Effectiveness of bio-effectors on maize, wheat and tomato performance and phosphorus acquisition from greenhouse to field scales in Europe and Israel: a meta-analysis.

Biostimulants (Bio-effectors, BEs) comprise plant growth-promoting microorganisms and active natural substances that promote plant nutrient-acquisition, stress resilience, growth, crop quality and yield. Unfortunately, the effectiveness of BEs, particularly under field conditions, appears highly variable and poorly quantified. Using random model meta-analyses tools, we summarize the effects of 107 BE treatments on the performance of major crops, mainly conducted within the EU-funded project BIOFECTOR with a focus on phosphorus (P) nutrition, over five years.

AI and narrative embeddings detect PTSD following childbirth via birth stories.

Free-text analysis using machine learning (ML)-based natural language processing (NLP) shows promise for diagnosing psychiatric conditions. Chat Generative Pre-trained Transformer (ChatGPT) has demonstrated preliminary initial feasibility for this purpose; however, whether it can accurately assess mental illness remains to be determined. This study evaluates the effectiveness of ChatGPT and the text-embedding-ada-002 (ADA) model in detecting post-traumatic stress disorder following childbirth (CB-PTSD), a maternal postpartum mental illness affecting millions of women annually, with no standard screening protocol.

Persistence of collective memory of corporate bankruptcy events discussed on X (Twitter) is influenced by pre-bankruptcy public attention.

Collective attention and memory involving significant events can be quantitatively studied via social media data. Previous studies analyzed user attention to discrete events that do not change post-event, and assume universal public attention patterns. However, dynamic events with ongoing updates are common, yielding varied individual attention patterns.

Short- and long-term effects of continuous compost amendment on soil microbiome community.

Organic amendment, and especially the use of composts, is a well-accepted sustainable agricultural practice. Compost increases soil carbon and microbial biomass, changes enzymatic activity, and enriches soil carbon and nitrogen stocks. However, relatively little is known about the immediate and long-term temporal dynamics of agricultural soil microbial communities following repeated compost applications.

Screening for post-traumatic stress disorder following childbirth using the Peritraumatic Distress Inventory.

Background: Post-traumatic stress disorder (PTSD) following traumatic childbirth may undermine maternal and infant health, but screening for maternal childbirth-related PTSD (CB-PTSD) remains lacking. Acute emotional distress in response to a traumatic experience strongly associates with PTSD. The Peritraumatic Distress Inventory (PDI) assesses acute distress in non-postpartum individuals, but its use to classify women likely to endorse CB-PTSD is unknown.

A diagnostic questionnaire for childbirth related posttraumatic stress disorder: a validation study.

Background: Labor and delivery can entail complications and severe maternal morbidities that threaten a woman's life or cause her to believe that her life is in danger. Women with these experiences are at risk for developing posttraumatic stress disorder. Postpartum posttraumatic stress disorder, or childbirth-related posttraumatic stress disorder, can become an enduring and debilitating condition.

OpenAI's Narrative Embeddings Can Be Used for Detecting Post-Traumatic Stress Following Childbirth Via Birth Stories.

Free-text analysis using Machine Learning (ML)-based Natural Language Processing (NLP) shows promise for diagnosing psychiatric conditions. Chat Generative Pre-trained Transformer (ChatGPT) has demonstrated preliminary initial feasibility for this purpose; however, whether it can accurately assess mental illness remains to be determined. This study evaluates the effectiveness of ChatGPT and the text-embedding-ada-002 (ADA) model in detecting post-traumatic stress disorder following childbirth (CB-PTSD), a maternal postpartum mental illness affecting millions of women annually, with no standard screening protocol.

Screening for Post-Traumatic Stress Disorder following Childbirth using the Peritraumatic Distress Inventory.

Background: Maternal psychiatric morbidities include a range of psychopathologies; one condition is post-traumatic stress disorder (PTSD) that develops following a traumatic childbirth experience and may undermine maternal and infant health. Although assessment for maternal mental health problems is integrated in routine perinatal care, screening for maternal childbirth-related PTSD (CB-PTSD) remains lacking. Acute emotional distress in response to a traumatic event strongly associates with PTSD.

PrismEXP: gene annotation prediction from stratified gene-gene co-expression matrices.

Background: Gene-gene co-expression correlations measured by mRNA-sequencing (RNA-seq) can be used to predict gene annotations based on the co-variance structure within these data. In our prior work, we showed that uniformly aligned RNA-seq co-expression data from thousands of diverse studies is highly predictive of both gene annotations and protein-protein interactions. However, the performance of the predictions varies depending on whether the gene annotations and interactions are cell type and tissue specific or agnostic.

HetIG-PreDiG: A Heterogeneous Integrated Graph Model for Predicting Human Disease Genes based on gene expression.

Graph analytical approaches permit identifying novel genes involved in complex diseases, but are limited by (i) inferring structural network similarity of connected gene nodes, ignoring potentially relevant unconnected nodes; (ii) using homogeneous graphs, missing gene-disease associations' complexity; (iii) relying on disease/gene-phenotype associations' similarities, involving highly incomplete data; (iv) using binary classification, with gene-disease edges as positive training samples, and non-associated gene and disease nodes as negative samples that may include currently unknown disease genes; or (v) reporting predicted novel associations without systematically evaluating their accuracy. Addressing these limitations, we develop the Heterogeneous Integrated Graph for Predicting Disease Genes (HetIG-PreDiG) model that includes gene-gene, gene-disease, and gene-tissue associations. We predict novel disease genes using low-dimensional representation of nodes accounting for network structure, and extending beyond network structure using the developed Gene-Disease Prioritization Score (GDPS) reflecting the degree of gene-disease association via gene co-expression data.

Identifications of Surfactin-Type Biosurfactants Produced by Species Isolated from Rhizosphere of Vegetables.

Surfactins are cyclic lipopeptides consisting of a β-hydroxy fatty acid of variable chain length and a peptide ring of seven amino acids linked together by a lactone bridge, forming the cyclic structure of the peptide chain. These compounds are produced mainly by species and are well regarded for their antibacterial, antifungal, and antiviral activities. For their surfactin production profiling, several strains isolated from vegetable rhizospheres were identified by their fatty acid methyl ester profiles and were tested against phytopathogen bacteria and fungi.

Identifying Women with Post-Delivery Posttraumatic Stress Disorder using Natural Language Processing of Personal Childbirth Narratives.

Background: Maternal mental disorders are considered a leading complication of childbirth and a common contributor to maternal death. In addition to undermining maternal welfare, untreated postpartum psychopathology can result in child emotional and physical neglect, and associated significant pediatric health costs. Some women may experience a traumatic childbirth and develop posttraumatic stress disorder (PTSD) symptoms following delivery (CB-PTSD).

Progress in and Opportunities for Applying Information Theory to Computational Biology and Bioinformatics.

This editorial is intended to provide a brief history of the application of Information Theory to the fields of Computational Biology and Bioinformatics; to succinctly summarize the current state of associated research, and open challenges; and to describe the scope of the invited content for this Special Issue of the journal with the theme of "Information Theory in Computational Biology" [...

Role-Aware Information Spread in Online Social Networks.

Understanding the complex process of information spread in online social networks (OSNs) enables the efficient maximization/minimization of the spread of useful/harmful information. Users assume various roles based on their behaviors while engaging with information in these OSNs. Recent reviews on information spread in OSNs have focused on algorithms and challenges for modeling the local node-to-node cascading paths of viral information.

Maintaining intensive agriculture overlying aquifers using the threshold nitrate root-uptake phenomenon.

Reducing nitrate leaching from agricultural land to aquifers has been a high priority concern for more than a half century. This study presents theory and observations of a threshold concentration of nitrate in the root zone (Cmax), which the leachate concentration increases at higher rates with increasing root-zone nitrate concentration. The value of Cmax is derived both by direct results from container experiments with varying nitrogen (N) fertigation and as a calibration parameter in N-transport models beneath commercial agricultural plots.

The COVID-19 Drug and Gene Set Library.

In a short period, many research publications that report sets of experimentally validated drugs as potential COVID-19 therapies have emerged. To organize this accumulating knowledge, we developed the COVID-19 Drug and Gene Set Library (https://amp.pharm.

The COVID-19 Gene and Drug Set Library.

The coronavirus (CoV) severe acute respiratory syndrome (SARS)-CoV-2 (COVID-19) pandemic has received rapid response by the research community to offer suggestions for repurposing of approved drugs as well as to improve our understanding of the COVID-19 viral life cycle molecular mechanisms. In a short period, tens of thousands of research preprints and other publications have emerged including those that report lists of experimentally validated drugs and compounds as potential COVID-19 therapies. In addition, gene sets from interacting COVID-19 virus-host proteins and differentially expressed genes when comparing infected to uninfected cells are being published at a fast rate.

EnrichrBot: Twitter bot tracking tweets about human genes.

Motivation: Micro-blogging with Twitter to communicate new results, discuss ideas and share techniques is becoming central. While most Twitter users are real people, the Twitter API provides the opportunity to develop Twitter bots and to analyze global trends in tweets.

Results: EnrichrBot is a bot that tracks and tweets information about human genes implementing six principal functions: (i) tweeting information about under-studied genes including non-coding lncRNAs, (ii) replying to requests for information about genes, (iii) responding to GWASbot, another bot that tweets Manhattan plots from genome-wide association study analysis of the UK Biobank, (iv) tweeting randomly selected gene sets from the Enrichr database for analysis with Enrichr, (v) responding to mentions of human genes in tweets with additional information about these genes and (vi) tweeting a weekly report about the most trending genes on Twitter.

Local/Global contagion of viral/non-viral information: Analysis of contagion spread in online social networks.

Contagion in online social networks (OSN) occurs when users are exposed to information disseminated by other users. Studies of contagion are largely devoted to the spread of viral information and to local neighbor-to-neighbor contagion. However, many contagion events can be non-viral in the sense of being unpopular with low reach size, or global in the sense of being exposed to non-adjacent neighbors.

CO and nitrogen interaction alters root anatomy, morphology, nitrogen partitioning and photosynthetic acclimation of tomato plants.

Nitrogen and CO supply interactively regulate whole plant nitrogen partitioning and root anatomical and morphological development in tomato plants. Nitrogen (N) and carbon (C) are the key elements in plant growth and constitute the majority of plant dry matter. Growing at CO enrichment has the potential to stimulate the growth of C plants, however, growth is often limited by N availability.

Effects of Different Cultivation Parameters on the Production of Surfactin Variants by a Strain.

Surfactins are lipopeptide-type biosurfactants produced mainly by species, consisting of a peptide loop of seven amino acids and a hydrophobic fatty acid chain (C⁻C). These molecules have been proven to exhibit various biological activities; thus, their therapeutic and environmental applications are considered. Within the surfactin lipopeptide family, there is a wide spectrum of different homologues and isomers; to date, more than 30 variants have been described.

High-Frequency Occurrence of Surfactin Monomethyl Isoforms in the Ferment Broth of a Strain Revealed by Ion Trap Mass Spectrometry.

Surfactins are cyclic lipopeptides consisting of a β-hydroxy fatty acid of various chain length and a peptide ring of seven amino acids linked together by a lactone bridge, forming the cyclic structure of the peptide chain. These compounds are produced mainly by species and possess numerous biological effects such as antimicrobial (antiviral, antibacterial, and antifungal) activities. A mixture of surfactins extracted from strain SZMC 6179J was examined by HPLC-ESI-IT-MS technique, enhancing their separation to reveal novel lipopeptide varieties with higher masses and to characterize their structures.