atoms and to make it possible to realize the kilogram from the Planck constant value.
Do Sibship Size and Birth Order Matter to Child Education?Evidence from Vietnam

The main measures the impact of sibship size and birth order on educational performance of children between 6 and 18 years old using regression and matching methods. Data used in this study are from Vietnam Household Living Standard Survey 2006. It is found that the number of siblings has a negative effect child school enrollment. An increase of one sibling leads to a decrease of around 2 percentage points in the percentage of school enrolment for children. Schooled children who have fewer siblings are also more likely to have better educational performance than school children who have more siblings. Having one additional brother or sister reduces the percentage of pupils with “excellent” educational performance as well as the percentage of pupils with “excellent and best” educational performance by around 2 percentage points. The birth order also matters to child education. Estimates from regressions and matching methods show that children with a high birth order tend to have higher rates of school enrollment than children with a low birth order.
Search for Tau Neutrinos in the τ → e Decay Channel in the OPERA Experiment

The OPERA (Oscillation Project with Emulsion tRacking Apparatus) experiment is a long baseline neutrino oscillation experiment that was designed to perform a conclusive test of the νμ → ντ oscillations hypothesis. The main aim of this experiment is a direct observation of τ leptons in ντ charged-current interactions. A good electromagnetic shower reconstruction is important for the τ detection in the τ → e decay channel. So far, 4 ντ candidates have been observed in the OPERA detector with a background of 0.23 events. Given the number of analyzed events and the low background, νμ → ντ oscillations are established with a significance of 4.2 σ. Reconstruction of electromagnetic showers is one of the fundamental tools needed by neutrino experiments and the estimation of their energy is a crucial issue. This is also true for the OPERA experiment where we have developed and tested an algorithm, in order to evaluate the energy of electron through the identification of its shower for the τ → e decay channel as well as νe interactions by using the OPERA bricks. Normal reconstruction of a shower event depends upon the shower being contained in one brick. However, when a shower is initiated in the upstream portion of the brick, the shower will begin in the first brick, pass through the Electronic Detector (ED) and end in the second brick; making normal reconstruction methods unusable. Nearly half of the electromagnetic shower events detected take place across two bricks, meaning that previously, half of detected events had to be discarded. I have expanded my code in such a way to analyze showers with one or two brick distributions, allowing us to include 50 percent more events in our data sets. This approach is using for the first time the information of the downstream brick to reconstruct and estimate the energy of electromagnetic showers in the OPERA experiment.