Search

We present the catalog of Interplanetary Network (IPN) localizations for 199 short-duration gamma-ray bursts (sGRBs) detected by the Konus-Wind (KW) experiment between 2011 January 1 and 2021 August 31, which extends the initial sample of IPN localized KW sGRBs (arXiv:1301.3740) to 495 events. We present the most comprehensive IPN localization data on these events, including probability sky maps in HEALPix format., Published in ApJS

We report the AGILE observations of GRB 220101A, which took place at the beginning of 1st January 2022 and was recognized as one of the most energetic gamma-ray bursts (GRBs) ever detected since their discovery. The AGILE satellite acquired interesting data concerning the prompt phase of this burst, providing an overall temporal and spectral description of the event in a wide energy range, from tens of keV to tens of MeV. Dividing the prompt emission into three main intervals, we notice an interesting spectral evolution, featuring a notable hardening of the spectrum in the central part of the burst. The average fluxes encountered in the different time intervals are relatively moderate, with respect to those of other remarkable bursts, and the overall fluence exhibits a quite ordinary value among the GRBs detected by MCAL. However, GRB 220101A is the second farthest event detected by AGILE, and the burst with the highest isotropic equivalent energy of the whole MCAL GRB sample, releasing E_iso=2.54x10^54 erg and exhibiting an isotropic luminosity of L_iso=2.34x10^52 erg/s (both in the 400 keV - 10 MeV energy range). We also analyzed the first 10^6 s of the afterglow phase, using the publicly available Swift XRT data, carrying out a theoretical analysis of the afterglow, based on the forward shock model. We notice that GRB 220101A is with high probability surrounded with a wind-like density medium, and that the energy carried by the initial shock shall be a fraction of the total E_iso, presumably near 50%., Comment: 16 pages, 9 figures, 4 tables

Nel presente rapporto (Report P9) sono raccolte le procedure integrate per l'individuazione e la stima del rischio fitopatologico su piante di rosa e di rucola, secondo quanto previsto nel quadro di attività sperimentali del progetto AgriDigit-agricoltura digitale sotto-progetto AGROFILIERE. Ai fini di una corretta comprensione di quanto di seguito esposto, si chiarisce che nello specifico contesto operativo, per procedura integrata s'intende una metodologia formale che regola il passaggio di informazioni e il trattamento dei dati all'interno e all'esterno dei sistemi di analisi (criteri, indici e modelli) implementati nel sistema o in specifici fogli di calcolo. Dopo un'analisi delle peculiarità dei siti sperimentali (Pescia Area Esterna, Pescia Serra e Pontecagnano Tunnel), il documento descrive i criteri utilizzati per la loro caratterizzazione micrometeorologica e spaziale, alla base della creazione di serie complete di dati agrometeorologici da utilizzare per la stima del rischio fungino, nelle diverse condizioni sperimentali. Vengono poi descritte le procedure implementate nei fogli di calcolo (xls), per lo studio, la calibrazione e la validazione dei modelli, suddivise per tipologia di pianta, rosa e rucola, e relative patologie (oidio, peronospora, botrite e ticchiolatura), rese coerenti dall'impiego di un set comune di dati e dal ricorso a informazioni di base omogenee, sullo sviluppo della coltura, del patogeno e sugli eventuali interventi gestionali. Le procedure ritenute affidabili e utilizzabili a livello operativo sono, quindi, implementate nel sistema informativo centrale, che impiega una procedura unificata per la stima dei livelli di rischio fungino sulle due colture. Al fine di consentire un controllo dei principali passaggi di analisi, le procedure sono state descritte con l'ausilio delle istruzioni di calcolo (foglio xls) e delle linee di codice (routine MATLAB®), opportunamente commentate.

La presente relazione finale rende conto dei lavori condotti dall'Unità Operativa del CNR-IBE in ottemperanza a quanto previsto dal "Contratto di affidamento del Servizio Specialistico" attivato dal CREA - Orticoltura e Floricoltura, nell'ambito del progetto "AgriDigit-agricoltura digitale, sotto-progetto AGROFILIERE, per lo sviluppo di sistemi di alert fitopatologico". Sulla base di quanto indicato nel Capitolato Tecnico, le attività condotte sono state organizzate nel modo seguente: oOttimizzazione delle componenti hardware e software di gestione di una rete di oSupporto alla gestione e mantenimento delle reti di monitoraggio nel corso del progetto. oSupporto all'integrazione ed elaborazione dei dati (da sensori ambiente e immagini multispettrali) per l'analisi della variabilità spazio-temporale del rischio fitopatologico. oSupporto alle attività di ricerca condotte nell'ambito del progetto AgriDigit. Tutte le attività descritte nei precedenti punti sono state condotte in linea con le indicazioni ricevute dai responsabili del CREA OF e dai ricercatori coinvolti nel progetto AgriDigit-agricoltura digitale sotto-progetto AGROFILIERE, in pieno spirito di collaborazione e trasparenza operativa. Oltre a offrire il proprio supporto nelle fasi di gestione della rete di monitoraggio, di raccolta, elaborazione e interpretazione dei dati, l'U.O. CNR-IBE ha condotto attività specifiche tese a rispondere a problematiche di carattere tecnico-operativo, gestionale o interpretativo, per quanto direttamente connesso ai temi oggetto dello studio. I dati raccolti e le informazioni prodotte, tramite le procedure di analisi definite e i modelli implementati, sono stati debitamente discussi con i ricercatori e i responsabili del CREA-OF, al fine di consentire un più largo e costruttivo confronto sulle problematiche interpretative, principalmente allo scopo di favorire la contestualizzazione delle informazioni trasmesse e la piena valorizzazione dei prodotti della ricerca intravisti. Nel corso del periodo di supporto sono stati anche creati scenari e simulazioni che permettessero alla committenza di valutare l'utilità e le potenzialità dei sistemi e delle procedure proposte, in modo da poterne orientare lo sviluppo, in base alle proprie esigenze operative. Numerose prove sono state inoltre condotte per la messa a punto di procedure affidabili per l'impiego delle fotocamere SONY ILCE-QX1 e dei relativi software, anche con l'ausilio di strumenti e programmi di confronto. Tutte le soluzioni individuate sono, infine, state implementate in fogli di calcolo o moduli software proprietari in linguaggio MATLAB® e descritti nei rapporti presentati, all'interno dei quali sono stati inseriti gli stessi codici, per una piena comprensione dei relativi passaggi logico-matematici. Le attività svolte dall'Unità Operativa del CNR-IBE nell'ambito di ciascuna fase, già oggetto di varie comunicazioni scritte e orali, sono riassunte nella presente relazione secondo l'ordine sopra ricordato e riportate con maggiore dettaglio nei diversi rapporti tecnici, in particolare il "Report tecnico P5 "Procedure di analisi dei dati e calibrazione dei modelli di rischio fitopatologici scelti - versione finale" e il Report tecnico P9 "Procedure integrate di analisi della variabilità spazio-temporale del rischio fitopatologico".

Background A thorough verification of the ability of genomic selection (GS) to predict estimated breeding values for pea (Pisum sativum L.) grain yield is pending. Prediction for different environments (inter-environment prediction) has key importance when breeding for target environments featuring high genotype × environment interaction (GEI). The interest of GS would increase if it could display acceptable prediction accuracies in different environments also for germplasm that was not used in model training (inter-population prediction). Results Some 306 genotypes belonging to three connected RIL populations derived from paired crosses between elite cultivars were genotyped through genotyping-by-sequencing and phenotyped for grain yield, onset of flowering, lodging susceptibility, seed weight and winter plant survival in three autumn-sown environments of northern or central Italy. The large GEI for grain yield and its pattern (implying larger variation across years than sites mainly due to year-to-year variability for low winter temperatures) encouraged the breeding for wide adaptation. Wider within-population than between-population variation was observed for nearly all traits, supporting GS application to many lines of relatively few elite RIL populations. Bayesian Lasso without structure imputation and 1% maximum genotype missing rate (including 6058 polymorphic SNP markers) was selected for GS modelling after assessing different GS models and data configurations. On average, inter-environment predictive ability using intra-population predictions reached 0.30 for yield, 0.65 for onset of flowering, 0.64 for seed weight, and 0.28 for lodging susceptibility. Using inter-population instead of intra-population predictions reduced the inter-environment predictive ability to 0.19 for grain yield, 0.40 for onset of flowering, 0.28 for seed weight, and 0.22 for lodging susceptibility. A comparison of GS vs phenotypic selection (PS) based on predicted genetic gains per unit time for same selection costs suggested greater efficiency of GS for all traits under various selection scenarios. For yield, the advantage in predicted efficiency of GS over PS was at least 80% using intra-population predictions and 20% using inter-population predictions. A genome-wide association study confirmed the highly polygenic control of most traits. Conclusions Genome-enabled predictions can increase the efficiency of pea line selection for wide adaptation to Italian environments relative to phenotypic selection. Electronic supplementary material The online version of this article (10.1186/s12864-019-5920-x) contains supplementary material, which is available to authorized users.

Mixed stand (MS) cropping of pea with small-grain cereals can produce more productive and environment-friendly grain crops relative to pure stand (PS) crops but may require selection to alleviate the pea competitive disadvantage. This study aimed to assess the pea variation for competitive ability and its associated traits and the efficiency of four phenotypic or genomic selection strategies. A set of 138 semi-leafless, semi-dwarf pea lines belonging to six recombinant inbred line populations and six parent lines were genotyped using genotyping-by-sequencing and grown in PS and in MS simultaneously with one barley and one bread wheat cultivar in two autumn-sown trials in Northern Italy. Cereal companions were selected in a preliminary study that highlighted the paucity of cultivars with sufficient earliness for association. Pea was severely outcompeted in both years albeit with variation for pea proportion ranging from nearly complete suppression (r≥ 0.46). The genetic correlation for pea yield across MS and PS conditions slightly exceeded 0.40 in both years. Later onset of flowering and taller plant height at flowering onset displayed a definite correlation with pea yield in MS (r≥ 0.46) but not in PS, whereas tolerance to ascochyta blight exhibited the opposite pattern. Comparisons of phenotypic selection strategies within or across populations based on predicted or actual yield gains for independent years indicated an efficiency of 52–64% for indirect selection based on pea yield in PS relative to pea yield selection in MS. The efficiency of an indirect selection index including onset of flowering, plant height, and grain yield in PS was comparable to that of pea yield selection in MS. A genome-wide association study based on 5,909 SNP markers revealed the substantial diversity of genomic areas associated with pea yield in MS and PS. Genomic selection for pea yield in MS displayed an efficiency close to that of phenotypic selection for pea yield in MS, and nearly two-fold greater efficiency when also taking into account its shorter selection cycle and smaller evaluation cost.

Il CNR-IBE, in riferimento a quanto previsto all'art. 6 del "Contratto di affidamento del Servizio Specialistico di supporto" da parte del CREA-OF, con la presente relazione informa dello stato di avanzamento dei lavori, con indicazione dettagliata delle attività svolte nel primo semestre. Le attività del Servizio specialistico previste a supporto della ricerca condotte nell'ambito del progetto AgriDigit-agricoltura digitale, sotto-progetto AGROFILIERE, per lo sviluppo di sistemi integrati di alert fitopatologico, sono le seguenti: oSviluppo di un piano di ottimizzazione delle componenti hardware e software di gestione di una rete di monitoraggio finalizzata alla realizzazione di studi agroambientali in coltura protetta e piena aria. oSupporto alla gestione e mantenimento delle reti di monitoraggio. oSupporto all'integrazione ed elaborazione dei dati per l'analisi della variabilità spazio-temporale del rischio fitopatologico e definizione di una procedura di analisi propedeutica allo sviluppo, calibrazione e messa a punto di modelli/sistemi di alert. oSupporto alle attività di ricerca condotte nell'ambito del progetto AgriDigit. Il cronoprogramma previsto per lo svolgimento delle attività/fasi oggetto dell'incarico, prevede una tempistica nella consegna dei diversi prodotti, alla quale l'UO CNR-IBE si attiene, in linea con quelle che sono le attività di ricerca condotte presso i siti sperimentali della committenza e le relative esigenze operative. Le attività svolte fino ad ora dall'Unità Operativa del CNR-IBE nell'ambito di ciascuna fase, già oggetto di varie comunicazioni scritte e orali, sono riassunte nella presente relazione secondo l'ordine sopra ricordato e riportate con maggiore dettaglio nel Report tecnico P5_vi "Procedure di analisi dei dati e calibrazione dei modelli di rischio fitopatologici scelti - versione intermedia".

Nel Report vengono forniti i risultati delle prime analisi dai dati raccolti dalle reti di monitoraggio installate presso i siti di progetto, elaborati tramite strumenti logico-matematici, statistici e geostatistici, alla luce delle osservazioni, informazioni e meta-informazioni acquisite e dei dati ancillari richiesti per le diverse analisi di ordine ambientale e fitosanitario. Nel corso di questo primo periodo, sono stati raccolti elementi sufficienti per poter confermare il ruolo decisivo dei fattori microclimatici nella determinazione del diverso rischio fungino su piante di rosa allevate a diverse densità in plot di dimensioni ridotte, sia in pieno campo che in serra. Per il momento rimangono da studiare le condizioni di rischio reale esistenti per la rucola allevata nella serra di Pontecagnano, sebbene le prime elaborazioni inducano a pensare che anche per questa coltura possano sussistere livelli di rischio diversi, da ricondursi principalmente alla loro posizione relativa rispetto alle aperture e ai termoconvettori presenti. Sulla base di queste prime risultanze, possiamo affermare che la conoscenza della variabilità dei fattori ambientali all'interno dell'area di coltivazione, unita alla considerazione degli aspetti genetici, fisiologici e tecnico-colturali, dovrebbe consentire di prevedere lo sviluppo del patogeno all'interno dei plot sperimentali, sia nel tempo che nello spazio, per l'individuazione e la messa a punto di procedure operative in grado di guidare gli interventi di lotta, sia in ambiente controllato che in campo. Il sistema progettato, attualmente in corso di sviluppo, si basa su una rete di sensori micrometeorologici in grado di fornire i dati necessari all'applicazione dei modelli fitopatologici scelti, attraverso i quali si intende caratterizzare le diverse aree produttive dal punto di vista del relativo rischio fungino. Un sistema di acquisizione di immagini multispettrali completa le informazioni relative alla densità dei vegetali, alla crescita della superficie fogliare e allo stato generale delle piante, nel tempo e nello spazio. Nel presente documento sono descritti i primi criteri adottati, le procedure di analisi implementate e le relative fasi di elaborazione, che dovranno essere sottoposte a test di verifica e possibilmente potenziate nel corso delle successive fasi di progetto.

La versione finale del Report P5, raccoglie le informazioni e le procedure di analisi automatica dei dati raccolti nel quadro del progetto AGRIDIGIT agricoltura digitale sotto-progetto AGROFILIERE, per l'individuazione e la stima del rischio fitopatologico su piante di rosa (Pescia) e rucola (Pontecagnano). Sulla base di quanto emerso e presentato nella versione intermedia del presente rapporto, preventivamente analizzato e discusso con la committenza, vengono qui proposte le soluzioni logico-matematiche, statistiche e geostatistiche, che sono state individuate come di potenziale interesse per la loro capacità di discriminare all'interno delle aree sperimentali tra condizioni ambientali diverse, più o meno favorevoli allo sviluppo dei patogeni fungini. Le modifiche e le integrazioni al precedente documento sono da considerarsi come necessario completamento del quadro informativo già presentato, avendo ricevuto, nel complesso, una sostanziale conferma di quanto già emerso nel corso della prima fase di progetto. Viene, infatti, confermato il ruolo decisivo dei fattori microclimatici nella determinazione del diverso rischio fungino su piante di rosa e rucola, nelle diverse condizioni sperimentali, e la possibilità di individuare procedure automatiche in grado di guidare gli interventi di lotta mirata (per area e per patogeno), sia in ambiente controllato che in campo. Le procedure proposte sono state messe a punto e calibrate con i dati raccolti nel corso della prima fase progettuale, confrontando le uscite dei sistemi di analisi con le osservazioni in campo, e validate - quando possibile - con i dati raccolti nel corso delle successive fasi sperimentali. Il sistema progettato si basa su una rete di monitoraggio micrometeorologico in grado di fornire i dati necessari all'applicazione dei modelli fitopatologici scelti, attraverso i quali si intende caratterizzare le diverse aree produttive dal punto di vista del relativo rischio fungino. Una procedura di acquisizione di immagini multispettrali completa le informazioni relative alla densità dei vegetali, alla crescita della superficie fogliare e allo stato generale delle piante, nel tempo e nello spazio. I risultati ottenuti fino a questo momento confermano l'importanza della raccolta e analisi di dati micrometeorologici, incoraggiando lo studio di modelli sempre più sensibili e in grado di simulare l'evoluzione del rischio in funzione delle reali condizioni ambientali e operative.

Terminal drought is the main stress limiting pea (Pisum sativum L.) grain yield in Mediterranean environments. This study aimed to investigate genotype &times, environment (GE) interaction patterns, define a genomic selection (GS) model for yield under severe drought based on single nucleotide polymorphism (SNP) markers from genotyping-by-sequencing, and compare GS with phenotypic selection (PS) and marker-assisted selection (MAS). Some 288 lines belonging to three connected RIL populations were evaluated in a managed-stress (MS) environment of Northern Italy, Marchouch (Morocco), and Alger (Algeria). Intra-environment, cross-environment, and cross-population predictive ability were assessed by Ridge Regression best linear unbiased prediction (rrBLUP) and Bayesian Lasso models. GE interaction was particularly large across moderate-stress and severe-stress environments. In proof-of-concept experiments performed in a MS environment, GS models constructed from MS environment and Marchouch data applied to independent material separated top-performing lines from mid- and bottom-performing ones, and produced actual yield gains similar to PS. The latter result would imply somewhat greater GS efficiency when considering same selection costs, in partial agreement with predicted efficiency results. GS, which exploited drought escape and intrinsic drought tolerance, exhibited 18% greater selection efficiency than MAS (albeit with non-significant difference between selections) and moderate to high cross-population predictive ability. GS can be cost-efficient to raise yields under severe drought.

We present the results of a systematic search and analysis of GRBs detected by the Astrorivelatore Gamma ad Immagini LEggero (AGILE) MiniCALorimeter (MCAL; 0.4–100 MeV) over a time frame of 13 yr, from 2007 to 2020 November. The MCAL GRB sample consists of 503 bursts triggered by MCAL, 394 of which were fully detected onboard with high time resolution. The sample consists of about 44% short GRBs and 56% long GRBs. In addition, 109 bursts triggered partial MCAL onboard data acquisitions, providing further detections that can be used for joint analyses or triangulations. More than 90% of these GRBs were also detected by the AGILE Scientific RateMeters (RMs), providing simultaneous observations between 20 keV and 100 MeV. We performed spectral analysis of these events in the 0.4–50 MeV energy range. We could fit the time-integrated spectrum of 258 GRBs with a single power-law model, resulting in a mean photon index 〈β〉of−2.3. Among them, 43 bursts could also be fitted with a Band model, with peak energy above 400 keV, resulting in a mean low-energy photon index 〈α〉 = −0.6, a mean high-energy photon index 〈β〉 = −2.5, and a mean peak energy 〈E p 〉 = 640 keV. The AGILE MCAL GRB sample mostly consists of hard-spectrum GRBs, with a large fraction of short-duration events. We discuss properties and features of the MCAL bursts, whose detections can be used to perform joint broad-band analysis with other missions, and to provide insights on the high-energy component of the prompt emission in the tens of mega electron volt energy range.

A reliable forecast of the pre-harvest grain quality is requested by stakeholders in the rice sector, which is increasingly oriented to the achievement of superior standards to meet the market demand. Despite its economic importance, very few simulation models of the qualitative aspects of rice productions including the effects of weather conditions and farming practices are available. This paper presents GLORIFY, a forecasting system targeting the simulation of head rice yield (HRY), which represents the main determinant of rice market price at global level. A new HRY model was developed using experimental data collected in Northern Italy in 2006–2013 and referred to Loto (japonica) and Gladio (tropical japonica) cultivars, and it was coupled to the WARM rice simulator. Historical simulations were then performed in the period 1994–2013 to reproduce observed HRY variability, with model outputs and weather variables used as independent variables to build multi-regression models. At field level, model performances denoted a good agreement between observed and simulated HRY (R2 and modelling efficiency in the range 0.73–0.93). At province level, best results were obtained for Loto variety, as the regression model was able to explain 78% of the HRY variability, with a root mean square error (RMSE) of 0.77%. The model accuracy slightly decreased when leave-one-out cross-validation was applied (R2 = 0.61, RMSE = 1.04%). The present study lays the basis for a reliable estimation of HRY variability under different management and weather conditions.

Grain quality is crucial to meeting market demand and preserve the sustainability of the European rice sector. However the relationships between agro-meteorological conditions and major features of pre-harvest quality are not well understood. The evaluation of available models is needed to assess their suitability for predicting grain quality for different environmental conditions. This study presents a multi-site and multi-year evaluation of 26 models for the simulation of rice grain composition, milling quality and cooking quality, in the main European rice district (Northern Italy). The analysis was performed using data from 16 sites where the cultivars Loto (japonica) and Gladio (tropical japonica) were grown in 2011–2014. Model performances denoted models’ ability to reproduce grain quality variables, with increased modelling efficiencies (EF) from grain composition (−0.78

Germplasm collections hold several thousands of white lupin (Lupinus albus L.) accessions. Genome-enabled models with good predictive ability for specific environments could provide a cost-efficient means to identify promising genetic resources for breeding programmes. This study provided an unprecedented assessment of genome-enabled predictions for white lupin grain yield, focusing on (i) a world collection of 109 landraces and 8 varieties phenotyped in three European sites with contrasting climate (Mediterranean, subcontinental or oceanic) and sowing time (data set 1); (ii) 78 geographically diversified landrace genotypes and three variety genotypes phenotyped in moisture-favourable and severely drought-prone managed environments (data set 2). The interest of predictions for individual genotypes was justified by large within-landrace variation for yield responses. Ridge regression BLUP (rrBLUP) and Bayesian Lasso (BL) models exploited allele frequencies (estimated from 3 to 4 genotypes per landrace) of 10,782 polymorphic SNPs for data set 1, and allele values of 9937 polymorphic SNPs for data set 2, following ApeKI-based genotyping-by-sequencing characterization. Compared with BL, rrBLUP displayed similar predictive ability for data set 1 and better predictive ability for data set 2. Best-predictive models displayed intra-environment predictive ability for the five test environments in the range 0.47–0.76. Cross-environment predictions between pairs of environments with positive genetic correlation, i.e., autumn-sown subcontinental vs Mediterranean sites, and moisture-favourable vs drought-prone environments, exhibited a predictive ability range of 0.40–0.51 and a predictive accuracy range of 0.48–0.61. Our results support the exploitation of genomic predictions and provide economic justification for the genotyping of germplasm collections of white lupin.

The peptidoglycan in Gram-negative bacteria is a dynamic structure in constant remodeling. This dynamism, achieved through synthesis and degradation, is essential because the peptidoglycan is necessary to maintain the structure of the cell but has to have enough plasticity to allow the transport and assembly of macromolecular complexes in the periplasm and outer membrane. In addition, this remodeling has to be coordinated with the division process. Among the multiple mechanisms bacteria have to degrade the peptidoglycan are the lytic transglycosidases, enzymes of the lysozyme family that cleave the glycan chains generating gaps in the mesh structure increasing its permeability. Because these enzymes can act as autolysins, their activity has to be tightly regulated, and one of the mechanisms bacteria have evolved is the synthesis of membrane bound or periplasmic inhibitors. In the present study, we identify a periplasmic lytic transglycosidase inhibitor (PhiA) in Brucella abortus and demonstrate that it inhibits the activity of SagA, a lytic transglycosidase we have previously shown is involved in the assembly of the type IV secretion system. A phiA deletion mutant results in a strain with the incapacity to synthesize a complete lipopolysaccharide but with a higher replication rate than the wild-type parental strain, suggesting a link between peptidoglycan remodeling and speed of multiplication.

The peptidoglycan in Gram-negative bacteria is a dynamic structure in constant remodeling. This dynamism, achieved through synthesis and degradation, is essential because the peptidoglycan is necessary to maintain the structure of the cell but has to have enough plasticity to allow the transport and assembly of macromolecular complexes in the periplasm and outer membrane. In addition, this remodeling has to be coordinated with the division process. Among the multiple mechanisms bacteria have to degrade the peptidoglycan are the lytic transglycosidases, enzymes of the lysozyme family that cleave the glycan chains generating gaps in the mesh structure increasing its permeability. Because these enzymes can act as autolysins, their activity has to be tightly regulated, and one of the mechanisms bacteria have evolved is the synthesis of membrane bound or periplasmic inhibitors. In the present study, we identify a periplasmic lytic transglycosidase inhibitor (PhiA) in Brucella abortus and demonstrate that it inhibits the activity of SagA, a lytic transglycosidase we have previously shown is involved in the assembly of the type IV secretion system. A phiA deletion mutant results in a strain with the incapacity to synthesize a complete lipopolysaccharide but with a higher replication rate than the wild-type parental strain, suggesting a link between peptidoglycan remodeling and speed of multiplication.

Participatory plant breeding (PPB) has gained increasing importance in developing countries, but its value for market-oriented breeding programmes of countries with developed agriculture that are committed to pure line selection (as needed to comply with DUS requirements) is unknown. This study aimed to compare PPB vs. conventional plant breeding of pea (Pisum sativum L.) targeted to organic systems of Italy, exploring phenotypic and genome-enabled selection approaches. Priority values assigned on a 0–5 scale to 14 traits by 18 farmers from Northern and Central Italy and six breeders were used to define weights of farmer and breeder selection indexes. Farmers and breeders attributed outmost importance to a visual acceptability score assigned a few weeks before crop maturity on a 1–9 scale, followed in importance by grain yield and tolerance to lodging. However, breeders and farmers differed (P

Background The aim of this study was to compare OTSC® proctology and fistulectomy with primary sphincter reconstruction results as treatment strategies for anorectal low trans-sphincteric fistula. Methods Between February 2012 and March 2013, patients affected by trans-sphincteric anal fistula were consecutively enrolled in the trial. Patients were randomized to receive fistulectomy with primary sphincter reconstruction or OTSC® Proctology. Demographic characteristics, comorbodities, previous anorectal treatments, and recurrent fistula data were acquired. Postoperative therapy data and pain and Wexner scores (30 and 60 dd) were acquired during follow-up. Furthermore, patients were contacted by telephone after six months, and were visited both one year and three years after surgery. Results Thirty consecutive patients were included in the study. 15 patients underwent the OTSC® Proctology procedure and 15 underwent the standard fistulectomy. The success rate was 93.3% in the OTSC group. The mean postoperative stay was 1.3 days in the OTSC® patients and 3.6 days in the fistulectomy group patients. The mean medications required for complete healing was 3.2 in the OTSC group and 8.9 in the FIPS group. Conclusions Our results suggest that OTSC® Proctology is an effective and safe treatment in achieving permanent closure of the internal fistula opening in selected patients, with excellent results in terms of pain, postoperative incontinence, healing time, and days of hospitalization.

BAPGEAPSI; Only a few studies on pea (Pisum sativum) investigated the association of single nucleotide polymorphisms (SNP) markers with key agronomic traits. This study aimed to explore the association of a standard set of 384 SNP with grain yield, seed protein content, seed weight, onset of flowering, plant height and lodging susceptibility, in three connected bi-parental recombinant inbred line (RIL) populations including 90 lines each. These RIL originated from crosses between three cultivars that displayed high and stable grain yield across Italian environments, namely, Attika (A), Isard (I), and Kaspa (K). The 270 lines were phenotyped in a spring-sown environment of Lodi (northern Italy; 45°19'N, 9°30'E). Variation among lines within the populations was significant (P < 0.01) in all cases except lodging susceptibility in one cross and, when expressed in terms of the genetic coefficient of variation, proved moderately large for most traits (including grain yield and seed protein content). Overall, we detected six quantitative trait loci (QTL) in the A × I linkage map, eight QTL in K × A, and nine QTL in K × I. Among them, there were three QTL in K × A and two QTL in K × I for grain yield, and one QTL in A × I and two QTL in both K × A and K × I for seed protein content. The consensus map, which included 130 markers (covering about 1094 cM), retained one QTL for grain yield and one for flowering time that co-located on LGII, and three for seed weight on LGIII, LGVI and LGVII. The QTL co-locating for yield and flowering time explained 8% and 31% of the overall phenotypic variation, respectively, for the two traits, and could be exploited in marker-assisted selection for adaptation to the target region.

GRB 190114C represents a breakthrough for the physics of gamma-ray bursts (GRBs), being the first GRB with delayed emission above 300 GeV, as reported by MAGIC. We present in this paper the sub-MeV/MeV data of the prompt and early afterglow emissions of GRB 190114C, as detected by AGILE and Konus-Wind, in the 20 keV-100 MeV energy range. The first stages of the burst exhibit multiple emission components, associated with an interesting spectral evolution. The first 2 s of the prompt emission can be described by a single "Band-like" spectral component. The successive 4 s show the presence of an additional high-energy spectral component, which quickly evolves into a "hard-flat" component of the νFν spectrum, extending up to 10-100 MeV and likely produced by inverse Compton radiation, whose onset and evolution are clearly shown in our data. After this phase, the νFν spectrum evolves into a "V shape," showing the persistence and spectral hardening of the additional high-energy component in substantial agreement with Fermi and Swift results. We also analyze the first ∼200 s of the early afterglow that show a reflaring episode near T0 + 15 s. We identify a new, so-far-unnoticed flux temporal break near T0 + 100 s, which is detected in hard X-rays by both Konus-Wind and INTEGRAL/SPI-ACS. We find this break incompatible with the commonly assumed adiabatic evolution of a fireball in a constant-density medium. We interpret this break as a consequence of radiative evolution of the early afterglow from a fireball expanding in a wind-like circumburst medium.

This study aimed to assess the predictive ability of genomic selection (GS) for biomass yield of alfalfa and pea, considering different data sets, GS models, and thresholds for genotype missing data. An additional aim was to briefly devise the incorporation of GS into breeding schemes of these crops. For alfalfa, the predictive ability of best GS models ranged from r = 0.18 to r = 0.36 in three data sets. The lowest value (observed in a data set with higher experimental error or lower genetic variation relative to the other data sets) may still be of practical interest, given the long selection cycle and the low narrow-sense heritability of biomass yield in this crop. For pea biomass yield, the predictive ability of best GS models averaged r = 0.45 across three recombinant inbred line (RIL) populations. Predictions were less accurate for this trait than for pea straw or grain yield. GS is a promising approach but its adoption implies important modifications of alfalfa and pea breeding schemes. We identified five stages of GS-based selection schemes, whose implementation depends largely on the reproductive system of the target species.

The marked underutilization of pea ecotype and old cultivar genetic resources need be reversed by producing information on elite germplasm and diversity patterns for key agronomic traits. This study generated such information for a world collection of 223 ecotypes or old cultivars of P. sativum subsp. sativum subdivided into 19 regional pools (each represented by 9–15 entries) and one reference pool of 11 locally-adapted varieties from France, Spain and Germany. The collection was evaluated for grain yield, seed protein content, straw biomass, phenology and other agronomic and morphological traits in a spring-sown, water-limited environment of Northern Italy. Variation within ecotype/old cultivar germplasm pools was at least two-fold greater for seed size and protein content and seven-fold greater for other traits relative to variation between pools, and was particularly large within pools from Central Europe, Near East and United Kingdom. The traditional germplasm, compared with improved variety material, tended to display higher dry grain yield (1.11 vs. 0.85 t/ha) and straw biomass, later crop maturity, smaller seed, and similar protein content. Traditional germplasm from Central Europe combined outstanding grain yield and total biomass with high seed protein content. High grain yield characterized, inter alia, the germplasm pools from Ukraine and Spain, the latter also featuring high protein content. Several ecotype/old cultivar accessions out-performed (P

Alpine bluegrass (Poa alpina L.) is common in mountain areas, where it represents a valuable forage resource and is widely used for ecological restoration of disturbed landscapes. This study assessed the variation of germplasm from the Rhaetian Alps, Italy. Both seed production and morphology were examined for potential use in restoration projects and for conservation and selection purposes of local genetic resources. The effects of the ex situ growing site were also assessed in two altitude-contrasting sites. Individual populations harboured great phenotypic diversity for all traits, including seed yield, while variation among valleys of origin was less manifest. Noticeable site-specific population yield responses were observed, suggesting that the environment adopted for seed production may steer the choice of materials to be multiplied. The longer vegetative period in the lowland site, combined with environment-tailored agronomic practices, contributed to better plant growth, seed yield and survival compared to the mountain site, suggesting that seed production of this Alpine species could be feasible in a lowland environment when adequate inputs were provided. One valley (Valchiavenna) displayed high average levels of seed yield and plant vigour across growing sites, indicating its possible value as a source of germplasm suitable for resowing at high altitude.

Despite the availability of a variety of models to simulate crop growth and development, few operational approaches have been developed to assess pre-harvest quality of agricultural productions as a function of the conditions actually explored by the crop during the season. This represents a clear gap of knowledge researchers are trying to fill, in light of the evidences of a climate change-driven decline in the nutritional properties of important food crops. Rice represents the staple food for half of the world’s population, and this explains the noticeable interest in rice grain quality, because of the direct implications on the economic value of productions, on their market destination, and on food security issues. This paper presents a framework-independent .NET software library, i.e., UNIMI.CropQuality, implementing models to simulate various aspects of rice quality: amylose, protein, lipids and starch content, viscosity profile, chalkiness, cracking and head rice yield. Alternate approaches for the simulation of the same quality property are included, to allow users to select the most suitable for specific modelling studies. A case study is also presented where the library was linked to the WARM rice model and used to simulate head rice yield and the percentage incidence of cracked and milky white kernels (severely chalky) for two rice varieties in the main European rice district. RRMSE ranged between 4.33% and 6.47% for head rice yield, 21.88% and 32.18% for cracking percentage, 35.92% and 55.01% for milky white chalkiness; modelling efficiency were always positive. The component, developed according to the state-of-the-art of component-oriented software development, is released with a Software Development Kit containing help and code documentation files, as well as sample applications showing how to use the library with different crop simulators.

Species adapted to prevailing soil and climatic conditions and native to the same geographic context are increasingly recommended for ecological restoration at high altitude. Better knowledge is required on the level of variation within these species for morpho-physiological traits and seed yield. Adequate and affordable seed production is a prerequisite for native species to be widely adopted for restoration interventions. This study evaluated the variation of yarrow (Achillea millefolium L.) germplasm collected in the Rhaetian Alps, Italy. The worth of yarrow for restoration at high altitude has been repeatedly noted. The main goal of the study was to identify promising materials for selection purposes. The explored mountain section harbored valuable variation for traits of interest. Altitude, climate, and soil should frame an ecological correspondence between the collection site and the target restoration site, to secure the value of the native germplasm outside the boundaries of its collection area. The work also assessed any interaction between the germplasm and the ex situ growing environments, represented by a mountain and a lowland site, and verified the feasibility of the latter for seed multiplication. The growing site remarkably affected several morpho-physiological characters. The lack of difference in mean seed yield between the two altitude-contrasting sites suggested that seed multiplication of yarrow could also be conveniently carried out in lowland environments, with advantages in terms of production costs. Nonetheless, different yield responses in the two sites were observed, emphasizing the need of choosing the material to multiply based on the environment adopted for seed production.

This study aimed to support field pea (Pisum sativum L.) breeding strategies for organic systems of southern European environments, by assessing the size of genotype × environment interaction (GEI) due to spatial and temporal factors across climatically contrasting regions and identifying plant characters associated with genotype adaptive responses. Twelve recent varieties were evaluated for grain yield and other traits in six organically managed environments (three sites × two cropping years) of northern, central and southern Italy. GEI for grain yield was large, with the variety × site × year interaction greatly exceeding the variety × site interaction. This finding, and the similar magnitude of the mean genetic correlations for variety yields across pairs of sites (rg = 0.56) and pairs of years (rg = 0.51), indicated the difficulty of exploiting variety × site interaction effects by breeding for specific climatic regions. Pattern analysis highlighted the large inconsistency across years for GEI pattern of the sites from central and southern Italy. GEI also complicated the targeting of varieties, owing to inconsistent top-performing material across years according to additive main effects and multiplicative interaction (AMMI)-modelled yields. Higher genotype mean yield was strictly associated (P < 0.01) with lower weed proportion (hence, greater competitiveness against weeds: r = –0.96), taller plants (r = 0.89) and larger seeds (r = 0.78), with looser associations with lower susceptibility to lodging and ascochyta blight. These traits, which also contributed to preferential adaptation to the moisture-favourable environments of northern Italy, could be selected in breeding widely adapted varieties.

Specific chromosomal aberrations might indicate the position of genes responsible for a particular disease. Neuroblastoma is characterized by frequent deletions and/or rearrangements of the subtelomeric 1p region which, accordingly, is believed to host one or more oncosuppressor gene(s) directly or indirectly involved in the development of this and other tumors. Identification of these genes could be facilitated if cell lines with well characterized interstitial deletions or reciprocal translocations could be available for application of 'positional cloning' strategies. In the present report we present additional and novel molecular data on three well established neuroblastoma cell lines (NLF, NMB and NGP). In one of these we have identified two sites that might be good candidates for hosting oncosuppressor genes; one of these is flanked by the D1S47 and ENO1 loci while the other is distal to the A12M2 locus.

Irrigazione e controllo dei fitoparassiti sono i fattori che maggiormente impattano sulla qualità delle piante e la sostenibilità economica e ambientale, a livello sia di produzione vivaistica sia di gestione di aree verdi urbane. Il lavoro descrive le attività svolte nell'ambito del progetto GARANTES, il cui obiettivo è stato quello di implementare e validare un sistema di supporto alle decisioni ai fini di una gestione ottimizzata degli interventi irrigui e fitosanitari nelle aree verdi pubbliche e private (giardini, parchi etc.). I componenti del sistema prototipale GARANTES sono in grado di supportare differenti attività/funzioni: i) monitoraggio; ii) gestione dei dati; iii) determinazione dei possibili rischi biotici e abiotici; iv) controllo degli interventi irrigui; v) caratterizzazione ambientale; vi) valutazione di sostenibilità; vii) sistema di allerta. I risultati preliminari, ottenuti attraverso test in siti pilota, hanno confermato l'importanza dell'automazione delle procedure e l'efficacia delle informazioni prodotte da GARANTES, sia nell'ottimizzazione dell'impiego di input agronomici (acqua, agrochimici, lavoro, etc.), sia nell'incrementare la sostenibilità gestionale di aree verdi in genere.

Yarrow (Achillea millefolium L.) is a herbaceous species common in the Alpine region of Europe and used in folk medicine since antiquity. Its organs are rich in monoterpenes and sesquiterpenes, two subclasses of plant terpenoids with relevant ecological significance, which were reported as valuable markers for the traceability of mountain dairy products. The variability in chemical composition of yarrow germplasm may be related with its genetic diversity, accounting for possible differences in medical properties, and supporting its use as a specific territorial marker. Aim of this work was to assess the leaf chemical composition of 16 yarrow populations collected at altitudes exceeding 1600 m in three valleys of the Rhaetian Alps, Italy, and jointly evaluated in a lowland site. The most abundant compounds detected generally differed from those of the germplasm from other countries. A trend of valley-specific pattern of composition was evident. However, the variability among individual populations was even more remarkable, regardless of their valley of origin. The concentrations of sesquiterpene hydrocarbons, oxygenated monoterpenes, and oxygenated sesquiterpenes discriminated the populations in multivariate analysis. A few prevailing chemotypes were characterized, which differed from those previously reported in the literature. The geographic isolation from other germplasms, and the local ecotypization, likely originated a chemically distinct gene pool.