Significant shifts in latitudinal optima of North American birds.

Changes in climate can alter environmental conditions faster than most species can adapt. A prediction under a warming climate is that species will shift their distributions poleward through time. While many studies focus on range shifts, latitudinal shifts in species' optima can occur without detectable changes in their range.

Superalkalis with Hydrogen as Central Electronegative Atom and their Possible Applications: Ab Initio and DFT Study.

Superalkalis are unusual species having ionization energies lower than that of the alkali metals. These species with various applications are of great importance in chemistry due to their low ionization energies and strong reducing property. A typical superalkali contains a central electronegative core decorated with excess metal ligands.

Using weather data to predict the presence of Lucilia spp. on sheep farms in New Zealand.

Flystrike remains an important animal health issue on New Zealand sheep farms. To date no useful predictive tool to assist farmers to develop control options has been available. The aim of this study was to use National Institute of Water and Atmospheric Research (NIWA) virtual climate station data in New Zealand to develop a weather-based model to accurately predict the presence of Lucilia spp.

Development of a multiplex droplet digital PCR assay for simultaneous detection and quantification of Escherichia coli, E. marmotae, and E. ruysiae in water samples.

Escherichia coli are widely used by water quality managers as Fecal Indicator Bacteria, but current quantification methods do not differentiate them from benign, environmental Escherichia species such as E. marmotae (formerly named cryptic clade V) or E. ruysiae (cryptic clades III and IV).

CRISPRals: A Web Database for Assessing the CRISPR Defense System in the Species Complex to Avoid Phage Resistance.

Clustered regularly interspaced short palindromic repeats (CRISPR) has been widely characterized as a defense system against phages and other invading elements in bacteria and archaea. A low percentage of species complex (RSSC) strains possess the CRISPR array and the CRISPR-associated proteins (Cas) that would confer immunity against various phages. To provide a wide-range screen of the CRISPR presence in the RSSC, we analyzed 378 genomes of RSSC strains to find the CRISPR locus.

CKAP5 stabilizes CENP-E at kinetochores by regulating microtubule-chromosome attachments.

Stabilization of microtubule plus end-directed kinesin CENP-E at the metaphase kinetochores is important for chromosome alignment, but its mechanism remains unclear. Here, we show that CKAP5, a conserved microtubule plus tip protein, regulates CENP-E at kinetochores in human cells. Depletion of CKAP5 impairs CENP-E localization at kinetochores at the metaphase plate and results in increased kinetochore-microtubule stability and attachment errors.

Draft genome sequences of spp. isolates from New Zealand environmental sources.

is often used as a fecal indicator bacterium for water quality monitoring. We report the draft genome sequences of 500 isolates including newly described species, namely , and , obtained from diverse environmental sources to assist with improved public health risk assessments.

BackMov: Individualized Motion Capture-Based Test to Assess Low Back Pain Mobility Recovery after Treatment.

Low back pain (LBP) is a common issue that negatively affects a person's quality of life and imposes substantial healthcare expenses. In this study, we introduce the (Back-pain Movement) BackMov test, using inertial motion capture (MoCap) to assess lumbar movement changes in LBP patients. The test includes flexion-extension, rotation, and lateralization movements focused on the lumbar spine.

Classification of the Pathological Range of Motion in Low Back Pain Using Wearable Sensors and Machine Learning.

Low back pain (LBP) is a highly common musculoskeletal condition and the leading cause of work absenteeism. This project aims to develop a medical test to help healthcare professionals decide on and assign physical treatment for patients with nonspecific LBP. The design uses machine learning (ML) models based on the classification of motion capture (MoCap) data obtained from the range of motion (ROM) exercises among healthy and clinically diagnosed patients with LBP from Imbabura-Ecuador.

High-resolution genomic analysis to investigate the impact of the invasive brushtail possum (Trichosurus vulpecula) and other wildlife on microbial water quality assessments.

Escherichia coli are routine indicators of fecal contamination in water quality assessments. Contrary to livestock and human activities, brushtail possums (Trichosurus vulpecula), common invasive marsupials in Aotearoa/New Zealand, have not been thoroughly studied as a source of fecal contamination in freshwater. To investigate their potential role, Escherichia spp.

A novel Bayesian Latent Class Model (BLCM) evaluates multiple continuous and binary tests: A case study for Brucella abortus in dairy cattle.

Bovine brucellosis, primarily caused by Brucella abortus, severely affects both animal health and human well-being. Accurate diagnosis is crucial for designing informed control and prevention measures. Lacking a gold standard test makes it challenging to determine optimal cut-off values and evaluate the diagnostic performance of tests.

A Torquoselective Thermal 6π-Electrocyclization Approach to 1,4-Cyclohexadienes via Solvent-Aided Proton Transfer: Experimental and Theoretical Studies.

The thermal 6π-electrocyclization of hexatriene typically delivers 1,3-cyclohexadiene (1,3-CHD). However, there is only limited success in directly synthesizing 1,4-cyclohexadiene (1,4-CHD) using such an approach, probably due to the difficulty in realizing thermally-forbidden 1,3-hydride shift after electrocyclic ring closure. The present study shows that by heating (2E,4E,6E)-hexatrienes bearing ester or ketone substituents at the C1-position in a mixture of toluene/MeOH or EtOH (2 : 1) solvents at 90-100 °C, 1,4-CHDs can be selectively synthesized.

Centrality statistics of symptom networks of schizophrenia: a systematic review.

The network theory of psychological disorders posits that systems of symptoms cause, or are associated with, the expression of other symptoms. Substantial literature on symptom networks has been published to date, although no systematic review has been conducted exclusively on symptom networks of schizophrenia, schizoaffective disorder, and schizophreniform (people diagnosed with schizophrenia; PDS). This study aims to compare statistics of the symptom network publications on PDS in the last 21 years and identify congruences and discrepancies in the literature.

Building a Binary Classification Machine-Learning Model: A Guide to Predicting Participation in a Lyme Disease Program at a Medical Institute.

The field of data analysis, preparation, and machine learning is rapidly expanding, offering numerous libraries and resources for exploration. Researchers gain knowledge through various channels, but few resources provide a comprehensive framework for building machine-learning models. We present a step-by-step framework for constructing a robust Random Forest classification model to fill this gap.

Applying BERT and ChatGPT for Sentiment Analysis of Lyme Disease in Scientific Literature.

This chapter presents a practical guide for conducting sentiment analysis using Natural Language Processing (NLP) techniques in the domain of tick-borne disease text. The aim is to demonstrate the process of how the presence of bias in the discourse surrounding chronic manifestations of the disease can be evaluated. The goal is to use a dataset of 5643 abstracts collected from scientific journals on the topic of chronic Lyme disease to demonstrate using Python, the steps for conducting sentiment analysis using pretrained language models and the process of validating the preliminary results using both interpretable machine learning tools, as well as a novel methodology of leveraging emerging state-of-the-art large language models like ChatGPT.

Real and synthetic Punjabi speech datasets for automatic speech recognition.

Automatic speech recognition (ASR) has been an active area of research. Training with large annotated datasets is the key to the development of robust ASR systems. However, most available datasets are focused on high-resource languages like English, leaving a significant gap for low-resource languages.

DL-PPI: a method on prediction of sequenced protein-protein interaction based on deep learning.

Purpose: Sequenced Protein-Protein Interaction (PPI) prediction represents a pivotal area of study in biology, playing a crucial role in elucidating the mechanistic underpinnings of diseases and facilitating the design of novel therapeutic interventions. Conventional methods for extracting features through experimental processes have proven to be both costly and exceedingly complex. In light of these challenges, the scientific community has turned to computational approaches, particularly those grounded in deep learning methodologies.

Chimeras in phase oscillator networks locally coupled through an auxiliary field: Stability and bifurcations.

We study networks in the form of a lattice of nodes with a large number of phase oscillators and an auxiliary variable at each node. The only interactions between nodes are nearest-neighbor. The Ott/Antonsen ansatz is used to derive equations for the order parameters of the phase oscillators at each node, resulting in a set of coupled ordinary differential equations.

Modelling variation in test sensitivity for monitoring leptospirosis in beef cattle.

When Bayesian latent class analysis is used for diagnostic test data in the absence of a gold standard test, it is common to assume that any unknown test sensitivities and specificities are constant across different populations. Indeed this assumption is often necessary for model identifiability. However there are a number of practical situations, depending on the type of test and the nature of the disease, where this assumption may not be true.

Technologies for Managing the Health of Older Adults with Multiple Chronic Conditions: A Systematic Literature Review.

Multimorbidity is defined as the presence of two or more chronic medical conditions in a person, whether physical, mental or long-term infectious diseases. This is especially common in older populations, affecting their quality of life and emotionally impacting their caregivers and family. Technology can allow for monitoring, managing, and motivating older adults in their self-care, as well as supporting their caregivers.

Microtubule search-and-capture model evaluates the effect of chromosomal volume conservation on spindle assembly during mitosis.

Variation in the chromosome numbers can arise from the erroneous mitosis or fusion and fission of chromosomes. While the mitotic errors lead to an increase or decrease in the overall chromosomal substance in the daughter cells, fission and fusion keep this conserved. Variations in chromosome numbers are assumed to be a crucial driver of speciation.

Reproductive biology of male common dolphins () in New Zealand waters.

Unlabelled: Reproductive parameters were assessed in 64 male common dolphins () examined post-mortem from strandings and bycatch in New Zealand between 1999 and 2020. The stages of male sexual maturation were assessed using morphological measurements and histological examination of testicular tissue. Age was determined via growth layer groups (GLGs) in teeth.

Machine-learning-based classification of diffuse large B-cell lymphoma patients by a 7-mRNA signature enriched with immune infiltration and cell cycle.

Background: Diffuse large B-cell lymphoma (DLBCL) exhibits remarkable heterogeneity but still remains undiagnosed in identifying the subpopulation of DLBCL to predict the prognosis and guide clinical treatment.

Methods: Molecular subgroups were identified in gene expression data from GSE10846 by a consensus clustering algorithm. And gene set enrichment analysis, immune infiltration, and the proposed cell cycle algorithm were applied to explore the biological functions of different subtypes.

Individualized Virtual Reality for Increasing Self-Compassion: Evaluation Study.

Background: Depression and anxiety are common and debilitating mental disorders with severe negative repercussions at both individual and societal levels. Although virtual reality (VR) has emerged as a safe and effective tool for the treatment of anxiety disorders, studies of the therapeutic application of VR to treat depression are more limited.

Objective: The purpose of this study was to test whether a novel type of individualized VR (iVR) can be used to improve self-compassion and decrease depressive symptoms and to evaluate the usability and acceptability of this approach, as rated by participants.

Environment and taxonomy shape the genomic signature of prokaryotic extremophiles.

This study provides comprehensive quantitative evidence suggesting that adaptations to extreme temperatures and pH imprint a discernible environmental component in the genomic signature of microbial extremophiles. Both supervised and unsupervised machine learning algorithms were used to analyze genomic signatures, each computed as the k-mer frequency vector of a 500 kbp DNA fragment arbitrarily selected to represent a genome. Computational experiments classified/clustered genomic signatures extracted from a curated dataset of [Formula: see text] extremophile (temperature, pH) bacteria and archaea genomes, at multiple scales of analysis, [Formula: see text].